Reviewerly/exHarmony huggingface dataset:
A vertical federation recommendation method based on clustering and latent factor model

Recommendation systems have been widely used for user data mining. However, considering the security of user privacy and legal regulations, various institutions have begun to strengthen data privacy protection gradually. Federated learning is a novel joint training privacy protection framework, which can be executed without knowing the original data of each participant. In this paper, we propose a method based on clustering and latent factor model under the vertical federated recommendation system. Taking into account the diversity of a large number of different users in each participant and the complexity of the matrix factorization of the user-item matrix, we cluster the users to reduce the dimension of the matrix and improve the accuracy of user recommendations. In addition, in order to ensure the security of user data privacy, we use homomorphic encryption to protect data. We conducted experiments on the MovieLens movie dataset. Experiments show that effective clustering can significantly improve the accuracy of the recommendation system of each participant.
A High-Speed NMS Coprocessor for Lightweight Ship Detection Algorithm

With the development of artificial intelligence research, the convolutional neural networks (CNN) triumphantly continue to conquer diverse visual tasks, with lots of technical and practical problems emerging. When the detection networks need to meet the high-speed processing requirement and the low power consumption constraint in edge computing, dedicated circuits and algorithm optimization are necessary. In this brief, we propose a non-maximum suppression (NMS) coprocessor that cooperated with a Vision Processor (VP) to speed up the detection. It contains a parallel process unit and dynamic memory management to accelerate NMS processing. Meanwhile, the software workflow provides a comprehensive method to convert the one-stage detection network to our processor in a post-training manner. The experimental results show that our co-design solution reaches 166 FPS to run a MobileNet-SSD, promising to deploy in edge-computing scenarios. Moreover, the detection accuracy on the Airbus dataset is 88.6%, favorable in practical applications.
A Novel Gaussian-Copula modeling for image despeckling in the shearlet domain

Image denoising, degraded by speckle noise, has turned into a prevalent problem in image processing. Speckle noise provides a granular perspective into the image and makes it difficult to have a clear interpretation of the singular and non-singular points. This paper proposes a novel statistical approach based on Gaussian Copula modeling in the Shearlet domain. The proposed multi-dimensional Minimum Mean Square Error (MMSE) processor consists of two key components. First, Bi Parameter Cauchy Gaussian Mixture model (BCGM) as the marginal distribution of shearlet coefficients is employed. Second, the joint-prior distribution modeling is formed, based on proposing the Gaussian copula, to model the dependency of the target coefficient respect to its neighbors. The closed form mathematical expression of proposed multi dimensional MMSE processor has some computational advantages, such as parallel computing. It will be shown that the proposed processor has adaptive behavior, meaning that, working non-linearly according to the estimated noise variance on each scale and direction of the shratlet transform. This behavior confirms that the designed processor is not sensitive to initial parameter settings, unlike the state-of-the-art filters in this area.
EMG-based IoT System using Hand Gestures for Remote Control Applications

Electromyography (EMG) has been widely used for detecting a person’s hand poses and remote control applications. However, the traditional EMG-based control systems have limitations such as short controlling range, and supporting the limited number of devices. There is a need for a more advanced system that can deal with the limitations while maintaining a high quality of services such as high accuracy level and controlling complex devices. Hence, we present a real-time and remote control Internet-of-Things system using EMG signals together with motion-related signals such as acceleration and angular velocity. A user wearing the Myo-band at his/her arm can remotely control devices via 8 different hand gestures. The entire system was implemented and tested via two use cases of home assistant and robot arm control. The results show that the presented system could achieve a high level of accuracy e.g., 100% accuracy for simple control and 90% accuracy for complex cases. This system can be a potential approach for smart home controlling and assisting disabled people.
Human Fall Detection with Wearable Sensors Using ML Algorithms

In the healthcare systems, wearable technology is enhanced by continuously monitoring people's physical activities and behaviors in daily life. A wearable system has two technologies, one based on sensors and the other based on cameras. The use of machine learning (ML) in health care applications, such as human fall detection, has become an active area of research. This research work has proposed a sensor based human fall detection system, which detect a fall by using sensor-based technology other than the vision-based technology because most people are concerned about their privacy very seriously. The overall performance of the proposed scheme was examined by using four machine learning algorithms namely, Support Vector Machine (SVM), k-nearest neighbour’s (KNNs), Decision Tree (DT), and Random Forest (RF). The proposed methodology achieves the best accuracy for human fall detection, i.e., 98% , by using ensemble technique as RF classifier.
Smart Seeing Eye Dog Wheeled Assistive Robotics

