Reviewerly/exHarmony huggingface dataset:
Multi-domain Opinion Mining: Authenticity of Data Using Sentiment Mining

With all the advancement of Internet telecommunications, a huge quantity of data is usually present for web users. Users make use of the resources offered and provide comments, resulting in the generation of more useful data. Because of the large number of people’s perspectives, beliefs, comments, and recommendations obtainable via online resources, it’s critical to study, examine, and categorize their viewpoints in order to make better decisions (ChandraKala and Sindhu in Opinion mining and sentiment classification: a survey, 2012). E-commerce websites have a significant impact on our day-to-day life. Whether it’s a handset or a vehicle, the average customer depends heavily on public feedback and comments provided (by others) on the Internet to learn about any goods prior to making a choice. The goal of our research study is to robotize the number of digital end-user ratings for each item or brand and assess those available evaluations for attitudes expressed regarding certain aspects. This process involves filtering irrelevant and unhelpful thoughts from multiple sources for checking their reliability and quantifying the feelings of thousands of user reviews.
Enhanced Association Rules and Python

Association rules mining and apriori algorithm are very known tools of data mining since the 1990s. However, enhanced association rules were introduced more than 20 years earlier as a tool of mechanizing hypothesis formation – an approach to exploratory data analysis. Various, both practical and theoretical results, were achieved in relation to enhanced association rules that provide much more general rules than apriori. A short overview of these results is presented. A new implementation of an analytical procedure dealing with enhanced association rules is introduced as well as novel algorithm to find association rules not by setting quantifiers but by setting expected number of association rules which corresponds with typical data scientists’ needs.
Study on the efficiency of methods for selecting a splitting attribute for constructing decision trees

One of the steps in building decision trees is to select a splitting attribute at each node. The quality of the classification depends on this selection. Classical decision tree learning algorithms, such as ID3 and CART, use exhaustive search over the entire attribute space. However, it is a very time-consuming process. The calculation of the objective function is performed for all objects according to all characteristics. The early studies proved that the efficiency of the hybridization of the attribute selection method and differential evolution in decision tree learning. It will help significantly speed up the learning process of a decision tree without losing the quality of classification. However, the research related to the selection of an attribute selection method has not been conducted. The authors of the paper compare nine of the most popular methods for splitting attribute selection. The attribute is of different complexity. Some methods use knowledge only about attributes; others use knowledge about class labels. The comparison was carried out while solving some classification problems. The authors selected decision tree learning time and classification accuracy were chosen as performance criteria. The estimation of methods for attribute selection is carried out as an average indicator for all classification problems.
Masked face recognition on mobile devices using deep learning

Deep Convolution Neural Network (DCNN) based facial recognition has made significant progress in recent years. Currently, facial recognition technology has emerged as an important authentication tool on mobile devices. Hence, a fast and lightweight DCNN model is required to work accurately in limited computing resources. Meanwhile, the outbreak of the COVID-19 pandemic has led to new challenges in face recognition due to the use of facemasks. Therefore, in this study, we develop a masked face recognition application using a lightweight and efficient DCNN, which is applicable to mobile devices. Two networks for face verification tasks, named MobileFaceNet and SeesawFaceNet are explored for this purpose. We train these models on the augmented version of CelebA dataset, which originally is a set of celebrity images. We put synthetic mask on the face images in CelebA to provide a training dataset contain mix of face images with and without mask. The trained models, which are able to recognize people either wearing or not wearing masks, are then retrained on the face dataset commonly used for verification purposes, i.e. LFW (face images without mask) and MFR2 (face images wearing masks). Transfer learning is utilized to improve the network learning ability, and cosine similarity is adopted to quantify the similarity for pairs of examples. In experiment, the SeesawFaceNet model obtains better performance, with 98.8% accuracy on LFW dataset, 96% accuracy on MFR2 masked dataset. In contrast, the experiment after deployment the models on a smartphone application, the MobileFaceNet model is more superior than the SeesawFaceNet with an accuracy of 85%, an average speed of 44 milliseconds, and model size of 4.9 MB.
Agile adoption challenges in Scrum event using TRIZ approach

