Reviewerly/exHarmony huggingface dataset:
Persisting Trust in Emerging Hybrid-Clouds and 6G Systems

Distributed ledger-based transaction management approaches and lineage data recording for dynamic management of DAG (Directed Acyclic Graph) structures can achieve trusted scalability of analytical transactions in some manner. Sub-modular and disjoint cluster sets increase connectivity performance of analytical systems. Unified batch/interactive/ad-hoc querying can enable trusted E2E (end-to-end) analytics for massive systems. Trust factor and trust cost optimization reinforce the trustworthiness of the overall system in varying context. However, security and privacy architectures are still context dependent. Dynamic change in a context have to be adapted to all system layers in real-time for a complete E2E trust mechanism and confidence in smart-ecosystems.In this study, we explored dynamic security and privacy aspects of E2E analytical transactions in varying context. Classical behavior modelling approaches; such as, cellular automata, chaotic systems, hierarchical block diagram modeling methods are cumbersome to adapt the dynamism. Persisting and ensuring the trust for varying contexts with an E2E trust mechanism enable to adapt the dynamism at massive scale. Initial arguments for data exchange over a hybrid-cloud node, instead of cell unit scenario in a simulated 5G environment with the trust mechanism, is evaluated. It is promising to meet zero latency requirement of MEC (Multi-access/Mobile Edge Computing) units. So that, we can say that transmitting data over a hybrid-cloud node rather than cell units can maximize mobility of 5/6G ecosystem with the E2E trust mechanism.
Personalized QoE Optimization with Edge-Aided Video Enhancement Services

Video enhancement technologies can not only improve video qualities but also enrich personalized video experiences. With these technologies, the requested videos can be enhanced either on end devices or cloud servers. However, client devices may not have sufficient computing resources or energy, while cloud servers may not satisfy users&#x0027; short delay requirement. Motivated by the 5G and edge computing technologies, we present veEdge in this paper, an edge-assisted video transmission framework for general-purpose video enhancement services. To balance the trade-off between video enhancement quality and processing delay, we formulate an optimization problem considering video requesting, processing, and playback time simultaneously. To solve the problem in an online fashion, we develop a deep reinforcement learning (DRL) based approach to select the best video enhancement model in favor of various targets, such as bandwidth and quality. The experimental results show that our solutions can significantly improve users&#x0027; quality of experience (QoE).
Face Recognition Using MATLAB

This work sought to investigate how face recognition can be implemented in MATLAB to correctly detect and identify an individual using their face. Face recognition is a biometric technology that is used to recognize and authenticate a detected face in images or videos. This technology can be used in various industries for various purposes. The main goal of this work is to correctly authenticate an individual face using Convolutional Neural Networks (called AlexNet) in MATLAB.
The application of Evolutionary and Nature Inspired Algorithms in Data Science and Data Analytics

In the past 30 years, scientists have searched nature, including animals and insects, and biology in order to discover, understand, and model solutions for solving large-scale science challenges. The study of bionics reveals that how the biological structures, functions found in nature have improved our modern technologies. In this study, we present our discovery of evolutionary and nature-inspired algorithms applications in Data Science and Data Analytics in three main topics of pre-processing, supervised algorithms, and unsupervised algorithms. Among all applications, in this study, we aim to investigate four optimization algorithms that have been performed using the evolutionary and nature-inspired algorithms within data science and analytics. Feature selection optimization in pre-processing section, Hyper-parameter tuning optimization, and knowledge discovery optimization in supervised algorithms, and clustering optimization in the unsupervised algorithms.
Stable Parallel Training of Wasserstein Conditional Generative Adversarial Neural Networks

We use a stable parallel approach to train Wasserstein Conditional Generative Adversarial Neural Networks (W-CGANs). The parallel training reduces the risk of mode collapse and enhances scalability by using multiple generators that are concurrently trained, each one of them focusing on a single data label. The use of the Wasserstein metric reduces the risk of cycling by stabilizing the training of each generator. We apply the approach on the CIFAR10 and the CIFAR100 datasets, two standard benchmark datasets with images of the same resolution, but different number of classes. Performance is assessed using the inception score, the Fr&#x00E9;chet inception distance, and image quality. An improvement in inception score and Fr&#x00E9;chet inception distance is shown in comparison to previous results obtained by performing the parallel approach on deep convolutional conditional generative adversarial neural networks (DC-CGANs). Weak scaling is attained on both datasets using up to 100 NVIDIA V100 GPUs on the OLCF supercomputer Summit.
Optimized Monocular Depth Estimation With Reparameterization on Embedded Devices

