Reviewerly/exHarmony huggingface dataset:
Developing a Lightweight Rock-Paper-Scissors Framework for Human-Robot Collaborative Gaming

We present a novel implementation of a Rock-Paper-Scissors (RPS) game interaction with a social robot. The framework is tailored to be computationally lightweight, as well as entertaining and visually appealing through collaboration with designers and animators. The fundamental gesture recognition pipeline employs a Leap motion device and two separate machine learning architectures to evaluate kinematic hand data on-the-fly. The first architecture is used to recognize and segment human motion activity in order to initialize the RPS play, and the second architecture is used to classify hand gestures into rock, paper or scissors. The employed tabletop robot interacts in the RPS play through unique animated gestural movements and vocalizations designed by animators which communicate the robot's choices as well as cognitive reflection on winning, losing and draw states. Performance of both learning architectures is carefully evaluated with respect to accuracy, reliability and run time performance under different feature and classifier types. Moreover, we assess our system during an interactive RPS play between robot and human. Experimental results show that the proposed system is robust to user variations and play style in real environment conditions. As such, it offers a powerful application for the subsequent exploration of social human-machine interaction.
Bidirectional heuristic search to find the optimal Bayesian network structure

Bayesian networks have many applications. Learning the optimal structure of a Bayesian network has always been important in this respect. In this paper, a bidirectional heuristic search algorithm is proposed for the order graph space commonly used in a Bayesian network. At the same time, heuristic functions that are admissible and consistent in terms of both forward and backward search are proposed to ensure convergence of the algorithm to the optimal solution. The experimental results show that, compared with traditional unidirectional heuristic search, in most cases, the bidirectional heuristic search proposed in this paper needs to expand fewer states, the convergence efficiency is higher, and less running time is needed.
ModelMine

Mining Software Repositories (MSR) has opened up new pathways and rich sources of data for research and practical purposes. This research discipline facilitates mining data from open source repositories and analyzing software defects, development activities, processes, patterns, and more. Contemporary mining tools are geared towards data extraction, analysis primarily from textual artifacts and have limitations in representation, ranking and availability. This paper presents ModelMine, a novel mining tool focuses on mining model-based artifacts and designs from open source repositories. ModelMine is designed particularly to mine software repositories, artifacts and commit history to uncover information about software designs and practices in open-source communities. ModelMine supports features that include identification and ranking of open source repositories based on the extent of presence of model-based artifacts and querying repositories to extract models and design artifacts based on customizable criteria. It supports phase-by-phase caching of intermediate results to speed up the processing to enable efficient mining of data. We compare ModelMine against a state-of-the-art tool named PyDriller in terms of performance and usability. The results show that ModelMine has the potential to become instrumental for cross-disciplinary research that combines modeling and design with repository mining and artifacts extraction. URL: https://www.smreza.com/projects/modelmine/
Complexity Determination of Stream Cipher Sequence based on Discrete-Time Signal Transformation

Recently, stream cipher systems have played a big role, especially in computer networks. Stream cipher systems depend on Pseudo-Random (P.R.) binary key sequences which are mixed with the plaintexts using addition with modulo two to produce the ciphertexts. The PR key sequences are characterized by three properties which are complexity, period, and randomness. In order to determine the Complexity degree of these Pseudo-Random sequences, many methods and techniques were used, like statistical method, Berlekamp-Massey method, information theory parameters method, and software computing techniques (Fuzzy logic, Neural Networks, Genetic Algorithms, and Adaptive Neural Fuzzy Inference System -ANFIS-). The main aim of this paper is to use a proposed method to determine the complexity degree of these Pseudo-Random sequences using one of the properties of the Z-transform, which is periodic sequence property. This proposed method enables the researchers to compute the complexity degree of any periodic sequence produced from linear or nonlinear generators, and accurate results are obtained. The steps of the proposed procedure are given, two different examples are illustrated one of them for a P.R. sequence over G.F. (2), and the second example for P.R. sequence over G.F. (5).
Knowledge Discovery in Simulation Data

This article provides a comprehensive and in-depth overview of our work on knowledge discovery in simulations. Application-wise, we focus on manufacturing simulations. Specifically, we propose and discuss a methodology for designing, executing, and analyzing large-scale simulation experiments with a broad coverage of possible system behavior targeted at generating knowledge about the system. Based on the concept of data farming, we suggest a two-phase process which starts with a data generation phase, in which a smart experiment design is used to set up and efficiently execute a large number of simulation experiments. In the second phase, the knowledge discovery phase, data mining and visually aided analysis methods are applied on the gathered simulation input and output data. This article gives insights into this knowledge discovery phase by discussing different machine learning approaches and their suitability for different manufacturing simulation problems. With this, we provide guidelines on how to conduct knowledge discovery studies within the manufacturing simulation context. We also introduce different case studies, both academic and applied, and use them to validate our methodology.
Blockchain as Key Enabling Technology for Future Electric Energy Exchange: A Vision

