Reviewerly/exHarmony huggingface dataset:
Enhancing Mobile Voice Assistants with WorldGaze

Contemporary voice assistants require that objects of inter-est be specified in spoken commands. Of course, users are often looking directly at the object or place of interest ? fine-grained, contextual information that is currently unused. We present WorldGaze, a software-only method for smartphones that provides the real-world gaze location of a user that voice agents can utilize for rapid, natural, and precise interactions. We achieve this by simultaneously opening the front and rear cameras of a smartphone. The front-facing camera is used to track the head in 3D, including estimating its direction vector. As the geometry of the front and back cameras are fixed and known, we can raycast the head vector into the 3D world scene as captured by the rear-facing camera. This allows the user to intuitively define an object or region of interest using their head gaze. We started our investigations with a qualitative exploration of competing methods, before developing a functional, real-time implementation. We conclude with an evaluation that shows WorldGaze can be quick and accurate, opening new multimodal gaze+voice interactions for mobile voice agents.
Approach Matters: Linking Practitioner Approaches to Technology Design for People with Dementia

Technology design for dementia is an active and growing area. Though work to date has largely addressed functional needs, there is a growing recognition of the importance of supporting meaningful activities. However, technology for active, rather than passive, engagement is relatively novel beyond specific applications (e.g., music or reminiscence therapy). To better understand how to support active engagement of people with dementia in activities, we interviewed nineteen practitioners. Our findings reveal differing approaches to making sense of the actions of people with dementia, as well as to engaging them in activities. We discuss the importance of tracing epistemological understandings of dementia to different configurations of technology for people living with dementia and provide a practical guide to support designers to do so. Finally, we discuss considerations for the design of dementia technologies around facilitating self-actualization and managing emotional exposure for care-providers.
IntMA: Dynamic Interaction-aware resource allocation for containerized microservices in cloud environments

The Information Technology sector has undergone tremendous changes arising due to the emergence and prevalence of Cloud Computing. Microservice Architectures have also been attracting attention from several industries and researchers. Due to the suitability of microservices for the Cloud environments, an increasing number of Cloud applications are now provided as microservices. However, this transition to microservices brings a wide range of infrastructural orchestration challenges. Though several research works have discussed the engineering of microservice-based applications, there is an inevitable need for research on handling the operational phases of the microservice components. Microservice application deployment in containerized datacenters must be optimized to enhance the overall system performance. In this research work, the deployment of microservice application modules on the Cloud infrastructure is first modelled as a Binary Quadratic Programming Problem. In order to reduce the adverse impact of communication latencies on the response time, the interaction pattern between the microservice components is modelled as an undirected doubly weighted complete Interaction Graph. A novel, robust heuristic approach IntMA is also proposed for deploying the microservices in an interaction-aware manner with the aid of the interaction information obtained from the Interaction Graph. The proposed allocation policies are implemented in Kubernetes. The effectiveness of the proposed approach is evaluated on the Google Cloud Platform, using different microservice reference applications. Experimental results indicate that the proposed approach improves the response time and throughput of the microservice-based systems.
On Sharding Open Blockchains with Smart Contracts

Current blockchain systems suffer from a number of inherent drawbacks in its scalability, latency, and processing throughput. By enabling parallel confirmations of transactions, sharding has been proposed to mitigate these drawbacks, which usually requires frequent communication among miners through a separate consensus protocol.In this paper, we propose, analyze, and implement a new distributed and dynamic sharding system to substantially improve the throughput of blockchain systems based on smart contracts, while requiring minimum cross-shard communication. Our key observation is that transactions sent by users who only participate in a single smart contract can be validated and confirmed independently without causing double spending. Therefore, the natural formation of a shard is to surround one smart contract to start with. The complication lies in the different sizes of shards being formed, in which a small shard with few transactions tends to generate a large number of empty blocks resulting in a waste of mining power, while a large shard adversely affects parallel confirmations. To overcome this problem, we propose an inter-shard merging algorithm with incentives to encourage small shards to merge with one another and form a larger shard, an intra-shard transaction selection mechanism to encourage miners to select different subsets of transactions for validation, as well as a parameter unification method to further improve these two algorithms to reduce the communication cost and improve system reliability.We analyze our proposed algorithms using the game theoretic approach, and prove that they converge to a Nash Equilibrium. We also present a security analysis on our sharding design, and prove that it resists adversaries who occupy at most 33% of the computation power. We have implemented our designs on go-Ethereum 1.8.0 and evaluated their performance using both real-world blockchain transactions and large-scale simulations. Our results show that throughput has been improved by 7.2×, and the number of empty blocks has been reduced by 90%.
A Novel FPGA Accelerator Design for Real-Time and Ultra-Low Power Deep Convolutional Neural Networks Compared With Titan X GPU

