Reviewerly/exHarmony huggingface dataset:
Agile software engineers’ affective states, their performance and software quality: A systematic mapping review

Nowadays, software development companies use agile methodologies to increase the speed and flexibility required by unpredictable working environments and streamline the software delivery process. Agile methodologies emphasize the software engineers' interactions rather than rigid software development processes. Therefore, agile software development processes (e.g., implementing user stories) should consider software engineers' affective states since these influence agile software project activities (e.g., team composition or decision-making). We conducted a systematic mapping review following the guidelines of Petersen, K. and Kitchenham, B. to answer our research question: "What affective states influence agile software engineers' performance and developed software quality?". We retrieved over 16,000 articles published between January 2010 and September 2021 and after applying selection criteria, 24 primary articles were identified. The results show that the affective states of software engineers influence the activities of the software project and the software development process. Furthermore, we found that there is a lack of well-defined and standard metrics to study the influence of software engineers' affective states on their performance and the quality of the resulting software. Finally, we concluded that studying and understanding the affective states of software engineers in agile environments is crucial to achieve their well-being at work and improve their performance.
Android-based Indoor Physical Activities Detection and Monitoring System using Pose Estimation

In the last decade, innovation in the fitness and health industry has exponentially expanded, resulting in new devices like fitness trackers, calorie counters, diet planners, and running trackers. However, a large proportion of the population is elder people who need technical assistance to live their life healthy and independently. In order to progress the advancement in this research area, we concentrated on developing a mobile application that can recognize and assist human actions during exercise and store their records. In this study, a computer vision-based application is developed that can recognize and monitor indoor physical exercises of people especially elders through a smartphone camera. The application uses the pose estimation method to recognize the exercises and further counts the number of exercises and repetitions. This application can recognize 12 different physical exercises, which are further divided into three main categories such as upper body (dumbbell press, overhead arms, back twist, chest, shoulder, front arm), lower body (chair stand, leg raise forward/backward, side leg raise, squats, balance walk), and whole-body (pushups) exercises. An android application is developed which combines OpenPose, deep learning framework, and OpenCV for pose estimation, learning, and visualization while the android studio is used for application development. We use the Media pipe library for the detection and tracking of 33 different points in the human body, including face points. For pose estimation and activity recognition, we calculate different angles between body-detected joints, which are used to recognize the exercise. Moreover, we also measure how many repetitions the user performs in a specific exercise. Finally, the application was tested on 15 different users, which gave positive feedback, and the results were quite accurate for detecting and counting the exercises. Furthermore, the application is very useful for elderly people who mostly stay in their homes or people who can’t join the Gym outside due to their busy schedules.
Evolutionary architecture search via adaptive parameter control and gene potential contribution

Evolutionary neural architecture search (ENAS) algorithms benefit from the non-convex optimization capability of evolutionary computation. However, most ENAS algorithms use a genetic algorithm (GA) with fixed parameter settings, which limits the algorithm’s search performance. Furthermore, the information generated during the evolutionary process is often ignored, while this information can be helpful for guiding the evolutionary direction of the population. This paper proposes a novel ENAS algorithm based on adaptive parameter control and gene potential contribution (AG-ENAS) to evolve neural networks efficiently. Firstly, an adaptive parameter adjustment mechanism is designed, based on population diversity and fitness. This enables better-informed adaptation of related parameters of genetic operators. Secondly, the mutation operator guided by the gene potential contribution of genes tends to produce better offspring. The gene potential contribution reflects the positive effect of the current gene on fitness. It guides the evolution by weighting the more valuable genes with the distribution index matrix. Finally, the concept of aging is introduced into the environmental selection, to offer more opportunities to the young generation and alleviate premature convergence. The proposed algorithm has been evaluated on eight different datasets, and compared against 44 state-of-the-art algorithms from the literature. The experimental results show that the network designed by AG-ENAS obtains higher classification accuracy than manual-designed networks such as SENet, DenseNet, and other ENAS algorithms such as Large-Evo and AE-CNN.
Online sequential non-tuned neural network—concept