The purpose of study is to explore the problems encountered during the implementation of the software development method under the Agile umbrella, namely Scrum. This study also implements TRIZ to find their problems. As a systematic method for solving inventive problems, TRIZ has been successful in engineering and technology environments. In a business and management environment such as the software development process, the use of Scrum will be challenging. Problem identification was performed at every Scrum event. The problem taking was obtained from 56 Sprints that had been carried out by eight software development teams in the IT department at a university for eight months. The TRIZ method applied was Engineering Contradiction with the IF-THEN-BUT pattern, so that it could get two conflict parameters: the improvement parameter and the worsening parameter. TRIZ offers 40 proven Inventive Principles (IP) as the solution patterns that help in finding specific solutions. To get recommendations from the 40 IPs, it was done using the contradiction matrix. The previous two parameters would be input to the contradiction matrix and the meeting point, both of which contained the TRIZ solution offer taken from 40 IPs. The results of problem identification produced 13 problems that most often arose from the 195 problems found. There were 11 solutions from the 40 IPs that appeared most. Finally, it involved five TRIZ practitioners to choose specific solutions and produced the three most recommended solutions: Spheroidality Curvature, Mechanical Substitution, and Parameter Change.
Fixed-point attack on Davies—Meyer hash function scheme based on SIMON, SPECK, and SIMECK algorithms

A hash function may use a block cipher algorithm as its basic structure to improve the efficiency and cost of hardware and software implementation. The SIMON, SPECK, and SIMECK algorithms are a family of lightweight block ciphers designed for implementation on devices with constrained resources. In this paper, we examine the robustness of the Davies-Meyer scheme using the SIMON-32, SPECK-32, and SIMECK-32 algorithms as the basic structure using fixed-point attacks. The result obtained is that the Davies-Meyer scheme based on SIMON, SPECK, and SIMECK algorithms shows the vulnerability to fixed-point attacks.
TROSD: A New RGB-D Dataset for Transparent and Reflective Object Segmentation in Practice

Transparent and reflective objects are omnipresent in our daily life, but their unique visual and optical characteristics are notoriously challenging even for state-of-the-art deep networks of semantic segmentation. To alleviate this challenge, we construct a new large-scale real-world RGB-D dataset called TROSD, which is more comprehensive than existing datasets for transparent and reflective object segmentation. Our TROSD dataset contains 11,060 RGB-D images with three semantic classes in terms of transparent objects, reflective objects, and others, covering a variety of daily scenes. Together with the dataset, we also introduce a novel network (TROSNet) as a high-standard baseline to assist other researchers to develop and benchmark their algorithms of transparent and reflective object segmentation. Moreover, extensive experiments also clearly show that the proposed TROSD dataset has an excellent capacity to facilitate the development of semantic segmentation algorithms with strong generalizability.
Toward Supporting CS1 Instructors and Learners With Fine-Grained Topic Detection in Online Judges

Online judges (OJ) are a popular tool to support programming learning.However, one major issue with OJs is that problems are often put together without any associated meta-information that could, for example, be used to help classify problems.This meta-information could be extremely valuable to help users quickly find what types of problems they need most.To face this problem, several OJ administrators have recently begun manually annotating the topics of problems based on computer science-related subjects, such as dynamic programming, graphs, and data structures.Initially, these topics were used to support programming competitions and experienced learners.However, with OJs being increasingly used to support CS1 classes, such topic annotation needs to be extended to suit CS1 learners and instructors.In this work, for the first time, to the best of our knowledge, we propose and validate a predictive model that can automatically detect fine-grained topics of problems based on the CS1 syllabus.After experimenting with many shallow and deep learning models with different word representations based on cutting-edge NLP techniques, our best model is a CNN, achieving an F1-score of 88.9%.We then present how our model can be used for various applications, including (i) facilitating the search process of problems for CS1 learners and instructors and (ii) how it can be integrated into a system to recommend problems in OJs.
Experimental Case Study of Self-Supervised Learning for Voice Spoofing Detection

This study aims to improve the performance of voice spoofing attack detection through self-supervised pre-training. Supervised learning needs appropriate input variables and corresponding labels for constructing the machine learning models that are to be applied. It is necessary to secure a large number of labeled datasets to improve the performance of supervised learning processes. However, labeling requires substantial inputs of time and effort. One of the methods for managing this requirement is self-supervised learning, which uses pseudo-labeling without the necessity for substantial human input. This study experimented with contrastive learning, a well-performing self-supervised learning approach, to construct a voice spoofing detection model. We applied MoCo’s dynamic dictionary, SimCLR’s symmetric loss, and COLA’s bilinear similarity in our contrastive learning framework. Our model was trained using VoxCeleb data and voice data extracted from YouTube videos. Our self-supervised model improved the performance of the baseline model from 6.93% to 5.26% for a logical access (LA) scenario and improved the performance of the baseline model from 0.60% to 0.40% for a physical access (PA) scenario.
Exploring Quantum Average-Case Distances: Proofs, Properties, and Examples

