Reviewerly/exHarmony huggingface dataset:
Design and Implementation of a Software Vulnerabilities and Application Research Tool

In a distributed environment that includes a large number of virtual machines managed by a single entity, maintaining an up-to-date inventory of the same is essential for several reasons. First of all, cybersecurity is an element of increasing importance that must necessarily be taken into consideration: in the event of a vulnerability, knowing quickly which virtual machines are affected in order to proceed with the fix minimizes problem resolution times and therefore the probability of attacks. Furthermore, knowing instantly which virtual machines contain certain versions of packages helps in identifying the best host on which to develop new projects according to specific needs. This paper proposes a possible solution already in use in a company that manages a considerable amount of servers and projects of decidedly heterogeneous types developed on them.
A Self-Supervised Monocular Depth Estimation Approach Based on UAV Aerial Images

The Unmanned Aerial Vehicles (UAVs) have gained increasing attention recently, and depth estimation is one of the essential tasks for the safe operation of UAVs, especially for drones at low altitudes. Considering the limitations of UAVs’ size and payload, innovative methods combined with deep learning techniques have taken the place of traditional sensors to become the mainstream for predicting per-pixel depth information.Since supervised depth estimation methods require a massive amount of depth ground truth as the supervisory signal. This article proposes an unsupervised framework to tackle the issue of predicting the depth map given a sequence of monocular images. Our model can solve the problem of scale ambiguity by training the depth subnetwork jointly with the pose subnetwork. Moreover, we introduce a modified loss function that utilizes a weighted photometric loss combined with the edge-aware smoothness loss to optimize the training. The evaluation results are compared with the model without weighted loss and other unsupervised monocular depth estimation models (Monodepth and Monodepth2). Our model shows better performance than the others, indicating potential assistance in enhancing the capability of UAVs to estimate distance with the surrounding environment.
Lexicon-Based Features on Naive Bayes Modification for Classification of Chinese Film

There are many Chinese movies on the internet for learning Chinese, one of which is on YouTube. This educational film provides negative and positive comments. To get a good movie to learn Chinese, we need to classify positive and negative comments ratings for Chinese learning that teachers can use in this video. In addition, a review of comments is an evolution of Chinese film ratings. The evaluation of comments included includes storytelling, content, model, visual effects, and more. The review has criticisms and comments that include feelings about the movie on Chinese language learning. Commentator helps movie students compare a movie's mood with positive or negative emotion groups. This research uses the naive Bayes taxonomy with the Lexicon Based function in sentiment analysis of comments. The classification process considers the appearance of words of emotional content in the score and the possible score values for positive or negative emotional classes. Based on test results, feature selection accuracy, precision, and recall in the form of stop word exclusion receive scores of 0.91, 0.87, and 0.98, respectively.
Based on improved yolo_v3 for college students’ classroom behavior recognition

The main purpose of the research on students’ classroom behavior recognition is to further systematically count all kinds of behavior data of students in class, and to provide a reliable technical support for education and teaching evaluation. Nowadays, the mainstream of target detection and recognition for multiple students in the classroom is to use various target detection and recognition technologies based on deep learning methods. These technologies optimize the model through self-learning of the data set through deep convolutional neural networks, thereby further Improve recognition efficiency. With the development of deep learning technology, the recognition efficiency has been greatly improved from the initial two-step detection to the current single-step detection algorithm. In the complex environment of the classroom, it is difficult to recognize students’ classroom behavior, which is effectively the problem of insufficient small target recognition accuracy. The original yolo-v3 network model is improved to make it suitable for students’ classrooms, which can solve this problem very well. According to the data fed back from the experimental results, the improved model has greatly improved the recognition efficiency.
Functional identification of the parameters multispecies Lotka-Volterra model

In this paper we set and solve the problem of functional identification of parameters of the Lotka-Volterra multispecies model using the ”Stripe” algorithm developed by V.A. Yakubovich. Assertions about some properties of the model are formulated and proved. The theoretical results are supported by modeling.
Impact of Video Conferencing Applications on Remote Workers

With the effect of the pandemic that has affected the worldwide in recent years, the spread of remote working has made it popular to conduct collaborative works such as pair programming, debugging and bug finding in software engineering through video conferencing applications. Although this situation makes it difficult for employees to synchronize and adapt with each other from time to time, the advantages of working remotely should not be ignored. In this study, some studies with eye tracking devices in the literature will be mentioned and the results will be summarized in order to measure the interaction of software teams with each other while working remotely with video conferencing applications. Then, the efficiency between face-toface working with the employees of a company that has adopted the semi-remote working methodology and remote paired programming using video conferencing applications will be compared.
MorphUz: Morphological Analyzer for the Uzbek Language

