Reviewerly/exHarmony huggingface dataset:
Virtually Rape: Should Cyber Sexual Offensiveness Constitute Rape?

Rape is the most severe sexual offense, involving one of the most feared and reviled acts a person can inflict on another. But what makes something "rape"? Initially, only penial-vagina forceful penetration; then, other forceful penial penetrations were added, oral and anal; and later the forceful insertion of inanimate objects as well. The requirement of using force lost its exclusiveness and much of its normative power, paving the way for other kinds of rape: sex by non-forceful coercion, sex by sedation, sex with incompetent victims, sex by fraud, and other forms of problematic sex. The normative debate about each form is ongoing, and in a manner of speaking, rape is a limitless idea. Where will the rape offense go next?Cyberspace, apparently. The Israeli Supreme Court has recently affirmed convictions of rape performed by distant words. The perpetrators conversed with children, teenagers and adult women online, using fraud and blackmail to manipulate them into masturbation and self-penetration. This groundbreaking judicial development is the inspiration to a normative analysis, revolving around Western notions of rape. Should such ill-intended communications constitute rape?The article will normatively scrutinize the virtual rape thesis. It will analytically deconstruct the normative notion of rape into three facets, and examine each separately: the physics of the offensive scenario; the settings thereof, the manner in which sexual autonomy is violated; and finally, the matter of proper criminal labeling. It will conclude that while sexual autonomy is indeed under attack in cyberspace, the framework of rape is unsuitable to handle this form of offensiveness.
Multi-agent Path Finding with Generalized Conflicts: An Experimental Study

This paper gives an overview of conflict reasoning in generalizations of multi-agent path finding (MAPF). MAPF and derived variants assume items placed in vertices of an undirected graph with at most one item per vertex. Items can be relocated across edges while various constraints depending on the concrete type of relocation problem must be satisfied. We recall a general problem formulation that encompasses known types of item relocation problems such as multi-agent path finding (MAPF), token swapping (TSWAP), token rotation (TROT), and token permutation (TPERM). We then focused on three existing optimal algorithms for MAPF: search-based CBS, and propositional satisfiability (SAT) - based MDD-SAT and SMT-CBS. These algorithms were modified to tackle various types of conflicts. The major contribution of this paper is a thorough experimental evaluation of CBS, MDD-SAT, and SMT-CBS on various types of relocation problems.
YoloSPoC: Recognition of Multiple Object Instances by Using Yolo-Based Proposals and Deep SPoC-Based Descriptors

The recognition of particular objects instances (e.g. my coffee cup or my wallet) is an important research topic in robotics, as it enables tasks like object manipulation in domestic environments in real-time. However, in recent years most efforts have been aimed to solve generic object detection and object class recognition problems. In this work, a method for performing recognition of particular objects instances, named YoloSPoC, is proposed. It is based on generation of high-quality object proposals by using YOLOv3, computing descriptors of these proposals using a MAC (Maximal Activation of Convolutions) based approach, recognizing the object instances using an open-set nearest neighbor classifier, and filtering of overlapping recognitions. The proposed method is compared to state-of the-art methods based on local features (SIFT and ORB based methods) using two datasets of home-like objects. The obtained results show that the proposed method outperforms existing methods in the reported experiments, being robust against conditions like (i) occlusions, (ii) illumination changes, (iii) cluttered backgrounds, (iv) presence of multiple objects in the scene, (v) presence of textured and non-textured objects, and (vi) object classes not available when training the proposal generator.
Assessment of HPV Risk Type in H&amp;E-stained Biopsy Specimens of the Cervix by Microscopy Image Analysis

Objectives: The objective of this study was (a) to identify, by computer processing of digitized images of hematoxylin and eosin (H&amp;E)-stained biopsy material of the cervix, differences in the structure of nuclei between high-risk (HR) and low-risk (LR) human papillomavirus virus (HPV) types and (b) to assess the HPV risk type by designing a decision-support system (DSS). Materials and Methods: Clinical material comprised H&amp;E-stained biopsies from squamous intraepithelial lesions of 55 patients with polymerase chain reaction-verified HR-HPV (26 patients) or LR-HPV (29 patients) infection. From each patient’s biopsy specimen, we digitized 1 region of interest, guided by the expert physician. After the segmentation of nuclei, we quantified from each nucleus 77 textural and morphologic features. We represented each patient by a 77-feature vector, the feature means of all nuclei, and we created 2 classes for HR-HPV and LR-HPV types. We carried out (a) a statistical analysis to determine features with statistically significant differences between the 2 classes and (b) a discriminant analysis, by designing a DSS, to estimate the HPV risk type. Results: Statistical analysis revealed 40 features with between-classes statistically significant differences and discriminant analysis showed that the best DSS design achieved a high accuracy of about 93% in identifying the HPV risk type on data not used in the design of the DSS. Conclusions: Nuclei of HR-HPV types were of higher intensity, contained larger structures, had higher edges, were coarser, rougher, had higher contrast, were larger, and attained more irregular shapes. The proposed DSS indicates that discrimination of HPV risk type from images of H&amp;E-stained biopsy material of the cervix is promising.
Linear generalized Kalman-Bucy filter

