Reviewerly/exHarmony huggingface dataset:
Maneuvering Multitargets Tracking System Using Surveillance Multisensors

Multisensors multitargets tracking is an important task, and the difficulty is how to form the real trajectory of each target accurately. In this article, we construct a complete system for tracking highly maneuvering multitargets in the case of multisensors asynchronous sampling. Our system divides the entire tracking task into three modules: space&#x2013;time calibration, point-trajectory data association, and trajectory data fusion. An improved least-squares (LS) virtual fusion method is proposed to correct the asynchronous sampling time, and the fast vector calibrates the multisensors&#x2019; spatial state. In the point-trajectory data association module, an adaptive K-nearest neighbors (AK-NN) algorithm is proposed, which employs the adaptive threshold forming multiple trajectories. In trajectory data fusion, a CV&#x002B;CT&#x002B;S multimotion model is proposed with the multisensors&#x2019; probabilistic data association filter (PDAF) algorithm to track highly maneuvering multitargets actively. The results show that our system performs accurately for moving maneuvering multitargets tracking in complex situations.
NUQSGD: Provably Communication-efficient Data-parallel SGD via Nonuniform Quantization

As the size and complexity of models and datasets grow, so does the need for communication-efficient variants of stochastic gradient descent that can be deployed to perform parallel model training. One popular communication-compression method for data-parallel SGD is QSGD (Alistarh et al., 2017), which quantizes and encodes gradients to reduce communication costs. The baseline variant of QSGD provides strong theoretical guarantees, however, for practical purposes, the authors proposed a heuristic variant which we call QSGDinf, which demonstrated impressive empirical gains for distributed training of large neural networks. In this paper, we build on this work to propose a new gradient quantization scheme, and show that it has both stronger theoretical guarantees than QSGD, and matches and exceeds the empirical performance of the QSGDinf heuristic and of other compression methods.
AliCoCo2: Commonsense Knowledge Extraction, Representation and Application in E-commerce

Commonsense knowledge used by humans while doing online shopping is valuable but difficult to be captured by existing systems running on e-commerce platforms. While construction of common- sense knowledge graphs in e-commerce is non-trivial, representation learning upon such graphs poses unique challenge compared to well-studied open-domain knowledge graphs (e.g., Freebase). By leveraging the commonsense knowledge and representation techniques, various applications in e-commerce can be benefited. Based on AliCoCo, the large-scale e-commerce concept net assisting a series of core businesses in Alibaba, we further enrich it with more commonsense relations and present AliCoCo2, the first commonsense knowledge graph constructed for e-commerce use. We propose a multi-task encoder-decoder framework to provide effective representations for nodes and edges from AliCoCo2. To explore the possibility of improving e-commerce businesses with commonsense knowledge, we apply newly mined commonsense relations and learned embeddings to e-commerce search engine and recommendation system in different ways. Experimental results demonstrate that our proposed representation learning method achieves state-of-the-art performance on the task of knowledge graph completion (KGC), and applications on search and recommendation indicate great potential value of the construction and use of commonsense knowledge graph in e-commerce. Besides, we propose an e-commerce QA task with a new benchmark during the construction of AliCoCo2, for testing machine common sense in e-commerce, which can benefit research community in exploring commonsense reasoning.
Optimization of the CNN Model for Hand Sign Language Recognition Using Adam Optimization Technique

To communicate with someone, hand signs prove to be more effective than just words to emphasize and structure the conversation and give more clarity. Some machines, like a driving car or a robot, are also operated by hand gestures as they are more convenient to interpret, but devising the recognition of hand gesture system is an extremely arcane commission since the model should be clever enough to recognize the hand gestures in distinct positions and orientation. In this research, the convolution neural network (CNN) model of deep learning (DL) is utilized for training the hand sign language image dataset. In addition to this, the Adam optimization technique, which is known to leverage the adaptive learning technique for figuring out the learning rate for every parameter, is utilized here for determining the optimized values of hyperparameters. The dataset is taken from GitHub and is contributed by the Turkey Ankara Ayrancı Anadolu High School students. The dataset consists of 218 hand sign sample images of each of the ten digits ranging from 0–9. After determining the optimized values of various hyperparameters, the proposed framework is then validated upon the validation dataset. The training and validation loss over the optimized number of epochs comes out to be 0.021 and 0.064, respectively. Conclusively, the investigational outcomes depict that the anticipated optimized CNN model displays an increased accuracy of 98%.
Towards a Novel Framework for Trust Driven Web URL Recommendation Incorporating Semantic Alignment and Recurrent Neural Network

