Reviewerly/exHarmony huggingface dataset:
Computing with Words — A Framework for Human-Computer Interaction

In this paper we explore the possibility of using computation with words (CWW) systems and CWW-based human-computer interface (HCI) and interaction to enable efficient computation and HCI. The application selected to demonstrate the problems and potential solutions is in the context of autonomous driving. The specific problem addressed is of a machine instructed by human word commands to execute the task of parking two manned or unmanned cars in a two-car garage using CWW. We divide the interaction process into two steps: (1) feasibility verification and (2) execution. In order to fulfill the task, we begin with verifications of feasibility in terms of assessing whether the garage is unoccupied, checking general ballpark dimensions, inspecting irregular shapes, and classifying the cars that need to be parked, in terms of size, types of vehicles, ranges of acceptable tolerances needed if the cars are manned or not, and means of collision avoidance. The execution of the autonomous driving part is directed by sensory non-numeric fuzzy information that indicates distances from walls or obstacles. The execution algorithm uses a sequence of driving instructions aimed at using the available space in a simple and efficient way without resorting to elaborate numerical calculations, such as making sure that the car is within 2 inches of the wall. The system and its usability are qualitatively analyzed. The analysis shows that the approach has a potential for reducing computational complexity and improving system usability.
Improving case-based reasoning with the aid of multi-criteria and group decision-making methods

Solving semi-formalized and semi-structured tasks on the basis of a case-based reasoning (CBR) provides to use the accumulated knowledge and experience. Wherein, sometimes it is necessary to improve CBR results, for example, to reduce the set of possible solutions and to ground their correctness not only with the use of a similarity measure. One way to solve this problem is to use mathematical multi-criteria and group decision-making methods. This paper discusses the combined use of the ARAMIS (Aggregation and Ranking Alternatives nearby the Multi-Attribute Ideal Situation), AIR (Aggregation of Individual Ranking), MOPC (Method Of Pairwise Comparison processing), COIP (Complex processing Of Individual Preferences) and a case-based reasoning. The algorithm and case study for solving problem of selecting construction materials in petrochemistry are described. The application of the approach did not result to new decisions and/or the revision of decisions based on a case-based reasoning approach. But it provides their grounding from the point of view of mathematical methods and the growth of confidence from the point of view of the experts.
Designing an Educational Multimedia System for Supporting Learning Difficulties in Arabic

Nowadays, schools are highly encouraged to use information technology in unconventional manners and also be creative in combining the use of various tools, while maintaining high standards. In spite of the latent advantage for the usage of assistive technology for children with learning difficulties (LD), many of the software applications, tools, and websites that those children deal with are designed without consideration of their required needs, making these software less effective or completely inaccessible for them. This paper presents an educational multimedia system to help children overcome their Arabic learning problems. The research work aims at proposing a practical educational strategy for automatically converting Arabic language text into an appropriate personalized multimedia content that would significantly enhance understanding, communication, and thinking skills for children with LD in elementary schools. The system will be also compared and evaluated with other existing educational multimedia systems.
Effect of Layout on User Performance and Subjective Evaluation in an Augmented-Reality Environment

In this study, the effect of layout on user performance and subjective evaluation in an augmented-reality (AR) environment was investigated. A scenario where participants had to work on three windows simultaneously was used. Three basic layouts of these windows have been examined, i.e., a horizontal layout, a vertical layout, and a diagonal layout. Additionally, two experimental tasks had to be completed; one was a reading comprehension task requiring a low switching frequency (LSF), and the other was a classification task requiring a high switching frequency (HSF). The results revealed that first, participants performed best in the diagonal layout in the LSF task, whereas they performed best in the vertical layout in the HSF task. Second, no significant differences were found in the disorientation between different layouts. Third, participants were significantly less satisfied with the diagonal layout in the HSF task. In conclusion, a horizontal layout is first recommended for general tasks and a vertical layout is recommended for HSF tasks. The switching distance and switching path are two important factors to be considered in the layout design in an AR environment.
Secure Cryptosystem Using Randomized Rail Fence Cipher for Mobile Devices

