Reviewerly/exHarmony huggingface dataset:
Temporal Knowledge Graph Incremental Construction Model for Recommendation

Knowledge graph (KG) has been proven to be effective to improve the performance of recommendation because of exploiting structural and semantic paths information in a static knowledge base. However, the KG is an incremental construction process with interactions occurring in succession. Although some works have been proposed to explore the evolution of knowledge graph, which updates the entity representations by considering the previous interactions of related entities. However, we believe that the semantic path information between the involved entities and the occurring interaction itself also can refine their representations. To this end, we propose a temporal knowledge graph incremental construction model, which updates the entity representations by considering interaction itself and high-order semantic paths information. Specifically, different length semantic paths between user and item are automatically extracted when an interaction occurs. Then we respectively employ recurrent neural network and standard multi-layer perceptron (MLP) to capture different length path semantic information and interaction itself information for updating the entity representations. Finally, we use MLP to predict the probability that a user likes an item after seamlessly integrating these variations into a unified representation. We conduct experiments on real-world datasets to demonstrate the superiority of our proposed model over all state-of-the-art baselines.
Cost-Minimizing System Design for Surveillance of Large, Inaccessible Agricultural Areas Using Drones of Limited Range

Drones are used increasingly for agricultural surveillance. The limited flight range of drones poses a problem for surveillance of large, inaccessible areas. One possible solution is to place autonomous, solar-powered charging stations within the area of interest, where the drone can recharge during its mission. This paper designs and implements a software system for planning low-cost drone coverage of large areas. The software produces a feasible, cost-minimizing charging station placement, as well as a drone path specification. Multiple optimizations are required, which are formulated as integer linear programs. In extensive simulations, the resulting drone paths achieved 70–90 percent of theoretical optimal performance in terms of minimizing mission time for a given number of charging stations, for a variety of field configurations.
Querying the IoT Using Multiresolution Contexts

People's daily life is increasingly intertwined with smart devices, which are more and more used in dynamic contexts. Therefore, searching and exploiting the wealth of information produced by the Internet of Things (IoT) require novel models, including a representation of the actual context of use. The definition of context is inherently difficult, due to the variety of application scenarios and user needs. In this article, we propose a general model for devices' contexts representing context components at different resolutions (or levels of granularity). This enables the definition of a multiresolution context-based algorithm for querying the IoT, according to given preferences and contexts that can be tightened or relaxed depending on the given application goal. Experimental results show how the proposed approach outperforms traditional solutions by increasing the retrieval of relevant results while keeping precision under control.
Institution-Based Encoding and Verification of Simple UML State Machines in CASL/SPASS

We present a new approach on how to provide institution-based semantics for UML state machines. Rather than capturing UML state machines directly as an institution, we build up a new logical framework \(\mathcal {M}^{\downarrow }_{\mathcal {D}}\) into which UML state machines can be embedded. A theoroidal comorphism maps \(\mathcal {M}^{\downarrow }_{\mathcal {D}}\) into the \(\textsc {Casl}\) institution. This allows for symbolic reasoning on UML state machines. By utilising the heterogeneous toolset \(\textsc {HeTS}\) that supports \(\textsc {Casl}\), a broad range of verification tools, including the automatic theorem prover \(\textsc {Spass}\), can be combined in the analysis of a single state machine.
Grade10: A Framework for Performance Characterization of Distributed Graph Processing

Graph processing is one of the most important and ubiquitous classes of analytical workloads. To process large graph datasets with diverse algorithms, tens of distributed graph processing frameworks emerged. Their users are increasingly expecting high performance for diversifying workloads. Meeting this expectation depends on understanding the performance of each framework. However, performance analysis and characterization of a distributed graph processing framework is challenging. Contributing factors are the irregular nature of graph computation across datasets and algorithms, the semantic gap between workload-level and system-level monitoring, and the lack of lightweight mechanisms for collecting fine-grained performance data. Addressing the challenge, in this work we present Grade10, an experimental framework for fine-grained performance characterization of distributed graph processing workloads. Grade10 captures the graph workload execution as a performance graph from logs and application traces, and builds a fine-grained, unified workload-level and system-level view of performance. Grade10 samples sparsely for lightweight monitoring and addresses the problem of accuracy through a novel approach for resource attribution. Last, it can identify automatically resource bottlenecks and common classes of performance issues. Our real-world experimental evaluation with Giraph and PowerGraph, two state-of-the-art distributed graph processing systems, shows that Grade10 can reveal large differences in the nature and severity of bottlenecks across systems and workloads. We also show that Grade10 can be used in debugging processes, by exemplifying how we find with it a synchronization bug in PowerGraph that slows down affected phases by 1.10-2.50×. Grade10 is an open-source project available at https://github.com/atlarge-research/grade10.
Analysis and application of martial arts video image based on fuzzy clustering algorithm

