Reviewerly/exHarmony huggingface dataset:
The influence of HRM practices and employees’ satisfaction on intention to leave

This study aims to examine the influence of HRM practices (employees' empowerment, job security, and employees' participation) and employees' satisfaction on intention to leave and the mediating role of employees' satisfaction from the perspective of faculty members in private universities in Kuwait.The survey questionnaire was gathered from 261 faculty members who were working in four private universities in Kuwait.The study used the software SmartPLS (version 3.2.8) to test the study hypotheses.The findings indicate that HRM practices (employees' empowerment and job security) and employees' satisfaction had negative effects on intention to leave, and employees' satisfaction partially mediated the relationship between HRM practices (employees' empowerment and job security) and intention to leave.Future studies may include other samples as public universities or other sectors in Kuwait such as the education sector or health care sector.
A novel credential protocol for protecting personal attributes in blockchain

This paper proposes a novel user-centric and privacy-preserving credential scheme over the blockchain. The proposed protocol allows users to access services without revealing sensitive attributes. This new paradigm is based on an efficient short signature, which uses pairing and self-blindable credentials that are verifiable on the blockchain. Our scheme achieves the advanced features of anonymity, unlinkability, and untraceability of users. Moreover, confidentiality of users’ attributes and unforgeability of their credentials are met. We provide security proofs and a real-world use-case where the protocol can be applied. To empirically assess the performance of our solution, the cryptographic components and communications between the various involved actors are implemented using GO and Java. In addition, an implementation for an online trading use case based on Hyperledger Fabric is provided. Finally, we prove the efficiency of our work by presenting some experimental results and exhibiting comparisons with known traditional credentials schemes.
FedRec: Privacy-Preserving News Recommendation with Federated Learning

News recommendation aims to display news articles to users based on their personal interest. Existing news recommendation methods rely on centralized storage of user behavior data for model training, which may lead to privacy concerns and risks due to the privacy-sensitive nature of user behaviors. In this paper, we propose a privacy-preserving method for news recommendation model training based on federated learning, where the user behavior data is locally stored on user devices. Our method can leverage the useful information in the behaviors of massive users to train accurate news recommendation models and meanwhile remove the need to centralized storage of them. More specifically, on each user device we keep a local copy of the news recommendation model, and compute gradients of the local model based on the user behaviors in this device. The local gradients from a group of randomly selected users are uploaded to server, which are further aggregated to update the global model in the server. Since the model gradients may contain some implicit private information, we apply local differential privacy (LDP) to them before uploading for better privacy protection. The updated global model is then distributed to each user device for local model update. We repeat this process for multiple rounds. Extensive experiments on a real-world dataset show the effectiveness of our method in news recommendation model training with privacy protection.
Going remote: Maintaining normalcy in our pathology laboratories during the COVID‐19 pandemic

To minimize disruptions to the practice of academic pathology, one can leverage modern information and communication technologies to create virtual connections. The overall sentiment after switching to a predominantly remote environment has been overwhelmingly positive, and this presents a novel opportunity to explore additional digital workflows.
Fast-SCNN: Fast Semantic Segmentation Network.

The encoder-decoder framework is state-of-the-art for offline semantic image segmentation. Since the rise in autonomous systems, real-time computation is increasingly desirable. In this paper, we introduce fast segmentation convolutional neural network (Fast-SCNN), an above real-time semantic segmentation model on high resolution image data (1024x2048px) suited to efficient computation on embedded devices with low memory. Building on existing two-branch methods for fast segmentation, we introduce our `learning to downsample' module which computes low-level features for multiple resolution branches simultaneously. Our network combines spatial detail at high resolution with deep features extracted at lower resolution, yielding an accuracy of 68.0% mean intersection over union at 123.5 frames per second on Cityscapes. We also show that large scale pre-training is unnecessary. We thoroughly validate our metric in experiments with ImageNet pre-training and the coarse labeled data of Cityscapes. Finally, we show even faster computation with competitive results on subsampled inputs, without any network modifications.
Graph Policy Gradients for Large Scale Robot Control

In this paper, we consider the problem of learning policies to control a large number of homogeneous robots. To this end, we propose a new algorithm we call Graph Policy Gradients (GPG) that exploits the underlying graph symmetry among the robots. The curse of dimensionality one encounters when working with a large number of robots is mitigated by employing a graph convolutional neural (GCN) network to parametrize policies for the robots. The GCN reduces the dimensionality of the problem by learning filters that aggregate information among robots locally, similar to how a convolutional neural network is able to learn local features in an image. Through experiments on formation flying, we show that our proposed method is able to scale better than existing reinforcement methods that employ fully connected networks. More importantly, we show that by using our locally learned filters we are able to zero-shot transfer policies trained on just three robots to over hundred robots.
Pulse-timing symmetry breaking in an excitable optical system with delay

