Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis of Indonesian Government's Effort to Overcome the Unemployment Problem during COVID-19 Pandemic

The impact of the COVID-19 pandemic has affected the unemployment problem in Indonesia, and it has become one of the public's concerns in the past two years. In August 2020, which was 9.8 million people and August 2021, which was 9.1 million people. Given these conditions, the government needs to make improvements regarding the current unemployment problem. The main objective of this research is to find out how public opinion is regarding the government's efforts to overcome the unemployment problem during the COVID-19 pandemic in Indonesia. Sentiment analysis was carried out on public opinion using Twitter as a data source. To measure the performance of the model, three algorithms are used, namely Naïive Bayes, Decision Tree, and Random Forest. The results of this study indicate that there are positive labels that have 1710 sentiments, and negative labels that have 1553 sentiments. The best algorithm obtained in this study is Random Forest, with an accuracy value of 79%. This study produces 15 features that affect the unemployment problem, the highest positive weight is ‘ignore’, and the influential feature with the highest negative weight is ‘stamp’.
Reconciling the Gap Between Seamless and Seamful Design Approach

This paper explores conceptually and empirically the complex and multi-layered networks of interaction where personal data is shared across different platforms. This is explored through the vocabulary and tensions established by ‘seamful’ and ‘seamless’ design approaches and their limitations in allowing people to track the flow of personal data across complex assemblages of digital services. These ideas are explored using a participatory method to support the design of Inclusive Digital Services where a prototype of seamless service related to data reuse and sharing is co-designed to address friction, effort, risk and cost (FERC) inherent to third sector services. The conclusion suggests that we need to foster a new attitude towards the development of trust in human-computer interaction, this can be achieved by promoting the turn to privacy by design and addressing the gap between ‘seamless’ and ‘seamful’ design through participatory methods.
Depth360: Self-supervised Learning for Monocular Depth Estimation using Learnable Camera Distortion Model

Self-supervised monocular depth estimation has been widely investigated to estimate depth images and relative poses from RGB images. This framework is promising because the depth and pose networks can be trained from just time-sequence images without the need for the ground truth depth and poses. In this work, we estimate the depth around a robot (360° view) using time-sequence spherical camera images, from a camera whose parameters are unknown. We propose a learnable axisymmetric camera model which accepts distorted spherical camera images with two fisheye camera images as well as pinhole camera images. In addition, we trained our models with a photo-realistic simulator to generate ground truth depth images to provide supervision. Moreover, we introduced loss functions to provide floor constraints to reduce artifacts that can result from reflective floor surfaces. We demonstrate the efficacy of our method using the spherical camera images from the GO Stanford dataset and pinhole camera images from the KITTI dataset to compare our method's performance with that of baseline method in learning the camera parameters.
Exploring Pixel Alignment on Shallow Feature for Weakly Supervised Object Localization

Weakly supervised object localization (WSOL) aims to cover the entire target object only under the image-level supervision. Most WSOL methods are stuck in mining the CAMs (class activation maps) of deep semantic features for they only focus on limited discriminative regions playing key role in classification. Recently, a new paradigm has emerged by localizing objects using the low-level feature through two stages. Existing two-stages methods usually train a classification network first to yield CAMs as pseudo labels to guide the learning of segment network, yet it does not consider the activations with more background noise or less discriminative area. In this paper, we propose a Pixel Alignment strategy to refine the object localization by improving the shallow-feature based CAMs generator with the joint supervision of pseudo-label mask, classification evaluation, and absolution size constraint on the activation map. More specifically, we utilize the class-specific pixel gradient to achieve a robust activation pseudo mask to background noise, which further supervises the activation generator with confident foreground and background regions. We also adapt a post-processing to excavate the target region in the conflict area (i.e., the non-overlap area of CAMs and the activations). Extensive experiments on CUB-2002011 and ILSVRC datasets indicate that our method outperforms the state-of-the-art among the two-stage works.
A Measure of Cluster Stability in Dynamic Cluster Analysis

Dynamic cluster analysis is understood as partitioning of spatio-temporal objects into groups which can change their composition over time. The aim of the paper is to propose the measure of cluster stability for such groups. The measure takes values from [0;1] interval. Some of its characteristics are discussed on the basis of simple example. The distribution of the measure under random membership is estimated by Monte Carlo simulation. The real example from the analysis of European Union countries in 15-year period is also provided.
An Ontology-based Knowledge Base System for Military Software Testing

