Reviewerly/exHarmony huggingface dataset:
Hybrid Sooty Tern Optimization and Differential Evolution for Feature Selection

In this paper, a hybrid model based on sooty tern optimization algorithm (STOA) is proposed to optimize the parameters of the support vector machine (SVM) and identify the best feature sets simultaneously. Feature selection is an essential process of data preprocessing, and it aims to find the most relevant subset of features. In recent years, it has been applied in many practical domains of intelligent systems. The application of SVM in many fields has proved its effectiveness in classification tasks of various types. Its performance is mainly determined by the kernel type and its parameters. One of the most challenging process in machine learning is feature selection, intending to select effective and representative features. The main disadvantages of feature selection processes included in classical optimization algorithm are local optimal stagnation and slow convergence. Therefore, the hybrid model proposed in this paper merges the STOA and differential evolution (DE) to improve the search efficiency and convergence rate. A series of experiments are conducted on 12 datasets from the UCI repository to comprehensively and objectively evaluate the performance of the proposed method. The superiority of the proposed method is illustrated from different aspects, such as the classification accuracy, convergence performance, reduced feature dimensionality, standard deviation (STD), and computation time.
A Survey on Stream Ciphers for Constrained Environments

Lightweight ciphers are defined as symmetric ciphers. They could be categorized into stream and block ciphers. A stream cipher is faster and less complex than block ciphers so it is suitable with the Internet of Things (IoT). The IoT is composed of many interconnected constrained devices that share and exchange data and information among each other continuously. Therefore, IoT devices must ensure basic security characteristics to protect that information. In this paper, we will make a survey on a solution that used stream cipher in cryptography. This survey investigates a detailed flow of the stream ciphers such as algorithm design pattern, key size, internal state, throughput, the vulnerability in security, and the initial vectors for comparison among various types of stream ciphers from lightweight cryptographic solutions. The goals of this survey are to discover the most effective IoT protection solution and to look at lightweight cryptographic solutions by taking into account the constraints the IoT devices have, as well as how does researched symmetric key cryptographic solution analysis work. The conclusion is the Fruit stream cipher has good resistance to known attacks, whereas the Enocoro128 and F-FCSR stream ciphers have large throughputs, as well as a WG, Grain, and MICKEY-128 stream ciphers are faster and more suitable to constrained devices (e.g., IoT) than other studied algorithms.
Feature trace recording

Tracing requirements to their implementation is crucial to all stakeholders of a software development process. When managing software variability, requirements are typically expressed in terms of features, a feature being a user-visible characteristic of the software. While feature traces are fully documented in software product lines, ad-hoc branching and forking, known as clone-and-own, is still the dominant way for developing multi-variant software systems in practice. Retroactive migration to product lines suffers from uncertainties and high effort because knowledge of feature traces must be recovered but is scattered across teams or even lost. We propose a semi-automated methodology for recording feature traces proactively, during software development when the necessary knowledge is present. To support the ongoing development of previously unmanaged clone-and-own projects, we explicitly deal with the absence of domain knowledge for both existing and new source code. We evaluate feature trace recording by replaying code edit patterns from the history of two real-world product lines. Our results show that feature trace recording reduces the manual effort to specify traces. Recorded feature traces could improve automation in change-propagation among cloned system variants and could reduce effort if developers decide to migrate to a product line.
Gamma Mixture Density Networks and their application to modelling insurance claim amounts

We discuss how mixtures of Gamma distributions with mixing probabilities, shape and rate parameters depending on features can be fitted with neural networks. We develop two versions of the EM algorithm for fitting so-called Gamma Mixture Density Networks, which we call the EM network boosting algorithm and the EM forward network algorithm, and we test their implementation together with the choices of hyperparameters. A simulation study shows that our algorithms perform very well on synthetic data sets. We further illustrate the application of the Gamma Mixture Density Network on a real data set of motor insurance claim amounts and conclude that Gamma Mixture Density Networks can improve the fit of the regression model and the predictions of the claim severities used for rate-making compared to classical actuarial techniques.
Agile, waterfall and iterative approach in information technology projects

Abstract The aim of this paper is to analyse the Agile methods implementation capability within software projects as a more viable alternative to the waterfall model and determine the key factors that guarantee the continuous delivery of software to market at the right and agreed quality level. Two Agile implementation methods were considered: Scrum and Kanban, which were evaluated based on factors like predictability, practicability and complexity. The conclusions show the selected methodologies advantages and disadvantages, and project key attributes can be used to choose the suitable methodology for a project. Regarding the Agile implementation methods, the most straightforward and predictable one was proven to be Scrum. This framework supports frequent delivery of software products that can address complex problems and adapt to the rapid market changes.
Study and Analysis of Implementing a Smart Attendance Management System Based on Face Recognition Tecqnique using OpenCV and Machine Learning

