Reviewerly/exHarmony huggingface dataset:
Digital humanities: prospects for knowledge transfer

The paper discusses the problems of knowledge transfer in the field of digital humanities (Digital humanities/DH).The transformation of ideas about digital humanities reflected in the latest publications is critically reviewed and the proposal to include relevant educational practices in this concept is grounded.The authors have made the forecast of digital humanities development based on the prospects of the 4th Industrial Revolution that radically change the relationship between the market and scientific environment.The paper presents the analysis of the factors that determine the importance of knowledge transfer in the humanities and the role of DH in the commercialization of intellectual potential.The authors suggest a new vision of knowledge transfer, pay special attention to the difficulties in its development in the Russian digital humanities and highlight promising areas of knowledge transfer programs for DH.The conclusion is made that it is necessary to update the knowledge transfer in the field of digital humanities re-focusing the research and development in this field.
Module minimisation based low‐complexity soft decoding of Reed–Solomon codes

The interpolation-based algebraic decoding for Reed–Solomon (RS) codes can correct errors beyond half of the code's minimum Hamming distance. Using soft information, the algebraic soft decoding (ASD) further improves the decoding performance. This paper presents a unified study of two classical ASD algorithms, the algebraic Chase decoding and the Koetter-Vardy decoding. Their computationally expensive interpolation is solved by the module minimisation (MM) technique which consists of basis construction and basis reduction. Compared with Koetter's interpolation, the MM interpolation yields a smaller computational cost for the two ASD algorithms. Re-encoding transform is further applied to reduce the decoding complexity by reducing the degree of module generators. Based on assessing the degree of module seeds, a complexity reducing approach is introduced to further facilitate the two ASD algorithms. Computational cost of the two algorithms as well as their re-encoding transformed variants will be analysed. Performance of the two ASD algorithms will be compared under decoding expenditure benchmark, providing more practical insights of their applications.
Integrated Model for Constructing Evolving Multi-Agent Subsystems

The paper provides evolutionary design methods for autonomous agents and multi-agent systems (MAS). Hybrid and fuzzy models development is based on design methods. Evolutionary principles and models, especially evolutionary design techniques, are used in the implementation of multi-agent systems. The agent and MAS design principle was developed. According to the principle, a design process includes basic self-organization components like interaction, crossing, adaptation to environment, etc. A general technique is developed: descendant agents are formed as a result of parent agent interaction. Different types of crossing-over are developed and described, also the idea of forming agencies (families) as evolving MAS simples was created. According to fuzzy coding principles, hybrid fuzzy and evolutionary models of forming agents and agencies were developed. Based on the fuzzy logic approaches such as triangle norms and conorms, fuzzy genetic operators development methods were provided. A program support system for agents and MAS evolutionary design was developed and implemented.
Path Planning for Robot Manipulator based on Obstacle-Guided Path Refinement

The Path planning is essential in the field of robotics as robots require a fast algorithm to develop not only the shortest path but also free of collision with any obstacles. Therefore, this paper describes a novel path planning algorithm based on Obstacle-Guided Path Refinement (OGPR). To illustrate the benefits and performance of the new algorithm, a comparison is established between OGPR and A* based on implementation of the two algorithms with SCARA robot model. The results of this comparison show that OGPR algorithm over advantage A* in terms of the path length and the computational time.
Cloud Computing in Lebanese Enterprises: Applying the Technology, Organization, and Environment (TOE) Framework

In the last few years, cloud computing (CC) has grown from being a promising business concept to one of the fastest growing segments of the IT industry.Many businesses including small, medium (SMEs) and large enterprises are migrating to this technology.The objective of this paper was to describe the opinions of enterprises about the benefits and challenges of cloud computing services in the private and public companies in South Lebanon.During 2019, a cross-sectional study which enrolled 29 enterprises used CC was conducted.The survey included questions on socio-demographic characteristics of representative of companies, and companies' factors in reference to the technology, organization, and environment (TOE) framework.Most (58.6%) of companies were private and micro and SMEs sized (86.8%).The cost saving (75.0%), scalability and flexibility (75.9%), security (44.8%), and improved service delivery were the main benefits that cloud offer to the business.The security aspect, the cost, and the limited provision of infra-structure remain a challenge for the adoption of CC.In conclusion, the research reveals the potential for the development of CC and obstacles for successful implementation of this new technology.
Analysis of key Factors for Organization Information Security