The linear generalized Kalman–Bucy filter problem has been studied in this work. A sum of a useful signal and a noise is an observed process. A signal and a noise are independent stationary auto-regressive processes with orders exceeding one. The filter estimates a signal, using an observed process. Two algorithms of filter are considered: a recurrent one and a direct one. Within the recurrent one, to find the next estimate of a signal, we use the current observation and several previous filter estimates. The direct algorithm uses all previous observations directly. The errors of estimates are found for both algorithms. The advantages and disadvantages of both algorithms are discussed in this paper. Calculations at the recurrent algorithm depend on the observation time. The direct algorithm is reduced to a linear algebraic system such that its order increases in time. On the other hand, the direct algorithm always converges in time, while this is not guaranteed for the recurrent algorithm. Numerical examples are given here.
Extraction of Eye Movements for a Communication System by EOG Signal Using DC Amplifier

Electrooculographic (EOG) signal reflects the eye movement. This paper proposes the detection of eye movements by EOG signal for a communication of people with movement disabilities. The system consists of a computer, analogue-to-digital converter, digital-to-analogue converter, a direct current (DC) amplifier and a monitor. The monitor is used to display a target for acquiring the eye-gaze position. For adjusting the DC amplifier, the eye-gaze position can be acquired. The target position on the monitor is changed at constant interval, and then the user looks at the target, relation of eye-gaze position and eye potential is derived. From the above, it was verified whether the DC amplifier can be adjusted and horizontal eye movements are detected. Then, the proposed system was used to normal healthy subjects. The result, eye movements is detected, and it can be expected to be used for the development of future communication aid system.
New Class of Non-binary Pseudorandom Number Generators

Pseudo-Random Number Generators (PRNG) has a wide range of applications. PRNG are the most important elements of any information security system. Scope of their application is very wide: in range from key information generation to entering unpredictability into the functioning of protection means and objects. The most interesting classes of PRNG are Linear Feedback Shift Registers (LFSR) and Non-Linear Shift Registers (NLFSR). New class of sequences are formed by NLFSR with structure determined by characteristic polynomials of particular form are considered in this paper.
A Probabilistic Path Planning Framework for Optimizing Feasible Trajectories of Autonomous Search Vehicles Leveraging the Projected-Search Reduced Hessian Method

This paper presents a new probabilistic algorithm for trajectory planning for autonomous vehicles (AV) in search and security applications. The goal is to compute optimized paths for the AVs in real time which maximize the probability of locating a fixed target, subject to constraints on the vehicle dynamics, within a prespecified time horizon. The likelihood of not detecting the target is modeled in a probabilistic manner based on approximate models of sensor acuity as a function of the distance to (and, the speed of) the sensor vehicle. For any possible vehicle path, a cost function is considered that accumulates the overall likelihood of not locating the target. Using an adjoint-based calculation, the gradient of this cost with respect to the control inputs is determined. The formulation of the cost function and the vehicle dynamics are decoupled, facilitating easy extension of the framework developed to other types of vehicles. The framework can also account for a priori estimates of the probability distribution of the target of interest. To accelerate convergence, we use the projected-search limited-memory reduced Hessian (LRHB), a recently developed gradient-based optimization method for constrained optimization; the LRH-B method significantly outperforms existing optimization algorithms as implemented in standard packages. Results indicate that our new framework can efficiently coordinate the search over the domain, and that LRH-B reduces the total computational cost during the search.
A Survey and Assessment of Intelligent Control Technologies and Algorithms for Helping Human Movement at National Borders

Intelligent control techniques and technologies can be used to control border activities which can become chaotic when there are large movements of people. This creates a lot of delays in processing of documentation and human movement. This paper provides a preliminary survey of intelligent techniques which are good candidates for controlling this migration challenge at frontiers. The paper presents certain queueing techniques and recommending algorithms which involve a range of intelligent applications. Challenges and different point of views that take into consideration fact of implementation in mobile devices (with limited processing power and memory) and being context-aware, are presented. It prepares criteria for assessing these techniques for the particular problem context as outlined, such as location-based service capability and appropriate recommending algorithms that can affect queue management and help human movement at national borders. This paper contributes a ranked assessment of these technologies and proposes a candidate for solving this particular human movement control problem. The paper then suggests further work to build applications to help control border movements which can be later deployed. This work is currently underway.
Optimizing Recommendations for Clustering Algorithms Using Meta-Learning

