Reviewerly/exHarmony huggingface dataset:
Factors affecting the adoption of cloud of things

Purpose The purpose of this study is to determine the significant factors affecting the adoption of Cloud of Things (CoT) by Indian small and medium-sized enterprises, using exploratory and confirmatory factor analysis. Design/methodology/approach Significant factors that impact CoT implementation were identified through a detailed literature survey. A conceptual framework and hypotheses were proposed for linking the significant factors so identified, namely, cost saving, relative advantage, sharing and collaboration, reliability, security and privacy, technical issues and adoption intention. The data were collected from 270 Indian SMEs using an online survey. Structural equation modelling (SEM) was used to test the proposed model. Findings It was observed that factors such as “sharing and collaboration”, “cost saving” and “relative advantage” had a positive influence on CoT adoption. Findings of the study also supported the hypothesis that “security and privacy” were the prime concerns for CoT adoption. Research limitations/implications Sample coverage across different geographical areas with qualitative data can be helpful. The SEM methodology is only capable of verifying linear relationships; to counter this, a hybrid approach with tools such as artificial neural network and multiple linear regression can be used. Practical implications This study intends to guide the managers of SMEs, cloud service providers and regulatory organisations for formulating an effective strategy to adopt CoT. It may be noted that CoT is the prime building block of Industry 4.0 and SMEs will benefit from government support for the same. Originality/value This paper highlights the influence of factors on the adoption intention of CoT with a focus on the SMEs of a developing country like India.
AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures

Learning to represent videos is a very challenging task both algorithmically and computationally. Standard video CNN architectures have been designed by directly extending architectures devised for image understanding to include the time dimension, using modules such as 3D convolutions, or by using two-stream design to capture both appearance and motion in videos. We interpret a video CNN as a collection of multi-stream convolutional blocks connected to each other, and propose the approach of automatically finding neural architectures with better connectivity and spatio-temporal interactions for video understanding. This is done by evolving a population of overly-connected architectures guided by connection weight learning. Architectures combining representations that abstract different input types (i.e., RGB and optical flow) at multiple temporal resolutions are searched for, allowing different types or sources of information to interact with each other. Our method, referred to as AssembleNet, outperforms prior approaches on public video datasets, in some cases by a great margin.
Mixed Precision DNNs: All you need is a good parametrization

Efficient deep neural network (DNN) inference on mobile or embedded devices typically involves quantization of the network parameters and activations. In particular, mixed precision networks achieve better performance than networks with homogeneous bitwidth for the same size constraint. Since choosing the optimal bitwidths is not straight forward, training methods, which can learn them, are desirable. Differentiable quantization with straight-through gradients allows to learn the quantizer's parameters using gradient methods. We show that a suited parametrization of the quantizer is the key to achieve a stable training and a good final performance. Specifically, we propose to parametrize the quantizer with the step size and dynamic range. The bitwidth can then be inferred from them. Other parametrizations, which explicitly use the bitwidth, consistently perform worse. We confirm our findings with experiments on CIFAR-10 and ImageNet and we obtain mixed precision DNNs with learned quantization parameters, achieving state-of-the-art performance.
Securing the Human

Recent global demand for cybersecurity professionals is promising, with the U.S. job growth rate at 28%, three times the national average [1]. In a global survey, 2,300 security managers reported that 59% of their security positions were unfilled, although 82% anticipated cyberattacks to their systems [2]. At the same time, the cybersecurity field is broadening, not only in technical concepts but also in human factors, business processes, and international law. The field has not become culturally diversified, however. Professionals hired in 2018 included only 24.9% women, 12.3% African Americans, and 6.8% Latinos [3]. These realities create an opportunity for higher education: diversify the profession while increasing the numbers of skilled computer scientists. New and integrated methods of attracting student populations in the field of cybersecurity are needed. This working group report analyzes the outcomes and approaches used in higher education to diversify the cybersecurity field through a review of the literature, identification of gaps, and recommendations for cybersecurity education researchers and practitioners.
Variable Convolution and Pooling Convolutional Neural Network for Text Sentiment Classification