We can make our Attendance Management System (AMS) intelligent by using a face-to-face recognition strategy. For that, we have to fix a CCTV camera in the classroom at any best point, which makes a person's picture at a fixed time and tests a face-to-face image. Traditionally, student attendance at the institutes is manually reported on the attendance sheets. It's not a productive operation, because it takes 5 or more minutes for attendance. Normally, the length of our class is 50 minutes, and every day we have more than 5 lessons. So, both courses spend more than 50 minutes, which is almost the same as our class time. For, solving this big issue we are proposed a novel automatic technique namely "Face Detection with OpenCV". The system will be connected with our master database which includes the student's name, images, roll numbers, and time of attendance. This application mainly follows three steps. Firstly, it will take images. Secondly, compare them with the existing images which are storing in the master database. Thirdly, it will mark present all the matched images automatically on a spreadsheet and the remaining students will be absent from that class.
Clustering-Based Approach for Building Code Computability Analysis

One common limitation of all automated code compliance-checking methods and tools is their inability to deal with all types of building-code requirements. More research is needed to better identify the different types of requirements, in terms of their syntactic and semantic structures and complexities, to gain more insights about the capabilities and limitations of existing methods and tools (i.e., which requirements they can automatically process, represent, or check, and which not). To address this need, this paper proposes a new set of syntactic and semantic features and complexity and computability metrics for code computability analysis. A clustering-based approach was used to identify the different types of code sentences, in terms of their computability, using the proposed features and metrics. The approach was implemented and tested on a corpus of 6,608 sentences from the International Building Code and its amendments. The sentence clusters and identified sentence types were evaluated using intrinsic and extrinsic evaluation methods. The evaluation results indicated good clustering performance, perfect alignment between the human- and computer-identified types, and good agreement in the assignment of sentences to the types.
A Dynamic Knowledge Diagnosis Approach Integrating Cognitive Features

The rapid development of data analytic technologies has advanced personalized learning and increased its popularity in K-12 education significantly. Specifically, one fundamental step in personalized learning is knowledge proficiency diagnosis which reveals blind spots in students’ knowledge. However, existing approaches to diagnosis either exploit data from a one-time assessment for the cognitive diagnosis (ignoring the previous historical interactions) or trace the knowledge state using recurrent neural networks to predict students’ future performance (ignoring the cognitive features). To this end, this study proposes a dynamic approach to knowledge diagnosis integrating cognitive features with a key-value memory network to store latent exercise information and capture long-term temporal features based on cognitive psychology. Specifically, given the characteristics of assessment data in China, our approach mainly aims to model sequence data with cognitive features, including forgetting and learning. Two corresponding gates are used to weaken the knowledge memory and strengthen the repeated knowledge memory over time, respectively, in the memory updating process. Finally, to evaluate our approach, we conducted extensive experiments on four real-world datasets collected from K-12 education. The results show that the approach can effectively process the time sequence in education, whose prediction results are better and more stable than other existing baseline models. We also conducted experiments for parameter sensitivity, different feature integration methods, and the effectiveness of cognitive features to ensure that the models achieved the best results. The application visualization further confirms the practicability of our approach in dealing with problems of dynamic knowledge diagnosis.
Robust face recognition based on multi-task convolutional neural network

&lt;abstract&gt; &lt;sec&gt;&lt;title&gt;Purpose&lt;/title&gt;&lt;p&gt;Due to the lack of prior knowledge of face images, large illumination changes, and complex backgrounds, the accuracy of face recognition is low. To address this issue, we propose a face detection and recognition algorithm based on multi-task convolutional neural network (MTCNN).&lt;/p&gt; &lt;/sec&gt; &lt;sec&gt;&lt;title&gt;Methods&lt;/title&gt;&lt;p&gt;In our paper, MTCNN mainly uses three cascaded networks, and adopts the idea of candidate box plus classifier to perform fast and efficient face recognition. The model is trained on a database of 50 faces we have collected, and Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measurement (SSIM), and receiver operating characteristic (ROC) curve are used to analyse MTCNN, Region-CNN (R-CNN) and Faster R-CNN.&lt;/p&gt; &lt;/sec&gt; &lt;sec&gt;&lt;title&gt;Results&lt;/title&gt;&lt;p&gt;The average PSNR of this technique is 1.24 dB higher than that of R-CNN and 0.94 dB higher than that of Faster R-CNN. The average SSIM value of MTCNN is 10.3% higher than R-CNN and 8.7% higher than Faster R-CNN. The Area Under Curve (AUC) of MTCNN is 97.56%, the AUC of R-CNN is 91.24%, and the AUC of Faster R-CNN is 92.01%. MTCNN has the best comprehensive performance in face recognition. For the face images with defective features, MTCNN still has the best effect.&lt;/p&gt; &lt;/sec&gt; &lt;sec&gt;&lt;title&gt;Conclusions&lt;/title&gt;&lt;p&gt;This algorithm can effectively improve face recognition to a certain extent. The accuracy rate and the reduction of the false detection rate of face detection can not only be better used in key places, ensure the safety of property and security of the people, improve safety, but also better reduce the waste of human resources and improve efficiency.&lt;/p&gt; &lt;/sec&gt; &lt;/abstract&gt;
Characterizing search activities on stack overflow

