Reviewerly/exHarmony huggingface dataset:
Text Summarization of News Articles

Text summarization gives condensed information of long texts and documents. Extractive and abstractive are two approaches for summarizing texts. Extractive text summarization is a type of forming summary by finding out the key phrases in a text or article. These important phrases are extracted to form the summary. In this paper, we aim to carry out text summarization of texts in two foreign languages, namely English and German, and two Indian languages: Marathi and Hindi. We have considered an entire sentence in a text as a feature to be extracted to form the summary. We have summarized news articles in the above-mentioned languages. For summarization, supervised or unsupervised algorithms can be used. We aim to find performance of unsupervised algorithms, namely weighted, TextRank, and fuzzy logic for summarizing texts in the above-mentioned languages. The performance evaluation of algorithms is done using ROUGE metric.
SUPPLE: Extracting Hand Skeleton with Spherical Unwrapping Profiles

Embedding a unified skeleton into diverse hand meshes is a prominent task both for animation and pose estimation. Most existing methods extracted skeletons from humanoid characters under simple poses, e.g. T- pose or A- pose. Applying them directly to hand meshes may yield inaccurate or implausible results because hands have higher dexterity and similar endpoints. Furthermore, these methods did not attempt to extract skeleton directly from a scan model which may be not watertight and has much more vertices. Our key idea is to unwrap meshes with different topologies in the same image-based representation, named SUPPLE (Spherical UnwraPping ProfiLEs), and then train a convolutional encoder-decoder to extract skeleton under this representation. Experiments demonstrate that our framework produces reliable and accurate skeleton estimation results across a broad range of datasets, from raw scans to artist-designed models.
SDN-Based QoE Evaluation Methods for HTTP Adaptive Video Streaming

Nowadays, video streaming has become the main traffic on the Internet. The demand of people for video services is growing higher and higher. In order to meet the requirements of users, the service providers such as network providers and application providers must guarantee good Quality of Experience (QoE). This paper reviews the influencing factors and main evaluation methods of user QoE, and introduces the research status and progress in this field. Then, combining with the most popular HTTP Adaptive Streaming (HAS) and SDN (Software-Defined Networking) technologies, we build an SDN-based QoE experimental platform for video streaming based some open-source software packages, in which we integrate a variety of QoE evaluation models and real extensible network environment modules. Finally, in the SDN-based virtual network, we perform videos multiple times, using different QoE evaluation methods each time, and obtain visual results. Our results prove that the platform architecture has the ability of helping researchers to carry out QoE evaluation testing in a variety of virtual network environments.
G-SLIDE: A GPU-Based Sub-Linear Deep Learning Engine via LSH Sparsification

Deep learning has been one of the trendiest research topics. However, as data quantities rise exponentially, training large neural networks can become prohibitively expensive with billions of parameters. Fortunately, recent research has discovered that not all of the computations in traditional network training are necessary. By selectively sparsifying the majority of the neurons during training, we can still obtain acceptable accuracy. SLIDE, a C++ OpenMP-based sub-linear deep learning engine, has been developed in this situation. SLIDE uses the algorithm of locality-sensitive hashing (LSH) to query neurons with high activation in sub-linear time. It achieves a remarkable speedup in training large fully-connected networks by making use of the network sparsity as well as multi-core parallelism. However, SLIDE is limited to CPUs, ignoring the popular GPU devices with greater parallel potential and computational capability. In this paper, we propose G-SLIDE, a GPU-based sub-linear deep learning engine, which combines the benefits of SLIDEs adaptive sparsification algorithms with GPUs high performance. The main challenges in developing G-SLIDE are efficiently using LSH to sparsify networks and training the special sparse neural networks on the GPU. To address these challenges, we propose several novel solutions, such as specific data formats and appropriate workload partitioning for threads to fully utilize the GPU resources. We evaluate G-SLIDE on two extremely sparse datasets with a 2080 Ti GPU, and the results demonstrate that for the time of one training epoch, G-SLIDE can achieve more than 16.4 speedup over SLIDE on a 32-core/64-thread CPU. Furthermore, on the same platform, G-SLIDE can earn an average of 16.2 speedup over TensorFlow-GPU and 30.8 speedup over TensorFlow-CPU.
Scammers exploit Covid-19 measures

While many scammers have simply used coronavirus-related themes in order to lure victims during the current pandemic, others are specifically targeting official government schemes.
Markov random fields model and applications to image processing

&lt;abstract&gt;&lt;p&gt;Markov random fields (MRFs) are well studied during the past 50 years. Their success are mainly due to their flexibility and to the fact that they gives raise to stochastic image models. In this work, we will consider a stochastic differential equation (SDE) driven by Lévy noise. We will show that the solution $ X_v $ of the SDE is a MRF satisfying the Markov property. We will prove that the Gibbs distribution of the process $ X_v $ can be represented graphically through Feynman graphs, which are defined as a set of cliques, then we will provide applications of MRFs in image processing where the image intensity at a particular location depends only on a neighborhood of pixels.&lt;/p&gt;&lt;/abstract&gt;
Digital Currency and its Implications for India

