Reviewerly/exHarmony huggingface dataset:
Information Visualization Design of Nuclear Power Control System Based on Attention Capture Mechanism

This paper takes information features of the main control room display of nuclear power plant for analysis, in which the design factors are extracted base on the monitoring interface task and visual cognitive law. With respect to the visual design method of interface presentation, we explore the design of the information display system of boron and water supply system. We determine the important information elements that can be improved in monitoring behavior, and optimize them according to their characteristic attributes. We group interface information according to task relevance, and design the color coding of the group information according to the structure of visual perception and the characteristics of color perception level. The information color between different levels could be adjusted according to readability and importance. The results of experiments verify the validity of the color coding method based on attention capture. According to the results, a visual design model of nuclear power control system information is established based on the research from the local interface icon to the overall presentation. This method can provide reference for the design of control system interface of nuclear power plant, and even improve the cognitive performance of operators and the safety and reliability of power plant operation.
Specimens in a Broader Context: The National Ecological Observatory Network and the extended specimen

Community innovations in both specimen digitization (e.g., Morphbank; SlideAtlas; Inselect, Hudson et al. 2015) and data standards (e.g., the National Science Foundation initative "Advancing Digitization of Biodiversity Collections", Page et al. 2015, Nelson and Shari 2019; Darwin Core (Darwin Core Task Group 2009)), have resulted in digitized specimens with rich contextual metadata and the capacity to share such specimen information widely. These extended specimens have allowed for the exploration of cross-scale research questions that traverse multiple taxonomic, spatial and temporal scales. As a relatively new collection organization, the National Ecological Observatory Network (NEON; Keller et al. 2008) has curated and archived &amp;gt;200,000 specimens to date and is projected to archive between 80,000 and 120,000 specimens annually through its 30-year, continental-scale environmental monitoring program. NEON has embraced the Extended Specimen paradigm (introduced by Webster 2017; NEON's implementation described in Lendemer et al. 2020), and each sample is physically and digitally curated from the point of collection enabling sample discoverability that maximizes specimen Findability, Accessibility, Interoperability, and Reusability (the FAIR standard; Wilkinson et al. 2016). All archived specimens are associated with precise spatial and temporal information and (where available/applicable) NEON also integrates specimen images, morphometrics, genetic sequences and taxonomic data with the specimen records within a Symbiota platform. Any additional analyses or derived specimens created by the research community are also linked in the specimen record. NEON has benefited substantially from community development of tools and standards, but the process of data integration has not been without problems. Here, we will discuss challenges NEON has faced in the implementation of the extended specimen as well as solutions.
Sequent Calculus for a Program-oriented Predicate Logic over Complex-Named Data

The multiplicity of data structures used in programming complicates analysis and verification of software systems. Nominative data aim to serve as a unified model of different data structures. The main feature of such data is usage of complex names to access or modify data components. This leads to a problem considered in the paper: to define and investigate a class of nominative data with complex names (complex-named data), operations on this class, properties of this class (specified as predicates over such data), and compositions of such predicates. We define an algebra of predicates over data with complex names and specify a first order logic that describes general properties of such algebras. For this logic we construct a sequent calculus and prove its soundness and completeness.
Prediction-Decision Network For Video Object Tracking

In this paper, we introduce an approach for visual tracking in videos that predicts the bounding box location of a target object at every frame. This tracking problem is formulated as a sequential decision-making process where both historical and current information are taken into account to decide the correct object location. We develop a deep reinforcement learning based strategy, via which the target object position is predicted and decided in a unified framework. Specifically, a RNN based prediction network is developed where local features and global features are fused together to predict object movement. Together with the predicted movement, some predefined possible offsets and detection results form into an action space. A decision network is trained in a reinforcement manner to learn to select the most reasonable tracking box from the action space, through which the target object is tracked at each frame. Experiments in an existing tracking benchmark demonstrate the effectiveness and robustness of our proposed strategy.
From 8-bit punk to 8-bit avant-garde

Music technology has advanced remarkably since the 1980s, yet the 8-bit sounds of computers and video game consoles from that era are still considered iconic and difficult to replicate. The sound chips originally used in these devices are no longer compatible with modern tools for music making, heavily constraining the further exploration of this popular aesthetics. With this paper, I present the ongoing development of a novel platform, built with open-source embedded technologies, and designed for the integration of vintage sound chips in widely used music programming and instrument design frameworks. The goal of the project is to innovate chiptune music practice, while preserving the role of authentic hardware and fostering the appropriation of its signature limitations.
Incorporating Verb Semantic Information in Visual Question Answering Through Multitask Learning Paradigm

