Reviewerly/exHarmony huggingface dataset:
EyeCoD

Eye tracking has become an essential human-machine interaction modality for providing immersive experience in numerous virtual and augmented reality (VR/AR) applications desiring high throughput (e.g., 240 FPS), small-form, and enhanced visual privacy. However, existing eye tracking systems are still limited by their: (1) large form-factor largely due to the adopted bulky lens-based cameras; (2) high communication cost required between the camera and backend processor; and (3) potentially concerned low visual privacy, thus prohibiting their more extensive applications. To this end, we propose, develop, and validate a lensless FlatCambased eye tracking algorithm and accelerator co-design framework dubbed EyeCoD to enable eye tracking systems with a much reduced form-factor and boosted system efficiency without sacrificing the tracking accuracy, paving the way for next-generation eye tracking solutions. On the system level, we advocate the use of lensless FlatCams instead of lens-based cameras to facilitate the small form-factor need in mobile eye tracking systems, which also leaves rooms for a dedicated sensing-processor co-design to reduce the required camera-processor communication latency. On the algorithm level, EyeCoD integrates a predict-then-focus pipeline that first predicts the region-of-interest (ROI) via segmentation and then only focuses on the ROI parts to estimate gaze directions, greatly reducing redundant computations and data movements. On the hardware level, we further develop a dedicated accelerator that (1) integrates a novel workload orchestration between the aforementioned segmentation and gaze estimation models, (2) leverages intra-channel reuse opportunities for depth-wise layers, (3) utilizes input feature-wise partition to save activation memory size, and (4) develops a sequential-write-parallel-read input buffer to alleviate the bandwidth requirement for the activation global buffer. On-silicon measurement and extensive experiments validate that our EyeCoD consistently reduces both the communication and computation costs, leading to an overall system speedup of 10.95×, 3.21×, and 12.85× over general computing platforms including CPUs and GPUs, and a prior-art eye tracking processor called CIS-GEP, respectively, while maintaining the tracking accuracy. Codes are available at https://github.com/RICE-EIC/EyeCoD.
Constructing twitter corpus of Iraqi Arabic Dialect (CIAD) for sentiment analysis

The number of Twitter users in Iraq has increased significantly in recent years.Major events, the political situation in the country, had a significant impact on the content of Twitter and affected the tweets of Iraqi users.Creating an Iraqi Arabic Dialect corpus is crucial for sentiment analysis to study such behaviors.Since no such corpus existed, this paper introduces the Corpus of Iraqi Arabic Dialect (CIAD).The corpus has been collected, annotated and made publicly accessible to other researchers for further investigation.Furthermore, the created corpus has been validated using eight different combinations of four feature-selections approaches and two versions of Support Vector Machine (SVM) algorithm.Various performance measures were calculated.The obtained accuracy, 78 %, indicates a promising potential application.
The Emergence of the Emergency in Higher Education in Argentina

The context of COVID-19 has deeply penetrated various aspects of university life such as academic and administrative management; teaching practices and learning have been challenged, shaken and, in some way, transformed. This arti-cle will describe an ongoing study of trends in relation to the inclusion of tech-nologies; the representations about online teaching itself, before and during the COVID-19 emergency, and also a projection into the post-COVID and the impli-cations for student’s achievements before and during the emergency. A compari-son will be made between the data obtained from these two Argentinean universi-ties and the information published by the IAU Global Survey Report [1] in order to develop a broader map about the challenges ahead for Higher Education (HE). Besides, the article will include some inquiries about whether post-pandemic ped-agogical practices will change according to teachers' own views. Based on the re-sults of the survey, we have delved into a qualitative study that focuses on chang-es in teaching and assessment practices in HE that allow us to extract from the voices of practicing professors with many years of experience, possible scenarios that account for transformations in teaching practices and ways of learning for the next few years. The empirical statistical survey carried out on more than 400 pro-fessors and the in-depth qualitative study offer results that are enlightening to re-view, in addition, management, training and relationship decisions with the stu-dent body for the coming years
Factorial of Sum of Nonnegative Integers for Computing and Algorithms

This paper presents a theorem for factorials with the sum of nonnegative integers. The results of theorems can be used as application in computing science and cryptography to develop algorithms like RSA algorithm and Elliptic Curve Cryptography.
Teacher Trainees' Experiences of the Components of ICT Competencies and Key Factors in ICT Competence Development in Work-Based Vocational Teacher Training in Finland