The intelligent scheduling algorithm for hierarchical data migration is a key issue in data management. Mass media content platforms and the discovery of content object usage patterns is the basic schedule of data migration. We add QPop, the dimensionality reduction result of media content usage logs, as content objects for discovering usage patterns. On this basis, a clustering algorithm QPop is proposed to increase the time segmentation, thereby improving the mining performance. We hired the standard C-means algorithm as the clustering core and used segmentation to conduct an experimental mining process to collect the ted QPop increments in practical applications. The results show that the improved algorithm has good robustness in cluster cohesion and other indicators, slightly better than the basic model.
Crafting Interactive Narrative Games with Adversarial Planning Agents from Simulations

This paper describes Adversario, an implemented system for creating interactive narrative games centered around an adversarial planner directing autonomous agents to achieve goals that conflict with player goals. Through our implementation, we present a novel framework for creating interactive narrative games wherein the autonomous agents guided by the adversarial planner have rich and varied attributes derived from a complex social simulation. By doing this, we show that it is possible to craft interactive narrative games with a set of carefully curated domain actions to produce interactions in a procedurally generated world with rich characters. Our system bridges several gaps between interactive narrative game design, procedural content generation, and classical AI planning. We demonstrate this by transforming a simulation-planning based narrative generation system into a system for streamlining the process of crafting interactive narrative games involving rich procedurally generated agents.
Markerless Rat Behavior Quantification With Cascade Neural Network

Quantifying rat behavior through video surveillance is crucial for medicine, neuroscience, and other fields. In this paper, we focus on the challenging problem of estimating landmark points, such as the rat's eyes and joints, only with image processing and quantify the motion behavior of the rat. Firstly, we placed the rat on a special running machine and used a high frame rate camera to capture its motion. Secondly, we designed the cascade convolution network (CCN) and cascade hourglass network (CHN), which are two structures to extract features of the images. Three coordinate calculation methods-fully connected regression (FCR), heatmap maximum position (HMP), and heatmap integral regression (HIR)-were used to locate the coordinates of the landmark points. Thirdly, through a strict normalized evaluation criterion, we analyzed the accuracy of the different structures and coordinate calculation methods for rat landmark point estimation in various feature map sizes. The results demonstrated that the CCN structure with the HIR method achieved the highest estimation accuracy of 75%, which is sufficient to accurately track and quantify rat joint motion.
Dense Subgraphs Summarization: An Efficient Way to Summarize Large Scale Graphs by Super Nodes

For large scale graphs, the graph summarization technique is essential, which can reduce the complexity for large-scale graphs analysis. The traditional graph summarization methods focus on reducing the complexity of original graph, and ignore the graph restoration after summarization. So, in this paper, we proposed a graph Summarization method based on Dense Subgraphs (DSS) and attribute graphs (dense subgraph contains cliques and quasi cliques), which recognizes the dense components in the complex large-scale graph and converts the dense components into super nodes after deep sub-graph mining process. Due to the nodes in the dense component are closely connected, our method can easily achieve the lossless reduction of the summarized graph. Experimental results show that our method performs well in execution time and information retention, and with the increase of data, DSS algorithm shows good scalability.
Opinion Mining System for Twitter Sentiment Analysis

After the paradigm shift produced by Web 2.0, the volume of opinion on the Internet has increased exponentially. The expansion of social media, whose textual content is somewhat subjective and comes loaded with opinions and assessments, can be very useful for recommending a product or brand. This information is an interesting challenge from the perspective of natural language processing, but is also an aspect of deep interest and great value not only as a marketing strategy for companies and political campaigns, but also as an indicator measuring consumer satisfaction with a product or service. In this paper, we present an opinion mining system that uses text mining techniques and natural language processing to automatically obtain useful knowledge about opinions, preferences and user trends. We studied improvements in the quality of opinion classification by using a voting system to choose the best classification of each tweet, base on of the absolute majority of the votes of the algorithms considered. In addition we developed a visualization tool that automatically combines these algorithms to assist end-user decision making. The opinion mining tool makes it possible to analyze and visualize data published on Twitter, to understand the sentiment analysis of users in relation to a product or service, by identifying the positive or negative sentiment expressed in Twitter messages.
Monitoring, modelling and simulation of bifacial PV modules over normal and high albedos

