Reviewerly/exHarmony huggingface dataset:
APDC-Net: Attention Pooling-Based Convolutional Network for Aerial Scene Classification

Deep learning methods have boosted the performance of a series of visual tasks. However, the aerial image scene classification remains challenging. The object distribution and spatial arrangement in aerial scenes are often more complicated than in natural image scenes. Possible solutions include highlighting local semantics relevant to the scene label and preserving more discriminative features. To tackle this challenge, in this letter, we propose an attention pooling-based dense connected convolutional network (APDC-Net) for aerial scene classification. First, it uses a simplified dense connection structure as the backbone to preserve features from different levels. Then, we propose a trainable pooling to down-sample the feature maps and to enhance the local semantic representation capability. Finally, we introduce a multi-level supervision strategy, so that features from different levels are all allowed to supervise the training process directly. Exhaustive experiments on three aerial scene classification benchmarks demonstrate that our proposed APDC-Net outperforms other state-of-the-art methods with much fewer parameters and validate the effectiveness of our attention-based pooling and multi-level supervision strategy.
A Deep Learning-Based End-to-End Composite System for Hand Detection and Gesture Recognition

Recent research on hand detection and gesture recognition has attracted increasing interest due to its broad range of potential applications, such as human-computer interaction, sign language recognition, hand action analysis, driver hand behavior monitoring, and virtual reality. In recent years, several approaches have been proposed with the aim of developing a robust algorithm which functions in complex and cluttered environments. Although several researchers have addressed this challenging problem, a robust system is still elusive. Therefore, we propose a deep learning-based architecture to jointly detect and classify hand gestures. In the proposed architecture, the whole image is passed through a one-stage dense object detector to extract hand regions, which, in turn, pass through a lightweight convolutional neural network (CNN) for hand gesture recognition. To evaluate our approach, we conducted extensive experiments on four publicly available datasets for hand detection, including the Oxford, 5-signers, EgoHands, and Indian classical dance (ICD) datasets, along with two hand gesture datasets with different gesture vocabularies for hand gesture recognition, namely, the LaRED and TinyHands datasets. Here, experimental results demonstrate that the proposed architecture is efficient and robust. In addition, it outperforms other approaches in both the hand detection and gesture classification tasks.
Concretely-Efficient Zero-Knowledge Arguments for Arithmetic Circuits and Their Application to Lattice-Based Cryptography

In this work we present a new interactive Zero-Knowledge Argument of knowledge for general arithmetic circuits. Our protocol is based on the “MPC-in-the-head”-paradigm of Ishai et al. (STOC 2009) and follows the recent “MPC-in-the-head with Preprocessing” as proposed by Katz, Kolesnikov and Wang (ACM CCS 2018). However, in contrast to Katz et al. who used the “cut-and-choose” approach for pre-processing, we show how to incorporate the well-known “sacrificing” paradigm into “MPC-in-the-head”, which reduces the proof size when working over arithmetic circuits. Our argument system uses only lightweight symmetric-key primitives and utilizes a simplified version of the so-called SPDZ-protocol. Based on specific properties of our protocol we then show how it can be used to construct an efficient Zero-Knowledge Argument of Knowledge for instances of the Short Integer Solution (SIS) problem. We present different protocols that are tailored to specific uses of SIS, while utilizing the advantages of our scheme. In particular, we present a variant of our argument system that allows the parties to sample the circuit “on the fly”, which may be of independent interest. We furthermore implemented our Zero-Knowledge argument for SIS and show that using our protocols it is possible to run a complete interactive proof, even for general SIS instances which result in a circuit with $${>}10^6$$ gates, in less than 0.5 s.
Malaysian Muslim investors’ behaviour towards the blockchain-based Bitcoin cryptocurrency market

Purpose This study aims to examine the factors that could increase the investment in the Bitcoin market among Malaysian Muslim communities. Design/methodology/approach The study used a survey questionnaire to collect data for a sample of 200 Muslim respondents in Malaysia. Subsequently, the collected data was analysed using structural equation modelling, as well as basic descriptive statistics and one sample test. Findings The findings showed that compatibility, awareness and facilitating conditions have a significant impact on Malaysian Muslim communities’ investment in the Bitcoin market. Originality/value It is worth emphasizing that this is one of the earliest studies that examine the Muslim communities’ perception and willingness to invest in the Bitcoin market. Hence, it is expected to be one of the foundations for future studies in this area.
The Dialogue Dodecathlon: Open-Domain Knowledge and Image Grounded Conversational Agents

