Reviewerly/exHarmony huggingface dataset:
Applying a New Questionnaire to Evaluate the Usability of Peruvian E-Government Websites

Usability has become an extremely relevant aspect that must be considered in the development of any software product. If usability is not considered, then there is a high probability that its users will reject the application. A software product is destined to fail in a highly competitive environment such as the current one, depending on its capability to be understood, learned, and attractive to its users. For this reason, a set of usability evaluation methods have been developed that allow specialists to systematically determine whether graphical interfaces are usable. Among the methods that exist, one of the most used due to its simplicity and low demand for resources is the usability assessment questionnaire. However, most of the questionnaires that have been designed are intended to be answered by end-users of the application. In contrast to this paradigm, in a previous work, a questionnaire was developed and designed that can be used by specialists in Human-Computer Interaction. This new questionnaire involves a set of 60 items with which it is possible to verify if the interfaces of a software product fulfill aspects that make them usable. In this study, the new questionnaire was employed to evaluate the usability of the main Peruvian E-Government Websites. The intention of this scientific article is to serve as a source for future improvement of the websites and to serve at a methodological level so that specialists can widely use the evaluation instrument and the conduction process that has been carried out in this research.
Construction of Credit System Prediction Model Based on L-M Optimization Algorithm

Using the relationship between the students’ historical score data and the curriculum, this paper constructs a credit score point prediction model based on BP neural network, which has certain theoretical and practical value. In the course, the neural network can be used to predict the complex relationship between credit and grade point. The prediction results of network trained by different algorithms are compared. It is found that the L-M optimization algorithm has the best preview performance. Finally, the function is used to simulate, and then the simulation results are compared with the sample data. It is verified that the L-M optimization algorithm has high accuracy and can be used to predict grade points.
Breaking Black Box Crypto-Devices Using Laser Fault Injection

Laser fault injection attacks on hardware implementations are challenging, due to the inherently large parameter space of the fault injection and the unknown underlying implementation of the attacked device. In this work we report details from an exemplary laser fault attack on the AES-based authentication chip Microchip ATAES 132A, which lead to full secret key extraction. In addition we were able to reveal some details of the underlying implementation. This chip claims to feature various countermeasures and tamper detection mechanisms and is therefore a representative candidate for devices to be found in many different applications. On this basis we describe a systematic approach for Laser fault attacks on devices in a black-box scenario. This includes the determination of all relevant attack parameters such as fault locations, timings, and energy settings.
A Survey on Mobile App Development Approaches With the Industry Perspective

Mobile applications (i.e., mobile apps) are dynamic from the user point of view, it is complex in some situations, and it is being developed in competitive and strict time frames. Therefore, the developers are required to pay attention to choose an appropriate software development process to cater these needs. Over the last decade, agile approaches have become very popular for software development in general, but there has only been limited research performed in the applicability of agile methods in the area of mobile app development. In this paper, a detailed review on mobile app development approaches with their best practices is prepared to explore the suitability of agile approaches. We have also conducted an online survey to know the current mobile app development trends in industries. It is observed that the agile approaches are the most suited approaches for mobile app development due to its flexibility in making changes, scalability, usability, etc. This crucial survey contributes towards a better comprehension of development trends in mobile app development industries.
Typing in Mid Air: Assessing One- and Two-Handed Text Input Methods of the Microsoft HoloLens 2

The Microsoft HoloLens 2 is a mixed reality (MR) headset that overlays virtual elements atop a user's view of their physical environment. To input text, the device has the ability to track hands and fingers, allowing for direct interaction with a virtual keyboard. This is an improvement over the HoloLens 1 device, which required head tracking and single-finger air-tapping input. The present study evaluated the performance (speed and accuracy), perceived usability, mental workload, and physical exertion of one-handed and two-handed text entry. A sample of 21 participants (12 male, 9 female) aged 18–32 years typed standardized phrases presented in random order. Typing with two hands was faster and more preferred than one-handed input; however, this input method was also less accurate. Exertion in some body parts was also higher in the two-handed condition. Findings suggest that while two-handed text input was better than one-handed, there is room for improvement to approximate typing on a physical or mobile device keyboard.
Path Planning of Mobile Robot Using Adaptive Particle Swarm Optimization

This paper proposes an adaptive particle swarm optimization (APSO)-driven algorithm for robot path planning and obstacle avoidance in an unknown environment. The proposed algorithm consists of two different obstacle avoidance methodologies; considering one obstacle at a time and all obstacles in the path between the robot position to the goal position. To avoid collision, the robot determines the tangential points on the safety circle. The simulation results are presented for different environmental situations. The proposed algorithm is applied on Webots, to validate its effectivity. It works efficiently, and the mobile robot has successfully avoided the obstacles while moving toward the goal position. A comparative study between the two obstacle avoidance methodologies is presented in terms of the minimum path length to reach the goal position. The proposed algorithm is also compared with the existing algorithms, and it provides satisfactory result.
Keylogger Awareness and Use in Cyber Forensics