The demonetization of currency in year 2016 by Modi government revolutionized the movement towards usage of digital payment methods in India. Though it was strenuous decision for country like India where 90% of the transactions are in cash, people adopted digital mode of payments in short span of time. The adoption of digital methods for payments is facilitated by phenomenal increase in ownership of smart phones and presence of user friendly payment modes like PAYTM and BHIM UPI. The paper tries to explore the global trends in digital currencies in selected countries and tries to examine the implications of digital currency for India through SWOT analysis.
SE-PSI: Fog/Cloud server-aided enhanced secure and effective private set intersection on scalable datasets with Bloom Filter

Private Set Intersection (PSI), which is a hot topic in recent years, has been extensively utilized in credit evaluation, medical system and so on. However, with the development of big data era, the existing traditional PSI cannot meet the application requirements in terms of performance and scalability. In this work, we proposed two secure and effective PSI (SE-PSI) protocols on scalable datasets by leveraging deterministic encryption and Bloom Filter. Specially, our first protocol focuses on high efficiency and is secure under a semi-honest server, while the second protocol achieves security on an economic-driven malicious server and hides the set/intersection size to the server. With experimental evaluation, our two protocols need only around 15 and 24 seconds respectively over one million-element datasets. Moreover, as a novelty, a multi-round mechanism is proposed for the two protocols to improve the efficiency. The implementation demonstrates that our two-round mechanism can enhance efficiency by almost twice than two basic protocols.
Emergence of Triple Entry Accounting in the Backdrop of Blockchain Technology: A Conceptual Study

In blockchain ecosystem, all the transactions are recorded and aggregated in the financial statements and get automatically confirmed as accurate. The third ledger serves as a linkage between the double entry ledgers and all records are made available to all participants. This has minimised the possibility of fraudulent attempts leading to errors or manipulations. This article attempts to explore how emergence of blockchain leads to the development of triple entry accounting, how it works and possible benefits and challenges.
Predictors to Use Mobile Apps for Monitoring COVID-19 Symptoms and Contact Tracing: Survey Among Dutch Citizens (Preprint)

<sec> <title>BACKGROUND</title> eHealth apps have been recognized as a valuable tool to reduce COVID-19’s effective reproduction number. The factors that determine the acceptance of COVID-19 apps remain unknown. The exception here is privacy. </sec> <sec> <title>OBJECTIVE</title> The aim of this article was to identify antecedents of acceptance of (1) a mobile app for COVID-19 symptom recognition and monitoring and (2) a mobile app for contact tracing, both by means of an online survey among Dutch citizens. </sec> <sec> <title>METHODS</title> Next to the demographics, the online survey contained questions focusing on perceived health, fear of COVID-19, and intention to use. We used snowball sampling via posts on social media and personal connections. To identify antecedents of the model for acceptance of the 2 mobile apps, we conducted multiple linear regression analyses. </sec> <sec> <title>RESULTS</title> In total, 238 Dutch adults completed the survey; 59.2% (n=141) of the responders were female and the average age was 45.6 years (SD 17.4 years). For the symptom app, the final model included the predictors age, attitude toward technology, and fear of COVID-19. The model had an r2 of 0.141. The final model for the tracing app included the same predictors and had an r2 of 0.156. The main reason to use both mobile apps was to control the spread of the COVID-19 virus. Concerns about privacy was mentioned as the main reason to not use the mobile apps. </sec> <sec> <title>CONCLUSIONS</title> Age, attitude toward technology, and fear of COVID-19 are important predictors of the acceptance of COVID-19 mobile apps for symptom recognition and monitoring and for contact tracing. These predictors should be taken into account during the development and implementation of these mobile apps to secure acceptance. </sec>
“Extreme Development” as a Means for Learning Agile

During the 2020 pandemic a new modality for the capstone project in Software Engineering was introduced to our third-year students in Computer Science. They have been tasked with the development of a non trivial software product - a Twitter client capable of visual analytics - using some Agile practices, exploiting a Scrum-like process model, and using only open source tools. Due to circumstances that were either planned (in the selection of tools and requirements) or unintended (the pandemic forbade any physical meeting), the project had some interesting outcomes. The project was not easy to enact, neither for the students nor for the instructors. The main problems were two: the students were not ready to practice agile teamwork, and the open source tools they had to use were demanding and only partly suitable for the goal they were chosen for. We term this experience - where students applied an agile discipline and were required to use only open source tools - an “extreme” agile development project. This paper - written by two students together with their instructor, summarises some lessons learnt: characteristics and features of the tools and practices used, the evolution of product artifacts and some difficulties encountered, along with the solutions we adopted. An important lesson learnt is that an agile project developed by Computer Science students requires specific training in communicating correct information at the right moment, and avoiding telling “social lies” concerning the status of both the product and its development process.
COGNITIVE AND EYE-MOVEMENT TECHNIQUES IN INDIRECT NOMINAL GROUPS ENTRENCHMENT

