Reviewerly/exHarmony huggingface dataset:
Monoscopic Phase Measuring Deflectometry Simulation and Verification

The three-dimensional (3D) shape of specular surfaces is important in aerospace, precision instrumentation, and automotive manufacturing. The phase measuring deflectometry (PMD) method is an efficient and highly accurate technique to measure specular surfaces. A novel simulation model with simulated fringe patterns for monoscopic PMD is developed in this study. Based on the pre-calibration and the ray-tracing model of the monoscopic PMD system, a comprehensive model from deformed pattern generation to shape reconstruction was constructed. Experimental results showed that this model achieved high levels of measuring accuracy in both planar and concave surfaces measurement. In planar surface measurement, the peak to valley (PV) value and root mean square (RMS) value of the reconstructed shape can reach 26.93 nm and 10.32 nm, respectively. In addition, the accuracy of the reconstructed concave surface can reach a micrometre scale. This work potentially fills critical gaps in monoscopic PMD simulation and provides a cost-effective method of PMD study.
Weak-keys and key-recovery attack for TinyJAMBU

Abstract In this paper, we study NIST lightweight 3 rd round candidate TinyJAMBU. The core component of TinyJAMBU is the keyed permutation P n , which which is based on a non-linear feedback shift register. By analysing this permutation carefully, we are able to find good cubes that are used to build distinguishers in the weak-key setting. These distinguishers outperform the best-known distinguishers, which were proposed in the esteemed Nature Scientific Reports by Teng et al. We also study the exact degree in nonce of the feedback polynomial of P n. This helped us in concluding that TinyJAMBU with more than 445 rounds is secure against distinguishers using 32 sized cubes in the normal setting. Finally, we give new key-recovery attacks against TinyJAMBU using the concepts of monomial trail presented by Hu et al. at ASIACRYPT 2020. Our attacks are unlikely to jeopardise the security of the entire 640 rounds TinyJAMBU, but we strongly anticipate that they will shed new lights on the cipher’s security.
EFFECTIVENESS OF ONLINE LEARNING WITH MICROSOFT TEAM APPLICATIONS IN POLIMAS

In the age of globalization, having access to the internet has become critical to enhancing human activities in areas such as learning, economics, culture, defence, and many others. Microsoft Teams is one of technological aspects that have been integrated into learning, particularly in e-learning platforms. Microsoft Teams has a number of unique features that enable instructors to develop a better online learning environment for their students. The main aim of this study was to investigate the effectiveness of online learning with the Microsoft Team application. A total of 4638 Polytechnique students were included in the study. An online survey questionnaire was used to obtain data from students who used the Microsoft Teams application during the 2020 and 2021 covid-19 pandemic periods. Survey questions were provided within System Pengurusan Maklumat pelajar (SPMP). Only 901 responses were received at the end of the survey. Results of the survey were evaluated descriptively. Preliminary research indicated that MS Team is effective in e-learning. Overall, the results of the study reveal that Microsoft Teams Application is an effective tool for online learning since it offers unique features that enhance interaction during the learning process. KEYWORDS: online learning, e-learning, MS Teams effectiveness, Web-based learning, students perception, students satisfaction.
CYBERCRIME IN RUSSIA: CURRENT CHALLENGES AND GOOD PRACTICES IN COMBATING CYBERCRIME

Globalization and the rapid development of computer and IT technologies have led to the emergence of a new type of offence — cybercrime. This concept has not yet been legislated, and there is a problem of classiﬁcation of crimes committed in cyberspace. The authors offer an analysis of current state of cybercrime in Russia for the last 10 years. An overview of modern countermeasures of cybercrime in Russia and abroad is presented. Some recommendations for ensuring corporate cyber security and promising areas of research are outlined.
Towards Creating a Model of IoT to be used in Library Activities for Saudi Arabia's Taibah University

The library must create a replacement organisation system in instruction to swap the existing system with the new one. There are several details why library operates need to adopt a new scheme, including the following: I have lost a lot of info on library books. The lack of familiarity with the literature Difficulty deciphering the fine print of library operations because of a sluggish system Difficulty in maintaining current information. Apart from orientation, the user interfaces are pleasant, and retraining was required. The researcher suggests that this approach be used on an ongoing basis to ensure the continued provision of library services. Other management responsibilities include serials and magazines, as well as reservations book, e-mail notice, and automated reminder, as well as the use of bar codes, scanners, and labels, as well as the custom of regularity identification tags to reduce book theft. Additionally, it is suggested that the library system engage in online surfing to enable users to access books and courses through the internet. The proposed research project would contribute to the development of a model IoT application for library operations at TIBA University in Saudi Arabia. The new system will manage and regulate all library information, resolving the aforementioned issues while also providing numerous advantages to staff and students.
The Relationship Between Ethical Leadership and Employee Job Satisfaction: The Mediating Role of Media Richness and Perceived Organizational Transparency