To date, approximately 12 million people over the age of 40 in the United States are visually impaired, 3 million of whom are blind. Meanwhile, many people are blinded by accidents. Guide dogs can assist blind people in their daily life. However, the cost to breed, raise, train, and match a Guiding Eyes dog with a person with vision loss is estimated to be approximately $50,000. Another weakness is that the life expectancy of a human is 73.4 years around the world and the average working life of a guide dog is around eight years. As a result, people with visual impairments need more guide dogs throughout their lives. If our design is put into use, we can reduce the cost significantly. In this paper, we are going to build a smart seeing eye dog, which is a kind of wheeled Assistive Robotics that could guide the blind people walk on the road like but perform better than a real seeing eye dog. We started this paper from four aspects. They are the structure of the mechanical dog, TensorFlow-based image recognition, ROS-based motion simulation and human-computer interaction. Because of the cost of training an assistive dog is extremely high and the dogs are all red-green color-blind, which means that they cannot recognize the traffic lights and road sign. In our paper, these problems can be solved very well. Soon, between this mechanical dog paper may help blind people to better integrate into society.
Blockchain Based RAN Data Sharing

Providing seamless connectivity and services across national borders are intricate challenges with multifarious underlying aspects, ranging from the network management to business and political considerations. Since the cross-border inter-Public Land Mobile Network (PLMN) network handover is currently not available in European cellular networks, we present a complementary approach, diminishing the connectivity gap to a minimum. By leveraging Distributed Ledger Technology (DLT), we establish a dynamic, secure data exchange and management solution between several Mobile Network Operators (MNOs) of different countries. Systematically integrating foreign cell and base station parameter (i.e., Radio Access Network (RAN) data) of border regions into the internal network management systems permits their usage in standardized Mobility Management procedures. We demonstrate that this type of collaboration on the inter-MNO network governance considerably improves the network quality and customer experience when crossing national borders. Since foreign RAN data is also required for the inter-PLMN network handover (and can serve many additional purposes) and provided that our solution is not relying on any specific mobile network technology generation (e.g., 4G or 5G), we conclude that it is a fundamental step towards an inter-MNO ecosystem beyond 5G.
Hiding the Lengths of Encrypted Messages via Gaussian Padding

Secure network protocols like TLS, QUIC, SSH and IPsec allow for additional padding to be used during encryption in order to hide message lengths. While it is impossible to conceal message lengths completely, without drastically degrading efficiency, such mechanisms aim at causing as much frustration as possible to the prospective attacker. However, none of the protocol specifications provide any guidance on how to select the length of this padding. Several works have highlighted how the leakage of message lengths can be exploited in attacks, but the converse problem of how to best defend against such attacks remains relatively understudied. We make this the focus of our work and present a formal treatment of length hiding security in a general setting. Prior work by Tezcan and Vaudenay suggested that sampling the padding length uniformly at random already achieves the best possible security. However we show that this is only true in the limited setting where only a single ciphertext is available to the adversary. If multiple ciphertexts are available to the adversary, then sampling the padding length according to a Gaussian distribution yields quantifiably better security for the same overhead. In fact, in this setting, uniformly random padding turns out to be among the worst possible choices. We confirm experimentally the superior performance of Gaussian padding over uniform padding in the context of the CRIME/BREACH attack.
Generating Image Captions using Deep Learning and Natural Language Processing

In today's world, there is rapid progress in the field of artificial intelligence and image captioning. It becomes a fascinating task that has saw widespread interest. The task of image captioning comprises image description engendered based on the hybrid combination of deep learning, natural language processing, and various approaches of machine learning and computer vision. In this work authors emphasize on how the model generates a short description as an output of the input image using the functionalities of Deep Learning and Natural Language Processing, for helping visually impaired people, and can also be cast-off in various web sites to automate the generation of captions reducing the task of recitation with great ease.
Detection of Phishing Websites Using Machine Learning Approach

With the development of e-commerce transaction, phishers and other cybercriminals are taking advantage and making the online platform insecure by triggering severe threats to users and industries as well as intimidating worldwide safety and economy. Currently, phishers are regularly developing different means for tempting user to expose their delicate facts. In order to elude falling target to phishers, it is essential to implement a phishing detection algorithm. Phishing is a way to deceive people in believing that the URL which they are visiting is genuine. Once the user establishes his/her trust on the website they enter their personal credentials like login password or account number etc. Attacker sends spam emails and develops identical websites which resembles the original websites and affects fields such as online business, e-commerce, digital marketing and banking. This paper aims to develop a system which identifies phishing URL with various machine learning methods and comparing it with hybrid stacking model to identify the approach which provides maximum accuracy rate and time effectively. In the proposed research work, different Machine Learning algorithms like Logistic Regression are compared with ensemble algorithms like Adaboost and Gradient boost. The research outcome shows that the proposed Stacking Classifier results 85.6 percentage of accuracy. The classifiers which provide better phishing predictions outcome are already available. However, with our comparative survey shows that the hybrid approach improves the accuracy prediction rate of phishing websites.
Development of NavIC synchronized fully automated inter-building QKD framework and demonstration of quantum secured video calling

