Reviewerly/exHarmony huggingface dataset:
Towards Federated COVID-19 Vaccine Side Effect Prediction

We propose FedCovid, a new federated learning system based on electronic health records (EHR), to predict COVID-19 vaccination side effects. Federated learning allows diverse data owners to work together to train machine learning models without sharing data, ensuring the privacy of EHR data. However, because EHR data is unique, directly using existing federated learning models may fail. The EHR data is diverse, with numerical and categorical characteristics as well as consecutive visits. Furthermore, each client’s data size is unequal, and the data labels are skewed due to the small number of patients that experience serious side effects. We present an adaptive approach to fuse heterogeneous EHR data and apply data augmentation techniques working with a margin loss to overcome the data imbalance issue in the client model training to address both challenges simultaneously in FedCovid. We recommend that when the server is updated, the data size of each client be taken into account to lessen the impact of clients with small data volumes. Finally, in order to train a stable and successful federated learning model, we suggest a new ordinal training technique. Experiments on a real-world dataset reveal that the suggested model is effective at predicting COVID-19 vaccination adverse effects. The performance increases by 14.35%, 17.81%, and 129.36% on the F1 score, Cohen’s Kappa, and PR-AUC, respectively, compared with the best baseline (The source code of the proposed FedCovid is available at https://github.com/JackqqWang/FedCovid.git ).
Explainable Anomaly Detection System for Categorical Sensor Data in Internet of Things

Internet of things (IoT) applications deploy massive number of sensors to monitor the system and environment. Anomaly detection on streaming sensor data is an important task for IoT maintenance and operation. However, there are two major challenges for anomaly detection in real IoT applications: (1) many sensors report categorical values rather than numerical readings; (2) the end users may not understand the detection results, they require additional knowledge and explanations to make decision and take action. Unfortunately, most existing solutions cannot satisfy such requirements. To bridge the gap, we design and develop an eXplainable Anomaly Detection System (XADS) for categorical sensor data. XADS trains models from historical normal data and conducts online monitoring. XADS detects the anomalies in an explainable way: the system not only reports anomalies’ time periods, types, and detailed information, but also provides explanations on why they are abnormal, and what the normal data look like. Such information significantly helps the decision making for users. Moreover, XADS requires limited parameter setting in advance, yields high accuracy on detection results and comes with a user-friendly interface, making it an efficient and effective tool to monitor a wide variety of IoT applications.
Systemic Review of AI Reshaped Blockchain Applications

The perceived benefits of integrating Blockchain Technology (BT) and Artificial Intelligence (AI) have profound importance from qualitative and quantitative perspectives to real-world applications. However, the academic literature connecting the two is largely absent. This study reviews the adoption of Blockchain applications in diversified sectors that contribute to its reshaping through AI and are not limited to digital currency, trade connection, or information processing. A term-searching mechanism addresses the research question through a formalized plan for reviewing field-wise blockchain papers. The keywords Blockchain, blockchain-based applications, and artificial intelligence assess the secondary literature published until the first quarter of 2021. Twenty-one use cases where BT proved its permanence theoretically or empirically about AI are identified based on equality and feasibility. Moreover, the potential of BT goes beyond the hindrances of techno-socio-economic space concerns, thus contributing to research and business innovation. This study can be considered one of the first academic articles connecting BT and its application spread over IT and related industrial environments/sectors/ fields for improving the movement of AI. In addition to trust and awareness, integrating AI and Blockchain will lead to a future beyond the reactive machine's limitation.
Code Smells: A Comprehensive Online Catalog and Taxonomy

Context: Code Smells—a concept not fully understood among programmers, crucial to the code quality, and yet unstandardized in the scientific literature. Objective: Goal (#1): To provide a widely accessible Catalog that can perform useful functions both for researchers as a unified data system, allowing immediate information extraction, and for programmers as a knowledge base. Goal (#2): To identify all possible concepts characterized as Code Smells and possible controversies. Goal (#3): To characterize the Code Smells by assigning them appropriate characteristics. Method: We performed a combined search of formally published literature and grey material strictly on Code Smell and related concepts where it might never have been mentioned, along with the term "Code Smell" as a keyword. The results were analyzed and interpreted using the knowledge gathered, classified, and verified for internal consistency. Results: We identified 56 Code Smells, of which 15 are original propositions, along with an online catalog. Each smell was classified according to taxonomy, synonyms, type of problem it causes, relations, etc. In addition, we have found and listed 22 different types of Bad Smells called hierarchies and drew attention to the vague distinction between the Bad Smell concepts and Antipatterns. Conclusion: This work has the potential to raise awareness of how widespread and valuable the concept of Code Smells within the industry is and fill the gaps in the existing scientific literature. It will allow further research to be carried out consciously because access to the accumulated information resource is no longer hidden or difficult. Unified data will allow for better reproducibility of the research, and the subsequent results obtained may be more definitive
LBAA: A novel load balancing mechanism in cloud environments using ant colony optimization and artificial bee colony algorithms

