Reviewerly/exHarmony huggingface dataset:
Binary-centric defense of production operating systems against kernel queue injection attacks

Kernel callback queues (KQs) are the established mechanism for event handling in modern kernels. Unfortunately, real-world malware has abused KQs to run malicious logic, through an attack called kernel queue injection (KQI). Current kernel-level defense mechanisms have difficulties with KQI attacks, since they work without necessarily changing legitimate kernel code or data. In this paper, we present the design, implementation, and evaluation of KQguard, an efficient and effective protection mechanism of KQs. KQguard employs static and dynamic analysis of kernel and device drivers to learn specifications of legitimate event handlers. At runtime, KQguard rejects all the unknown KQ requests that cannot be validated. We implement KQguard on the Windows Research Kernel (WRK), Windows XP, and Linux, using source code instrumentation or binary patching. Our extensive experimental evaluation shows that KQguard is effective (i.e., it can have zero false positives against representative benign workloads after enough training and very low false negatives against 125 real-world malware), and it incurs a small overhead (up to ~5%). We also present the result of an automated analysis of 1,528 real-world kernel-level malware samples aiming to detect their KQ Injection behaviors. KQguard protects KQs in both Windows and Linux kernels, can accommodate new device drivers, and can support closed source device drivers through dynamic analysis of their binary code.
Scalable IP Core for Feed Forward Random Networks

Feed forward random networks enable classification and regression tasks at high speed compared to networks using back propagation. In this research, a scalable and reconfigurable IP core for inference and training of one such network, known as the extreme learning machine (ELM), is proposed. The architecture utilizes a compute-lite learning rule that supports deployment on low-end mobile platforms. A representative study shows that the network achieves $${\sim }$$ 91% classification accuracy on the MNIST dataset with 16-bit precision and 200 hidden layer neurons. The design facilitates training an MNIST image in 70 $$\upmu $$ s at 90 MHz operating frequency on a Virtex-7 (XC7VX690T) FPGA.
Machine Learning Methods for Opinion Mining In text: The Past and the Future

Sentiment analysis, which is also referred as opinion mining, attracts continuous and increasing interest not only from the academic but also from the business domain. Countless text messages are exchanged on a daily basis within social media, capturing the interest of researchers, journalists, companies, and governments. In these messages people usually declare their opinions or express their feelings, their beliefs and speculations, i.e., their sentiments. The massive use of on-line social networks and the large amount of data collected through them, has raised the attention to analyze the rich information they contain. In this chapter we present a comprehensive overview of the various methods used for sentiment analysis and how they have evolved in the age of big data.
Ranking Hotel Reviews Based on User's Aspects Importance and Opinions

Online product reviews have become fundamental to users' purchasing decisions. Many websites provide rating-based ranking of entities, but analyzing the set of textual reviews is still time-consuming. Indeed, each user (reader) must build his/her own judgment from the set of reviews of the other users (writers), who might not have the same expectations and needs. To speed up this process, work have proposed more personalized rankings, which are restricted to the writer's perspective. In this work, we present an approach to rank reviews of an entity of interest, a hotel, based on the reader's profile. The method extracts a profile from free-text reviews and uses it to assess the degree of relevance of each review to rank according to the user's interests. The results obtained in the experiment exhibit a Mean Reciprocal Rank (MRR) of 0.72%, which is higher than comparable approaches of the literature. This paper also emphasizes the lack of available material to undertake such research, and sketches a methodology for evaluation.
Comparative Analysis of Controlling Drone

The drones are entering our lives in many areas. There are many problems as well. Various studies are under way to solve these problems. The conventional drone manipulation method is a PUI (Physical User Interface) based which provides a two dimensional control and physically input value, but the newly presented manipulation method is a three dimensional NUI (Natural User Interface) based manipulation method which is mapped naturally with the movement of the drone. There were studies to analyze each manipulation method, but there was no precedent study to compare and analyze the characteristics of these two manipulation methods based on the experiment. Therefore, in this study, the designated sections are to be driven by each manipulation method, and the data obtained based on motion capture and subjective experience surveys are compared through the experiment.
ANTHROPOLOGICAL COMPREHENSION OF A WOMAN-AUTHOR AS THE SUBJECT OF CULTURE THROUGH THE PRISM OF LANGUAGE AND LITERATURE (GENDER ASPECT)