With the popularity of the internet, the expression of emotions and methods of communication are becoming increasingly abundant, and most of these emotions are transmitted in text form.Text sentiment classification research mainly includes three methods based on sentiment dictionaries, machine learning and deep learning.In recent years, many deep learning-based works have used TextCNN (text convolution neural network) to extract text semantic information for text sentiment analysis.However, TextCNN only considers the length of the sentence when extracting semantic information.It ignores the semantic features between word vectors and only considers the maximum feature value of the feature image in the pooling layer without considering other information.Therefore, in this paper, we propose a convolutional neural network based on multiple convolutions and pooling for text sentiment classification (variable convolution and pooling convolution neural network, VCPCNN).There are three contributions in this paper.First, a multiconvolution and pooling neural network is proposed for the TextCNN network structure.Second, four convolution operations are introduced in the word embedding dimension or direction, which are helpful for mining the local features on the semantic dimensions of word vectors.Finally, average pooling is introduced in the pooling layer, which is beneficial for saving the important feature information of the extracted features.The verification test was carried out on four emotional datasets, including English emotional polarity, Chinese emotional polarity, Chinese subjective and objective emotion and Chinese multicategory.Our apporach is effective in that its result was up to 1.97% higher than that of the TextCNN network.
Chaos Based Cryptographic Pseudo-Random Number Generator Template with Dynamic State Change

This article presents a configurable, high-throughput pseudo-random number generator template targeting cryptographic applications. The template is parameterized using a chaotic map that generates data, an entropy builder that is used to periodically change the parameters of the map and a parameter change interval, which is the number of iterations after which the entropy builder will change the generator’s parameters. The system is implemented in C++ and evaluated using the TestU01 and NIST RNG statistical tests. The same implementation is used for a stream cipher that can encrypt and decrypt PNG images. A Monte-Carlo analysis of the seed space was performed. Results show that for certain combinations of maps and entropy builders, more than 90% of initial states (seeds) tested pass all statistical randomness tests. Also, the throughput is large enough so that a 8 K color image can be encrypted in 2 s on a modern laptop CPU (exact specifications are given in the paper). The conclusion is that chaotic maps can be successfully used as a building block for cryptographic random number generators.
Segmentation and Recognition for Historical Tibetan Document Images

As a shining pearl in traditional Tibetan culture, historical Tibetan documents have received extensive attention from historians, linguists and Buddhist scholars. These documents are converted into digital form using Tibetan document segmentation and recognition methods. The document digitization is of great significance for the research, protection and inheritance of Tibetan history. This paper proposes an overall segmentation and recognition framework for historical Tibetan document images. Firstly, the historical Tibetan document image is preprocessed to correct imbalanced illumination, tilt and noises, and is further transformed into the binarized image. Secondly, we propose a layout segmentation method based on block projection to segment Tibetan document images into texts, lines and frames. Thirdly, in order to solve the problems of touching strokes between text-lines and curvilinear text-lines, we present a text-line segmentation method based on graph model for historical Tibetan text-line segmentation. Lastly, we present a touching segmentation method to segment touching Tibetan character string, and then recognize Tibetan characters. Experimental results show our proposed methods on layout segmentation, text-line segmentation and touching character string segmentation, achieve the satisfactory performance. The proposed methods can also be applied to other fonts in Tibetan font family.
An Antiquity to the contemporary of Secret Sharing Scheme

Secret Sharing (SS) scheme divides a secret image into shadow images (referred to as shares) in a way that requires the shadows in a certain privileged coalition for the secret reconstruction. However, the secret image cannot be revealed if they are not combined in a prescribed way. In this paper, the evolution of the traditional secret sharing scheme to the contemporary of secret sharing scheme has been presented based on its technology adoption. The basic comparison between the traditional secret sharing scheme and Visual cryptography is presented. The image-based share generation process has been explained in the context of pixel sharing patterns and its process. The reconstruction image is obtained by the XOR /OR Boolean operation. The threshold-based share generation schemes are explained mathematically and the comparative analysis of these schemes also presented.
CCTV Surveillance for Unprecedented Violence and Traffic Monitoring

Monitoring of traffic and unprecedented violence has become very much necessary in the urban as well as the rural areas, so the paper attempts to develop a CCTV surveillance for unprecedented violence and traffic monitoring. The proffered method performs the synchronization of the videos and does proper alliance employing the algorithms of motion detection and contour filtering. The steps in motion detection identifies the movement of the objects such as vehicles and unprecedented activities whereas the filtering is used to identify the object itself using its color. The synchronization and the alignment process affords to provide the details of the each objects on the scenario. The proposed algorithm is developed in Java which assists its model using its library that is open source. The validation of the proposed model was carried out using the data set acquired from real time and results were acquired. Moreover the results acquired were compared with the algorithms that were created in the early stages, the comparison proved that the proffered model was capable of obtaining a consecutive quick outcomes of 12.3912 *factor than the existing methods for the resolution of the video used in testing was 240.01x 320.01 with 40 frames per second with cameras of high definition. Further the results acquired were computed to run the application of the embedded CPU and the GPU processors.
Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data