Monocular depth estimation (MDE) has been a basic task in many fields. Currently, researchers usually improve the accuracy of MDE by designing deep neural networks, including complex branches or other components. Although complex networks achieve increased precision, they bring slow speed and high memory access cost (MAC) in embedded systems. To balance the speed, accuracy, and MAC for embedded systems, we propose a directly connected model (DCM), which relies on existing networks, for fast inference in embedded systems. DCM balances the speed, precision, and MAC with structural reparameterization that decouples its training and inference networks. To further reduce the MAC and latency of DCM for embedded systems, we construct a network optimization frame-work based on recent template-based optimization methods. In the framework, we respectively adopt the machine learning LightGBM and contextual simulated annealing algorithm as the cost model and optimizer to complete rapid optimization in embedded systems. Our comprehensive experiments confirm that our methods achieve a better trade-off among precision, latency, and MAC. The accuracy of our original DCM inference networks reaches at least 6.13&#x0025; better than the state of the arts. The optimized DCM inference networks decrease latency, main MAC, and power consumption by 46.49&#x0025;, 90.97&#x0025;, and 65.31&#x0025; on Jetson TX2 GPU, without unchanged accuracy.
A survey on security and human-related challenges in agile software deployment

Over the last two decades, agile methods have transformed and brought notable changes to software development by strongly emphasizing team collaboration, customer involvement, and chance tolerance. However, it also has some challenges. This study aims to accumulate these challenges for large-scale agile development. It has been found that there were 11 human-related challenges and solutions for scaling agile methodology. In addition, there were other significant challenges to agile security and organization.
Feature Selection Based on Semantic Information in Complex Indoor Environment

The construction of 3D indoor models is of great significance and is applied to indoor services such as AR, VR, 3D printing, and virtual fitting. In the visual community, there is a large drift in image-based pose estimation, which becomes one of the main factors affecting the accuracy of 3D reconstruction. Feature selection plays an essential role in pose estimation. We aim to select better features in a complex indoor environment, and obtain an accurate, robust, and efficient visual odometer to estimate the pose. This paper integrates semantic information into feature extraction, combined with geometric methods. We provide a scoring framework to filter the features of non-information areas and highlight visual saliency features. Weaken the selection of features in non-information areas such as walls, ceilings, and floors, and strengthen feature selection in areas with more information such as special colors, sizes, and shapes. We use fewer points to obtain a more efficient and robust visual odometer by fusing semantic information. We evaluated our methods on the TUM dataset, and the results showed that our algorithm could extract more efficient matching in complex indoor scenes.
Improved algorithm of thermal image target detection based on Yolo v4

As people have higher requirements for object detection accuracy and adaptability, this research proposes a single-stage algorithm for thermal image target detection based on Yolo v4. The algorithm combines the visible light image and the thermal image to complement the image information, and uses the TMDF (Two Modol Differential Fusion) module to perform feature fusion on the visible light image and the thermal image and then enter the network for training. Adding the DCT(Discrete Cosine Transform) adjustment network to adjust the final output can finally increase the mAP (mean Average Precision) value of the model on the KAIST data set by 3.7&#x0025;, and MR (missed detection rate) value decreased by 3.5&#x0025;.
Novel Framework for Resources Optimization to Solve Class Imbalance Problems

In the actual world, AI is being used to address issues of class inequality. This is especially true when the information is not just unbalanced, but also multidimensional. When there is a class imbalance, a large dimensionality of datasets is always present, and both difficulties must be considered jointly. When using examples to evaluate each component, standard element picking algorithms usually provide equal weights to tests from different classes. As a result, they are unable to operate effectively with unbalanced data. When the costs of misclassification of different classes are different, cost-effective learning procedures are typically used. Different processes in writing have been established to deal with concerns related to class discomfort.
Universal Adversarial Attack Against 3D Object Tracking

3D object tracking has great potential applications in computing intensive cyber-physical systems, particularly auto-nomic driving. However, Object tracking based on deep neural networks is vulnerable to adversarial examples. Previous works generate adversarial examples by applying perturbations to samples, which suffer from the generation of an individual adversarial perturbation for each sample. Algorithms have been proposed to generate a universal adversarial perturbation, which is one single perturbation able to make classifiers misclassify most images. In this paper, we propose the universal adversarial attack method against 3D object tracking. This method gen-erates adversarial samples by applying a universal adversarial perturbation to tracking templates, leading to the deviation of the predictions from the ground truth. A novel objective function, which combines confidence loss, direction loss and distance loss, generates an atomic perturbation for each tracking template. By aggregating these atomic perturbations, the universal adversarial perturbation can be derived. Experiments conducted on the KITTI dataset show that the proposed method can effectively reduce the performance of 3D object tracking.
Quantifying the Right Combination of Knowledge and Population Diversity for Transferred Populations

