Reviewerly/exHarmony huggingface dataset:
Seamless Grid: an off-chain model proposal for scalable P2P electricity markets and grids management

The increasing interest shown in Blockchain Technology in recent years has involved also the electric energy sector which is seeking new solutions to manage the rising penetration into the grid of Distributed Energy Resources (DERs) and Demand Response (DR) users and their secure operational integration into the electricity markets. The participation of small distributed resources to a Peer-to-peer (P2P) electricity market running without the intermediation of a trusted third party represents an additional challenge for which the research has addressed Blockchain as a possible game changer solution. This work aims to propose a model based on Bitcoin Blockchain and the Lightning Network - an off-chain second layer protocol - in order to manage fast and secure transactions of electricity in a prototype 3 nodes grids. This model represents a novel solution that, even based on a solid blockchain system, can overcome the intrinsic trustlessness versus scalability limits that affects traditional public blockchains.
Binary Classification Using Pairs of Minimum Spanning Trees or N-Ary Trees

One-class classifiers are trained only with target class samples. Intuitively, their conservative modeling of the class description may benefit classical classification tasks where classes are difficult to separate due to overlapping and data imbalance. In this work, three methods leveraging on the combination of one-class classifiers based on non-parametric models, Trees and Minimum Spanning Trees class descriptors (MST_CD) are proposed. These methods deal with inconsistencies arising from combining multiple classifiers and with spurious connections that MST-CD creates in multi-modal class distributions. Experiments on several datasets show that the proposed approach obtains comparable and, in some cases, state-of-the-art results.
Digital Forensics in Industrial Control Systems

The increasing complexity of industrial control systems (ICS) and interconnection with other systems poses more safety- and/or security-related challenges due to a rising number of attacks and errors. The event reconstruction is the goal of the new field of ICS forensics differing from well-established Desktop-IT forensics. We identify ICS properties, implications and the impact on the forensic process. Our primary contribution is the identifcation of ICS specific properties and their impact on the forensic process in order to foster forensic capabilities and forensic readiness in ICS. An existing model for Desktop-IT forensics is successfully adapted for use in ICS.
A Question-Driven Source Code Recommendation Service Based on Stack Overflow

In order to help users get the source code in SO directly, this paper proposes a question-driven source code recommendation service based on the source code in Stack Overflow(SO). The service utilizes a question-code matching model to recommend users source code snippets which can solve the development problems users encounter. We evaluate the recommendation accuracy of the recommendation service and verify its feasibility. This service outperforms other approaches on recommendation accuracy, and the effectiveness of the service is also discussed.
Study on the Path Planning of the Spherical Mobile Robot based on Fuzzy Control

To improve the adaptability of the spherical mobile robot in unknown environment, this paper proposed the behavior-based fuzzy control method, and integrated fuzzy obstacle avoidance performance into this method. Firstly, the sensors were arranged according to the motion model of the spherical mobile robot, and which improved the flexibility and autonomy of the spherical mobile robot in unstructured environment. In the process of motion, the spherical mobile robot use multi-sensor, which is used to collect the obstacles and the target position information in the environment, to complete the path planning. Then, the MobotSim simulation platform was used to design the behavior-based control experiments for the spherical mobile robot, including wall-following, through narrow road, obstacle avoidance and trending target experiments.etc. The simulation results verified the validity and reliability of the fuzzy control system. Finally, the path planning experiments in the real environment was completed, which controlled the spherical mobile robot behavior by fuzzy controller and priority decisions, including obstacle avoidance, wall-following, cruising, tending to target and unlocking the deadlock behaviors, and so on, and modified the pose of the robot based on the angle of real-time feedback information to avoid obstacles precisely. The experimental results showed that the spherical mobile robot could complete control method based on behavior, further verified the effectiveness and practicability of the fuzzy control system.
Irregular MDS Array Codes With Fewer Parity Symbols

An irregular MDS array code is a class of array codes allowing unequal sizes of each columns. In this paper, two classes of irregular MDS array codes are proposed to reduce the number of parity symbols at certain cases. The first code is based on polynomial remainder codes, and the second code uses the code bundling method. In addition, a systematic encoding approach of the polynomial remainder code is also presented.
Recommender Systems in the Offline Retailing Domain: A Systematic Literature Review