This paper investigates the intriguing question of whether we can create learning algorithms that automatically generate training data, learning environments, and curricula in order to help AI agents rapidly learn. We show that such algorithms are possible via Generative Teaching Networks (GTNs), a general approach that is, in theory, applicable to supervised, unsupervised, and reinforcement learning, although our experiments only focus on the supervised case. GTNs are deep neural networks that generate data and/or training environments that a learner (e.g. a freshly initialized neural network) trains on for a few SGD steps before being tested on a target task. We then differentiate through the entire learning process via meta-gradients to update the GTN parameters to improve performance on the target task. GTNs have the beneficial property that they can theoretically generate any type of data or training environment, making their potential impact large. This paper introduces GTNs, discusses their potential, and showcases that they can substantially accelerate learning. We also demonstrate a practical and exciting application of GTNs: accelerating the evaluation of candidate architectures for neural architecture search (NAS), which is rate-limited by such evaluations, enabling massive speed-ups in NAS. GTN-NAS improves the NAS state of the art, finding higher performing architectures when controlling for the search proposal mechanism. GTN-NAS also is competitive with the overall state of the art approaches, which achieve top performance while using orders of magnitude less computation than typical NAS methods. Speculating forward, GTNs may represent a first step toward the ambitious goal of algorithms that generate their own training data and, in doing so, open a variety of interesting new research questions and directions.
Demystifying Distributed Ledger Technologies: Limits, Challenges, and Potentials in the Energy Sector

The success of the sustainable transformation of the energy sector, both in terms of planning and operation, relies on new entities, business models, and technologies. The shift from a relatively small number of centralized bulk producers and single direction energy flow to a decentralized multi-actor renewable system with a two-way flow of energy and multi-way flow of information needs to be accompanied by new technological solutions. Blockchain and other Distributed Ledger Technologies (DLT) represent a new technology for the energy sector, creating both opportunities and challenges for different aspects of energy systems, such as energy production, peer-to-peer (P2P) energy markets, green certificate registries, etc. Due to its decentralized nature and no need for intermediaries, DLT can facilitate energy democratization processes and decentralized energy production. In this paper, we present a systematic review of DLT principles, its theoretical background, and the most notable implementations, as well as an in-depth analysis of representative research projects and companies researching DLT use cases in the energy sector, taking into consideration technical aspects of DLT. We provide an insight into the benefits and limitations of DLT and identify technical challenges that need to be solved to enable widespread usage of DLT in energy systems. Additionally, we provide suggestions and guidelines for implementing DLT in different categories of use cases in the energy sector.
PARADE: Passage Representation Aggregation forDocument Reranking

Pre-trained transformer models, such as BERT and T5, have shown to be highly effective at ad hoc passage and document ranking. Due to the inherent sequence length limits of these models, they need to process document passages one at a time rather than processing the entire document sequence at once. Although several approaches for aggregating passage-level signals into a document-level relevance score have been proposed, there has yet to be an extensive comparison of these techniques. In this work, we explore strategies for aggregating relevance signals from a document’s passages into a final ranking score. We find that passage representation aggregation techniques can significantly improve over score aggregation techniques proposed in prior work, such as taking the maximum passage score. We call this new approach PARADE. In particular, PARADE can significantly improve results on collections with broad information needs where relevance signals can be spread throughout the document (such as TREC Robust04 and GOV2). Meanwhile, less complex aggregation techniques may work better on collections with an information need that can often be pinpointed to a single passage (such as TREC DL and TREC Genomics). We also conduct efficiency analyses and highlight several strategies for improving transformer-based aggregation.
Recent Progress in Sensing and Computing Techniques for Human Activity Recognition and Motion Analysis