Software testing is a knowledge-intensive activity of software quality assurance. Due to professionalism and time constraints, the military software testing task brings many challenges to the validity and reliability of the testing results for the testing industry. The large amount of historical test data accumulated by the testing institution contains rich test experience and domain knowledge, which have not been used effectively to meet these challenges. Given great importance to knowledge for software testing, using semantic web technologies, an ontology-based knowledge base system and its technical implementation scheme are introduced to military domain, which is an effective means to support knowledge representation, processing, storage and retrieval. At last, we analysis how software testing ontologies and knowledge base system can be used to improve acquiring and sharing of organizational knowledge, as well as supporting the engineering practices of military software testing.
Comparison of Bioinspired Optimization Techniques for Improving the Performance of Dynamic Sliding Mode Controllers

Bio-inspired optimization algorithms have been shown in recent years to produce optimal solutions to various complex computational issues in science and engineering. This paper presents a comparative examination of bio-inspired algorithms to better understand and measure their efficacy in searching for the best tuning parameters for a Dynamical Sliding Mode Control for integrating systems with an inverse response and dead time. The comparative analysis includes four bioinspired algorithms: particle swarm optimization, artificial bee colony, ant colony optimization, and genetic algorithms, and how they can improve the performance of the controller by searching for optimum tuning parameter solutions. The parameters of each algorithm have different impacts on the searching mechanism and were evaluated in two simulated systems. Ant colony optimization shows a significant capability over other algorithms to find optimal solutions for our problem.
Document-Level Relation Extraction with a Dependency Syntax Transformer and Supervised Contrastive Learning

Document-level Relation Extraction is more challenging than its sentence-level counterpart, extracting unknown relational facts from a plain text at the document level. Studies have shown that the Transformer architecture models long-distance dependencies without regard to the syntax-level dependencies between tokens in the sequence, which hinders its ability to model long-range dependencies. Furthermore, the global information among relational triples and local information around entities is critical. In this paper, we propose a Dependency Syntax Transformer and Supervised Contrastive Learning model (DSTSC) for document-level relation extraction. Specifically, dependency syntax information guides Transformer to enhance attention between tokens with dependency syntax relation in the sequence. The ability of Transformer to model document-level dependencies is improved. Supervised contrastive learning with fusion knowledge captures global information among relational triples. Gaussian probability distributions are also designed to capture local information around entities. Our experiments on two document-level relation extraction datasets, CDR and GDA, have remarkable results.
Trust-aware location recommendation for user groups

Recommender systems have become indispensable part of online environments. Product recommendations in ecommerce applications, location recommendations in location related applications, and user and post recommendations in online social networks are just a few examples in the world of online recommendations. The recommendations generated in such environments are mostly for individuals. However, increasing use of social networks and online communities lead to need for generating recommendations for a group of users for joint activities, such as eating out as a group or seeing a movie with friends. In the literature, there are studies proposing recommendation solutions for groups, but the number of such studies is very limited. In this work, we address the problem of location recommendation to a group of users, and the proposed solutions are based on Random Walk with Restart (RWR) algorithm on the social network graph. Another novel aspect of the proposed work is the use of trust factor of users in location-based social networks (LBSNs). In generating group recommendations, we follow two alternative paths. The first one aggregates the location recommendations that are generated with the Random Walk algorithm for each member in the group, taking the preferences and objectivity scores of the individuals into account. The second one is based on creating a group profile by blending preferences and venue category types, and using this group profile to run the Random Walk algorithm once. In both approaches trust factor of users is incorporated into the solutions within the social network graph. The experiments conducted on the data collected from the location based social network platform Foursquare have shown that the trust factor of users improves the performance of group recommendation system and the proposed algorithm provides recommendations to groups with high accuracy compared to the baselines.
Gender Analysis of Labor Force Outcomes: Evidence from Cameroon