Context: This research was conducted in the context of Finnish vocational education and training (VET) teacher training. The teacher training was work-based, meaning that each teacher trainee was already working as an unqualified VET teacher. Workplaces were comprehensively utilised as the learning environment, with teacher training support also being provided in the form of contact days, webinars, and online learning activities. Approach: We aimed to explore the main components of information and communications technology (ICT) competencies among vocational teacher trainees within work-based training and also to reveal the supportive and challenging factors related to developing ICT competencies within the training. Overall, 44 vocational teacher trainees participated in our study. The data were analysed via qualitative content analysis. Findings: Six main ICT competence components were identified during the work-based training: (1) The use and creation of digital learning materials, (2) the planning and use of digital learning environments, (3) synchronous digitally enhanced teaching, (4) general ICT competencies, (5) digital interaction, and (6) digital assessment. The development of teachers' ICT competencies was supported by their own active use of ICT and by experimenting with the ICT software and ideas presented by the teacher training staff. Challenging factors in ICT development included the continuous feeling of haste and the lack of time in the workplace, as well as the failure to recognise prior ICT competencies within the teacher training. This could limit the development of ICT competencies.Conclusions: Work-based VET teacher training has the potential to develop teacher trainees' ICT competencies because it allows immediate implementation and experimentation with regard to new ICT ideas and tools. By aligning this research with prior research, it is possible to construct a comprehensive ICT competence framework to support VET teacher training and workplace development.
Multi-Level Federated Network Based on Interpretable Indicators for Ship Rolling Bearing Fault Diagnosis

The federated learning network requires all the connection weights to be shared among the server and clients during training which increases the risk of data leakage. Meanwhile, the traditional federated learning method has a poor diagnostic effect for non-independently identically distributed data. In order to address these issues, a multi-level federated network based on interpretable indicators was proposed in this manuscript. Firstly, an interpretable adaptive sparse deep network is constructed based on the interpretability principle. Secondly, the relevance map of the network is constructed based on interpretable indicators. Based on this map, the contribution of the connection weights in the network is used to build a multi-level federated network. Finally, the effectiveness of the proposed algorithm has been proved through experimental validation in the paper.
Android-Based Digital Library Application Development

Digital technology has enabled the dissemination of all types of information, replacing traditional formats with automated systems. As a result of the development of digital technology, the library has also entered a new era, which is then called the digital library era. Digital library is considered as one form of modern library where the dependence will be on modern technology in the conversion of information and data to digital formats to achieve greater effectiveness and efficiency in storing and processing information and then transmitting it. Therefore, the purpose of this study is to develop an Android-Based Digital Library Application at the Department of Education Management, State University of Surabaya. This study uses the research and development method proposed by Borg and Gall by applying the nine stages. The results of this study indicate that the development of an Android-Based Digital Library Application at the Education Management Department, State University of Surabaya has been successfully implemented with the results of the trial getting an average percentage above 80% which is included in the very feasible category to be implemented. Even so, evaluation and monitoring must continue to be carried out to prevent errors or problems that may occur, as well as material to continue developing this Android-Based Digital Library Application.
Natural Language Processing to Identify Abnormal Breast, Lung, and Cervical Cancer Screening Test Results from Unstructured Reports to Support Timely Follow-up

Cancer screening and timely follow-up of abnormal results can reduce mortality. One barrier to follow-up is the failure to identify abnormal results. While EHRs have coded results for certain tests, cancer screening results are often stored in free-text reports, which limit capabilities for automated decision support. As part of the multilevel Follow-up of Cancer Screening (mFOCUS) trial, we developed and implemented a natural language processing (NLP) tool to assist with real-time detection of abnormal cancer screening test results (including mammograms, low-dose chest CT scans, and Pap smears) and identification of gynecological follow-up for higher risk abnormalities (i.e. colposcopy) from free-text reports. We demonstrate the integration and implementation of NLP, within the mFOCUS system, to improve the follow-up of abnormal cancer screening results in a large integrated healthcare system. The NLP pipelines have detected scenarios when guideline-recommended care was not delivered, in part because the provider mis-identified the text-based result reports.
Proteus: A Self-Designing Range Filter

We introduce Proteus, a novel self-designing approximate range filter, which configures itself based on sampled data in order to optimize its false positive rate (FPR) for a given space requirement. Proteus unifies the probabilistic and deterministic design spaces of state-of-the-art range filters to achieve robust performance across a larger variety of use cases. At the core of Proteus lies our Contextual Prefix FPR (CPFPR) model - a formal framework for the FPR of prefix-based filters across their design spaces. We empirically demonstrate the accuracy of our model and Proteus' ability to optimize over both synthetic workloads and real-world datasets. We further evaluate Proteus in RocksDB and show that it is able to improve end-to-end performance by as much as 5.3x over more brittle state-of-the-art methods such as SuRF and Rosetta. Our experiments also indicate that the cost of modeling is not significant compared to the end-to-end performance gains and that Proteus is robust to workload shifts.
Detection and Classification of Artifact Distortions in Optical Motion Capture Sequences

