Reviewerly/exHarmony huggingface dataset:
SparseTrain: Exploiting Dataflow Sparsity for Efficient Convolutional Neural Networks Training

Training Convolutional Neural Networks (CNNs) usually requires a large number of computational resources. In this paper, SparseTrain is proposed to accelerate CNN training by fully exploiting the sparsity. It mainly involves three levels of innovations: activation gradients pruning algorithm, sparse training dataflow, and accelerator architecture. By applying a stochastic pruning algorithm on each layer, the sparsity of back-propagation gradients can be increased dramatically without degrading training accuracy and convergence rate. Moreover, to utilize both natural sparsity (resulted from ReLU or Pooling layers) and artificial sparsity (brought by pruning algorithm), a sparse-aware architecture is proposed for training acceleration. This architecture supports forward and back-propagation of CNN by adopting 1-Dimensional convolution dataflow. We have built a cycle-accurate architecture simulator to evaluate the performance and efficiency based on the synthesized design with 14nm FinFET technologies. Evaluation results on AlexNet/ResNet show that SparseTrain could achieve about 2.7× speedup and 2.2× energy efficiency improvement on average compared with the original training process.
Deep Concept-wise Temporal Convolutional Networks for Action Localization

Existing action localization approaches adopt shallow temporal convolutional networks (i.e., TCN) on 1D feature map extracted from video frames. In this paper, we empirically find that stacking more conventional temporal convolution layers actually deteriorates action classification performance, possibly ascribing to that all channels of 1D feature map, which generally are highly abstract and can be regarded as latent concepts, are excessively recombined in temporal convolution. To address this issue, we introduce a novel concept-wise temporal convolutional network (C-TCN) as an alternative to TCN for training deeper action localization networks. To address this issue, we introduce a novel concept-wise temporal convolution (CTC) layer as an alternative to conventional temporal convolution layer for training deeper action localization networks. Instead of recombining latent concepts, CTC layer deploys a number of temporal filters to each concept separately with shared filter parameters across concepts. Thus can capture common temporal patterns of different concepts and significantly enrich representation ability. Via stacking CTC layers, we proposed a deep concept-wise temporal convolutional network (C-TCN), which boosts the state-of-the-art action localization performance on THUMOS'14 from 42.8 to 52.1 in terms of mAP(%), achieving a relative improvement of 21.7%. Favorable result is also obtained on ActivityNet.
Exploring the Role of Argument Structure in Online Debate Persuasion

Online debate forums provide users a platform to express their opinions on controversial topics while being exposed to opinions from diverse set of viewpoints.Existing work in Natural Language Processing (NLP) has shown that linguistic features extracted from the debate text and features encoding the characteristics of the audience are both critical in persuasion studies.In this paper, we aim to further investigate the role of discourse structure of the arguments from online debates in their persuasiveness.In particular, we use the factor graph model to obtain features for the argument structure of debates from an online debating platform and incorporate these features to an LSTM-based model to predict the debater that makes the most convincing arguments.We find that incorporating argument structure features play an essential role in achieving the better predictive performance in assessing the persuasiveness of the arguments in online debates.
Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation Learning

Learning representations of sets of nodes in a graph is crucial for applications ranging from node-role discovery to link prediction and molecule classification. Graph Neural Networks (GNNs) have achieved great success in graph representation learning. However, expressive power of GNNs is limited by the 1-Weisfeiler-Lehman (WL) test and thus GNNs generate identical representations for graph substructures that may in fact be very different. More powerful GNNs, proposed recently by mimicking higher-order-WL tests, only focus on representing entire graphs and they are computationally inefficient as they cannot utilize sparsity of the underlying graph. Here we propose and mathematically analyze a general class of structure-related features, termed Distance Encoding (DE). DE assists GNNs in representing any set of nodes, while providing strictly more expressive power than the 1-WL test. DE captures the distance between the node set whose representation is to be learned and each node in the graph. To capture the distance DE can apply various graph-distance measures such as shortest path distance or generalized PageRank scores. We propose two ways for GNNs to use DEs (1) as extra node features, and (2) as controllers of message aggregation in GNNs. Both approaches can utilize the sparse structure of the underlying graph, which leads to computational efficiency and scalability. We also prove that DE can distinguish node sets embedded in almost all regular graphs where traditional GNNs always fail. We evaluate DE on three tasks over six real networks: structural role prediction, link prediction, and triangle prediction. Results show that our models outperform GNNs without DE by up-to 15\% in accuracy and AUROC. Furthermore, our models also significantly outperform other state-of-the-art methods especially designed for the above tasks.
Friend, Collaborator, Student, Manager: How Design of an AI-Driven Game Level Editor Affects Creators

