Reviewerly/exHarmony huggingface dataset:
Building Effective Software Development Teams Based on the Personality of Its Members

Software development teams are built to achieve the highest level of performance and ensure the fulfillment of the project's objectives. Social and human factors are consolidated as fundamental aspects to ensure this purpose. In addition, personality is another determining aspect to build a team due to the role played by the interaction between the members of the work team and its influence on the conformation of the software development team. Therefore, the objective of this study is to identify the procedures and recommendations that allow building software development teams based on the personality of their members. To achieve this objective, a systematic literature review was carried out with 26 primary studies, from which 14 procedures were identified to build work teams in Software Engineering, where personality is measured using psychometric tests. The results of the empirical studies allowed consolidating recommendations for assigning roles according to personality traits. The most relevant result indicates that the management of software development teams can be improved by including social and human factors within their measurement and decision-making processes.
Progressive Transformer Machine for Natural Character Reenactment

Character reenactment aims to control a target person’s full-head movement by a driving monocular sequence that is made up of the driving character video. Current algorithms utilize convolution neural networks in generative adversarial networks, which extract historical and geometric information to iteratively generate video frames. However, convolution neural networks can merely capture local information with limited receptive fields and ignore global dependencies that play a crucial role in face synthesis, leading to generating unnatural video frames. In this work, we design a progressive transformer module that introduces multi-head self-attention with convolution refinement to simultaneously capture global-local dependencies. Specifically, we utilize the non-lapping window-based multi-head self-attention mechanism with hierarchical architecture to obtain the larger receptive fields at low-resolution feature map and thus extract global information. To better model local dependencies, we introduce the convolution operation to further refine the attentional weight in the multi-head self-attention mechanism. Finally, we use several stacked progressive transformer modules with the down-sampling operation to encode information of appearance information of previously generated frames and parameterized 3D face information of the current frame. Similarly, we use several stacked progressive transformer modules with the up-sampling operation to iteratively generate video frames. In this way, it can capture global-local information to facilitate generating video frames that are globally natural while preserving sharp outlines and rich detail information. Extensive experiments on several standard benchmarks suggest that the proposed method outperforms current leading algorithms.
Bayesian Matrix Completion for Planning Diabetes Treatment Based on Urban Cases

For patients in the early stages of diabetes, it is crucial for patients and doctors to make treatment decisions to prevent the condition from getting worse and developing complications such as diabetic eyes and feet. Both undertreatment and overtreatment can do harm to the patient. In this paper, the probability of taking each treatment option for different symptoms is complemented by a database of existing successful cases of diabetic treatment options to recommend an appropriate treatment for patients with specific symptoms. The matrix completion process adopts the assumption of high rank and completes the matrix based on the unique properties of Bayesian matrices. The results demonstrate the practicality and effectiveness of this algorithm.
Research on Transmission Line Foreign Object Detection Based on Edge Calculation

Transmission line foreign object detection plays an important role in improving the security, reliability and stability of transmission system. It's a challenge that transmission line foreign object detection achieves real-time on edge calculation as well as high performance. This paper proposes an object detector with both speed and accuracy based on YOLOv5, named YOLOv5-GHK. First, replacing Convolution, Ghost module has less parameters and calculation to achieve real-time speed. Then, network prediction with high-resolution feature improves accuracy of small transmission line foreign objects. Last, knowledge distillation ensures that lightweight model has less loss of precision. As a consequence, the proposed method runs at 36 FPS on NPU with a state-of-the-art accuracy.
Secure Virtual Machine Allocation for Prevention of Side Channel Attacks in Cloud Computing

Cloud computing allows customers to use a variety of computing resources on-demand and with no maintenance overhead. One of the major issue concerning cloud computing is security. From the end user's perception, migrating to cloud exposes them to additional security risks that are entirely considered to be produced by other occupants who may have some access to shared resources. The co-location or co-residence attack, otherwise called as co-resident assault, is the focus of this research. This is a type of attack in which malevolent individuals construct side channels and steal confidential information from VMs that share the same server. Here we have studied on the co-resident attacks and the mechanisms to detect and prevent the attack. To address this issue, we have focused on the PSSF VM allocation policy as PSSF policy has high security with low energy consumption.
Adversarial Attacks against Machine Learning Classifiers: A Study of Sentiment Classification in Twitter

