diff --git a/examples/example_image/example_files/example_init.py b/examples/example_image/example_files/example_init.py
index e75a116..b318ee8 100644
--- a/examples/example_image/example_files/example_init.py
+++ b/examples/example_image/example_files/example_init.py
@@ -8,6 +8,7 @@ import torch
 
 class Model(BaseModel):
     def __init__(self):
+        super().__init__(input_shape=(3, 32, 32), output_shape=(10,))
         dir_path = os.path.dirname(os.path.abspath(__file__))
         self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
         self.model = ConvModel(channel=3, n_random_features=10).to(self.device)
diff --git a/examples/example_image/main.py b/examples/example_image/main.py
index 70589dc..757fe44 100644
--- a/examples/example_image/main.py
+++ b/examples/example_image/main.py
@@ -4,7 +4,7 @@ from get_data import *
 import os
 import random
 from utils import generate_uploader, generate_user, ImageDataLoader, train, eval_prediction
-from learnware.learnware import Learnware, JobSelectorReuser, EnsembleReuser
+from learnware.learnware import Learnware, JobSelectorReuser, AveragingReuser
 import time
 
 from learnware.market import EasyMarket, BaseUserInfo
@@ -40,17 +40,17 @@ semantic_specs = [
     {
         "Data": {"Values": ["Tabular"], "Type": "Class"},
         "Task": {"Values": ["Classification"], "Type": "Class"},
-        "Device": {"Values": ["GPU"], "Type": "Tag"},
+        "Library": {"Values": ["Pytorch"], "Type": "Class"},
         "Scenario": {"Values": ["Business"], "Type": "Tag"},
         "Description": {"Values": "", "Type": "String"},
         "Name": {"Values": "learnware_1", "Type": "String"},
     }
 ]
 
-user_senmantic = {
+user_semantic = {
     "Data": {"Values": ["Tabular"], "Type": "Class"},
     "Task": {"Values": ["Classification"], "Type": "Class"},
-    "Device": {"Values": ["GPU"], "Type": "Tag"},
+    "Library": {"Values": ["Pytorch"], "Type": "Class"},
     "Scenario": {"Values": ["Business"], "Type": "Tag"},
     "Description": {"Values": "", "Type": "String"},
     "Name": {"Values": "", "Type": "String"},
@@ -133,10 +133,10 @@ def prepare_market():
 
 def test_search(gamma=0.1, load_market=True):
     if load_market:
-        image_market = EasyMarket()
+        image_market = EasyMarket(market_id="image")
     else:
         prepare_market()
-        image_market = EasyMarket()
+        image_market = EasyMarket(market_id="image")
     logger.info("Number of items in the market: %d" % len(image_market))
 
     select_list = []
@@ -151,7 +151,7 @@ def test_search(gamma=0.1, load_market=True):
         user_label = np.load(user_label_path)
         user_stat_spec = specification.utils.generate_rkme_spec(X=user_data, gamma=gamma, cuda_idx=0)
         user_info = BaseUserInfo(
-            id=f"user_{i}", semantic_spec=user_senmantic, stat_info={"RKMEStatSpecification": user_stat_spec}
+            id=f"user_{i}", semantic_spec=user_semantic, stat_info={"RKMEStatSpecification": user_stat_spec}
         )
         logger.info("Searching Market for user: %d" % (i))
         sorted_score_list, single_learnware_list, mixture_score, mixture_learnware_list = image_market.search_learnware(
@@ -167,13 +167,14 @@ def test_search(gamma=0.1, load_market=True):
             acc_list.append(acc)
             logger.info("search rank: %d, score: %.3f, learnware_id: %s, acc: %.3f" % (idx, score, learnware.id, acc))
         # test reuse
+
         reuse_baseline = JobSelectorReuser(learnware_list=mixture_learnware_list)
         reuse_predict = reuse_baseline.predict(user_data=user_data)
         reuse_score = eval_prediction(reuse_predict, user_label)
         job_selector_score_list.append(reuse_score)
         print(f"mixture reuse loss: {reuse_score}\n")
 
