Reviewerly/exHarmony huggingface dataset:
Listen, Adapt, Better WER: Source-free Single-utterance Test-time Adaptation for Automatic Speech Recognition

Although deep learning-based end-to-end Automatic Speech Recognition (ASR) has shown remarkable performance in recent years, it suffers severe performance regression on test samples drawn from different data distributions. Test-time Adaptation (TTA), previously explored in the computer vision area, aims to adapt the model trained on source domains to yield better predictions for test samples, often out-of-domain, without accessing the source data. Here, we propose the Single-Utterance Test-time Adaptation (SUTA) framework for ASR, which is the first TTA study on ASR to our best knowledge. The single-utterance TTA is a more realistic setting that does not assume test data are sampled from identical distribution and does not delay on-demand inference due to pre-collection for the batch of adaptation data. SUTA consists of unsupervised objectives with an efficient adaptation strategy. Empirical results demonstrate that SUTA effectively improves the performance of the source ASR model evaluated on multiple out-of-domain target corpora and in-domain test samples.
Knowledge Graph Random Neural Networks for Recommender Systems

In recent years, knowledge graph networks for recommendation have attracted extensive attention, since these methods can capture structured information by linking items with their attributes instead of using only interaction data between users and items. However, existing Graph Neural Networks Methods on Knowledge Graph suffer from over-smoothing and data sparsity, leading to the inability to build deeper networks. To address these problems, the Knowledge Graph Random Neural Networks for Recommender Systems (KRNN) is proposed. Specifically, a random dropout strategy is designed to generate the perturbed entities feature matrices. Then, a feature propagation method is proposed over the perturbed feature matrices for capturing high-order neighbor information, and locating the novel entities representation. The data augmentation matrices are generated by using the new entity representation from the previous step. The consistency regularization is designed to optimize the prediction across different data augmentation matrices in multiple random dropout. Extensive experiments on real datasets demonstrate the proposed method is superior to state of the baselines in alleviating over-smoothing and predicting user preferences, especially in data sparsity scenarios.
Image Detection and Recognition of different species of animals using Deep Learning

Deep Learning has grown in popularity as a method for solving computer vision difficulties. Deep learning models are becoming increasingly efficient in solving complex real-world issues, as evidenced by numerous recent articles. However, training the networks slowly takes time and money for certain real-world situations that are difficult to handle with deep learning. It is vital to find ways to collect data rapidly so that it can use it in real-time applications. With a growing awareness of the need for habitat conservation, researchers are examining animal density, presence, and absence to determine the visibility of endangered species. Animal detection tools are required to estimate and monitor the quantity of animals in a specific area. This can be considered a subset of environmental monitoring. Camera-based technologies, as well as acoustic and seismic measures, can all be used to detect animals. Because cameras are a helpful tool for monitoring wildlife behaviour and assessing ecosystems, they have a long history of use. Animals, on the other hand, can move quickly and conceal themselves to avoid being spotted. The YOLOV5 model could be used to detect several types of animals in the forest. From photos, real-time camera feeds, and recorded videos, the YOLOV5 model can recognize horses, sheep, cows, elephants, bears, zebras, and giraffes etc. It also has the ability to detect birds. Objects can be detected in real time by YOLOV5 using a camera feed attached to the CPU via USB connection. The advantages of the YOLOv5 network&#x2019;s is lightweight so it reduces the deployment cost of the identification model. Furthermore, animal detection may be required for a variety of purposes, including livestock management, wildlife management, pest control, and security.
Robust Low-Rank Analysis with Adaptive Weighted Tensor for Image Denoising

In order to obtain better denoising results, this paper proposes the Robust Low-Rank Analysis with Adaptive Weighted Tensor (AWTD) method for image denoising tasks. On one hand, it uses the latest adaptive weight tensor, which obtains the low-rank approximation of the tensor by adding adaptive weights to the unfolding matrix of the tensor. The adaptive weight tensor can effectively retain useful singular values and better preserve the low-rank properties of the unfolding matrix. On the other hand, the proposed algorithm considers the spatial information and spectral information at the same time: for the RGB images, it retains the structural information inside the image patch and the connection between different channels (the spatial information of the image); for the hyperspectral images, it also retains the spectral information of the hyperspectral images. The experimental results show that the proposed method is superior to other test methods.
Self-supervised flow field decoupling for Controllable face reenactment