The study reveals the reading and construal techniques employed in the process of textual entrenchment of indirect nominal groups. Entrenchment is characterized by the conventionalization of the nominal group form and the repeated referent construal scheme. The research discloses whether any repeated indirect nominal group use contributes to its entrenchment which is assessed in the oculographic experiment. It traces the eye-movement techniques and the reference identification index variations signifying the changes in the cognitive load distribution. To define the construal differences occurring in the first and repeated indirect nominal group use, we apply the system of parameters of focusing and process the parametric analysis of the text fragments. We initially hypothesized that the repeated indirect nominal group use will employ defocusing and repeated focusing schemes, however it also employed new focusing schemes. The changing roles of focusing and defocusing were evaluated oculographically and revealed the techniques of both cognitive load decrease and increase, with fixation duration decrease and stable reading regimes correlating with defocusing / repeated focusing, and identification index decrease and longer fixation duration correlating with new focusing. The results give evidence that textual entrenchment is in most cases accompanied by novel construal, and is constrained not solely by the number of repeated indirect nominal group uses but by its new roles in the text and referent construal.
A federated learning based semi-supervised credit prediction approach enhanced by multi-layer label mean

Learning based credit prediction has attracted great interest from academia and industry. Different institutions hold a certain amount of credit data with limited users to build model. An institution has the requirement to obtain data from other institutions for improving model performance. However, due to the privacy protection and subject to legal restrictions, they encounter difficulties in data exchange. This affects the performance of credit prediction. In order to solve the above problem, this paper proposes a federated learning based semi-supervised credit prediction approach enhanced by multi-layer label mean, which can aggregate parameters of each institution via joint training while protecting the data privacy of each institution. Moreover, in actual production and life, there are usually more unlabeled credit data than labeled ones, and the distribution of their feature space presents multiple data-dense divisions. To deal with these, local meanNet model is proposed with a multi-layer label mean based semi-supervised deep learning network. In addition, this paper introduces a cost-sensitive loss function in the supervised part of the local mean model. Conducted on two public credit datasets, experimental results show that our proposed federated learning based approach has achieved promising credit prediction performance in terms of Accuracy and F1 measures. At the same time, the framework design mode that splits data aggregation and keys uniformly can improve the security of data privacy and enhance the flexibility of model training.
Multi-objective Particle Swarm Optimization Based Enhanced Fuzzy C-Means Algorithm for the Segmentation of MRI Data

Fuzzy c-means algorithm and its variants are popular for the segmentation of magnetic resonance imaging (MRI) data. The enhanced fuzzy c-means approach is one among them that comprises weighted local spatial data. However, the quantity of spatial data added with input MRI image differs and that depends on the noise content and sequence of MRI. Hence, the value of weight factor needs to be chosen appropriately and automatically to attain the accurate segmentation results. In this perspective, the current work focuses to generate optimum weight values and presents an optimized enhanced fuzzy c-means algorithm for MRI data. The proposed method utilizes the multi-objective particle swarm optimization to control the weight parameter that leads to maximum segmentation accuracy. The new approach is tested and validated on a standard simulated BrainWeb MRI dataset. The outcome shows that the proposed approach is flexible and robust to noise content as compared to the conventional algorithms.
Acceleration of Rotated Object Detection on FPGA

In this brief, an FPGA-based solution is proposed to show the computing efficiency on rotated object detection based on R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> Det algorithm. The key idea of our approach is firstly to design reconfigurable neural processing units (NPU) for convolutional neural networks (CNN) and a specific architecture for spatial operations, and then to adopt a novel scheduling scheme to deal with the data dependency on these modules. When implemented on cost-effective Kintex Ultrascale+ XCKU15P FPGA, the proposed solution achieves nearly 3.20 times energy efficiency compared to NVIDIA V100 GPU. The proposed reconfigurable NPU achieves 73.20% DSP efficiency, 30.39% improvement compared to the 56.14% DSP efficiency of the state-of-the-art CNN accelerator on ResNet-50.
Environment Classification and Deinterleaving using Siamese Networks and Few-Shot Learning

