Reviewerly/exHarmony huggingface dataset:
A deep variational matrix factorization method for recommendation on large scale sparse dataset

Traditional recommendation methods based on matrix factorization techniques have yielded immense success because of their good scalability. However, they still face the problem of data sparsity, which may lead to a reduction in recommendation performance. As it is hard to learn good latent features in the sparse user-item rating matrix. In recent years, deep learning is very appealing in learning effective representations. Its non-linear characteristics just remedy the shortcomings of matrix factorization. In this paper, a novel method deep variational matrix factorization recommendation (DVMF) is proposed for large scale sparse dataset. DVMF is based on latent factors to predict the ratings. The latent features of the users and items are respectively obtained through a deep nonlinear structure. Based on the latent factors and combined with matrix factorization method, the paper presents algorithm optimization method of DVMF. The experiments on three real-world datasets from different domains show that DVMF is able to provide higher accuracy than recommendation algorithms based on matrix factorization or deep learning individually on large scale sparse dataset.
Fast RGB-T Tracking via Cross-Modal Correlation Filters

This paper studies how to perform RGB-T object tracking in the correlation filter framework. Given the input RGB and thermal videos, we utilize the correlation filter for each modality due to its high performance in both of accuracy and speed. To take the interdependency between RGB and thermal modalities, we introduce the low-rank constraint to learn filters collaboratively, based on the observation that different modality features should have similar filters to make them have consistent localization of the target object. For optimization, we design an efficient ADMM (Alternating Direction Method of Multipliers) algorithm to solve the proposed model. Experimental results on the benchmark datasets (i.e., GTOT, RGBT210 and OSU-CT) suggest that the proposed approach performs favorably in both accuracy and efficiency against the state-of-the-art RGB-T methods.
Type-2 Fuzzy Sets as Well as Computing with Words

Lotfi Zadeh, the father of fuzzy sets and fuzzy logic, introduced many of their important concepts, two of which are discussed in this article namely, type-2 fuzzy sets and computing with words. Much has happened since he introduced them, and it is the purpose of this article to bring the readers up-to-speed on both topics. It is also hoped that this article will whet the reader's appetite to read further on one or both of these topics.
Collision-Free Encoding for Chance-Constrained Nonconvex Path Planning

The path planning methods based on nonconvex constrained optimization, such as mixed-integer linear programming (MILP), have found various important applications, ranging from unmanned aerial vehicles (UAVs) and autonomous underwater vehicles (AUVs) to space vehicles. Moreover, their stochastic extensions have enabled risk-aware path planning, which explicitly limits the probability of failure to a user-specified bound. However, a major challenge of those path planning methods is constraint violation between discrete time steps. In the existing approach, a path is represented by a sequence of waypoints and the safety constraints (e.g., obstacle avoidance) are imposed on waypoints. Therefore, the trajectory between waypoints could violate the safety constraints. A naive continuous-time extension results in unrealistic computation cost. In this paper, we propose a novel approach to ensure constraint satisfaction between waypoints without employing a continuous-time formulation. The key idea is to enforce that the same inequality constraint is satisfied on any two adjacent time steps, under assumptions of polygonal obstacles and straight line trajectory between waypoints. The resulting problem encoding is MILP, which can be solved efficiently by commercial solvers. Thus, we also introduce novel extensions to risk-allocation path planners with improved scalability for real-world scenarios and run-time performance. While the proposed encoding approach is general, the particular emphasis of this paper is placed on the chance-constrained, nonconvex path-planning problem (CNPP). We provide extensive simulation results on CNPP to demonstrate the path safety and scalability of our encoding and related path planners.
Reinforcement learning and model predictive control for robust embedded quadrotor guidance and control

A new method for enabling a quadrotor micro air vehicle (MAV) to navigate unknown environments using reinforcement learning (RL) and model predictive control (MPC) is developed. An efficient implementation of MPC provides vehicle control and obstacle avoidance. RL is used to guide the MAV through complex environments where dead-end corridors may be encountered and backtracking is necessary. All of the presented algorithms were deployed on embedded hardware using automatic code generation from Simulink. Results are given for flight tests, demonstrating that the algorithms perform well with modest computing requirements and robust navigation.
Deep convolutional network with locality and sparsity constraints for texture classification