The extreme learning machine (ELM) is a unified framework for a single-layer feed-forward neural network (SLFFNN). In the current chapter, a new algorithm premised on the online sequential learning approach, known as the online sequential extreme learning machine (OSELM), is introduced for an SLFFNN. In general, the main learning framework of the OSELM is similar to the original ELM—the input weights and bias of hidden neurons are randomly allocated, and the output weights are analytically calculated. The difference between the algorithms of the OSELM and the original ELM is related to the calculation of the model output weight. To calculate this matrix of output weights, the ELM applied batch learning algorithms, while the OSELM considers an online learning approach. In this case, the training observations are sequentially presented to the learning algorithm, one by one or chunk-by-chunk (as a block of data). In the following section, a general background of the SLFFNN, especially the ELM, is reviewed. Following this, a detailed description of the batch learning approach utilized in the original ELM is presented, as well as the difference between batch learning and online learning. A brief review of the mathematical definition of the original ELM will be provided such that the main changes required to arrive at the OSELM are clear
A self-adaptive soft-recoding strategy for performance improvement of error-correcting output codes

The technique of error-correcting output codes (ECOC) has been proven to be of high discriminative ability in many classification applications. However, most algorithms on the ECOC were designed based on the binary or ternary codes (referred to as the hard codes), which might fail to precisely correct errors in dealing with tough tasks. In this study, a Soft-Recoding strategy based on a self-adaptive algorithm is proposed, which replaces the traditional hard codes with the real-value elements to better fit the output distribution of the base learners. This is achieved by minimizing the ratio of two distances: the distance of the output vector to the ground-truth class, and the average distance of the output vector to the remaining classes. Extensive experiments using five different hard ECOC algorithms and the corresponding softened versions on twenty datasets with diversified numbers of features and classes confirm the effectiveness of our Soft-Recoding strategy in promoting the performance of the original ECOC algorithms. Our source code and additional results are available at: github.com/MLDMXM2017/SA-soft-recoding.
Storytelling Method using Finger Pool to Improve the Expressive Language Ability of Moderately Independent Students Class II SDN Raharja Tanjungsari

The purpose of this research is to improve the expressive language skills of mentally retarded students in class II SLB Raharja Mandiri using the storytelling method through finger puppet media. This study uses the Mix Methods Research method, which is a combination of qualitative and quantitative methods. The research design carried out in this study was the Exploratory Mixed Methods Research Design, which is a combination research method that combines qualitative and quantitative methods. The experimental method used in this research is Single Subject Research (SSR). Meanwhile, the type of experimental design that will be used in this second stage is the multiple baseline cross-subject type. The use of telling stories using finger puppets is believed to be able to improve expressive language skills with moderate mental retardation.
The Effect of Instructional Media “DAPACA” to Improve Reading Skills for Students with Learning Disability

This research developed Instructional Media for student’s with learning disability using media "DAPACA". The purpose of this study was to improve the ability of beginning reading with DAPACA media, DAPACA media is designed using several arrays of syllable cards when put together can make a word and sentence, the subject of this research is 5 student’s with learning disability specific to early reading skills. The research method used in this study uses an experimental method using a single group design pre-test post-test. The data collection technique used in this study was a test. The test given in the form of an instrument of early reading skills. Data collection techniques are carried out during the session Pre-test and Post-test. The results showed that DAPACA instructional media can improve early reading skills, can be seen with the results of the acquisition of the average ability score for the pre-test results is 78 and The average for the post-test results is 112, there was an increase in the score of 34.
Personalized literature recommendation based on heterogeneous entity academic network

Although researchers have benefited from big scholarly data, it is still very difficult for them to quickly and accurately find the suitable literature in the massive literature. In recent years, the research on personalized literature recommendation with the help of academic data has gradually attracted the attention of scholars. However, the existing works are mainly based on the similarity of literature content, and ignore the important information of scholars such as research fields and affiliations, which leads to insufficient personalization of recommendation results and there is still room for improvement in accuracy. In this paper, a new personalized literature recommendation method named PR-HeAN is proposed. This method also considers the similarity of literature, and more importantly, introduces a heterogeneous entity academic network. It combines the literature similarity obtained from a K-order literature co-citation network with the literature recommendation probability obtained from the heterogeneous entity academic network to generate the recommendation results. The heterogeneous network is generated from the K-order literature co-citation network, which integrates five types of academic entities, including literature, scholars, research fields, affiliations and publication venues，and enriches the representation information of the literature. The experimental results on two datasets show that the proposed method outperforms four baseline algorithms in terms of recall, accuracy and F1 value.
Semi-supervised Cycle-GAN for face photo-sketch translation in the wild