We introduce dodecaDialogue: a set of 12 tasks that measures if a conversational agent can communicate engagingly with personality and empathy, ask questions, answer questions by utilizing knowledge resources, discuss topics and situations, and perceive and converse about images. By multi-tasking on such a broad large-scale set of data, we hope to both move towards and measure progress in producing a single unified agent that can perceive, reason and converse with humans in an open-domain setting. We show that such multi-tasking improves over a BERT pre-trained baseline, largely due to multi-tasking with very large dialogue datasets in a similar domain, and that the multi-tasking in general provides gains to both text and image-based tasks using several metrics in both the fine-tune and task transfer settings. We obtain state-of-the-art results on many of the tasks, providing a strong baseline for this challenge.
Neural Extractive Summarization with Hierarchical Attentive Heterogeneous Graph Network

Sentence-level extractive text summarization is substantially a node classification task of network mining, adhering to the informative components and concise representations.There are lots of redundant phrases between extracted sentences, but it is difficult to model them exactly by the general supervised methods.Previous sentence encoders, especially BERT, specialize in modeling the relationship between source sentences.While, they have no ability to consider the overlaps of the target selected summary, and there are inherent dependencies among target labels of sentences.In this paper, we propose HAHSum (as shorthand for Hierarchical Attentive Heterogeneous Graph for Text Summarization), which well models different levels of information, including words and sentences, and spotlights redundancy dependencies between sentences.Our approach iteratively refines the sentence representations with redundancy-aware graph and delivers the label dependencies by message passing.Experiments on large scale benchmark corpus (CNN/DM, NYT, and NEWSROOM) demonstrate that HAHSum yields ground-breaking performance and outperforms previous extractive summarizers.
Do Not Mask What You Do Not Need to Mask: A Parser-Free Virtual Try-On

The 2D virtual try-on task has recently attracted a great interest from the research community, for its direct potential applications in online shopping as well as for its inherent and non-addressed scientific challenges. This task requires fitting an in-shop cloth image on the image of a person, which is highly challenging because it involves cloth warping, image compositing, and synthesizing. Casting virtual try-on into a supervised task faces a difficulty: available datasets are composed of pairs of pictures (cloth, person wearing the cloth). Thus, we have no access to ground-truth when the cloth on the person changes. State-of-the-art models solve this by masking the cloth information on the person with both a human parser and a pose estimator. Then, image synthesis modules are trained to reconstruct the person image from the masked person image and the cloth image. This procedure has several caveats: firstly, human parsers are prone to errors; secondly, it is a costly pre-processing step, which also has to be applied at inference time; finally, it makes the task harder than it is since the mask covers information that should be kept such as hands or accessories. In this paper, we propose a novel student-teacher paradigm where the teacher is trained in the standard way (reconstruction) before guiding the student to focus on the initial task (changing the cloth). The student additionally learns from an adversarial loss, which pushes it to follow the distribution of the real images. Consequently, the student exploits information that is masked to the teacher. A student trained without the adversarial loss would not use this information. Also, getting rid of both human parser and pose estimator at inference time allows obtaining a real-time virtual try-on.
A multiple criteria group decision-making approach for the assessment of small and medium-sized enterprise competitiveness

Purpose Small- and medium-sized enterprises (SMEs) have become increasingly important in national and international markets because they contribute to the development of local and national economies. SMEs often face serious challenges when competing with multinational companies. The purpose of this paper is to develop a method for assessing SMEs’ competitiveness. Design/methodology/approach Based on a constructivist epistemology, this study makes an integrated use of cognitive mapping and the measuring attractiveness by a categorical-based evaluation technique (MACBETH). To this end, face-to-face sessions were conducted with a panel of entrepreneurs and senior managers who deal with the challenges of maintaining SME competitiveness every day. The proposed assessment system was tested and validated by the panel members. Findings The methodological processes adopted in this study provide promising results for decision makers seeking to identify the most competitive SMEs. Specifically, the results emphasize, among other points, the importance of innovation and the human dimension to gaining competitive advantages. Research limitations/implications The evaluation system developed in this study is extremely versatile and confirms the usefulness of integrating cognitive mapping and MACBETH to facilitate evaluations of SME competitiveness. However, due to its idiosyncratic and process-oriented nature, generalizations need to be done with caution. Practical implications The proposed method can be valuable to researchers seeking to develop mechanisms for evaluating SMEs’ entrepreneurial performance and include specialized know-how and sensemaking in organizational decision-making processes. Originality/value The integrated use of cognitive maps and MACBETH contributes to a better understanding of how to assess SMEs’ competitiveness. No prior work reporting the use of this dual methodology in this study context has been found.
Framework for designing motivational augmented reality applications in vocational education and training