-        reuse_ensemble = EnsembleReuser(learnware_list=mixture_learnware_list, mode="vote")
+        reuse_ensemble = AveragingReuser(learnware_list=mixture_learnware_list, mode="vote")
         ensemble_predict_y = reuse_ensemble.predict(user_data=user_data)
         ensemble_score = eval_prediction(ensemble_predict_y, user_label)
         ensemble_score_list.append(ensemble_score)
@@ -198,4 +199,4 @@ def test_search(gamma=0.1, load_market=True):
 if __name__ == "__main__":
     # prepare_data()
     # prepare_model()
-    test_search(load_market=True)
+    test_search(load_market=False)
diff --git a/examples/example_m5/example_init.py b/examples/example_m5/example_init.py
index d875d96..e0aabdd 100644
--- a/examples/example_m5/example_init.py
+++ b/examples/example_m5/example_init.py
@@ -1,13 +1,15 @@
 import os
 import joblib
 import numpy as np
+import lightgbm as lgb
 from learnware.model import BaseModel
 
 
 class Model(BaseModel):
     def __init__(self):
+        super(Model, self).__init__(input_shape=(82,), output_shape=())
         dir_path = os.path.dirname(os.path.abspath(__file__))
-        self.model = joblib.load(os.path.join(dir_path, "model.out"))
+        self.model = lgb.Booster(model_file=os.path.join(dir_path, "model.out"))
 
     def fit(self, X: np.ndarray, y: np.ndarray):
         pass
diff --git a/examples/example_m5/main.py b/examples/example_m5/main.py
index 6b7544e..310bb40 100644
--- a/examples/example_m5/main.py
+++ b/examples/example_m5/main.py
@@ -1,13 +1,14 @@
 import os
 import fire
 import zipfile
+import numpy as np
 from tqdm import tqdm
 from shutil import copyfile, rmtree
 
 import learnware
 from learnware.market import EasyMarket, BaseUserInfo
 from learnware.market import database_ops
-from learnware.learnware import Learnware, JobSelectorReuser
+from learnware.learnware import Learnware, JobSelectorReuser, AveragingReuser
 import learnware.specification as specification
 from m5 import DataLoader
 
@@ -16,17 +17,17 @@ semantic_specs = [
     {
         "Data": {"Values": ["Tabular"], "Type": "Class"},
         "Task": {"Values": ["Classification"], "Type": "Class"},
-        "Device": {"Values": ["GPU"], "Type": "Tag"},
+        "Library": {"Values": ["Scikit-learn"], "Type": "Class"},
         "Scenario": {"Values": ["Business"], "Type": "Tag"},
         "Description": {"Values": "", "Type": "String"},
         "Name": {"Values": "learnware_1", "Type": "String"},
     }
 ]
 
-user_senmantic = {
+user_semantic = {
     "Data": {"Values": ["Tabular"], "Type": "Class"},
     "Task": {"Values": ["Classification"], "Type": "Class"},
-    "Device": {"Values": ["GPU"], "Type": "Tag"},
+    "Library": {"Values": ["Scikit-learn"], "Type": "Class"},
     "Scenario": {"Values": ["Business"], "Type": "Tag"},
     "Description": {"Values": "", "Type": "String"},
     "Name": {"Values": "", "Type": "String"},
@@ -122,13 +123,20 @@ class M5DatasetWorkflow:
 
         m5 = DataLoader()
         idx_list = m5.get_idx_list()
+        os.makedirs("./user_spec", exist_ok=True)
+        single_score_list = []
+        random_score_list = []
+        job_selector_score_list = []
+        ensemble_score_list = []
 
         for idx in idx_list:
             train_x, train_y, test_x, test_y = m5.get_idx_data(idx)
             user_spec = specification.utils.generate_rkme_spec(X=test_x, gamma=0.1, cuda_idx=0)
+            user_spec_path = f"./user_spec/user_{idx}.json"
+            user_spec.save(user_spec_path)
 
