Reviewerly/exHarmony huggingface dataset:
The Different Effects of Collapsing Categories on Graded Response Model and Generalized Partial Credit Model

The Different Effects of Collapsing Categories On Graded Response Model And Generalized Partial Credit ModelIn ordinal data analysis, when an item has some category with low endorsement rate, it is common practice to collapse the adjacent categories.However, until recently the effect of collapsing on polytomous item response theory (IRT) modeling has not been thoroughly investigated.Harel ( 2014) provided an in-depth analysis on the effect of collapsing categories on partial credit model (PCM) and generalized partial credit model (GPCM), claiming that collapsing would lead to model misspecification and hence should be avoided in most circumstances.In this paper, we provide theoretical and simulation results to show that graded response model (GRM), another commonly used polytomous IRT model, is a safer option when collapsing categories is required.
Spatiotemporal Gait Guidance Using Audiovisual Cues of Synchronized Walking Avatar in Augmented Reality

There has been a growing interest in augmented reality (AR) methods for gait guidance and rehabilitation.These methods have been effective in guiding spatial gait parameters, such as stride length, and temporal cycle times when paired with rhythmic stimuli.However, few studies have explored the simultaneous spatial and temporal guidance of gaits and have primarily focused on one of several aspects.Gait parameters are related, and changing one might have unintended effects on others; thus, simultaneous guidance is required.In this study, we designed and evaluated a system that provides simultaneous spatial and temporal guidance using a synchronized walking avatar in AR.The system requires a head-mounted display (HMD) and presents a walking avatar involving auditory cues synchronized with the foot-contact timing of the participant via a mutual entrainment model.Spatial feedback is provided by distance changes between the participant and avatar, and the effects on stride length are observed.Phase difference changes between the avatar and participant serve as temporal cues that are used to guide the cycle time.The stride length and cycle-time of eight participants walking along a straight corridor wearing the HMD were recorded and analyzed.Four experimental conditions were applied comprising combinations of increases and decreases in the spatial and temporal cues.Furthermore, the results demonstrated that spatial and temporal feedback had almost independent effects on respective gait parameters.These findings reveal that this combination of spatiotemporal cueing can be extremely effective in gait guidance without loss of effectiveness from single modality cues.
Multiscale Hybrid Convolutional Deep Neural Networks with Channel Attention

Attention mechanisms can improve the performance of neural networks, but the recent attention networks bring a greater computational overhead while improving network performance. How to maintain model performance while reducing complexity is a hot research topic. In this paper, a lightweight Mixture Attention (MA) module is proposed to improve network performance and reduce the complexity of the model. Firstly, the MA module uses multi-branch architecture to process the input feature map in order to extract the multi-scale feature information of the input image. Secondly, in order to reduce the number of parameters, each branch uses group convolution independently, and the feature maps extracted by different branches are fused along the channel dimension. Finally, the fused feature maps are processed using the channel attention module to extract statistical information on the channels. The proposed method is efficient yet effective, e.g., the network parameters and computational cost are reduced by 9.86% and 7.83%, respectively, and the Top-1 performance is improved by 1.99% compared with ResNet50. Experimental results on common-used benchmarks, including CIFAR-10 for classification and PASCAL-VOC for object detection, demonstrate that the proposed MA outperforms the current SOTA methods significantly by achieving higher accuracy while having lower model complexity.
Beyond linear regression: mapping models in cognitive neuroscience should align with research goals

Many cognitive neuroscience studies use large feature sets to predict and interpret brain activity patterns. Feature sets take many forms, from human stimulus annotations to representations in deep neural networks. Of crucial importance in all these studies is the mapping model, which defines the space of possible relationships between features and neural data. Until recently, most encoding and decoding studies have used linear mapping models. Increasing availability of large datasets and computing resources has recently allowed some researchers to employ more flexible nonlinear mapping models instead; however, the question of whether nonlinear mapping models can yield meaningful scientific insights remains debated. Here, we discuss the choice of a mapping model in the context of three overarching desiderata: predictive accuracy, interpretability, and biological plausibility. We show that, contrary to popular intuition, these desiderata do not map cleanly onto the linear/nonlinear divide; instead, each desideratum can refer to multiple research goals, each of which imposes its own constraints on the mapping model. Moreover, we argue that, instead of categorically treating the mapping models as linear or nonlinear, we should instead aim to estimate the complexity of these models. We show that, in many cases, complexity provides a more accurate reflection of restrictions imposed by various research goals. Finally, we outline several complexity metrics that can be used to effectively evaluate mapping models.
Assessing the safety effect through Google Maps usage: FMEA approach (Case study: Indonesia)