Excitable systems with delayed feedback are important in areas from biology to neuroscience and optics. They sustain multistable pulsing regimes with different number of equidistant pulses in the feedback loop. Experimentally and theoretically, we report on the pulse-timing symmetry breaking of these regimes in an optical system. A bifurcation analysis unveils that this originates in a resonance phenomenon and that symmetry-broken states are stable in large regions of the parameter space. These results have impact in photonics for e.g. optical computing and versatile sources of optical pulses.
Streaming transformer ASR with blockwise synchronous inference

The Transformer self-attention network has shown promising performance as an alternative to recurrent neural networks in end-to-end (E2E) automatic speech recognition (ASR) systems. However, Transformer has a drawback in that the entire input sequence is required to compute both self-attention and source--target attention. In this paper, we propose a novel blockwise synchronous beam search algorithm based on blockwise processing of encoder to perform streaming E2E Transformer ASR. In the beam search, encoded feature blocks are synchronously aligned using a block boundary detection technique, where a reliability score of each predicted hypothesis is evaluated based on the end-of-sequence and repeated tokens in the hypothesis. Evaluations of the HKUST and AISHELL-1 Mandarin, LibriSpeech English, and CSJ Japanese tasks show that the proposed streaming Transformer algorithm outperforms conventional online approaches, including monotonic chunkwise attention (MoChA), especially when using the knowledge distillation technique. An ablation study indicates that our streaming approach contributes to reducing the response time, and the repetition criterion contributes significantly in certain tasks. Our streaming ASR models achieve comparable or superior performance to batch models and other streaming-based Transformer methods in all tasks considered.
Dynamic Longest Common Substring in Polylogarithmic Time

The longest common substring problem consists in finding a longest string that appears as a (contiguous) substring of two input strings. We consider the dynamic variant of this problem, in which we are to maintain two dynamic strings S and T, each of length at most n, that undergo substitutions of letters, in order to be able to return a longest common substring after each substitution. Recently, Amir et al. [ESA 2019] presented a solution for this problem that needs only 𝒪(n^(2/3)) time per update. This brought the challenge of determining whether there exists a faster solution with polylogarithmic update time, or (as is the case for other dynamic problems), we should expect a polynomial (conditional) lower bound. We answer this question by designing a significantly faster algorithm that processes each substitution in amortized log^𝒪(1) n time with high probability. Our solution relies on exploiting the local consistency of the parsing of a collection of dynamic strings due to Gawrychowski et al. [SODA 2018], and on maintaining two dynamic trees with labeled bicolored leaves, so that after each update we can report a pair of nodes, one from each tree, of maximum combined weight, which have at least one common leaf-descendant of each color. We complement this with a lower bound of Ω(log n/ log log n) for the update time of any polynomial-size data structure that maintains the LCS of two dynamic strings, even allowing amortization and randomization.
One Sample Stochastic Frank-Wolfe.

One of the beauties of the projected gradient descent method lies in its rather simple mechanism and yet stable behavior with inexact, stochastic gradients, which has led to its wide-spread use in many machine learning applications. However, once we replace the projection operator with a simpler linear program, as is done in the Frank-Wolfe method, both simplicity and stability take a serious hit. The aim of this paper is to bring them back without sacrificing the efficiency. In this paper, we propose the first one-sample stochastic Frank-Wolfe algorithm, called 1-SFW, that avoids the need to carefully tune the batch size, step size, learning rate, and other complicated hyper parameters. In particular, 1-SFW achieves the optimal convergence rate of $\mathcal{O}(1/\epsilon^2)$ for reaching an $\epsilon$-suboptimal solution in the stochastic convex setting, and a $(1-1/e)-\epsilon$ approximate solution for a stochastic monotone DR-submodular maximization problem. Moreover, in a general non-convex setting, 1-SFW finds an $\epsilon$-first-order stationary point after at most $\mathcal{O}(1/\epsilon^3)$ iterations, achieving the current best known convergence rate. All of this is possible by designing a novel unbiased momentum estimator that governs the stability of the optimization process while using a single sample at each iteration.
Contrast Enhancement of Medical Images Using Statistical Methods with Image Processing Concepts