In recent years, the rapid evolution of social networks has played a significant role in people's lives. The wide use of social networks like Twitter, Facebook, and Instagram has shortened the communication distance between users. Besides communication, the social network has also become a platform for expressing one's thoughts, emotions, and opinions, becoming a medium for expressing attitudes towards entities such as products, services, events, etc. Therefore, the use of social media to attain various functions has become an investigation area in recent years. The extensive application of machine learning (ML) and natural language processing (NLP), and the improvements in computer performance, have been widely applied in various fields. In terms of social media, machine learning algorithms help in implementing functions like the sentimental analysis. To provide weighted sentiment scores to entities, the sentiment analysis system for text analysis integrates NLP and machine learning approaches. ML algorithms are sensitive to adversarial examples by adding perturbations or changes that are not detectable to the human naked eye and mislead the classifier to predict a false result. In this paper, we propose two attacks, LeetSpeakAttack and PhonologicalErrorAttack, that test the accuracy of a classifier. We also evaluate the classifiers' performance before and after the adversarial attack.
Trustful Charity Foundation platform based on Hyperledger Fabric

Charity plays a significant role in our society and is considered by many people as the most common way to repay our social debt. Therefore a significant amount of donated funds circulate around the world. In recent years, there has been an increase in the number of non-profit organizations and charitable foundations collecting funds for various charity needs. Unfortunately, the reputation of charity organizations is often undermined by the actions of unscrupulous organizations. Significant reputational damage caused by cases of fraud and corruption reduces the level of trust and decreases the amount of collected funds. We strongly believe that blockchain technology can help charity organizations to build trust, increase efficiency and transparency. We are presenting a blockchain-based charity foundation platform implemented on the top of Hyperledger Fabric. This paper examines the significance of blockchain technology for modern philanthropy. We believe that a blockchain-based platform can mobilize new actors to participate in charity activities by increasing transparency and building trust.
Research and Application of Automatic Text Summarization Technology Based on Deep Learning

It takes a lot of time and energy for users to obtain useful information from the massive data generated by the Internet. The text abstract is a refined expression of the content of the article, which can summarize the main content of the article. Text summarization technology can quickly allow users to obtain information that is valuable to them, and to a certain extent alleviate the problem of information overload in the era of big data. In this paper, we use the knowledge enhancement model to learn the semantic relationship of the real world by modeling the entity concept and other prior semantic knowledge in massive data, so as to overcome the disadvantage of using only the original language signal in the previous language model. Then the generative pre-training model is used to solve some specific problems in natural language generation, such as the exposure bias problem. The experimental results show that the model used in this paper works well on the Gigaword and CNN / DailyMail data sets. At the same time, the abstract generated on the nlpcc2017 Chinese abstract data has good accuracy and readability.
A Concurrent Intelligent Natural Language Understanding Model for an Automated Inquiry System

The work is intended to tackle a vital field that lies at the intersection of speech processing and natural language processing: Spoken Language Understanding (SLU). Its idea is to understand the essence of machine-directed human speech in order to facilitate its further processing and take on board its cognitive impact. The proposed system is CIDIS -Concurrent Intelligent Model for Dialogue Act Classification, Intent Detection and Slot Filling, that uses a deep concurrent multi-task paradigm to perform the three fundamental tasks of the SLU domain: Dialogue Act Classification, Intent Detection and Slot Filling. Since the model is orchestrated in a multi-task fashion, every task interacts with the other to have a global understanding of the input query. It follows an intelligent encoding strategy involving concatenation of the query’s BERT and CharCNN embedding to handle all possible edge cases and ambiguities involved in human speech queries. This intelligent encoding is passed through a Stacked BiLSTM architecture followed by task-specific attention layers. The three supplementary outputs are in turn fed to the final module that generates the expected query response in real-time based on the dialogue act, intent and slot. The developed models are evaluated against standard benchmark datasets like ATIS, TRAINS and FRAMES and the achieved state-of-the-art performances are eventually tabulated.
Document Image Forgery Detection Based on Deep Learning Models