Abstract Face reenactment is a face image generation method. Its main task is to generate a new image given a source image and a driving image, which has the facial motion information of the driving image while retaining the content information of the source image. Existing flow-based approaches have demonstrated high-quality results, but these works regard the head movement as a whole, and cannot achieve more flexible movement control, and often suffer from the loss of identity information. In this paper, we propose a novel Controllable multi-identity face reenactment(CFReenet), which uses the prior information of facial motion to decompose the movement as two parts of pose and expression and use the curriculum strategy to decouple movement and identity information and better maintain identity information. The experimental results demonstrate the effectiveness of our method.
Cloud computing security based on OWASP

In recent years, the use of cloud computing is grown up due to several reasons such as economic and efficiency. The major issue faced in cloud computing is security and how to ensure files protection. The vulnerabilities in cloud computing are considered a big challenge for the end-users that are not filled yet. The main objective of this paper is to enable people to protect their services and products running on clouds by controlling threats and attacks based on the operations, experience of development, and security communities. We proposed in this paper a new model based on the security issues related to tracking tasks, ideas, and problems which can implement in various forms such as using the threats in Software Development Life Cycle (SDLC) and using control stories to make sure the identified threats are mitigated. To evaluate the performance of the proposed model, we used the execution time. For our test, we used a 654 byte file for encryption and decryption. The obtained results indicate that our model is efficient especially for the small files in encryption. Moreover, the proposed model was decrypting the file much faster than the traditional model.
A note on a class of permutation trinomials

Let [Formula: see text] denote the finite field with [Formula: see text] elements. In this paper, we investigate the trinomial [Formula: see text] over the finite field [Formula: see text], where [Formula: see text] with [Formula: see text] being a positive integer. We prove that the trinomial [Formula: see text] permutes [Formula: see text] if and only if [Formula: see text] and [Formula: see text] is even. This work is a continuation of the previous work of Bai and Xia [A new class of permutation trinomials constructed from Niho exponents, Cryptogr. Commun. 10 (2018) 1023–1036].
Research of readiness of pedagogical workers for innovations

The purpose is empirical research of the interdependence of variables and construction of the factor structure of the readiness of pedagogical workers for innovation. Research methods: theoretical analysis and generalization, valid test methods with standardized questionnaires, factor analysis. The structure of readiness of pedagogical workers for innovative pedagogical activity has been determined using ANOVA factor analysis with Varimax rotation. The most significant interdependencies of variables and researched relationships were established at the statistically significant level (p ≤ .01; p ≤ .05). The structure of readiness of pedagogical workers for innovations united five main factors. It has been found that the main is F2 “informational readiness”, which is positively correlated with F1 “motivational operational readiness”, F3 “sense readiness”, F4 “procedural readiness” and F5 “distat-resulting preparedness”. It is expedient to implement the acquired knowledge into the program of reforms and development of primary and secondary education. The research of a specific type of readiness for innovation with the development of an implementation algorithm requires further attention.
IEMI Fault Injection Method Using Continuous Sinusoidal Wave with Controlled Frequency, Amplitude, and Phase

A fault injection attack based on an intentional electromagnetic interference (IEMI) using a continuous sinusoidal wave can generate a fault that is applicable for the differential fault analysis (DFA) against cryptographic module non-invasively. However, previous studies often generate multi-byte faults in the output ciphertext which are not applicable for the DFA, and they increase the analysis time dramatically. In this paper, we propose an IEMI-based fault injection method that increases the occurrence of a one-byte fault in the output ciphertext by controlling the frequency, phase, and amplitude of the injected sinusoidal wave. Specifically, we control the frequency and phase of the sinusoidal wave injected into the cryptographic module implemented with the advanced encryption standard (AES). At the same time, the number of faults of the output ciphertext is monitored so that a glitch is generated in-phase at each rising edge of the clock. At this condition, the amplitude is controlled to further increase the timing difference between the rising timing of the original clock and the overclocking occurrence timing associated with the glitch. Experimental results validated that the proposed method can increase the ratio of the number of one-byte faults to the number of faults. From the experimental results, we discuss a direction to further improve the method to maximize one-byte faults for the future study.
Investigating Display Position of a Head-Fixed Augmented Reality Notification for Dual-task

Providing additional information in the proper position of augmented reality (AR) head-mounted display (HMD) can help increase AR performance and usability for dual-task. Therefore, our study inves-tigated how to place notifications for the dual-task to address this. We compared eight display positions and two tasks (single and dual tasks) to identify the appropriate area for displaying notifications. We confirmed that the middle-right reduces response time and task load. In contrast, the top-left is the location, which should avoid providing any notification in AR dual-task. Our study contributes to designing AR notifications on HMDs to enhance everyday AR experiences.
Deformable Torso Anatomy Education with Three-Dimensional Autostereoscopic Visualization and Free-Hand Interaction