Machine learning advances have afforded an increase in algorithms capable of creating art, music, stories, games, and more. However, it is not yet well-understood how machine learning algorithms might best collaborate with people to support creative expression. To investigate how practicing designers perceive the role of AI in the creative process, we developed a game level design tool for Super Mario Bros.-style games with a built-in AI level designer. In this paper we discuss our design of the Morai Maker intelligent tool through two mixed-methods studies with a total of over one-hundred participants. Our findings are as follows: (1) level designers vary in their desired interactions with, and role of, the AI, (2) the AI prompted the level designers to alter their design practices, and (3) the level designers perceived the AI as having potential value in their design practice, varying based on their desired role for the AI.
Adam and the Ants: On the Influence of the Optimization Algorithm on the Detectability of DNN Watermarks

As training Deep Neural Networks (DNNs) becomes more expensive, the interest in protecting the ownership of the models with watermarking techniques increases. Uchida et al. proposed a digital watermarking algorithm that embeds the secret message into the model coefficients. However, despite its appeal, in this paper, we show that its efficacy can be compromised by the optimization algorithm being used. In particular, we found through a theoretical analysis that, as opposed to Stochastic Gradient Descent (SGD), the update direction given by Adam optimization strongly depends on the sign of a combination of columns of the projection matrix used for watermarking. Consequently, as observed in the empirical results, this makes the coefficients move in unison giving rise to heavily spiked weight distributions that can be easily detected by adversaries. As a way to solve this problem, we propose a new method called Block-Orthonormal Projections (BOP) that allows one to combine watermarking with Adam optimization with a minor impact on the detectability of the watermark and an increased robustness.
Multi-Level P2P Traffic Classification Using Heuristic and Statistical-Based Techniques: A Hybrid Approach

Peer-to-peer (P2P) applications have been popular among users for more than a decade. They consume a lot of network bandwidth, due to the fact that network administrators face several issues such as congestion, security, managing resources, etc. Hence, its accurate classification will allow them to maintain a Quality of Service for various applications. Conventional classification techniques, i.e., port-based and payload-based techniques alone, have proved ineffective in accurately classifying P2P traffic as they possess significant limitations. As new P2P applications keep emerging and existing applications change their communication patterns, a single classification approach may not be sufficient to classify P2P traffic with high accuracy. Therefore, a multi-level P2P traffic classification technique is proposed in this paper, which utilizes the benefits of both heuristic and statistical-based techniques. By analyzing the behavior of various P2P applications, some heuristic rules have been proposed to classify P2P traffic. The traffic which remains unclassified as P2P undergoes further analysis, where statistical-features of traffic are used with the C4.5 decision tree for P2P classification. The proposed technique classifies P2P traffic with high accuracy (i.e., 98.30%), works with both TCP and UDP traffic, and is not affected even if the traffic is encrypted.
An Unsupervised Behavioral Modeling and Alerting System Based on Passive Sensing for Elderly Care

Artificial Intelligence in combination with the Internet of Medical Things enables remote healthcare services through networks of environmental and/or personal sensors. We present a remote healthcare service system which collects real-life data through an environmental sensor package, including binary motion, contact, pressure, and proximity sensors, installed at households of elderly people. Its aim is to keep the caregivers informed of subjects’ health-status progressive trajectory, and alert them of health-related anomalies to enable objective on-demand healthcare service delivery at scale. The system was deployed in 19 households inhabited by an elderly person with post-stroke condition in the Emilia–Romagna region in Italy, with maximal and median observation durations of 98 and 55 weeks. Among these households, 17 were multi-occupancy residences, while the other 2 housed elderly patients living alone. Subjects’ daily behavioral diaries were extracted and registered from raw sensor signals, using rule-based data pre-processing and unsupervised algorithms. Personal behavioral habits were identified and compared to typical patterns reported in behavioral science, as a quality-of-life indicator. We consider the activity patterns extracted across all users as a dictionary, and represent each patient’s behavior as a ‘Bag of Words’, based on which patients can be categorized into sub-groups for precision cohort treatment. Longitudinal trends of the behavioral progressive trajectory and sudden abnormalities of a patient were detected and reported to care providers. Due to the sparse sensor setting and the multi-occupancy living condition, the sleep profile was used as the main indicator in our system. Experimental results demonstrate the ability to report on subjects’ daily activity pattern in terms of sleep, outing, visiting, and health-status trajectories, as well as predicting/detecting 75% hospitalization sessions up to 11 days in advance. 65% of the alerts were confirmed to be semantically meaningful by the users. Furthermore, reduced social interaction (outing and visiting), and lower sleep quality could be observed during the COVID-19 lockdown period across the cohort.
Legal Aspects of Central Bank Digital Currency: Central Bank and Monetary Law Considerations

