Reviewerly/exHarmony huggingface dataset:
Identity and Policy Based Signcryption Scheme for AMI Downlink Transmission

The smart grid is an upgraded power network that integrates advanced metering infrastructure to provide seamless two-way communication between consumers and suppliers. Many researchers have proposed solutions to confidentiality, integrity, and authenticity in uplink transmission, but there are few pieces of literature on the security of downlink multicast communication. In this paper, we first introduce a new cryptographic concept called identity and policy based signcryption (IPSC) scheme. Then we give a concrete IPSC scheme, and prove the proposed scheme is IND-CPA secure under the DBDH assumption and UF-CMIA secure under the ECDL assumption. Finally, we present a secure and efficient smart grid downlink transmission framework by exploiting our IPSC scheme, which can ensure origin non-repudiation, data confidentiality and integrity, and can also provide fine-grained access control on downlink multicast information.
Human Factors in the Study of Automatic Software Repair

Automatic software repair represents a significant development in software engineering, promising considerable potential change to the working procedures and practices of software engineers. Technical advances have been the focus of many recent publications. However, there has not been an equivalent growth of studies of human factors within automatic software repair. This position paper presents the case for increased research in this area and suggests three key focuses and approaches for a future research agenda. All three of these enable industry-based software engineers not just to provide feedback on automatic software repair tools but to participate in shaping these technologies so that they meet developer and industry needs.
Construction and Application of Knowledge Bases

In order to reduce the impact on complex information, a comprehensive knowledge base is of great urgency to be constructed and the knowledge needs to be hosted on the question-answering robot. A new kind of knowledge base - Association Map is constructed, which is based on human's association of entities and associate related entities. And this paper shows the application of the knowledge base in knowledge retrieval and reasoning of question-answering robot.
Semantic stereo using semiglobal matching and convolution neural networks

3D reconstruction from satellite stereo images is important for numerous applications like 3D city modelling and urban mapping. The Semi Global Matching (SGM) is the state-of-the-art algorithm for dense image matching of satellite stereo images. However, the results of dense image matching are controlled by many factors like, occlusions (presence of some features only in one image), radiometric differences, shadows, texture less surfaces and perspective distortions, which results in significant amount of outliers and missing data in the resulting disparity maps. This problem can be reduced if image segmentation is available. This work explores the fusion of the image segmentation with 3D reconstruction not only to improve the 3D reconstruction results but also to provide the semantic label for each pixel of the image. The image segmentation is itself a challenging problem. Here, we leverage the recent advancements in deep learning to train a Convolution Neural Network (CNN) for segmentation of satellite images in to buildings, roads, water and vegetation classes. The CNN architecture used here is based on the popular U-Net architecture for semantic segmentation. The data from 2019 IEEE GRSS data fusion contest track 2 is used for training of the CNN. The trained network is used for semantic segmentation of the satellite images. These segmentation masks are used to refine the SGM based disparity maps by filtering the disparities and void filling only in the connected component of each segment, which helped in computing smoother disparity maps with fewer outliers and missing data. The evaluation of results of refined disparity and image segmentation was done according to GRSS semantic Labeling, on the bases of IoU. Our net mIoU was 0.7612. As compared to results of simple disparities from SGM results of refined disparity maps were better.
Content Based Image Retrieval on Natural and Artificial Images

Abstract Data or information now is not only presented in text, but can also be presented in other forms for example in the form of images. Still image data and moving picture require storage which is much bigger than the text data. As a consequence, we need a method that can speed up the process of indexing and retrieving digital images as well as to reduce storage needed. So increasing data in the form of digital images does not reduce the speed and performance of computers and image data transfer in the internet. In this paper, we try to explore the JPEG image which is currently considered as an international standard for images in WWW. Most of the images currently stored in a media that we use frequently or on the internet are in JPEG format. This JPEG format has many advantages compared to other formats one of the advantage of JPEG is the size smaller compared to other such as PNG, and TIF. Therefore, JPEG has a very important role in saving storage without reducing quality of image itself. The research was carried out to determine the effectiveness and speed of retrieving natural images and artificial images using the CBIR method, research using no less than 5,000 images, and applying queries from 50 to 80. The results showed than in terms of effectiveness, images artificially better than natural images while natural speed is better. On the hand, speed of retrieving of natural images much better compared to the artificial images.
Preliminary studies on deep-penetrating geochemical methods in exploration for concealed volcanic-type uranium deposit