Today hacking is not far enough from the reach of common people. But still today people are using untrusted systems (e.g. Cyber cafés, Internet labs in organizations etc.) to deal with their important financial, confidential and password accounts, where Keylogger is playing a vital role for these cyber criminals to peep into anyone’s account. Keylogger, Keystroke tracker or KeyRecorder is software which records the key pressed and stores them in secondary memory storage. This hack-attack is widely used in today’s era but still unfortunately some people are unaware of this technology. Not only Keylogger is a threat but Screen Recording software is also one of the severe attacks, to steal the password of the user which is a counter attack to the use of virtual keyboards. This paper focuses on public interest in such security training. This paper is divided into two phases. Phase I deals with dark side of Key Loggers i.e. Key Logger as a Hacking tool and Phase II deals with the silver side i.e. Key Logger as a Security tool. At last there will be some tricks that will help to keep our data away from such Keylogging softwares.
Self-paced Safe Co-training for Regression

In semi-supervised learning, co-training is successfully in augmenting the training data with predicted pseudo-labels. With two independently trained regressors, a co-trainer iteratively exchanges their selected instances coupled with pseudo-labels. However, some low-quality pseudo-labels may significantly decrease the prediction accuracy. In this paper, we propose a self-paced safe co-training for regression (SPOR) algorithm to enrich the training data with unlabeled instances and their pseudo-labels. First, a safe mechanism is designed to enhance the quality of pseudo-labels without side effects. Second, a self-paced learning technique is designed to select “easy” instances in the current situation. Third, a “qualifier-based” treatment is designed to remove “weak” instances selected in previous rounds. Experiments were undertaken on nine benchmark datasets. The results show that SPOR is superior to both popular co-training regression methods and state-of-the-art semi-supervised regressors.
Validated Digital Literacy Measures for Populations with Low Levels of Internet Experiences

A growing body of evidence suggests that digital literacy, defined as the ability to effectively find information online, is an important barrier constraining adoption and use of Internet and digital technologies in the developing world. Digital literacy by enabling people to effectively find valuable information online, can play a crucial role in expanding economic opportunities, thereby leading to human development and poverty reduction. Unfortunately, there is a dearth of validated survey measures for capturing digital literacy of populations who have limited prior exposure to technology. We present a novel approach for measuring digital literacy of low literacy and new Internet users, an important segment of users in developing countries. Using a sample of 143 social media users in Pakistan, which includes a significant fraction of low literacy individuals, we measure digital literacy by observing the effectiveness of participants in completing a series of tasks and by recording a set of self-reported survey measures. We then use machine learning methods (e.g., Random Forest) to identify a parsimonious set of survey measures that are most predictive of ground truth digital literacy established through participant observation. Our approach is easily scalable in low-resource settings and can aid in tracking digital literacy as well as designing interventions and policies tailored to users with different levels of digital literacy.
RAM360: Robust Adaptive Multi-Layer 360$^\circ$ Video Streaming With Lyapunov Optimization

Viewport-adaptive streaming approaches are emerging as the most promising way to deliver high-quality 360 videos over mobile networks. However, the viewport prediction is only reliable within a short prediction window, i.e., a short playback buffer, which conicts with maintaining a long buffer to avoid playback rebuffering. To deal with this problem, we present RAM360, a Robust Adaptive Multi-layer 360 video streaming system, to ensure high viewport quality and low stall ratio concurrently. We make three technical contributions. First, we design a QoE-driven robust multi-layer streaming framework, where each chunk is encoded by multiple independent layers with different quality levels. The client dynamically decides which chunk and layer to be downloaded by their QoE contributions. Thus, the base-layer could be prefetched to avoid the risk of stalling while the viewport quality is improved by downloading enhancement layer. Second, we establish a novel QoE model to represent the quality of whole playback session, not that of chunk. It aims to maximize the overall QoE of playback session. Third, we introduce the Lyapunov optimization theory to solve the QoE optimization problem, which is an online algorithm with near-optimality solution. We demonstrate that RAM360 can significantly outperform the existing schemes regarding viewport quality, stall ratio, and QoE through extensive experiments with public datasets.
Design and Implementation of an Intelligent Education Management Platform Based on Face Recognition