Summary Recently, cloud computing has been recognized as an effective paradigm for offering an on‐demand platform, software services, and an efficient infrastructure to cloud clients. Due to the exponential growth of cloud tasks and the rapidly increasing number of cloud users, scheduling and balancing these tasks among involved heterogeneous virtual machines becomes an Non‐deterministic Polynomial hard (NP‐hard) optimization problem considering significant constraints, such as high rate of resource usage, low scheduling time, and low implementation cost. Therefore, various meta‐heuristic algorithms have been widely used to tackle the issue. The current paper proposes a novel load balancing mechanism using the ant colony optimization and artificial bee colony algorithms, called LBAA, which aims to balance the load division among systems in data centers. The simulation outcomes confirm that our algorithm outperforms previous works regarding response time, imbalance degree, makespan, and resource utilization up to 25%, 15%, 12%, and 10%, respectively.
A Prescriptive Machine Learning Approach for Assessing Goodwill in the Automotive Domain

Car manufacturers receive thousands of goodwill requests for vehicle defects per year. At BMW, these requests for repair-cost contributions are either assessed automatically by a set of fixed rules or manually by human experts. To decrease manual effort, which is still around 50%, we propose a machine learning approach with the goal to discover so far unknown assessment patterns in human decisions. Since the assessment contribution data is heavily imbalanced, we structure the learning task hierarchically: The first layer's task is to predict the main rank of the request (no contribution, partial contribution, or full contribution). Then, in the case where partial contribution is suggested, the second layer predicts the concrete percentage using a regression model. To optimize our model and tailor it to certain strategies (e.g., customer friendly or more cost oriented), we make use of a custom-defined cost matrix. We also outline how the model can be used in a scenario in which it prescribes appropriate monetary contributions for requested repair-costs. This can initially happen in the form of a decision support system (DSS) and, in the next step, through automated decision making (ADM), where a certain part of goodwill requests is processed automatically by the prescriptive model.
Axiomatic Epistemology

This article is the first complete axiomatic presentation of the theory of cognition (epistemology). It presents the axioms of the theory of cognition, the definitions of the concepts and categories used in the text of the theory, and also provides detailed proofs of the propositions (theorems) arising from them. Based on this axiomatic system, evidence-based solutions to the problems (aporias) of the theory of cognition are proposed. This system of axiomatic epistemology can serve as a step in building a rigorous scientific theory of human cognition. The axioms of the proposed system follow the Aristotelian general understanding of the essence of human cognition.
DeepDual-SD: Deep Dual Attribute-Aware Embedding for Binary Code Similarity Detection

Abstract Binary code similarity detection (BCSD) is a task of detecting similarity of binary functions which are not available to the corresponding source code. It has been widely utilized to facilitate various kinds of crucial security analysis in software engineering. Because of the complexity of the program compilation process, identifying binary code similarity presents tough challenges. The most sensible binary similarity detector relies on a robust vector representation of binary code. However, few BCSD approaches are suitable to form vector representations for analyzing similarities between binaries, which may not only diverge in semantics but also in structures. And the existing solutions which only depend on hands-on feature engineering to form feature vectors, fail to take into consideration the relationships between instructions. To resolve these problems, we propose a novel and unified approach called DeepDual-SD that aims to combine the dual attributes (semantic and structural attribute). More specifically, DeepDual-SD consists of two branches, in which one text-based feature representation is driven by semantic attribute learning to exploit instruction semantics, another graph-based feature representation for structural attribute learning to investigate structural differences. Meanwhile deep embedding (DE) technology is utilized to map this information into low-dimensional vector representation. In addition, to get together the dual attributes, a fusion mechanism based on gate architecture is designed for learning to pay proper attention between the two attribute-aware embeddings. Experimental verifications are conducted on Openssl and Debian datasets for several tasks, including cross-compiler, cross-architecture and cross-version scenarios. The results demonstrate that our method outperforms the state-of-the-art BCSD methods in different scenarios in terms of detection accuracy.
Stochastic Optimal Selection and Analysis of Allowable Photovoltaic Penetration Level for Grid-Connected Systems Using a Hybrid NSGAII-MOPSO and Monte Carlo Method