Recent studies have demonstrated advantages of the representations learned by Convolutional Neural Networks (CNNs) in providing an appealing paradigm for visual classification tasks. Most existing methods adopt activations from the last fully connected layer as the image representation. This paper advocates exploiting appropriately convolutional layer activations to constitute a powerful descriptor for texture classification under an end-to-end learning framework. The main component of our method is a new locality-aware coding layer conducted with the locality constraint, where the dictionary and the encoding representation are learned simultaneously. The layer is readily amenable to training via the backpropagation as the locality-aware coding process has an analytical solution. It is capable of capturing class-specific information which makes the learned convolutional features more robust. The resulting representation is particularly useful for texture classification. Comprehensive experiments on the DTD, FMD and KTH-T2b datasets show that our approach notably outperforms the state-of-the-art methods.
Euphoria: A Scalable, event-driven architecture for designing interactions across heterogeneous devices in smart environments

Context: From personal mobile and wearable devices to public ambient displays, our digital ecosystem has been growing with a large variety of smart sensors and devices that can capture and deliver insightful data to connected applications, creating thus the need for new software architectures to enable fluent and flexible interactions in such smart environments. Objective: We introduce Euphoria, a new software architecture design and implementation that enables easy prototyping, deployment, and evaluation of adaptable and flexible interactions across heterogeneous devices in smart environments. Method: We designed Euphoria by following the requirements of the ISO/IEC 25010:2011 standard on Software Quality Requirements and Evaluation applied to the specific context of smart environments. Results: To demonstrate the adaptability and flexibility of Euphoria, we describe three application scenarios for contexts of use involving multiple users, multiple input/output devices, and various types of smart environments, as follows: (1) wearable user interfaces and whole-body gesture input for interacting with public ambient displays, (2) multi-device interactions in physical-digital spaces, and (3) interactions on smartwatches for a connected car application scenario. We also perform a technical evaluation of Euphoria regarding the main factors responsible for the magnitudes of the request-response times for producing, broadcasting, and consuming messages inside the architecture. We deliver the source code of Euphoria free to download and use for research purposes. Conclusion: By introducing Euphoria and discussing its applicability, we hope to foster advances and developments in new software architecture initiatives for our increasingly complex smart environments, but also to readily support implementations of novel interactive systems and applications for smart environments of all kinds.
Probabilistic Appearance-Based Place Recognition Through Bag of Tracked Words

A key feature in robotics applications is to recognize whether the current environment observation corresponds to a previously visited location. Should the place be recognized by the robot, a Loop Closure Detection (LCD) has occurred. The letter in hand deploys a novel low complexity LCD method based on the representation of the route by unique visual features (VFs). Each of these VFs, referred to as “Tracked Word” (TW), is generated on-line through a tracking technique coupled with a guided feature-detection mechanism and belongs to a group of successive images. During the robot's navigation, new TWs are added to the database forming a bag of tracked words. When querying the database seeking for loop closures, the new local-feature descriptors are associated with the nearest neighboring TWs in the map casting votes to the corresponding instances. The system relies on a probabilistic method to select the most suitable loop closing pair, based on the number of votes each location polls. The proposed system depends solely on the appearance information of the scenes on the trajectory, without requiring any pre-training phase. The evaluation of the method is administered via a variety of tests with several community datasets, thus proving its capability of achieving high recall rates for perfect precision.
Learning Blockchain Delays

Despite the growing interest in cryptocurrencies, the delays incurred to confirm transactions are one of the factors that hamper the wide adoption of systems such as Bitcoin. Bitcoin transactions usually are confirmed in short periods (minutes), but still much larger than conventional credit card systems (seconds). In this work, we propose a framework encompassing machine learning and a queueing theory model to (i) identify which transactions will be confirmed; and (ii) characterize the confirmation time of confirmed transactions. The proposed queueing theory model accounts for factors such as the activity time of blocks and the mean time between transactions. We parameterize the model for transactions that are confirmed within minutes, suggesting that its integration into a more general framework is a step towards building scalability to Bitcoin.
Deterministic bidirectional communication and remote entanglement generation between superconducting qubits

Abstract We propose and experimentally demonstrate an efficient scheme for bidirectional and deterministic photonic communication between two remote superconducting modules. The two chips, each consists of a transmon, are connected through a one-meter long coaxial cable that is coupled to a dedicated “communication” resonator on each chip. The two communication resonators hybridize with a mode of the cable to form a dark “communication mode” that is highly immune to decay in the coaxial cable. We overcome the various restrictions of quantum communication channels established by other recent approaches in deterministic communication for superconducting qubits. Our approach enables bidirectional communication, and eliminates the high insertion loss and large volume footprint of circulators. We modulate the transmon frequency via a parametric drive to generate sideband interactions between the transmon and the communication mode. We demonstrate bidirectional single-photon transfer with a success probability exceeding 60%, and generate an entangled Bell pair with a fidelity of 79.3 ± 0.3%.
Cryptocarbon: The promises and pitfalls of forest protection on a blockchain