Virtual reality/augmented reality has advantages in immersive learning and conveying meta-information in anatomy education. In this study, we present an interactive naked-eye 3D torso anatomy education environment based on population anatomical information. In particular, we utilize the deformable anatomy models, constructed from big data of healthy adults, to convey the anatomy knowledge of truthfulness organ shapes and population anatomical variances. In addition, the proposed anatomy education system is conveyed by free-hand 3D autostereoscopic visualization, which supports multi-user and glass-free. A user study with fourteen students was implemented and demonstrates that the system is appropriate and useful for torso anatomy education.
Modal decomposition of an incoherent combined laser beam based on the combination of residual networks and a stochastic parallel gradient descent algorithm

With the increase of the superimposed eigenmodes number, the traditional numerical modal decomposition (MD) technique will inevitably suffer from ambiguity and local minima problems and thus is typically unsuitable for conducting modal decomposition of an incoherent combined laser beam. In this paper, we propose a novel, to the best of our knowledge, MD algorithm, named ResNet-SPGD, which combines the advantages of residual networks (ResNet) and stochastic parallel gradient descent (SPGD) algorithm. Via setting the modal mode coefficients obtained from the CNN model as the initial value of the SPGD algorithm, such algorithm shows an attractive solution to mitigate the problem of modal ambiguity. The proposed algorithm is preliminarily applied to the modal decomposition of an incoherent combined laser beam, and the feasibility is demonstrated via numerical simulations. Complete MD is performed with high accuracy, and the only cost is the sacrifice of some real-time capacity.
[DC] Context-Aware Inference and Adaptation in Augmented Reality

Augmented Reality(AR) offers the potential for easy and efficient information access, reinforcing the wide belief that AR Glasses are the next-generation of personal computing devices. However, to realize this all-day AR vision, the AR interface must be able to address the challenges that constant and pervasive presence of virtual content can cause for the user. The optimal interface, that is the most efficient yet least intrusive, in one context may be the worst interface for another context. Throughout the day, as the user switches context, an optimal all-day interface must adapts its virtual content display and interactions as well. This work aims to propose a research agenda to design and validate different adaptation techniques and context-aware AR interfaces and introduce a framework for the design of such intelligent interfaces.
A Comparative Study of Pose Estimation

In the past years, the vision of computers has been able to achieve massive momentum that could provide the absolute best of expectations among the many fields and sectors. A research paper on Human Pose Estimation would open new opportunities and help those who researched in finding the best techniques and methods for human pose detection. In this paper, we proposed that by using Open Pose 2D based pose estimation model technique to track and calculate every minute movement and perform analysis in real time. This technique which has been chosen has an amazing advantage of reasoning about pose in a comprehensive manner and has a straightforward but yet efficacious conceptualization which takes advantage of the recent advances in the Computer Vision Sector. We have presented an elaborate factual analysis with performance that is one step ahead based on four scholastic standards of varied real world representations.
Two-step Bayesian methods for generalized regression driven by partial differential equations

In certain non-linear regression models, the functional form of the regression function is not explicitly available, but is only described by a set of differential equations. For regression models described by a set of ordinary differential equations (ODEs), both Bayesian and non-Bayesian methods for inference were developed in the literature. In this paper, we consider a Bayesian approach to non-linear regression with respect to a multidimensional predictor variable given by a set of partial differential equations (PDEs). We consider a computationally convenient two-step approach by first representing the functions nonparametrically, constructing a finite random series prior using tensor products of B-splines and directly inducing a posterior distribution on parameter space through an appropriate projection map. By considering three different choices of the projection map, we propose three different approaches with their merits. We allow generalized non-linear regression with the response variable following an exponential family of distributions, extending the method beyond regression with additive normal errors. We establish Bernstein-von Mises type theorems which show n-consistency and asymptotically correct frequentist coverage of Bayesian credible regions. We also conduct a simulation study to evaluate the finite sample performances of the proposed methods.
HiddenCPG: Large-Scale Vulnerable Clone Detection Using Subgraph Isomorphism of Code Property Graphs