The performance of face photo-sketch translation has improved a lot thanks to deep neural networks. GAN based methods trained on paired images can produce high-quality results under laboratory settings. Such paired datasets are, however, often very small and lack diversity. Meanwhile, Cycle-GANs trained with unpaired photo-sketch datasets suffer from the steganography phenomenon, which makes them not effective to face photos in the wild. In this paper, we introduce a semi-supervised approach with a noise-injection strategy, named Semi-Cycle-GAN (SCG), to tackle these problems. For the first problem, we propose a pseudo sketch feature representation for each input photo composed from a small reference set of photo-sketch pairs, and use the resulting pseudo pairs to supervise a photo-to-sketch generator Gp2s. The outputs of Gp2s can in turn help to train a sketch-to-photo generator Gs2p in a self-supervised manner. This allows us to train Gp2s and Gs2p using a small reference set of photo-sketch pairs together with a large face photo dataset (without ground-truth sketches). For the second problem, we show that the simple noise-injection strategy works well to alleviate the steganography effect in SCG and helps to produce more reasonable sketch-to-photo results with less overfitting than fully supervised approaches. Experiments show that SCG achieves competitive performance on public benchmarks and superior results on photos in the wild.
Theoretical Bases for Designing a Methodology of Training Teachers for Intercultural Interaction

The intensification of the processes of globalization and integration of cultures has set before many multi-ethnic countries the task of building a multicultural society that develops according to the laws of tolerance, cooperation and mutual respect. This problem is particularly acute in the regions due to the differences in ethno-cultural and migration processes in any of them, different degree of dynamics and constant changeability of these processes. These factors determine the specific nature of training teachers for intercultural interaction in each particular region. Currently there is a situation when it is impossible to use a unified experience in training teachers for intercultural interaction equally in a single region: it is obvious that each region needs its own methodology. This determines the relevance of the research due to the existing trend of dynamic regional development under the influence of migration and ethno-cultural processes as well as the necessity to find mechanisms for training teachers for intercultural interaction in the dynamic ethno-cultural environment of the region through the analysis of various precedents. Thus, the purpose of this study is to design a methodology for training teachers for intercultural interaction in the context of dynamic ethno-cultural processes. Research objectives: to analyze the current state of knowledge of the problem of training teachers for intercultural interaction in the system of higher pedagogical education in Russia, as well as the current state of international studies on the problem of multicultural education based on the ideas of inter-ethnic dialogue; and to develop an algorithm for designing a methodology for training teachers for intercultural interaction in the context of dynamic migratory and ethno-cultural processes in the regions. The results of the study make it possible to propose a systematic algorithm for designing a methodology for training teachers for intercultural interaction based on the generalized regional experience; and to characterize the content of each stage of this algorithm. The study expands the ideas about the training of teachers for intercultural interaction in an ethno-cultural region, the specifics of implementing the principle of regionalization and comparative studies. The proposed methodology will make it possible to identify general, particular and single changes in the training of teachers for intercultural interaction, taking into account the regional specificity and dynamism of ethno-cultural processes in each region.
Multiscale feature fusion deep network for single image dehazing with continuous memory mechanism

Haze scenes seriously degrade the performance of computer vision systems. Thus, the single image dehazing based on convolutional neural network (CNN), as an important direction of computer vision, has always attracted extraordinary attentions of researchers and made great progress on dehazing effects. However, there are still some non-trivial issues such as uneven dehazing and color distortion, which are not well addressed. To alleviate these issues, this work proposes a new multiscale feature fusion image dehazing network incorporating a contiguous memory mechanism (MFFDN-CM). Specifically, the pixel attention mechanism, continuous memory strategy and residual dense blocks are jointly integrated into the dehazing model with a prevalent encoder-decoder structure(U-Net). Firstly, our model obtains multiscale feature maps by subsampling operations, and further employs skip connections between the corresponding network layers to connect the feature maps between the encoder and the decoder for good feature fusion. Then, we introduce a continuous memory residual block to strengthen the information flows for feature reuse. Moreover, to leverage detail representation and accomplish adaptive dehazing according to the haze density, MFFDN-CM adopts a pixel attention module on the skip connections to combine the residual dense block module of the corresponding decoding layers. Finally, multiple residual blocks are exploited on the bottleneck in encoder-decoder structure to prevent network performance degradation due to vanishing gradients. Experimental results demonstrate the proposed model can achieve better dehazing performance than the state-of-the-art methods based on other deep neural networks.
A truncated generalized Huber prior for image smoothing