In this commentary, we explore how blockchain is being leveraged to address the fundamental problems with market-based forest protection globally. In doing so, we consider the ways ‘cryptocarbon’ initiatives are creating new challenges that have so far escaped critical scrutiny. A blockchain is a distributed and immutable electronic database – a ledger of every transaction that has ever taken place on a network, stored as cryptographically secured blocks, strung together in a chain. The technology is being increasingly hyped as applicable for a whole range of industries, social service provisions, and environmental management concerns. This includes the facilitation of natural asset market mechanisms, like Reducing Emissions from Deforestation and Forest Degradation (REDD+). The original aim of REDD+ was to incentivise conservation, making tropical forests more valuable standing than cut down. Multiple factors, including lack of consumer interest, created an oversupply of carbon commodities. Ninety-five percent of the world’s avoided deforestation credits, representing millions of hectares of conserved forest, were stuck without a buyer. Several flagging REDD+ projects are now hoping that blockchain technology can carry them to new heights of market capitalisation. However, like with any powerful new technology, the benefits remain ambiguous.
Hybrid Nonsmooth Barrier Functions With Applications to Provably Safe and Composable Collision Avoidance for Robotic Systems

Robots are entering an age of ubiquity, and to operate effectively, these systems must typically satisfy a series of constraints (e.g., collision avoidance, obeying speed limits, maintaining connectivity). In addition, modern applications hinge on the completion of particular tasks, such as driving to a certain location or monitoring a crop patch. The dichotomy between satisfying constraints and completing objectives creates a need for constraint-satisfaction frameworks that are composable with a pre-existing primary objective. Barrier functions have recently emerged as a practical and the composable method for constraint satisfaction, and prior results demonstrate a system of Boolean logic for nonsmooth barrier functions as well as a composable controller-synthesis framework; however, this prior work does not consider dynamically changing constraints (e.g., a robot sensing and avoiding an obstacle). Consequently, the main theoretical contribution of this letter extends nonsmooth barrier functions to time-varying barrier functions with jumps. In a practical instantiation of the theoretical main results, this letter revisits a classic problem by formulating a collision-avoidance framework and composing it with a nominal controller. Experimental results show the efficacy of this framework on a light detection and ranging (LIDAR)-equipped differential-drive robot in a real-time obstacle-avoidance scenario.
What Drives Cryptocurrency Prices?

The Google Trends 1 search analysis service and the Telegram 2 messaging platform are investigated to determine their respective relationships to cryptocurrency price behaviour. It is shown that, in contrast to earlier findings, the relationship between cryptocurrency price movements and internet search volumes obtained from Google Trends is no longer consistently positive, with strong negative correlations detected for Bitcoin and Ethereum during June 2018. Sentiment extracted from cryptocurrency investment groups on Telegram is found to be positively correlated to Bitcoin and Ethereum price movements, particularly during periods of elevated volatility. The number of messages posted on a Bitcoin-themed Telegram group is found to be an indicator of Bitcoin price action in the subsequent week. A long shortterm memory (LSTM) recurrent neural network is developed to predict the direction of cryptocurrency prices using data obtained from Google Trends and Telegram. It is shown that Telegram data is a better predictor of the direction of the Bitcoin market than Google Trends. The converse is true for Ethereum. The LSTM model produces the most accurate results when predicting price movements over a one-week period.
RED: A Simple but Effective Baseline Predictor for the TrajNet Benchmark

In recent years, there is a shift from modeling the tracking problem based on Bayesian formulation towards using deep neural networks. Towards this end, in this paper the effectiveness of various deep neural networks for predicting future pedestrian paths are evaluated. The analyzed deep networks solely rely, like in the traditional approaches, on observed tracklets without human-human interaction information. The evaluation is done on the publicly available TrajNet benchmark dataset [39], which builds up a repository of considerable and popular datasets for trajectory prediction. We show how a Recurrent-Encoder with a Dense layer stacked on top, referred to as RED-predictor, is able to achieve top-rank at the TrajNet 2018 challenge compared to elaborated models. Further, we investigate failure cases and give explanations for observed phenomena, and give some recommendations for overcoming demonstrated shortcomings.
Enhancing two-qubit quantum coherence in a correlated dephasing channel