A code property graph (CPG) is a joint representation of syntax, control flows, and data flows of a target application. Recent studies have demonstrated the promising efficacy of leveraging CPGs for the identification of vulnerabilities. It recasts the problem of implementing a specific static analysis for a target vulnerability as a graph query composition problem. It requires devising coarse-grained graph queries that model vulnerable code patterns. Unfortunately, such coarse-grained queries often leave vulnerabilities due to faulty input sanitization undetected. In this paper, we propose, a scalable system designed to identify various web vulnerabilities, including bugs that stem from incorrect sanitization. We designed to find a subgraph in a target CPG that matches a given CPG query having a known vulnerability, which is known as the subgraph isomorphism problem. To address the scalability challenge that stems from the NP-complete nature of this problem, leverages optimization techniques designed to boost the efficiency of matching vulnerable subgraphs. found confirmed vulnerabilities including CVEs among 2,464 potential vulnerabilities in real-world CPGs having a combined total of 1 billion nodes and 1.2 billion edges.
Privacy preserving Data security model for Cloud Computing Technology

New advancements in cloud computing technology enable the usage of cloud platforms for business purposes rapidly increasing every day. Data accumulation related to business transactions, Communications, business model architecture and much other information are stored in the cloud platform and access Dubai the business Associates commonly. Considering the security point of view data stored in the cloud need to be highly secured and accessed through authentication. The proposed system is focused on evaluating a cloud integrity auditing model in which the security and privacy preserving system is being audited, privacy is decided using a machine learning algorithm. The proposed model is developed using a hybrid CatBoost algorithm (HCBA) in which the input data is stored into the cloud platform using Bring your own encryption Key (BYOEK). The security of BYOEK model is evaluated and validated with respect to the given test model in terms of Execution time comparison Vs. Data transactions.
Understanding People’s Experience for Physical Activity Planning and Exploring the Impact of Historical Records on Plan Creation and Execution

Making and executing physical activity plans can help people improve their physical activity levels. However, little is known about how people make physical activity plans in everyday settings and how people can be assisted in creating more successful plans. In this paper, we developed and deployed a mobile app as a probe to investigate the in-the-wild physical activity planning experience for 28 days with 17 participants. Additionally, we explored the impact of presenting successful and unsuccessful planning records on participants' planning behaviors. Based on interviews before, during, and after the deployment, we offer a description of what factors participants considered to fit their exercise plans into their existing routines, as well as factors leading to plan failures and dissatisfaction with planned physical activity. With access to historical records, participants derived insights to improve their plans, including trends in successes and failures. Based on those findings, we discuss the implications for better supporting people to make and execute physical activity plans, including suggestions for incorporating historical records into planning tools.
Predicting defects in imbalanced data using resampling methods: an empirical investigation

The development of correct and effective software defect prediction (SDP) models is one of the utmost needs of the software industry. Statistics of many defect-related open-source data sets depict the class imbalance problem in object-oriented projects. Models trained on imbalanced data leads to inaccurate future predictions owing to biased learning and ineffective defect prediction. In addition to this large number of software metrics degrades the model performance. This study aims at (1) identification of useful metrics in the software using correlation feature selection, (2) extensive comparative analysis of 10 resampling methods to generate effective machine learning models for imbalanced data, (3) inclusion of stable performance evaluators-AUC, GMean, and Balance and (4) integration of statistical validation of results. The impact of 10 resampling methods is analyzed on selected features of 12 object-oriented Apache datasets using 15 machine learning techniques. The performances of developed models are analyzed using AUC, GMean, Balance, and sensitivity. Statistical results advocate the use of resampling methods to improve SDP. Random oversampling portrays the best predictive capability of developed defect prediction models. The study provides a guideline for identifying metrics that are influential for SDP. The performances of oversampling methods are superior to undersampling methods.
Interval Type-3 Fuzzy Logic Systems (IT3FLS)

In this chapter we present the basic concepts and methods for building the Interval type-3 fuzzy systems.
Effective monetary policy, banks' pricing behaviour and human development in Africa

This paper empirically examines the effect of monetary policy effectiveness on human development in Africa. We employ both micro-bank level and macro-country level data. Bank level data are taken from the bank scope database maintained by Fitch/IBCA/Bureau Van Dijk. Series are yearly, covering a sample of 320 banks across 29 African countries. Panel fixed effects, random effects and IV regressions were estimated for the period 2002 to 2013. For our IV estimation, the paper explores an instrumental variable based on the fact that effective monetary policy is conditional on the independence of the central bank. The regression results that ensued suggest that; first, effective monetary policy translates to high banks' loan and deposit prices. Building on these results and employing various specifications of banks' pricing strategy, the second test suggests that, high banks' pricing induced by effective monetary policy tends to increase human development. Results of the net effects eventually suggest that effective monetary policy, overall, does not improve human development.