Reviewerly/exHarmony huggingface dataset:
Matching user accounts with spatio-temporal awareness across social networks

• A user identification method that considers the spatiotemporal correlation and the inconsistency of check-in records. • A KDE-based method to measure the proximity of two users’ check-in records. • A method to penalize the similarity based on the number of conflictive check-in records. User identification aims at matching user accounts across social sites, which benefits many real-world applications. Existing works based on user trajectories usually address spatial and temporal data separately while not fully utilizing the coupling relation between them. Differently, in this work, we jointly consider spatialtemporal information in users’ acitvities to improve the user identification method. In particular, we observe that check-in records of different users tend to create inconsistent spatialtemporal information. These inconsistencies are useful for eliminating false user matching. Inspired by this observation, we propose a novel user identification method that captures the correlation of spatial and temporal information and the inconsistency in check-in records. It contains three main steps. 1) We measure the similarity of users’ trajectories based on a kernel density estimation, which considers spatial and temporal information simultaneously. 2) We assign a weight to each check-in record to favor discriminative ones. 3) We utilize the inconsistency among check-in records to compute penalties for trajectory similarity. The pair of accounts with higher similarity (than a predefined threshold) is then considered to be from the same user. We evaluate our approach on three ground-truth datasets. The results show that the proposed method offers competitive performance, with F1 values reaching 86.12%, 85.08% and 78.34%, which demonstrates the superiority of the proposed method over state-of-the-art methods.
Performance differences between instructions on paper vs digital glasses for a simple assembly task

Providing instruction for assembly tasks is essential in modern manufacturing industry, as well as in households for customers that buy products to be assembled at home. Recent technological developments might be able to assist in completing an assembly task faster and more accurately. The aim of this study was to evaluate whether performance and usability differs when instructions for an assembly task are presented on digital glasses versus paper. Participants (n = 63) completed one of three versions of an assembly task (between-subject-design) with LEGO® bricks: (1) with paper instruction (P), (2) with text instructions presented stepwise via digital glasses (GT), (3) with stepwise text and auditory instruction (in parallel) on digital glasses (GA). Outcome measures on performance were completion time and errors. Furthermore, usability was measured by the User Experience Questionnaire, the Standardized Usability Questionnaire, the Post-Study Usability Questionnaire, and cognitive processing skills were assessed by the Trail Making Test and different versions of the Eriksen Flanker Task. Analyses were adjusted for the confounding factors age, gender, experience with glasses and LEGO, and problems with instruction. Findings indicate that task completion was faster with the paper instructions compared to both versions of instruction via digital glasses (GT, GA). We observed no difference in accuracy and usability between the instructions. “Novelty” was rated higher for instructions for both GT and GA, compared to P. Results show that instructions on digital glasses may not always be more effective for assembly than the traditional paper-based instructions. Further studies are necessary to investigate whether effectiveness may depend on task complexity, target group, experience of the user with task and device, and how the information is presented.
TIE: A Framework for Embedding-based Incremental Temporal Knowledge Graph Completion

Reasoning in a temporal knowledge graph (TKG) is a critical task for information retrieval and semantic search. It is particularly challenging when the TKG is updated frequently. The model has to adapt to changes in the TKG for efficient training and inference while preserving its performance on historical knowledge. Recent work approaches TKG completion (TKGC) by augmenting the encoder-decoder framework with a time-aware encoding function. However, naively fine-tuning the model at every time step using these methods does not address the problems of 1) catastrophic forgetting, 2) the model's inability to identify the change of facts (e.g., the change of the political affiliation and end of a marriage), and 3) the lack of training efficiency. To address these challenges, we present the Time-aware Incremental Embedding (TIE) framework, which combines TKG representation learning, experience replay, and temporal regularization. We introduce a set of metrics that characterizes the intransigence of the model and propose a constraint that associates the deleted facts with negative labels.
Entity Retrieval Using Fine-Grained Entity Aspects

Using entity aspect links, we improve upon the current state-of-the-art in entity retrieval. Entity retrieval is the task of retrieving relevant entities for search queries, such as "Antibiotic Use In Livestock". Entity aspect linking is a new technique to refine the semantic information of entity links. For example, while passages relevant to the query above may mention the entity "USA", there are many aspects of the USA of which only few, such as "USA/Agriculture", are relevant for this query. By using entity aspect links that indicate which aspect of an entity is being referred to in the context of the query, we obtain more specific relevance indicators for entities. We show that our approach improves upon all baseline methods, including the current state-of-the-art using a standard entity retrieval test collection. With this work, we release a large collection of entity-aspect-links for a large TREC corpus.
Set2setRank