Image smoothing is a fundamental task in computer vision and graphics. This paper presents a new image smoothing method based on a truncated generalized Huber prior. The proposed model is neither convex nor concave and is hard to optimize. We first transform the prior into a concave one, then utilize the technique of half-quadratic minimization to get an equivalent convex surrogate function. Thus the numerical algorithm is obtained by solving a weighted least square problem and iteratively updating the weights. The convergence of the algorithm is theoretically guaranteed. The proposed method is flexible and powerful in preserving edge/structure and eliminating undesired information. The effectiveness of the proposed method is demonstrated by several applications, including scale space filtering, texture removal, and clip-art JPEG artifacts removal.
Database memory forensics: Identifying cache patterns for log verification

Cyberattacks continue to evolve and adapt to state-of-the-art security mechanisms. Therefore, it is critical for security experts to routinely inspect audit logs to detect complex security breaches. However, if a system was compromised during a cyberattack, the validity of the audit logs themselves cannot necessarily be trusted. Specifically, for a database management system (DBMS), an attacker with elevated privileges may temporarily disable the audit logs, bypassing logging altogether along with any tamper-proof logging mechanisms. Thus, security experts need techniques to validate logs independent of a potentially compromised system to detect security breaches. This paper demonstrates that SQL query operations produce a repeatable set of patterns within DBMS process memory. Operations such as full table scans, index accesses, or joins each produce their own set of distinct forensic artifacts in memory. Given these known patterns, we propose that collecting forensic artifacts from a trusted memory snapshot allows one to reverse-engineer query activity and validate audit logs independent of the DBMS itself and outside the scope of a database administrator's privileges. We rely on the fact the queries must ultimately be processed in memory regardless of any security mechanisms they may have bypassed. This work is generalized to all relational DBMSes by using two representative DBMSes, Oracle and MySQL.
Contributions of Individual Intellectual Integrations to the Academic Achievement of Humanities Students

The contributions of individual intellectual integrations to the academic achievement of humanities students from the standpoint of the system’s principle, the system-integrative approach and the concept of dual systems were studied. System 1 (integral individuality) and system 2 (intelligence and creativity) were considered in the context of the cultural and educational environment. The purpose of the study was to identify the effects that this dual system produces on the academic achievement of students. The effects were tested under two conditions: (1) systems 1 and 2 operated together, and (2) systems 1 and 2 operated in parallel and separately. The study involved 415 humanities students of higher educational institutions of Perm, including 293 females and 122 males aged 17 to 22 years ( M = 18.6; SD = 1.0). Specially developed methods were used to study the properties of individuality (nervous system, temperament and personality), indicators of crystallized and/or fluid intelligence, and indicators of creativity (fluency, originality and flexibility). During the study, three models (full, correlated and uncorrelated) were developed and tested. The full model had an acceptable fit with the data and indicated the contribution of Conscientiousness (system 1) and Crystallized Intelligence (system 2) to the academic achievement. The correlated model was in good fit with the data and indicated the contribution of Conscientiousness , Neuroticism (system 1), and Crystallized Intelligence (system 2) to the academic achievement. Taken together, these properties explained 19% of the variance in the academic achievement. The uncorrelated model was consistent with the correlated model, except for the integration of systems 1 and 2, and was badly fitted to the data. The results of the study correlated well with the results of meta-analyses. The individual intellectual integrations were limited and selective. They affected some variables but did not touch other ones. Thus, the considered dual system functions rather jointly than separately.
Using relational graphs for exploratory analysis of network traffic data

The human brain is designed to perceive the surrounding world as associations. These associations between the individual pieces of information allow us to analyze and categorize new inputs and thus understand them. However, the support for association-based analysis in traditional network analysis tools is only limited or not present at all. These tools are mostly based on manual browsing, filtering, and aggregation, with only basic support for statistical analyses and visualizations for communicating the general characteristics. Yet, it is the relationship diagram that could allow the analysts to get a broader context and reveal the associations hidden in the data. In this paper, we explore the possibilities of relational analysis as a novel paradigm for network forensics. We provide a set of user requirements based on the discussion with domain experts and introduce a novel visual analysis tool utilizing multimodal graphs for modeling relationships between entities from captured packet traces. Finally, we demonstrate the relational analysis process on two use cases and discuss feedback from domain experts.