One of the greatest advantages of augmented reality (AR) in education is that AR increases student motivation. Nevertheless, there is a gap between the research on student motivation in AR and the definition of frameworks to inform and guide the design and development of AR applications that effectively support student motivation. In this paper, we attempt to bridge that gap as we introduce and evaluate a framework for designing motivational AR applications. Our framework has been built upon three theoretical foundations: motivational design, universal design for learning and co-creation. The evaluation study was conducted with 58 chemistry students enrolled in the vocational education and training (VET) program for Laboratory Operations, and we found that the framework not only effectively supports the four dimensions of Keller's (2010) ARCS (attention, relevance, confidence and satisfaction) model of motivation, but also demonstrates exceptional results in the Attention and Confidence dimensions of motivation.
Unsupervised Learning of Object Keypoints for Perception and Control

The study of object representations in computer vision has primarily focused on developing representations that are useful for image classification, object detection, or semantic segmentation as downstream tasks. In this work we aim to learn object representations that are useful for control and reinforcement learning (RL). To this end, we introduce Transporter, a neural network architecture for discovering concise geometric object representations in terms of keypoints or image-space coordinates. Our method learns from raw video frames in a fully unsupervised manner, by transporting learnt image features between video frames using a keypoint bottleneck. The discovered keypoints track objects and object parts across long time-horizons more accurately than recent similar methods. Furthermore, consistent long-term tracking enables two notable results in control domains -- (1) using the keypoint co-ordinates and corresponding image features as inputs enables highly sample-efficient reinforcement learning; (2) learning to explore by controlling keypoint locations drastically reduces the search space, enabling deep exploration (leading to states unreachable through random action exploration) without any extrinsic rewards.
Being the Victim of Intimate Partner Violence in Virtual Reality: First- Versus Third-Person Perspective

Immersive virtual reality is widely used for research and clinical purposes. Here we explored the impact of an immersive virtual scene of intimate partner violence experienced from the victim’s perspective (first person), as opposed to witnessing it as an observer (third person). We are ultimately interested on the potential of this approach to rehabilitate batterers and in understanding mechanisms underlying this process. For this, non-offender men experienced the scene either from the perspective of the victim’s virtual body (a female avatar), which moved synchronously with the participants’ real movements, or from the perspective of an observer, while we recorded their behavior and physiological responses. We also evaluated through questionnaires, interviews and implicit association tests their subjective impressions and potential pre/post changes in implicit gender bias following the experience. We found that in all participants, regardless of perspective, the magnitude of the physiological reactions to virtual threatening stimuli was related to how vulnerable they felt for being a woman, the sensation that they could be assaulted, how useful the scene could be for batterer rehabilitation, and how different it would have been to experience the scenario on TV. Further, we found that their level of identification with the female avatar correlated with the decrease in prejudice against women. Although the first-person perspective (1PP) facilitated taking the scene personally, generated a sensation of fear, helplessness, and vulnerability, and tended to induce greater behavioral and physiological reactions, we show that the potential for batterer rehabilitation originates from presence and identification with the victim, which in turn is more easily, but not exclusively, achieved through 1PP. This study is relevant for the development of advanced virtual reality tools for clinical purposes.
Knowledge management processes, innovation capability and organizational performance

Purpose The purpose of this study is to introduce a unified framework which integrates knowledge management processes (knowledge creation, sharing, storage and documentation and acquisition), innovation capability (product, process, marketing and organizational and organizational performance (operational, financial and product quality). Then empirically test the effect of knowledge management processes (KMP) on innovation capability (IC), the effect of IC on organizational performance (OP) and the impact of KMP on OP through the mediator (IC). Design/methodology/approach A questionnaire was designed and addressed to the general managers of the Jordanian companies making up the target population. Statistical techniques employed included confirmatory factor analysis and structural equation modeling (SEM) using AMOS 24 to test the hypotheses. Findings The results show that KMP influences IC, IC affects OP and KMP impacts OP through the mediator (IC). Research limitations/implications The use of longitudinal data and comparisons with this study would provide further insights that would assist in generalizing knowledge related to the relationships among KMP, IC and Os' P. Practical implications If organizations fully comprehend KMP and IC, they would be able to implement them successfully, creating value for their companies and improving OP. Originality/value The existing research on KMP and IC is primarily conceptual and descriptive in nature, and empirical research confirming the real impact of KMP when developing IC is lacking. Also. the relationship between innovation capabilities and OP has not been adequately studied. Hence, this study introduces a conceptual framework which integrates KMP, IC and OP and simultaneously empirically tests the relationships among them.
Asynchronous Interaction Aggregation for Action Detection