Generally, the main focus of the grid-linked photovoltaic systems is to scale up the photovoltaic penetration level to ensure full electricity consumption coverage. However, due to the stochasticity and nondispatchable nature of its generation, significant adverse impacts such as power overloading, voltage, harmonics, current, and frequency instabilities on the utility grid arise. These impacts vary in severity as a function of the degree of penetration level of the photovoltaic system. Thus, the design problem involves optimizing the two conflicting objectives in the presence of uncertainty without violating the grid’s operational limitations. Nevertheless, existing studies avoid the technical impact and scalarize the conflicting stochastic objectives into a single stochastic objective to lessen the degree of complexity of the problem. This study proposes a stochastic multiobjective methodology to decide on the optimum allowable photovoltaic penetration level for an electricity grid system at an optimum cost without violating the system’s operational constraints. Five cutting-edge multiobjective optimization algorithms were implemented and compared using hypervolume metric, execution time, and nonparametric statistical analysis to obtain a quality solution. The results indicated that a Hybrid NSGAII-MOPSO had better convergence, diversity, and execution time capacity to handle the complex problem. The analysis of the obtained optimal solution shows that a practical design methodology could accurately decide the maximum allowable photovoltaic penetration level to match up the energy demand of any grid-linked system at a minimum cost without collapsing the grid’s operational limitations even under fluctuating weather conditions. Comparatively, the stochastic approach enables the development of a more sustainable and affordable grid-connected system.
The Effect of Video Clips on Students’ Speaking Performances

The literature review is aimed at enhancing the quality of speaking English learning through video clips. The paper's objectives are to use technology to stimulate students’ interest in learning English and to how teachers accelerate students’ speaking English learning in their classroom. One of the reasons is that frameworks of language function constrain the design of teaching methods and curricula, and so this skill is often only used as a motivator or to enrich language teaching activities. The participants are one hundred eleven students at Thanh Loc high school 11th Grade and Five English Teachers. Data collection includes analyzing and discussing the obtained data in light of the theoretical basis of combining teaching speaking skills to develop inter-communication competence for learners, and the teacher makes specific recommendations on classroom teaching design and the professional development of learners in the file of technology.
Biofidelic image registration for head and neck region utilizing an in-silico articulated skeleton as a transformation model

Objective.We propose an integration scheme for a biomechanical motion model into a deformable image registration. We demonstrate its accuracy and reproducibility for adaptive radiation therapy in the head and neck region.Approach. The novel registration scheme for the bony structures in the head and neck regions is based on a previously developed articulated kinematic skeleton model. The realized iterative single-bone optimization process directly triggers posture changes of the articulated skeleton, exchanging the transformation model within the deformable image registration process. Accuracy in terms of target registration errors in the bones is evaluated for 18 vector fields of three patients between each planning CT and six fraction CT scans distributed along the treatment course.Main results. The median of target registration error distribution of the landmark pairs is 1.4 ± 0.3 mm. This is sufficient accuracy for adaptive radiation therapy. The registration performs equally well for all three patients and no degradation of the registration accuracy can be observed throughout the treatment.Significance. Deformable image registration, despite its known residual uncertainties, is until now the tool of choice towards online re-planning automation. By introducing a biofidelic motion model into the optimization, we provide a viable way towards an in-build quality assurance.
An Investigation into an Always Listening Interface to Support Data Exploration

Natural Language Interfaces that facilitate data exploration tasks are rapidly gaining in interest in the research community because they enable users to focus their attention on the task of inquiry rather than the mechanics of chart construction. Yet, current systems rely solely on processing the user's explicit commands to generate the user's intended chart. These commands can be ambiguous due to natural language tendencies such as speech disfluency and underspecification. In this paper, we developed and studied how an always listening interface can help contextualize imprecise queries. Our study revealed that an always listening interface is able to use an on-going conversation to fill in missing properties for imprecise commands, disambiguate inaccurate commands without asking the user for clarification, as well as generate charts without being explicitly asked.
Human Activities Recognition using Semi-Supervised SVM and Hidden Markov Models