Ethical leadership (EL) is a vital component driving firms' practice, significantly affecting employee satisfaction (ES). The objective of this study was to investigate the relationship between EL and employee job satisfaction. Moreover, the study investigates the mediating effect of media richness (MR), perceived organizational transparency (OT) on EL, and ES. In addition, the study used a convenient sampling technique for collecting the data from 276 employees working in the service sector of Macau. Essentially, questionnaires had used as the fundamental tool guiding the data collection method. The findings indicate a positive and significant relationship between EL, ES, MR, and perceived OT. The MR and perceived OT have significant mediating effects between EL and ES. The study is beneficial for the service sector of Macau to plan the strategies for their employees in terms of organizational sustainability and success. The study is also helpful for the government to understand EL and for the researcher to search the new aspects of EL in organizations for future research direction.
Fusing Local and Global Information for One-Step Multi-View Subspace Clustering

Multi-view subspace clustering has drawn significant attention in the pattern recognition and machine learning research community. However, most of the existing multi-view subspace clustering methods are still limited in two aspects. (1) The subspace representation yielded by the self-expression reconstruction model ignores the local structure information of the data. (2) The construction of subspace representation and clustering are used as two individual procedures, which ignores their interactions. To address these problems, we propose a novel multi-view subspace clustering method fusing local and global information for one-step multi-view clustering. Our contribution lies in three aspects. First, we merge the graph learning into the self-expression model to explore the local structure information for constructing the specific subspace representations of different views. Second, we consider the multi-view information fusion by integrating these specific subspace representations into one common subspace representation. Third, we combine the subspace representation learning, multi-view information fusion, and clustering into a joint optimization model to realize the one-step clustering. We also develop an effective optimization algorithm to solve the proposed method. Comprehensive experimental results on nine popular multi-view data sets confirm the effectiveness and superiority of the proposed method by comparing it with many state-of-the-art multi-view clustering methods.
Analisis Pengalaman Pengguna Aplikasi Gojek dan Grab dengan Pendekatan User Experience Questionnaire

Technological developments have an impact on the presence of Super Apps, such as: Gojek and Grab. The presence of these two applications has an effect on the lifestyle in society which is increasingly facilitated by various services that are presented in only one application. The Gojek and Grab applications have many similarities, including in terms of their use. These two applications are very popular and favored by the community because they are practical and help various community activities, for example: food ordering services, delivery of goods, transportation, and non-cash payments. This study was conducted to determine whether there are differences in user experience with the application with case studies in people living in Yogyakarta. User experience measurement is carried out using the User Experience Questionnaire (UEQ) approach on attractive, perspicuity, efficiency, dependability, stimulation, and novelty variables. This study uses a comparative descriptive method with a quantitative approach. The results showed that all Gojek and Grab application user experience variables got positive values ??and there were no significant differences in all variables. However, based on the UEQ measurement, it is known that the Gojek application is superior to the Grab application in perspicuity and novelty variables. Meanwhile, the Grab application is superior to the Gojek application in terms of efficiency, dependability, and stimulation variables. To improve the user experience for the Gojek and Grab applications, it is necessary to improve the quality of the perspicuity and dependability variables.
DETERMINANT OF NON-PERFORMING LOAN IN DEVELOPMENT BANK OF ETHIOPIA