As the most popular navigation application used in Indonesia, Google Maps provides route information with the shortest route or the fastest travel time. Unfortunately, Google Maps does not consider the safety aspect regarding warnings about the unsafe roads or the unsuitability of road conditions with the type of vehicles that may increase the safety risks for road users. This research aims to assess the safety effect of Google Maps usage by using the Failure Mode and Effects Analysis (FMEA) approach to identify the results or effects of an item's failure on system operation and to classify each potential failure according to its severity. In the end, FMEA will also analyze the safety aspects of Google Maps by determining the Risk Priority Number (RPN) as the product of severity, occurrence, and detection to prioritize the anticipatory actions, mitigations, and strategies against the highest level of risk. The data source was received from questionnaires distributed to more than a thousand respondents through an online survey that represented the user characteristics of using Google Maps in Indonesia. As the result, the potential failure mode of "Google Maps doesn't give a speeding warning" is assessed as the highest risk level of RPN, thus indicating the recommended action is by providing a speeding warning in the form of audible notification, without disturbing the driver while driving.
Vocational Interests of Students in an International Network Learning Environment

Despite the fact that vocational interests constitute one of the main variables that influence decision-making, their study does not begin until ages that could require late hours, such as Secondary Education. In order to alleviate this deficit, a longitudinal study was carried out, whose main objective was to analyse to what extent a networked learning environment contributes to a better behaviour of primary school students towards their vocational interests. An educational program was developed and implemented based on the international R&amp;D e-Cultures Project. Spain, Portugal and several Latin American countries participated in the research. The evaluation of the program was carried out using a quasi-experimental pretest-posttest design in which an ad hoc questionnaire was applied to collect the data. The results showed a stronger career decision in boys (as opposed to girls). On the other hand, vocational interests were less related to the country of residence. It is concluded that vocational guidance, working collaboratively in a networked learning environment with an intercultural perspective, enhances the decision-making process and definition of professional goals, contributing to the teaching of science in primary school students.
On the Performance of Convolutional Neural Networks with Resizing and Padding

As performance of convolutional neural networks in image classification experiments largely depends on appropriate feature extraction, resizing and/or padding scenarios need to be studied as it affects feature extraction directly. While direct resizing may alter the texture of an image, appropriate padding is not straightforward. In this paper, possible resizing and/or padding scenarios have been empirically studied to have more insights into preprocessing in image classification experiments. It is found that appropriate padding while maintaining the aspect ratio appears to be reasonably prospective than direct resizing. Specifically, padding with estimated background appears to be well-performing in experiments where textures are significant. In all the experiments carried out, resizing with some kind of padding is found to yield the highest classification accuracy. Fairly significant improvement in classification accuracy, i.e., 2–3% in the range of over 90% is obtained with suitable padding.
Optimizing Hill Climbing Algorithm Parameters for Generation of Cryptographically Strong S-Boxes

Abstract Nonlinear substitutions (S-boxes) are an important component of modern symmetric ciphers. They are designed to complicate the plaintext-ciphertext dependency. According to modern ideas, the S-box should be bijective, have high nonlinearity and algebraic immunity, low delta uniformity and linear redundancy. These criteria directly affect the cryptographic strength of ciphers, providing resistance to statistical, linear, algebraic, differential, and other cryptanalysis techniques. Many researchers have used various heuristic techniques to generate random S-boxes with high nonlinearity. However, the complexity of this task is still high. For example, the best-known algorithm to generate a random 8-bit bijective S-box with nonlinearity 104 requires high computational effort (i.e. more than 65 thousand intermediate estimates [search iterations]). In this article, we explore an iterative hill-climbing algorithm and optimize the heuristic search parameters. We show that the complexity of generating S-boxes can be significantly reduced. To search for a random bijective S-box with nonlinearity 104, only about 50 thousand intermediate search iterations are required. In addition, we generate Cryptographically Strong S-Boxes for which additional criteria are provided. We present estimates of the complexity of the search and estimates of the probabilities of generating substitutions with various cryptographic indicators. The extracted results demonstrate a significant improvement in our approach compared to the state of the art in terms of providing linear nonredundancy, nonlinearity (104), algebraic immunity (3) and delta uniformity (8).
STUDENTS’ MOTIVATION FOR PERFORMING RESEARCH WORK AND ITS RELATIONSHIP WITH THEIR ORIENTATION TOWARD BUILDING AN ACADEMIC CAREER AT UNIVERSITY

