Reviewerly/exHarmony huggingface dataset:
A Recommendation System for Customizable Items

The amount of data on the Internet is growing rapidly. This information overload has increased the popularity of recommendation systems over the past few years. Recommendation systems are software tools that help users by providing them suggestions for items or products according to their preferences. They have been around for decades, over which time a strong community of researchers and industry practitioners have risen. Recommendation systems generate product recommendations that are mainly useful for users who lack adequate proficiency in the respective product domain. The generated recommendations may be personalized or non-personalized (in magazines or in newspapers) depending upon the requirement. There are several techniques involved in building a recommendation system. This work mainly emphasizes content-based filtering and constraint-based filtering approaches, and how they are used for recommending descriptive items that are customized to user’s taste.
Acquiring Consensus Solutions by Multi-human-agent-based Evolutionary Computation

The paper proposes a system to acquire consensus solutions among people to a multiobjective problem which does not require direct communication among the people. It relies on multi-human-agent-based evolutionary computation (Mhab-EC) with a mechanism for making solutions of agents converged. In the system, people prepare their own agents who can be tuned by themselves, and then submit their agents to a simulation of Mhab-EC and acquire a convergence solution from the simulation. Then, examining the simulation result, they tune their agents again to be closer to their desired solutions and submit the agents to the simulation and obtain a convergence solution. The mechanism repeats this procedure pre-determined times and outputs a convergence solution of the final simulation as the consensus solution. In basic evaluation of the system, we focus on if the system can make solution converged and show that it can indeed do it.
Evaluating the influence of scope on feature location

Feature Location (FL) is a widespread technique that is used to maintain and evolve a software product. FL is also helpful in reengineering a family of software products into a Software Product Line (SPL). Despite the popularity of FL, there is no study that evaluates the influence of scope (single product or product family) when engineers perform FL. The goal of this paper is to compare the performance, productivity, and perceived difficulty of manual FL when scope changes from a single product to a product family. We conducted a crossover experiment to compare the performance, productivity, and perceived difficulty of manual FL when scope changes. The experimental objects are extracted from a real-world SPL that uses a Domain-Specific Language to generate the firmware of its products. Performance and productivity decrease significantly when engineers locate features in a product family regardless of their experience. For these variables the impact of the FL Scope is medium–large. On contrast, for perceived difficulty, the magnitude of the difference is moderate and is not significant. While performance and productivity decrease significantly when engineers locate features in a product family, the difficulty they perceive does not predict the significant worsening of the results. Our work also identifies strengths and weaknesses in FL. This can help in developing better FL approaches and test cases for evaluation.
A Study of MongoDB Data Models and A Novel Hybrid Data Modeling Approach

NoSQL databases have become increasingly popular. With the introduction of Web 2.0, mobile apps such as Facebook, Twitter, and Whatsapp produce massive amounts of unstructured data on a regular basis. Today's difficult task is to effectively manage such data. Many businesses are turning to NoSQL databases to manage massive amounts of data. These NoSQL databases fall in one of the categories viz. key/value, columnar, document oriented, graph-based databases. MongoDB is most popular NoSQL document-oriented database. MongoDB is capable of handling tasks such as search recommendations, cloud management, metadata storage, merchandise categorization, and so on. One of eBay's functionality is search recommendations, which are available on the website. MongoDB responds easily with all of the search suggestions. Many websites and organizations rate MongoDB among the top five NoSQL databases in the world. The main task in a database application is schema design. The behavior of MongoDB in relation to evolving modelling styles is investigated in this paper. The study demonstrates some primary characteristics discovered during MongoDB data modelling. To boost the database, a novel approach of Hybrid data modelling is suggested.
Segmentation of Overlapping Red Blood Cells for Malaria Blood Smear Images by U-Net Architecture

Automated segmentation of red blood cells is a widely applied task in order to evaluate red blood cells for certain diseases. Counting of malaria parasites requires individual red blood cell segmentation in order to evaluate the severity of infection. For such an evaluation, correct segmentation of red blood cells is required. However, it is a difficult task due to the presence of overlapping red blood cells. Existing methodologies employ preprocessing steps in order to segment red blood cells. We propose a deep learning approach that has a U-Net architecture to provide fully automated segmentation of red blood cells without any initial preprocessing. While red blood cells were segmented, irrelevant objects such as white blood cells, platelets and artifacts were removed. The network was trained and tested on 5600 and 600 samples respectively. Segmentation of overlapping red blood cells was achieved with 93.8% Jaccard similarity index. To the best of our knowledge, our results surpassed previous outcomes.
Calcareous nannobiostratigraphy of the Hartha Formation, East Baghdad well No.6, Central Iraq