The Uzbek language is an agglutinative language in that words are derived from stems (root) by concatenating affixes. This property makes a large number of combinations of morphemes, and greatly increases the word-vocabulary size. Therefore, words are split into certain sub-word units and applied to text and speech processing applications. Proper sub-word units not only provide high coverage and smaller lexicon size, but also provide semantic and syntactic information that is necessary for downstream applications. This paper discusses a morphological analyzer tool for natural language processing and machine learning purpose. The tool named MorphUz, which can split a text of words into a sequence of morphemes. Morphological analyzer is one of the main part of the natural language processing. MorphUz analyzer is an open-source morphological analyzer for the Uzbek language. The MorphUz analyzer is available as a website for exploration. MorphUz analyzer implements the morphology of the Uzbek language following a two-level approach using stemming and suffix analyzer. The implementation of MorphUz analyzer done by using PHP and JavaScript scripts and MySQL database.
Deep USRNet Reconstruction Method Based on Combined Attention Mechanism

Single image super-resolution (SISR) based on deep learning is a key research problem in the field of computer vision. However, existing super-resolution reconstruction algorithms often improve the quality of image reconstruction through a single network depth, ignoring the problems of reconstructing image texture structure and easy overfitting of network training. Therefore, this paper proposes a deep unfolding super-resolution network (USRNet) reconstruction method under the integrating channel attention mechanism, which is expected to improve the image resolution and restore the high-frequency information of the image. Thus, the image appears sharper. First, by assigning different weights to features, focusing on more important features and suppressing unimportant features, the details such as image edges and textures are better recovered, and the generalization ability is improved to cope with more complex scenes. Then, the CA (Channel Attention) module is added to USRNet, and the network depth is increased to better express high-frequency features; multi-channel mapping is introduced to extract richer features and enhance the super-resolution reconstruction effect of the model. The experimental results show that the USRNet with integrating channel attention has a faster convergence rate, is not prone to overfitting, and can be converged after 10,000 iterations; the average peak signal-to-noise ratios on the Set5 and Set12 datasets after the side length enlarged by two times are, respectively, 32.23 dB and 29.72 dB, and are dramatically improved compared with SRCNN, SRMD, PAN, and RCAN. The algorithm can generate high-resolution images with clear outlines, and the super-resolution effect is better.
A Divide and Conquer Strategy for Sweeping Coverage Path Planning

One of the main challenges faced by floor treatment service robots is to compute optimal paths that completely cover a set of target areas. Short paths are of noticeable importance because their length is directly proportional to the energy used by the robot. Such a problem is known to be NP-hard; therefore, efficient algorithms are needed. In particular, computation efficiency is important for mobile robots with limited onboard computation capability. The general problem is known as coverage path planning (CPP). The CPP has several variants for single regions and for disjoint regions. In this research, we are investigating the solutions for disjoint target regions (rooms) that have fixed connection points (doors). In particular, we have found effective simplifications for the cases of rooms with one and two doors, while the challenging case of an unbounded number of rooms can be solved by approximation. As a result, this work presents a divide and conquer strategy (DnCS) to address the problem of finding a path for a sweeping robot that needs to sweep a set of disjoint rooms that are connected by fixed doors and corridors. The strategy divides the problem into computing the sweeping paths for the target rooms and then merges those paths into one solution by optimising the room visiting order. In this strategy, a geometrical approach for room coverage and an undirected rural postman problem optimisation are strategically combined to solve the coverage of the entire area of interest. The strategy has been tested in several synthetic maps and a real scenario showing short computation times and complete coverage.
The Role of Randomization in Trustworthy Machine Learning

Machine learning has been perhaps this decade's most significant technological development, with the prospect of becoming a general-purpose technology. Applications range from autonomous driving to assisting with court decisions. In many of these settings, the worst-case performance of machine learning is critical. Yet, the predictions of machine learning often appear fragile, with no hint as to the reasoning behind them-and may be dangerously wrong. This situation is in large part due to the absence of security considerations in the design of machine learning algorithms. This is unacceptable: society must be able to trust and hold machine learning accountable.
PFSA-ID: an annotated Indonesian corpus and baseline model of public figures statements attributions