In this work, we present an in-depth study of average-case quantum distances introduced in Maciejewski et al. (2022). The average-case distances approximate, up to the relative error, the average Total-Variation (TV) distance between measurement outputs of two quantum processes, in which quantum objects of interest (states, measurements, or channels) are intertwined with random quantum circuits. Contrary to conventional distances, such as trace distance or diamond norm, they quantify <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">average-case</i> statistical distinguishability via random quantum circuits. We prove that once a family of random circuits forms an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> -approximate 4-design, with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\delta =o(d^{-8})$ </tex-math></inline-formula> , then the average-case distances can be approximated by simple explicit functions that can be expressed via simple degree two polynomials in objects of interest. For systems of moderate dimension, they can be easily explicitly computed – no optimization is needed as opposed to diamond norm distance between channels or operational distance between measurements. We prove that those functions, which we call quantum average-case distances, have a plethora of desirable properties, such as subadditivity w.r.t. tensor products, joint convexity, and (restricted) data-processing inequalities. Notably, all distances utilize the Hilbert-Schmidt (HS) norm, which provides this norm with a new operational interpretation. We also provide upper bounds on the maximal ratio between worst-case and average-case distances, and for each of them, we provide an example that saturates the bound. Specifically, we show that for each dimension <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> this ratio is at most <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d^{\frac {1}{2}}, d, d^{\frac {3}{2}}$ </tex-math></inline-formula> for states, measurements, and channels, respectively. To support the practical usefulness of our findings, we study multiple examples in which average-case quantum distances can be calculated analytically.
Cybersecurity Alert Prioritization in a Critical High Power Grid With Latent Spaces

High-Power electric grid networks require extreme security in their associated telecommunication network to ensure protection and control throughout power transmission. Accordingly, supervisory control and data acquisition systems form a vital part of any critical infrastructure, and the safety of the associated telecommunication network from intrusion is crucial. Whereas events related to operation and maintenance are often available and carefully documented, only some tools have been proposed to discriminate the information dealing with the heterogeneous data from intrusion detection systems and to support the network engineers. In this work, we present the use of deep learning techniques, such as Autoencoders or conventional Multiple Correspondence Analysis, to analyze and prune the events on power communication networks in terms of categorical data types often used in anomaly and intrusion detection (such as addresses or anomaly description). This analysis allows us to quantify and statistically describe high-severity events. Overall, portions of alerts around 5-10% have been prioritized in the analysis as first to handle by managers. Moreover, probability clouds of alerts have been shown to configure explicit manifolds in latent spaces. These results offer a homogeneous framework for implementing anomaly detection prioritization in power communication networks.
A Model-Free Moving Object Detection and Tracking Framework Based on UAV Data

At present, object detection and target tracking play an important role in the development of unmanned aerial vehicle(UAV) applications. The detection and tracking algorithms based on deep learning show excellent performance on aerial data. However, these algorithms, depending on GPU and high-quality datasets, are difficult to migrate to terminals with microcomputers. In this paper, we develop a tracking method combining DeepSort and Histogram of Oriented Gradient (HOG), named HOGSort within a model-free and non-deep learning framework for detection and tracking of moving objects. Moving objects are detected by inter-frame difference algorithms and tracked by HOGSort. Experiments based on aerial videos show that most trackers can continuously track their respective targets, proving that our framework is effective. ...
Research on Intelligent Cooperative Command and Control System of Unmanned Equipment in Amphibious Operations

Focusing on the requirements of amphibious operations, this paper mainly analyzes the composition of unmanned force, typical combat missions, command and control relations, etc. in amphibious operations. Combined with the analysis of the development trend of unmanned equipment and command and control systems, this paper constructs the architecture of intelligent cooperative command and control system of unmanned equipment in amphibious operations, and proposes the system composition and the key technologies to break through, including mission planning, route planning, multi-source information intelligence processing, decision, integrated control of large-scale cross domain, effect evaluation of land-attacking based on image understanding. It can provide reference for the development and standardization of intelligent cooperative command and control system of unmanned equipment.
Flight Corridor Construction Method for Fixed-Wing UAV Obstacle Avoidance

