Reviewerly/exHarmony huggingface dataset:
Combining Data Preprocessing Methods With Imputation Techniques for Software Defect Prediction

Software Defect Prediction (SDP) models are used to predict, whether software is clean or buggy using the historical data collected from various software repositories. The data collected from such repositories may contain some missing values. In order to estimate missing values, imputation techniques are used, which utilizes the complete observed values in the dataset. The objective of this study is to identify the best-suited imputation technique for handling missing values in SDP dataset. In addition to identifying the imputation technique, the authors have investigated for the most appropriate combination of imputation technique and data preprocessing method for building SDP model. In this study, four combinations of imputation technique and data preprocessing methods are examined using the improved NASA datasets. These combinations are used along with five different machine-learning algorithms to develop models. The performance of these SDP models are then compared using traditional performance indicators. Experiment results show that among different imputation techniques, linear regression gives the most accurate imputed value. The combination of linear regression with correlation based feature selector outperforms all other combinations. To validate the significance of data preprocessing methods with imputation the findings are applied to open source projects. It was concluded that the result is in consistency with the above conclusion.
P‐12.14: The Overview and Prospects of Privacy Technology

With the advent of big data information age and the multi‐field application of display screens, personal private information and commercial secrets are easily leaked through the display device. A privacy display with narrow viewing angle can effectively solve this problem. In this paper, we are going to introduce some advanced privacy technologies existing on the market, including technical principles, application scenarios, advantages and disadvantages. Prospects are made for the development direction of privacy products according to customer demands at the end of the article.
Gleason Grading of Prostate Cancer Based on Improved AlexNet

Prostate cancer is a common malignant tumor in male genitourinary system, its morbidity is increasing in recent years. Puncture pathological examination with Gleason scoring is the ultimate means of diagnosing prostate cancer. Early detection of prostate cancer is obviously very important for the treatment and prognosis of the cancer. However, the pathological image of prostate cancer has a complicated texture structure, especially the difference between Gleason Grade 3 and Gleason Grade 4. Therefore, pathological images with a Gleason score of 7 are difficult to distinguish between "3+4" and "4+3". The misjudgment of "3+4" and "4+3" impact on quality of life in patients with prostate cancer after operation can be profound. In order to improve the classification accuracy of histopathological images of prostate cancer especially for detecting "3+4" and "4+3", this paper proposed an image classification model based on improved AlexNet. On the basis of ALexNet, Res1_block and Res2_block structures are added to extract the features of pathological images. Experimental results show that our approach can automatically classify prostate cancer pathological images, and the test accuracy can reach 78.4%.
STATE OF TEXAS V. YSLETA DEL SUR PUEBLO Case No. EP-17-CV-179-PRM (W.D. Tex. Feb. 14, 2019)

Gaming Law ReviewVol. 23, No. 4 CasesSTATE OF TEXAS V. YSLETA DEL SUR PUEBLO Case No. EP-17-CV-179-PRM (W.D. Tex. Feb. 14, 2019)Published Online:25 Apr 2019https://doi.org/10.1089/glr2.2019.2345AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetailsCited byDisrupting Frivolous DefensesSSRN Electronic Journal, Vol. 276FRAND Licensing Levels under EU LawSSRN Electronic JournalReal Insider TradingSSRN Electronic JournalOnline Sovereignty: The Law and Economics of Tribal Electronic CommerceSSRN Electronic Journal Volume 23Issue 4May 2019 InformationCopyright 2019, Mary Ann Liebert, Inc., publishersTo cite this article:STATE OF TEXAS V. YSLETA DEL SUR PUEBLO Case No. EP-17-CV-179-PRM (W.D. Tex. Feb. 14, 2019).Gaming Law Review.May 2019.248-259.http://doi.org/10.1089/glr2.2019.2345Published in Volume: 23 Issue 4: April 25, 2019Online Ahead of Print:March 15, 2019PDF download
Research and Application of Instrument Reading Recognition Algorithm Based on Deep Learning

As an important part of China's social production, instruments are scientifically applied in many fields such as industrial development and military development, as well as in substations with complex environment. Therefore, it is necessary to ensure the accuracy of identification readings in instrument application, so as to provide real and effective data information for instrument application. Based on the connotation of deep learning, this paper proposes a pointer and digital multi and multi type instrument reading recognition algorithm based on Mask -RCNN. Then, the experimental results of instrument identification reading algorithm are systematically analyzed. Finally, the application of instrument identification reading algorithm is discussed in order to improve the scientificity and accuracy of instrument identification reading algorithm.
HTM Theory