The field of machine learning has seen explosive growth over the past decade, largely due to increases in technology and improvements of implementations. As powerful as machine learning solutions can be, they are still reliant on human input to select the optimal algorithms and parameters. Clustering algorithms, in particular, are typically chosen by trial and error, as researchers will select a number of algorithms and choose whichever provides the most desirable result.This study will use a process called meta-learning to evaluate and analyze datasets and extract a series of meta-features. These meta-features can then be used to intelligently recommend an optimal clustering algorithm without the cost of having to manually run the algorithm. To accomplish this, we will experiment using 135 datasets and determine their expected outcomes using only their meta-features. The outcomes being optimized are performance (accuracy) and runtime.Results are then ranked separately for performance and runtime and we can determine how accurately the learning model was able to choose the optimal algorithm for each objective.With respect to runtime, we are able to predict the top-performing algorithm 71.1% of the time, one of the top two algorithms 89.6% of the time, and an algorithm in the top three 93.3% of the time. Performance is correctly predicted in the top two 50.4% of the time and in the top three at a rate of 63.7%.
IMPACT OF INSTITUTION FACTORS TO UNIVERSITY-INDUSTRY KNOWLEDGE EXCHANGE: A STUDY BASED ON SRI LANKAN UNIVERSITY SYSTEM

Purpose: The study explored the impact of institutional factors have on the university-industry knowledge exchange based on the Sri Lankan university system.&#x0D; Methodology: The study is quantitative and explanatory by nature and it applied the deductive method and questionnaire survey strategy. The study conducted with minimum interference of researcher and individual academics is the unit of analysis. The types of knowledge interaction, university-industry knowledge exchange, and institutional factors were the independent, dependent and moderating variables respectively. A Structural Equation Model is deployed on collected data to explore the moderating impact of the institutional factor on the university-industry knowledge exchange.&#x0D; Implications: It implies that the level of joint, contract research activities, human resource mobility, and training of academic staff are largely wider on the conducive environment and sophisticated facilities of the university.&#x0D; Main Findings: First, study evidence that there are statistically significant impacts of type of interactions and institutional factors on university-industry knowledge exchange. Further, the study confirmed the moderating power of institutional factors over the knowledge exchange process.&#x0D; Novelty: There is a lack of research literature discussing the moderating effect of institutional factors on the university-industry knowledge exchange process.
Success Factors for SOA Implementation in Network Centric Environment

This paper discusses challenges and success factors for service oriented architecture (SOA) implementation in network centric environment. The authors identify 9 fundamental challenges for the SOA approach in order to make the biggest benefit for the NATO NEC (NNEC) and increase the mission effectiveness to the highest extent. They cover the areas of applicability to existing military communications and the ability to reflect military processes. Their range is quite broad, pointing out technological as well as SOA governmental problems. The authors emphasize that any COTS solution available on the market today is able to overcome all of them at once. However, they propose solutions to some of the problems and present quick wins that can speed up the process of achieving capabilities in a heterogeneous multinational NEC environment.
High‐dimensional spectral data classification with nonparametric feature screening

Abstract Two nonparametric feature screening methods, namely, the Kolmogorov filter and model free, marginally measure the relationship between categorical response and predictor variables without the parametrical assumption. And they can select important variables in the high‐dimensional classification data. Random forest, as a classical nonparametric method, can solve various classification problems. In this paper, we combine the two nonparametric feature screening methods with random forest to handle with spectral data classification. And then other conventional classification methods are compared with ours on three spectral datasets. The comparison results illustrated that our methods have more desirable ability about classification performance and variable selection than other methods.
Knowing/showing/doing

Abstract In this visual essay, I present a body of work surrounding and investigating the letterpress workshop as a site of knowledge production. Print practice generates a sort of performative, embodied knowledge about and expressed by the objects and materials of printing. Ornament, alongside the alphabet, participates in the printing process, and may be extended from a form of 'enhancement' into an active site of communication. The printed matter presented here uses ornament to illustrate and record aspects of print practice that cannot adequately be communicated through or represented in text. In the article, I use theories of technology and documentation from Flusser and Gitelman to provide context and critical reflection on the printed matter. Together, writing and printing engage in a 'feedback loop' that records on and extends a developing printing practice.
Modeling and Analyzing Incremental Natures of Developing Software

