Reviewerly/exHarmony huggingface dataset:
Customer Support In The Era of Continuous Deployment: A Software-Intensive Embedded Systems Case Study

Supporting customers after they acquire the prod-uct is essential for companies producing and selling software-intensive embedded systems products. Generally, customer sup-port is the first interaction point between the product users and the product vendor. Customer support is often engaged with answering customers' questions, troubleshooting, fault identification, and fixing product faults. While continuous deployment advocates for closer cooperation between the ones operating the software and the ones developing it, the means of such collaboration in general and the role of customer support, in particular, has not been addressed in the context of software-intensive embedded systems. Therefore, to better understand the impact that continuous deployment has on customer support and the role customer support should play in this context, we conducted a case study at a multinational company developing and selling telecommunications networks infrastructure. We focused on the 4th and 5th Generation (4G and 5G) Radio Access Networks (RAN) products, which can be considered a high volume product as they cover more than 80% of the world's population. Our study reveals that customer support needs to transition from a transaction-based and passive function triggered by customer support requests, to take an active role characterized by being proactive and preemptive to cope with the shorter operational time of a software version introduced by continuous deployment. In addition, customer support plays an essential role in making the feedback actionable by aggregating and consolidating feedback data to the R&D organization.
Violence Detection using Feature Fusion of Optical Flow and 3D CNN on AICS-Violence Dataset

In this paper, we introduce a new self-developed violence dataset named AICS-Violence. It contains 7576 high-resolution video clips of violent and non-violent scenarios collected by outdoor security cameras. It includes two different test sets with additional non-violent but seemingly violent actions as well. One of those test sets has data collected using a different camera angle compared to that of the training set and the remaining test set. To focus on each group of people in frames, we develop a method to automatically crop candidate boxes from detected human bounding boxes. Furthermore, two methods named 3D DenseNet Fusion OF RGB and 3D DenseNet Fusion OFnom RGB are proposed. In both methods, two 3D DenseNets are used to extract features from RGB and visualized optical flow respectively. Then, two different ways of fusion namely addition and normalization-based multiplication are applied to the two methods respectively. Evaluation results show that our methods achieve slightly better performance in the first test set and especially significantly better generation than those of some other state-of-the-art methods on the second test set.
The Construction of Smart Chinese Medicine Cloud Health Platform Based on Deep Neural Networks

In order to improve the efficiency of doctors’ diagnosis and treatment, the state has built a Chinese medicine cloud health platform. However, most medical institutions currently use internal networks, and the technical standards and specifications are not uniform. Some information is not compatible and patient information cannot be shared. Therefore, the construction of a smart traditional Chinese medicine (TCM) cloud health platform based on deep neural networks has become a current research hotspot. The research results show that the deep neural network technology has a theoretical basis and feasibility in the smart Chinese medicine cloud health platform. Combining with deep neural network technology, a cloud-based Chinese medicine cloud health system has been developed through big data analysis technology. Through the investigation and research method, this paper found that the smart Chinese medicine cloud health platform based on deep neural networks was more popular with citizens, which could improve the quality and efficiency of management. The average management quality of a smart TCM health cloud platform based on deep neural networks was 81.1, and the average management quality of a common TCM cloud health platform was 75. The efficiency of the smart Chinese medicine cloud health platform based on deep neural networks was relatively stable, with an average of 84%. The efficiency of the general TCM cloud health platform fluctuated significantly and was relatively unstable, with an average of 73.5%. The efficiency of the smart Chinese medicine cloud health platform was 10.5% higher than that of the ordinary Chinese medicine cloud health platform. This shows that the construction of the intelligent Chinese medicine cloud health platform under the deep neural network is relatively successful.
A Novel, Scalable N*N Reversible Logic Multiplier Design

In EDA, Reversible logic has its applications in designing computer graphics, digital signal processing, quantum computing, nanotechnology and cryptography. It is a prospective design style in quantum computing and modern nanotechnology. Although noteworthy proposals have been put forth towards defining new reversible logic gates and sub units of ALUs, design of efficient Multiplier block remains crucial to the realization of a high-performance Reversible Logic ALU that can be leveraged by Deep Neural Networks for high performance MAC operations. This paper proposes the design and comparison of a component scalable Reversible Logic Multiplier using the proposed SAK gate. The verification and simulation have been carried out using Vivado&#x00AE; by Xilinx and further implemented on an Artix&#x00AE;-7 based cost effective FPGA. The proposed design has 27% lesser Gate Count and 12.5% lesser Quantum Cost compared to prior designs.
Improving Rating and Relevance with Point-of-Interest Recommender System