             user_info = BaseUserInfo(
-                id=f"user_{idx}", semantic_spec=user_senmantic, stat_info={"RKMEStatSpecification": user_spec}
+                id=f"user_{idx}", semantic_spec=user_semantic, stat_info={"RKMEStatSpecification": user_spec}
             )
             (
                 sorted_score_list,
@@ -141,18 +149,36 @@ class M5DatasetWorkflow:
             print(
                 f"single model num: {len(sorted_score_list)}, max_score: {sorted_score_list[0]}, min_score: {sorted_score_list[-1]}"
             )
+            loss_list = []
             for score, learnware in zip(sorted_score_list, single_learnware_list):
                 pred_y = learnware.predict(test_x)
-                loss = m5.score(test_y, pred_y)
-                print(f"score: {score}, learnware_id: {learnware.id}, loss: {loss}")
+                loss_list.append(m5.score(test_y, pred_y))
+            print(
+                f"Top1-score: {sorted_score_list[0]}, learnware_id: {single_learnware_list[0].id}, loss: {loss_list[0]}"
+            )
 
             mixture_id = " ".join([learnware.id for learnware in mixture_learnware_list])
-            print(f"mixture_learnware: {mixture_id}\n")
-
-            reuse_baseline = JobSelectorReuser(learnware_list=mixture_learnware_list)
-            reuse_predict = reuse_baseline.predict(user_data=test_x)
-            reuse_score = m5.score(test_y, reuse_predict)
-            print(f"mixture reuse loss: {reuse_score}\n")
+            print(f"mixture_score: {mixture_score}, mixture_learnware: {mixture_id}")
+
+            reuse_job_selector = JobSelectorReuser(learnware_list=mixture_learnware_list, use_herding=False)
+            job_selector_predict_y = reuse_job_selector.predict(user_data=test_x)
+            job_selector_score = m5.score(test_y, job_selector_predict_y)
+            print(f"mixture reuse loss (job selector): {job_selector_score}")
+
+            reuse_ensemble = AveragingReuser(learnware_list=mixture_learnware_list)
+            ensemble_predict_y = reuse_ensemble.predict(user_data=test_x)
+            ensemble_score = m5.score(test_y, ensemble_predict_y)
+            print(f"mixture reuse loss (ensemble): {ensemble_score}\n")
+
+            single_score_list.append(loss_list[0])
+            random_score_list.append(np.mean(loss_list))
+            job_selector_score_list.append(job_selector_score)
+            ensemble_score_list.append(ensemble_score)
+
+        print(f"Single search score: {np.mean(single_score_list)}")
+        print(f"Job selector score: {np.mean(job_selector_score_list)}")
+        print(f"Average ensemble score: {np.mean(ensemble_score_list)}")
+        print(f"Random search score: {np.mean(random_score_list)}")
 
 
 if __name__ == "__main__":
diff --git a/examples/example_market_db/example_db.py b/examples/example_market_db/example_db.py
index 11eb4cc..b4e146a 100644
--- a/examples/example_market_db/example_db.py
+++ b/examples/example_market_db/example_db.py
@@ -15,7 +15,7 @@ semantic_specs = [
     {
         "Data": {"Values": ["Tabular"], "Type": "Class"},
         "Task": {"Values": ["Classification"], "Type": "Class"},
-        "Device": {"Values": ["GPU"], "Type": "Tag"},
+        "Library": {"Values": ["Scikit-learn"], "Type": "Class"},
         "Scenario": {"Values": ["Nature"], "Type": "Tag"},
         "Description": {"Values": "", "Type": "String"},
         "Name": {"Values": "learnware_1", "Type": "String"},
@@ -23,7 +23,7 @@ semantic_specs = [
     {
         "Data": {"Values": ["Tabular"], "Type": "Class"},
         "Task": {"Values": ["Classification"], "Type": "Class"},
-        "Device": {"Values": ["GPU"], "Type": "Tag"},
+        "Library": {"Values": ["Scikit-learn"], "Type": "Class"},
         "Scenario": {"Values": ["Business", "Nature"], "Type": "Tag"},
         "Description": {"Values": "", "Type": "String"},
         "Name": {"Values": "learnware_2", "Type": "String"},
@@ -31,7 +31,7 @@ semantic_specs = [
     {
         "Data": {"Values": ["Tabular"], "Type": "Class"},
         "Task": {"Values": ["Regression"], "Type": "Class"},
-        "Device": {"Values": ["GPU"], "Type": "Tag"},
+        "Library": {"Values": ["Scikit-learn"], "Type": "Class"},
         "Scenario": {"Values": ["Business"], "Type": "Tag"},
         "Description": {"Values": "", "Type": "String"},
         "Name": {"Values": "learnware_3", "Type": "String"},
@@ -40,11 +40,8 @@ semantic_specs = [
 