The development of artificial intelligence has greatly changed people’s work and lifestyle. Intelligence has appeared in various fields, which has brought great convenience to people. Education is considered to be one of the most important topics for a family. With the family’s emphasis on education, the number of students is increasing year by year, and there is a serious imbalance between the number of teachers and the number of students. Teachers cannot guarantee the quality of education while taking into account the classroom status of all students, assisted by artificial intelligence. Education helps to realize the fairness of education. In recent years, face recognition technology has achieved remarkable results in security, attendance and other fields, providing ideas for intelligent education. This paper studies the intelligent education management platform based on face recognition design and implementation.
Pseudoeosinophilia on automated analyser (Sysmex XN-1000) a guide to malaria detection - An incidental finding

Rationale: Malaria is a protozoan disease which has a worldwide distribution in tropical and subtropical regions. The peripheral blood smear examination is a gold standard and an immunochromatographic rapid diagnostic test is an adjuvant diagnostic tool for the detection of malaria. For the past two decades, the utility of automated blood cell counters for the detection of malarial infection is growing in the presumptive diagnosis of malaria in developing countries. Patient Concerns: Here, we report a case in which a young male came for surgical evaluation for inguinal hernia surgery. On routine haemogram evaluation, malaria was suspected with the help of flow cytometry based haematology analyser (Sysmex XN 1000) generated histogram showing erroneously high eosinophil count (pseudoeosinophilia) and atypical white blood cell scattergram. Diagnosis: Peripheral smear and malarial antigen examinations confirmed Plasmodium vivax species. Outcomes: Patient was treated with antimalarial medications and elective surgery to repair inguinal hernia was planned after 10 days. Lessons: Here, we hope to increase the awareness of laboratory physicians to explore the possibilities of diagnosing malaria through hints provided by automated haematology analyser generated histograms.
Perceiving the Invisible: Proposal-Free Amodal Panoptic Segmentation

Amodal panoptic segmentation aims to connect the perception of the world to its cognitive understanding. It entails simultaneously predicting the semantic labels of visible scene regions and the entire shape of traffic participant instances, including regions that may be occluded. In this work, we formulate a proposal-free framework that tackles this task as a multi-label and multi-class problem by first assigning the amodal masks to different layers according to their relative occlusion order and then employing amodal instance regression on each layer independently while learning background semantics. We propose the PAPS architecture that incorporates a shared backbone and an asymmetrical dual-decoder consisting of several modules to facilitate within-scale and cross-scale feature aggregations, bilateral feature propagation between decoders, and integration of global instance-level and local pixel-level occlusion reasoning. Further, we propose the amodal mask refiner that resolves the ambiguity in complex occlusion scenarios by explicitly leveraging the embedding of unoccluded instance masks. Extensive evaluation on the BDD100K-APS and KITTI-360-APS datasets demonstrate that our approach set the new state-of-the-art on both benchmarks.
The Negative BER Loss Function for Deep Learning Decoders

After certain training epochs of the deep learning decoders, the binary cross entropy (BCE) loss function ignores the training of some unsuccessful decoded information bits, thereby degrading the training efficiency. In this letter, we propose the negative bit error rate (NBER) loss function to increase the training concentration degree on the unsuccessful decoded information bits by modifying the gradient on different information bits, so that the training efficiency and decoding performance can be improved. The simulation results show that NBER could achieve significant performance improvement over BCE both in the deep neural network decoders and the weighted belief propagation decoders.
An Ensemble-Based Machine Learning for Predicting Fraud of Credit Card Transactions

Recently, using credit cards has been considered one of the essential things of our life due to its pros of being easy to use and flexible to pay. The critical impact of the increment of using credit cards is the occurrence of fraudulent transactions, which allow the illegal user to get money and free goods via unauthorized usage. Artificial Intelligence (AI) and Machine Learning (ML) have become effective techniques used in different applications to ensure cybersecurity. This paper proposes our fraud detection system called Man-Ensemble CCFD using an ensemble-learning model with two stages of classification and detection. Stage one, called ML-CCFD, utilizes ten machine learning (ML) algorithms to classify credit card transactions to class 1 as a fraudulent transaction or class 0 as a legitimate transaction. As a result, we compared their classification reports together, precisely precision, recall (sensitivity), and f1-score. Then, we selected the most accurate ML algorithms based on their classification performance and prediction accuracy. The second stage, known Ensemble-learning CCFD, is an ensemble model that applies the Man-Ensemble method on the most effective ML algorithms from stage one. The output of the second stage is to get the final prediction instead of using common types of ensemble learning, such as voting, stacking, boosting, and others. Our framework's results showed the effectiveness and efficiency of our fraud detection system compared to using ML algorithms individually due to their weakness issues, such as errors, overfitting, bias, prediction accuracy, and even their robustness level.
A New PPE Algorithm Based on Parallel Communication Strategy