Purpose. To study the phenomenon of a woman-author as a subject of culture and philosophy from a development of literary aspect in the works both Western and Ukrainian scientists. To define the significance of the philosophical representation of the gender stereotypes to reconsider their place and role in the socio cultural discourse. Theoretical basis. To investigate the theoretical framework in the postmodern philosophy the cross-disciplinary approach is used. The comparative approach is methodologically important to clarify the problems concerning a woman-author as a subject of culture. It is underlined that the boundary line between literature and philosophy is movable, which coincides with the shapes of the human experience. Based on the conviction that gender has integrated into all social relations, that means it is a gender context of any social interaction, it is important to emphasize the productivity of a new scientific methodology of sociocultural constructing of gender. Originality. Is in systematic literary analysis of Ukrainian and Western women’s prose as specific philosophical phenomenon. It was proved that the investigation of women’s literature, its identity is an important focus of both philosophy and culture, which helps find philosophical problems in literary texts. Besides the analysis of gender implications in texts allows to start theoretical dialogue on gender problems, which means the participation in the discussion about the targets of our cultural life. Conclusions. It has been proved that literature of the ХХth-XXIst centuries is characterized by strengthening interaction between philosophical systems and literary works that reflects mainstreaming of intellectual and thinking bases. It was revealed that women’s philosophical and literary conceptions have created a unique woman’s world of being and an image of "a new woman", thus leading the way towards the new stereotypes based on comprehension that sex differences should not be determining factors both in cultural and social coexistence.
Locust Search Algorithm Applied for Template Matching

For a wide-range of image processing tasks (such as feature tracking, object recognition, stereo matching and remote sensing), Template Matching (TM) serves as a crucial strategy to allow the localization and recognition of objects and/or patterns within digital images. The purpose of a TM approach is to find the location (region) within a source image which manifest the best possible resemblance to a given sub-image (commonly known as image template). Any TM algorithm (independently of their particularities) incorporates two important elements: 1. a search strategy; and 2. a similarity measurement. In its simplest form, a TM method involves exhaustive search processes were a similarity measurement (such as the Normalized Cross-Correlation) is applied for each available location within the source image. Intuitively, such an extensive amount of function evaluations has a strong impact on the algorithms computational cost, an issue that happens to be extremely detrimental for many real-world applications. In the last few years, Evolutionary Algorithms (EAs) have been proposed as an alternative to aid on the search process of TM approaches by reducing the amount of locations that are evaluated within a given source image; however, it is known that many of these methods carry with them several critical flaws which could negatively impact the TM process, including an insufficient exploration of the source image (which often leads to premature convergence). In this chapter, the swarm optimization algorithm known as Locust Search (LS) is applied to aid on solving the of template matching. The LS method includes a series of unique evolutionary operators that allows to explicitly avoid the concentration of search agents toward the best-known solutions, which in turn allows these agents to better explore of the available image’s search region. According to a series of experimental results, when compared to other TM approaches which integrates EAs as a part of their search strategy, LS achieves the best between estimation accuracy and computational load.
Optimized Memory Management for a Java-Based Distributed In-memory System

Several Java-based distributed in-memory systems have been proposed in the literature, but most are not aiming at graph applications having highly concurrent and irregular access patterns to many small data objects. DXRAM is addressing these challenges and relies on DXMem for memory management and concurrency control on each server. DXMem is published as an open-source library, which can be used by any other system, too. In this paper, we briefly describe our previously published but relevant design aspects of the memory management. However, the main contributions of this paper are the new extensions, optimizations, and evaluations. These contributions include an improved address translation which is now faster compared to the old solution with a translation cache. The coarse-grained concurrency control of our first approach has been replaced by a very efficient per-object read-write lock which allows a much better throughput, especially under high concurrency. Finally, we compared DXRAM for the first time to Hazelcast and Infinispan, two state-of-the-art Java-based distributed cache systems using real-world application-workloads and the Yahoo! Cloud Serving Benchmark in a distributed environment. The results of the experiments show that DXRAM outperforms both systems while having a much lower metadata overhead for many small data objects.
Human visual based perception of steganographic images