Optical motion capture systems are prone to errors connected to marker recognition (e.g., occlusion, leaving the scene, or mislabeling). These errors are then corrected in the software, but the process is not perfect, resulting in artifact distortions. In this article, we examine four existing types of artifacts and propose a method for detection and classification of the distortions. The algorithm is based on the derivative analysis, low-pass filtering, mathematical morphology, and loose predictor. The tests involved multiple simulations using synthetically-distorted sequences, performance comparisons to human operators (concerning real life data), and an applicability analysis for the distortion removal.
Application of Factorial and Binomial identities in Communication and Cybersecurity

Abstract This paper presents application of the binomial and factorial identities and expansions that are used in communication and cybersecurity. Cybersecurity is the practice of protecting the computing systems, communication networks, data and programs from cyber-attacks. Its objective is to reduce the risk of cyber-attacks and protect against the unauthorized exploitation of systems and networks. For this purposes, we need a strong security mathematical algorithm like RSA algorithm and Elliptic Curve Cryptography. In this connection, computing technique based on factorial and binomial identities is developed for the researchers who are working in communication and cybersecurity.
Revisiting the Extension of Matsui’s Algorithm 1 to Linear Hulls: Application to TinyJAMBU

At EUROCRYPT ’93, Matsui introduced linear cryptanalysis. Both Matsui’s Algorithm 1 and 2 use a linear approximation involving certain state bits. Algorithm 2 requires partial encryptions or decryptions to obtain these state bits after guessing extra key bits. For ciphers where only part of the state can be obtained, like some stream ciphers and authenticated encryption schemes, Algorithm 2 will not work efficiently since it is hard to implement partial encryptions or decryptions. In this case, Algorithm 1 is a good choice since it only involves these state bits, and one bit of key information can be recovered using a single linear approximation trail. However, when there are several strong trails containing the same state bits, known as the linear hull effect, recovering key bits with Algorithm 1 is infeasible. To overcome this, Röck and Nyberg extended Matsui’s Algorithm 1 to linear hulls. However, Röck and Nyberg found that their theoretical estimates are quite pessimistic for low success probabilities and too optimistic for high success probabilities. To deal with this, we construct new statistical models where the theoretical success probabilities are in a good accordance with experimental ones, so that we provide the first accurate analysis of the extension of Matsui’s Algorithm 1 to linear hulls. To illustrate the usefulness of our new models, we apply them to one of the ten finalists of the NIST Lightweight Cryptography (LWC) Standardization project: TinyJAMBU. We provide the first cryptanalysis under the nonce-respecting setting on the full TinyJAMBU v1 and the round-reduced TinyJAMBU v2, where partial key bits are recovered. Our results do not violate the security claims made by the designers.
A Two-Period Decision Model for Central Bank Digital Currencies and Households

Central bank digital currencies (CBDCs) give rise to many possibilities including those of negative interest rates. A two-period decision model is presented between one central bank and one representative household. The central bank applies the Taylor (1993) rule to choose its interest rate. The household allocates its resources strategically to production, consumption, CBDC holding, and non-CBDC holding. The results are determined analytically and illustrated numerically by varying 19 parameter values. Interesting novelties of the article are that the central bank may choose negative CBDC interest rates when the household holds far more CBDC than non-CBDC, for low inflation rates, low real interest rates, low household’s potential production, low weight assigned to inflation in the Taylor (1993) rule, high target inflation rate, and high household’s production parameter. That usually causes the household to decrease its CBDC holding and increase its non-CBDC holding, production and consumption. The central bank may increase its CBDC interest rate to compete with an increasing non-CBDC interest rate if the household’s transaction efficiencies for CBDC and non-CBDC increase, or the household’s transaction efficiency for consumption decreases. Shocks to production, inflation and interest rates are analyzed.
SCLERA: A Framework for Privacy-Preserving MLaaS at the Pervasive Edge

The increasing data generation rate and the proliferation of deep learning applications have led to the development of machine learning-as-a-service (MLaaS) platforms by major Cloud providers. The existing MLaaS platforms, however, fall short in protecting the clients’ private data. Recent distributed MLaaS architectures such as federated learning have also shown to be vulnerable against a range of privacy attacks. Such vulnerabilities motivated the development of privacy-preserving MLaaS techniques, which often use complex cryptographic prim-itives. Such approaches, however, demand abundant computing resources, which undermine the low-latency nature of evolving applications such as autonomous driving.To address these challenges, we propose SCLERA–an efficient MLaaS framework that utilizes trusted execution environment for secure execution of clients’ workloads. SCLERA features a set of optimization techniques to reduce the computational complexity of the offloaded services and achieve low-latency inference. We assessed SCLERA’s efficacy using image/video analytic use cases such as scene detection. Our results show that SCLERA achieves up to 23× speed-up when compared to the baseline secure model execution.
Development of Algorithm for Person Re-Identification Using Extended Openface Method