This paper explains the modelling and simulation of we modeled and simulate the power output of bifacial PV modules installed over different albedos using monofacial PV performance models (analytical and empirical). The analytical model and empirical models were evaluated using experimental data obtained from the testbed consisting of three bifacial PV arrays mounted in the same configuration but with different rear irradiance conditions: (1) backside covered by white plastic, (2) over normal albedo, and (3) over high albedo. The models use the measured effective solar irradiances and the PV module temperatures to simulate the power output in a healthy condition; they were adapted to bifacial modules by introducing the irradiance bifacial gain in the models' equations. The results of the comparison showed that both models achieved good performance to simulate the instantaneous output of the PV arrays. Daily energy relative errors during sunny days were much less than overcast days, or about 1% for both models, excluding the shading effect. However, compared to the empirical model, the analytical model achieved better performances considering the bifaciality of the PV modules. In addition, the error for the entire monitored period (74 days during March, April, and May 2020) was less than 1.5% for the analytical model, including sunny and cloudy days; such high accuracy in the energy output forecast shows that the analytical model can accurately predict the generation of bifacial PV modules under different skies conditions.
The study of narrative acts with and for digital media

Abstract In this article, we introduce the concept of narrative act which denotes a meta-action—an action on an action—that contributes to the narrative sequence. Narrative acts have been often described in narrative theories, in particular within structuralism, but never systematically studied. Examples of narrative acts include Ask, Order, or Forbid. Moreover, in the interactive digital narrative (IDN) field, such narrative acts may take a central role when they are used as building blocks in combinatorial generative systems. Therefore, this article proposes a unique effort to establish an extensive catalog of such narrative acts, with the view that this catalog is a work in progress by definition since it targets the IDN domain, which consists of a very limited corpus of works. It is, however, a necessary step toward a more theoretical approach of narrative acts in IDN. The catalog contains more than 200 narrative acts, for which a number of features have been collected. Among those is the source of the act, either a narrative or speech act theory or a theoretical work or an implemented system. A general three-level taxonomy is also proposed, associated with an interactive visualization tool available online, allowing researchers and creative authors to consult and expand the catalog.
Vitrivr-Explore: Guided Multimedia Collection Exploration for Ad-hoc Video Search

vitrivr is an open-source system for indexing and retrieving multimedia data based on its content and it has been a fixture at the Video Browser Showdown (VBS) in the past years. While vitrivr has proven to be competitive in content-based retrieval due to the many different query modes it supports, its functionality is rather limited when it comes to exploring a collection or searching result sets based on content. In this paper, we present vitrivr-explore, an extension to the vitrivr stack that allows to explore multimedia collections using relevance feedback. For this, our implementation integrates into the existing features of vitrivr and exploits self-organizing maps. Users initialize the exploration by either starting with a query or just picking examples from a collection while browsing. Exploration can be based on a mixture of semantic and visual features. We describe our architecture and implementation and present first results of the effectiveness of vitrivr-explore in a VBS-like evaluation. These results show that vitrivr-explore is competitive for Ad-hoc Video Search (AVS) tasks, even without user initialization.
PGE distribution in the chromitite layers at Eastern Chrome Mine, Eastern Bushveld Complex, South Africa: A descriptive study with comparison of EPMA and LA-ICP-MS methods for detection of trace PGE in base metal sulphides

Abstract A descriptive petrographic study was carried out on the chromitites (LG-6 to UG-3a) of a single borehole from the Winterveld Chrome Mine, Eastern BC, along with analysis by both EPMA and LA-ICP-MS of the PGE contents of base metal sulphides (BMS) found within these layers. EPMA measurements were optimized to measure trace amounts of PGE by increased probe current and counting time, yielding very low detection limits for this method (12 to 38 ppm). However, analytical error proved to be very high (over ~30%) in samples that showed PGE values below 100 ppm. Also, the possibility of interference on PGE spectra by other elements, especially on Rh, is high. Therefore, these measurements can only be considered semi-quantitative indicators of elemental PGE concentration. Our findings confirmed that pentlandite of the BC chromitites contains Pd and Rh, probably in solid solution. Pt was found in discrete minerals (braggite, cooperite) throughout the sequence, and Ru, Ir and Os were found as early-stage minerals such as laurite, associated with chromite rather than sulphide. The PPGE increase upwards in the chromitite layers relative to the IPGE. An immiscible sulphide phase contributed to collection of the PGE in the chromitites of the MG-4 and above. The chromitites of the LG-6 to MG-1 show less evidence of hosting an immiscible sulphide phase, and pentlandite of these layers does not appear to host Rh, although Pd was found. Although this borehole is located only 1.5 km away from the centre of the Steelpoort Fault Zone, a possible feeder zone for the BC, large-scale disruption of the chromitite layers was not observed. An intrusive pegmatoidal pyroxenite was found to occur beneath the LG-6, and contained PGE-bearing sulphides, although the origin of this mineralization is most likely from the LG-6 chromitite itself. A sulphide vein occurring in silicate was also analysed and was found to be PGE-deficient compared to the sulphides of the chromitite layers.
Opportunities and Challenges of Quantum Radar