To solve programming issues, developers commonly search on Stack Overflow to seek potential solutions. However, there is a gap between the knowledge developers are interested in and the knowledge they are able to retrieve using search engines. To help developers efficiently retrieve relevant knowledge on Stack Overflow, prior studies proposed several techniques to reformulate queries and generate summarized answers. However, few studies performed a large-scale analysis using real-world search logs. In this paper, we characterize how developers search on Stack Overflow using such logs. By doing so, we identify the challenges developers face when searching on Stack Overflow and seek opportunities for the platform and researchers to help developers efficiently retrieve knowledge. To characterize search activities on Stack Overflow, we use search log data based on requests to Stack Overflow's web servers. We find that the most common search activity is reformulating the immediately preceding queries. Related work looked into query reformulations when using generic search engines and found 13 types of query reformulation strategies. Compared to their results, we observe that 71.78% of the reformulations can be fitted into those reformulation strategies. In terms of how queries are structured, 17.41% of the search sessions only search for fragments of source code artifacts (e.g., class and method names) without specifying the names of programming languages, libraries, or frameworks. Based on our findings, we provide actionable suggestions for Stack Overflow moderators and outline directions for future research. For example, we encourage Stack Overflow to set up a database that includes the relations between all computer programming terminologies shared on Stack Overflow, e.g., method name, data structure name, design pattern, and IDE name. By doing so, Stack Overflow could improve the performance of search engines by considering related programming terminologies at different levels of granularity.
Sensor-based Complex Human Activity Recognition from Smartwatch Data using Hybrid Deep Learning Network

Sensor-based human activity recognition (HAR) research is being used for tasks like healthcare tracking, fall detection, and misbehavior prevention. Because of the sophistication of hand gesture signals, complex human activity (CHA) recognition is a difficult task in HAR research. Compared to simple human behavior (SHA), the CHA has more input and long sequential information to deal with. To solve the CHA problem, we proposed a hybrid deep learning model that combines a CNN network and an LSTM network in this paper. The proposed model is an end-to-end model that automatically extracts high features. The model is tested on the daily human activity (DHA) dataset, which is a publicly accessible dataset of complex human activities. The results of the experiments show that the proposed hybrid deep learning model outperforms the current state-of-the-art recognition model.
Face Depth Prediction by the Scene Depth

Depth map, also known as range image, can directly reflect the geometric shape of the objects. Due to several issues such as cost, privacy and accessibility, face depth information is not easy to obtain. However, the spatial information of faces is very important in many aspects of computer vision especially in the biometric identification. In contrast, scene depth information is related easier to obtain with the development of autonomous driving technology in recent years. An idea of face depth estimation inspired is to bridge the gap between the scene depth and the face depth. Previously, face depth estimation and scene depth estimation were treated as two completely separate domains. This paper proposes and explores utilizing scene depth knowledge learned to estimate the depth map of faces from monocular 2D images. Through experiments, we have preliminarily verified the possibility of using scene depth knowledge to predict the depth of faces and its potential in face feature representation.
Cyclic Arbitrage in Decentralized Exchange Markets

Decentralized Exchanges (DEXes) enable users to create markets for exchanging any pair of cryptocurrencies. The direct exchange rate of two tokens may not match the cross-exchange rate in the market, and such price discrepancies open up arbitrage possibilities with trading through different cryptocurrencies cyclically. In this paper, we conduct a systematic investigation on cyclic arbitrages in DEXes. We propose a theoretical framework for studying cyclic arbitrage. With our framework, we analyze the profitability conditions and optimal trading strategies of cyclic transactions. We further examine exploitable arbitrage opportunities and the market size of cyclic arbitrages with transaction-level data of Uniswap V2. We find that traders have executed 292,606 cyclic arbitrages over eleven months and exploited more than 138 million USD in revenue. However, the revenue of the most profitable unexploited opportunity is persistently higher than 1 ETH (4,000 USD), which indicates that DEX markets may not be efficient enough. By analyzing how traders implement cyclic arbitrages, we find that traders can utilize smart contracts to issue atomic transactions and the atomic implementations could mitigate users' financial loss in cyclic arbitrage from the price impact.
Factors Influencing the Popularity of a Health-Related Answer on a Chinese Question-and-Answer Website: Case Study