This study aimed at examining the determinant of non-performing loans in the development bank of Ethiopia. Other objectives included determining bank-specific and macroeconomic factors for default loan performance. Based on Development Bank of Ethiopia (DBE) reports from 1990-2019 there was an increase in NPLs during the studied period. Due to this, the performance of the bank is highly affected and the bank leads to insolvency. The thesis aimed to determine determinants of non-performing loans in DBE by determining theoretical and empirical evidence that help answer the research objective. The study used an explanatory design and a mixed research approach. The primary data by interviewing the staff of DBE those who were senior credit officers and team managers. And the secondary data was obtained from the bank’s annual financial performance report and the National Bank’s annual report for thirty consecutive fiscal periods from 1990 up to 2019. This study used an autoregressive distributed lag (ARDL) model or Bound Testing approach to co-integration. Non-performing loan ratio was taken as the dependent variable while Return on asset (earning capacity), liquidity, capital adequacy, bank size, exchange rate, lending rate (interest rate), inflation, and GDP have been taken as independent variables. The study found that there was a significant negative relationship between earning ability (ROA), Interest rate, Gross domestic products, inflation rate, and nonperforming loans of development banks of Ethiopia. The relationship between bank size and liquidity with non-performing loans was found to be positive. In addition to bank-specific factors and macroeconomic variables related to variables such Bank lends by the project itself as collateral with small lending rate relatively other commercial banks of the country, a long delay on implantation, poor known your customer assessment, political and social instability, inadequate coordination among stakeholder, governance, and structure, excessive external intervention The implications of the study suggest that Development Bank of Ethiopia should modified business model, bank credit policy-related issue such as appropriateness, timelines risk management and alignment with the current political macroeconomic realities, the bank should regulating interference of government bodies on loan approval, processes, and implementation, the bank should reduce project delays.
Age-related changes in visuo-proprioceptive processing in perceived body position

This study investigated age-related change in visuo-proprioceptive processing in the perceived body position using mirror hand/foot illusions, focusing on its temporal characteristics, its dependency on body parts, and its association with older adults' fall risk. Either immediately or 15 s after the exposure to the mirror-induced inconsistency of visuo-proprioceptive signals regarding the right hand or foot position, participants performed a reaching task using the unseen, illusion-affected hand or foot. Results showed clear age group differences. Specifically, older adults exhibited larger reaching errors than younger adults in the hand condition, and after the 15 s delay in the foot condition. Further, the reaching errors were constant across time for older adults but decreased after the delay in young adults, regardless of the tested body part. Particularly, older adults' risk of falling, which was assessed by the timed up-and-go test, was negatively correlated with the reduction of reaching error across time. This suggests that older adults, especially those with a high risk of falling, face difficulties in appropriately processing visual and proprioceptive information for body perception in accordance with their external environment.
Transkimmer: Transformer Learns to Layer-wise Skim

Transformer architecture has become the defacto model for many machine learning tasks from natural language processing and computer vision.As such, improving its computational efficiency becomes paramount.One of the major computational inefficiency of Transformer-based models is that they spend the identical amount of computation throughout all layers.Prior works have proposed to augment the Transformer model with the capability of skimming tokens to improve its computational efficiency.However, they suffer from not having effectual and end-to-end optimization of the discrete skimming predictor.To address the above limitations, we propose the Transkimmer architecture, which learns to identify hidden state tokens that are not required by each layer.The skimmed tokens are then forwarded directly to the final output, thus reducing the computation of the successive layers.The key idea in Transkimmer is to add a parameterized predictor before each layer that learns to make the skimming decision.We also propose to adopt reparameterization trick and add skim loss for the end-to-end training of Transkimmer.Transkimmer achieves 10.97× average speedup on GLUE benchmark compared with vanilla BERT base baseline with less than 1% accuracy degradation.
Managing the Live-Sound Audio Engineer’s Most Essential Critical Listening Tool

Critical listening is the live-sound audio engineer's most essential tool for informed sonic assessment.In producing a cohesive mix that fulfills an event's aims, audio engineers affect the experience and well-being of all live-sound participants.This study compares the results from a 2020 international audio engineer survey with published research.The findings demonstrate that although in theory, engineers recognize their hearing as being their most essential critical listening tool, in practice, many have not found ways to manage their hearing and optimize their assessment ability effectively.Many engineers with impeded or impaired hearing continue to mix, believing that any negative impact on participants is minimal or nonexistent.The livesound experience and participant health and well-being are improved by promoting and acting on appropriate hearing management practices.
IMPROVED EDGE DETECTION FOR SATELLITE IMAGES

Abstract. Edges are a key feature employed in various computer vision applications namely segmentation, object recognition, feature tracking and 3D reconstruction. Edges provide key information with regards to object presence, shape, form and detail which aid in many computer vision tasks. While there are various edge detection techniques in literature, challenges in edge detection remain. Varying image contrast due to non uniform scene illumination and imaging resolution affects the edge information obtained from any given image. The edge detection results are characterised by missing edges, edge fragmentation and some false positive edges. Gradient based edge detectors are the most commonly used detectors. These detectors all suffer from aforementioned challenges. In this, paper we present an edge detection framework that aims to recover long unfragmented edges from satellite images. This is achieved by using an edge accumulator that operates on the entire edge detection parameter space. Gradient based edge detectors rely on thresholding to retrieve salient edges. This usually results in missed or noisy edges. To counter this, the accumulator is run over a wide parameter space, growing edges at each accumulator level while maintaining edge position using a localisation filter. The results are longer unbroken edges that are detected for most objects, even in shadowy regions and low contrast areas. The results show improved edge detection that preserves the form and detail of objects when compared to current gradient based detectors.
Practical Teaching of Business English Majors Based on Intelligent Machine Teaching