Recommender systems have over the years proved to be effective in overcoming the challenges related to the incredible growth of the information on the Web, and nowadays they are evidently popular in a variety of apparently disparate domains, e.g., e-commerce, tourism and healthcare. Nonetheless, no Systematic Literature Review (SLR), and even no literature survey, aimed at analyzing the current state of academic and industrial research knowledge on recommender systems for the offline retailing domain is reported in the literature. There is, therefore, a need for conducting a SLR that allows revealing the trends and challenges in the research and development of recommender systems for that domain. In this chapter, we present the details about the planning, execution and analysis of results of a SLR of the state of the art of recommender systems for the offline retailing domain. The findings of the analysis of results shed light, among other things, on the necessity of further research on recommendation systems and algorithms for the offline retailing domain, specially, for small local stores and chain stores in the categories of department stores, drugstores and convenience stores.
Accuracy for Sentiment Analysis of Twitter Students on ELearning in Indonesia using Naive Bayes Algorithm Based on Particle Swarm Optimization

Abstract Students can use social media such as Twitter for online learning (E-Learning). This study aims to analyse an accuracy for the sentiments of students about E-Learning who use Indonesian on Twitter social media both positive and negative opinions. The algorithms used are Naive Bayes (NB). Then to optimize the accuracy of the calculation results, the Particle Swarm Optimization (NB-PSO) approach is used. In order to optimize accurate results, this study uses three experimental sequences (scenario 1, scenario 2, and scenario 2) for both NB and NB-PSO algorithms. Each scenario uses different positive and negative comments. The results of the experiment show that in scenario 1 an increase in accuracy is 10.00% for NB-PSO. Scenario 2 there is an increase in accuracy of 13.33% on NB-PSO. Meanwhile, in scenario 3 an increase in accuracy is 27.22% for NB-PSO. These results prove that the accuracy of NB-PSO is better than NB for all scenarios.
3D Visualization of Network Including Nodes with Labels

Visual cluttering is still a severe problem of large-scale network visualization techniques, and therefore many improvements on network visualization have been presented. Meanwhile, many network datasets in our daily life contain label information. We are developing a network visualization technique which overlays a node-link diagram for visualizing the connectivity and a set visualization for representing the label information. Our technique firstly places a set of nodes in an input dataset into a 3D space and provides an interactive mechanism to manipulate viewpoints in the 3D space. The technique then projects the nodes onto a 2D plane and generates a Delaunay triangular mesh connecting the nodes which have the user-specified label on the 2D plane. Finally, it displays the outer boundary of the triangular mesh to represent the region enclosing the nodes which have the label. In addition, supposing that multiple weighted labels can be assigned to the same node, our implementation draws nodes like pie charts to clearly represent the weights of labels assigned to the nodes. This paper shows examples of the visualization applying a co-authorship network dataset.
Visual Analysis of Formula One Races

In this paper we describe an interactive web-based visual analysis tool for Formula one races. It first provides an overview about all races on a yearly basis in a calendar-like representation. From this starting point, races can be selected and visually inspected in detail. We support a dynamic race position diagram as well as a more detailed lap times line plot for showing the drivers' lap times in comparison. Many interaction techniques are supported like selections, filtering, highlighting, color coding, or details-on-demand. We illustrate the usefulness of our visualization tool by applying it to a Formula one dataset while we describe the different dynamic visual racing patterns for a number of selected races and drivers.
A Study on 2D and 3D Parallel Coordinates for Pattern Identification in Temporal Multivariate Data

Parallel coordinates are commonly used for non-temporal multivariate data, but there is little support for their usability for displaying temporal multivariate data. In this paper, we introduce a study evaluating the usability of 2D and 3D parallel coordinates for pattern identification in temporal multivariate data. The results indicate that 3D parallel coordinates have higher usability, as measured with higher accuracy and faster response time as well as subjective ratings, compared to 2D.
Blockchain as a Disruptive Technology

Blockchain is a digital, distributed, and decentralized network to store information in a tamper-proof way with an automated way to enforce trust among different participants. An open distributed ledger can record all transactions between different parties efficiently in a verifiable and permanent way. It captures and builds consensus among participants in the network. Each block is uniquely connected to the previous blocks via a digital signature which means that making a change to a record without disturbing the previous records in the chain is not possible, thus rendering the information tamper-proof. Blockchain holds the potential to disrupt any form of transaction that requires information to be trusted. This means that all intermediaries of trust, as they exist today, exposed to disruption in some form with the initiation of Blockchain technology. Blockchain works by validating transactions through a distributed network in order to create a permanent, verified, and unalterable ledger of information.
Information Constrained Control Analysis of Eye Gaze Distribution Under Workload

We describe a novel model of human eye gaze behavior under workload, derived from the basic principle of information constrained control. The model assumes two distributions over the visual field: A saliency distribution, which is nongoal oriented, and a reward task-related distribution. The eye gaze behavior is determined by the tradeoff between these two distributions, where the goal is to preserve the task-related constraints, while remaining as close as possible to the saliency distribution representing a comfort zone. Based on minimum Kullback-Liebler divergence principles, the model gives rise to a family of gaze distributions controlled by a single tradeoff parameter. The model was evaluated experimentally in a driving simulator that consisted of an immersive environment with clear tasks and accurate monitoring capabilities. The findings confirm the theoretical predictions with respect to the low rank manifold and order relations in the data. We show that the model can be used to visualize the unknown reward function associated with a task, and predict human workload based on gaze pattern.
Help by Predicting What to Do