 user_senmantic = {
     "Data": {"Values": ["Tabular"], "Type": "Class"},
-    "Task": {
-        "Values": ["Classification"],
-        "Type": "Class",
-    },
-    "Device": {"Values": ["GPU"], "Type": "Tag"},
+    "Task": {"Values": ["Classification"], "Type": "Class"},
+    "Device": {"Values": ["GPU"], "Type": "Class"},
     "Scenario": {"Values": ["Business"], "Type": "Tag"},
     "Description": {"Values": "", "Type": "String"},
     "Name": {"Values": "learnware", "Type": "String"},
diff --git a/examples/example_pfs/example_init.py b/examples/example_pfs/example_init.py
index d875d96..88b788a 100644
--- a/examples/example_pfs/example_init.py
+++ b/examples/example_pfs/example_init.py
@@ -6,6 +6,7 @@ from learnware.model import BaseModel
 
 class Model(BaseModel):
     def __init__(self):
+        super(Model, self).__init__(input_shape=(31,), output_shape=())
         dir_path = os.path.dirname(os.path.abspath(__file__))
         self.model = joblib.load(os.path.join(dir_path, "model.out"))
 
diff --git a/examples/example_pfs/main.py b/examples/example_pfs/main.py
index 5d3ae6a..95e2662 100644
--- a/examples/example_pfs/main.py
+++ b/examples/example_pfs/main.py
@@ -17,17 +17,17 @@ semantic_specs = [
     {
         "Data": {"Values": ["Tabular"], "Type": "Class"},
         "Task": {"Values": ["Classification"], "Type": "Class"},
-        "Device": {"Values": ["GPU"], "Type": "Tag"},
+        "Library": {"Values": ["Scikit-learn"], "Type": "Class"},
         "Scenario": {"Values": ["Business"], "Type": "Tag"},
         "Description": {"Values": "", "Type": "String"},
         "Name": {"Values": "learnware_1", "Type": "String"},
     }
 ]
 
-user_senmantic = {
+user_semantic = {
     "Data": {"Values": ["Tabular"], "Type": "Class"},
     "Task": {"Values": ["Classification"], "Type": "Class"},
-    "Device": {"Values": ["GPU"], "Type": "Tag"},
+    "Library": {"Values": ["Scikit-learn"], "Type": "Class"},
     "Scenario": {"Values": ["Business"], "Type": "Tag"},
     "Description": {"Values": "", "Type": "String"},
     "Name": {"Values": "", "Type": "String"},
@@ -122,7 +122,7 @@ class PFSDatasetWorkflow:
         pfs = Dataloader()
         idx_list = pfs.get_idx_list()
         os.makedirs("./user_spec", exist_ok=True)
-        sinle_score_list = []
+        single_score_list = []
         random_score_list = []
         job_selector_score_list = []
         ensemble_score_list = []
@@ -134,7 +134,7 @@ class PFSDatasetWorkflow:
             user_spec.save(user_spec_path)
 
             user_info = BaseUserInfo(
-                id=f"user_{idx}", semantic_spec=user_senmantic, stat_info={"RKMEStatSpecification": user_spec}
+                id=f"user_{idx}", semantic_spec=user_semantic, stat_info={"RKMEStatSpecification": user_spec}
             )
             (
                 sorted_score_list,
@@ -152,13 +152,13 @@ class PFSDatasetWorkflow:
                 pred_y = learnware.predict(test_x)
                 loss_list.append(pfs.score(test_y, pred_y))
             print(
-                f"Top1-score: {sorted_score_list[0]}, learnware_id: {single_learnware_list[0].id}, loss: {loss_list[-1]}"
+                f"Top1-score: {sorted_score_list[0]}, learnware_id: {single_learnware_list[0].id}, loss: {loss_list[0]}"
             )
 
             mixture_id = " ".join([learnware.id for learnware in mixture_learnware_list])
             print(f"mixture_score: {mixture_score}, mixture_learnware: {mixture_id}")
 