Understanding interaction is an essential part of video action detection. We propose the Asynchronous Interaction Aggregation network (AIA) that leverages different interactions to boost action detection. There are two key designs in it: one is the Interaction Aggregation structure (IA) adopting a uniform paradigm to model and integrate multiple types of interaction; the other is the Asynchronous Memory Update algorithm (AMU) that enables us to achieve better performance by modeling very long-term interaction dynamically without huge computation cost. We provide empirical evidence to show that our network can gain notable accuracy from the integrative interactions and is easy to train end-to-end. Our method reports the new state-of-the-art performance on AVA dataset, with 3.7 mAP gain ( $$12.6\%$$ relative improvement) on validation split comparing to our strong baseline. The results on datasets UCF101-24 and EPIC-Kitchens further illustrate the effectiveness of our approach. Source code will be made public at: https://github.com/MVIG-SJTU/AlphAction .
DeepFakes Evolution: Analysis of Facial Regions and Fake Detection Performance

Media forensics has attracted a lot of attention in the last years in part due to the increasing concerns around DeepFakes. Since the initial DeepFake databases from the \(\text {1}^{\text {st}}\) generation such as UADFV and FaceForensics++ up to the latest databases of the \(\text {2}^{\text {nd}}\) generation such as Celeb-DF and DFDC, many visual improvements have been carried out, making fake videos almost indistinguishable to the human eye. This study provides an exhaustive analysis of both \(\text {1}^{\text {st}}\) and \(\text {2}^{\text {nd}}\) DeepFake generations in terms of facial regions and fake detection performance. Two different methods are considered in our experimental framework: i) the traditional one followed in the literature and based on selecting the entire face as input to the fake detection system, and ii) a novel approach based on the selection of specific facial regions as input to the fake detection system.Among all the findings resulting from our experiments, we highlight the poor fake detection results achieved even by the strongest state-of-the-art fake detectors in the latest DeepFake databases of the \(\text {2}^{\text {nd}}\) generation, with Equal Error Rate results ranging from 15% to 30%. These results remark the necessity of further research to develop more sophisticated fake detectors.
XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation

Sebastian Ruder, Noah Constant, Jan Botha, Aditya Siddhant, Orhan Firat, Jinlan Fu, Pengfei Liu, Junjie Hu, Dan Garrette, Graham Neubig, Melvin Johnson. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 2021.
Big data and targeted machine learning in action to assist medical decision in the ICU

Historically, personalised medicine has been synonymous with pharmacogenomics and oncology. We argue for a new framework for personalised medicine analytics that capitalises on more detailed patient-level data and leverages recent advances in causal inference and machine learning tailored towards decision support applicable to critically ill patients. We discuss how advances in data technology and statistics are providing new opportunities for asking more targeted questions regarding patient treatment, and how this can be applied in the intensive care unit to better predict patient-centred outcomes, help in the discovery of new treatment regimens associated with improved outcomes, and ultimately how these rules can be learned in real-time for the patient.
Cryptanalysis and Improvement of the Image Encryption Scheme Based on 2D Logistic-Adjusted-Sine Map

In recent years, many scholars have been committed to improving the security and practicability of chaotic image encryption. In addition, their proposed schemes mainly focus on two aspects, new or improved chaotic systems and new or improved encryption processes. However, according to cryptanalysis works of scholars, encryption processes of chaotic image encryption schemes deserve more attentions. In this paper, a recently reported chaotic image encryption scheme named the 2D logistic-adjusted-sine-map-based image encryption scheme is comprehensively investigated, and some security, practicability, and rationality problems are found. Therefore, we first point out these problems existing in the reported encryption scheme and make some improvements in practicability and rationality. Next, under the conditions of chosen plaintext attack, we cryptanalyze it and propose a corresponding attack algorithm. For our attack algorithm, simulation test results show that it can completely recover plain images without knowing any secret key related information. Finally, we also present some possible improvements for the security problems of chaotic image encryption scheme under study.
Token Sales and Initial Coin Offerings: <i>Introduction</i>

Token sales or initial coin offerings (ICOs) are smart contracts on a blockchain designed to raise external finance by issuing tokens or coins. This introduction provides an overview of this novel financing method. Differences between tokens and coins, types of tokens, and various ICO mechanisms are discussed. The author also describes the evolution of the ICO market and surveys some advantages of ICOs. <b>TOPICS:</b>Currency, volatility measures
Multi-object tracking with discriminant correlation filter based deep learning tracker

In this paper, we extend the discriminant correlation filter (DCF) based deep learning tracker to multi-object tracking. For each object, we use an individual tracker to estimate the position. Two different pre-trained networks are used as feature extractors, respectively. The response peak and osc illation are both considered to validate the tracking. When the object is lost, the discriminative appearance model achieved by DCF is considered as a part of the feature representation between the object and detection for data association. In order to validate our method, we analyze and test our approach on the MOT2D2015 and MOT17 benchmarks for multiple pedestrian tracking. The results show that our approach performs superiorly against several recent state-of-the-art online multi-object trackers.