Abstract Volcanic-type uranium deposit is one of the most important type of uranium resources in China. It has been proved by the scientific deep drilling work accomplished in 2015 that uranium mineralization can be detected at the depth of more than one thousand meters, where there are no significant radiometric or other exploration geochemical anomalies that can be acquired on ground surface. With the progress of exploration for uranium resources in China, deposits which are exposed on surface or shallow-buried are all almost discovered already. It has become a challenging task to explore the deep-buried deposits for uranium geologists. Application experiments are carried out in Xiangshan uranium deposit field using two kinds of deep-penetrating geochemical methods, including the Selective Leaching of Mobile Metals(MOMEO) and Electro-geochemistry techniques(CHIM).The U ore bodies are manifested as a clear CHIM anomaly of U. The adsorbed and exchangeable forms of U obtained with the MOMEO technique is demonstrated as lower single peak anomalies compared with the CHIM technique. The ‘total’ U element content shows no clear anomaly over the uranium mineralization, while there is only a single point high value with an intensity up to 12.5 ppm of U. The polonium-210 shows no significant anomalies over the uranium mineralization.According to the results obtained in this paper, two kinds of deep-penetrating geochemical signatures demonstrate more effective role in detecting the concealed uranium mineralization than traditional methods. The deep-penetrating geochemical methods are bound to play an important role in exploring for deep-buried and concealed uranium deposits in China.
Keyword Extraction Approach Based on Probabilistic-Entropy, Graph, and Neural Network Methods

Nowadays, methods of automatic keyword extraction are developed based on statistical and graph features of texts. The transfer of learning approaches allows one to use additional word features obtained from deep neural network models fitted to solve different tasks. The paper proposes an integrated approach to keyword extraction based on a classification model that aggregates results of probabilistic-entropy, graph methods, and word features extracted from a neural network for text title generation. To validate the method, a dataset of news texts was gathered, with keywords manually selected through crowdsourcing. For the proposed approach F1-measure weighted by classes accuracy of keyword extraction is 72%, which is approximately 5% better in comparison with the existing methods.
Bayesian Edge Detector Using Deformable Directivity-Aware Sampling Window

Conventional image entropy merely involves the overall pixel intensity statistics which cannot respond to intensity patterns over spatial domain. However, spatial distribution of pixel intensity is definitely crucial to any biological or computer vision system, and that is why gestalt grouping rules involve using features of both aspects. Recently, the increasing integration of knowledge from gestalt research into visualization-related techniques has fundamentally altered both fields, offering not only new research questions, but also new ways of solving existing issues. This paper presents a Bayesian edge detector called GestEdge, which is effective in detecting gestalt edges, especially useful for forming object boundaries as perceived by human eyes. GestEdge is characterized by employing a directivity-aware sampling window or mask that iteratively deforms to probe or explore the existence of principal direction of sampling pixels; when convergence is reached, the window covers pixels best representing the directivity in compliance with the similarity and proximity laws in gestalt theory. During the iterative process based on the unsupervised Expectation-Minimization (EM) algorithm, the shape of the sampling window is optimally adjusted. Such a deformable window allows us to exploit the similarity and proximity among the sampled pixels. Comparisons between GestEdge and other edge detectors are shown to justify the effectiveness of GestEdge in extracting the gestalt edges.
Synchronizing Game and AI Design in PCG-Based Game Prototypes

Procedural content generation (PCG)-based game design aims to reach a new way of playing games by focusing gameplay around algorithmic game content generation. However, positioning interaction with PCG systems and generated content to the center of player experience poses design challenges for both game design and AI design. In order to create the wanted affordances, rich contextual information is required to make informed decisions on the generated content. While previous research has presented excellent developments on PCG’s possibilities, further considering context and affordances in the early stages of prototyping may aid designers reach these possibilities in a more consistent manner. This study is set to discuss how context, affordances and the game’s overall design can be considered during the prototyping process of PCG-based games. Misaligned game context and affordances can result in deeply rooted design issues that may later manifest as subpar gameplay experiences and increased development effort. These emergent issues are examined through a post-mortem case study to produce an extended PCG-based design process, featuring actionable steps, that takes context, affordances, and the game’s overall design into account through meaningful play.
Video Synopsis: A Systematic Review

Automated analysis of surveillance video is necessary due to its redundant and uninteresting content. This has led to a rapidly evolving research area called video synopsis or otherwise called as rapid recap system. As the name suggests, it can highly condense a lengthy input surveillance video by extracting and shifting the moving objects in temporal axis and then displays them simultaneously in an non-overlapping fashion. This article investigates the prior arts for synopsis video generation for both single and multi-camera network. This article focuses on providing a brief introduction to the area of video synopsis by discussing the state-of-the-art techniques. Then the end of the article also focuses on a new trend in synopsis generation methods applied to the challenging area of multi-camera networks. This literature identifies various optimization techniques, dataset, and evaluation matrix used for each approach.
On the dimension of ideals in group algebras, and group codes