Robots assisting humans with some specific tasks have been demonstrated on several occasions. A further challenging idea is to anticipate human needs by mining the future demand from the next action prediction. To trigger this anticipation mechanism a robot has to recognize what the human is doing now, foresee what the human will do next, and from their connection guesstimating what to do to help. We propose here a deep network combining the essential components of this challenging process leading to foreseeing the help that can be provided in human-robot collaboration.
An Online Crowd Semantic Segmentation Method Based on Reinforcement Learning

In this paper, we propose an online crowd segmentation method based on reinforcement learning. Existing approaches for the task suffers from the fixed parameters applied to different sceneries. We propose to utilize the reinforcement learning to adaptively adjust the critical parameters for better clustering. Specifically, we first propose the velocity-constrained natural nearest neighbor algorithm to adaptively determine the original fixed parameter K in K-nearest-neighbor, thus better reflecting the neighborhood relationship in velocity correlation calculation. Then, we construct an online reinforcement learning module in the grouping process to adaptively decide the segmentation threshold. Furthermore, we combine the motion image semantics with feature point grouping to obtain the pixel-level segmentation. This method enhances the generalization and robustness in different scenarios to increase the accuracy. Comprehensive experimental results conducted on different datasets demonstrate the effectiveness and validity of our method.
A Constant-Time Algorithm for Checking Reachability of Arrival Times and Arrival Velocities of Autonomous Vehicles

A fast algorithm for checking whether an autonomous vehicle can arrive at a position at a given arrival time and velocity is the key to Autonomous Intersection Management (AIM). This paper presents a complete set of closed form equations that fully describes the set of all reachable arrival configurations in longitudinal motion planning if the vehicle's controller is a double integrator with bounded acceleration. This result improves the running time of the algorithm for checking the reachability of an arrival configuration from logarithmic time to constant time. We also apply the result to check the reachability in a segmented road and discuss how the algorithm can be applied to real vehicles.
Gan Based Multi-Exposure Inverse Tone Mapping

High dynamic range (HDR) imaging provide larger range of luminosity and wider color gamut than conventional low dynamic range (LDR) imaging. The method which transforms LDR contents to HDR contents is called inverse tone mapping. After deep neural networks are used in inverse tone mapping problem, researchers mostly focus on transforming normal exposure LDR images to HDR. However, when people use inverse tone mapping in practice, they get some ill-exposed images as well. The state-of-art algorithms can't transform these images to HDR well.In this work, we propose an end-to-end multi-exposure inverse tone mapping (MITM) framework based on existing generative adversarial network (GAN). This framework can transform a single LDR image not only at normal exposure, but also at unsuitable exposure to a normal exposure HDR image. We use histogram equalization to preprocess the luma of the input LDR images; when training the model, we use intrinsic image decomposition to divide the output HDR images into illuminance and reflectance components and use these two components to constrain the luminance information and the color information separately. This framework can adjust the unsuitable exposure and provide a better viewing experience than other state-of-art algorithms in the experimental results.
Portrait Segmentation by Deep Refinement of Image Matting

Portrait segmentation is becoming a hot topic nowadays. In this paper we propose a novel framework to cope with the high precision requirements that portrait segmentation demands on boundary area by deep refinement of the portrait matting. Our approach introduces three novel techniques. First, a trimap is proposed by fusing information coming from two well-known techniques for image segmentation, i.e., Mask R-CNN and DensePose. Second, an alpha matting algorithm runs over the previous trimap generate. From this mate result we generate a couple of masks, one of them boundary-sensitive kernel, called boundary and the other one inside-sensitive kernel called leftover. Third, we refine the portrait by a pre-trained CNN-based model, followed by a transposed convolution. We have evaluated our approach on the PFCN dataset as well as the portrait images collected from COCO dataset. Experimental results demonstrate the better performance of our algorithm over previous methods.
Improving Session Based Recommendation by Diversity Awareness