Quantum key distribution (QKD) is a revolutionary communication technology that promises ultimate security assurance by exploiting the fundamental principles of quantum mechanics. In this work, we report design and development of a fully automated inter-building QKD framework for generation and distribution of cryptographic keys, securely and seamlessly, by executing weak coherent pulse based BB84 protocol. This framework is experimentally validated by establishing a quantum communication link between two buildings separated by ∼300 m of free-space atmospheric channel. A novel synchronization technique enabled with indigenous NavIC (IRNSS) constellation is developed and implemented. This QKD system demonstrates generation of secure key rate as high as 300 Kbps with QBER< 3% for mean photon no. per pulse (μ) of 0.15. The intercept–resend eavesdropping attack has been emulated within the system and evaluated during experiment. A novel quantum secured end-to-end encrypted video calling app (QuViC) is also developed and integrated with QKD framework to demonstrate unconditionally secure two-way communication over Ethernet, functioning alongside with quantum communication.
Direction-Aware User Recommendation Based on Asymmetric Network Embedding

User recommendation aims at recommending users with potential interests in the social network. Previous works have mainly focused on the undirected social networks with symmetric relationship such as friendship, whereas recent advances have been made on the asymmetric relationship such as the following and followed by relationship. Among the few existing direction-aware user recommendation methods, the random walk strategy has been widely adopted to extract the asymmetric proximity between users. However, according to our analysis on real-world directed social networks, we argue that the asymmetric proximity captured by existing random walk based methods are insufficient due to the inbalance in-degree and out-degree of nodes. To tackle this challenge, we propose InfoWalk, a novel informative walk strategy to efficiently capture the asymmetric proximity solely based on random walks. By transferring the direction information into the weights of each step, InfoWalk is able to overcome the limitation of edges while simultaneously maintain both the direction and proximity. Based on the asymmetric proximity captured by InfoWalk, we further propose the qualitative (DNE-L) and quantitative (DNE-T) directed network embedding methods, capable of preserving the two properties in the embedding space. Extensive experiments conducted on six real-world benchmark datasets demonstrate the superiority of the proposed DNE model over several state-of-the-art approaches in various tasks.
Block-Division-Based Wireless Coded Computation

Coded computation is an efficient way to mitigate the straggler effect in a distributed computing system. In this letter, we study an open problem for distributed computing that how to exploit the computed results of stragglers in wireless networks. A block-division-based coded computation scheme exploiting the straggles’ results is proposed and analyzed in wireless networks. The nodes are assumed to have different capabilities to compute tasks and transmit results, and re-transmission is considered when the transmitted packets are lost. Each node divides the local data block into several sub-blocks and transmits the results of each sub-block immediately when its computation is finished. Then, the derived overall latency shows that existing works of coded computation can be generalized by the proposed scheme and there exists a trade-off between computation and transmission concerning the number of the divided sub-blocks. To achieve the optimal trade-off, we formulate optimization problems and obtain the (sub)optimal solutions. The effectiveness of the proposed scheme is verified through simulation results.
Sentiment Analysis of The Covid-19 Vaccine For Arabic Tweets Using Machine Learning

With exposure through social media, everyone has the freedom to publish their opinions and feelings with no restrictions. These opinions and feelings may be about individual or general trends of society in general. These feeling that are related to topics or general trends must be analysed. The purpose of this paper is to analyze sentiment for tweets posted about the COVID-19 vaccine. For this purpose, a dataset focusing on the tweets which were recorded between the February 19th to the 20th of March 2021 related to the COVID-19 vaccine was collected. Several classic machine learning methods were used, namely, Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), AdaBoost, K-Nearest Neighbors (KNN), and Gaussian Naive Bayes (GNB) were utilized, the preprocessing and annotation process steps on the proposed dataset. The TF -IDF feature extraction method where the dataset is trained based on unigram, bigram, and trigram features was applied. The best results were achieved through the bigram, where the LR model achieved the highest percentage in the accuracy with a value of 87.0%.
Cryptanalysis of the Privacy-Preserving Ride-Hailing Service TRACE