Several relations and bounds for the dimension of principal ideals in group algebras are determined by analyzing minimal polynomials of regular representations. These results are used in the two last sections. First, in the context of semisimple group algebras, to compute, for any abelian code, an element with Hamming weight equal to its dimension. Finally, to get bounds on the minimum distance of certain MDS group codes. A relation between a class of group codes and MDS codes is presented. Examples illustrating the main results are provided.
Detection of Iterative Adversarial Attacks via Counter Attack

Abstract Deep neural networks (DNNs) have proven to be powerful tools for processing unstructured data. However, for high-dimensional data, like images, they are inherently vulnerable to adversarial attacks. Small almost invisible perturbations added to the input can be used to fool DNNs. Various attacks, hardening methods and detection methods have been introduced in recent years. Notoriously, Carlini–Wagner (CW)-type attacks computed by iterative minimization belong to those that are most difficult to detect. In this work we outline a mathematical proof that the CW attack can be used as a detector itself. That is, under certain assumptions and in the limit of attack iterations this detector provides asymptotically optimal separation of original and attacked images. In numerical experiments, we experimentally validate this statement and furthermore obtain AUROC values up to $$99.73\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>99.73</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> on CIFAR10 and ImageNet. This is in the upper part of the spectrum of current state-of-the-art detection rates for CW attacks.
Procedural Generation for Divination and Inspiration

This paper presents a series of experiments that map the expressive space of specific procedural generation techniques with playful aleatory interventions. They are tools for inspiration based on divinatory practices. This paper connects these ancient procedural techniques to contemporary technologies like Twitter bots. We challenge the limits of these technologies in order to playfully explore the role they can play in everyday life. The first experiment, Nostrandomus, remixes ancient prophecies. The second one, Five Sparrows on a Vampire, generates proposals for dining experiences featuring recipes and accompanying eating instructions. The third tool, Haikookies, is a self-help inspired twitter bot that shares fortune cookie-style wisdom in haiku form. The final experiment, Tiphareth, is a set of partially procedurally generated tarot cards. Additionally, Ephemerald, a tool for streamlined Tracery-based [6] procedural content generation is introduced. The takeaway of this paper is that data curation is a fundamental component of working with generative systems. In other words, the human aspect needs to be present in order to create meaningful results.
Visualization for Analyzing Usage Status from Dialogue Systems

Dialogue systems, which give users quick and easy access to required information interactively, have been widely used in various fields. To increase user satisfaction, it is important to analyze usage status in detail after introduction of dialogue systems. However, since usage status is analyzed from a large number of logs and other collected information, it can take much time and effort to find beneficial information. This study proposes a method that utilizes the dialogue system introduced for public relations in universities to support the analysis of usage status by visualization, aiming discovery by intuitive awareness. In the results of evaluation using real data of the dialogue system, a variety of information that matches different purposes and unexpected information were discovered by intuitive awareness and supporting functions.
A cost effective‐ secure algorithm for work‐flow scheduling in cloud computing

The work‐flow is emerging as the promising paradigm for the different computing infrastructures of distributed nature. It is applied widely in various scientific fields and simple job execution. To manage with the enormous flow of data in the work‐flow, the requirement of huge infrastructure and the execution in the reasonable time. The work ‐flow adapts to the cloud environment. Though cloud environment provides with the large‐scale infrastructure and consistency and efficiency on comparing to the system operated over non‐cloud, a proper scheduling is entailed for the execution of tasks satisfying the specified constraints with the necessary security measure that has to be provided as the cloud does not provide with the counter measures for the security threats. More over as the real world tasks are most probably heterogeneous requiring virtual machines of different instance of series. So the paper proffers a cost‐effective algorithm based on the multi‐populated genetic algorithm (Multi‐populated GA) implanted with particle swarm optimization (PSO) for the heterogeneous networks so as to minimize the cost of the execution and meet out the QOS constraints, the results serve as the guiding principle for the security level improvement for the (sensitive) critical tasks. The performance evaluation of the scheduling using the workflowsim is performed to evidence the maximum reduction in the cost of the execution and the QOS constraints met using the proposed system.
Slicing based efficient privacy preservation technique with multiple sensitive attributes for safe data distribution