Visual Question Answering (VQA) concerns providing answers to Natural Language questions about images. Several deep neural network approaches have been proposed to model the task in an end-to-end fashion. Whereas the task is grounded in visual processing, if the question focuses on events described by verbs, the language understanding component becomes crucial. Our hypothesis is that models should be aware of verb semantics, as expressed via semantic role labels, argument types, and/or frame elements. Unfortunately, no VQA dataset exists that includes verb semantic information. Our first contribution is a new VQA dataset (imSituVQA) that we built by taking advantage of the imSitu annotations. The imSitu dataset consists of images manually labeled with semantic frame elements, mostly taken from FrameNet. Second, we propose a multi-task CNN-LSTM VQA model that learns to classify the answers as well as the semantic frame elements. Our experiments show that semantic frame element classification helps the VQA system avoid inconsistent responses and improves performance. Third, we employ an automatic semantic role labeler and annotate a subset of the VQA dataset (VQA sub ). This way, the proposed multi-task CNN-LSTM VQA model can be trained with the VQA sub as well. The results show a slight improvement over the single-task CNN-LSTM model.
Applying the Closed World Assumption to SUMO-Based FOL Ontologies for Effective Commonsense Reasoning.

Most commonly, the Open World Assumption is adopted as a standard strategy for the design, construction and use of ontologies. This strategy limits the inferencing capabilities of any system because non-asserted statements (missing knowledge) could be assumed to be alternatively true or false. As we will demonstrate, this is especially the case of first-order logic (FOL) ontologies where non-asserted statements is nowadays one of the main obstacles to its practical application in automated commonsense reasoning tasks. In this paper, we investigate the application of the Closed World Assumption (CWA) to enable a better exploitation of FOL ontologies by using state-of-the-art automated theorem provers. To that end, we explore different CWA formulations for the structural knowledge encoded in a FOL translation of the SUMO ontology, discovering that almost 30 % of the structural knowledge is missing. We evaluate these formulations on a practical experimentation using a very large commonsense benchmark obtained from WordNet through its mapping to SUMO. The results show that the competency of the ontology improves more than 50 % when reasoning under the CWA. Thus, applying the CWA automatically to FOL ontologies reduces their ambiguity and more commonsense questions can be answered
Quantum estimation through a bottleneck

We study the estimation of a single parameter characterizing families of unitary transformations acting on two systems. We consider the situation with the presence of bottleneck, i.e., only one of the systems can be measured to gather information. The estimation capabilities are related to unitaries’ generators. In particular, we establish continuity of quantum Fisher information with respect to generators. Furthermore, we find conditions on the generators to achieve the same maximum quantum Fisher information we would have in the absence of bottleneck. We also discuss the usefulness of initial entanglement across the two systems as well as across multiple estimation instances.
Some Means Supporting Responsibility and Openness in Information and Control Systems

The structure of many current systems is independent of related responsibility borders. It comes from the fact that they are designed by programmers that as a rule give more importance to efficient data moving than to reflecting existing business and control processes and to precise personal or organizational responsibility. It results in non-trivial issues when looking for reasons and responsibility for improper system behaviour and its overall quality. Another serious issue is that the requirements on the systems are changing during their development and use. Accommodation to the changes often introduces new serious issues to the systems. Proper support for responsibility and flexibility is a necessary precondition for solving the issues or for recovery from them. We cope with those issues by proposing software architecture simplifying alignment of software with responsibility domains. The solution is enabled by a proper application of specific software service types described here. The proposed solution significantly improves system and organizational flexibility and openness and brings many other interesting engineering advantages. The proposed solution is based on observations from human behaviour and roles within organizations and their cooperating groups.
Adaptive Aggregated Tracklet Linking for Multi-Face Tracking

This paper addresses the problem of multi-face tracking. The proposed framework first assembles discrete detections into short reliable sequences (code-named `tracklets') and second, links them via hierarchical clustering. A key challenge in face tracking in real world scenarios `on the wild' or under adverse conditions is the drastic variations across scenes (e.g: variations in illumination, pose, occlusion, motion blur, etc.). To this end, the paper presents a simple hierarchical clustering approach which exploits the intra-scene compactness while minimizing the impact of inter-scene variations. The algorithm adapts to different domains by dynamically learning domain-specific parameters through density dispersion analysis. The proposed framework is evaluated on three databases, which demonstrate competitive performance with state-of the-art clustering and tracklet linking approaches. In addition, the paper presents a challenging database which is publicly available, on which the end-to-end tracking performance is reported.
A Knowledge-based Multi-entity and Cooperative System Architecture