The recommendation of points of interest (POIs) is essential in location-based social networks. It makes it easier for users and locations to share information. Recently, researchers tend to recommend POIs by treating them as large-scale retrieval systems that require a large amount of training data representing query-item relevance. However, gathering user feedback in retrieval systems is an expensive task. Existing POI recommender systems make recommendations based on user and item (location) interactions solely. However, there are numerous sources of feedback to consider. For example, when the user visits a POI, what is the POI is about and such. Integrating all these different types of feedback is essential when developing a POI recommender. In this paper, we propose using user and item information and auxiliary information to improve the recommendation modelling in a retrieval system. We develop a deep neural network architecture to model query-item relevance in the presence of both collaborative and content information. We also improve the quality of the learned representations of queries and items by including the contextual information from the user feedback data. The application of these learned representations to a large-scale dataset resulted in significant improvements.
Modeling Disengaged Guessing Behavior in a Vocabulary Learning App using Student, Item, and Session Characteristics

Disengagement from task content in educational apps may have a severe negative impact on learning outcomes. In the current study, we propose repeated mistakes as an indicator of disengaged guessing behavior that may be detrimental to learning. Furthermore, we propose a hierarchical generalized linear model to examine predictors of disengaged guessing behavior relating to student, item and session characteristics. Knowledge of how different characteristics contribute to the prediction of disengaged guessing may provide important information to teachers regarding which students are likely to engage in such behavior, as well as to app developers regarding the functioning of different types of task and session content.
Photovoltaic system monitoring and fault detection using peer systems

Abstract Monitoring residential scale photovoltaic (PV) systems is important for maximizing the energy yield and detecting malfunctions. Analytical‐based approaches are not reliable in these systems because of the lack of on‐site measurements and detailed PV system specifications. In this paper, a collaborative approach is proposed which does not depend on weather data but on similar PV systems. Based on the so‐called performance‐to‐peer approach, the aim of this work is to improve this baseline model by adding PV systems characteristics and by optimizing with machine learning techniques. The methodology has been tested in a fleet of more than 12,000 PV systems located in the Netherlands with up to 7 years of data per system. The proposed model achieves an average of 94.1% and a NRMSE of 0.05, outperforming in terms of the baseline model by 1.4 points, and the analytical approach by 3.8. The data requirements of this model are not high: With 1,700 years of PV system data with daily resolution, the maximum performance can be achieved as long as a minimum of 6 months of data per system and 100 PV systems are considered. The application of this model for fault detection and categorization has also been shown. The proposed approach has shown its strengths with respect to other methods through its ability of distinguishing between system mismatch and actual fault and of adapting to new situations via retraining.
QAnswer: Towards Question Answering Search over Websites

Question Answering (QA) is increasingly used by search engines to provide results to their end-users, yet very few websites currently use QA technologies for their search functionality. To illustrate the potential of QA technologies for the website search practitioner, we demonstrate web searches that combine QA over knowledge graphs and QA over free text – each being usually tackled separately. We also discuss the different benefits and drawbacks of both approaches for web site searches. We use the case studies made of websites hosted by the Wikimedia Foundation (namely Wikipedia and Wikidata). Differently from a search engine (e.g. Google, Bing, etc), the data are indexed integrally, i.e. we do not index only a subset, and they are indexed exclusively, i.e. we index only data available on the corresponding website.
Health Monitoring Approaches towards Face Recognition based System

Face recognition is the most advanced technique of identification and authorization, having evolved significantly over the last ten years. Face recognition software is now widely utilized in a variety of applications, including security and other businesses. The purpose of this study is to develop a facial recognition-based automated attendance management system with active learning capabilities. To create a dependable and secure attendance tracking system, the proposed proposal employs a number of technologies. Active learning is added to address the issue of children's facial feature alterations. The technique is recommended to be used in schools, universities, businesses, and prisons.
Forecasting Bitcoin