It has been recognized for over half a century that quantum mechanics defines the ultimate limits for sensing devices, and products of that recognition are now on the horizon of practical implementation. One of the most promising of those products is quantum radar, which offers a capability to significantly improve the tradeoff between energy and detection sensitivity compared to classical alternatives. We provide a back-of-the-envelope and implementation-agnostic analysis to glimpse the kinds of expected improvement that a quantum radar could provide for applications of interest. Our analysis relies on relatively simplistic models that are only intended to facilitate an intuitive grasp of quantum-versus-classical distinctions. Such analyses of course cannot lead to definitive conclusions, but we believe they provide evidence that in some contexts quantum radar can be expected to offer realizable practical advantages over classical alternatives. We conclude with a discussion of theoretical and practical obstacles and a high-level summary of what can be said about the present status of quantum radar.
Lightweight Implementation of the LowMC Block Cipher Protected Against Side-Channel Attacks

LowMC is a parameterizable block cipher developed for use in Multi-Party Computation (MPC) and Fully Homomorphic Encryption (FHE). In these applications, linear operations are much less expensive in terms of resource utilization compared to the non-linear operations due to their low multiplicative complexity. In this work, we implemented two versions of LowMC -- unrolled and lightweight. Both implementations are realized using RTL VHDL. To the best of our knowledge, we report the first lightweight implementation of LowMC and the first implementation protected against side-channel analysis (SCA). For the SCA protection, we used a hybrid 2/3 shares Threshold Implementation (TI) approach, and for the evaluation, the Test Vector Leakage Assessment (TVLA) method, also known as the T-test. Our unprotected implementations show information leakage at 10K traces, and after protection, they could successfully pass the T-test for 1 million traces. The Xilinx Vivado is used for the synthesis, implementation, functional verification, timing analysis, and programming of the FPGA. The target FPGA family is Artix-7, selected due to its widespread use in multiple applications. Based on our results, the numbers of LUTs are 867 and 3,328 for the lightweight and the unrolled architecture with unrolling factor U = 16, respectively. It takes 14.21 μs for the lightweight architecture and 1.29 μs for the unrolled design with U = 16 to generate one 128-bit block of the ciphertext. The fully unrolled architecture beats the best previous implementation by Kales et al. in terms of the number of LUTs by a factor of 4.5. However, this advantage comes at the cost of having 2.9 higher latency.
A method of depth extension in 3D shape measurement by geometric constraints

Abstract In this paper a new method to extend the measurement depth limited by geometric constraints for objects with large depth variances is presented. This method mainly includes the following steps: (1) using the watershed image segmentation algorithm to extract the correct part of the discontinuous absolute phase distribution obtained by geometric constraints, and making a binary mask of the correspondent region; (2) using the window Fourier filter-quality guided phase unwrapping algorithm to obtain the relative phase distribution of the tested object; (3) using the binary mask to get the difference between the relative and the absolute phases; (4) converting the relative phase distribution to absolute phase distribution by referring to their phase difference in the mask region. The experimental results prove the effectiveness and correctness of the proposed method.
Performance Optimization Mechanism of Adolescent Physical Training Based on Reinforcement Learning and Markov Model

Upon the teenagers’ failure to obtain the plenty of physical exercises at the growth and development stage, the related central nervous system is prone to degeneration and the physical fitness starts to decline gradually. In fact, through monitoring the exercise process real-timely and quantifying the exercise data, the adolescent physical training can be effectively conducted. For such process, it involves two issues, i.e., the real-time data monitoring and data quantification evaluation. Therefore, this paper proposes a novel method based on Reinforcement Learning (RL) and Markov model to monitor and evaluate the physical training effect. Meanwhile, the RL is used to optimize the adaptive bit rate of surveillance video and help the real-time data monitoring; the Markov model is employed to evaluate the health condition on the physical training. Finally, we develop a real-time monitoring system on exercise data and compare with the state-of-the-art mechanisms based on this system platform. The experimental results indicate that the proposed performance optimization mechanism can be more efficient to conduct the physical training. Particularly the average evaluation deviation rate based on Markov model is controlled within 0.16%.
iNNk

This paper presents iNNK, a multiplayer drawing game where human players team up against an NN. The players need to successfully communicate a secret code word to each other through drawings, without being deciphered by the NN. With this game, we aim to foster a playful environment where players can, in a small way, go from passive consumers of NN applications to creative thinkers and critical challengers.