-            reuse_job_selector = JobSelectorReuser(learnware_list=mixture_learnware_list)
+            reuse_job_selector = JobSelectorReuser(learnware_list=mixture_learnware_list, use_herding=False)
             job_selector_predict_y = reuse_job_selector.predict(user_data=test_x)
             job_selector_score = pfs.score(test_y, job_selector_predict_y)
             print(f"mixture reuse loss (job selector): {job_selector_score}")
@@ -168,12 +168,12 @@ class PFSDatasetWorkflow:
             ensemble_score = pfs.score(test_y, ensemble_predict_y)
             print(f"mixture reuse loss (ensemble): {ensemble_score}\n")
 
-            sinle_score_list.append(loss_list[0])
+            single_score_list.append(loss_list[0])
             random_score_list.append(np.mean(loss_list))
             job_selector_score_list.append(job_selector_score)
             ensemble_score_list.append(ensemble_score)
 
-        print(f"Single search score: {np.mean(sinle_score_list)}")
+        print(f"Single search score: {np.mean(single_score_list)}")
         print(f"Job selector score: {np.mean(job_selector_score_list)}")
         print(f"Average ensemble score: {np.mean(ensemble_score_list)}")
         print(f"Random search score: {np.mean(random_score_list)}")
diff --git a/examples/workflow_by_code/main.py b/examples/workflow_by_code/main.py
index 01f09f9..97d2fd0 100644
--- a/examples/workflow_by_code/main.py
+++ b/examples/workflow_by_code/main.py
@@ -19,21 +19,21 @@ semantic_specs = [
     {
         "Data": {"Values": ["Tabular"], "Type": "Class"},
         "Task": {"Values": ["Classification"], "Type": "Class"},
-        "Device": {"Values": ["GPU"], "Type": "Tag"},
+        "Library": {"Values": ["Scikit-learn"], "Type": "Class"},
         "Scenario": {"Values": ["Business"], "Type": "Tag"},
         "Description": {"Values": "", "Type": "String"},
         "Name": {"Values": "learnware_1", "Type": "String"},
     }
 ]
 
-user_senmantic = {
+user_semantic = {
     "Data": {"Values": ["Tabular"], "Type": "Class"},
     "Task": {
         "Values": ["Classification"],
         "Type": "Class",
     },
-    "Device": {"Values": ["GPU"], "Type": "Tag"},
-    "Scenario": {"Values": ["Business"], "Type": "Tag"},
+    "Library": {"Values": ["Scikit-learn"], "Type": "Tag"},
+    "Scenario": {"Values": ["Business"], "Type": "Class"},
     "Description": {"Values": "", "Type": "String"},
     "Name": {"Values": "", "Type": "String"},
 }
@@ -130,7 +130,7 @@ class LearnwareMarketWorkflow:
         with zipfile.ZipFile(zip_path, "r") as zip_obj:
             zip_obj.extractall(path=unzip_dir)
 
-        user_info = BaseUserInfo(id="user_0", semantic_spec=user_senmantic)
+        user_info = BaseUserInfo(id="user_0", semantic_spec=user_semantic)
         _, single_learnware_list, _ = easy_market.search_learnware(user_info)
 