Privacy preservation in data publishing is the major topic of research in the field of data security. Data publication in privacy preservation provides methodologies for publishing useful information; simultaneously the privacy of the sensitive data has to be preserved. This work can handle any number of sensitive attributes. The major security breaches are membership, identity and attribute disclosure. In this paper, a novel approach based on slicing that adheres to the principle of k-anonymity and l-diversity is introduced. The proposed work withstands all the privacy threats by the incorporation of k-means and cuckoo-search algorithm. The experimental results with respect to suppression ratio, execution time and information loss are satisfactory, when compared with the existing approaches.
An Efficient and Revocable Auditing Scheme for the Internet of Things

With the rapid development and popularization of Internet of Things (IoT) technology, the problem of limited data storage and computing power of smart devices is becoming more and more obvious, and cloud computing can provide computing and storage services. IoT data is outsourced in cloud storage, and how to ensure the integrity of the data is worth studying. A Certificateless Provable Data Possession (CL-PDP) scheme for the Internet of Things environment is proposed. To solve the problem of weak user computing power and malicious third-party problems, a KTC fog alliance structure model is designed. The user revocation by the cloud service provider reduces the computational burden of the KGC. Finally, through theoretical analysis and experimental verification, it shows that the scheme has less calculation, revocation overhead and higher credibility than other schemes.
P‐28: Investigating the Usability of Touchscreen Interfaces in a Turbulent Flight Deck – For Panning and Numeric Data Entry Tasks

This paper studies the use of touchscreen displays on the flight deck, focusing on the usability of touch interfaces to complete panning and numeric entry tasks. Results from this study show that the usability of a drag gesture to meet a pan function, surpasses the performance of a simple tapping interface under all turbulent conditions tested. Also, that this drag interface is more useable in an inter‐seat flight deck position, than a central one. It was also found that touchscreen interfaces can surpass the performance of the traditional mechanical dial interface for numeric data entry tasks, under all turbulent conditions tested.
80‐4: Human Visual System Uses Just a Few Transfer Functions Depending on Various Environments to Realize Normalized Visual Percept: Investigation Using Real Photographic Images

We investigate global tone mapping operators (GTMOs) that convert physical stimuli in various real HDR environments (i.e., luminance contrast ratio (CR) is up to approx. 10 5 :1) into normalized visual percept (NVP) by actually comparing real scenes with reproduced photographic images. Subjective test results suggest that the images reproduced by our proposed method are successfully converted into SDR (i.e., CR is approx. 10 2 :1). We also demonstrate that these NVPs recorded in SDR images can be re‐converted into LDR images (i.e., CR is approx. 10:1) while preserving natural tone.
Parsing a markup language that supports overlap and discontinuity

Text As Graph Markup Language (TAGML) is a recently developed markup language that offers core support for overlapping and discontinuous markup. Designing and implementing a markup language technology stack that supports overlap poses numerous challenges; the most prominent being that the markup language cannot be expressed in a recursive context-free (CF) grammar. In this short paper we discuss our experiments with parsing TAGML based on a context-sensitive grammar. Our current approach implements an attribute grammar, which consists of a CF grammar with semantic actions. We discuss the advantages and disadvantages of our approach, and sketch several alternative methods.
Data Linking as a Service: An Infrastructure for Generating and Publishing Linked Data on the Web

Companies, government, and even ordinary people have been producing and publishing huge amounts of data. This phenomena, known as big data, leveraged the interest in advanced analytics and data science. Many observers, though, are pointing out that extracting knowledge from such datasets requires suitable tools for handling and integrating data. Research in the last years has shown that taking into account the semantics of data is crucial for fostering data integration solutions. However, there is a lack of solutions for data publishing that follow the best practices for exposing and connecting data. With this regard, this work proposed DLaaS, an infrastructure for generating and publishing linked data on the Web. It aims at facilitating the execution of necessary processes to properly publish high quality linked data. Its main goal is to improve the reuse of data by connecting entities based on heterogeneous datasets that share a certain level of data intersection or semantic relationship.
Balancing Exploration and Exploitation in the Memetic Algorithm via a Switching Mechanism for the Large-Scale VRPTW

This paper presents an effective memetic algorithm for the large-scale vehicle routing problem with time windows (VRPTW). Memetic algorithms consist of an evolutionary algorithm for the global exploration and a local search algorithm for the exploitation. In this paper, a switching mechanism is introduced to balance quantitatively between exploration and exploitation, to improve the convergent performance. Specifically, a similarity measure and a sigmoid function is defined to guide the crossover. Experimental results on Gehring and Homberger's benchmark show that this algorithm outperforms previous approaches and improves 34 best-known solutions out of 180 large-scale instances. Although this paper focuses on the VRPTW, the proposed switching mechanism can be applied to accelerate more general genetic algorithms.