In 2014 it was estimated that 1.8 billion images were uploaded daily to the Internet, and in 2018 it is estimated that 3.2 billion images are shared daily. Some of these uploaded images may contain hidden information that can potentially be malicious (e.g. an image that contains hidden information regarding terrorism recruitment) or may cause serious damage (e.g. an employee wishing to hide sensitive company details in an image file and exporting the image to third parties). This research studied the most effective methods in manipulating images to hide information (Data Loss). Significant work has been done on computational algorithmic detection. Yet the desired output from this work was to find the point at which a human can no longer visually establish the difference between an original image and a manipulated image. This research examines the extent of use for file formats, bit depth alterations, least significant bits, message and audio concealment and watermark and filtering techniques for image steganography. The findings of this study indicated that audio insertion and picture insertion into cover image files are the strongest in deceiving the human eye. These results have been categorised for human visual perception in image-based steganography.
A Fuzzy Neural Tree for the Evaluation of Shape in an Architectural Design

A learning strategy for fuzzy neural tree is presented that is based on combining the knowledge-driven and data-driven modeling paradigms. The knowledge-driven aspect of the strategy is expressing knowledge via the connection topology of a neural tree. The tree is driven by inputs associated with fuzzy logic. In this type of neural tree, the connection weights are determined in an unsupervised manner. However, the fuzzy logic related parameters are subject to data-driven identification, and they are comparatively few in number. For this reason, a low number of input-output data-pairs suffice to establish the neural representation in the new approach. This makes it suitable for representing evaluation processes of mind that have been difficult to bring into explicit form. An example of this is the evaluation of shape quality in an architectural design, and it is used to verify the effectiveness of the approach by experiment.
TEACHING NUMBER TRACING TO AT-RISK DYSCALCULIA PUPIL: A SINGLE CASE STUDY IN LINUS 2.0 NUMERACY REMEDIAL CLASS IN SANDAKAN, SABAH

This study aims to administrate a qualitative single case study about the teaching of number tracing using a new idea which is known as â€œReconnecting Learning-Mediated number tracingâ€. This teaching strategy is developed to teach number tracing and to support pupils at-risk of dyscalculia to master number tracing skills which are a prerequisite to learn number writing. This research blends the active learning theory and mediated learning to form a new teaching strategy. Data were collected from a primary school remedial mathematics Miss Z and a pupil at-risk of dyscalculia in Sandakan, Sabah through lesson observations for synthesizing the pupil's learning progression and conducted semi-structured interviews with the particular teacher. On top of that, analysis of documents such as reflective journals and electronic daily lesson plans (E-rph) was performed in order to gain a comprehensive picture of this teaching strategy. Findings reveal that this new teaching strategy was a structured and practical learning to scaffold the pupil who was at-risk of dyscalculia in particular to number tracing. At the end of this study, the pupil was able to rewrite the numbers correctly using LINUS 2.0 numeracy instrument.
Substantive and Procedural Legislation in Belgium to Combat Webcam-Related Sexual Child Abuse

The aim of this chapter is to examine whether Belgian substantive law and criminal procedure are adequate to clamp down on webcam-related sexual child abuse. After an introduction to the Belgian legal system, the chapter gives an overview of the relevant criminal offences involving minors (sexual assault, rape, child prostitution, child pornography, voyeurism, corruption of children, grooming, cyber luring). We conclude that the Belgian substantive law offers many possibilities to prosecute and try perpetrators of these crimes. Criminal liability of the offender for cyber luring, for instance, is not affected when the avatar is operated by a police officer. As regards the criminal procedure, several legislative reforms have given extended powers to the public prosecutor and the investigating judge. Law enforcement authorities currently have a wide array of investigative measures at their disposal, such as the online infiltration, the IT sneak and peek operation and the covert access to private communication and computer data. This chapter argues that, as a consequence, under Belgian criminal procedure, evidence gathered by use of an avatar, is lawfully collected. A few question marks remain, as it is doubtful that the extended powers are fully consistent with human rights law.
Optimal Solution of Structural Engineering Design Problems using Crow Search Algorithm

In real world, the structural engineering design problems are large scale non-linear constrained problems. In the present study, crow search algorithm (CSA) is applied to find the optimal solution of structural engineering design problems such as pressure vessel design problem, welded beam design problem and tension/ compression string design problem. The numerical results are compared with the existing results reported in the literature including metaheuristic algorithms and it is found that the results obtained by the crow search algorithm are better than other existing algorithms. Further, the effectiveness of the algorithm is verified to be better than the existing algorithms by statistical analysis using mean, median, best case, and worst case scenarios. The present study confirms that the crow search algorithm may be easily and effectively applied to various structural design problems.
A Coordinated Multi-channel Information Presentation Framework for Data Exploration