Convolutional neural networks (CNNs) based deep learning algorithms require high data flow and computational intensity. For real-time industrial applications, they need to overcome challenges such as high data bandwidth requirement and power consumption on hardware platforms. In this work, we have analyzed in detail the data dependency in the CNN accelerator and propose specific pipelined operations and data organized manner to design a high throughput CNN accelerator on FPGA. Besides, we have optimized the kernel operations to obtain a high power efficiency. The proposed CNN accelerator supports image classification and real-time object detection with high accuracy. The evaluation results show that our CNN-based FPGA accelerator can achieve 740 Giga operations per second (GOPS) at 200 MHz with kernel power of 12.2 watts on Intel Arria 10 FPGA. For object detection tasks, our system can achieve 105 fps with 56.5 mAP or 25 fps with 73.6 mAP on VOC dataset. Since we use the mixed fixed-point data representation, the detection accuracy is comparable with the GPU-based YOLO V2 framework. The power efficiency of our system is 3.3× better than Titan X GPU and 418× better than Intel E5-2620 V4 CPU.
Fall Detection Based on Dual-Channel Feature Integration

Falls have caught great harm to the elderly living alone at home. This paper presents a novel visual-based fall detection approach by Dual-Channel Feature Integration. The proposed approach divides the fall event into two parts: falling-state and fallen-state, which describes the fall events from dynamic and static perspectives. Firstly, the object detection model (Yolo) and the human posture detection model (OpenPose) are used for preprocessing to obtain key points and the position information of a human body. Then, a dual-channel sliding window model is designed to extract the dynamic features of the human body (centroid speed, upper limb velocity) and static features (human external ellipse). After that, MLP (Multilayer Perceptron) and Random Forest are applied to classify the dynamic and static feature data separately. Finally, the classification results are combined for fall detection. Experimental results show that the proposed approach achieves an accuracy of 97.33% and 96.91% when tested with UR Fall Detection Dataset and Le2i Fall Detection Dataset.
Analysis and Variants of Broad Learning System

The broad learning system (BLS) is designed based on the technology of compressed sensing and pseudo-inverse theory, and consists of feature nodes and enhancement nodes, has been proposed recently. Compared with the popular deep learning structures, such as deep neural networks, BLS has the ability of rapid incremental learning and can remodel the system without the usual tedious retraining process. However, given that BLS is still in its infancy, it still needs analysis, improvements, and verification. In this article, we first analyze the principle of fast incremental learning ability of BLS in depth. Second, in order to provide an in-depth analysis of the BLS structure, according to the novel structure design concept of deep neural networks, we present four brand-new BLS variant networks and their incremental realizations. Third, based on our analysis of the effect of feature nodes and enhancement nodes, a new BLS structure with a semantic feature extraction layer has been proposed, which is called SFEBLS. The experimental results show that SFEBLS and its variants can increase the accuracy rate on the NORB dataset 6.18%, Fashion-MNIST dataset by 3.15%, ORL data by 5.00%, street view house number dataset by 12.88%, and CIFAR-10 dataset by 18.42%, respectively, and the four brand-new BLS variant networks also obviously outperform the original BLS.
Attention and boundary guided salient object detection

In recent years, fully convolutional neural network (FCN) has broken all records in various vision task. It also achieves great performance in salient object detection. However, most of the state-of-the-art methods have suffered from the challenge of precisely segmenting the entire salient object with uniform region and explicit boundary and effectively suppressing the backgrounds on complex images. There is still a large room for improvement over the FCN-based saliency detection approaches. In this paper, we propose an attention and boundary guided deep neural network for salient object detection to better locate and segment the salient objects with uniform interior and explicit boundary. A channel-wise attention module is utilized to emphasize the important regions, which selects the important feature channels and assigns large weights to them. A boundary information localization module is proposed for suppressing the irrelevant boundary information to better locate and explore the useful structure of objects. The proposed approach achieves state-of-the-art performance on four well-known benchmark datasets.
Malicious Domain Detection Using Machine Learning On Domain Name Features, Host-Based Features and Web-Based Features