Aiming at the problem of autonomous obstacle avoidance of fixed-wing UAVs, this paper proposes a safe flight corridor construction method based on the motion model of it, and realizes the path planning of fixed-wing UAV based on the safe flight corridor. At present, most of the planning-based obstacle avoidance methods for UAV are oriented to the rotor UAV. Because the motion of the fixed-wing UAV is limited by its minimum turning radius, and can not hover like the rotor, how to solve its optimal obstacle avoidance path is a problem to be solved. Firstly, according to the obstacle avoidance requirements of fixed-wing UAVs, standard path planning methods and flight corridor construction methods are introduced. Secondly, by modelling the fixed-wing UAV, the initialization corridor space required for its actual flight is solved by using the Dubins curve, and the initialization corridor is cut according to the front-end planning data and obstacle information to obtain the flight corridor for the safe flight of the fixed-wing UAV. The flight path of a UAV is optimized by taking flight corridor conditions as constraints. Finally, through simulation experiments, the superiority of this method in autonomous obstacle avoidance of fixed-wing UAV is proved.
Peran Orang Tua dalam Meningkatkan Minat Membaca Anak Usia Sekolah Dasar

This study aims to determine the role and attention of parents to increase children's interest in reading and the obstacles experienced. This research uses descriptive qualitative. The subjects of this study were parents of students of primary school age or Madrasah Ibtidaiyah (SD/MI). Data collection techniques through literature review by searching, tracing, reading, analyzing, and drawing conclusions from journals related to the research topic and then recording, processing, and analyzing them. The results of this study indicate that the role of parents is important in increasing children's interest in reading. Some of these roles include: a) being a role model and providing reading facilities; b) as a teacher and motivator; c) apply parenting and instill good values; and d) become a mentor or adviser. Some of these roles can be a factor in increasing children's interest in reading. Home is the best place to develop a child's interest in reading. One of the supports needed to increase children's interest in reading is the role of parents.
VR training and imagery training in esports

Abstract This article focused on the recent interest in VR in sport and how this technology can and can not help Esport athletes. Summarizing that VR is likely not a tool needed in esports, the authors highlight ways that Esport athletes can incorporate imagery into their first-person perspective gaming film review in a way that increased their psychological skills and performance.
Ano Raw: A Deep Learning Based Approach to Transliterating the Filipino Sign Language

Deaf people communicate best through sign language. It is one of the most vital languages worldwide. Sign languages, like spoken languages, are sophisticated, naturally formed structures that are arranged around a set of conversational activities. In the Philippines, practicing Filipino Sign Language (FSL) already had improved communication for deaf people. However, the community’s main difficulty is that most Filipinos do not comprehend or use FSL. Within the deaf community, these gestures, whether ASL or FSL, are still limited. People who are normal and hearing would never attempt to learn sign language. This results in a significant communication gap between the deaf and the hearing. Human translators are needed in order for deaf people to be understood. While they can be helpful, they are not always available or attainable. Filipino Sign Language Alphabet Recognition using Convolutional Neural Networks (CNN) is proposed to address this growing challenge. The proposed solution can recognize and forecast a letter using an image training model. The model peaked at the 15th epoch, with an accuracy rate of 92% and validation accuracy of 93%. The study aims to bridge the gaps between the deaf community with the hearing in the Philippines.
A survey on Transformers for long sentences and its application to medical notes

The Self-attention mechanism in Transformer has been successful so far. However, it needed adequate performance in electronic health records and text summarization tasks that operate on long sentences due to computational complexity during self-attention calculation. Therefore, this paper provides a survey on the proposed method to uncover sparse attention that operates on long sentences and examine the case of improving the performance in the EHR task.
Hardware-Based Isolation Technique to Guarantee Availability of Security Controls in a Gateway for Industrial Networks

Protocols such as DNP and Modbus are widely used in many industrial networks, and security controls are often employed in a protocol gateway placed in-between public and trusted networks. In this paper, an architecture is proposed to protect the security controls running in a hardware-isolated space by providing an isolation environment to the protocol stack and security controls via TrustZone, even if the protocol stack is compromised. In addition, we evaluate whether our proposed architecture can protect against attack scenarios such as manipulation commands, information leakage, and fuzzing attacks, and we compare the performance of the gateway with and without TrustZone.
Fine-Grained Data Augmentation using Generative Adversarial Networks

This paper presents fine-grained data augmentation, a data augmentation method for deep neural network training that can be applied to tasks with a small number of images, such as in the medical field or vision-inspection tasks. For small-datasets, the number of images per class is usually unbalanced and overfitting occurs when training small-datasets. In this paper, data augmentation skills using generative adversarial network for image super-resolution tasks is presented. Data augmentation with generative adversarial network for image super-resolution tasks retains the overall shape and form, but changes only the details of features. The proposed method achieves better performance when training CIFAR-100 and CUB-200-2011 datasets from scratch. The proposed method is being actively developed to further improve the performance of image classification and will be applicable to object detection.