Automatic human activity recognition is an area of interest for developing health, security, and sports applications. Currently, it is necessary to develop methods that facilitate the training process and reduce the costs of this process. This paper explores a methodology to classify human physical activities in a semi-supervised paradigm. With this approach, it is possible to reduce the number of labels necessary to train the learning model and the complexity of this process. This process begins by deducting the number of micro-movements or sub-movements where the data should be grouped and assigning the label through a clustering technique. We perform this procedure for a specific group of micro-movements whose label is unknown. Later, the classification process starts by using two methods, a Support Vector Machine (SVM) that identifies the micro-movements and a Markov Hidden Model that detects the human physical activity as a function of sequences. The results show that with a percentage of 80 % of the known labels, we achieved outcomes like the supervised paradigms found in the literature. This facilitates training these learning models by reducing the number of examples requiring labels and reduces the economic costs, which is one of the significant limitations of machine learning processes.
MLPQ: A Dataset for Path Question Answering over Multilingual Knowledge Graphs

Knowledge Graph-based Multilingual Question Answering (KG-MLQA), as one of the essential subtasks in Knowledge Graph-based Question Answering (KGQA), emphasizes that questions on the KGQA task can be expressed in different languages to solve the lexical gap between questions and knowledge graph(s). However, the existing KG-MLQA works mainly focus on the semantic parsing of multilingual questions but ignore the questions that require integrating information from cross-lingual knowledge graphs (CLKG). This paper extends KG-MLQA to Cross-lingual KG-based multilingual Question Answering (CLKGQA) and constructs the first CLKGQA dataset over multilingual DBpedia named MLPQ, which contains 300K questions in English, Chinese, and French. We further propose a novel KG sampling algorithm for KG construction, making the MLPQ support the research of different types of methods. To evaluate the dataset, we put forward a general question answering workflow whose core idea is to transform CLKGQA into KG-MLQA. We first use the Entity Alignment (EA) model to merge CLKG into a single KG and get the answer to the question by the Multi-hop QA model combined with the Multilingual pre-training model. By instantiating the above QA workflow, we establish two baseline models for MLPQ, one of which uses Google translation to obtain alignment entities, and the other adopts the recent EA model. Experiments show that the baseline models are insufficient to obtain the ideal performances on CLKGQA. Moreover, the availability of our benchmark contributes to the community of question answering and entity alignment.
Multivariate Time Series Anomaly Detection With Generative Adversarial Networks Based on Active Distortion Transformer

Detecting anomalies for multivariate time series is of great importance in modern industrial applications. However, due to the complex temporal dynamics in modern systems, finding a distinguishable judge criterion is hard, which makes accurate anomaly detection still a challenging task. In order to better capture the anomalous features and design a more informative judge criterion, this article presents an unsupervised generative adversarial network (GAN) for multivariate time series anomaly detection, which highlights a novel active distortion transformer (ADT) block. Different from the vanilla transformer, the ADT block can make good use of the prior knowledge of time sequences’ overall associations by actively conducting distortion during the reconstruction of input sequences. Benefiting from the ADT block, the network simultaneously utilizes the sequence associations and reconstruction error to recognize anomalies. In the online detection phase, anomalous data points tend to be less correlated with the overall sequence and have greater reconstruction errors than normal ones, so that an irrelevance score and a reconstruction error score can be obtained. We combine the two scores to generate a more powerful anomaly score as the judge criterion. Extensive experiments are conducted on four publicly available sensor datasets, and we also make comparisons with the recent baselines. Results show that our model outperforms the recent state-of-the-art methods, demonstrating the effectiveness of our method.
Time Delay Signature Extraction of Optical-Feedback-Induced Chaos With Reservoir Computing

The time delay signature is a crucial secure key for chaos communication using optical feedback semiconductor lasers. Herein, we propose a single node-based photonic reservoir computing method for extracting the time delay signature of optical-feedback-induced chaos. Through drawing the phase diagram of the chaotic time series and its delayed copies, we transform the time delay signature into the shape feature of the obtained two-dimensional image. After training the optimized photonic reservoir with these two-dimensional images, we numerically demonstrate that our method can achieve an overall extraction accuracy rate of 97.3% for different optical feedback semiconductor lasers. Considering its good performance and simple configuration, we believe that this single node-based photonic reservoir computing offers a hardware-friendly solution for time delay signature extraction.
A Cloud based Improved File Handling and Duplicate Removal using MD5

In the era of data duplication, the document checksum approach is commonly used to quickly and accurately identify redundant information. The calculated value of numbers and letters used to validate the validity of a file's contents It can determine whether duplicate data exists. However, there are numerous deficiencies. They have a longer processing time, a chance of misleading results, are not user-friendly, and are tough to maintain. The primary objectives of this research work is to minimise the number of duplicates in the cloud database, maximise the process performance to the point where the backup window is slightly impacted, and design with horizontal scaling in mind so that it runs competitively on a cloud platform. The proposed MD5 method outperforms the current best practises.