Our energy scenario is nowadays shaped by progressive electrification of energy final use.In this context, electricity networks are seeing a growing multitude of distributed assets entering from the edges of the grid and acquiring new ICT capabilities that were limited before to a restricted number of major players.Particularly, assets like Photovoltaic Inverter (PvI), Electric Vehicle (EV) chargers, wind turbines controllers, programmable loads, storage systems, and other Distributed Energy Resources (DER) are now able to communicate through different technologies and make conscious choices under humandecisions or even independently.This is leading to a decentralization of the system's view by increasing single actor independence.Notwithstanding, a problem arises when current centrally-managed electricity networks struggle to coordinate massive amounts of new figures and adapt to this new decentralized paradigm.Therefore, a decentralized coordination-and-control framework will ensure better integration of DERs and new figures as prosumers, while allowing higher exploitation of their potential compared to centrally managed systems.This article seeks in Blockchains the enabling technology for designing and supporting such a grid infrastructure.It develops a first framework to address this need by envisioning a gridsystem based on the direct participation of nowadays-used embedded-energy-devices within a decentralized platform hosting specific coordination procedures.The platform was developed in an experimental research campaign performed at ABB Laboratories basing on embedded-devices currently designed as controlconnectivity boards for smart-inverters.Therefore this article introduces the background theory and reasons behind this proposed system.The intent here is not to give all the specific details of the implementation, but introduce the supporting reason, high-level design, and required characteristic of the Blockchain-based platform for coordinating grid operations.Blockchain technology is seen here as the appropriate technology to enable the realization of a multi-actor energy-management system and enable distributed coordination in power grids.
On the data set’s ruins

Abstract Computer vision aims to produce an understanding of digital image’s content and the generation or transformation of images through software. Today, a significant amount of computer vision algorithms rely on techniques of machine learning which require large amounts of data assembled in collections, or named data sets. To build these data sets a large population of precarious workers label and classify photographs around the clock at high speed. For computers to learn how to see, a scale articulates macro and micro dimensions: the millions of images culled from the internet with the few milliseconds given to the workers to perform a task for which they are paid a few cents. This paper engages in details with the production of this scale and the labour it relies on: its elaboration. This elaboration does not only require hands and retinas, it also crucially zes mobilises the photographic apparatus. To understand the specific character of the scale created by computer vision scientists, the paper compares it with a previous enterprise of scaling, Malraux’s Le Musée Imaginaire , where photography was used as a device to undo the boundaries of the museum’s collection and open it to an unlimited access to the world’s visual production. Drawing on Douglas Crimp’s argument that the “musée imaginaire”, a hyperbole of the museum, relied simultaneously on the active role of the photographic apparatus for its existence and on its negation, the paper identifies a similar problem in computer vision’s understanding of photography. The double dismissal of the role played by the workers and the agency of the photographic apparatus in the elaboration of computer vision foreground the inherent fragility of the edifice of machine vision and a necessary rethinking of its scale.
Financial systems, regulatory quality, and economic growth

This study examined symmetric and asymmetric causal relationships among financial systems, regulatory quality, and economic performance in selected African countries. The patterns of causality and impulse responses were found to vary across the selected countries, and the following were confirmed: symmetric demand-following, symmetric supply-leading, symmetric feedback, and neutrality hypotheses. Also confirmed were negative and positive demand-following hypotheses, negative and positive supply-leading hypotheses, and negative and positive feedback hypotheses. Overall, our recommendation is that in cases where supply-leading hypothesis is confirmed, policy target should be financial development so as to either stimulate economic growth or prevent economic decline, whereas in cases where demand-following hypothesis is confirmed, emphasis should be placed on growth-enhancing policies in order to either achieve financial development or prevent crisis in the financial system. We also argue that the quality of regulation plays an important role in the finance-growth nexus as it has a mediating effect on both the real and financial sectors. We further argue that it is important to consider asymmetric dynamics when testing causality in finance-growth relationships. It is possible that the economy (financial system) would react differently to changes in financial system (economy), depending on whether the changes are positive or negative.
TACAM: Topic And Context Aware Argument Mining