Protecting sensitive information from illegal access and misuse is crucial to all organizations. An inappropriate Information Security (IS) policy and procedures are not only a suitable environment for an outsider attack but also a good chance for the insiders' misuse. In this paper, we will discuss the roles of an organization in information security and how human behavior affects the Information Security System (ISS). How an organization can create and instill an effective information security culture in an organization to improve their information safeguards. The findings in this review can be used to further researches and will be useful for organizations to improve their information security structure (ISC).
Utilizing Process Data for Cognitive Diagnosis

Process data, different from item responses, essentially shows the interactions between test-takers, item presentation including stems and options, technology-enhanced help features, as well as the computer interface. With the availability of process data in addition to product data, additional auxiliary information from the response process can be utilized to serve different assessment purposes such as enhancing accuracy in ability estimation, facilitating cognitive diagnosis, and aberrant responding behavior detection. Response time (RT) is the most frequently studied process data contained in log files in current psychometric modeling, although other process data is available such as the number of clicks, the frequency of use of help features, frequency of answer changes, and data collected using eye-tracking devices. Process data is worthy of exploration and the integration with product data can enhance our evidence base for assessment purposes. This chapter will focus on the use of RT as one important type of process data in cognitive diagnostic modeling.
Human Intention Estimation Using Fuzzy Inference Based on Gaze Tracking and Saliency Map

Recently, the development of nursing and welfare robots intended for people with disabilities and elderly has been advanced. The research of the method to transmit human intentions to the robot by using the biological information of the operator is proceeding. Particularly, in the gaze information, there are multiple important information including the human psychological condition. In this research we propose a method to perform the locomotive control of omni-directional wheelchair by a gaze instruction system based on the human intention. The human intention is estimated by using fuzzy inference combining the gaze map and visual saliency map. We performed the operating experiment using a gaze tracking device, and we confirmed that the operator can successfully perform the moving control of wheelchair according to the instructions.
Comparative Efficiency of Methods of Cooperative Processing of Imagery Information

A problem of detection of 3D objects on the basis of imagery information provided by two or three different sensors is considered. The efficiency of object detection with the use of algorithms of integration and fusion of information from different sensors and also by means of formation of a unified synthetic image from two initial images is estimated. Conditions are determined at which the use of the data integration algorithm and the method of forming a single synthetic image provides a lower efficiency as compared to the use of only one image among those available for detection.
Virtual Reality Rendered Video Precognition with Deep Learning for Crowd Management

We describe an AI driven model based on the Nethra Video Analytic platform, optimised for overhead detection and designed specifically for CCTV and which can detect and categorize people from any angle. The model runs in real time in crowded and noisy environments and can be in- stalled on devices as in edge analytics or applied directly to existing video feeds. By mapping an entire space, we link together individual camera feeds and data points to calculate the total number of people to assist with capacity planning and to pin point bottlenecks in people flows. Solving the problem of aggregating multiple 360-degree video camera feeds into a single combined rendering, we further describe a novel use of interactive Virtual Reality. This model renders St Pancras International Station in VR and can track people movement in real time. Real people are rep- resented by avatars in real-time in the model. Users are able to change their viewpoint to look at any angle. The movement of the avatars exactly mirrors what can be seen in the cameras.
Detecting Feature Interactions in FORML Models

Requirement engineers must know how features (units of functionality) interact, in order to resolve undesired interactions. Model checking has been proposed as an effective method for detecting feature interactions. We propose a method for (1) modelling features as distinct modules (explicating intended interactions with other features), (2) composing feature modules into a system model that preserves intended interactions, (3) translating this rich model into the input language of a model checker, and (4) automatically generating correctness properties whose violations reveal unintended feature interactions.
Twitter Sentiment Based Mining for Decision Making using Text Classifiers with Learning by Induction

Abstract The amount of data residing in social media currently untapped is certainly limitless as millions of people are constantly posting a message or the other to public forums on the internet. Twitter being one of the largest social media networks with over 336 million monthly active users has proven to be a fertile ground for harvesting opinion from multiple people. This work explores how opinion can be extracted from tweets to discover people’s view concerning a certain subject matter. It focuses mainly on overcoming the limitation of the current approach to social media sentiment based mining for decision making which is that opinions derived from multiple sources are limited to available connections on the social media platforms and lack of improved accuracy of mined opinions. In order to achieve this, the proposed framework provides a platform to mine opinions from more than the available friends and connections on the social media platform and in addition, improve the quality of the opinion mined by implementing supervised learning algorithms with learning by induction in Twitter data analysis. In this research, three different supervised machine learning algorithms were applied to a dataset curated by graduate students at Stanford in order to accurately classify tweets into either positive or negative sentiment based on its content. It was discovered that Maximum Entropy had the highest accuracy of 83.5% among the three algorithms. The research has provided a web application which would enable users such as CEOs, Market Analysts, and random users make quality decision based on others’ opinions.
Optimization of settlement land use through carbon footprint approach in The North Balikpapan