Internet has plenty of vulnerabilities which are exploited by cyber criminals to send spam, commit financial frauds, perform phishing, indulge in command & control, disseminate malware and other malicious activities. Many times these exploits are carried out through malicious domain names which are the vital part of an Internet resource URL. Few vulnerabilities in the Internet setup and its related administrative policies allows such malicious domain names to be registered with the DNS servers. Though blacklisting happens to be the simplest and quickest solution to identify such malicious domains, the technique cannot cope up with the speed at which the domain names are generated and registered, and hence we look forward for other effective means of identifying malicious domains. The researchers have been using features from DNS data and features from lexical analysis of domain names, but there exists a need to identify more related features and introduce machine-learning to meet challenges due to IP flux and domain flux. In this paper, we have introduced usage of web-based features of domain names in addition to using blacklists, DNS data and lexical features to identify malicious domains. Using the features extracted from the domain names, we build a classifier model using the logistic regression classification algorithm and use that classifier to identify benign and malicious domains. Our experiment is based on active DNS analysis and we look forward to take this work for passive DNS analysis.
How Dataset Characteristics Affect the Robustness of Collaborative Recommendation Models

Shilling attacks against collaborative filtering (CF) models are characterized by several fake user profiles mounted on the system by an adversarial party to harvest recommendation outcomes toward a malicious desire. The vulnerability of CF models is directly tied with their reliance on the underlying interaction data ---like user-item rating matrix (URM) --- to train their models and their inherent inability to distinguish genuine profiles from non-genuine ones. The majority of works conducted so far for analyzing shilling attacks mainly focused on properties such as confronted recommendation models, recommendation outputs, and even users under attack. The under-researched element has been the impact of data characteristics on the effectiveness of shilling attacks on CF models.
Farmer-to-farmer digital network as a strategy to strengthen agricultural performance in Kenya: A research note on ‘Wefarm’ platform

The potential to utitilize mobile phone innovations in providing customized agricultural information and services to improve smallholder farmers’ performance in sub-Saharan Africa (SSA) remains largely untapped. This is at the backdrop of the increasing emergence and adoption of ICT services as a means of relaying agricultural information to small-scale farmers and extension workers. One such technology is ‘Wefarm’ in Kenya, an SMS-based knowledge-sharing platform that enables farmers to connect with their peers and exchange knowledge about their production systems. Even though targeted to improve farmers’ access to information related to agricultural production, marketing and financial services, the extent to which these goals are achieved has not been evaluated. A pathway to improve technology adoption is by proving that the technology can enable farmers to attain their livelihood objectives. This short research note, therefore, identifies critical knowledge gaps for which evidence is required to inform policy and practice to enable stakeholders to harness the potential of ICT based innovations offered for agricultural development in SSA.
Web of Scholars: A Scholar Knowledge Graph

In this work, we demonstrate a novel system, namely Web of Scholars, which integrates state-of-the-art mining techniques to search, mine, and visualize complex networks behind scholars in the field of Computer Science. Relying on the knowledge graph, it provides services for fast, accurate, and intelligent semantic querying as well as powerful recommendations. In addition, in order to realize information sharing, it provides open API to be served as the underlying architecture for advanced functions. Web of Scholars takes advantage of knowledge graph, which means that it will be able to access more knowledge if more search exist. It can be served as a useful and interoperable tool for scholars to conduct in-depth analysis within Science of Science.
Towards Real-time Cooperative Deep Inference over the Cloud and Edge End Devices

Deep neural networks (DNNs) have been widely used in many intelligent applications such as object recognition and automatic driving due to their superior performance in conducting inference tasks. However, DNN models are usually heavyweight in computation, hindering their utilization on the resource-constraint Internet of Things (IoT) end devices. To this end, cooperative deep inference is proposed, in which a DNN model is adaptively partitioned into two parts and different parts are executed on different devices (cloud or edge end devices) to minimize the total inference latency. One important issue is thus to find the optimal partition of the deep model subject to network dynamics in a real-time manner. In this paper, we formulate the optimal DNN partition as a min-cut problem in a directed acyclic graph (DAG) specially derived from the DNN and propose a novel two-stage approach named quick deep model partition (QDMP) to solve it. QDMP exploits the fact that the optimal partition of a DNN model must be between two adjacent cut vertices in the corresponding DAG. It first identifies the two cut vertices and considers only the subgraph in between when calculating the min-cut. QDMP can find the optimal model partition with response time less than 300ms even for large DNN models containing hundreds of layers (up to 66.3x faster than the state-of-the-art solution), and thus enables real-time cooperative deep inference over the cloud and edge end devices. Moreover, we observe one important fact that is ignored in all previous works: As many deep learning frameworks optimize the execution of DNN models, the execution latency of a series of layers in a DNN does not equal to the summation of each layer's independent execution latency. This results in inaccurate inference latency estimation in existing works. We propose a new execution latency measurement method, with which the inference latency can be accurately estimated in practice. We implement QDMP on real hardware and use a real-world self-driving car video dataset to evaluate its performance. Experimental results show that QDMP outperforms the state-of-the-art solution, reducing inference latency by up to 1.69x and increasing throughput by up to 3.81x.
Matchinggan: Matching-Based Few-Shot Image Generation