In this work we address the problem of argument search. The purpose of argument search is the distillation of pro and contra arguments for requested topics from large text corpora. In previous works, the usual approach is to use a standard search engine to extract text parts which are relevant to the given topic and subsequently use an argument recognition algorithm to select arguments from them. The main challenge in the argument recognition task, which is also known as argument mining, is that often sentences containing arguments are structurally similar to purely informative sentences without any stance about the topic. In fact, they only differ semantically. Most approaches use topic or search term information only for the first search step and therefore assume that arguments can be classified independently of a topic. We argue that topic information is crucial for argument mining, since the topic defines the semantic context of an argument. Precisely, we propose different models for the classification of arguments, which take information about a topic of an argument into account. Moreover, to enrich the context of a topic and to let models understand the context of the potential argument better, we integrate information from different external sources such as Knowledge Graphs or pre-trained NLP models. Our evaluation shows that considering topic information, especially in connection with external information, provides a significant performance boost for the argument mining task.
Association Rules Enhanced Knowledge Graph Attention Network

Embedding knowledge graphs into continuous vector spaces has recently attracted increasing interest in knowledge base completion. However, in most existing embedding methods, only fact triplets are utilized, and logical rules have not been thoroughly studied for the knowledge base completion task. To overcome the problem, we propose an association rules enhanced knowledge graph attention network (AR-KGAN). In this paper, triplets and logical rules are jointly modeled in the proposed unified framework to achieve more predictive entity and relation embeddings. Association rules and corresponding correlation degrees between them can be automatically obtained according to our designed mining algorithm. The major component of AR-KGAN is an encoder of an effective neighborhood aggregator, which addresses the problems by aggregating neighbors with both association rules based and graph based attention weights. The decoder enables AR-KGAN to be translational between entities and relations while keeping the superior link prediction performance. Then, the global loss is minimized over both atomic and complex formulas to achieve the embedding task. In this manner, we learn embeddings compatible with triplets and association rules, which are certainly more predictive for knowledge acquisition and inference. The results show that the proposed AR-KGAN model achieves significant and consistent improvements over state-of-the-art methods on three benchmark datasets.
Boundaries of Hate: Ethical Implications of the Discursive Construction of Hate Speech in U.S. Opinion Journalism

In the United States, hate speech sits at the intersection of ethical and legal debates and has a complex relationship with journalism. The First Amendment provides broad legal protections for hate speech, and U.S. journalists have historically been known for their advocacy for speech freedoms. However, ethical debate persists about the extent to which hate speech should be used within U.S. public discourse. At the nexus of this debate are journalists with intersecting and – possibly – competing duties. This study examines the discursive construction of hate speech in U.S. opinion journalism, analyzing journalistic discourse published in U.S. editorials and opinion columns between 1998 and 2019. The study finds five key themes that, together, indicate the use of hate speech as a means to articulate a libertarian interpretation of public discourse, facilitating a reductionist conception of freedom of expression that does not live up to journalism's duties to democracy.
Anonymous Auction Protocol Based on Time-Released Encryption Atop Consortium BlockChain

The Ethereum block chain as a decentralized platform is so successful that many applications deployed on it.However, for the inherent transparency properties and the lack of privacy, deploying a financial application on top of it is always a challenge.In this paper, we tackle this challenge and propose an anonymous sealed-bid auction protocol based on time-released encryption atop Consortium Block chain.We adopt a strict digital certificate-based identity mechanism of the consortium block chain to permit legitimate participants, and utilize the blind signature based on elliptic curve technology to allowing anonymous participation.Moreover, a timed release public key encryption algorithm is adopted to encrypt bids and prevent auctioneer from colluding with bidders.This is completely different from the method (zero-knowledge proof) used in other papers to prevent collusion between auctioneer and bidder.We provide a specific analysis of our protocol, which shows that our protocol meets anonymity and applicability.
Characterizing quantum states via sector lengths

Correlations in multiparticle systems are constrained by restrictions from quantum mechanics. A prominent example for these restrictions are monogamy relations, limiting the amount of entanglement between pairs of particles in a three-particle system. A powerful tool to study correlation constraints is the notion of sector lengths. These quantify, for different $k$, the amount of $k$-partite correlations in a quantum state in a basis-independent manner. We derive tight bounds on the sector lengths in multi-qubit states and highlight applications of these bounds to entanglement detection, monogamy relations and the $n$-representability problem. For the case of two- and three qubits we characterize the possible sector lengths completely and prove a symmetrized version of strong subadditivity for the linear entropy.
A NOTE ON DERIVABILITY CONDITIONS