In this paper, we forecast Bitcoin's returns and return jumps using a self-exciting process embedded in a stochastic volatility model. We show the existence of the jump clustering feature, which varies depending on the frequency of the data. In an out-of-sample setting, we use a particle filter to sample latent states and conduct one-step-ahead probabilistic forecasting on future jumps (underlying intensities). We assess the forecasts by a continuous ranked probability score. We further develop a statistic that takes the discrepancies between the predicted probabilities of positive and negative jumps. We find that high and low values of the difference in predicted probabilities of positive and negative jumps is able to predict returns, and that a trading strategy based on this has a Sharpe ratio of 4.36.
Safety and Security Analysis using LDA based on Case Reports: Case Study and Trust Evaluation Method

There are many cases where the safety and security of systems are threatened by accidental or intentional human error. This study focuses on the fact that there is information available about human error in design and operation documents and case reports, and they are in natural language. Therefore, we propose a method to analyze the impact of human error on safety and security using Latent Dirichlet Allocation (LDA), which is one of the topic model methods. First, we matched the given information to create a list of similarities (co-occurrence list) between documents. Based on this co-occurrence list, a fault and attack tree was constructed. While manually considering them, the critical points were identified through sensitivity analysis. We show the effectiveness of this proposed method through two characteristic case studies of cyber-based connected car design deficiencies and physical-based manufacturing inspection fraud. Both analyzes add a way to leverage big data interoperability in manufacturing processes using the IoT.
Demo: PhishChain: A Decentralized and Transparent System to Blacklist Phishing URLs

Blacklists are a widely-used Internet security mechanism to protect Internet users from financial scams, malicious web pages, and other cyber attacks based on blacklisted URLs. This demo introduces PhishChain, a transparent and decentralized system for blacklisting phishing URLs. At present, public/private domain blacklists, such as PhishTank, CryptoScamDB, and APWG, are maintained by a centralized authority, but operate in a crowd sourcing fashion to create a manually verified blacklist periodically. In addition to being a single point of failure, the blacklisting process utilized by such systems is not transparent. We utilize the blockchain technology to support transparency and decentralization, where no single authority is controlling the blacklist and all operations are recorded in an immutable distributed ledger. Further, we design a page rank based truth discovery algorithm to assign a phishing score to each URL based on crowd sourced assessment of URLs. As an incentive for voluntary participation, we assign skill points to each user based on their participation in URL verification.
Evaluating multimodal strategies for multi-label movie genre classification

We present the evaluation of different multimodal strategies to classify movies genres in a multi-label scenario. We used a dataset consisting of posters, synopses, subtitles, and trailers taken from 10,594 movies. For this purpose, we first built deep networks for each media. Then these classifiers were combined using two deep models and three late fusion strategies. Despite its simplicity, the late fusion strategies achieved better performance than the deep models. We obtained 0.649 of F-Score and 0.626 of AUC-PR using fusion by the sum rule. Furthermore, it was possible to notice the complementarity among the modalities and the improvement in performance obtained by combining them.
Detecting Regulation Violations for an Indian Regulatory Body through Multi Label Classification

The Securities and Exchange Board of India (SEBI) is the regulatory body for securities and commodities in India. SEBI creates, and enforces regulations that must be followed by all listed companies. To the best of our knowledge, this is the first work on identifying the regulation(s) that a SEBI-related case violates, which could be of substantial value to companies, lawyers, and other stakeholders in the regulatory process. We create a dataset for this task by automatically extracting violations from publicly available case-files. Using this data, we explore various multi-label text classification methods to determine the potentially multiple regulations violated by (the facts of) a case. Our experiments demonstrate the importance of employing contextual text representations to understand complex financial and legal concepts. We also highlight the challenges that must be addressed to develop a fully functional system in the real-world.
A Performed Optimized Load Balancing Genetic Approach Technique in Cloud Environment

Cloud computing is often referred to as a model that provides limitless computing services with a pay-as-you-go model. Modern cloud infrastructures provide resources as the VMs to physical machines using virtualization technology. Every virtual machine focuses on running its own operating system, and it leads to utilize resources from its host physical machine (PM). For load balancing, cloud is capable of migrating VMs from PMs which have heavy load to the ones which have light load. The objective of this process is to use the resources of a physical machine below certain threshold. Uncertainty can be a major reason of the overloading of virtual machines. Previously proposed load balancing method used genetic algorithm for the migration of the virtual machine. The delay of this algorithm increases in the network as virtual machines are migrated. This work puts forward a new algorithm, namely butterfly optimization for VM migration. The proposed optimization algorithm has been implemented in the MATLAB software. The achieved results are compared against the outcomes of the previous algorithm. The introduced approach is evaluated over three performance parameters including delay, bandwidth used and space used.
Assessing the Topics and Motivating Factors Behind Human-Social Chatbot Interactions: Thematic Analysis of User Experiences