Fifteen samples of Limestone from the Hartha Formation were obtained from East Baghdad well No.6, Central Iraq, from the depth (2247-2261 meter) about 14 meters thickness. Detail investigation was carried out, from which fifteen species of calcareous nannofossils identified; eleven were descried from other regions and four were left under an open name because of lack of material or rare of samples. The recorded calcareous nannofossil assemblages recognized two biozones these from the lower to the upper part of the section:&#x0D; 1 - Eiffellithus eximius Interval Zone (CC19) part&#x0D; 2- Ceratolithoides aculeus Interval Zone (CC20) part &#x0D; These biozones are correlated with other calcareous nannofossils biozones from both local and regional sections that lead to conclude the age of the Middle Campanian.
Plume: Fast Finality Blockchain without Single Failure Point

Bitcoin’s slow finality is an important factor preventing it from being widely used in real life. And other public single-chain blockchains have the same problem. In this paper, we propose a practical new protocol called Plume to achieve consensus with fast finality by utilizing a (slow) traditional blockchain. Plume combines the DAG structure and traditional blockchain to increase throughput and speed up confirmation. DAG structure is proposed to increase throughput, but how to construct a secure consensus protocol based on DAG is pretty hard. In Plume, we use the traditional blockchain to generate a randomness which is used to generate fast blocks in DAG. And the order of fast blocks in DAG is dependent on a global clock and a hash function. Same as bitcoin, Plume is a probabilistic protocol. However, due to DAG structure, the creation rate of blocks in Plume can be higher without reducing security. Thus the confirmation time of blocks can be accelerated. This paper also analyzes the probability of confirmation in different conditions and also proves that Plume is secure no matter the randomness from traditional single chain is unbiased or not. At last, this paper simulates performance with 1000 nodes using NS-3. The throughput can be improved to 400 Tps in the case that block size is 100K bytes and time interval of fast block is 1 second. Also the confirmation time can be reduced to within 2 minutes in the case that block size is 10K bytes and time interval is 1 second.
Co-design of Advanced Architectures for Graph Analytics using Machine Learning

A graph is an excellent way of representing relationships among entities. We can use graph analytics to synthesize and analyze such relational data, and extract relevant features that are useful for various tasks such as machine learning. Considering the crucial role of graph analytics in various domains, it is important and timely to investigate the right hardware configurations that can achieve optimal performance for graph workloads on future high-performance computing systems. Design space exploration studies facilitate the selection of appropriate configurations (e.g. memory) to achieve a desired system performance. Recently, the approach of accelerating graph analytics using persistent non-volatile memory has gained a lot of attention. Traditional system simulators such as Gem5 and NVMain can be used to explore the design space of these advanced memory architectures for graph workloads. However, these simulators are slow in execution thus limiting the efficiency of design space exploration studies. To overcome this challenge, we proposed a machine learning based approach to co-design advanced memory architectures for graph workloads. We tested our approach with DRAM, non-volatile memory, and hybrid memory (DRAM+NVM) using a breadth first search benchmark algorithm. Our results showed the applicability of the proposed machine learning based approach to the co-design of the advanced memory architectures. In this paper, we provide recommendations on selecting advanced memory architectures to achieve desired performance for graph workloads. We also discuss the performances of different machine learning models that were considered in this study.
Bayesian nonparametrics for directional statistics

We introduce a density basis of the trigonometric polynomials that is suitable to mixture modelling. Statistical and geometric properties are derived, suggesting it as a circular analogue to the Bernstein polynomial densities. Nonparametric priors are constructed using this basis and a simulation study shows that the use of the resulting Bayes estimator may provide gains over comparable circular density estimators previously suggested in the literature. From a theoretical point of view, we propose a general prior specification framework for density estimation on compact metric space using sieve priors. This is tailored to density bases such as the one considered herein and may also be used to exploit their particular shape-preserving properties. Furthermore, strong posterior consistency is shown to hold under notably weak regularity assumptions and adaptive convergence rates are obtained in terms of the approximation properties of positive linear operators generating our models.
Development of the Remote Anonimous Voting System and the Analysis of its Vulnerabilities