Social question-and-answer (Q&A) sites have become an important venue for individuals to obtain and share human papillomavirus (HPV) vaccine knowledge.This study aims to examine how different features of an HPV vaccine-related answer are associated with users' response behaviors on social Q&A websites.A total of 2953 answers and 270 corresponding questions regarding the HPV vaccine were collected from a leading Chinese social Q&A platform, Zhihu. Three types of key features, including content, context, and contributor, were extracted and coded. Negative binomial regression models were used to examine their impact on the vote and comment count of an HPV vaccine-related answer.The findings showed that both content length and vividness were positively related to the response behaviors of HPV vaccine-related answers. In addition, compared with answers under the question theme benefits and risks, answers under the question theme vaccination experience received fewer votes and answers under the theme news opinions received more votes but fewer comments. The effects of characteristics of contributors were also supported, suggesting that answers from a male contributor with more followers and no professional identity would attract more votes and comments from community members. The significant interaction effect between content and context features further showed that long and vivid answers about HPV vaccination experience were more likely to receive votes and comments of users than those about benefits and risks.The study provides a complete picture of the underlying mechanism behind response behaviors of users toward HPV vaccine-related answers on social Q&A websites. The results help health community organizers develop better strategies for building and maintaining a vibrant web-based community for communicating HPV vaccine knowledge.
BDNNSurv: Bayesian Deep Neural Networks for Survival Analysis Using Pseudo Values

There has been increasing interest in modeling survival data using deep learning methods in medical research.In this paper, we proposed a Bayesian hierarchical deep neural networks model for modeling and prediction of survival data.Compared with previously studied methods, the new proposal can provide not only point estimate of survival probability but also quantification of the corresponding uncertainty, which can be of crucial importance in predictive modeling and subsequent decision making.The favorable statistical properties of point and uncertainty estimates were demonstrated by simulation studies and real data analysis.The Python code implementing the proposed approach was provided.
Separable Convolutions for Optimizing 3D Stereo Networks

Deep learning based 3D stereo networks give superior performance compared to 2D networks and conventional stereo methods. However, this improvement in the performance comes at the cost of increased computational complexity, thus making these networks non-practical for the real-world applications. Specifically, these networks use 3D convolutions as a major work horse to refine and regress disparities. In this work first, we show that these 3D convolutions in stereo networks consume up to 94% of overall network operations and act as a major bottleneck. Next, we propose a set of “plug-&-run” separable convolutions to reduce the number of parameters and operations. When integrated with the existing state of the art stereo networks, these convolutions lead up to $7\times$ reduction in number of operations and up to $3.5\times$ reduction in parameters without compromising their performance. In fact these convolutions lead to improvement in their performance in the majority of cases <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> This work is part of the project DeepStereoVision (FRE: 01IS18024B) sponsored by the German Ministry of Education & Research (BMBF).
A classification benchmark for Arabic alphabet phonemes with diacritics in deep neural networks

Although the Arabic language is the fourth most popular language in the world, it has not received sufficient attention in artificial intelligence research, especially in automatic speech recognition (ASR). The key feature of the Arabic language is that its words are pronounced exactly as they are written. Above all, taking into account the diacritics,2 there are no words with similar pronunciation and writing. This motivates us to think of building an Arabic ASR system by recognizing its alphabet phonetics. Therefore, the Arabic alphabet phonemes classification must be studied, this is what the paper aims to achieve. In this paper, we create a new dataset, called Arabic alphabet phonetics dataset (AAPD). AAPD was collected by taking sound recordings of 1420 persons. We build several Arabic alphabet phonemes classification systems using three feature extraction techniques and four deep neural networks. Based on AAPD, we designed numerous experiments to compare the performance of feature extraction and classification methods, which can be used as a benchmark. Experimental results showed that Mel-frequency Cepstral Coefficient (MFCC) is considered most effective to feature extraction due to its highest accuracy, particularly when using 20 for Mel-bands number the training time is the least. Additionally, the appropriate model that achieved the highest accuracy with the least computational load is the proposed model VGG–based, where acquired an accuracy of 95.68%.