Transfer Learning (TL) is a process of leveraging knowledge from one problem called the source problem (S) to a related problem called the target problem (T). Experiments and previous research (see Section II) showed that the knowledge from solving a related problem and population diversity are able to aid machine learning algorithm to solve a related problem more easily. To understand the right amount of knowledge and diversity of the transferred population, we modeled the TL process using Evolutionary Computation (EC). Our model gives us more access and control over the TL components, especially the content of the transferred population. The results showed that the relationship R between the source S and the target T problems can quantify the right combination of each component of the transferred population.
An Efficient Adaptive Noise Correction Framework for Size Measurement over Data Streams

With the rapid development of the Internet of Things (IoT), massive high-speed data streams are produced every moment, making accurate size estimation a challenging task. Many sketches have been proposed to summarize real-time high-speed data streams and provide per-flow size estimations. However, sketches have to share the memory units to fit in limited on-chip space, inevitably introducing noises to all flows and resulting in over-estimation problems. Prior work adopts an average denoising strategy to remove the same noise from raw sketch estimations. However, they overlook that the noise distribution is highly skewed, leading to inaccurate results for most flows. This paper proposes an efficient Adaptive Noise Correction (ANC) framework, which analyzes the noise of each flow on a case-by-case basis and provides accurate size estimations. The key of our design is to build an ML model to predict a weight coefficient that indicates the noises in raw estimations, which is conducted for each flow by analyzing the neighbor flows whose memory units overlap with the given flow. Then we introduce a novel Probabilistic Cold Filter to block the tiny flows and assist in noise correction. Experimental results based on real Internet traces show that our framework can effectively remove the noises for different sketches, showing better estimation accuracy than the state-of-the-art.
Code-Switching By Multilingual Pakistanis On Twitter: A Qualitative Analysis

Code-switching is practicing two different grammatical systems where multilinguals also move between two languages or between two dialects or registers of the same language. In the current article, code-switching is characterized as the simultaneous use of two or more languages or dialects within a conversation. The current study focused on code-switching practices on the social media website Twitter. While posting on Twitter, multilinguals may use several languages. The aim of this study was to describe and analyze code-switched Tweets for any recurring patterns and practices. The population of this study involved Twitter users living in the Rawalpindi-Islamabad area of Pakistan. The Tweets were collected on the basis of time and location through a random cluster sampling method. A qualitative analysis of the individual Tweets was done, and recurring patterns were pointed out. This was purely observational research. It was found that the sampled Tweets only code-switched between Urdu and English. Code-switching at the intra-sentential level was more common than at the inter-sentential level. Code-switching at the level of clauses was the most common form of intra-sentential code-switching. Over half of the inter-sentential code-switching had the English sentence(s) preceding the Urdu sentence(s). The findings suggest that code-switching between English and Urdu occurs more commonly at the intra-sentential level. They further imply that the population generally prefers to start inter-sentential code-switching with English before code-switching to Urdu. The results of this study may be useful in demystifying the phenomenon of code-switching in online spaces.
Local Interaction and Global Guidance Based Low and High-level Feature Fusion for RGB-D Fixation Prediction

Saliency prediction on RGB-D images is an underexplored and challenging task in computer vision. We propose a channel-wise attention and contextual interaction asymmetric network for RGB-D saliency prediction. In the proposed network, a common feature extractor provides cross-modal complementarity between the RGB image and the corresponding depth map. In addition, we introduce a four-stream feature-interaction module that fully leverages multiscale and cross-modal features for extracting contextual information. Moreover, we propose a channel-wise attention module to highlight the feature representation of salient regions. Finally, we refine coarse maps through a corresponding refinement block. Experimental results show that the proposed network achieves performance comparable with state-of-the-art saliency prediction methods on two representative datasets.
Infinileap: Modern High-Performance Networking for Distributed Java Applications based on RDMA

In this paper, we propose Infinileap, a modern networking framework enabling high-performance memory transfer mechanisms like Remote Direct Memory Access (RDMA) for applications written in Java. Infinileap is based on the Open Communication X (UCX) framework, which is accessed from Java. This is accomplished through Oracle's Project Panama, which is currently in the preview phase and aims to significantly improve interoperability between Java and “foreign” languages, such as C. In contrast to often used internal and unsupported JDK APIs, Project Panama's APIs are explicitly intended for use and developers are encouraged to adapt their existing code accordingly. Using Project Panama, we implement an object as well as future-oriented framework based on UCX. Our experiments show that Infinileap and thus Project Panama's innovations work reliably and efficiently under heavy load and also, within benchmarks implemented for this purpose based on the Java Microbenchmark Harness (JMH), achieve very good performance results with over 110 million messages per second and round-trip latencies below two microseconds with a single ConnectX-5 InfiniBand (single-port) network interface controller.
Sequential Recommendation for Online Consumption by ACFNN