Deep learning has risen in popularity as a face recognition technology in recent years.Facenet, a deep convolutional neural network (DCNN) developed by Google, recognizes faces with 128 bytes per face.It also claims to have achieved 99.96% on the reputed Labelled Faces in the Wild (LFW) dataset.However, the accuracy and validation rate of Facenet drops down eventually, there is a gradual decrease in the resolution of the images.This research paper aims at developing a new facial recognition system that can produce a higher accuracy rate and validation rate on low-resolution face images.The proposed system Extended Openface performs facial recognition by using three different features i) facial landmark ii) head pose iii) eye gaze.It extracts facial landmark detection using Scattered Gated Expert Network Constrained Local Model (SGEN-CLM).It also detects the head pose and eye gaze using Enhanced Constrained Local Neural field (ECLNF).Extended openface employs a simple Support Vector Machine (SVM) for training and testing the face images.The system's performance is assessed on low-resolution datasets like LFW, Indian Movie Face Database (IMFDB).The results demonstrated that Extended Openface has a better accuracy rate (12%) and validation rate (22%) than Facenet on low-resolution images.
Digital transformation of higher education from the standpoint of interdisciplinary approach: review of humanitarian studies

The purpose of the study is to identify the actual problems in the field of digital transformation of higher education, studied by modern scientists – teachers, psychologists, sociologists, philosophers, economists. A theoretical and methodological review of the authors' positions and interpretations of the problems under study related to the process of digitalization of education in a digital society is presented. The advantages and risks of the digital transformation of higher education are assessed. The scientific novelty lies in the fact that the author has analyzed the latest (2019-2021) Russian scientific sources in the humanities and pedagogy from the standpoint of an interdisciplinary approach. As a result, key areas of research of digitalization processes in education were identified, aimed at updating the content, forms and methods of professional training in higher education in the context of digital realities.
Improving Online Learning through the Use of Learning Management System Platform: A Technology Acceptance Model-Technology Readiness Index Combination Model Approach

The COVID-19 pandemic push the learning revolution. The previous onsite learning is suddenly change to online learning. Current technological developments support the online learning application through the use of Learning Management System Platform. However, some problems are found during the application of online learning. Thus, this research aims to improve online learning through the use of Learning Management System Platform. The Technology Acceptance Model and the Technology Readiness Index model were combined to identify the critical factors influencing the intention to use Learning Management System. Fourteen hypotheses were proposed and tested using the Structural Equation Modelling method run in Partial Least Square software. The results indicated that eight hypotheses were accepted. The accepted hypothesis represents the criticality to consider innovativeness, job relevance, perceived enjoyment, perceived security and subjective norm in the Learning Management System usage during online learning during the COVID-19 Pandemic
Univariate and Multivariate LSTM Models for One Step and Multistep PV Power Forecasting

The energy demand is increasing due to population growth and economic development. To satisfy this energy demand, the use of renewable energy is essential to face global warming and the depletion of fossil fuels. Photovoltaic energy is one of the renewable energy sources, widely used by several countries over the world. The integration of PV energy into the grid brings significant benefits to the economy and environment, however, high penetration of this energy also brings some challenges to the stability of the electrical grid, due to the intermittency of solar energy. To overcome this issue, the use of a forecasting system is one of the solutions to guarantee an effective integration of PV plants in the electrical grid. In this paper, a PV power ultra short term forecasting has been done by using univariate and multivariate LSTM models. Different combinations of input variables of the models and different timesteps forecasting were tested and compared. The main aim of this work is to study the influence of the different combinations of variables on the accuracy of the LSTM models for one-step forecasting and multistep forecasting and comparing the univariate and multivariate LSTM models with MLP and CNN models . The results show that for one step forecasting, the use of a univariate model based on historical data of PV output power is sufficient to get accurate forecasting with 28.98W in MAE compared to multivariate models that can reach 35.39W. Meanwhile, for multistep forecasting, it is mandatory to use a multivariate model that has historical data of meteorological variables and PV output power in the input of LSTM model. Moreover, The LSTM model shows great accuracy compared to MLP and CNN especially in multistep PV power forecasting.