As users often express their preferences with binary behavior data~(implicit feedback), such as clicking items or buying products, implicit feedback based Collaborative Filtering~(CF) models predict the top ranked items a user might like by leveraging implicit user-item interaction data. For each user, the implicit feedback is divided into two sets: an observed item set with limited observed behaviors, and a large unobserved item set that is mixed with negative item behaviors and unknown behaviors. Given any user preference prediction model, researchers either designed ranking based optimization goals or relied on negative item mining techniques for better optimization. Despite the performance gain of these implicit feedback based models, the recommendation results are still far from satisfactory due to the sparsity of the observed item set for each user. To this end, in this paper, we explore the unique characteristics of the implicit feedback and propose Set2setRank framework for recommendation. The optimization criteria of Set2setRank are two folds: First, we design an item to an item set comparison that encourages each observed item from the sampled observed set is ranked higher than any unobserved item from the sampled unobserved set. Second, we model set level comparison that encourages a margin between the distance summarized from the observed item set and the most "hard'' unobserved item from the sampled negative set. Further, an adaptive sampling technique is designed to implement these two goals. We have to note that our proposed framework is model-agnostic and can be easily applied to most recommendation prediction approaches, and is time efficient in practice. Finally, extensive experiments on three real-world datasets demonstrate the superiority of our proposed approach.
Stock market index prediction based on reservoir computing models

Prediction of the financial market price is critical for financial decision-making and market policy-making. Recently, various machine learning and deep learning methods have been adopted to predict financial markets’ movements using historical time series of prices. However, accurate prediction of financial prices is still a long-standing challenge that always calls for new approaches. In this study, a novel machine learning model of reservoir computing is developed to predict stock market indices. The performance of the proposed new model is systematically evaluated using the time series of daily closing prices of seven major international stock market indices including S&P500 Index, New York Stock Exchange Composite, Dow Jones Industrial Average, Nasdaq Composite Index, Financial Times Stock Exchange 100 Index, Nikkei 225 Index, and Shanghai Stock Exchange Index between January 4, 2010, and December 31, 2018 covering 2,272 trading days. The results show that our model outperforms the widely used deep learning methods of long short-term memory and recurrent neural network in most cases. To further evaluate the predictive capability of our model, we compare our model to the other two newly reported deep learning methods in recent studies. Comparative results also show that our model is competitive to those deep learning methods in predicting stock market indices. Our study contributes to the literature by developing novel reservoir computing models for financial market predictions. Meanwhile, our results also provide practical implications for financial practitioners of potential financial applications of reservoir computing in financial time series analysis and predictions.
Action Coherence Network for Weakly-Supervised Temporal Action Localization

Weakly-supervised Temporal Action Localization (W-TAL) aims at simultaneously classifying and locating all action instances with only video-level supervision. However, current W-TAL methods have two limitations. First, they ignore the difference in video representations between an action instance and its surrounding background when generating and scoring action proposals. Second, the unique characteristics of the RGB frames and optical flow are largely ignored when fusing these two modalities. To address these problems, an Action Coherence Network (ACN) is proposed in this paper. Its core is a new coherence loss which exploits both classification predictions and video content representations to supervise action boundary regression and thus leads to more accurate action localization results. Besides, the proposed ACN explicitly takes into account the specific characteristics of RGB frames and optical flow by training two separate sub-networks, each of which is able to generate modality-specific action proposals independently. Finally, to take advantage of the complementary action proposals generated by two streams, a novel fusion module is introduced to reconcile them and obtain the final action localization results. Experiments on the THUMOS14 and ActivityNet datasets show that our ACN outperforms the state-of-the-art W-TAL methods, and is even comparable to some recent fully-supervised methods. Particularly, ACN achieves a mean average precision of 26.4% on the THUMOS14 dataset under the IoU threshold 0.5.
Popularity-Aware 360-Degree Video Streaming