Nowadays, remote voting is in demand, so in this paper we discuss popular voting services, investigate different schemes for remote anonymous voting and present new anonymous voting scheme. One of the good ways to form and sign voting ballot is the blind electronic signature algorithm and the scheme proposed by authors also uses a blind signature protocol. In paper we describe in detail the protocol for creating an anonymous data transmission channel and prove the impossibility of unambiguous comparison of the bulletin and its sender. In addition, in this work we examined and analyzed potential threats to system security and presented ways to prevent from them. Thus, we analyzed the reliability of the presented system and demonstrated that the anonymous remote voting system is not susceptible to these vulnerabilities. The last part of this work presents the practical implementation of described schema in the remote electronic voting system with description of its realization features.
Detection of Suspicious Activities of Human from Surveillance Videos

Video surveillance has been used from a long time to provide security in many sensitive places, so with this great progress in various aspects of life the traditional surveillance operations are facing many problems because of the large amounts of information that must be handled manually in a limited time also the possibility of information loss which can contain important things such as suspicious behaviors. So recently, a large amount of research has been conducted on video surveillance. This paper will present a system to support the smart surveillance for detecting abnormal behaviors that represent security risk. The proposed algorithms are intended to detect two cases of human activities namely, walking and running. No restrictions were imposed on the number of people in the scene, and the direction of the motions. However, we restrict the videos to indoor color videos, where the video are captured by one stationary camera. The moving objects which correspond to people in the scene are detected by background subtraction algorithm . We consider the displacement rate of the centroids of the segmented foreground areas and the rate of change in the size of the segmented areas as the two main features for activity classification. Briefly, in this study, a sequential of procedures were applied to detect moving objects (suspicious activity) in video, these procedures consisted in dividing the video into frames and separating the background of the video from the objects inside it, as well as removal the noise from the images by using morphological operations thus the mathematical operations are performed to which to determined which images contains suspicious activity. The proposed algorithms determine the activity type with a high accuracy rate.
A New Deep Architecture for Digital Signal Modulation Classification over Rician Fading

In this paper, we simulate digital signals of six usual modulation patterns considering Rician fading and propose a new deep neural network structure (CGDNN) combining Convolutional Neural Networks (CNNs) with Gated Recurrent Unit (GRU). Simulation results show that the proposed structure has the ability to classify the signal modulation patterns regardless the influence of different Rician K-factors and has better performance than conventional structures including CNNs and CLDNNs.
Child-Computer Interaction in the Global South: Designing for Children on the Margins

The Global South is a reference to the underdeveloped regions of the world and though it is often synonymous with the marginalisation of people through living standards and access to resources, not everyone falls under this margin. The children in the margins in the Global South encompasses those with little or no access to modern communication, technologies and academic resources. This spectrum also includes children with learning and developmental disabilities, mental and physical health issues and hostile home or school environments. Marginalization in the Global South places a significant portion of the children at an elevated risks of inequality from lack of access, knowledge and training of ICTs to digital exclusion. Moreover, the recent global pandemic has exacerbated these challenges. This workshop aims at tracing research lines and questions around methodological and technological in designing tools to support children on the margins in the Global South.
Study on user quitting in the Puffer live TV video streaming service

Video streaming is an important application on the Internet. To ensure user satisfaction, video streaming service providers monitor their services in terms of quality and engagement. This enables them to ensure high quality services and grow their platform and incomes. However, studying user engagement is difficult as the decision of a user to quit or continue watching videos is jointly affected by many factors such as interest towards content, time available, and service quality. Therefore, the reason for quitting is difficult to identify. To address this, this study is based on usage data of a real-world TV service called Puffer and aims to study the relationship between service quality and quitting actions. Data from December 2020 were collected and correspond to 230,880 distinct viewing sessions. On the basis of these data, performing analysis at different scales (hour, day, month) enables the identification of different reasons for users to quit videos. By using this analysis, quality-related quitting events are identified and put into relation with quality-related parameters as well as state-of-the-art video quality and user quitting prediction models. Results show that quitting prediction models can be used to identify such events. Finally, by the means of logistic regression, this work describes the first steps towards mapping quitting prediction on the basis of models trained using data from laboratory experiments to real-world scenarios and shows a classification accuracy of 75.9%.
Comparisons of Full Body and Facial Dog Identification