Future intelligent systems will become more complex and they will be composed of potentially very different artificial agents like mobile robots, smart home infrastructure, and smartphones that collect data and that have capabilities to perform certain tasks. The challenges will be that the elements such a system is composed of will not be known in advance and might change dynamically. Further, the tasks that need to be fulfilled will be unknown beforehand and might require cooperation with humans to make use of their abilities. In this paper we provide a description of a system with which we try to tackle these challenges. Our system uses available resources (robots, humans) and the actual state of the environment to provide a plan in order to satisfy a given request. We demonstrate the flexibility of our system through varying the available resources and the state of the environment. To our knowledge there are few approaches only that (i) explicitly model and use humans together with intelligent agents in the same plan in this kind of abstraction level, and (ii) that have a system using heterogeneous agents at the same time.
An Attack-Immune Trusted Architecture for Supervisory Intelligent Terminal

With the wide application of the mobile Internet, many aspects such as mobile payment, mobile office, private-data protection, security control of terminals and so on will face huge secure pressure. For that reason, this paper presents a supervisory control architecture based on secure SOC system with attack-immune and trustworthiness for intelligent terminals, which possesses ability for dynamic integrity measurement (DIM) without interference and trusted escrow application, meanwhile, this trusted architecture is fully verified based on FPGA prototype system. Compared with other schemes, this trusted architecture has higher security and dynamic integrity measurement efficiency, which can thoroughly supervise running of mobile OS and applications.
Legal Aspects of Genetic Testing Regarding Insurance and Employment

The legal analysis of genetic testing in insurance and employment reveals layered tensions that are central to the governance of contemporary social risk. The development of genomic medicine promises much, through the ability to identify risk on an individualized basis. However, individualization inevitably raises concerns about privacy protection, all the more pressing when genetic testing is not a fully refined, predictive technology. The individualization of risk yields still more profound tensions to the extent that the Post-War consensus in many societies has been to seek to transfer certain social risks away from the individual, by collectivizing them through a range of state-supported social security mechanisms. On the occasion of the 20th General Congress of the International Academy of Comparative Law held on 24 July 2018 at Kyushu University in Fukuoka, Japan, legal experts worldwide prepared reports on the legal aspects of genetic testing in the contexts of insurance and employment, fourteen of which are revised as chapters in this edited volume. This general report highlights commonalities and differences between legal regimes on their approach to genetic testing in these contexts. It chronicles the justifications for and against the collection of genetic information by insurers and employers, legal definitions, as well as the specific rights and obligations and generalist frameworks that apply from a privacy rights and anti-discrimination lens. This comparative endeavor concludes with a reflection on the relevance of transnational standards to address the legal challenges of genetic testing in insurance and employment.
An automated framework for gaming platform to test multiple games

Game testing is a necessary but challenging task for gaming platforms. Current game testing practice requires significant manual effort. In this paper, we proposed an automated game testing framework combining adversarial inverse reinforcement learning algorithm with evolutionary multi-objective optimization. This framework aims to help gaming platform to assure market-wide game qualities as the framework is suitable to test different games with minimum manual customization for each game.
Supervised Multi-View Distributed Hashing

Multi-view hashing efficiently integrates multi-view data for learning compact hash codes, and achieves impressive large-scale retrieval performance. In real-world applications, multi-view data are often stored or collected in different locations, where hash code learning is more challenging yet less studied. To fulfill this gap, this paper proposes a novel supervised multi-view distributed hashing (SMvDisH) for hash code learning from multi-view data in a distributed manner. SMvDisH yields the discriminative latent hash codes by joint learning of latent factor model and classifier. With local consistency assumption among neighbor nodes, the distributed learning problem is divided into a set of decentralized sub-problems. The sub-problems can be solved in parallel, and the computational and communication costs are low. Experimental results on three large-scale image datasets demonstrate that SMvDisH achieves competitive retrieval performance and trains faster than state-of-the-art multi-view hashing methods.
Unsupervised Features Extracted using Winner-Take-All Mechanism Lead to Robust Image Classification

