Reviewerly/exHarmony huggingface dataset:
MS-DLD: Multi-Sensors Based Daily Locomotion Detection via Kinematic-Static Energy and Body-Specific HMMs

More adaptable and user-independent techniques are required for multi-sensors based daily locomotion detection (MS-DLD).This research study proposes a couple of locomotion detection methods using body-worn multi-sensors to successfully categorize several locomotion transitions.This research presents both standard and state-of-the-art methods for MS-DLD.Conventionally, to improve MS-DLD process, the proposed methodology consists of a wavelet transformed Quaternion-based filter for the inertial signals, patterns recognition in the form of kinematic-static energies, and state-of-the-art multi-features extraction.These features include entropy, spectral, and cepstral coefficients domains.Then, fuzzy logicbased optimization has been introduced in order to achieve the selective features by converting them into codewords.These nominated features with enhanced patterns are further detected by HMMs to improve the accuracy for classifying daily life locomotion actions.This paper also introduces another state-ofthe-art way to model daily locomotion detection and derives body-specific modified HMMs.The model divides the sensor data into three active body-specific parts including head sensors, mid-body sensors, and lower body sensors.Body-specific modified HMMs have been provided with raw data for the three active body-specific sensors and gave better results with less computational complexities when compared to the conventional methods.The proposed systems have been experimentally assessed and trialed over three diverse publicly available datasets: the UP-Fall dataset consisting of falling and other daily life activities, the IM-WSHA dataset comprising everyday locomotion actions, and the ENABL3S gait and locomotion dataset consisting of multiple gait movements.Experimental outcomes indicate that the proposed conventional technique achieved improved results and outperformed existing systems based on detection accuracies of 90.0% and 87.5% over UP-Fall, 86.0% and 88.3% over IM-WSHA, and 86.7% and 90.0% over ENABL3S datasets for kinematic and static energy patterns, respectively.Further, the results show that the state-ofthe-art body-specific modified HMMs method achieved 94.3% and 95.0% over UP-Fall, 92.0% and 93.3% over IM-WSHA, and 90.0% and 95.0% over ENABL3S datasets for kinematic and static patterned signals, respectively.The results of state-of-the-art efficient system show a significant increase in detection accuracy when compared to standard systems.
Masked-face recognition using deep metric learning and FaceMaskNet-21

The coronavirus disease 2019 (COVID-19) has made it mandatory for people all over the world to wear facial masks to prevent the spread of the virus. The conventional face recognition systems used for security purposes have become ineffective in the current situation since the face mask covers most of the important facial features such as nose, mouth, etc. making it very difficult to recognize the person. We have proposed a system that uses the deep metric learning technique and our own FaceMaskNet-21 deep learning network to produce 128-d encodings that help in the face recognition process from static images, live video streams, as well as, static video files. We achieved a testing accuracy of 88.92% with an execution time of fewer than 10 ms. The ability of the system to perform masked face recognition in real-time makes it suitable to recognize people in CCTV footage in places like malls, banks, ATMs, etc. Due to its fast performance, our system can be used in schools and colleges for attendance, as well as in banks and other high-security zones to grant access to only the authorized ones without asking them to remove the mask.The online version contains supplementary material available at 10.1007/s10489-021-03150-3.
A federated learning framework for cyberattack detection in vehicular sensor networks

Abstract Vehicular Sensor Networks (VSN) introduced a new paradigm for modern transportation systems by improving traffic management and comfort. However, the increasing adoption of smart sensing technologies with the Internet of Things (IoT) made VSN a high-value target for cybercriminals. In recent years, Machine Learning (ML) and Deep Learning (DL) techniques attracted the research community to develop security solutions for IoT networks. Traditional ML and DL approaches that operate with data stored on a centralized server raise major privacy problems for user data. On the other hand, the resource-constrained nature of a smart sensing network demands lightweight security solutions. To address these issues, this article proposes a Federated Learning (FL)-based attack detection framework for VSN. The proposed scheme utilizes a group of Gated Recurrent Units (GRU) with a Random Forest (RF)-based ensembler unit. The effectiveness of the suggested framework is investigated through multiple performance metrics. Experimental findings indicate that the proposed FL approach successfully detected the cyberattacks in VSN with the highest accuracy of 99.52%. The other performance scores, precision, recall, and F1 are attained as 99.77%, 99.54%, and 99.65%, respectively.
Incorporating Hierarchy into Text Encoder: a Contrastive Learning Approach for Hierarchical Text Classification