We investigate how the classical correlations between successive actions of a colored dephasing channel affect the decoherence of a two-qubit system. Our calculation shows that the l1 norm of coherence, the relative entropy of coherence, and the robustness of coherence can be noticeably enhanced by increasing the strength of classical correlations in the whole time interval during which the two qubits pass through the channel, while the coherence weight is enhanced only when exceeds a critical value. Moreover, in the infinite-time limit, all the coherence measures reach their steady values which are determined only by .
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Real world datasets, particularly in the current context of Big Data applications, suffer from the problem of imbalanced representation of samples from different categories. Most classifiers and learning techniques are inept to deal with this problem, with the majority of them tending to overlook the issue. Typical data balancing methods in literature resort to data sampling that constitutes of either undersampling the majority class samples or oversampling the minority class samples. An intelligent combination of undersampling the majority class and oversampling the minority class is expected to improve the learning performance. In this paper, data balancing is achieved prior to classification, through a novel three-step sequence of intelligent undersampling of the majority class followed by the oversampling of the minority class, which is further followed by the intelligent undersampling of the minority class that has now become the majority class due to the oversampling. The recently proposed Sample Subspace Optimization (SSO) that uses Particle Swarm Optimization (PSO) as an intelligent agent to find globally optimum solutions in the search space, is our choice for the intelligent undersampling technique. The oversampling in the second step is achieved through Synthetic Minority Oversampling (SMOTE) as well as intelligent variants such as Borderline SMOTE, ADASYN and MWMOTE. The increase in computational complexity is compensated by the higher performance achieved due to relevant sampling. Experiments on benchmark datasets from the UCI repository establish the efficiency of our three-step approach SSOMaj–SMOTE–SSOMin as observed from the higher AUC scores from the Receiver Operating Characteristics.
Robust capped L1-norm twin support vector machine

Twin support vector machine (TWSVM) is a classical and effective classifier for binary classification. However, its robustness cannot be guaranteed due to the utilization of squared L2-norm distance that can usually exaggerate the influence of outliers. In this paper, we propose a new robust capped L1-norm twin support vector machine (CTWSVM), which sustains the advantages of TWSVM and promotes the robustness in solving a binary classification problem with outliers. The solution of the proposed method can be achieved by optimizing a pair of capped L1-norm related problems using a newly-designed effective iterative algorithm. Also, we present some theoretical analysis on existence of local optimum and convergence of the algorithm. Extensive experiments on an artificial dataset and several UCI datasets demonstrate the robustness and feasibility of our proposed CTWSVM.
Big data and IoT solution for patient behaviour monitoring

The study of patient behaviours (vital sign, physical action and emotion) is crucial to improve one’s quality of life. The only solution for handling and managing millions of people’s behaviours and health would be big data and IoT technology because most of the countries are lack of medical professionals. In this paper, a big data and IoT-based patient behaviour monitoring system have proposed. Qualitative studies are carried out on the selected behaviours analytics, cardiovascular disease identification and fall detection. At last, authors have summarised the general challenges like trust, privacy, security and interoperability as well as special challenges in various sectors: government, legislators, research institutions, information technology companies and patients.
Siamese: scalable and incremental code clone search via multiple code representations

This paper presents a novel code clone search technique that is accurate, incremental, and scalable to hundreds of million lines of code. Our technique incorporates multiple code representations (i.e., a technique to transform code into various representations to capture different types of clones), query reduction (i.e., a technique to select clone search keywords based on their uniqueness), and a customised ranking function (i.e., a technique to allow a specific clone type to be ranked on top of the search results) to improve clone search performance. We implemented the technique in a clone search tool, called Siamese, and evaluated its search accuracy and scalability on three established clone data sets. Siamese offers the highest mean average precision of 95% and 99% on two clone benchmarks compared to seven state-of-the-art clone detection tools, and reported the largest number of Type-3 clones compared to three other code search engines. Siamese is scalable and can return cloned code snippets within 8 seconds for a code corpus of 365 million lines of code. Using an index of 130,719 GitHub projects, we demonstrate that Siamese’s incremental indexing capability dramatically decreases the index preparation time for large-scale data sets with multiple releases of software projects. The paper discusses the applications of Siamese to facilitate software development and research with two use cases including online code clone detection and clone search with automated license analysis.