This chapter presents the general background information about the Hierarchical Temporal Memory (HTM). HTM is a recently proposed cognitive learning algorithm that is intended to emulate the overall structural and functionality of the human neocortex responsible for the high-order functions such as cognition, learning and making predictions. The main properties of HTM is hierarchical structure, sparsity and modularity. HTM consists of two main parts: HTM Spatial Pooler (SP) and HTM Temporal Memory (TM). The HTM SP performs the encoding of the input data and produces sparse distributed representation (SDR) of the input pattern useful for visual data processing and classification tasks. The HTM TM detects the temporal changes in the input data and performs prediction making.
Structure Versus Hardness Through the Obfuscation Lens

Much of modern cryptography, starting from public-key encryption and going beyond, is based on the hardness of structured (mostly algebraic) problems like factoring, discrete log, or finding short lattice vectors.While structure is perhaps what enables advanced applications, it also puts the hardness of these problems in question.In particular, this structure often puts them in low (and so-called structured) complexity classes such as \sansN \sansP \cap \sansc \sanso \sansN \sansP or statistical zero-knowledge (\sansS \sansZ \sansK ).Is this structure really necessary?For some cryptographic primitives, such as one-way permutations and homomorphic encryption, we know that the answer is yes---they imply hard problems in \sansN \sansP \cap \sansc \sanso \sansN \sansP and \sansS \sansZ \sansK , respectively.In contrast, one-way functions do not imply such hard problems, at least not by black-box reductions.Yet, for many basic primitives such as public-key encryption, oblivious transfer, and functional encryption, we do not have any answer.We show that the above primitives, and many others, do not imply hard problems in \sansN \sansP \cap \sansc \sanso \sansN \sansP or \sansS \sansZ \sansK via black-box reductions.In fact, we first show that even the very powerful notion of indistinguishability obfuscation (IO) does not imply such hard problems, and then deduce the same for a large class of primitives that can be constructed from IO.
Toward a Working Definition of Digital Literacy

Literacy is generally understood to be the combined abilities to read and write, speak and listen; however, the advancement of technology has broadened what it means to be literate to encompass the notion of digital literacy. This chapter is divided into four major sections. First, a comprehensive definition of digital literacy will be presented. Then, digital literacy within a socio-cultural framework will be briefly highlighted. Next, three prominent issues that have surfaced around digital literacy will be examined: the dissonance between digital natives and digital immigrants, how and why some forms of digital literacy enjoy acceptance and legitimacy, and attaining and not gaining access to digital literacy formats – the digital divide. The final section of this chapter is devoted to a discussion of implications of digital literacy in K-12 education, college, and career.
Security Tokens

This chapter synthesizes the economics, law, and technology of security tokens and Security Token Offerings (STOs). Security tokens are blockchain-based investment contracts that are subject to securities law. Interoperability, fractional ownership, market liquidity, and rapid settlement are among the main reasons why security tokens are a primary catalyst for the digitization of finance. We also empirically compare STOs with Initial Exchange Offerings (IEOs) and Initial Coin Offerings (ICOs). STOs take longer and raise more funding, however, controlling for other factors, the valuation of STOs and IEOs is lower than that in utility-token ICOs. These findings suggest an interesting avenue for future research. Moreover, both the law and the technology of security tokens needs to address important challenges related to the competent jurisdiction in multinational activities as well as blockchain interoperability, scalability, and natural resource degradation, respectively.
Web Application Vulnerabilities: Exploitation and Prevention

Web application security has become a major challenge due to the common vulnerabilities found in web applications. Attackers possess a never-ending list of vulnerabilities and payloads to exploit them in order to gain access over various web applications maliciously. Each time when there are any changes made at some layer of web-application architecture, there exists a chance of creating novel vulnerabilities. In this paper, we brief out our analysis on common and familiar vulnerabilities like Sql Injection (SQLi), Cross site Scripting (XSS) and Cross site Request Forgery (CSRF) and demonstrate the exploitation of these vulnerabilities by considering DVWA, a highly vulnerable web application designed for education purpose. We carry out exploitation both manually and through automated tools. We conclude our research by inferring some preventive mechanisms to be adopted while designing the web applications to mitigate such types of attacks.
Blockchain Technology as a Bridging Infrastructure Among Formal, Non-Formal, and Informal Learning Processes