We investigate relationships between versions of derivability conditions for provability predicates. We show several implications and non-implications between the conditions, and we discuss unprovability of consistency statements induced by derivability conditions. First, we classify already known versions of the second incompleteness theorem, and exhibit some new sets of conditions which are sufficient for unprovability of Hilbert--Bernays' consistency statement. Secondly, we improve Buchholz's schematic proof of provable $\Sigma_1$-completeness. Then among other things, we show that Hilbert--Bernays' conditions and L\"ob's conditions are mutually incomparable. We also show that neither Hilbert--Bernays' conditions nor L\"ob's conditions accomplish G\"odel's original statement of the second incompleteness theorem.
Tree++: Truncated Tree Based Graph Kernels

Graph-structured data arise ubiquitously in many application domains. A fundamental problem is to quantify their similarities. Graph kernels are often used for this purpose, which decompose graphs into substructures and compare these substructures. However, most of the existing graph kernels do not have the property of scale-adaptivity, i.e., they cannot compare graphs at multiple levels of granularities. Many real-world graphs such as molecules exhibit structure at varying levels of granularities. To tackle this problem, we propose a new graph kernel called Tree++ in this paper. At the heart of Tree++ is a graph kernel called the path-pattern graph kernel. The path-pattern graph kernel first builds a truncated BFS tree rooted at each vertex and then uses paths from the root to every vertex in the truncated BFS tree as features to represent graphs. The path-pattern graph kernel can only capture graph similarity at fine granularities. In order to capture graph similarity at coarse granularities, we incorporate a new concept called super path into it. The super path contains truncated BFS trees rooted at the vertices in a path. Our evaluation on a variety of real-world graphs demonstrates that Tree++ achieves the best classification accuracy compared with previous graph kernels.
A Novel Coding Architecture for Multi-Line LiDAR Point Clouds Based on Clustering and Convolutional LSTM Network

Light detection and ranging (LiDAR) plays an indispensable role in autonomous driving technologies, such as localization, map building, navigation and object avoidance. However, due to the vast amount of data, transmission and storage could become an important bottleneck. In this article, we propose a novel compression architecture for multi-line LiDAR point cloud sequences based on clustering and convolutional long short-term memory (LSTM) networks. LiDAR point clouds are structured, which provides an opportunity to convert the 3D data to 2D array, represented as range images. Thus, we cast the 3D point clouds compression as a range image sequence compression problem. Inspired by the high efficiency video coding (HEVC) algorithm, we design a novel compression framework for LiDAR data that includes two main techniques: intra-prediction and inter-prediction. For intra-frames, inspired by the depth modeling modes (DMM) adopted in 3D-HEVC, we develop a clustering-based intra-prediction technique, which can utilize the spatial structure characteristics of point clouds to remove the spatial redundancy. For inter-frames, we design a prediction network model using convolutional LSTM cells. The network model is capable of predicting future inter-frames using the encoded intra-frames. As a result, temporal redundancy can be removed. Experiments on the KITTI dataset demonstrate that the proposed method achieves an impressive compression ratio (CR), with 4.10% at millimeter precision, which means the point clouds can compress to nearly 1/25 of their original size. Additionally, compared with the well-known octree, Google Draco, and MPEG TMC13 methods, our algorithm yields better performance in compression ratio.
Linguistic focus guides attention during the encoding and refreshing of Working Memory content

Focus is a linguistic device that marks a piece of information within an utterance as most relevant, as when emphasis is placed by the speaker on a word using phonological stress, special intonation, or prosodic prominence. The question addressed in the present study is whether the use of linguistic focus is best seen as a means of directing the listener’s attention. We investigated attention allocation on the part of the listener to linguistically focused elements in Working Memory in a series of eye-tracking experiments. We concentrated on two processes: the encoding of the focused element and its retention. Attentional load during encoding was measured by pupil dilation, and attention allocation during retention was estimated from fixations to locations of previously present visual stimuli on a blank screen. It was found that i) more attention was allocated during the processing of sentences with linguistic focus and ii) linguistically focused elements received more attention during memory retention. However, when the task demanded the sharing of attention, the advantage of the focused element during retention disappeared. Further experiments showed that when verbal stimuli whose prominence was not linguistically marked were presented, the patterns of attention allocation associated with linguistic focus during retention replicated. These results lend further support to the claim that linguistic focus is a grammaticalized means of expressing prominence, and as such, functions as an attention capturing device.