         print("User info:", user_info.get_semantic_spec())
@@ -159,7 +159,7 @@ class LearnwareMarketWorkflow:
             user_spec = specification.rkme.RKMEStatSpecification()
             user_spec.load(os.path.join(unzip_dir, "svm.json"))
             user_info = BaseUserInfo(
-                id="user_0", semantic_spec=user_senmantic, stat_info={"RKMEStatSpecification": user_spec}
+                id="user_0", semantic_spec=user_semantic, stat_info={"RKMEStatSpecification": user_spec}
             )
             (
                 sorted_score_list,
diff --git a/learnware/config.py b/learnware/config.py
index 18c2a15..be274ba 100644
--- a/learnware/config.py
+++ b/learnware/config.py
@@ -79,12 +79,17 @@ semantic_config = {
             # "Generation",
             "Segmentation",
             "Object Detection",
+            "Others",
         ],
         "Type": "Class",  # Choose only one class
     },
-    "Device": {
-        "Values": ["CPU", "GPU"],
-        "Type": "Tag",
+    # "Device": {
+    #     "Values": ["CPU", "GPU"],
+    #     "Type": "Tag",
+    # },  # Choose one or more tags
+    "Library": {
+        "Values": ["Scikit-learn", "PyTorch", "TensorFlow", "Others"],
+        "Type": "Class",
     },  # Choose one or more tags
     "Scenario": {
         "Values": [
diff --git a/learnware/learnware/reuse.py b/learnware/learnware/reuse.py
index ccd1da8..97ee44e 100644
--- a/learnware/learnware/reuse.py
+++ b/learnware/learnware/reuse.py
@@ -17,7 +17,7 @@ logger = get_module_logger("Reuser")
 class JobSelectorReuser(BaseReuser):
     """Baseline Multiple Learnware Reuser uing Job Selector Method"""
 
-    def __init__(self, learnware_list: List[Learnware], herding_num: int = 1000):
+    def __init__(self, learnware_list: List[Learnware], herding_num: int = 1000, use_herding: bool = True):
         """The initialization method for job selector reuser
 
         Parameters
@@ -29,6 +29,7 @@ class JobSelectorReuser(BaseReuser):
         """
         super(JobSelectorReuser, self).__init__(learnware_list)
         self.herding_num = herding_num
+        self.use_herding = use_herding
 
     def predict(self, user_data: np.ndarray) -> np.ndarray:
         """Give prediction for user data using baseline job-selector method
@@ -53,13 +54,15 @@ class JobSelectorReuser(BaseReuser):
 
         return selector_pred_y
 
-    def job_selector(self, user_data: np.ndarray):
+    def job_selector(self, user_data: np.ndarray, use_herding: bool = True):
         """Train job selector based on user's data, which predicts which learnware in the pool should be selected
 
         Parameters
         ----------
         user_data : np.ndarray
             User's labeled raw data.
+        use_herding: bool
+            Whether create job selector training samples by herding
         """
         if len(self.learnware_list) == 1:
             user_data_num = user_data.shape[0]
@@ -69,26 +72,32 @@ class JobSelectorReuser(BaseReuser):
                 learnware.specification.get_stat_spec_by_name("RKMEStatSpecification")
                 for learnware in self.learnware_list
             ]
-            task_matrix = np.zeros((len(learnware_rkme_spec_list), len(learnware_rkme_spec_list)))
 
-            for i in range(len(self.learnware_list)):
-                task_rkme1 = learnware_rkme_spec_list[i]
-                for j in range(i, len(self.learnware_list)):
-                    task_rkme2 = learnware_rkme_spec_list[j]
-                    task_matrix[i][j] = task_matrix[j][i] = task_rkme1.inner_prod(task_rkme2)
-
-            task_mixture_weight = self._calculate_rkme_spec_mixture_weight(
-                user_data, learnware_rkme_spec_list, task_matrix
-            )
+            if self.use_herding:
+                task_matrix = np.zeros((len(learnware_rkme_spec_list), len(learnware_rkme_spec_list)))
+                for i in range(len(self.learnware_list)):
+                    task_rkme1 = learnware_rkme_spec_list[i]
+                    task_matrix[i][i] = task_rkme1.inner_prod(task_rkme1)
+                    for j in range(i + 1, len(self.learnware_list)):
+                        task_rkme2 = learnware_rkme_spec_list[j]
+                        task_matrix[i][j] = task_matrix[j][i] = task_rkme1.inner_prod(task_rkme2)
+
+                task_mixture_weight = self._calculate_rkme_spec_mixture_weight(
+                    user_data, learnware_rkme_spec_list, task_matrix
+                )
 