With the improvement of the communication speed and the popularization of the Internet, images have become the most common information medium in life. At the same time, the adverse effects of forged images in the media, credit investigation, finance and academic fields are becoming more and more significant. Therefore, in recent years, the research on forged image identification algorithms has been active worldwide. Image forgery has different classification methods. According to whether the forgery uses deep learning methods, it can be divided into deep forged images and traditional forged images. It can also be divided into ordinary image forged and document image forged according to whether the image is a text image. Different forgery methods will leave different forgery traces in the image, corresponding to different forgery identification methods. Aiming at document forgery images, this paper proposes a forgery detection algorithm based on deep learning and fusion of error level analysis (ELA) information. Compared with the previous forgery identification algorithms, the algorithm in this paper can not only identify whether the document image is forged, but can also locate the forged text area. The algorithm proposed in this paper supports the detection of document image forgery generated by cutting, copying, erasing and deep learning methods. The detection algorithm of this paper participated in the fifth forgery detection competition of Ali Tianchi and won the 32nd place among 1470 participating teams.
Research on personnel data management model based on blockchain

With the development of society, the importance of personnel data has becoming more and more important. In recent years, many problems have been exposed in personnel data management: First, the security of data can not be guaranteed. Second, it is difficult to share data in time. Third, personnel data are opaque to the person concerned and lack supervision. To solve these problems, this paper proposes a personnel data management model based on blockchain, which solves the problem of insufficient block storage space by building key-on-chain data model, and effectively makes use of the characteristics of blockchain, such as decentralization, strong tamper resistance and information traceability to protect the security and authenticity of personnel files. The distributed ledger of different nodes can share data in time. Finally, Attribute-Based Access Control (ABAC) is deployed on the smart contract to realize fine-grained access control.
Deep-Learning Based Multi-Object Detection and Tracking using Range-Angle Map in Automotive Radar Systems

In this paper, a machine learning-based object detection and tracking approach in radar system is proposed via using the range-angle map as the input. Specifically, by using the You Only Look Once (YOLO) for object detection and Deep Simple Online and Realtime Tracking (D-SORT) for tracking, the proposed approach can improve the detection and tracking performance, reducing the parameters needed to be manually selected, and providing more relevant information, such as the shape, size, and category of the object. We conduct the realistic simulations to evaluate the proposed approach. Results show that our proposed approach can outperform the conventional radar processing approach in terms of detection and tracking performance. Furthermore, results indicate that the object categorization of the proposed approach is accurate.
A New Measurement Method of Real-time Pose Estimation for an Automatic Hydraulic Excavator

Construction machinery automation has drawn more and more research interests in recent years. To realize the automatic operation of a hydraulic excavator, a key technology is the effective pose estimation of its multiple actuators. In this work, a new measurement method of real-time pose estimation is developed by using machine vision and artificial neural network (ANN) to overcome the limitations of existing methods. A high-resolution camera is installed on the excavator cab to capture the pose image with a constant view angle even when the excavator swings. Images are processed in hue, saturation and value space to extract the centroid pixel coordinate (CPC) of the markers fixed on the actuator joints. Taking the centroids as featured points, the mapping relations between the pixel coordinates and the joint angles are derived by training the ANN. A number of experiments are implemented under various conditions and the results show that the proposed method has good real-time performance and robustness. This work can provide basis for the feedback control of the actuators in the excavator.
Wave Forecast using Bidirectional GRU and GRU Method Case Study in Pangandaran, Indonesia

Wave forecasting is needed to support marine activities, especially planning for ship navigation, maintenance, and offshore engineering construction. Especially in an open sea, forecasting waves can be challenging due to the stochastic nature of the waves. In this research, we use deep learning models to forecast significant wave height, i.e., the Gated Recurrent Unit (GRU) both unidirectional and bidirectional variants. As a case study, we use six-year time series wave data in Pangandaran, West Java, Indonesia. We use the historical wave data to forecast the next one, three, and seven days’ wave conditions. Results of prediction show that the GRU gives better results than the experiment’s Bidirectional GRU (BiGRU). To predict one day ahead, the GRU gives an RMSE value of 0.2184 and R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> value of 0.9863, whereas the BiGRU gives a prediction with an RMSE value of 0.2206 and R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> value of 0.9869.
Partial Cycle Structure of FSRs and Its Applications in Searching De Bruijn Sequences

We propose the concept of partial cycle structure of feedback shift registers, and study its applications in searching the characteristic functions of de Bruijn sequences. We show that, if a function generates de Bruijn sequences then its partial cycle structure does not contain cycles, and conversely, if the partial cycle structure of a function does not contain cycles then it can be extended into a function that generates de Bruijn sequences. By using this property, we analyze the low degree terms in the characteristic functions of de Bruijn sequences, and in particular give a full description of the linear terms in them. We also design an algorithm to search the characteristic functions of de Bruijn sequences which should perform better than the random search algorithm.
An Efficient Digital Twin Assisted Clustered Federated Learning Algorithm for Disease Prediction

