Reviewerly/exHarmony huggingface dataset:
Trustworthy Isolation of DMA Enabled Devices

We present a mechanism to trustworthy isolate I/O devices with Direct Memory Access (DMA), which ensures that an isolated I/O device cannot access sensitive memory regions. As a demonstrating platform, we use the Network Interface Controller (NIC) of an embedded system. We develop a run-time monitor that forces NIC reconfigurations, defined by untrusted software, to satisfy a security rule. We formalized the NIC in the HOL4 interactive theorem prover and we verified the design of the isolation mechanism. The verification is based on an invariant that is proved to be preserved by all NIC operations and that ensures that all memory accesses address allowed memory regions only. We demonstrate our approach by extending an existing Virtual Machine Introspection (VMI) with the monitor. The resulting platform prevents code injection in a connected and untrusted Linux (The HOL4 proofs and the source code of the monitor are published at https://github.com/kth-step/NIC-formalization-monitor .).
Application of Linear Congruent Generator in Affine Cipher Algorithm to Produce Dynamic Encryption

Abstract Modification of the original message (plaintext) with the LCG random number generator algorithm and encryption with the affine cipher algorithm to produces a dynamic and safe random message (ciphertext) compared to just the affine cipher algorithm. The plaintext is converted to ASCII code and stored in a data list. The data list that contain the plaintext changes the position of the data where each plaintext column is moved to an odd column and even columns are filled by random numbers generated by LCG. The data list is then changed again by adding value to each column with random numbers generated by the programming language and then it encrypted with the affine cipher algorithm. The total number of ciphertexts can be twice as many as the plaintext. The results obtained are not the same ciphertext character even though the plaintext has the same character. Ciphertext generated in the first run program will be different for the next run program or dynamic encryption. To restore a ciphertext to the plaintext is done based on the affine cipher decryption function and the steps to modify the message that was done before.
Multi-Control Virtual Reality Driving Simulator

Immersive technologies (virtual and augmented reality) started gaining a lot of ground over the past few years initially in the video game industry, followed by simulators and finally integrated into many other classic applications. This paper aims to analyze how users respond to using new controlling devices in a scene designed as a driving simulator (race car track). The landscape and the controls are specifically tailored for interactions within immersive environments. In order to understand the capabilities of the experimental application and evaluate the new controls, several tests were conducted. The results are presented from a user experience perspective, emphasizing qualities, side-effects, and problems of such an approach.
Encoding features robust to unseen modes of variation with attentive long short-term memory

Long short-term memory (LSTM) is a type of recurrent neural networks that is efficient for encoding spatio-temporal features in dynamic sequences. Recent work has shown that the LSTM retains information related to the mode of variation in the input dynamic sequence which reduces the discriminability of the encoded features. To encode features robust to unseen modes of variation, we devise an LSTM adaptation named attentive mode variational LSTM. The proposed attentive mode variational LSTM utilizes the concept of attention to separate the input dynamic sequence into two parts: (1) task-relevant dynamic sequence features and (2) task-irrelevant static sequence features. The task-relevant dynamic features are used to encode and emphasize the dynamics in the input sequence. The task-irrelevant static sequence features are utilized to encode the mode of variation in the input dynamic sequence. Finally, the attentive mode variational LSTM suppresses the effect of mode variation with a shared output gate and results in a spatio-temporal feature robust to unseen variations. The effectiveness of the proposed attentive mode variational LSTM has been verified using two tasks: facial expression recognition and human action recognition. Comprehensive and extensive experiments have verified that the proposed method encodes spatio-temporal features robust to variations unseen during the training.
A Study on IT Security Control by Comparison of Secure Software Development Process

This research will begin by mapping the stages of software development and secure software development so that the comparison of the needs of both parties can be seen, where the application of security concepts will refer to the Software Security Touchpoint, and for the risk side a comparison of risk assessment methods that must support a development secure software, seen from the stages and output that will be generated in the form of security controls. So that a guideline can be produced in making IT security control as a solution in developing secure software based on risk during Software Development Life Cycle.
A flexible logic-based approach to closeness using order of magnitude qualitative reasoning

Abstract In this paper, we focus on a logical approach to the important notion of closeness, which has not received much attention in the literature. Our notion of closeness is based on the so-called proximity intervals, which will be used to decide the elements that are close to each other. Some of the intuitions of this definition are explained on the basis of examples. We prove the decidability of the recently introduced multimodal logic for closeness and, then, we show some capabilities of the logic with respect to expressivity in order to denote particular positions of the proximity intervals.
Research on Wheel-wall Gap Calculation Method for Four-Magnetic Wheel Wall-Climbing Robot Walking on Cylindrical Tank