In the age of digital communications, radar receivers prove to be essential for applications involving classification such as air traffic control towers, defense systems, and navigation systems. Detecting Emitters within a Radar Environment presents hurdles to the System Designers such as accounting for interference and trying to classify multiple emitters when they are stacked. This paper presents a few-shot machine learning model that utilizes Siamese networks with classification. Given a relatively small dataset, the Siamese network's task is to find the difference between stacked pulses and normal pulse trains, as well as classify the pulse-descriptor words (PDWs), of the signals in the environment. The PDWs will characterize various aspects of the signal with help from a dynamic-thresholding deinterleaving algorithm. The data for this experiment are laboratory generated signals that are transmitted and received using MATLAB, the Zynq Ultrascale+ MPSoC ZCU104 FPGA board, and the AD-FMCOMMS2-EBZ RF module.
Artificial intelligence technologies for more flexible recommendation in uniforms

Purpose This research aims to collect human body variables via 2D images captured by digital cameras. Based on those human variables, the forecast and recommendation of the Digital Camouflage Uniforms (DCU) for Taiwan's military personnel are made. Design/methodology/approach A total of 375 subjects are recruited (male: 253; female: 122). In this study, OpenPose converts the photographed 2D images into four body variables, which are compared with those of a tape measure and 3D scanning simultaneously. Then, the recommendation model of the DCU is built by the decision tree. Meanwhile, the Euclidean distance of each size of the DCU in the manufacturing specification is calculated as the best three recommendations. Findings The recommended size established by the decision tree is only 0.62 and 0.63. However, for the recommendation result of the best three options, the DCU Fitting Score can be as high as 0.8 or more. The results of OpenPose and 3D scanning have the highest correlation coefficient even though the method of measuring body size is different. This result confirms that OpenPose has significant measurement validity. That is, inexpensive equipment can be used to obtain reasonable results. Originality/value In general, the method proposed in this study is suitable for applications in e-commerce and the apparel industry in a long-distance, non-contact and non-pre-labeled manner when the world is facing Covid-19. In particular, it can reduce the measurement troubles of ordinary users when purchasing clothing online.
Narrative Text Generation from Abductive Interpretations Using Axiom-Specific Templates

Structured story graphs have proven to be useful for representing content in pipelines for automated interpretation and narration. Recent progress on interpretation using logical abduction has made it possible to construct these representations automatically, and several methods for converting these structures into narrative text have been proposed. In this paper, we describe a technical approach to narrative text generation from structured story graphs that prioritizes simplicity and ease-of-use, employing full-sentence templates associated with the specific axioms used to construct graphs during the interpretation process. We evaluate our approach using the TriangleCOPA benchmark for narrative interpretation and text generation, comparing our results to human-authored narratives and to the results of previous work.
DNN-Based Brain MRI Classification Using Fuzzy Clustering and Autoencoder Features

Worldwide interest has been noted in medical image analysis and classification using machine learning techniques.Magnetic resonance imaging (MRI) is one of the safe and painless procedures for human brain scanning.During the MRI procedure, magnetic fields and radio waves are used to scan and map the extended view of brain tissues for further pathological processes and analysis.For a qualitative and quantitative MRI analysis, the manual capability of radiologists and/or doctors is limited and time-consuming in complex and group-level diagnoses.Hence, the development of an intelligent, robust, and reliable support system for the diagnosis of brain-related diseases is a top priority.In this paper, a new deep neural network based MRI image classification approach is proposed that uses fuzzy c-mean (FCM) and an autoencoder to classify brain MRI as normal or abnormal, diminishing human error during the diagnosis of diseases in MRI scans.Here, FCM is utilized for abnormal tissue segmentation from brain MRI images, followed by an autoencoder, for extraction and dimensionality reduction of features.Finally, a deep neural network was used for the classification of brain MRI images that were trained using FCM-extracted features and sample data.Considering the availability of raw MRI data, data augmentation techniques have also been used to increase the number of data required to train a deep neural network.The experiment results achieved 96% accuracy and a 95% sensitivity rate for classification.The results demonstrate that the proposed well-trained deep learning technology has the potential to make solid predictions regarding brain abnormalities; therefore, it can be used as a prominent tool in clinical practice.
Learning correlation filter with fused feature and reliable response for real-time tracking

Object tracking is a key component of machine vision system and getting much attention in different walk of life. Recently, correlation filters have been successfully applied to visual tracking. However, how to design effective features and deal with model drifts remain open issues for online tracking. This paper tackles these challenges by proposing a real-time correlation tracking algorithm (RCT) based on two ideas. First, we propose a method to fuse features to more naturally describe the gradient and color information of the tracked object, and introduce the fused feature into a background-aware correlation filter to obtain the response map. Second, we present a novel strategy to significantly reduce noise in the response map and therefore ease the problem of model drift. Systematic comparative evaluations performed over multiple tracking benchmarks demonstrate the efficacy of the proposed approach. The results show that the proposed RCT significantly improves the performance compared to the baseline tracker while still maintaining a real-time tracking speed of 26 fps in MATLAB implementation.