In-depth analysis of medical data through machine learning to achieve disease prediction is beneficial to the early detection and treatment of diseases. However, medical data involves mass patient privacy, and datasets of different medical institutions cannot be directly shared due to privacy protection. So medical data often exists in the form of data islands, which makes it difficult for most existing prediction models to complete disease prediction. In this paper, a digital twin assisted efficient clustering Federated Learning (FL) algorithm for disease prediction is proposed. It can break data islands to predict diseases on the premise of privacy security. Firstly, we design an efficient clustering Federated Learning with Client Selection (FLCS) protocol based on heterogeneity and contribution to improve the training efficiency and prediction accuracy. Secondly, we use digital twin to assist the FLCS protocol to carry out large-scale prediction. In addition, the shapley value introduced in the calculation of client contribution makes the model interpretable and enhances the reliability of prediction results. Finally, the evaluation results show that compared with the common prediction models and FedAvg algorithm, the FLCS protocol assisted by digital twin has better efficiency and accuracy in binary classification prediction.
Enabling Edge-based Federated Learning through MQTT and OMA Lightweight-M2M

The increasing processing capabilities of massively deployed Internet of Things (IoT) and edge devices coupled with the rising demands for fast and autonomous decision-making pave the way for accurate Machine Learning (ML) model training in proximity to where data is collected, as proposed by the Federated Learning (FL) paradigm. The devices contributing to the local training (clients) are typically large in number and differ in terms of computing and battery capabilities, experienced link quality conditions and owned datasets. If clients are not properly selected by the entity in charge of aggregating the individual models (FL server) both the convergence speed and the accuracy of the resulting model can be affected. The interest in the literature for the optimization of the client selection policies is manifest, but less attention has been devoted to the design of the practical implementation of such policies with focus on the client discovery procedures. In order to fill this gap, an edge-based framework is proposed to improve the FL client discovery procedures by leveraging (i) the Message Queue Telemetry Transport (MQTT) protocol for FL clients-server interactions aimed to retrieve the capabilities of the potential clients and (ii) the Open Mobile Alliance (OMA) Lightweight-Machine-to-Machine (LwM2M) for the semantic description of such capabilities. A proof-of-concept is provided to assess the communication footprint incurred by the proposal.
Separating Hate Speech from Abusive Language on Indonesian Twitter

Social media is an effective tool for connecting with people and distributing information. However, many people often use social media to spread hate speech and abusive languages. In contrast to hate speech, abusive languages are frequently used as jokes with no purpose of offending individuals or groups, even though they may contain profanities. As a result, the distinction between hate speech and abusive language is often blurred. In many cases, individuals who spread hate speech may be prosecuted as it has legal implications. Previous research has focused on binary classification of hate speech and normal tweets. This study aims to classify hate speech, abusive language, and normal messages on Indonesian Twitter. Several machine learning models, such as logistic regression and BERT models, are utilized to accomplish text classification tasks. The model's performance is assessed using the F1-Score evaluation metric. The results show that BERT models outperform other models in terms of F1-Score, with the BERT-indobenchmark model, which was pretrained on social media text data, achieving the highest F1-Score of 85.59. This also demonstrates that pretraining the BERT model using social media data improves the classification model significantly. Developing such classification model that can distinguish between hate speech and abusive language would help individuals in preventing the spread of hate speech that has legal implications.
Mobile Applications for Privacy-Preserving Digital Contact Tracing

Mobile applications for triggering Covid-19 exposure notifications without sacrificing the users' privacy are a promising tool for complementing manual contact tracing that is a resource-demanding and labor-intensive task when the infections grow rapidly. This advanced seminar presents the fundamental concepts behind the realization of large-scale Mobile Contact Tracing Apps (MCTA). We provide an overview of this emerging field, while focusing on Bluetooth-based privacy-preserving solutions. We tackle the topic from multiple perspectives: background, state-of-the-art technologies, protocols, real-life implementations, performance indicators, security and privacy aspects, as well as future directions. The seminar presents the big picture, so that the target audience can further expand their knowledge by studying the material and following the references. Our presentation will be delivered through the lens of 2 country-wide MCTA, namely the Corona-Warn-App (CWA) and the CovTracer-Exposure Notification (CovTracer-EN) app deployed in Germany and Cyprus, respectively.