Liang, Lu-Lu Du, Zhi-Gang Shieh, Chin-Shiuh Hu, Chia-Cheng Chu, Shu-Chuan Feng, QingThe Phasmatodea population evolution algorithm (PPE) is a relatively new swarm intelligence algorithm at present. A PSO algorithm with a parallel communication strategy can largely optimize the advantages of PSO and overcome some of its shortcomings. Combining PPE and Parallel particle swarm optimization, this paper proposes a parallel Phasmatodea population evolution algorithm (PPPE) with communication strategy. In this thesis, all populations are grouped to communicate and compete. This approach can change the location and number of populations and thus expand the search for populations on a global scale. Inspired by Parallel PSO, the authors propose two exchange strategies in this paper. The first communication strategy is modelled on the first communication style of Parallel PSO. The second communication strategy is modelled on the third communication style of Parallel PSO. The algorithm was tested and analysed on 30 benchmark functions. The test results indicate that, under the same conditions, the improved algorithm outperforms the best fit and degree of convergence obtained by other similar algorithms.
Blockchain-Based Enterprise Ballots in an Oil and Gas Consortium

Enterprise ballot is a term often used to represent a deliberation process within a company or a consortium of companies. Such ballots are usually applied to support the decision-making process in scenarios where stakeholders and the organization have to decide on a certain subject before performing an action. However, there are few reported cases in the literature regarding enterprise ballots and their requirements. Hence, the enterprise needs are not mapped in depth, particularly in a consortium scenario. This presents challenges regarding process transparency, system reliability and user access restrictions. Therefore, an enterprise ballot system should have its own requirements to deal with this environment, ensuring a correct, verifiable and private ballot process. In this sense, this paper presents BallotBR, an enterprise ballot system built under the Libr@Blockchain project, which is supported by Libra consortium based on the ANP’s Research, Development and Innovation clauses. The requirements were collected with the companies stakeholders, and the system was implemented in a permissioned blockchain, Hyperledger Fabric. Furthermore, a permissioned blockchain network was proposed to provide correctness, verifiability and privacy to the ballot process. Such a network aims to promote a trustworthy, cooperative, and reliable environment. Still, the network intends not only to enable an enterprise ballot system for a specific consortium, but to allow that, in the future, other applications be attached, connecting other companies in this ecosystem. Since we used a permissioned blockchain, companies can exchange information privately through exclusive communication channels. Moreover, we developed an integration with a digital identity and signature solution to control access to the system, and ensure the identity of users’ actions within BallotBR. Last but not least, we present the identified threats to validity to expose and discuss the main concerns of our system design choices.
Impact of Likelihoods on Class Enumeration in Bayesian Growth Mixture Modeling

Bayesian methods have been widely used to estimate models with complex structures. To assess model fit and compare different models, researchers typically use model selection criteria such as Deviance Information Criteria (DIC), Watanabe-Akaike Information Criteria (WAIC) and leave-one-out cross validation (LOO-CV), the calculation of which is based on the likelihoods of the models. When models contain latent variables, the likelihood is often specified as conditional on the latent variables in popular Bayesian software (e.g., BUGS, JAGS, and Stan). Although this practice reduces computation work and does not affect model estimation, the previous literature has shown that model comparisons based on the conditional likelihood could be misleading. In contrast, marginal likelihoods can be obtained by integrating out the latent variables and be used to calculate model selection criteria. In this study, we evaluate the effect of using conditional likelihoods and marginal likelihoods in model selection for growth mixture models. Simulation results suggest that marginal likelihoods are much more reliable and should be generally used for growth mixture modeling.
Multipath Data Fusion With Recursive ML-PDA and Generative ML-PMHT for VLO Targets in Underwater Environment

We discuss and extend two approaches to target detection and motion parameter estimation in a very low observable (VLO) underwater multipath environment. The maximum likelihood probabilistic data association (ML-PDA) algorithm considers all possible path–measurement hypotheses, whereas the maximum likelihood probabilistic multiple hypothesis tracker (ML-PMHT) assumes independence among the returns’ origins leading to a simpler likelihood formulation. In particular, we present a recursive form of the multipath ML-PDA algorithm for improved computational efficiency and a generative multipath ML-PMHT algorithm that accounts for the prior probability distribution of the number of received measurements. By simulation of an active sonar tracking scenario, we show that while the two algorithms perform similarly for few returns, the added fidelity of the ML-PDA outperforms the ML-PMHT in parameter estimation as returns accumulate over multiple scans and dimensions. However, the computational cost of the ML-PDA discourages its use beyond simple scenarios. This cost, coupled with the comparable detection performance of the two algorithms, suggests that the simpler ML-PMHT is the better choice for practical use. We also show how both the ML-PDA and ML-PMHT can be applied to realistic ocean environments using ocean acoustic modeling software. This article is an extension of previous work on comparing the standard versions of the ML-PDA and ML-PMHT algorithms.