In order to improve the effect of business English teaching, this paper combines intelligent digital methods to intelligently process images of online business English teaching. Under the condition of weak feedback, the self-mixing interference signal has a sinusoidal shape, and the calculated envelope phase jump is small. Moreover, under moderate feedback conditions, the self-mixing interference signal is in the form of a sawtooth wave. The principle based on phase unwrapping is adopted, which solves the situation of finding the displacement very well. The experimental research shows that the practical teaching system for business English majors based on intelligent machine teaching proposed in this paper has good effects and can play an important role in business English teaching.
Contrastive Supervised Distillation for Continual Representation Learning

In this paper, we propose a novel training procedure for the continual representation learning problem in which a neural network model is sequentially learned to alleviate catastrophic forgetting in visual search tasks. Our method, called Contrastive Supervised Distillation (CSD), reduces feature forgetting while learning discriminative features. This is achieved by leveraging labels information in a distillation setting in which the student model is contrastively learned from the teacher model. Extensive experiments show that CSD performs favorably in mitigating catastrophic forgetting by outperforming current state-of-the-art methods. Our results also provide further evidence that feature forgetting evaluated in visual retrieval tasks is not as catastrophic as in classification tasks. Code at: https://github.com/NiccoBiondi/ContrastiveSupervisedDistillation.
Smart Home: Deep Learning as a Method for Machine Learning in Recognition of Face, Silhouette and Human Activity in the Service of a Safe Home

Despite the general improvement of living conditions and the ways of building buildings, the sense of security in or around them is often not satisfactory for their users, resulting in the search and implementation of increasingly effective protection measures. The insecurity that modern people face every day, especially in urban centers regarding their home security, led computer science to the development of intelligent systems, aiming to mitigate the risks and ultimately lead to the consolidation of the feeling of security. In order to establish security, smart applications were created that turned a house into a Smart and Safe Home. We first present and analyze the deep learning method and emphasize its important contribution to the development of the process for machine learning, both in terms of the development of methods for safety at home, but also in terms of its contribution to other sciences and especially medicine where the results are spectacular. We then analyze in detail the back propagation algorithm in neural networks in both linear and non-linear networks as well as the X-OR problem simulation. Machine learning has a direct and effective application with impressive results in the recognition of human activity and especially in face recognition, which is the most basic condition for choosing the most appropriate method in order to design a smart home. Due to the large amount of data and the large computing capabilities that a system must have in order to meet the needs of a safe, smart home, technologies such as fog and cloud computing are used for both face recognition and recognition of human silhouettes and figures. These smart applications compose the systems that are created mainly through “Deep Learning” methods based on machine learning techniques. Based on the study we have done and present in this work, we believe that with the use of DL technology, the creation of a completely safe house has been achieved to a large extent today, covering an urgent need these days due to the increase in crime.
Delay Encryption by Cubing

Delay Encryption (often called Timed-Release Encryption) is a scheme in which a message is sent into the future by ensuring its confidentiality only for a given amount of time. We propose a new scheme based on a novel time-lock puzzle. This puzzle relies on the assumption that repeated squaring is an inherently sequential process. We perform an extensive and practical analysis of many classical and quantum attacks on our scheme and conclude that it is secure given some precautions.
Deep Learning Methods for Speed Estimation of Bipedal Motion from Wearable IMU Sensors

The estimation of the speed of human motion from wearable IMU sensors is required in applications such as pedestrian dead reckoning. In this paper, we test deep learning methods for the prediction of the motion speed from raw readings of a low-cost IMU sensor. Each subject was observed using three sensors at the shoe, shin, and thigh. We show that existing general-purpose architectures outperform classical feature-based approaches and propose a novel architecture tailored for this task. The proposed architecture is based on a semi-supervised variational auto-encoder structure with innovated decoder in the form of a dense layer with a sinusoidal activation function. The proposed architecture achieved the lowest average error on the test data. Analysis of sensor placement reveals that the best location for the sensor is the shoe. Significant accuracy gain was observed when all three sensors were available. All data acquired in this experiment and the code of the estimation methods are available for download.