In order to better study the adsorption property of the magnetic wheeled wall-climbing robot for its stably walking on the wall of the cylindrical tank, the calculation method of the gap from the magnetic wheel to tank wall is proposed. The three-wheel or four-wheel contact mode of the robot on the cylindrical tank wall is analyzed, and the contact points between magnetic wheels and the tank wall is determined. The position and orientation of the robot on the tank wall are described by coordinate transformation, and the change law of wheel-wall gap along the wheel axis is established by geometric method. The whole solution process is numerically calculated by MATLAB finally. The results can be used in the design of magnetic wheel structure and magnetic force.
Stepwise Evolutionary Learning Using Deep Learned Guidance Functions

This paper explores how Learned Guidance Functions (LGFs)—a pre-training method used to smooth search landscapes—can be used as a fitness function for evolutionary algorithms. A new form of LGF is introduced, based on deep neural network learning, and it is shown how this can be used as a fitness function. This is applied to a test problem: unscrambling the Rubik's Cube. Comparisons are made with a previous LGF approach based on random forests, and with a baseline approach based on traditional error-based fitness.
Cancer Incidence Prediction Using a Hybrid Model of Wavelet Transform and LSTM Networks

Cancer is a major public health concern. Being able to predict the number of future cancer incidences is vital to allocate appropriate healthcare resources and research funding. Real data, such as cancer incidences, exhibit non-linear characteristics along with a high degree of fluctuations, which makes the modelling process difficult. This study explores the potential of time series modelling, especially the long short-term memory (LSTM) recurrent neural networks, to predict the number of cancer incidences. A novel hybrid model of the wavelet transform and LSTM is proposed with the goal of increasing forecasting accuracy. The evaluation of the proposed models for the three most common types of cancer in the Kingdom of Saudi Arabia shows that the proposed hybrid model has better accuracy than the original LSTM model.
Variance Ranking for Multi-Classed Imbalanced Datasets: A Case Study of One-Versus-All

Imbalanced classes in multi-classed datasets is one of the most salient hindrances to the accuracy and dependable results of predictive modeling. In predictions, there are always majority and minority classes, and in most cases it is difficult to capture the members of item belonging to the minority classes. This anomaly is traceable to the designs of the predictive algorithms because most algorithms do not factor in the unequal numbers of classes into their designs and implementations. The accuracy of most modeling processes is subjective to the ever-present consequences of the imbalanced classes. This paper employs the variance ranking technique to deal with the real-world class imbalance problem. We augmented this technique using one-versus-all re-coding of the multi-classed datasets. The proof-of-concept experimentation shows that our technique performs better when compared with the previous work done on capturing small class members in multi-classed datasets.
Application of Q-Learning and RBF Network in Chinese Chess Game System

Abstract Machine game is one of the important directions of artificial intelligence research, and Chinese chess is a typical game process. This paper first introduces the game principle of Chinese chess. Then Q-learning method and evaluation function are added to train data through a large amount of self-learning. A chess game system based on Q-learning is designed and developed. The experimental results show that Chinese chess with Q-learning algorithm has the ability of evolutionary learning, which effectively improves the game level of Chinese chess.
Tweet Moodifier: Towards giving emotional awareness to Twitter users

Emotional contagion in online social networks has been of great interest over the past years. Previous studies have focused mainly on finding evidence of affect contagion in homophilic atmospheres. However, these studies have overlooked users' awareness of the sentiments they share and consume online. In this paper, we present an experiment with Twitter users that aims to help them better understand which emotions they experience on this social network. We introduce Tweet Moodifier (T-Moodifier), a Google Chrome extension that enables Twitter users to filter and make explicit (through colored visual marks) the emotional content in their News Feed. We compare behavioral changes between 55 participants and 5089 of their public “friends.” The comparison period spans from two weeks before installing T-Moodifier to one week thereafter. The results suggest that the use of T-Moodifier might help Twitter users increase their emotional awareness: T-Moodifier users who had access to emotional statistics about their posts produced a significantly higher percentage of neutral content. This behavioral change suggests that people could behave differently while using real-time mechanisms that increase their affect reflection. Also, post-experience, those who completed both pre- and post-surveys could assert more confidently the main emotions they shared and perceived on Twitter. This shows T-Moodifier's potential to effectively make users reflect on their News Feed.
Automatic Evaluation of Motion Picture Contents by Estimation of Facial Expression Intensity