Hierarchical text classification is a challenging subtask of multi-label classification due to its complex label hierarchy.Existing methods encode text and label hierarchy separately and mix their representations for classification, where the hierarchy remains unchanged for all input text.Instead of modeling them separately, in this work, we propose Hierarchyguided Contrastive Learning (HGCLR) to directly embed the hierarchy into a text encoder.During training, HGCLR constructs positive samples for input text under the guidance of the label hierarchy.By pulling together the input text and its positive sample, the text encoder can learn to generate the hierarchy-aware text representation independently.Therefore, after training, the HGCLR enhanced text encoder can dispense with the redundant hierarchy.Extensive experiments on three benchmark datasets verify the effectiveness of HGCLR.
Perfectionism and rational-emotive behaviour as a motivational policy for human resource management

The article considers the concept of a perfectionist motivational policy, which promotes achieving a high level of intrinsic employee motivation that ensures accomplishment of the organisational duties maximum amount; meanwhile, additional compensation produces a motivation displacement eﬀect. Theoretically, perfectionism, as the organisation corporate culture value, contributes to a signiﬁcant resources saving for the personnel maintenance and development. Employees who ﬁnd themselves in a corporate perfectionist culture have the advantage of achieving a high autonomy level due to developing integrative abilities for regulating social actions. Perfectionist motivational policy is more typical for the company`s growth cyclical stage and more beneﬁcial for young people who have high goals regarding their professional development. Rational-emotive employees behaviour is a perfectionism operational correlate oriented towards catastrophic attitudes which stimulate continuous activity, maximum involvement in the organisation activities and the achievement of not only operational but also long-term company goals. Perfectionist motivational policy requires an independent employee performance appraisal, which limits the individuals’ personal inﬂuence on the organisation’s human resources policy. Inconsistency in implementation of the motivation structural elements not only hinders the motivation internalisation, but also provokes a rise in distrust of power structures in general due to the personnel management low culture in the organisation.
Wikidata

Library catalogues may be connected to the linked data cloud through various types of thesauri. For name authority thesauri in particular I would like to suggest a fundamental break with the current distributed linked data paradigm: to make a transition from a multitude of different identifiers to using a single, universal identifier for all relevant named entities, in the form of the Wikidata identifier. Wikidata (https://wikidata.org) seems to be evolving into a major authority hub that is lowering barriers to access the web of data for everyone. Using the Wikidata identifier of notable entities as a common identifier for connecting resources has significant benefits compared to traversing the ever-growing linked data cloud. When the use of Wikidata reaches a critical mass, for some institutions, Wikidata could even serve as an authority control mechanism.
Developing Competency in Programming among Future Software Engineers

Introduction. Due to the intensive development of the software industry, there is a constant update of technologies and languages used in professional programming. These processes bring up to date the tasks of improving the quality of education and ensuring the development of competency in programming among students in specializations where they are trained for the information technology industry. The purpose of the article is to highlight the results of scientific research aimed at developing and implementation at higher education institutions a system of future software engineers tr aining in professional activities. Materials and Methods. The study involved 139 students from several Ukrainian universities. We studied the influence of the proposed forms, methods and training tools on the success of the formation of future software engineers’ programming competence. The study was organized using the parallel experiment method (data collection was carried out in the control and experimental groups). To collect data on the level of students’ competence in programming, their learning outcomes in a number of professionallyoriented disciplines were analysed. To analyse the data, methods of grouping, construction of pivot tables, and graphical presentation of data were used. The reliability of the data was verified using Fisher’s test and Pearson’s chi-square test. Results. The authors were first who developed a system of training o future software engineers in professional activities and experimentally tested the effectiveness of students’ programming competence moulding in the framework of this system. Here, presented are results of the study of formation of future software engineers’ professional competency, in particular competency in the field of programming, during their college education. A brief description of the professional competence structure and the system of future software engineers training for professional activities is given. Description of the pedagogical experiment stages and its contents is given, along with quantitative data in the form of tables and bar charts and its analysis. At the decisive stage of the experiment, more than a third of the students had low levels of competence under consideration, which indicated the inadequate level of professional training. According to the results of the control stage, it was established that in the experimental group, the level of competence in programming increased significantly. Discussion and Conclusion. The study results are of practical value for the scientific and pedagogical staff of universities for the development of educational programs for professional training of future software engineers, in particular, in the field of programming.
Improving the Performance of the Picnic Signature Scheme