             herding_X, train_herding_X, val_herding_X = None, None, None
             herding_y, train_herding_y, val_herding_y = [], [], []
             for i in range(len(self.learnware_list)):
                 task_spec = learnware_rkme_spec_list[i]
-                task_herding_num = max(5, int(self.herding_num * task_mixture_weight[i]))
+                if self.use_herding:
+                    task_herding_num = max(5, int(self.herding_num * task_mixture_weight[i]))
+                    herding_X_i = task_spec.herding(task_herding_num).detach().cpu().numpy()
+                else:
+                    herding_X_i = task_spec.z.detach().cpu().numpy()
+                    task_herding_num = herding_X_i.shape[0]
                 task_val_num = task_herding_num // 5
 
-                herding_X_i = task_spec.herding(task_herding_num).detach().cpu().numpy()
                 train_X_i = herding_X_i[:-task_val_num]
                 val_X_i = herding_X_i[-task_val_num:]
 
@@ -267,7 +276,7 @@ class AveragingReuser(BaseReuser):
                 # print(pred_y.shape)
                 if not isinstance(pred_y, np.ndarray):
                     pred_y = pred_y.detach().cpu().numpy()
-                softmax_pred = softmax(pred_y, axis=1)
+                softmax_pred = softmax(pred_y, axis=0)
                 if mean_pred_y is None:
                     mean_pred_y = softmax_pred
                 else:
diff --git a/learnware/market/__init__.py b/learnware/market/__init__.py
index 10654c7..1620a5e 100644
--- a/learnware/market/__init__.py
+++ b/learnware/market/__init__.py
@@ -3,3 +3,4 @@ from .base import BaseUserInfo, BaseMarket
 from .evolve_anchor import EvolvedAnchoredMarket
 from .evolve import EvolvedMarket
 from .easy import EasyMarket
+from .heterogeneous_feature import HeterogeneousFeatureMarket
diff --git a/learnware/market/easy.py b/learnware/market/easy.py
index 7e2de88..5e7bf80 100644
--- a/learnware/market/easy.py
+++ b/learnware/market/easy.py
@@ -119,37 +119,38 @@ class EasyMarket(BaseMarket):
         """
         if not os.path.exists(zip_path):
             logger.warning("Zip Path NOT Found! Fail to add learnware.")
-            return None, False
+            return None, self.INVALID_LEARNWARE
 
         try:
             if len(semantic_spec["Data"]["Values"]) == 0:
                 logger.warning("Illegal semantic specification, please choose Data.")
-                return None, False
+                return None, self.INVALID_LEARNWARE
             if len(semantic_spec["Task"]["Values"]) == 0:
                 logger.warning("Illegal semantic specification, please choose Task.")
-                return None, False
-            if len(semantic_spec["Device"]["Values"]) == 0:
+                return None, self.INVALID_LEARNWARE
+            if len(semantic_spec["Library"]["Values"]) == 0:
                 logger.warning("Illegal semantic specification, please choose Device.")
-                return None, False
+                return None, self.INVALID_LEARNWARE
             if len(semantic_spec["Name"]["Values"]) == 0:
                 logger.warning("Illegal semantic specification, please provide Name.")
-                return None, False
+                return None, self.INVALID_LEARNWARE
             if len(semantic_spec["Description"]["Values"]) == 0 and len(semantic_spec["Scenario"]["Values"]) == 0:
                 logger.warning("Illegal semantic specification, please provide Scenario or Description.")
-                return None, False
+                return None, self.INVALID_LEARNWARE
             if (
                 semantic_spec["Data"]["Type"] != "Class"
                 or semantic_spec["Task"]["Type"] != "Class"
-                or semantic_spec["Device"]["Type"] != "Tag"
+                or semantic_spec["Library"]["Type"] != "Class"
                 or semantic_spec["Scenario"]["Type"] != "Tag"
                 or semantic_spec["Name"]["Type"] != "String"
                 or semantic_spec["Description"]["Type"] != "String"
             ):
                 logger.warning("Illegal semantic specification, please provide the right type.")
-                return None, False
+                return None, self.INVALID_LEARNWARE
         except:
+            print(semantic_spec)
             logger.warning("Illegal semantic specification, some keys are missing.")
-            return None, False
+            return None, self.INVALID_LEARNWARE
 