The requirements for university graduates include the possession of research skills, which implies the inclu-sion of this type of activity in the curriculum. Research work of students is included in both compulsory types of work and those performed at will of the student himself (over and above the curriculum), the latter being associated with a high level of students’ motivation. The article aims to analyze the structure of students’ motivation for carrying out research work at university and to identify its connection with the orienta-tion of young people to building an academic career at university after graduation. The methodology is based on Talcott Parsons’ model of reference variables. It allowed us to describe the motivation of students at the theoretical level. Some of the selected theoretical positions (affective neutrality, self-orientation) were tested using empirical material collected through a survey of students of Perm national research universities (Perm State University, Perm National Research Politechnic University, Higher School of Economics – Perm). Based on the ideas of T.V. Razina and other researchers of motivation, we developed and tested a methodology for identifying the structure of students’ motivation to conducting research work at university. The following were identified as the most pronounced motives of the interviewed students: firstly, they car-ry out research work for self-development in the broad sense of the word; secondly, the process itself pro-vides them with interest. Cluster analysis made it possible to classify respondents into 4 groups, based on the specifics of their motivation: strategists, enthusiasts, dependents, and and random people. A connection was revealed between the specifics of students’ motivation and their determination to build an academic ca-reer at university after graduation. As a result, we were able to identify and describe the group of students who are more determined to devote themselves to research work in the future. The paper proposes a number of recommendations for scientific supervisors and managing personnel of universities, which can be helpful in building the personnel policy of universities.
Societal XR—A Vision Paper

Most current research and developments in the field of extended reality follow a rather one-sided agenda: Creating virtual worlds and even metaverses which require all users to wear suitable head-mounted displays. They thereby reduce the application of XR to limited spaces, (relatively) few users, and (social) activities in these virtual worlds. Only few approaches such as Spatial Augmented Reality and Social XR overcome at least some of these limitations. So far, a vision for a truly public XR that can be used by everyone without wearing technology on their bodies, is missing. This vision paper therefore wants to give XR research and development an additional new direction. It coins the term “Societal XR” for a form of extended reality that moves all technology into the environment, becomes accessible to everyone including children and people with disabilites, and integrates virtuality into their users' reality and everyday life.
Critical technology enablers for near-to-eye AR and VR

Virtual reality (VR) and Augmented reality (AR) near-to-eye systems can offer virtual surround-the-user screen sizes, excellent portability, telepresence, and enhanced situational awareness, to name just a few attributes for both military and civilian applications. Advancements of 8 critical technology enablers may soon resolve the largest issues of economic, functionality, and human factors that have impeded broad-acceptance of near-to-eye imaging. There have recently been substantial improvements in these enabling technologies which are reaching critical viability or are moving out of earlystage research and development with cost-effective manufacturing potential. Each of these enabling technologies are discussed and their potential impact on the future of AR and VR assessed.
SIKE Channels

We present new side-channel attacks on SIKE, the isogeny-based candidate in the NIST PQC competition. Previous works had shown that SIKE is vulnerable to differential power analysis, and pointed to coordinate randomization as an effective countermeasure. We show that coordinate randomization alone is not sufficient, because SIKE is vulnerable to a class of attacks similar to refined power analysis in elliptic curve cryptography, named zero-value attacks. We describe and confirm in the lab two such attacks leading to full key recovery, and analyze their countermeasures.
An Energy-Efficient Strategy and Secure VM Placement Algorithm in Cloud Computing

One of the important and challenging tasks in cloud computing is to obtain the usefulness of cloud by implementing several specifications for our needs, to meet the present growing demands, and to minimize energy consumption as much as possible and ensure proper utilization of computing resources. An excellent mapping scheme has been derived which maps virtual machines (VMs) to physical machines (PMs), which is also known as virtual machine (VM) placement, and this needs to be implemented. The tremendous diversity of computing resources, tasks, and virtualization processes in the cloud causes the consolidation method to be more complex, tedious, and problematic. An algorithm for reducing energy use and resource allocation is proposed for implementation in this article. This algorithm was developed with the help of a Cloud System Model, which enables mapping between VMs and PMs and among tasks of VMs. The methodology used in this algorithm also supports lowering the number of PMs that are in an active state and optimizes the total time taken to process a set of tasks (also known as makespan time). Using the CloudSim Simulator tool, we evaluated and assessed the energy consumption and makespan time. The results are compiled and then compared graphically with respect to other existing energy-efficient VM placement algorithms.
Sentiment Analysis of Kampus Mengajar 2 Toward the Implementation of Merdeka Belajar Kampus Merdeka Using Naïve Bayes and Euclidean Distence Methods