The format of the video evaluation questionnaire is varied. The user can search high-evaluation videos by referring to the video evaluation, and the video producer is motivated to produce the video by getting a good evaluation, and by analyzing the evaluation, they can get clues to make a better video. However, the evaluation can be affected by several elements which are not directly related to the video itself. In addition, not all of the users evaluate the video because the evaluation is voluntary. Hence, it is difficult to obtain accurate video evaluation. This research focuses on the importance of the fun of the video in evaluating the video. We calculate the degree of smile from the face of the user at the time of viewing the video by the expression intensity estimation, and use it to evaluate the video automatically. First, smiley face images and the expression intensity of the face image are obtained by subject experiments. Using this data, an expression intensity estimation model by a support vector machine is created. Next, the expression intensity is calculated for each frame from the face video of the person at the time of watching the video, and it is linked to the evaluation on the video. As the performance evaluation, the video evaluation obtained through the questionnaire is compared with one obtained by the proposed method. The comparison result indicates that the expression intensity is useful for the video evaluation.
Label-Based Deep Semantic Hashing for Cross-Modal Retrieval

With the arrival of the era of big data, multimodal data increases explosively and the cross-modal retrieval has drawn increasing research interests. Due to benefits of low storage cost and fast query speed, hashing-based methods have made great advancements in cross-modal retrieval. Most of the previous hashing methods design a similarity-preserving matrix based on labels to simply describe binary similarity relationship between multimodal data, i.e., similar or dissimilar. This method is applicable to single-label data, but it fails to make use of labels to explore rich semantic information for multi-label data. In this paper, we propose a new cross-modal retrieval method, called Label-Based Deep Semantic Hashing (LDSH). In this method, a new similarity-preserving matrix is given according to multi-label to describe the degree of similarity between multimodal data. Moreover, the last fully connected layer of the deep neural network is designed as a Block Structure (B-Structure) to reduce the redundancy between generated bits. In order to accelerate the convergence speed of neural network, the Batch Normalization Layer (BN-Layer) is adopted after the B-Structure. Extensive experiments on two real datasets with image-text modalities demonstrate the superiority of the proposed method in cross-modal retrieval tasks.
Topology optimization on geometry of 3D printed “Impulse RC Alien 4 Inch” racing quadcopter frame with polylactic acid material

The racing quadcopter is one of Unmanned Aerial Vehicle (UAV) type which is for high speed. A frame of the racing quadcopter used carbon fiber material because of its lightness and rigidity; however, it is costly. Because of that, it is essential to consider alternative material, such as polylactic acid (PLA). However, the PLA frame needs some optimization treatments to achieve similar mass and rigidity as the carbon fiber. One of the structural optimization methods that can be used is topology optimization. Although this method will generate some intricate details in geometry, which is difficult for conventional manufacturing, however, with the additive manufacturing technology, the complex geometry is no longer a barrier. The aim of this research is prototyping "the best design" frame using a 3D printer, based on simulation results of stress and total deformation of the racing quadcopter from topology optimization method. The methodology started from the quadcopter modeling, initial simulation with carbon fiber frame and PLA frame using the static structural simulation of finite element software. Continued by topology optimization simulation with some retain masses to obtain some redesign models. The next stage is the final simulation on the redesign models using a similar method as the initial simulation, and then the analysis is established to achieve "the best design". The last stage is model manufacturing using a 3D printer, model testing, and result evaluation. As a result, "the best design" is achieved from doubling the thickness of PLA frame, establishing redesign of 60% retains mass topology optimization. Then both "the best design" manufacturing and testing are done well, although with some defects.
Thai Character Segmentation in Handwriting Images using Four Directional Depth First Search

One of the key processes for converting handwriting images into digital texts is the character segmentation. It is very challenge especially for the case of segmenting the hand-writing due to intra-variations of various writing styles and overlapping of characters between consecutive characters. This paper works on Thai characters in handwriting images. Thai characters consist of different types of consonants, tones and vowels, which are written in different manners. This paper proposes the 4 directional depth first search based approach for segmenting individual characters in both vertical and horizontal cutting aspects. The vertical cut is applied to segment each text column, while the horizontal cut is applied to segment individual characters. Then, the erosion with two structuring elements is used to split overlapped consecutive characters that may be remained after the main segmentation process. The proposed method is validated with 11,949 Thai characters in handwriting images. It achieves up to 90.76 % of the successful segmentation.
Laboratory Information Management Systems for Electron Microscopy: Evaluation of the 4CeeD Data Curation Platform