Free-roaming dog population survey is very important in the veterinary research area. Modern technologies can promote the automation process of dog surveys. This paper investigates two networks, which are Facenet and Dlib for dog identification. These two networks predict embedded vectors which are a reduced dimension vector of images containing distance information that is aligned with dog identities. The full body and face images of dogs are fed into the networks. Our experiments show that facial images provide more discriminative embedded vector than full body images. The average accuracy of both networks with face and full body images are 91.43% and 87.32%, respectively. In overall, both networks provide good accuracy and promising results for dog recognition applications.
Optimal Network Defense Decision-making Method Based on Delay-Time Differential Game

Abstract In the process of attack and defense, there will be a period of delay in the implementation of the decision-making and strategy, therefore, the paper is based on the theory of the dynamic and time-delay differential game theory, and puts forward the network security status evolution model, and puts forward the network attack defense delay differential game model, and the design of the method of the optimal defense strategy of the most optimal defense strategy is designed, and the optimal decision is achieved. Finally, the validity of the model and the algorithm is verified by experiment.
A Novel Cloud Authentication Framework

Cloud computing is a medium that provides cost reduction with storage and fast computation environment. Various organization are marching to the cloud because of the several advantages provided such as remote access, cost efficiencies and simplified IT infrastructure and management. However, for any distributed or shared system, security is the main requirement. The research work discuss the numerous researchers work regarding cloud security issues, threats, known vulnerabilities and their solutions. Hence, the available threats in the cloud system are discussed to come up with an improved framework that provide data security through multiple techniques. The proposed research work combines cryptography, authorization and authentication mechanisms in order to ensure access control.
Fast Spatio-Temporal Multi-Branch Fusion Network for Video Deraining

High-quality videos are essential for outdoor vision systems. Rain is one of the most familiar factors for degrading video quality. Existing works tend to design the complex network architectures with lots of parameters. Unfortunately, they always generate the nonideal results with residual rain streaks and insufficient details, although the network parameters are ample. To handle these issues, we develop a fast Spatio-Temporal Multi-Branch Fusion Network (STMBFN) for effectively handling video deraining. To be concrete, we design a temporal information generation module to align different frames. Different from existing works to directly concatenate these aligned frames, we divide these frames to ensure the closest relationship between the current and other frames. In this way, we can weaken the burden of the network. As a result, we design a multi-branch fusion network based on three three-layers basic networks. In the experimental part, we present sufficient analyses in terms of our STMBFN to illustrate the effectiveness. Further, plenty of experiments are conducted to fully verify our superiority against existing state-of-the-art approaches.
Requirements Elicitation via Fit-Gap Analysis: A View Through the Grounded Theory Lens

While requirements elicitation remains a key success factor for software projects, there is little empirical research on the elicitation methods. We focus on fit-gap analysis, a requirements elicitation technique that is common in practice, but hardly studied in requirements engineering research. Fit-gap analysis is a method for matching software products with the needs of customers, with the aim to identify needs that are supported as fits, and needs that are not as gaps. Through a grounded theory investigation of recording transcripts from fit-gap analysis sessions, we provide empirical knowledge about this elicitation technique. We determine and discuss the different categories of the topics contained in a fit-gap analysis. Additionally, as a first step toward assisting analysts in processing and exploring their analyses, we build and share a set of keywords and phrases that can help automatically identify those categories within the transcripts. We conduct an experiment for early validation, involving both students and practitioners, that determines the relative perceived importance of the identified fit-gap categories. Finally, we derive implications for research in the field that include our perspective on how tooling can assist analysts in fit-gap analysis.
A GraphBLAS implementation in pure Java

Analyzing connected data in forms of graphs is more relevant than ever. To allow users to write their own custom graph algorithms, graph computation models such as GraphBLAS have been developed. Unfortunately, the popular Java programming language was mostly neglected by existing GraphBLAS implementations so far. To overcome that issue, we present our implementation of essential GraphBLAS concepts in the Java programming language in this paper. For our purpose, we extended the linear algebra library Efficient Java Matrix Library (EJML). To show the benefits of our implementation, we compare us against existing graph algorithm libraries in Java using real world graphs and three graph algorithms.