Picnic is a digital signature algorithm designed to provide security against attacks by quantum computers. The design uses only symmetric-key primitives, and is an efficient instantiation of the MPC-in-the-head paradigm. In this work, we explore the Picnic design in great detail. We investigate and benchmark different parameter choices and show that there exist better parameter choices than those in the current specification. We also present improvements to the MPC protocol that shorten signatures and reduce signing time. The proposed MPC changes tailor the protocol to the circuit of interest in Picnic, but may also be of independent interest. Taken together, these changes give a new instantiation of Picnic that signs messages 7.9 to 13.9 times faster, and verifies signatures 4.5 to 5.5 times faster than the existing “Picnic2” design, while having nearly the same signature sizes.
HiPad: Text entry for Head-Mounted Displays Using Circular Touchpad

Text entry in virtual reality (VR) is currently a common activity and a challenging problem. In this paper, we introduce HiPad, leveraging a circular touchpad with a circular virtual keyboard, to support the one-hand text entry in mobile head-mounted displays (HMDs). The design of HiPad’s layout is based on a circle and a square with rounded corners, where the outer circle is subdivided into six keys’ regions containing letters. This technique input text by a common hand-held controller with a circular touchpad for HMDs and disambiguates the word based on the sequence of keys pressed by the user. In our first study, three potential layouts are considered and evaluated, leading to the design containing six keys. By analyzing the touch behavior of users, we optimize the 6-keys layout and conduct the second study, showing that the optimized layout has better performance. Then the third study is conducted to evaluate the performance of 6-keys HiPad with VE-layout and TP-layout and to study the learning curves. The results show that novices can achieve 13.57 Words per Minute (WPM) with VE-layout and 11.60 WPM with TP-layout and the speeds increase by 74.42% for VE-layout users and by 81.53% for TP-layout users through a short 60-phrase training.
Prediction of Breast Cancer using Machine Learning Approaches

Breast cancer is considered one of the most common cancers in women caused by various clinical, lifestyle, social, and economic factors. Machine learning has the potential to predict breast cancer based on features hidden in data.This study aimed to predict breast cancer using different machine-learning approaches applying demographic, laboratory, and mammographic data.In this analytical study, the database, including 5,178 independent records, 25% of which belonged to breast cancer patients with 24 attributes in each record was obtained from Motamed cancer institute (ACECR), Tehran, Iran. The database contained 5,178 independent records, 25% of which belonged to breast cancer patients containing 24 attributes in each record. The random forest (RF), neural network (MLP), gradient boosting trees (GBT), and genetic algorithms (GA) were used in this study. Models were initially trained with demographic and laboratory features (20 features). The models were then trained with all demographic, laboratory, and mammographic features (24 features) to measure the effectiveness of mammography features in predicting breast cancer.RF presented higher performance compared to other techniques (accuracy 80%, sensitivity 95%, specificity 80%, and the area under the curve (AUC) 0.56). Gradient boosting (AUC=0.59) showed a stronger performance compared to the neural network.Combining multiple risk factors in modeling for breast cancer prediction could help the early diagnosis of the disease with necessary care plans. Collection, storage, and management of different data and intelligent systems based on multiple factors for predicting breast cancer are effective in disease management.
Students' Opinions about the Educational Use of the Metaverse

This study aims to determine students' opinions about the educational use of the Metaverse. The study was conducted during the spring semester of the 2021-2022 academic year. The sample of the study consists of 34 second-year students at the Department of Computer Engineering of a state university in the Southeast Anatolia region of Turkey. The students participated in and evaluated the Metaverse. After the application, interviews were conducted with the participants to find out the students' opinions about Metaverse. In this study, a mixed-method in which both qualitative and quantitative research methods were considered together was used. As a result of the study, it was found that the majority of students had never used Metaverse before, but wanted to use the Metaverse environment in the classroom. Students indicated that Metaverse can be used in many fields and disciplines. However, they were hesitant to use Metaverse in all faculty courses. Students believe that the Metaverse can enhance their knowledge of the subject. In addition, students consider that Metaverse can make the course content more enjoyable and increase their motivation for the course. They also believe that Metaverse has pedagogical advantages and will be used in the classroom in the near future. As for the disadvantages, they mentioned that Metaverse makes learning difficult and distracted, students disconnected from real life, and violates class discipline.
THE IMPLEMENTATION OF ENTERPRISE RESOURCE PLANNING (ERP) IN THE UNITED ARAB EMIRATES: A CASE OF MUSANADA CORPORATION