Blockchain is an online decentralized and distributed ledger technology that has the ability to keep and track records in a safe, verifiable, and transparent manner. More significantly, it has an infrastructure that is compatible with Web 3.0, which offers great potential for lifelong learning. This chapter explains the different modalities of learning (formal, non-formal, informal), blockchain technology, and its current use in educational processes. Based on the findings, the authors suggest that blockchain technology can be used to connect and interlink different educational experiences that occur in different educational modalities, enabling us to evaluate educational processes holistically and thus promote lifelong learning through the use of cutting-edge technologies.
The Use of Software Tools in Linked Data Publication and Consumption

To reduce the complexity intrinsic to LD manipulation, software tools are used to publish or consume data associated to LD activities. However, few developers have a broad understanding of how software tools may be used in publication or consumption of Linked Data. The goal of this work is to investigate the use of software tools in Linked Data publication and consumption processes. More specifically, understanding how these software tools are related to process of publication or consumption of LD. In order to meet their goal, the authors conducted a Systematic Literature Review (SLR) to identify the studies on the use of software tools in these processes. The SLR gathered 6473 studies, of which only 80 studies remained for final analysis (1.25% of the original sample). The highlights of the study are: (1) initial steps of the publication process are fairly supported by the software tools; (2) Non-RDF serialization is fairly supported in publication and consumptions process by the software tools; and (3) there are non-supported steps in consumption and publication processes by the tools.
Wearable Device for Immersive Virtual Reality Control and Application in Upper Limbs Motor Rehabilitation

Virtual Reality (VR) has been used in several areas such as video games, technical training, movies and teaching. VR-based interventions have also been applied for motor rehabilitation, e.g., to help the patient recovery from disabilities provoked by stroke, cognitive deficit or musculoskeletal problem. VR combined with wearable tracking devices creates new possibilities to apply immersive approaches during motor rehabilitation. This can enhance the health care making it more interesting and pleasant for patients as well as more effective for physicians and physiotherapists. However, the costs related to this technology may be impracticable for a wide application by public health system. Therefore, this paper introduces preliminary results for a low-cost wearable device, which is integrated to VR environments aiming to provide a better quality rehabilitation process for most patient with motor disabilities.
Corpus PaGeS

This chapter presents the bilingual parallel corpus PaGeS, compiled by the research group SpatiAlEs from the University of Santiago de Compostela. PaGeS currently amounts to nearly 20 million tokens and consists of texts originally written in German and in Spanish and their correspondent translations into the other language, as well as a small portion of German and Spanish translations from third languages. The present contribution introduces the main characteristics of the PaGeS corpus, focusing on its design and compilation. It first explains the criteria for the selection of the texts and the details of text pre-processing, automatic alignment and manual review. It then addresses the search and display features describing the server architecture and indexing process. Finally, the intended development of the PaGeS corpus is briefly discussed.
The ‘future Internet’ and crime: towards a criminology of the Internet of Things

The Internet of Things (IoT) is poised to revolutionise the way we live and communicate and the manner in which we engage with our social and natural world. In the IoT, objects such as household items, vending machines and cars have the ability to sense and share data with other things, via wireless, Bluetooth or Radio-Frequency Identification (RFID) technology. ‘Smart things’ have the capability to control their performance as well as our experiences and decisions. In this exploratory paper, we overview recent developments in the IoT technology and their relevance for criminology. Our aim is to partially fill the gap in the literature by flagging emerging issues criminologists and social scientists ought to engage with in the future. The focus is exclusively on the IoT, while other advances, such as facial recognition technology, are only lightly touched upon. This paper, thus, serves as a starting point in the conversation, as we invite scholars to join us in forecasting—if not preventing—the unwanted consequences of the ‘future Internet’.
Edge Repartitioning via Structure-Aware Group Migration

Graph partitioning is a mandatory step in distributed graph computing systems. Some existing systems use edge partitioning methods to partition static graphs. However, the structure of the real-world graphs changes dynamically, which leads to unnecessary vertex replicas and load imbalance, reducing the performance of graph computation. In this article, we focus on improving the lower partitioning quality caused by the dynamics of the graph structure. We propose an edge repartitioning algorithm via structure-aware group migration (SAGM-ER). We define a special structure edge group (EG) consisting of multiple edges, which can reduce vertex replicas by migrating to other partitions. In repartitioning, we search for EGs in parallel by a method based on a structure-aware priority and then migrate EGs to reduce vertex replicas. Compared to the state of the art, SAGM-ER can reduce more vertex replicas. We implement SAGM-ER on Powergraph, which reduces the redundant replicas by 63.33%, thus reducing executing time and communication costs in graph computation by 33.72% and 37.51%, respectively.
Filtering and sensor optimization applied to angle-only navigation