         logger.info("Get new learnware from %s" % (zip_path))
         id = "%08d" % (self.count)
@@ -476,7 +477,7 @@ class EasyMarket(BaseMarket):
         """
         learnware_num = len(learnware_list)
         if learnware_num == 0:
-            return [], []
+            return None, [], []
         if learnware_num < max_search_num:
             logger.warning("Available Learnware num less than search_num!")
             max_search_num = learnware_num
@@ -639,7 +640,6 @@ class EasyMarket(BaseMarket):
                 weight_list = []
                 mixture_learnware_list = []
 
-            # convert dist to score
             if mixture_dist is None:
                 sorted_score_list = self._convert_dist_to_score(sorted_dist_list)
                 mixture_score = None
diff --git a/learnware/market/heterogeneous_feature.py b/learnware/market/heterogeneous_feature.py
new file mode 100644
index 0000000..b25e2e8
--- /dev/null
+++ b/learnware/market/heterogeneous_feature.py
@@ -0,0 +1,102 @@
+import numpy as np
+from typing import Tuple, Any, List, Union, Dict
+
+from .evolve import EvolvedMarket
+from ..learnware import Learnware
+
+
+class MappingFunction:
+    def __init__(self) -> None:
+        pass
+
+    def transform(X: np.ndarray) -> np.ndarray:
+        """transform the data in one feature space to another feature space.
+
+        Parameters
+        ----------
+        X : np.ndarray
+            data in one feature space
+
+        Returns
+        -------
+        np.ndarray
+            transformed data in other feature space
+        """
+        pass
+
+
+class HeterogeneousFeatureMarket(EvolvedMarket):
+    """Organize learnwares with heterogeneous feature spaces
+
+    Parameters
+    ----------
+    EvolvedMarket : _type_
+        Market version with evolved learnwares
+    """
+
+    def __init__(self):
+        super(HeterogeneousFeatureMarket, self).__init__()
+        self.mapping_function_list = {}
+
+    def _mapping_function_list_initialization(self, learnware_list: List[Learnware]):
+        """Initialize mapping functions with all submitted learnwares
+
+        Parameters
+        ----------
+        learnware_list : List[Learnware]
+            list of learnwares
+        """
+        self.mapping_function_list = self.learn_mapping_functions(learnware_list)
+
+    def learn_mapping_functions(self, learnware_list: List[Learnware]) -> List[MappingFunction]:
+        """Use all statistical specifications of submitted learnwares to generate mapping functions from each original feature space to subsapce and vice verse.
+
+        Parameters
+        ----------
+        learnware_list : List[Learnware]
+            list of learnwares
+
+        Returns
+        -------
+        List[MappingFunction]
+            list of mapping functions
+        """
+        pass
+
+    def transform_original_to_subspace(
+        self, original_feature_space_idx: int, original_feature: np.ndarray
+    ) -> np.ndarray:
+        """Transform feature in a original feature space to the subspace.
+
+        Parameters
+        ----------
+        original_feature_space_idx : int
+            index of the original feature space
+        original_feature : np.ndarray
+            data in the original feature space
+
+        Returns
+        -------
+        np.ndarray
+            mapped data in the subspace
+        """
+        pass
+
+    def transform_subspace_to_original(
+        self, original_feature_space_idx: int, subspace_feature: np.ndarray
+    ) -> np.ndarray:
+        """Transform feature in the subspace to a original feature space.
+
+        Parameters
+        ----------
+        original_feature_space_idx : int
+            index of the original feature space
+        subspace_feature : np.ndarray
+            data in the subspace
+
+        Returns
+        -------
+        np.ndarray
+            mapped data in the original feature space
+        """
+        pass