Purpose By far, the corpus for the quotation extraction and quotation attribution tasks in Indonesian is still limited in quantity and depth. This study aims to develop an Indonesian corpus of public figure statements attributions and a baseline model for attribution extraction, so it will contribute to fostering research in information extraction for the Indonesian language. Design/methodology/approach The methodology is divided into corpus development and extraction model development. During corpus development, data were collected and annotated. The development of the extraction model entails feature extraction, the definition of the model architecture, parameter selection and configuration, model training and evaluation, as well as model selection. Findings The Indonesian corpus of public figure statements attribution achieved 90.06% agreement level between the annotator and experts and could serve as a gold standard corpus. Furthermore, the baseline model predicted most labels and achieved 82.026% F-score. Originality/value To the best of the authors’ knowledge, the resulting corpus is the first corpus for attribution of public figures’ statements in the Indonesian language, which makes it a significant step for research on attribution extraction in the language. The resulting corpus and the baseline model can be used as a benchmark for further research. Other researchers could follow the methods presented in this paper to develop a new corpus and baseline model for other languages.
Disclosure as a critical-feminist design practice for Web-based data stories

We present findings from design research on disclosure at the intersection of data visualization, digital storytelling, and feminism. While there is an increased awareness of power structures in data science, computing, and design, there is little design research to confront these. This work explores the potential of disclosing context information of data stories, i.e., digital storytelling formats utilizing data visualizations, to enable critical-feminist readings of and reflections on these stories. Drawing from a growing body of feminist scholarship in human-computer interaction, data science, and beyond, we identify key aspects and forms of disclosure for embedding them into visual data story interfaces. We devise and validate these aspects and forms within a case study: a Web-based scrollytelling article explaining the feminist concept of intersectionality using a combination of animated illustration, data visualization, and text. With this work, we demonstrate and discuss the potentials and pitfalls of disclosure practices in data storytelling.
Exploring uncertainty measures in convolutional neural network for semantic segmentation of oral cancer images

Oral cancer is one of the most prevalent cancers, especially in middle- and low-income countries such as India. Automatic segmentation of oral cancer images can improve the diagnostic workflow, which is a significant task in oral cancer image analysis. Despite the remarkable success of deep-learning networks in medical segmentation, they rarely provide uncertainty quantification for their output.We aim to estimate uncertainty in a deep-learning approach to semantic segmentation of oral cancer images and to improve the accuracy and reliability of predictions.This work introduced a UNet-based Bayesian deep-learning (BDL) model to segment potentially malignant and malignant lesion areas in the oral cavity. The model can quantify uncertainty in predictions. We also developed an efficient model that increased the inference speed, which is almost six times smaller and two times faster (inference speed) than the original UNet. The dataset in this study was collected using our customized screening platform and was annotated by oral oncology specialists.The proposed approach achieved good segmentation performance as well as good uncertainty estimation performance. In the experiments, we observed an improvement in pixel accuracy and mean intersection over union by removing uncertain pixels. This result reflects that the model provided less accurate predictions in uncertain areas that may need more attention and further inspection. The experiments also showed that with some performance compromises, the efficient model reduced computation time and model size, which expands the potential for implementation on portable devices used in resource-limited settings.Our study demonstrates the UNet-based BDL model not only can perform potentially malignant and malignant oral lesion segmentation, but also can provide informative pixel-level uncertainty estimation. With this extra uncertainty information, the accuracy and reliability of the model’s prediction can be improved.
Framework for Classroom Student Grading with Open-Ended Questions: A Text-Mining Approach

The purpose of this paper is to present a framework based on text-mining techniques to support teachers in their tasks of grading texts, compositions, or essays, which form the answers to open-ended questions (OEQ). The approach assumes that OEQ must be used as a learning and evaluation instrument with increasing frequency. Given the time-consuming grading process for those questions, their large-scale use is only possible when computational tools can help the teacher. This work assumes that the grading decision is entirely a teacher’s task responsibility, not the result of an automatic grading process. In this context, the teacher is the author of questions to be included in the tests, administration and results assessment, the entire cycle for this process being noticeably short: a few days at most. An attempt is made to address this problem. The method is entirely exploratory, descriptive and data-driven, the only data assumed as inputs being the texts of essays and compositions created by the students when answering OEQ for a single test on a specific occasion. Typically, the process involves exceedingly small data volumes measured by the power of current home computers, but big data when compared with human capabilities. The general idea is to use software to extract useful features from texts, perform lengthy and complex statistical analyses and present the results to the teacher, who, it is believed, will combine this information with his or her knowledge and experience to make decisions on mark allocation. A generic path model is formulated to represent that specific context and the kind of decisions and tasks a teacher should perform, the estimated results being synthesised using graphic displays. The method is illustrated by analysing three corpora of 126 texts originating in three different real learning contexts, time periods, educational levels and disciplines.
Multi-Wavelength Photonic Neuromorphic Computing for Intra and Inter-Channel Distortion Compensations in WDM Optical Communication Systems