This study aims to examine the most significant factors that contributed to the system's success (ERP), how to implement it in the Musanada Corporation in the United Arab Emirates, and also the resistance of certain employees to this system. This study follows the qualitative methodology and data collection from company members (stakeholders in the company). A complete analysis of the study and submitted proposals to develop the system. This paper presents the study on the field so far and the results reached by the group's members and through data collection from stakeholders for the organization, providing the field study within analysis steps of implementation and application of the system in the institutions of the United Arab Emirates. It summarizes the advantages of the ERP system and the ability of companies in its implement it. Furthermore, it facilitates the external and internal processes (business processes and operations) and the internal development of the network in the company. ERP is significant in facilitating the task and reaching the desired goal (for the company's benefit). New technology has helped develop traditional systems and the presence of more security, ease of access to the desired goal, and the company's success to pass several actions in a short time.
STUDYING HUMAN ROBOT INTERACTION AND ITS CHARACTERISTICS

The process of human-robot interaction focuses on the analysis of different forms of communication between humans and the robots through the application of technologies like artificial intelligence and machine learning. However, there can be different challenges associated with the process like security risks and challenges related to mapping environment and manufacturing procedures.&#x0D; For analysis of the process of mitigation of the challenges, secondary qualitative data have been collected from different journals and websites. Theoretical analysis has been done for the collected data and the application of a cognitive modelling model has been done for this study. The main results include that application of a cognitive model can help in simulating the human problem-solving process and can help to improve the human cognition techniques.
Congruence and Plausibility, Not Presence: Pivotal Conditions for XR Experiences and Effects, a Novel Approach

Presence often is considered the most important quale describing the subjective feeling of being in a computer-generated and/or computer-mediated virtual environment. The identification and separation of orthogonal presence components, i.e., the place illusion and the plausibility illusion, has been an accepted theoretical model describing Virtual Reality (VR) experiences for some time. This perspective article challenges this presence-oriented VR theory. First, we argue that a place illusion cannot be the major construct to describe the much wider scope of Virtual, Augmented, and Mixed Reality (VR, AR, MR: or XR for short). Second, we argue that there is no plausibility illusion but merely plausibility, and we derive the place illusion caused by congruent and plausible generation of spatial cues, and similarly for all the current model's so-defined illusions. Finally, we propose congruence and plausibility to become the central essential conditions in a novel theoretical model describing XR experiences and effects.
Stochastic Komodo Algorithm

A novel metaheuristic algorithm is proposed in this paper, namely stochastic komodo algorithm (SKA).This proposed algorithm is an improved version of Komodo mlipir algorithm (KMA), which is inspired by the behaviour of Komodo during foraging and mating.The improvement is conducted by simplifying the basic form of KMA.Like KMA, it consists of three types of Komodo: big male, female, and small male.Male Komodo focuses on intensification.On the other side, females conduct diversification or intensification based on the search space radius.It eliminates sorting mechanism at the beginning of the iteration.Rather than determined from the quality (fitness score), the distribution of the types of Komodo is conducted stochastically at the beginning of every iteration.This proposed algorithm is then tested by using ten functions.Five functions are unimodal, while the five others are multimodal.The proposed algorithm is also compared with several well-known algorithms: football game-based optimization, hide objects game optimization, cloud-theory-based simulated annealing, harmony search, and KMA.The result shows that this proposed algorithm is very competitive compared with these benchmark algorithms in both unimodal and multimodal functions.A female-dominant formation is proven to achieve optimal result.
Modality-Adaptive Mixup and Invariant Decomposition for RGB-Infrared Person Re-identification

RGB-infrared person re-identification is an emerging cross-modality re-identification task, which is very challenging due to significant modality discrepancy between RGB and infrared images. In this work, we propose a novel modality-adaptive mixup and invariant decomposition (MID) approach for RGB-infrared person re-identification towards learning modality-invariant and discriminative representations. MID designs a modality-adaptive mixup scheme to generate suitable mixed modality images between RGB and infrared images for mitigating the inherent modality discrepancy at the pixel-level. It formulates modality mixup procedure as Markov decision process, where an actor-critic agent learns dynamical and local linear interpolation policy between different regions of cross-modality images under a deep reinforcement learning framework. Such policy guarantees modality-invariance in a more continuous latent space and avoids manifold intrusion by the corrupted mixed modality samples. Moreover, to further counter modality discrepancy and enforce invariant visual semantics at the feature-level, MID employs modality-adaptive convolution decomposition to disassemble a regular convolution layer into modality-specific basis layers and a modality-shared coefficient layer. Extensive experimental results on two challenging benchmarks demonstrate superior performance of MID over state-of-the-art methods.
An Efficiency Study for SPLADE Models

Latency and efficiency issues are often overlooked when evaluating IR models based on Pretrained Language Models (PLMs) in reason of multiple hardware and software testing scenarios. Nevertheless, efficiency is an important part of such systems and should not be overlooked.