In a typical ride-hailing service, the service provider (RS) matches a customer (RC) with the closest vehicle (RV) registered to this service. Ride-hailing services have gained tremendous popularity over the past years, and several works have been proposed to ensure privacy of riders and drivers during ride-matching. TRACE is an efficient privacy-preserving ride-hailing service proposed by Wang et al. (IEEE Trans. Vehicular Technology 2018). TRACE uses masking along with other cryptographic techniques to ensure efficient and accurate ride-matching. RS computes a (secret) spatial division of a region into quadrants. The RS uses masked location information to match RCs and RVs within a quadrant without obtaining their exact locations, thus ensuring privacy. Additionally, an RC only gets to know location of the closest RV finally matched to it, and not of other responding RVs in the region.
Insurance Fraud in Korea, Its Seriousness, and Policy Implications

Several characteristics of insurance fraud including its chronic nature justifies the need for identifying feasible proposals which can be expected to bring about significant impacts. Recent statistics show that insurance fraud is now consistently on the increase. However, insurance fraud is highly fragmented and each offence is not significant enough to elicit active interest among the public or interventions from the police. Three problems have been identified and diagnosed. These were a lack of awareness, an absence of a national leadership and also limited attention directed to insurance fraud by the investigating authorities. Based on these, three recommendations have been suggested. (1) Embarking on and developing a national initiative by central government, (2) Taking a dynamic concentration approach to send deterrent threats to potential fraudsters, and (3) Using big data technologies to detect clandestine activities by organised groups.
PopDMMO: A general framework of population-based stochastic search algorithms for dynamic multimodal optimization

Dynamic multimodal optimization problems (DMMOPs) are a class of problems consisting of two characteristics, i.e., dynamic and multimodal natures. The former characteristic reveals that the properties of DMMOPs change over time, which is derived from dynamic optimization problems (DOPs). The latter one indicates that there exist multiple global or acceptable local optima, which comes from the multimodal optimization problems (MMOPs). Although there has been much attention to both DOPs and MMOPs in the field of meta-heuristics, there is little work devoting to the connection between the dynamic and multimodal natures in DMMOPs. To solve DMMOPs, the strategies dealing with dynamic and multimodal natures in the algorithms should cooperate with each other. Before looking deeply into the connections between two natures, there is necessary to measure the performances of the methods dealing with two natures in DMMOPs. In this paper, first, considering the dynamic and multimodal natures of DMMOPs, we design a set of benchmark problems to simulate various dynamic and multimodal environments. Then, we propose the optimization framework called PopDMMO containing several popular algorithms and methods to test and compare the performances of these algorithms, which gives a general view of solving DMMOPs.
Using Tsetlin Machine to discover interpretable rules in <scp>natural language processing</scp> applications

Abstract Tsetlin Machines (TM) use finite state machines for learning and propositional logic to represent patterns. The resulting pattern recognition approach captures information in the form of conjunctive clauses, thus facilitating human interpretation. In this work, we propose a TM‐based approach to three common natural language processing (NLP) tasks, namely, sentiment analysis, semantic relation categorization and identifying entities in multi‐turn dialogues. By performing frequent itemset mining on the TM‐produced patterns, we show that we can obtain a global and a local interpretation of the learning, one that mimics existing rule‐sets or lexicons. Further, we also establish that our TM based approach does not compromise on accuracy in the quest for interpretability, via comparison with some widely used machine learning techniques. Finally, we introduce the idea of a relational TM, which uses a logic‐based framework to further extend the interpretability.
Classifiers selection based on analytic hierarchy process and similarity score for spam identification

The overrun of images and corpus spam on the web constitutes a threat to users producing more troubles, diminishing internet bandwidth, and increasing network overload. In this paper, we introduce a multi-criteria decision-making (MCDM) framework for spam classification based on an analytic hierarchy process. The proposed approach permits the selection of the most optimal classifier among six machine learning algorithms according to a set of performance metrics. The ranking consistency of classifiers has been evaluated with a consistency ratio almost inferior to 0.1 on all dataset. The proposed framework can recommend top-K classifiers with high accuracy by using Euclidean distance on basic characteristics of test and train data that was already trained on a set of classifiers and fed into the MCDM framework. The experiment has been performed on eight publicly available datasets and tested on four datasets using only known features from the dataset and using Euclidean distance measure to identify from the most performant classifier algorithm. The followed methodology has accurately identified the most suited classifier on training datasets with average accuracy 95% for both images and corpus spam out and achieved 100% accuracy on a personalimageham+spamarchive_jml dataset that performs better than existing methods. We performed Friedman statistical test on the dataset that showed significance, then a post hoc Conover test performed on each data to make a pairwise comparison that demonstrated the effectiveness of the proposed approach based AHP framework. The proposed approach outperforms existing research works in term of spam identification on both image and corpus spam data and was successful in recommending classifiers to unknown datasets.