Tile-based streaming techniques have been widely used to save bandwidth in 360° video streaming. However, it is a challenge to determine the right tile size which directly affects the bandwidth usage. To address this problem, we propose to encode the video by considering the viewing popularity, where the popularly viewed areas are encoded as macrotiles to save bandwidth. We propose techniques to identify and build macrotiles, and adjust their sizes considering practical issues such as head movement randomness. In some cases, a user's viewing area may not be covered by the constructed macrotiles, and then the conventional tiling scheme is used. To support popularity-aware 360° video streaming, the client selects the right tiles (a macrotile or a set of conventional tiles) with the right quality level to maximize the QoE under bandwidth constraint. We formulate this problem as an optimization problem which is NP-hard, and then propose a heuristic algorithm to solve it. Through extensive evaluations based on real traces, we demonstrate that the proposed algorithm can significantly improve the QoE and save the bandwidth usage.
Robust Control Optimization for Quantum Approximate Optimization Algorithms

Quantum variational algorithms have garnered significant interest recently, due to their feasibility of being implemented and tested on noisy intermediate scale quantum (NISQ) devices. We examine the robustness of the quantum approximate optimization algorithm (QAOA), which can be used to solve certain quantum control problems, state preparation problems, and combinatorial optimization problems. We demonstrate that the error of QAOA simulation can be significantly reduced by robust control optimization techniques, specifically, by sequential convex programming (SCP), to ensure error suppression in situations where the source of the error is known but not necessarily its magnitude. We show that robust optimization improves both the objective landscape of QAOA as well as overall circuit fidelity in the presence of coherent errors and errors in initial state preparation.
Cybercrime is (often) boring: Infrastructure and alienation in a deviant subculture

Abstract The boredom and alienation produced by capitalist societies and countervailing forces of attraction and excitement are at the heart of the subcultural account of crime. The underground hacker subculture is no exception, commonly represented as based around exciting, technically skilled practices and high-profile deviance. However, the illicit economy associated with these practices has become industrialized, developing shared infrastructures that facilitate the sale of illicit services rather than skilled technical work. We explore how this shift in the nature of work has shaped the culture and experiences of this subculture. Developing a novel concept—the ‘illicit infrastructure’—and drawing on an extensive analysis of empirical data from interviews and novel data sources such as forums and chat channels, we argue that as they industrialize, deviant subcultures can begin to replicate the division of labour, cultural tensions and conditions of alienation present in mainstream capitalist economies.
Blockchain-Watermarking for Compressive Sensed Images

With the application of multimedia big data, the problems such as information leakage and data tampering have emerged.The security of images which is one of the most typical multimedia has become a major problem facing the large-scale open network environment.This paper proposed a blockchain-watermarking scheme to protect the privacy, integrity and availability of compressed sensed images, which effectively combines multimedia watermarking, compressed sensing, Interplanetary File System (IPFS) and blockchain technologies.Based on the reliable authentication of watermarking, the confidentiality protection of compressed sensing, the secure storage of IPFS, and the decentralization and non-tamperability of blockchain, the all-round security protection of the image big data based on compressive sensing can be realized.Experiments show that the proposed scheme is effective and feasible.
Performance Analysis and Comparison of Faster R-CNN, Mask R-CNN and ResNet50 for the Detection and Counting of Vehicles

Traffic congestion is one of the major issues of urban cities. The conventional techniques used usually to control traffic via different types of sensors are less precise and expensive. Intelligent solutions using deep learning algorithms provide promising results in terms of better performance, prompt decision making and cost effectiveness. This article aims at providing an easy, more accurate and less expensive solution for the traffic control issues specifically at the traffic signals. Three deep neural network (DNN) frameworks i.e. Faster R-CNN, Mask R-CNN and ResNet-50 have been implemented and compared for vehicle detection, classification and counting. A dataset of 3200 images of different vehicles is used for the training of the models. The training is carried out on NVIDIA 1060TI 3GB GPU. Trained system is tested on indigenous recorded video data of 8 hours for two routes at a traffic signal. Results demonstrated that the overall detection accuracy of Faster R-CNN and Mask R-CNN is >80%, whereas detection accuracy of ResNet-50 is >75%. The counting accuracies of Faster R-CNN, Mask R-CNN and ResNet-50 are >75%, >70% and >62% respectively. Various error analysis have been carried out to validate the performance of the aforementioned frameworks. Furthermore, a prototype has also been developed by interconnecting the DNN results with Arduino via Serial communication.
Real-time healthcare monitoring using smart systems: A step towards healthcare service orchestration Smart systems for futuristic healthcare