Recommender systems help users to discover and filter new and interesting products based on their preferences. Session-Based Recommender systems are powerful tools for anonymous e-commerce visitors to understand their behaviours and recommend useful products. Diversity in the recommendations is an important parameter due to increasing the opportunity of recommending new and less similar items that users interacted. Effect of diversity has been investigated in many works for the collaborative filtering-based Recommender systems. However, for session-based Recommender systems, exploring the effect of diversity is still an open area. In this paper, we propose an approach to calculate the diversity level of the items in the session logs and analyse the effect of diversity level on the session-based recommendation. In order to test the impact of diversity awareness, we propose a sequential Item-KNN recommendation model. The final recommendation list is created as a contribution of the interacted items in the session that depends on the diversity level between last interacted item of the session. We conduct several experiments to validate our diversity aware model on a real-world dataset. The results show that diversity awareness in the sessions helps to improve the performance of Recommender system in terms of recall and precision evaluation metrics. Also, the proposed method can be applied to other sequential Recommender system methods, including deep-learning based Recommender systems.
The Classical Capacity of a Quantum Erasure Queue-Channel

We consider a setting where a stream of qubits is processed sequentially. We derive fundamental limits on the rate at which classical information can be transmitted using qubits that decohere as they wait to be processed. Specifically, we model the sequential processing of qubits using a single server queue, and derive expressions for the classical capacity of such a quantum `queue-channel.' Focusing on quantum erasures, we obtain an explicit single-letter capacity formula in terms of the stationary waiting time of qubits in the queue. Our capacity proof also implies that a `classical' coding/decoding strategy is optimal, i.e., an encoder which uses only orthogonal product states, and a decoder which measures in a fixed product basis, are sufficient to achieve the classical capacity of the quantum erasure queue-channel. More broadly, our work begins to quantitatively address the impact of decoherence on the performance limits of quantum information processing systems.
Person Re-Identification by Ranking Ensemble Representations

Existing deep learning algorithms for person re-identification (re-id) typically rely on single-sample classification or pairwise matching constraints. This indicates a breach of deployment due to ignoring the probe-specific matching information against the gallery set encoded in ranking lists. In this work, we address this problem by exploring the idea of RANkinG Ensembles (RANGE) that learns such information from the ranking lists. Specifically, given an off-the-self deep re-id feature representation model, we construct per-probe ranking lists and exploit them to learn inter ranking ensemble representation. To mitigate the harm of inevitable false gallery positives, we further introduce a complementary intra ranking ensemble representation. Extensive experiments show that both supervised and unsupervised re-id benefit from the proposed RANGE method on four challenging benchmarks: MSMT17, Market-1501, DukeMTMC-ReID, and CUHK03.
Piggyback IDE Support for Language Product Lines

The idea to treat domain-specific languages (DSL) as software product lines (SPL) of compilers/interpreters led to the introduction of language product lines (LPL). Although there exist various methodologies and tools for designing LPLs, they fail to provide basic IDE services for language variants-such as, syntax highlighting, auto completion, and debugging support-that programmers normally expect. While state-of-the-art language development tools permit the generation of basic IDE services for a specific language variant, most tools fail to consider and support reuse of basic IDE services of families of DSLs. Consequently, to provide basic IDE services for an LPL, one either generates them for the many language variants or designs a separate SPL of IDEs scattering language concerns. In contrast, we aim to piggyback basic IDE services on language features and provide an IDE for LPLs, which fosters their reuse when generating language variants. In detail, we extended the Neverlang language workbench to permit piggybacking syntax highlighting and debugging support on language components. Moreover, we developed an LPL-driven Eclipse-based plugin that includes a syntax highlighting editor and debugger for an LPL with piggybacked basic IDE services, i.e., where modular language features include the definition for syntax highlighting and debugging. Within this work, we introduce a general mechanism for fostering the basic IDE services' reuse and demonstrate its feasibility by realizing context-aware syntax highlighting for a Java-based family of role-oriented programming languages and providing debugging support for the family of JavaScript-based languages.
Efficient Deep Neural Networks for Edge Computing

Deep Neural Networks(DNNs) have demonstrated state-of-art performance on many problems in different fields such as computer vision, natural language processing, and others. Training of (DNNs) is computationally an expansive because of the backpropagation algorithm. In the inference stage,(DNNs) model requires an expansive computation and high storage cost, which makes them a challenging problem for devices that have constrained resources on the edge. In order to mitigate this issue, a plethora of methods has been applied and published in the literature in recent years. In this paper, we present FPTT, a two-stage pipeline: filter pruning, and Tensor Train Decomposition, that work together to reduce the storage and computation requirements of (DNNs) in order to deploy them easily on the edge. Our work demonstrates the same accuracy or tiny degradation of accuracy with retraining after applying both stages. In particular, for the VGG-16 on CIFAR-10, CIFAR-100 and ImageNet we report compression reduction of 85%, 84.03%, and 93.69% respectively and for Alexnet on CIFAR-10, CIFAR-100 we report compression reduction of 96.5% and 94.5%, and for LeNet-5 on MNIST and CIFAR-10 we report compression rate of 93.2% and 98.71%.