Abstract Limited land in the downtown area as well as the increasing amount of new activities centre causes residential development leads to North Balikpapan. This area is an urban fringe with vast protected forests as buffer zone and catchment area for the city and surrounding area. Land conversion in this area will increase hazard risk of inundation, water quality decrease and increased CO 2 emissions. Therefore, development should be maintained environmental stability. One of the rights applicated approach is carbon footprint that is capable to measure the balance between production and absorption needs of CO 2 emissions. To find the optimal land allocation, we used carbon footprint calculation from the household activities, identify the factors of settlement growth, and use Linear Programming analysis. Analysis’ results show that settlement activities in North Balikpapan produce 108.362,4 tCO 2 /year or equivalent with 618,50 Ha green space. Meanwhile, the development of settlement in North Balikpapan is affected by social demographic, developer initiative, environmental condition, public facilities availability, economical structure, and policy factors. According to those factors, optimal allocation of settlement area in North Balikpapan is only about 4,510.01 Ha. With that condition, it still able to absorb CO 2 emissions from inside or outside the area around 2.751 tCO 2 /year.
Learning to listen for design

In his essay, Designed as Designer, Richard Gabriel suggests that artifacts are agents of their own design. Building on Gabriel’s position, this essay makes three observations (1) Code “speaks” to the programmer through code smells, and it talks about the shape it wants to take by signalling design principle violations. By “listening” to code, even a novice programmer can let the code itself signal its own emergent natural structure. (2) Seasoned programmers listen for code smells, but they hear in the language of design principles (3) Design patterns are emergent structures that naturally arise from designers listening to what the code is signaling and then responding to these signals through refactoring transformations. Rather than seeing design patterns as an educational destination, we see them as a vehicle for teaching the skill of listening. By showing novices the stories of listening to code and unfolding design patterns (starting from code smells, through refactorings, to arrive at principled structure), we can open up the possibility of listening for emergent design.
Evaluating Software Diversity in Branch Prediction Analyses for static WCET Estimation

Static worst-case execution time analysis enables to obtain guaranteed timing bounds for programs, which is required for safety-critical hard real-time systems. This comprises micro-architectural analyses that rely on full knowledge of the executed program. An example are current approaches to statically bound the time penalty of mispredicted branches in systems using static or dynamic branch predictors. On the other hand, in artificial software diversity, uncertainty in program aspects is used to render code-reuse attacks useless, making the system considerably more secure. We solve this conflict by proposing adapted static analyses for static and dynamic branch prediction that are able to cope with diversity, and by quantifying the impact of diversity onto the analysis results through extensive evaluation.
VYTEDU-CW: Difficult Words as a Barrier in the Reading Comprehension of University Students

We present VYTEDU-CW (Educational Videos and Texts - Complex Word) as a corpus in Spanish made up of university educational texts containing difficult words. In its construction process, it worked with students of the different careers that make up the University of Guayaquil (Ecuador); the students were selected according to the level of studies with respect to the content of the texts of the corpus, this with the purpose that the text is not very easy for students who are in higher grades. The work consisted in the recognition and labeling of the complex words contained in the different texts that make up the VYTEDU corpus. It should be noted that one of the difficulties in reading comprehension is complex words, which become a barrier especially for those with special cognitive abilities. This new corpus had its origin in the VYTEDU corpus, whose demonstration was made at the congress of the SEPLN (Murcia-Spain, 2017) and on which several experiments were carried out. Currently, VYTEDU-CW is being used to continue researching the Automatic Simplification of Spanish Texts in the field of Natural Language Processing. A sample of students was taken from the different faculties and from the results the corpus statistics were obtained. The objective of this work is to determine that difficult words in educational texts become one of the main problems in the reading comprehension in the day to day of university students.
Bayesian LPV-FIR Identification of Wheelchair Dynamics

This paper constructs a model of wheelchair dynamics in a data-driven manner. In particular, we focus on the forward movement of the wheelchair, for which we adopt a Linear-Parameter-Varying Finite-Impulse-Response (LPV-FIR) model. To avoid the overfitting behavior, we employ the Bayesian estimation method. We show by experimental results that the constructed model reproduces the observed data more precisely than linear models.
An Automatic and a Machine-assisted Method to Clean Bilingual Corpus