As the information age changes the world around us, humans are interacting with ever-increasing amounts of data. While it can be difficult to remember details from data, presenting it in a coordinated manner can aid in its retention and recall. In this work, we investigate whether placing a greater degree of emphasis on elements of coordination in a multi-channel presentation system aids in recall of information. The interface described in this paper presents three coordinated channels of information: a pair of interactive time-series graphs, a text narrative and its vocalization, and salient external search data. 36 subjects were asked to use the interface to explore stock market data in one of two groups. For subjects in the increased-emphasis group, the interface color-coordinates related data across visual elements to emphasize the coordination between channels of information. Subjects in the control group received the same interface with no color coordination. It was hypothesized that increasing emphasis on coordination would produce a positive effect on recall of information from the interface. Subjects in the increased-emphasis group exhibited significantly greater accuracy than the control group when attempting to recall dates, but only for graph patterns presented with more detailed descriptions. However, subjects in both groups exhibited significantly worse accuracy when attempting to recall graph patterns that were presented with more detailed descriptions than those presented with simpler ones. Thus, increasing emphasis on coordination only helped accuracy of recall for dates, and only when the patterns associated with those dates were more difficult to recall to begin with.
Misuse of Police Information Systems: Predicting Perceived Likelihood of Misuse among Unsworn Police Employees

Abstract Police information systems (ISs) contain highly sensitive data. Misuse of these systems poses a significant risk to agency integrity and personal privacy. In order to prevent this form of misconduct, police agencies must implement strategies that address factors associated with employee misuse. The aim of this research was to determine factors associated with the perceived likelihood of engaging in IS misuse among a sample of employees from an Australian police agency. Two hundred and eighty-seven unsworn police staff completed an online survey that investigated a number of factors, shown in previous research, to be relevant to IS misuse. Findings demonstrated that access to the IS, perceived appropriateness of and concern regarding IS misuse, and perceived certainty of punishment were relevant in explaining the perceived likelihood of IS misuse. Implications are discussed in relation to the focus of agency prevention strategies and the need for future research in the area.
Open Educational Resources in the Digital Economy: Legal Regulatory Framework for Free Software License

Relevance of the problem under examination is subjected to the active development of the digital economy and introduction of information technologies into the educational environment, formation of the open educational resources, which can be used only in compliance with the intellectual rights of authors and copyright holders of the works. The purpose of this article is to inquire into the free licenses used in the open educational resources: open licenses and Creative Commons licenses in Russia. The key method of research is a particular-scientific formal legal method, rendering it possible to arrive at the concept of the open educational resources, to determine the major characteristics of the open licenses and Creative Commons licenses. Following the research, it is concluded that the open license mechanism is the most convenient for arranging and distributing content of the open educational resource in the digital economy. The research has shown that the modern legal structure of the open license as recorded in the Civil Code of the Russian Federation makes it possible to integrate the Creative Commons license used worldwide into practical activities to form the open educational resources in the Russian Federation. The Creative Commons licenses can be used in the modern law enforcement activity without significant amendments into the Russian law. The article’s materials are of practical value for professionals and scholars concerned with the intellectual property protection, as well as for those implementing and using the open educational resources.
Formal Representations of the Knowledge

This paper, describes the problem of the representation of the knowledge. After a brief discussion on the phase of coding - decoding are mentioned some of the characteristics and conditions that must assemble all scheme of representation of the knowledge in artificial intelligence (AI). The desire of to formalize the representation of the knowledge leads us to the introduction of the logic of propositions. Nevertheless, the limitations of the logic of propositions, which allows us neither represent several examples of the same entity, nor to quantify; it leads us to preferring the logic of predicates as scheme of formal representation. The results of the investigation demonstrate that the representation of the knowledge by logic of propositions is not versatile and thus come up the need of using, always from the formal optics, the logic of predicates.
Unsupervised Feature Selection Using RBF Autoencoder

In this paper, a novel learning approach to solve unsupervised feature selection in high-dimensional data is proposed, namely Radial Basis Function Autoencoder feature selection (RAFS). This method based on autoencoder uses the radial basis function to achieve mapping instead of the weight. We also consider penalty to give a powerful constraint on redundant features. In extensive experiments, our method shows its outperformance in fair comparison with several other methods.