The process of generating pictorial representations of the inner human body for medical analysis and intervention is called medical imaging (MI). MI strives for revealing inner structures concealed by the existence of bones and skin to identify diseases in order to be treated properly. MI can be achieved through the use of various imaging systems, where each has different technology necessities. The output of these imaging systems is digital images that have meaningful medical information. Such images, however, do not own a high perceived quality due to the existence of image degradations. Despite modern technology advancement, various medical systems are still producing images with poor contrast owing to incorrect settings and device limitations. Such images must be processed efficiently to become clearer for better analysis, understanding and interpretation. In this study, a simple algorithm is developed, where it utilizes a combination of image processing concepts and statistical methods. The developed algorithm is appraised with a dataset of real-degraded low-contrast images only, assessed with one specialized no-reference metric and compared with four known contrast enhancement methods. From the conducted experiments, the proposed algorithm showed promising performances, since it produced acceptable-quality results rapidly and outperformed the comparison methods in different important aspects.
Real-Time Tweet Analytics Using Hybrid Hashtags on Twitter Big Data Streams

Twitter is a microblogging platform that generates large volumes of data with high velocity. This daily generation of unbounded and continuous data leads to Big Data streams that often require real-time distributed and fully automated processing. Hashtags, hyperlinked words in tweets, are widely used for tweet topic classification, retrieval, and clustering. Hashtags are used widely for analyzing tweet sentiments where emotions can be classified without contexts. However, regardless of the wide usage of hashtags, general tweet topic classification using hashtags is challenging due to its evolving nature, lack of context, slang, abbreviations, and non-standardized expression by users. Most existing approaches, which utilize hashtags for tweet topic classification, focus on extracting hashtag concepts from external lexicon resources to derive semantics. However, due to the rapid evolution and non-standardized expression of hashtags, the majority of these lexicon resources either suffer from the lack of hashtag words in their knowledge bases or use multiple resources at once to derive semantics, which make them unscalable. Along with scalable and automated techniques for tweet topic classification using hashtags, there is also a requirement for real-time analytics approaches to handle huge and dynamic flows of textual streams generated by Twitter. To address these problems, this paper first presents a novel semi-automated technique that derives semantically relevant hashtags using a domain-specific knowledge base of topic concepts and combines them with the existing tweet-based-hashtags to produce Hybrid Hashtags. Further, to deal with the speed and volume of Big Data streams of tweets, we present an online approach that updates the preprocessing and learning model incrementally in a real-time streaming environment using the distributed framework, Apache Storm. Finally, to fully exploit the batch and stream environment performance advantages, we propose a comprehensive framework (Hybrid Hashtag-based Tweet topic classification (HHTC) framework) that combines batch and online mechanisms in the most effective way. Extensive experimental evaluations on a large volume of Twitter data show that the batch and online mechanisms, along with their combination in the proposed framework, are scalable, efficient, and provide effective tweet topic classification using hashtags.
AI-Powered Oracle Bone Inscriptions Recognition and Fragments Rejoining

Oracle Bone Inscriptions (OBI) research is very meaningful for both history and literature. In this paper, we introduce our contributions in AI-Powered Oracle Bone (OB) fragments rejoining and OBI recognition. (1) We build a real-world dataset OB-Rejoin, and propose an effective OB rejoining algorithm which yields a top-10 accuracy of 98.39%. (2) We design a practical annotation software to facilitate OBI annotation, and build OracleBone-8000, a large-scale dataset with character-level annotations. We adopt deep learning based scene text detection algorithms for OBI localization, which yield an F-score of 89.7%. We propose a novel deep template matching algorithm for OBI recognition which achieves an overall accuracy of 80.9%. Since we have been cooperating closely with OBI domain experts, our effort above helps advance their research. The resources of this work are available at https://github.com/chongshengzhang/OracleBone.
A Convolutional Neural Network based system for Colorectal cancer segmentation on MRI images

The aim of the study is to present a new Convolutional Neural Network (CNN) based system for the automatic segmentation of the colorectal cancer. The algorithm implemented consists of several steps: a pre-processing to normalize and highlights the tumoral area, the classification based on CNNs, and a post-processing aimed at reducing false positive elements. The classification is performed using three CNNs: each of them classifies the same regions of interest acquired from three different MR sequences. The final segmentation mask is obtained by a majority voting. Performances were evaluated using a semi-automatic segmentation revised by an experienced radiologist as reference standard. The system obtained Dice Similarity Coefficient (DSC) of 0.60, Precision (Pr) of 0.76 and Recall (Re) of 0.55 on the testing set. After applying the leave-one-out validation, we obtained a median DSC=0.58, Pr=0.74, Re=0.54. The promising results obtained by this system, if validated on a larger dataset, could strongly improve personalized medicine.
3D reconstruction of objects with occlusion and surface reflection using a dual monocular structured light system