Leading mainstream image processing approaches produce excellent performance using convolutional neural networks trained by backpropagation (BP) learning rules. Unsupervised learning approaches have been popular due to their biological significance, though they typically underperform compared to BP results. In this work, we demonstrate that features extracted in an unsupervised manner using the biologically inspired Hebbian learning rule in a winner-take-all setting, perform competitively with BP on the image classification task. The convolutional filters learned by Hebbian rule are smoother than filters learned using BP. The quality of the two training approaches is compared based on metrics such as the speed of training and classification accuracy. We demonstrate that the extracted features of unsupervised learning are more robust to noise as compared to BP.
Deep Autoencoder Architectures For Foreground Object Detection In Video Sequences Based On Probabilistic Mixture Models

Foreground object detection algorithms should be insensitive to noise present in the analyzed video sequences. In this work, a study of a type of non-supervised deep learning network, called autoencoder, is performed. They are suited to reduce input dimensionality and capture the most relevant information present in a region or image. Therefore, different types of autoencoders, deterministic and variational, with different architectures, activation functions and number of layers, are analyzed. This neural network is combined with a probabilistic mixture model which attempts to classify each video frame region as background and foreground.
Quantum steganography based on reflected gray code for color images

Digital steganography is the art and science of hiding information in covert channels, so as to conceal the information and prevent the detection of hidden messages. On the classic computer, the principle and method of digital steganography has been widely and deeply studied, and has been initially extended to the field of quantum computing. Quantum image steganography is a relatively active branch of quantum image processing, and the main strategy currently used is to modify the LSB of the cover image pixels. For the existing LSB-based quantum image steganography schemes, the embedding capacity is no more than 3 bits per pixel. Therefore, it is meaningful to study how to improve the embedding capacity of quantum image steganography. This work presents a novel steganography using reflected Gray code for color quantum images, and the embedding capacity of this scheme is up to 6 bits per pixel. In proposed scheme, the secret qubit sequence is considered as a sequence of 6-bit segments. For 6 bits in each segment, the first 3 bits are embedded into the second LSB of RGB channels of the cover image, and the remaining 3 bits are embedded into the LSB of RGB channels of the cover image using reflected-Gray code to determine the embedded bit from secret information. Following the transforming rule, the LSBs of stego-image are not always same as the secret bits and the differences are up to almost 50%. Experimental results confirm that the proposed scheme shows good performance and outperforms the previous ones currently found in the literature in terms of embedding capacity.
Parallel SCC Detection Based on Reusing Warps and Coloring Partitions on GPUs

Detecting strongly connected components (SCCs) in directed graphs is crucial for analyzing the structure of graphs. It can accelerate the process of large-scale graphs with applying GPUs to graph computation. However, GPU-based SCC detection still faces the challenge from graphs, especially real-world graphs, skewing and strict synchronization requirement. In this paper, we proposed a new paradigm of identifying SCCs with reusing warps and coloring partitions. The scheme of reusing warps assigns multiple vertices to each virtual warp at one time, which greatly reduces the number of warps that are applied but assigned few tasks. Furthermore, we proved that the partitions formed by coloring independently contains all SCCs in them, so we use coloring to identify more partitions and parallel detect SCCs in each partition to get more SCCs. Finally, the colored partitions are deeply combined with the following traversal to optimize the algorithm. We conduct extensive theoretical and experimental analysis to demonstrate the efficiency and accuracy of our approach. The experimental results expose that our approach can achieve 7.23\(\times \), 30.55\(\times \), 1.75\(\times \) and 1.26\(\times \) speedup for SCC detection using NVIDIA K80 compared with algorithms of Tarjan, Barnat, Hong and Slota respectively.
Cycleptz: The Learning-Based Control Method For Master-Slave Camera Systems

A master-slave setup consists of fixed and PTZ cameras monitoring a scenario to provide high-resolution images of target regions. Most of the works in literature focus on the unrealistic setting with a single person and single ground plane. Differently, in this work we proposed the CyclePTZ, a learning-based method that learns the mappings between master-slave and slave-master using a cycle-consistent neural network. While the master-slave mapping is used to control the PTZ cameras, the slave-master works as a supplementary supervisor for network training and to perform hard sample mining. More importantly, as both functions are learned simultaneously using the cycle loss, the CyclePTZ is able to learn a better mapping between fixed and PTZ cameras. Experimental results demonstrate that the proposed CyclePTZ is able to follow targets in multiple ground planes and to record corresponding points with multiple people in the scene. We compare the proposed method with the current literature in real-time experiments that demonstrate favorable performance of CyclePTZ (i.e., reducing the target center error in 10.92 percentage points).