This paper analyzes the legal foundations of central bank digital currency (CBDC) under central bank and monetary law. Absent strong legal foundations, the issuance of CBDC poses legal, financial and reputational risks for central banks. While the appropriate design of the legal framework will up to a degree depend on the design features of the CBDC, some general conclusions can be made. First, most central bank laws do not currently authorize the issuance of CBDC to the general public. Second, from a monetary law perspective, it is not evident that 'currency' status can be attributed to CBDC. While the central bank law issue can be solved through rather straithforward law reform, the monetary law issue poses fundmental legal policy challenges.
Self-Regulated Learning for Egocentric Video Activity Anticipation

Future activity anticipation is a challenging problem in egocentric vision. As a standard future activity anticipation paradigm, recursive sequence prediction suffers from the accumulation of errors. To address this problem, we propose a simple and effective Self-Regulated Learning framework, which aims to regulate the intermediate representation consecutively to produce representation that (a) emphasizes the novel information in the frame of the current time-stamp in contrast to previously observed content, and (b) reflects its correlation with previously observed frames. The former is achieved by minimizing a contrastive loss, and the latter can be achieved by a dynamic reweighing mechanism to attend to informative frames in the observed content with a similarity comparison between feature of the current frame and observed frames. The learned final video representation can be further enhanced by multi-task learning which performs joint feature learning on the target activity labels and the automatically detected action and object class tokens. SRL sharply outperforms existing state-of-the-art in most cases on two egocentric video datasets and two third-person video datasets. Its effectiveness is also verified by the experimental fact that the action and object concepts that support the activity semantics can be accurately identified.
The Understanding of Deep Learning: A Comprehensive Review

Deep learning is a computer-based modeling approach, which is made up of many processing layers that are used to understand the representation of data with several levels of abstraction. This review paper presents the state of the art in deep learning to highlight the major challenges and contributions in computer vision. This work mainly gives an overview of the current understanding of deep learning and their approaches in solving traditional artificial intelligence problems. These computational models enhanced its application in object detection, visual object recognition, speech recognition, face recognition, vision for driverless cars, virtual assistants, and many other fields such as genomics and drug discovery. Finally, this paper also showcases the current developments and challenges in training deep neural network.
Credit Card Fraud Detection Based on Machine Learning

In recent years, the rapid development of e-commerce exposes great vulnerabilities in online transactions for fraudsters to exploit. Credit card transactions take a salient role in nowadays’ online transactions for its obvious advantages including discounts and earning credit card points. So credit card fraudulence has become a target of concern. In order to deal with the situation, credit card fraud detection based on machine learning is been studied recently. Yet, it is difficult to detect fraudulent transactions due to data imbalance (normal and fraudulent transactions), for which Smote algorithm is proposed in order to resolve data imbalance. The assessment of Light Gradient Boosting Machine model which proposed in the paper depends much on datasets collected from clients’ daily transactions. Besides, to prove the new model’s superiority in detecting credit card fraudulence, Light Gradient Boosting Machine model is compared with Random Forest and Gradient Boosting Machine algorithm in the experiment. The results indicate that Light Gradient Boosting Machine model has a good performance. The experiment in credit card fraud detection based on Light Gradient Boosting Machine model achieved a total recall rate of 99% in real dataset and fast feedback, which proves the new model’s efficiency in detecting credit card fraudulence.
A Sharp Analysis of Model-based Reinforcement Learning with Self-Play

Model-based algorithms---algorithms that explore the environment through building and utilizing an estimated model---are widely used in reinforcement learning practice and theoretically shown to achieve optimal sample efficiency for single-agent reinforcement learning in Markov Decision Processes (MDPs). However, for multi-agent reinforcement learning in Markov games, the current best known sample complexity for model-based algorithms is rather suboptimal and compares unfavorably against recent model-free approaches. In this paper, we present a sharp analysis of model-based self-play algorithms for multi-agent Markov games. We design an algorithm \emph{Optimistic Nash Value Iteration} (Nash-VI) for two-player zero-sum Markov games that is able to output an ϵ-approximate Nash policy in O~(H3SAB/ϵ2) episodes of game playing, where S is the number of states, A,B are the number of actions for the two players respectively, and H is the horizon length. This significantly improves over the best known model-based guarantee of O~(H4S2AB/ϵ2), and is the first that matches the information-theoretic lower bound Ω(H3S(A+B)/ϵ2) except for a min{A,B} factor. In addition, our guarantee compares favorably against the best known model-free algorithm if min{A,B}=o(H3), and outputs a single Markov policy while existing sample-efficient model-free algorithms output a nested mixture of Markov policies that is in general non-Markov and rather inconvenient to store and execute. We further adapt our analysis to designing a provably efficient task-agnostic algorithm for zero-sum Markov games, and designing the first line of provably sample-efficient algorithms for multi-player general-sum Markov games.
Application of Virtual Reality and Artificial Intelligence Technology in Fitness Clubs