The Ministry of Education and Culture initiated the Merdeka Belajar Kampus Merdeka (MBKM) program. Several programs in Merdeka Belajar Kampus Merdeka (MBKM) Program include industrial internships, independent projects, student exchanges, community service projects, humanitarian programs, and so on. Kampus Mengajar 2 is one of the programs that hadbeen running. The program received various responses from the public, which were expressed on social media. The Supervisor at kampus mengajar 2 was also active in providing various comments on the kampus mengajar 2 telegramgroups in the form of good, bad, and neutral comments. These comments have the potential to generate a growing sentiment among the general public and academics. Based on these issues, the researcher analyzed the kampus mengajar 2 sentiments toward the implementation of the Merdeka Belajar Kampus Merdeka program with the data source being comments on the supervisors' telegram group. The data obtained from the telegram group is classified as good, bad, or neutral using the Naive Bayes method and K-Nearest Neighbors on up to 591 data points. The data is then divided into two parts: training data and testing data. Testing data can account for up to 20 percent of total data, with the remaining 80 percent serving as training data. The accuracy results on sentiment analysis show that the Naive Bayes method outperforms the KNN method, with 99.30 percent for Naive Bayes and 97.20 percent for K-Nearest Neighbors
Continuous Nonintrusive Mobile Device Soft Keyboard Biometric Authentication

Mobile banking, shopping, and in-app purchases utilize persistent authentication states for access to sensitive data. One-shot authentication permits access for a fixed time period. For instance, a username/password-based authentication allows a user access to all the shopping and payments data in the Amazon shopping app. Traditional user passwords and lock screens are easily compromised. Snooping attacks—observing an unsuspecting user entering passwords—and smudge attacks—examining touchscreen finger oil residue—enable compromised user authentication. Mobile device interactions provide robust human and device identity data. Such biometrics enhance authentication. In this paper, behavioral attributes during user input constitute the password. Adversary password reproduction difficulty increases since pure observation is insufficient. Current mobile continuous authentication schemes use, among others, touchscreen–swipe interactions or keyboard input timing. Many of these methods require cumbersome training or intrusive authentication. Software keyboard interactions provide a consistent biometric data stream. We develop biometric profiles using touch pressure, location, and timing. New interactions authenticate against a profile using a divergence measure. In our limited user–device data sets, the classification achieves virtually perfect accuracy.
TWIENG: A Multi-Domain Twi-English Parallel Corpus for Machine Translation of Twi, a Low-Resource African Language

A Twi-English parallel corpus is certainly an important resource for Machine Translation of Twi (ISO 639-3), a Low-Resource African Language (LRAL) which is mainly spoken in Ghana and Ivory Coast. Currently large-scale multi-domain Twi-English parallel corpus is still unavailable partly due to the difficulties and the arduous efforts required in its design. In this paper, we present TWIENG: a large-scale multi-domain Twi-English parallel corpus. We crawled the sentences from the web using web crawlers, translated, aligned, tokenized and compiled to create the corpus. We crawled English sentences from Ghanaian indigenous electronic news portals, Ghanaian Parliamentary Hansards, Twi Bible and crowdsourcing via google forms. The sentences were translated by professional translators and linguists, they were then aligned, tokenized and compiled. The corpus was curated using the sketch engine, a corpus manager and analysis software developed by Lexical Computing Limited. The corpus was manually evaluated by Twi professional linguists. The Corpus has 5,419 parallel sentences.
Simulation regarding solar panel tracking system based on Sun’s position

The following article describes a tracking system to keep a solar panel aligned with the Sun. It was designed in Matlab using Simulink. The model consists in a panel, a motor and a controller which is used to track the position of the Sun. The physical system is based of two equations of motion, one for the panel and the other one for the motor. The purpose of building the tracking system is keeping a maximum intensity of producing electricity during all day.
Face Detection Using Artificial Neural Network and Wavelet Neural Network

Recognition and detection of face are the most important aspects in different fields like video surveillance, interface of computer with human and database management of images. This paper discusses about the feature extraction of an image using Gabor transform and then the extracted output is used as the input to the neural network. The training of the neural network is done with input image data. The Gabor wavelet gives emphasis to the vital parts of the face containing cheeks, mouth, nose and eye. Neural network used as a classifier technique for doing the recognition and unrecognizing of face. Also in this paper a hybrid technique using wavelet transform and neural network called as wavelet neural network (WNN) is used for the face detection. The multi scale analysis quality of wavelet transforms and the classification ability of artificial neural network is combined in the WNN. The accuracy of face detection is more with this hybrid WNN approach. Three main issues of face recognition are pre-processing of image, feature extraction of the image and classification of the input image. The results of both the proposed images are disused to show its accuracy.