Recommendation System plays an important role in delivering relevant data to the user. A recommender system is also used to display relevant websites with respect to the user query. As the amount of malicious web pages in World Wide Web is quite enormous, there is a huge probability that the URL might be harmful to the user. This paper proposes a Trust-based URL recommendation technique using Semantic Alignment driven Knowledge aggregation methodology along with Artificial neural network and Glowworm Swarm Optimization. The data used for training the Recurrent Neural Network is the URL Trees formulated from the dataset combined with data after classification from fact-checkers, which is later used to check the Threat level of the URL from the initial solution set. Based on this index, the URL is recommended in such a way that the URL is more relevant and Threat is minimized. The architecture's performance is calculated and compared with the baseline approaches and it is clearly observed that the proposed trust-based URL recommendation system is dominating in terms of performance and attained a precision and accuracy of 96.84% and 95.87% respectively.
Scheduling Microservice-based Workflows to Containers in On-demand Cloud Resources

Though microservices process and communicate with lightweight mechanisms, finer tasks result in much more complicated precedence constraints. Different tasks have distinct resource requirements and different VMs (Virtual Machine) have various configurations and prices. In this paper, we consider the problem of scheduling microservice tasks of workflow applications to containers configured on on-demand VMs to minimize the total rental cost. The problem is mathematically modelled using integer programming and an algorithm framework is proposed. For dynamic available containers and resource requirements, a task scheduling heuristic is presented for scheduling precedence-constrained or independent tasks to available containers. All parameters and components of the proposed algorithm framework are statistically calibrated by the Analysis of Variance technique on a large number of random instances. Performance of the the proposed algorithm is verified over a lot of instances.
A deep variational Bayesian framework for blind image deblurring

Blind image deblurring is an important but challenging problem in image processing. Traditional optimization-based methods typically formulate this task as a maximum-a-posteriori estimation or variational inference problem, whose performance highly relies on handcrafted priors for both the latent image and blur kernel. In contrast, recent deep learning methods generally learn from a large collection of training images. Deep neural networks (DNNs) directly map the blurry image to the clean image or to the blur kernel, paying less attention to the physical degradation process of the blurry image. In this study, we present a deep variational Bayesian framework for blind image deblurring. Under this framework, the posterior of the latent clean image and blur kernel can be jointly estimated in an amortized inference manner with DNNs, and the involved inference DNNs can be trained by fully considering the physical blur model, and the supervision of data driven priors for the clean image and blur kernel, which is naturally led to by the lower bound objective. Comprehensive experiments were conducted to substantiate the effectiveness of the proposed framework. The results show that it can achieve a promising performance with relatively simple networks and incorporate existing deblurring DNNs to enhance their performance.
Investigation of clinical target volume segmentation for whole breast irradiation using three-dimensional convolutional neural networks with gradient-weighted class activation mapping

This study aims to implement three-dimensional convolutional neural networks (3D-CNN) for clinical target volume (CTV) segmentation for whole breast irradiation and investigate the focus of 3D-CNNs during decision-making using gradient-weighted class activation mapping (Grad-CAM). A 3D-UNet CNN was adopted to conduct automatic segmentation of the CTV for breast cancer. The 3D-UNet was trained using three datasets of left-, right-, and both left- and right-sided breast cancer patients. Segmentation accuracy was evaluated using the Dice similarity coefficient (DSC). Grad-CAM was applied to trained CNNs. The DSCs for the datasets of the left-, right-, and both left- and right-sided breasts were on an average 0.88, 0.89, and 0.85, respectively. The Grad-CAM heatmaps showed that the 3D-UNet used for segmentation determined the CTV region from the target-side breast tissue and by referring to the opposite-side breast. Although the size of the dataset was limited, DSC ≥ 0.85 was achieved for the segmentation of breast CTV using the 3D-UNet. Grad-CAM indicates the applicable scope and limitations of using a CNN by indicating the focus of such networks during decision-making.
Multi-stage stochastic gradient method with momentum acceleration

Multi-stage optimization which invokes a stochastic algorithm restarting with the returned solution of previous stage, has been widely employed in stochastic optimization. Momentum acceleration technique is famously known for building gradient-based algorithms with fast convergence in large-scale optimization. In order to take the advantage of this acceleration in multi-stage stochastic optimization, we develop a multi-stage stochastic gradient descent with momentum acceleration method, named MAGNET, for first-order stochastic convex optimization. The main ingredient is the employment of a negative momentum, which extends the Nesterov’s momentum to the multi-stage optimization. It can be incorporated in a stochastic gradient-based algorithm in multi-stage mechanism and provide acceleration. The proposed algorithm obtains an accelerated rate of convergence, and is adaptive and free from hyper-parameter tuning. The experimental results demonstrate that our algorithm is competitive with some state-of-the-art methods for solving several typical optimization problems in machine learning.
Knowledge Exchange Between Domain-Adversarial and Private Networks Improves Open Set Image Classification