Two different methods of corpus cleaning are presented in this article. One is a machine-assisted technique, which is good to clean small-sized parallel corpus, and the other is an automatic method, which is suitable for cleaning large-sized parallel corpus. A baseline SMT (MOSES) system is used to evaluate these methods. The machine-assisted technique used two features: word alignment and length of the source and target language sentence. These features are used to detect mistranslations in the corpus, which are then handled by a human translator. Experiments of this method are conducted on the English-to-Indian Language Machine Translation (EILMT) corpus (English-Hindi). The Bilingual Evaluation Understudy (BLEU) score is improved by 0.47% for the clean corpus. Automatic method of corpus cleaning uses a combination of two features. One feature is length of source and target language sentence and the second feature is Viterbi alignment score generated by Hidden Markov Model for each sentence pair. Two different threshold values are used for these two features. These values are decided by using a small-sized manually annotated parallel corpus of 206 sentence pairs. Experiments of this method are conducted on the HindEnCorp corpus, released in the workshop of the Association of Computational Linguistics (ACL 2014). The BLEU score is improved by 0.6% on clean corpus. A comparison of the two methods is also presented on EILMT corpus.
Training Powered Wheelchair Manoeuvres in Mixed Reality

We describe a mixed reality environment that has been designed as an aid for training driving skills for a powered wheelchair. Our motivation is to provide an improvement on a previous virtual reality wheelchair driving simulator, with a particular aim to remove any cybersickness effects. The results of a validation test are presented that involved 35 able bodied volunteers divided into three groups: mixed reality trained, virtual reality trained, and a control group. No significant differences in improvement was found between the groups but there is a notable trend that both the mixed reality and virtual reality groups improved more than the control group. Whereas the virtual reality group experienced discomfort (as measured using a simulator sickness questionnaire), the mixed reality group experienced no side effects.
AADL Vulnerability Modeling and Security Analysis Method

In the system architecture design phase, there may be flaws introduced by the access control design. The flaws make the system design vulnerable. Vulnerabilities that are not detected during the design phase will be taken to the subsequent phases of the system development. This reduces system security and leads to more repair cost. In order to detect the access control flaws in the design phase as early as possible, we propose the security analysis method using Architecture Analysis and Design Language (AADL). This method analyzes the access control policies between components. It can analyze and detect the access control flaws in the AADL architecture model. For describing the access control policies, vulnerability model annex is created. We use a case study, based on a module from an aircraft intelligent information system, to introduce the application of modeling and analysis methods.
Fast forward of adiabatic spin dynamics : An application to quantum annealing model in triangle spin systems.

Abstract We propose a scheme of the fast forward of adiabatic spin dynamics in a triangular 3 spin cluster of quantum annealing model and reveal the structure and role of driving pair-wise interaction and three-body interactions. We settle the quasi-adiabatic spin dynamics (QASD) by adding the regularization terms to the original Hamiltonian and accelerate it with use of a large time-scaling factor which realizes QASD on shortened time scale. Assuming the candidate regularization Hamiltonian consisting of three-body interactions besides the pair-wise exchange interactions and magnetic field, we solved the regularization terms. These terms multiplied by the velocity function give rise to the state-dependent counter-diabatic terms (CDTs) for each of adiabatic states. Applying this method to quantum annealing model, we find CDTs which include pair-wise interaction and the 3-body interactions. The driving pairwise and three-body interaction in the fast-forward scheme guarantees the complete fidelity of accelerated states.
Improving Visual Attention Guiding by Differentiation between Fine and Coarse Navigation

Attention guiding improves human performance in search tasks in various applications. With the current generation of Head-Mounted-Displays, new designs for addressing new requirements like stereoscopic cues or a restricted field of view are needed. Besides the design of the navigation system itself, research for two-dimensional interfaces shows that a differentiation into coarse and fine navigation could improve performance. Scenarios with information in a 360° area around the user may especially profit from attention guiding techniques adapting to different navigation modes. To analyse potential performance improvements, two common guiding techniques for fine navigation were combined with an arrow for coarse navigation. Within a laboratory study, the effects of the combination of an arrow with the attention funnel and SWAVE techniques on search time and rotation overhead, as well as subjective preference, have been examined. Results show that the combination partially improves the performance of attention guiding techniques when considering objective and subjective parameters. As the findings are not generally applicable, more in-depth research on the combination of different designs for coarse navigation, as well as other factors of interest, like distraction caused by the attention guiding techniques, should be carried out.