The recent scientific and technical advances in Internet of Things (IoT) based pervasive sensing and computing have created opportunities for the continuous monitoring of human activities for different purposes. The topic of human activity recognition (HAR) and motion analysis, due to its potentiality in human–machine interaction (HMI), medical care, sports analysis, physical rehabilitation, assisted daily living (ADL), children and elderly care, has recently gained increasing attention. The emergence of some novel sensing devices featuring miniature size, a light weight, and wireless data transmission, the availability of wireless communication infrastructure, the progress of machine learning and deep learning algorithms, and the widespread IoT applications has promised new opportunities for a significant progress in this particular field. Motivated by a great demand for HAR-related applications and the lack of a timely report of the recent contributions to knowledge in this area, this investigation aims to provide a comprehensive survey and in-depth analysis of the recent advances in the diverse techniques and methods of human activity recognition and motion analysis. The focus of this investigation falls on the fundamental theories, the innovative applications with their underlying sensing techniques, data fusion and processing, and human activity classification methods. Based on the state-of-the-art, the technical challenges are identified, and future perspectives on the future rich, sensing, intelligent IoT world are given in order to provide a reference for the research and practices in the related fields.
Test-Time Unsupervised Domain Adaptation

Convolutional neural networks trained on publicly available medical imaging datasets (source domain) rarely generalise to different scanners or acquisition protocols (target domain). This motivates the active field of domain adaptation. While some approaches to the problem require labelled data from the target domain, others adopt an unsupervised approach to domain adaptation (UDA). Evaluating UDA methods consists of measuring the model’s ability to generalise to unseen data in the target domain. In this work, we argue that this is not as useful as adapting to the test set directly. We therefore propose an evaluation framework where we perform test-time UDA on each subject separately. We show that models adapted to a specific target subject from the target domain outperform a domain adaptation method which has seen more data of the target domain but not this specific target subject. This result supports the thesis that unsupervised domain adaptation should be used at test-time, even if only using a single target-domain subject.
A survey on knowledge graph embeddings with literals: Which model links better literal-ly?

Knowledge Graphs (KGs) are composed of structured information about a particular domain in the form of entities and relations. In addition to the structured information KGs help in facilitating interconnectivity and interoperability between different resources represented in the Linked Data Cloud. KGs have been used in a variety of applications such as entity linking, question answering, recommender systems, etc. However, KG applications suffer from high computational and storage costs. Hence, there arises the necessity for a representation able to map the high dimensional KGs into low dimensional spaces, i.e., embedding space, preserving structural as well as relational information. This paper conducts a survey of KG embedding models which not only consider the structured information contained in the form of entities and relations in a KG but also its unstructured information represented as literals such as text, numerical values, images, etc. Along with a theoretical analysis and comparison of the methods proposed so far for generating KG embeddings with literals, an empirical evaluation of the different methods under identical settings has been performed for the general task of link prediction.
Unsupervised Reference-Free Summary Quality Evaluation via Contrastive Learning

Evaluation of a document summarization system has been a critical factor to impact the success of the summarization task. Previous approaches, such as ROUGE, mainly consider the informativeness of the assessed summary and require human-generated references for each test summary. In this work, we propose to evaluate the summary qualities without reference summaries by unsupervised contrastive learning. Specifically, we design a new metric which covers both linguistic qualities and semantic informativeness based on BERT. To learn the metric, for each summary, we construct different types of negative samples with respect to different aspects of the summary qualities, and train our model with a ranking loss. Experiments on Newsroom and CNN/Daily Mail demonstrate that our new evaluation method outperforms other metrics even without reference summaries. Furthermore, we show that our method is general and transferable across datasets.
Natural Language Rationales with Full-Stack Visual Reasoning: From Pixels to Semantic Frames to Commonsense Graphs

Natural language rationales could provide intuitive, higher-level explanations that are easily understandable by humans, complementing the more broadly studied lower-level explanations based on gradients or attention weights. We present the first study focused on generating natural language rationales across several complex visual reasoning tasks: visual commonsense reasoning, visual-textual entailment, and visual question answering. The key challenge of accurate rationalization is comprehensive image understanding at all levels: not just their explicit content at the pixel level, but their contextual contents at the semantic and pragmatic levels. We present RationaleˆVT Transformer, an integrated model that learns to generate free-text rationales by combining pretrained language models with object recognition, grounded visual semantic frames, and visual commonsense graphs. Our experiments show that free-text rationalization is a promising research direction to complement model interpretability for complex visual-textual reasoning tasks. In addition, we find that integration of richer semantic and pragmatic visual features improves visual fidelity of rationales.