Both target-specific and domain-invariant features can facilitate Open Set Domain Adaptation (OSDA). To exploit these features, we propose a Knowledge Exchange (KnowEx) model which jointly trains two complementary constituent networks: (1) a Domain-Adversarial Network (DAdvNet) learning the domain-invariant representation, through which the supervision in source domain can be exploited to infer the class information of unlabeled target data; (2) a Private Network (PrivNet) exclusive for target domain, which is beneficial for discriminating between instances from known and unknown classes. The two constituent networks exchange training experience in the learning process. Toward this end, we exploit an adversarial perturbation process against DAdvNet to regularize PrivNet. This enhances the complementarity between the two networks. At the same time, we incorporate an adaptation layer into DAdvNet to address the unreliability of the PrivNet's experience. Therefore, DAdvNet and PrivNet are able to mutually reinforce each other during training. We have conducted thorough experiments on multiple standard benchmarks to verify the effectiveness and superiority of KnowEx in OSDA.
SEND: A novel dissimilarity metric using ensemble properties of the feature space for clustering numerical data

The clustering is an unsupervised learning technique for grouping the unlabeled data based on the proximity between the data points. Therefore, the performance of clustering techniques mainly depends on the proximity measures. The computation of dissimilarity in high dimensional and noisy datasets as well as datasets with imbalanced feature scale, which appear in various applications, is a challenging task. To counter these challenges, we propose a new distance metric to compute the dissimilarity between data points by combining the ensemble properties, entropy and weight information of feature vectors. We consider the statistical information and entropy along each features to compute the dissimilarity between the points. Then each feature is associated with weight based on its distribution information. The proposed Similarity measure based on Entropy for Numerical Datasets (SEND), is free from any domain specific parameters and there are no underlying assumptions about the distribution of the data. The proposed metric is applied on different type of clustering techniques to evaluate its performance. Experimental analyses on synthetic as well as real datasets demonstrate the efficacy of the proposed metric in terms of cluster quality, accuracy, execution time, robustness against noise and its ability to handle the high dimension datasets.
Human, Technical, and Organizational Drivers Affecting Sustainability of Content Firms through Management and Innovation Capability during COVID-19

Individuals and businesses engage in a number of activities in the digital environment. During unexpected circumstances, such as COVID-19, many people used the internet for purposes such as education, work, and entertainment. This has led to a rapid growth in the content industry. Thus, studies of the sustainability and success of content companies are essential. Few empirical studies focus on these aspects of content companies, particularly concentrating on the importance of management and innovation capability. This study examines the roles of management and innovation capabilities and the human, technical, and organizational factors that affect these capabilities. This, in turn, positively affects the sustainability and success of content companies. The proposed research model includes content development ability and leadership as a human factor, technical support and information technology (IT) infrastructure as a technical factor, and top management and financial support as an organizational factor. A total of 255 responses were collected from upper management-level employees at various firms in the content industry. Covariance-based structural equation modeling was used with Amos 22.0. The results indicated that all proposed hypotheses were supported with the exception of the hypothesis that tests the relationship between content development and management capability. The study findings provide information necessary for future sustainability and success of content companies.
Runtime Verification Under Access Restrictions

We define a logical framework that permits runtime verification to take place when a monitor has incomplete or uncertain information about the underlying trace. Uncertainty is modeled as a stateful access control proxy that has the capacity to turn events into sets of possible events, resulting in what we call a “multi-trace”. We describe a model of both proxy and monitor as extensions of Mealy machines, and provide an algorithm to lift a classical monitor into a sound, loss-tolerant monitor. Experiments on various scenarios show that the approach can account for various types of data degradation and access limitations, provides a tighter verdict than existing works in some cases, and preserves scalable performance of the model.
Building Arabic Paraphrasing Benchmark based on Transformation Rules

Measuring semantic similarity between short texts is an important task in many applications of natural language processing, such as paraphrasing identification. This process requires a benchmark of sentence pairs that are labeled by Arab linguists and considered a standard that can be used by researchers when evaluating their results. This research describes an Arabic paraphrasing benchmark to be a good standard for evaluation algorithms that are developed to measure semantic similarity for Arabic sentences to detect paraphrasing in the same language. The transformed sentences are in accordance with a set of rules for Arabic paraphrasing. These sentences are constructed from the words in the Arabic word semantic similarity dataset and from different Arabic books, educational texts, and lexicons. The proposed benchmark consists of 1,010 sentence pairs wherein each pair is tagged with scores determining semantic similarity and paraphrasing. The quality of the data is assessed using statistical analysis for the distribution of the sentences over the Arabic transformation rules and exploration through hierarchical clustering (HCL). Our exploration using HCL shows that the sentences in the proposed benchmark are grouped into 27 clusters representing different subjects. The inter-annotator agreement measures show a moderate agreement for the annotations of the graduate students and a poor reliability for the annotations of the undergraduate students.
Ensemble Based Hinglish Hate Speech Detection