Online consumption promotes more and more users to purchase stuff and receive service by the Internet. Digging out users&#x2019; underlying preferences chronologically can benefit both customers and servicers. While several approaches have been built on the basis of the Markov chain method and convolutional neural network, they still insufficiency unveil tangled relationships that are involved in users&#x2019; historical behavior. In this paper, we deliver an Attention-based Convolutional Fusion Neural Network (ACFNN) for the sequential recommendation. Specifically, we first conduct pairwise encoding between items and then put them into two branches of CNN block, i.e., one is the naive CNN module and another is combined with the attention mechanism. In this way, we can extract more rich representations of sequential features and capture useful item-item correlations. Comparative experiments on real datasets show that ACFNN outperforms the state-of-the-art methods in the sequential recommendation task.
Pose Adaptive Margin Loss for Face Verification

Face verification is to determine whether two faces belong to the same person, and is widely used in ordinary life. Deep Learning-based Face verification (FV) has made great progress and achieved very high performance on LFW in recent years. And FV is an open-set problem, so is expected to learn discriminative features which intra-class variation of features is small and inter-class difference is large. While at the same time, we notice that the variance in pose greatly degrades the verification performance, due to the intrinsic difficulties in generalization and robust feature extraction from relative few non-frontal face examples. Margin based Softmax loss has achieved outstanding improvements, and we argue that the margin should be adjusted according to different poses. In this paper, we propose an pose adaptive margin loss to improve the generalization ability of margin based Softmax loss in face verification problems, where faces with different poses have different additive margin. Extensive experiments demonstrate that our method achieves better performance than popular margin-based approaches.
Research on Cross-Camera Person Re-Identification Using Overlapping Field of View

Person re-identification in cross-camera images involves matching the identical individual from a secondary camera filmed frame with a post-identified ID. In a practically public scenario, multiple view angles from different cameras may change greatly, and accordingly the person appearance features between disparate images are quite different. It affects appearance feature-based person re-identification adversely. In this paper, we propose an algorithm that matches location features in images from diverse devices and therefore captures spatial mapping matrix for dual-view overlapping portions. Person&#x0027;s location feature is mapped crossing cameras based on the environment he is in. The Siamese Network is leveraged for appearance similarity calculation. The location similarity and appearance similarity are fused for person re-identification. Solid cross-camera videos are adopted as experimental dataset. Experimental results show the algorithm&#x0027;s effectiveness in cross-camera person re-identification with overlapping field of view.
On-Camera Content Filtering for Real-Time Video Analytics

In the past several years, pervasive surveillance cameras have generated massive video records continually, and the video records can be used for several applications (e.g., tracking and object detection). Machine Learning (ML), especially Deep Learning, is a powerful method extensively used for video analytics. Typically, cameras do not have enough computational power to analyze videos locally. A commonly used strategy is gathering videos from cameras and processing them in the cloud or edge server. However, limited bandwidth between cameras and the server results in the inefficiency of existing central-based video analytics systems. In this paper, we effectively reduce the bandwidth demands by proposing a novel object filtering method while keeping the resolution of the video. Specifically, we adopt the moving object detection algorithm on the camera side to find regions in the video and encode these regions into high-resolution video. Through extensive experiments, our system can reduce the bandwidth demands of the video to be analyzed by at least 36.2&#x0025; without losing analysis for main objects.
A Real-Time Translation of Arabic Video Contents to Arabic Sign Language

The shift to online education due to the COVID-19 pandemic has brought various challenges to different individuals. Among these affected individuals are deaf and hearing-impaired. They face difficulties in understanding online videos because they do not provide real-time translation into Sign languages. The main idea of this project is derived from this point. It proposes a PC-based application called "Al-Banan" that supports deaf and hearing-impaired in the Arabic region to understand media in general including learning video and live virtual classes by translating Basic Arabic Speech into Arabic Sign language using a three-dimensional avatar. The application was built done using Python and C# languages. The application was tested on many deaf through social media and proved to be effective if the word exists in the dictionary a three-dimensional cartoon avatar showing the corresponding Saudi Arabia Sign Language. Otherwise, the word is represented by finger spelling. The results proved the understanding and acceptance of the idea by the deaf.