With the modern technological innovations every industry is transforming itself to produce better goods and services for client satisfaction. Healthcare is also such an industry which is using modern technology for transforming the pattern of patient care and treatment. In the technological era, the aim of the healthcare ecosystem is not only disease management but an overall well being of patients. Sensor enabled IoT based smart healthcare systems along with integration of artificial intelligence creates orchestration of healthcare services and enables healthcare professionals to monitor the routine activities of patients in real-time enabling on time response for patients’ healthcare needs. The study discusses how artificial intelligence and sensor enabled smart devices creates smart systems for continuous monitoring of patient’s health and creates orchestrated services for the benefits of the patients. Study also creates the literature-based propositions which can be empirically validated in the future.
Adaptive Artificial Potential Fields with Orientation Control Applied to Robotic Manipulators

This paper proposes the integration of an Adaptive Artificial Potential Fields algorithm with a new end effector orientation control technique for real-time robot path planning. The development of autonomous robotic systems has undergone several advances in path planning algorithms. These systems generate object collision-free paths in the robot’s workspace. In this context, the Artificial Potential Fields technique has been the focus of improvements in recent years due to its simplicity of application and efficiency in real-time systems, since it does not require a global mapping of the robot’s workspace. In spite of its efficiency, this technique is susceptible to local minimum problems of different natures, such as Goals Non-Reachable with Obstacles Nearby (GNRON). To solve this problem, we suggest the use of an improvement called Adaptive Artificial Potential Fields used in conjunction with the proposed end effector orientation control technique, which allows reaching a desired orientation of the end effector. The resulting force, generated from the Adaptive Artificial Potential Field, guides the robot end effector to the goal. The Robot Operating System (ROS) framework and a collaborative robot manipulator UR5 are used to validate the proposed method on an approaching task for an object on a 3D printer tray.
Error Detection Algorithm for Cloud Outsourced Big Data

Cloud storage is a fast-growing technology allowing multiple users to store data at one place. It can accommodate big data which is beyond the capacity of local storage due to limited space. Moreover, it is economic place as it charges customers on pay per use basis. When users transfer data from their local storage to cloud, during transmission, errors are detected and corrected by Secured Socket Layer (SSL) and Transport Layer Security (TLS). But what happen if it is altered after storing on cloud space. Supposed data is stored or uploaded on cloud space, and somebody enters into that space with malicious intention and makes some changes in the sensitive as well as financial data; then, how the users will come to know that their data had been breached and altered during it was stored on cloud space. For maintaining the data integrity, users encrypt it by some well-known algorithm like Data Encryption Standard (DES), Advanced Encryption Standard (AES), Rivest–Shamir–Adleman (RSA), Blowfish, International Data Encryption Algorithm (IDEA), and Dynamic Algorithm. These algorithms may be cracked by hit and trial method using large no. of available computing elements offered by cloud. This chapter proposes an algorithm that detects errors or unauthorized changes that may encounter in big data for the duration it was stored on cloud space.
Rule-Based Reinforcement Learning for Efficient Robot Navigation With Space Reduction

For real-world deployments, it is critical to allow robots to navigate in complex environments autonomously.Traditional methods usually maintain an internal map of the environment, and then design several simple rules, in conjunction with a localization and planning approach, to navigate through the internal map.These approaches often involve a variety of assumptions and prior knowledge.In contrast, recent reinforcement learning (RL) methods can provide a model-free, self-learning mechanism as the robot interacts with an initially unknown environment, but are expensive to deploy in realworld scenarios due to inefficient exploration.In this paper, we focus on efficient navigation with the RL technique and combine the advantages of these two kinds of methods into a rulebased RL (RuRL) algorithm for reducing the sample complexity and cost of time.First, we use the rule of wall-following to generate a closed-loop trajectory.Second, we employ a reduction rule to shrink the trajectory, which in turn effectively reduces the redundant exploration space.Besides, we give the detailed theoretical guarantee that the optimal navigation path is still in the reduced space.Third, in the reduced space, we utilize the Pledge rule to guide the exploration strategy for accelerating the RL process at the early stage.Experiments conducted on real robot navigation problems in hex-grid environments demonstrate that RuRL can achieve improved navigation performance.