To generate new images for a given category, most deep generative models require abundant training images from this category, which are often too expensive to acquire. To achieve the goal of generation based on only a few images, we propose matching-based Generative Adversarial Network (GAN) for few-shot generation, which includes a matching generator and a matching discriminator. Matching generator can match random vectors with a few conditional images from the same category and generate new images for this category based on the fused features. The matching discriminator extends conventional GAN discriminator by matching the feature of generated image with the fused feature of conditional images. Extensive experiments on three datasets demonstrate the effectiveness of our proposed method.
Scene-Adaptive Video Frame Interpolation via Meta-Learning

Video frame interpolation is a challenging problem because there are different scenarios for each video depending on the variety of foreground and background motion, frame rate, and occlusion. It is therefore difficult for a single network with fixed parameters to generalize across different videos. Ideally, one could have a different network for each scenario, but this is computationally infeasible for practical applications. In this work, we propose to adapt the model to each video by making use of additional information that is readily available at test time and yet has not been exploited in previous works. We first show the benefits of 'test-time adaptation' through simple fine-tuning of a network, then we greatly improve its efficiency by incorporating meta-learning. We obtain significant performance gains with only a single gradient update without any additional parameters. Finally, we show that our meta-learning framework can be easily employed to any video frame interpolation network and can consistently improve its performance on multiple benchmark datasets.
Decision Boundary-Based Anomaly Detection Model Using Improved AnoGAN From ECG Data

Arrhythmia detection through deep learning is mainly classified through supervised learning. Supervised learning progresses through the labeled data. However, in the medical field, it is challenging to collect ECG data of patients with arrhythmia than ECG data of healthy people, and thus data bias occurs. Therefore, if you use a supervised learning model, there are problems with lack of data and imbalance between labels that arise during learning. Accordingly, this study proposes the decision boundary-based Anomaly detection model using improved AnoGAN from ECG data. In this study, at the time of learning, the loss of the Generator does not reduce, but the loss of a Discriminator lowers. Even if the Generator and Discriminator were designed to have the same learning count, the learning competency of Generator was judged to be lowered. In repeated experiments, it was found that the best loss balance was achieved when the learning count of Discriminator was 1 and that of Generator was 4. Another problem is that the decision boundary of AnoGAN is subjective. Accordingly, the repeated experiments based on F-measure are conducted to determine a decision boundary. For performance evaluation, the accuracy of the model is evaluated on the basis of Epoch, and the goodness-of-fit of the model is evaluated on the basis of AUC and F-measure. According to the evaluation of F-measure, the model has the best performance when the decision boundary is 200. In terms of Epoch, the model has the highest accuracy when the Epoch is 10. In addition, the proposed model has better goodness-of-fit than AnoGAN.
Fast Unsupervised Anomaly Detection in Traffic Videos

Anomaly detection in traffic videos has been recently gaining attention due to its importance in intelligent transportation systems. Due to several factors such as weather, viewpoint, lighting conditions, etc. affecting the video quality of a real time traffic feed, it still remains a challenging problem. Even though the performance of state-of-the-art methods on the available benchmark dataset has been competitive, they demand a massive amount of external training data combined with significant computational resources. In this paper, we propose a fast unsupervised anomaly detection system comprising of three modules: preprocessing module, candidate selection module and backtracking anomaly detection module. The preprocessing module outputs stationary objects detected in a video. Then, the candidate selection module removes the misclassified stationary objects using a nearest neighbor approach and then uses K-means clustering to identify potential anomalous regions. Finally, the backtracking anomaly detection algorithm computes a similarity statistic and decides on the onset time of the anomaly. Experimental results on the Track 4 test set of the NVIDIA AI CITY 2020 challenge show the efficacy of the proposed framework as we achieve an F1-score of 0.5926 along with 8.2386 root mean square error (RMSE) and are ranked second in the competition.