Digital signal processing (DSP) has been widely applied in optical communication systems to mitigate various signal distortions and has become one of the key technologies that have sustained data traffic growth over the past decade. However, the strict energy budget of application-specific integrated circuit-based DSP chips has prevented the deployment of some powerful but computationally costly DSP algorithms in real applications. As a result, fiber nonlinearity-induced signal distortions impede fiber communication systems, especially in wavelength-division multiplexed (WDM) transmission systems. To solve these challenges in DSP, there has been a surge of interest in implementing neural networks-based signal processing using photonics hardware (i.e., photonic neural networks). Photonic neural networks promise to break performance limitations in electronics and gain advantages in bandwidth, latency, and power consumption in solving intellectual tasks that are unreachable by conventional digital electronic platforms. This work proposes a photonic recurrent neural network (RNN) capable of simultaneously resolving dispersion and both intra and inter-channel fiber nonlinearities in multiple WDM channels in the photonic domain, for the first time to our best knowledge. Furthermore, our photonic RNN can directly process optical WDM signals in the photonic domain, avoiding prohibitive energy consumption and speed overhead in analog to digital converters (ADCs). Our proposed photonic RNN is fully compatible with mature silicon photonic fabrications. We demonstrate in simulation that our photonic RNN can process multiple WDM channels simultaneously and achieve a reduced bit error rate compared to typical DSP algorithms for all WDM channels in a pulse-amplitude modulation 4-level (PAM4) transmission system, thanks to its unique capability to address inter-channel fiber nonlinearities. In addition to signal quality performance, the proposed system also promises to significantly reduce the power consumption and the latency compared to the state-of-the-art DSP chips, according to our power and latency analysis.
Forest fire predicting using Naive Bayes and KNN algorithm

Forest fires are one of the disasters that cause problems for the environment. Forest fires can cause damage and threats, not only to forest resources but also to the entire ecosystem, both fauna and plants that can damage biodiversity and the environment of an area and can endanger human life. The source of forest fires was initially thought to come from a dry and hot environment, but in some cases, forest fires are triggered by human activities in clearing land for agriculture or other purposes. One of the factors that influence the spread of forest fires is several variables combined with humidity levels, wind speed, and rainfall. In this study, researchers used machine learning algorithms KNN and Naïve Bayes to predict forest fires and compare the results of the performance levels of each method used. The results obtained indicate that the naive Bayes method has an accuracy value of 53.33% and K-NN has an accuracy value of 62.66%
Chaos-Based Lightweight Cryptographic Algorithm Design and FPGA Implementation

With the massive application of IoT and sensor technologies, the study of lightweight ciphers has become an important research topic. In this paper, an effective lightweight LZUC (lightweight Zu Chongzhi) cipher based on chaotic system is proposed to improve the traditional ZUC algorithm. In this method, a further algorithm is designed for the process of integrating chaos into the lightweighting of ZUC. For the first time, this design introduces the logistic chaotic system into both the LFSR (linear feedback shift register) and nonlinear F-function of the cryptographic algorithm. The improved LZUC algorithm not only achieves a certain effect in lightweighting, but also has good statistical properties and security of the output sequence. To verify the performance of the LZUC cipher, we performed NIST statistical tests and information entropy analysis on its output key streams and discussed the typical attacks on the algorithm's resistance to weak key analysis, guess-determination analysis, time-stored data trade-off analysis, and algebraic analysis. In addition, we completed the design of an image security system using the LZUC cipher. Histogram analysis and correlation analysis are used to analyze both plaintext and ciphertext data. At the end of the article, the plaintext and ciphertext images displayed by LCD can be further visualized to verify the encryption effectiveness of the LZUC cipher.