The basic premise of iterative and evolutionary project management is that a project is divided into early, frequent, and short duration delivery steps. Each step attempts to deliver some real value to stakeholders. The increment size and iteration length usually depend on profitability, finance, deadline, and so on, rather than the functionality of a developing system. It is difficult to guarantee the correctness in every iteration step. In this article, we propose a method of ensuring the correctness of iterative design in terms of deadlock-freedom of the behavior of software. The method first obtains the correct (deadlock-free) atomic subsystems of a system using a decomposition approach. In the iterative development process, the method then requires that one atomic subsystem or the composition of multiple atomic subsystems should be regarded as one increment. Every increment is naturally correct and can be completely independently developed, independently deployed, and independently maintained. The currently released system in each iteration step is naturally guaranteed to be correct. It is not necessary for developers to consider the composition of the increment and the previously released system may cause flaws and errors. We also discuss the approach for ensuring correctness when design modifications are made in an iteration step. Finally, we explore the automatic decomposition of a system into multiple atomic subsystems and present the corresponding algorithm. A case demonstrates these results.
OmniEyes: Analysis and Synthesis of Artistically Painted Eyes

Faces in artistic paintings most often contain the same elements (eyes, nose, mouth...) as faces in the real world, however they are not a photo-realistic transfer of physical visual content. These creative nuances the artists introduce in their work act as interference when facial detection models are used in the artistic domain. In this work we introduce models that can accurately detect, classify and conditionally generate artistically painted eyes in portrait paintings. In addition, we introduce the OmniEyes Dataset that captures the essence of painted eyes with annotated patches from 250 K artistic paintings and their metadata. We evaluate our approach in inpainting, out of context eye generation and classification on portrait paintings from the OmniArt dataset. We conduct a user case study to further study the quality of our generated samples, asses their aesthetic aspects and provide quantitative and qualitative results for our model’s performance.
Unsupervised Keyword Extraction Method Based on Chinese Patent Clustering

Recently, patent data analysis has attracted a lot of attention, and patent keyword extraction is a hot problem. Most existing methods for patent keyword extraction are based on the frequency of words without semantic information. In this paper, we propose an Unsupervised Keyword Extraction Method (UKEM) based on Chinese patent clustering. More specifically, we use a Skip-gram model to train word embeddings based on a Chinese patent corpus. Then each patent is represented as a vector called patent vector. These patent vectors are clustered to obtain several cluster centroids. Next, the distance between each word vector in patent abstract and cluster centroid is computed to indicate the semantic importance of this word. The experimental results on several Chinese patent datasets show that the performance of our proposed method is better than several competitive methods.
Ensuring readiness for teachers to forming at school future specialists of digital economy on the base of the concept of open educational resources

The article discusses the need to attract school teachers of mathematics and computer science to implement the program “Personnel of the digital economy”. The problem is the steady reduction in recent years in the share of school graduates who choose the exam in specialized mathematics, which does not allow them to continue their studies at universities to acquire IT specialties. This casts doubt on the achievement of the indicators of the program “Development of the Digital Economy of the Russian Federation” in part of staffing of digitalization. To implement the plans, it is necessary to solve the problem of creating the appropriate professional focus of schoolchildren. This, in turn, requires bringing the system of training and retraining of teachers of mathematics and computer science at universities in line with the provisions of the Program in a meaningful, methodological and technological aspects. The article discusses the substantive aspect of the problem and offers some options for solving it: career guidance work with schoolchildren, conducting elective courses on the study of modern IT technologies, programming, robotics, encouraging the design activities of IT-oriented schoolchildren, etc. It is proposed to use open educational resources (OER), issued with free licenses such as Creative Commons, as a methodological and technological basis for training. The advantages of OER in connection with the solution of the problem are discussed. It is proposed to use a cloud-based LMS such as Google Classroom (Google Suite for Education) as a hosting platform. It is concluded that the involvement of school teachers in the preparation of potential (future) cadres of the digital economy is possible on the basis of the approaches proposed in the article.
Research on Fault Attack of SMS4 Block Cipher

The fault attack is a serious threat to the security of the cryptography because it can be actively planned by the attacker. Many fault attacks of the SMS4 block cipher have been released. In these methods, fault injection is between the 28th round input and the 32nd round of the algorithm, and the round-key of the last round can be successfully recovered. In this paper, a new differential fault analysis (DFA) of the SMS4 block cipher is proposed. Injecting a single-bit fault in the 27th round of the SMS4 can be used to recover the round-key rk <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">31</sub> of the last round.