The mixing of multiple languages in speech or text is termed as the phenomenon of code-mixing. The easier access of internet to a larger population, fluent in various regional languages and the more convenient usage of ubiquitous languages like English for technical terms, sports related words, scientific concepts, etc. has led to an increasing presence of code-mixed content on the world wide web, especially socializing and microblogging platforms like of Facebook, twitter, Instagram, etc. The code-mixing of Hindi, the predominant language of South Asia, with English is colloquially referred to as “Hinglish”, a portmanteau resulting from the names of these two languages. Hinglish is considerably different from its parent languages in syntax, phonetics, grammar and even usage of punctuations. The accent and sentiments are drawn from Hindi, the vocabulary is comprised of varying English (Roman) transliteration of Hindi words, certain English terms. The proposed research work aims to build a self-sufficient model, independent of models meant for English language, which can classify code-mixed posts or tweets, or any other text material for that matter, into the categories: Non-Offensive, Abusive and Hate-Inducing. Our work proposes two ensemble models. The first consisting of some basic machine learning models, first experimented individually experimented with, then as an ensemble. The other ensemble is made by stacking some deep learning models. The aim was to combine the learning of models as weak learners into one for getting better results.
Avoiding pitfalls: Key insights and lessons learnt from customizing and rolling out a national web‐based system in Tanzania

Abstract Tanzania is among the first countries to adopt District Health Information System version 2 (DHIS2) as the national health management information system (HMIS). In this article, we share experiences on achievements and challenges encountered in the process of customizing and rolling out the DHIS2 in Tanzania. This instrumental case study was conducted using a qualitative approach involving desk reviews, key informant interviews, and field observations. Tanzania successfully adopted the DHIS2 as evidenced by innovation of new apps; improving availability of quality data and enhancing data analysis. These achievements were attributed to inbuilt system strengths that provided the necessary flexibility for customization; the use of local experts to customize and maintain the system; cascaded training; and formation of a consortium to coordinate and support the rollout process. Prior existence of a robust paper‐based HMIS and lessons from other countries' experience favored smooth transition. However, several challenges hindered the DHIS2 implementation including donor dependency and ineffective cascade training that left many users untrained. Countries envisaging to adopt the DHIS2 need to consider the use of cost‐effective training approaches to accommodate all users including the use of champions to conduct on‐the‐job training, support, and mentoring.
QiOi: Performance Isolation for Hyperledger Fabric

This paper investigates the performance interference of blockchain services that run on cloud data centers. As the data centers offer shared computing resources to multiple services, the blockchain services can experience performance interference due to the co-located services. We explore the impact of the interference on Fabric performance and develop a new technique to offer performance isolation for Hyperledger Fabric, the most popular blockchain platform. First, we analyze the characteristics of the different components in Hyperledger Fabric and show that Fabric components have different impacts on the performance of Fabric. Then, we present QiOi, component-level performance isolation technique for Hyperledger Fabric. The key idea of QiOi is to dynamically control the CPU scheduling of Fabric components to cope with the performance interference. We implement QiOi as a user-level daemon and evaluate how QiOi mitigates the performance interference of Fabric. The evaluation results demonstrate that QiOi mitigates performance degradation of Fabric by 22% and improves Fabric latency by 2.5 times without sacrificing the performance of co-located services. In addition, we show that QiOi can support different ordering services and chaincodes with negligible overhead to Fabric performance.
A Holistic Approach to Urdu Language Word Recognition using Deep Neural Networks

Urdu is one of the most popular languages in the world. It is a Persianized standard register of the Hindi language with considerable and valuable literature. While digital libraries are constantly replacing conventional libraries, a vast amount of Urdu literature is still handwritten. Digitizing this handwritten literature is essential to preserve it and make it more accessible. Nevertheless, the scarcity of Urdu Optical Character Recognition (OCR) research limits a digital library's scope to a manual document search. The limited research work in this area is mainly due to the complexity of Urdu Script. Unlike the English language, the Urdu writing style is cursive, bidirectional, and character shapes and sizes highly vary depending on their position. Holistic word recognition is found to be a better solution among many other text segmentation techniques as it takes the complete word into account instead of segmenting it explicitly or implicitly. For this project, the data of five different Urdu words were collected for training and testing a convolutional neural network and 96% recognition accuracy was achieved.