An evaluation of the feasibility and the requirements associated with a facility-wide deployment of a laboratory information management system (LIMS) at an electron microscopy facility was conducted. 4CeeD, an open-source LIMS, was selected for the focus study. This report summarizes data infrastructure prerequisites, critical and desirable features, and lessons learned from using and interacting with 4CeeD, and broader LIMS adoption recommendations for this facility.
Application of Machine Intelligence in Digital Pathology: Identification of Falciparum Malaria in Thin Blood Smear Image

Advancement of microscopic imaging has contributed to the development of digitized blood smear images. Application of automated algorithms to analyse these images can help pathologist for a rapid and accurate diagnosis. This chapter proposes a new algorithm that uses a combination of both supervised and unsupervised learning methods as well as rule-based methods, for faster and effective segmentation of parasite-infected cells. Morphological and textural features are extracted for identification of parasite stage using publicly available MaMic dataset. The infected cells are segmented with very high accuracy for better classification results. Single classifiers and ensemble of classifiers were used for best classification outcome. The best overall image-level accuracy of 98.74% was achieved by ensemble classifier.
Stylistic palimpsests: Computational stylistic perspectives on precursory authorship in Aphra Behn’s drama

Abstract Computational stylistics has developed various methods for investigating and attributing authorship of collaborative literary texts. This article investigates ‘precursory authorship’ (Love, 2002): that is, the authorial traces of a source text that inform—to a greater or lesser degree—a subsequent literary output, in order to establish its relevance for our approach to and understanding of the linguistic properties of literary style. Precursory authorship and derivative adaptations are common features of early modern English drama, and the study focusses on two case studies relating to the plays of Restoration playwright, Aphra Behn (c. 1640–89). Using a combination of quantitative methods (Rolling Delta (RD), principal components analysis (PCA), Delta, and Hierarchical Cluster Analysis), the investigation highlights the presence of precursory authorial style in Behn’s The Rover and an anonymous work associated with Behn, The Counterfeit Bridegroom. The results suggest that precursory authorial style is identifiable in both cases, not only through a similarity with the source text but, to a lesser degree, other texts by the precursory author as well. The anonymous play yields complex and non-confirmatory evidence for Behn’s authorship. Methodologically, RD is most sensitive to precursory collaboration. Collectively, the findings highlight the importance of stylistic factors when describing and interpreting literary linguistic quantitative data: precursory authorial style is another facet that intersects with properties such as time period and genre. The article urges a more critical and theoretically informed view of authorially aligned linguistic style.
Comparison of study motivation and job expectations of Russian full-time and part-time university students

Purpose The purpose of this paper is to present the results of the authors’ questionnaire survey focused on the comparison of study motivation and job expectations of full-time and part-time students of Russian universities and identify main problems of higher education and graduate employment and suggest their possible solutions. Design/methodology/approach The authors’ questionnaire survey was conducted from March to October 2018. The respondents were full-time and part-time bachelor’s and master’s students from 30 regions across Russia. The relevant data were obtained from 1,051 students. The data analysis was based on the calculation of relative frequencies (as a share from the total number of respondents) and the evaluation of the dependence of responses on the form of study (full-time students and part-time students) using contingency tables and χ 2 tests of independence. Findings The results of the authors’ questionnaire survey support the assumption that the current generation of full-time and part-time students of Russian universities studies to succeed in the future. Surveyed university students certainly have a high motivation to study, but at the same time, they seem to have too high expectations about their future work and career, which can negatively affect their future success in their jobs if they do not have appropriate knowledge, skills and abilities. Originality/value The results of the authors’ questionnaire survey show some unique tendencies in the Russian university students’ attitudes to study, work and career that are worth attention both from the point of view of universities and from the point of view of employers and their current approaches to the education and the employment of the current generation of young people. The results open up new possibilities for further research focused on the higher education and the employability of the new generation of work force.
Content of heavy metals and petroleum products in the soil on the territory of oilproducing areas of the Republic of Tatarstan

The paper presents data on the hygienic assessment of the content of heavy metals and petroleum products in the soil on the territory of oil-producing areas of the Republic of Tatarstan. The average total content of lead (10,1 mg/kg), cadmium (0.073 mg/kg), manganese (792.4 mg/kg), cobalt (14.2 mg/kg), chromium (87.6 mg/kg), nickel (65.2 mg/ kg), arsenic (8.0 mg/kg), copper (36.6 mg/kg) and zinc (521 mg/kg) in soil for agricultural purposes near objects oil production was shown not to exceed the established hygienic standards. Soil contamination with oil products leads to an increase in its toxicity and mutagenicity. Soil contamination with oil products at the level of as low as 30 mg/ kg leads to the gain in its toxicity (low toxicity) and mutagenicity (average) (specific gravity of germinated seeds was 70.7±0.67%, aberrations - 1.78±0.19%).