The present study contributes to the extant literature by assessing how financial and human developments moderate the incidence of vulnerable female employment on female labour force participation in Cameroon for the period 1987 to 2020 using the generalized least squares (GLS) estimation approach. Human development and financial development moderating or policy variables are also employed in order to improve space for policy implication. It is apparent from the findings that human development in the perspective of the human development index (HDI) and broad money supply are necessary and sufficient conditions to moderate vulnerable female employment for female labour force participation. Accordingly, HDI thresholds of between 0.591 and 0.634 are needed to reverse to the negative incidence of female vulnerable employment on female labour force participation. Furthermore, a threshold of 30.294 (% of GDP) of broad money supply is also needed to reverse the negative incidence of vulnerable female employment on female labour force participation. Other implications for policy are discussed.
A Legal Information Retrieval System for Statute Law

The information retrieval task for statute law requires a system to retrieve the relevant legal articles given a legal bar exam query. The Transformer-based approaches have demonstrated robustness over traditional machine learning and information retrieval methods for legal documents. However, those approaches are mainly domain adaptation without attempting to tackle the challenges in the characteristics of the legal queries and the legal documents. This paper specifies two challenges related to the characteristics of the two legal materials and proposes methods to tackle them effectively. Specifically, the challenge of different language used (while the articles use abstract language, the queries may use the language to describe a specific scenario) is addressed by a specialized model. Besides, another specialized model can overcome the challenge of long articles and queries. As shown in the experimental results, our proposed system achieved a state-of-the-art F2 score of 76.87%, with an improvement of 3.85% compared to the previous best system. The code will be available at https://github.com/nguyenlab/statute_law_IR .
Analysis characterisation of coolant monitoring system for nuclear research reactor digital instrumentation

Nuclear energy is now one of the most impactful forms of energy generation today, and it is still widely utilized. This research presents a pre-analysis on the Characterization of Coolant Monitoring System for Nuclear Research Reactor Digital Instrumentation. The process passes from the primary coolant system to the Nuclear Research Reactor Digital Instrumentation monitoring system. Less study was conducted on variable modelling to determine the lifespan of PUSPATI TRIGA Reactor (RTP) operations in Malaysia. The principal coolant deprived the heat exchanger’s performance and temperature regulation for prediction analysis data. This paper presents an analysis for pre-studied modelling for preventive maintenance on cooling monitoring systems for plant reactor at RTP. Data collection on the flow, temperature, and pressure for the cooling monitoring system was identified for both primary and secondary conditions. A day data was collected based on the interarrival time that is counted about 1000k+ data in time. Basic statistics based on the water flow, temperature, and pressure were analyzed. The result presents a normal condition status for the coolant system at RTP. The characterization of data is important in modelling future analysis for digital preventive maintenance. The significance of this study is to ensure that RTP’s primary coolant system is functioning well and to raise awareness of the possibility of an accident occurring without proper monitoring.
Analysis of Avalanche effect in Cryptographic Algorithms

- Information security is becoming significantly more crucial for data transmission and storage due to the rapid development of digital information exchange in digital mode. Since the information is private, it is crucial that only authorized individuals have access to it. Hence, in order to protect information security systems from unwanted attacks, cryptography has emerged as a key solution. This security feature scrambles data using some algorithms into incomprehensible text that can only be decoded by the entity possessing the appropriate key. The two most popular symmetric encryption methods, data encryption standard (DES) and advanced encryption standard (AES), and an asymmetric algorithm RSA have both been implemented in this paper using the Python programming language. The algorithms are compared based on the metric Avalanche effect by changing the single character of plain text from different places.
Anonymous Random Allocation and Its Applications

Random Allocation -the random assignment of the data to the parties- is a well-studied topic in the analysis of medical or judicial data, and the context of resource distribution. Random allocation reduces the chance of bias or corruption in the relevant applications, which makes the results more reliable. This is done by preventing a special or pre-planned assignment of the data to accommodate the assessment toward the desired results. This paper provides the first formal syntax and security notion of a random allocation scheme. Based on our new security notions of anonymity, confidentiality, and data-integrity, random allocation can cover more applications such as the distributed audit system where the confidentiality of data and the anonymity of auditors are of paramount importance. Our protocol allows the parties to stay anonymous during the concurrent executions of the protocol even if they have revealed themselves at a certain execution. The revelation property gives the possibility to the parties to claim certain advantages/faults at the end of a protocol-execution (without breaking the data-privacy or anonymity in other protocol-executions). We instantiate our syntax and prove the security based on simple cryptographic components and assumptions such as the Diffie-Hellman assumption, in the random oracle model.
Performance Evaluation of Phishing Classification Techniques on Various Data Sources and Schemes