Passive target estimation is a widely investigated problem of practical interest for which particle filters represent a popular class of methods. We propose an adaptation of the Laplace Particle Filter applied to angle-only navigation using landmarks. In this specific context, a high number of aiding landmarks or features could be hard to handle in terms of computational cost. Hence, this paper introduces a Cross-entropy algorithm that selects landmarks having a high contribution to the state estimation. This parsimonious approach reduces the resources required for navigation systems while holding a good accuracy. These methods are discussed through numerical results on an Angle-only navigation scenario.
Mobile Forensics

In this chapter, we forensically examine a Samsung Galaxy S7 running Android Version 8.0.0 and an iPhone 7 running iOS version 12.1, and report on the findings of the forensic examination.
Observable Models

Epistemic reading of Kripke models relies on a hidden assumption of common knowledge of the model which is too restrictive in epistemic contexts since agents may have different views of the situation. We explore possible worlds models in their full generality without common knowledge assumptions. Our starting point is a collection of possible worlds with accessibility relations "whatever is known in u is true in v." We call such a structure an observable model since, contrary to the popular belief, it is not generally a Kripke model but rather an "observable section" of some Kripke model. We sketch a theory of observable models and argue that they bring a new conceptual clarity to epistemic modeling. In practical terms, observable models are as manageable as Kripke models and have advantages over the latter in representing (un)awareness and ignorance. Similar analysis applies to intuitionistic models.
Deploying Risk Access Models in a Cloud Environment: Possibilities and Challenges

The importance of the cloud has increased due to its capabilities and features, which could facilitate its users. Cloud computing is a full package of virtual servers that can be utilized by customers and cloud service providers to store and manipulate their data. It is a new computer model that provides computing infrastructure resources as on-demand electronic services. The cloud's bounties need to be protected in terms of the ways they can be accessed. An access control model could control access to such a dynamic environment. Although cloud computing provides unique features for users, it brings new challenges to data security and access control such as unauthorized access with possibilities of data leakage. Since traditional access control models were designed for particular environments and could not be deployed directly in the cloud without security investigations, risk-monitoring models are quite new models that trying to overcome the cloud security constraints. Risk-based access control models provide many security solutions to risk management in dynamic environments such as cloud computing. This paper illustrates an extensive investigation to access control models and displays several risk access control models. Moreover, it looks at the possibility and challenges that make a risk-based access control deployed in a cloud computing environment.
Aparecium

Aparecium is an XQuery / XSLT library for reading non-XML data as XML, under the control of an “invisible XML” grammar describing the structure of the input. The use of the library is illustrated with an application, and some technical issues in the development of the library are discussed.
Steganography in Color Animated Image Sequence for Secret Data Sharing Using Secure Hash Algorithm

Steganography is one of the leading strategy that is employed to hide restricted content into a cover media so that the person unaware of the fact can’t predict that the cover media can acquire some secret information. The effectiveness of any steganographic approach is appraised on imperceptibility of the stego-media, payload of the cover media, integrity of the secret information, security, and computational complexity. The work presented in this paper focuses on these important concerns of secured data transmission. The proposed steganographic approach uses multimedia files including animated image sequences as cover media for concealing secret messages. The entire secret information to be embedded is converted to a code using Secure Hash Algorithm-1 (SHA-1) (National Institute of Standards, and Technology (US). Technology Administration. Secure hash standard, 1993) and shared with the secret message to validate the authenticity of the transmitted secret information. The whole secret message is converted to its ASCII to embed them by replacing the least significant digit (LSD) (Liu et al. In Proceedings of the 2012 IEEE International Conference on Communications (ICC), IEEE, 2012). The receiver validates the transmitted information with SHA-1 again generating a code, which validates with the existing code sent over from the transmitting end. The proposed algorithm is worked upon a variety of text messages with a large set of color animated image sequences as cover media and also has been tested carefully by different parametric measures. This algorithm gives robust outputs in terms of visual imperceptibility, safety, and embedding capability.
A QUOTA-DDPG Controller for Run-to-Run Control

A novel distributional reinforcement learning controller, i.e., the quantile option structure-based deep deterministic policy gradient (QUOTA-DDPG), is proposed for run-to-run (R2R) control in the chemical mechanical polishing (CMP) process. The algorithm provides a new policy dimension for exploration, which learns a higher-level strategy via QUOTA to adaptively select the appropriate quantile to be used for action selection during environmental interactions. A reward function similar is designed to guide the learning process of an agent-based on the connection between reinforcement learning and feedback control. Simulation experiments demonstrate that the proposed controller can effectively compensate for the abnormal variation of the CMP environment adaptively.