With the continuous development and progress of virtual reality technology in recent years, the application of virtual reality technology in all aspects of real life is no longer limited to the military field, medical, or film production fields, but it gradually appears in front of the public, into the lives of ordinary people. The human-computer interaction method in virtual reality and the presentation effect of the virtual scene are the two most important aspects of the virtual reality experience. How to provide a good human-computer interaction method for virtual reality applications and how to improve the final presentation effect of the virtual reality scene is also becoming an important research direction. This paper takes the virtual fitness club experience system as the application background, analyzes the function and performance requirements of the virtual reality experience system in the virtual reality environment, and proposes the use of Kinect as a video acquisition device to extract the user’s somatosensory operation actions through in-depth information to achieve somatosensory control. This article adopts a real human-computer interaction solution, uses Unity 3D game engine to build a virtual reality scene, defines shaders to improve the rendering effect of the scene, and uses Oculus Rift DK2 to complete an immersive 3D scene demonstration. This process greatly reduces resource consumption; it not only enables users to experience unprecedented immersion as users but also helps people create unprecedented scenes and experiences through virtual imagination. The virtual fitness club experience system probably reduces resource consumption by nearly 70%.
Measuring Decentralization in Bitcoin and Ethereum using Multiple Metrics and Granularities

Decentralization has been widely acknowledged as a core virtue of blockchains. However, in the past, there have been few measurement studies on measuring and comparing the actual level of decentralization between existing blockchains using multiple metrics and granularities. This paper presents a new comparison study of the degree of decentralization in Bitcoin and Ethereum, the two most prominent blockchains, with various decentralization metrics and different granularities within the time dimension. Specifically, we measure the degree of decentralization in the two blockchains during 2019 by computing the distribution of mining power with three metrics (Gini coefficient, Shannon entropy, and Nakamoto coefficient) as well as three granularities (days, weeks, and months). Our measurement results with different metrics and granularities reveal the same trend that, compared with each other, the degree of decentralization in Bitcoin is higher, while the degree of decentralization in Ethereum is more stable. To obtain the cross-interval information missed in the fixed window based measurements, we propose the sliding window based measurement approach. The corresponding results demonstrate that the use of sliding windows could reveal additional cross-interval information overlooked by the fixed window based measurements, thus enhancing the effectiveness of measuring decentralization in terms of continuous trends and abnormal situations. We believe that the methodologies and findings in this paper can facilitate future studies of decentralization in blockchains.
Knowledge graph-based rich and confidentiality preserving Explainable Artificial Intelligence (XAI)

The paper proposes a novel architecture for explainable artificial intelligence based on semantic technologies and artificial intelligence. We tailor the architecture for the domain of demand forecasting and validate it on a real-world case study. The explanations provided result from knowledge fusion regarding concepts describing features relevant to a particular forecast, related media events, and metadata regarding external datasets of interest. The Knowledge Graph enhances the quality of explanations by informing concepts at a higher abstraction level rather than specific features. By doing so, explanations avoid exposing sensitive details regarding the demand forecasting models, thus preserving confidentiality. In addition, the Knowledge Graph enables linking domain knowledge, forecasted values, and forecast explanations while also providing insights into actionable aspects on which users can take action. The ontology and dataset we developed for this use case are publicly available for further research.
Role of Leadership in Adoption of Blockchain Technology in Small and Medium Enterprises in Saudi Arabia

This research aims to determine the role of the leadership and potential benefits that blockchain adoption may bring to SME financing in the Kingdom, as well as the foreseeable challenges that may hinder small businesses from benefiting from the adoption of blockchain. It is interesting aspect to see how leadership manages to adopt new changes amid several challenges and threats. This article also outlines policy and regulatory trends that SMEs can save operating costs and improve efficiency, thereby increasing transparency and easier access to funds. Digital technology and creative business models have the potential to help narrow the financing gap for SMEs. E-commerce and the sharing economy provide SMEs with more market access and new business models, as well as the data-driven business prospects generated through data sharing under the framework of open banking. This study provides recommendation that there is a dire need to pay attention on the complete mechanism of the SME’s in order to support them as well as promote them to show their distinction in the contribution of social and economic development. This study provides implications for the financial institutions, government agencies and society to come forward equally for the common interest.