Three-dimensional (3D) vision plays an important role in industrial vision, where occlusion and reflection have made it challenging to reconstruct the entire application scene. In this paper, we present a novel 3D reconstruction framework to solve the occlusion and reflection reconstruction issues in complex scenes. A dual monocular structured light system is adopted to obtain the point cloud from different viewing angles to fill the missing points in the complex scenes. To enhance the efficiency of point cloud fusion, we create a decision map that is able to avoid the reconstruction of repeating regions of the left and right system. Additionally, a compensation method based on the decision map is proposed for reducing the reconstruction error of the dual monocular system in the fusion area. Gray-code and phase-shifting patterns are utilized to encode the complex scenes, while the phase-jumping problem at the phase boundary is avoided by designing a unique compensation function. Various experiments including accuracy evaluation, comparison with the traditional fusion algorithm, and the reconstruction of real complex scenes are conducted to validate the method’s accuracy and the robustness to the shiny surface and occlusion reconstruction problem.
Drug cryptomarket futures: structure, function and evolution in response to law enforcement actions

Developments in encryption technology have facilitated new forms of digital drug trading. In this chapter, we provide an overview of cryptomarkets and the technologies that underpin them. Our examination of the history of cryptomarket development shows they have evolved in response to enforcement operations and threats from within the community, yet policy responses, whilst increasingly varied and sophisticated, remain in line with prohibition and suppression. Our evaluation of the transformative potential of cryptomarkets on the global drugs trade suggests it is limited to specific drugs, countries and forms of violence, and its impact will be greater in countries where drug consumption is highest rather than producer countries like Colombia or Afghanistan. Whilst the literature is emergent, we argue that it demands better scrutiny from law enforcement and policy makers in designing appropriate responses to the online drugs trade to avoid unintentionally increasing harms both for drug takers and the public.
Effect of risk of using computerized AIS on external auditor's work quality in Yemen

This study aimed to identify the effect of the risks of using computerized AIS in facilities subject to auditing on external auditor's work quality. The study was applied to Yemeni external auditors, with a targeted sample size of 120 people who were randomly selected. Data were collected through questionnaires. The collected data were processed using SPSS version 23. The results of this study revealed a statistically significant effect of risk associated with the use of computerized AIS (human risks, environmental risks, and viruses’ risks) on external auditor's work quality. However, human risks are the most risk to the external auditor in the audited facilities. From a practical standpoint, AIS and IT auditors, and facilities subject to auditing all stand to gain from the results of this study.
Knowledge Graphs and COVID-19: Opportunities, Challenges, and Implementation

The COVID-19 pandemic has been truly global and multidimensional in scope, with ramifications extending well beyond health.Yet, unlike previous crises, there is hope that timely release of relevant data sets, as well as advents in AI (artificial intelligence) technology, could lead to compressed timescales in finding a vaccine or cure.Despite the huge existing body of academic literature on the coronavirus family, searching through such a corpus, including new research that has emerged in the wake of the crisis, is a daunting task even for experts.Simple keyword search over such corpora is insufficient for experts who want answers to questions that require linking together multiple pieces of information across documents.In this article, we review an innovative AI technology called a knowledge graph (KG) that could be used to fulfill such complex information needs.We detail the potential for KGs to play an important role in the fight against COVID-19.We also cover challenges and ongoing collaborative implementations of COVID-19 KGs in industry and academia.
WNUT-2020 Task 1 Overview: Extracting Entities and Relations from Wet Lab Protocols

This paper presents the results of the wet lab information extraction task at WNUT 2020.This task consisted of two sub tasks: (1) a Named Entity Recognition (NER) task with 13 participants and (2) a Relation Extraction (RE) task with 2 participants.We outline the task, data annotation process, corpus statistics, and provide a high-level overview of the participating systems for each sub task.
Semi-supervised Formality Style Transfer using Language Model Discriminator and Mutual Information Maximization

Formality style transfer is the task of converting informal sentences to grammaticallycorrect formal sentences, which can be used to improve performance of many downstream NLP tasks.In this work, we propose a semisupervised formality style transfer model that utilizes a language model-based discriminator to maximize the likelihood of the output sentence being formal, which allows us to use maximization of token-level conditional probabilities for training.We further propose to maximize mutual information between source and target styles as our training objective instead of maximizing the regular likelihood that often leads to repetitive and trivial generated responses.Experiments showed that our model outperformed previous state-of-theart baselines significantly in terms of both automated metrics and human judgement.We further generalized our model to unsupervised text style transfer task, and achieved significant improvements on two benchmark sentiment style transfer datasets.