Although social chatbot usage is expected to increase as language models and artificial intelligence improve, very little is known about the dynamics of human-social chatbot interactions. Specifically, there is a paucity of research examining why human-social chatbot interactions are initiated and the topics that are discussed.We sought to identify the motivating factors behind initiating contact with Replika, a popular social chatbot, and the topics discussed in these interactions.A sample of Replika users completed a survey that included open-ended questions pertaining to the reasons why they initiated contact with Replika and the topics they typically discuss. Thematic analyses were then used to extract themes and subthemes regarding the motivational factors behind Replika use and the types of discussions that take place in conversations with Replika.Users initiated contact with Replika out of interest, in search of social support, and to cope with mental and physical health conditions. Users engaged in a wide variety of discussion topics with their Replika, including intellectual topics, life and work, recreation, mental health, connection, Replika, current events, and other people.Given the wide range of motivational factors and discussion topics that were reported, our results imply that multifaceted support can be provided by a single social chatbot. While previous research already established that social chatbots can effectively help address mental and physical health issues, these capabilities have been dispersed across several different social chatbots instead of deriving from a single one. Our results also highlight a motivating factor of human-social chatbot usage that has received less attention than other motivating factors: interest. Users most frequently reported using Replika out of interest and sought to explore its capabilities and learn more about artificial intelligence. Thus, while developers and researchers study human-social chatbot interactions with the efficacy of the social chatbot and its targeted user base in mind, it is equally important to consider how its usage can shape public perceptions and support for social chatbots and artificial agents in general.
Students Perceptions of Mobile Apps: Learning Features Impacts on EFL Vocabulary Learning

Although mobile apps have been used for many educational purposes, little is known about how effective these apps are in EFL (English as a Foreign Language) vocabulary learning. Students’ expectations on their usage of mobile apps are also lacking. To fill this gap, further improve the app development and teaching, this study centered on the effectiveness of mobile apps on EFL vocabulary learning from students’ perspectives. A total of 25 articles were collected from 3 selected databases—Web of Science, Eric, and Academic Search Complete. The findings were analyzed through content analysis. Also, an empirical study was conducted between 2 classes of the same grade and same average proficiency level of English in Tianjin No.21 high school. The participants were asked to take part in pretest and mid-term diagnostic test to specify their vocabulary level proficiency and words retentions. Questionnaire and selected interview were adopted to analyze students’ perceptions of mobile app--Bubei. The results provide a profile of using contexts of mobile applications for EFL vocabulary learning and the impacts of using mobile apps on EFL vocabulary learning outcomes. Mobile applications are mostly used in informal learning contexts and adopted in higher education for EFL vocabulary learning. The studies also identified 8 categories of impacts, including vocabulary acquisition and retention, administration for learning, pronunciation feature, usage frequency, learners’ perceptions and attitudes, motivation and interest, feedback and evaluation, and learning environments. Implications are discussed, and suggestions for future research are provided.
Multi-scale perturbation fusion adversarial attack on MTCNN face detection system

Researchers have made significant progress in fake face image detection in recent years, which can accurately identify fake face images, but it is still challenging to prevent malicious face-swapping. By analyzing the popular face swap program faceswap, we found that adding perturbations to images to disable face detectors is an effective way to avert face swaps from happening. We propose an adversarial attack method that performs multi-scale interpolating perturbations for the MTCNN face detection system. This method can improve the stability of adversarial samples, resist the perturbation failure caused by scale changes, and achieve attack effects on single and multi-person images while maintaining the cleanliness of images so that images can continue to upload to social media platforms.
Studies on Human Recognition Activities Based on Federated Learning

The traditional Human activity recognition (Human Activity Recognition, HAR) failed to protect user data effectively and had low recognition accuracy in the process of training model by machine learning. Under protecting the privacy of user data, improving the accuracy of HAR recognition has become a hot spot. This paper designs the HFL framework for solving human recognition activity based on federated learning and deep learning, and proposes the P-FedAvg algorithm. The P-FedAvg algorithm solves the model bias caused by direct averaging and improves the accuracy of aggregation. Experiments show that our method improves the accuracy of HAR recognition while protecting user's privacy.