Phishing attacks have become a perilous threat in recent years, which has led to numerous studies to determine the classification technique that best detects these attacks. Several studies have made comparisons using only specific datasets and techniques without including the most crucial aspect, which is the performance evaluation of data changes. Hence, classification techniques cannot be generalized if they only use specific datasets and techniques. Therefore, this research determined the performance of classification techniques on changing data through a subset of schemes in a dataset. It was conducted using unbalanced and balanced phishing datasets, as well as subset schemes in ratios of 90:10, 80:20, 70:30, and 60:40. The thirteen most recent classification techniques used in preliminary phishing studies were compared and evaluated against ten performance measures. The results showed that the proposed schemes successfully uncover the maximum and minimum performance obtained by a classification technique. These comparisons can provide deeper insights into phishing classification techniques than related research.
Binman: An Autonomous Beach Cleaning Robot

Pollution on beaches increasing day by day. The major wastes that are collected from beaches include liquor bottles, cigarettes, and plastic bottles. Out of these, plastic bottles were the major ones. These wastes ended up in the sea if they didn’t collect properly destroying the aquatic life. Manual collection of this waste will result in health problems for the workers. So, there is a need for a machine that will collect this waste and dispose of it with minimum human intervention and cause zero-carbon footage. This paper discusses the design and simulation of a beach cleaning robot named Binman which includes discussions on the overall structure of the robot, motor, and battery selection, and implementation of GPS waypoint navigation using ROS framework.
An Expansion on Prioritized Experience Replay with Round Robin Scheduling

From a fantasy on the silver screen to beating human players at tic-tac-toe, chess, classic Atari games and Go, Artificial Intelligence (AI) has made great strides in the last 60 years. Specifically, the field of Reinforcement Learning (RL) has been an area of much interest given its recent success and its still relatively novel nature. The bulk of RL research has been focused on developing learning algorithms such as State <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</inf> , Action <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</inf> , Reward <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</inf> , State <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t+1</inf> , Action <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t+1</inf> (SARSA), SARSAmax (Q-Learning), and Genetic Algorithms. Therefore, little attention has been paid to the tools used to help these algorithms (i.e. the Experience Replay Buffer). This paper briefly covers the Deep Q-Learning (DQN) algorithm, as it is the learning algorithm used in this study. Most importantly, it demonstrates a new Experience Replay Butter sampling technique, the Round Robin Prioritized Experience Replay Buffer (RRPERB). This technique successfully demonstrated that it could help RL agents learn with less episodes and also generalized better to rarely seen states by not completely depriving them of experiences which are ranked as less priority.
Coordinated Scheduling and Decentralized Federated Learning Using Conflict Clustering Graphs in Fog-Assisted IoD Networks

Despite the advantages of fog-assisted internet of drones (IoD) networks for federated learning (FL) model aggregations, it is restricted by the limited battery capacity of drones and wireless channel conditions between a fog and drones. In this paper, we address the deployment problem of decentralized FL and the secrecy rate maximization problem in fog-assisted IoD networks. In particular, we leverage the overlapped zones of drones to spread their local aggregated models to the network. As such, the global model aggregation is achieved without any participation from the fog. Then, aiming to secure the proposed decentralized FL-based framework, we consider the secrecy rate maximization problem, which can be addressed by scheduling users to drones and their available radio resource blocks (RRBs). This coordinated scheduling problem of users-drones/RRBs is NP-hard. To efficiently solve it using graph theory techniques, we construct a centralized conflict clustering graph and reformulate the considered scheduling problem as a maximum-weight independent set (MWIS) problem in the constructed conflict graph. To alleviate the need for the coordinated scheduling policy at the fog, we propose to solve the coordinated scheduling problem in a distributed manner. We conduct extensive simulations to verify the effectiveness of the proposed schemes over existing schemes. Selected numerical results show that the proposed distributed scheduling schemes provide similar performances in terms of secrecy rate and energy consumption as compared with the centralized scheme, with reasonable amount of information exchanges among the drones.