Transferring files between mobile devices through a wireless medium has security implications. Particularly for applications that transfer personal information like healthcare records or location information. Designing an efficient and secure cryptosystem for these resource-constrained modern smart devices is a challenging task. In this paper, we address the same and propose a light-weight, energy-efficient and secure cryptosystem based on rail-fence cipher (RFC). We refer the proposed algorithm as "randomized rail fence cipher (RRFC)" as it uses pseudorandom numbers to select the fences for transposing characters of a file. The idea is to remove security vulnerabilities of RFC by increasing the complexity of determining the pattern used for the transposition. We implement the proposed algorithm and experimentally analyze the performances of the algorithm on mobile devices. We also present attack analysis for the proposed cryptosystem.
Wajeez: An Extractive Automatic Arabic Text Summarisation System

The volume of Arabic information is rapidly increasingly nowadays, and thus, access to the corrects is arguably one of the most difficult research problems facing readers and researchers. Text Summarisation Systems are utilised to produce a short text describing significant portions of the original text. That is by selecting the most important sentences, following several steps: preprocessing, stemming, scoring, and summary extraction. Nevertheless, summarisation systems remain still in their infancy for the Arabic language. Therefore, this paper proposes an automatic Arabic text summarisation systems, entitled Wajeez, that introduces a new inclusive scoring formula that generates a final summary from several top-ranking sentences. Wajeez was applied on two different datasets: the Essex Arabic Summaries Corpus (EASC) and a manual summary to assess its performance using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) set of metrics. In comparison to two other competitions systems, Wajeez performed comparatively well when a title is provisioned to support summarisation.
Computer Network Security Evaluation Based on LM-BP Neural Network

The assessment of computer network security is a significant system in computer network security assurance. Aiming at the shortcomings of BP neural network in network security evaluation, such as the slow convergence speed, the difficulty of globally optimal solution, the low accuracy of diagnosis and the uncertainty of network structure. Taking all these disadvantages into account, this paper aims to revise BP neural network by using levenberg-marquardt algorithm and combine the actual sample data to operate simulation test. As a result, LM-BP neural network algorithm, which possesses the advantages of fast learning speed and strong generalization ability, provides an effective, accurate and reliable method for the evaluation of computer network security.
Micro-level Data Analysis and Visualization of the Interrelation Between Confidence and Athletic Performance

Studies in sport psychology have shown that confidence is one of the most critical mental skill for elite athletes. In this paper, we present a method to analyze and visualize the interrelation between confidence and athletic performance in professional tennis matches. Specifically, we have developed a method to estimate the relative confidence level of a tennis player based on his/her micro-level performance success. Our program then analyzes the interrelation between the athlete's relative confi- dence level with the player's subsequent performance metrics, such as unforced errors, forced errors, winners, serve directions, first-serve faults, double faults, aces, and rally lengths.
An empirical Bayes approach for learning directed acyclic graph using MCMC algorithm

Abstract One hypothetically well‐founded approach for learning a Directed Acyclic Graph (DAG) is to utilize the Markov Chain Monte Carlo (MCMC) techniques. In the MCMC, the uniform noninformative priors on all of the possible graphs are considered. This brings about computational costs, making them impractical for learning the structure of DAGs with numerous variables. In this paper, we focus on the discrete variables and use the data information to restrict the space of possible graphs. This approach can be interpreted as an empirical Bayes paradigm. This means that we use an empirical Bayes approach to make zero prior probability of some possible graphs. For this purpose, we first estimate the potential neighbors using L1‐Regularized Markov Blanket and then determine the candidate causes for each variable by introducing a new criterion. This perspective makes it possible to reduce the search space in the process of the MCMC simulation. The results on the well‐known DAGs show that our method has higher accuracy. The source code is available at http://bs.ipm.ac.ir/softwares/mcmccode.rar .
Combination of Local Interaction with Remote Interaction in ARM-COMS Communication

ARM-COMS detects the orientation of a human subject face by the face-detection tool based on an image processing technique, and mimics the head motion of a remote partner during video conversation in an effective manner to enhance entrainment as reported before. However, ARM-COMS does not make any appropriate reactions if a communication partner speaks without move in video communication. Therefore, audio signal from the human subject is another option to use as a driving force of ARM-COMS to enhance the physical entrainment. In this study, a configuration of voice signal-based local interaction subsystem was implemented. Using this subsystem, handing of two types of individual input signals were studied: one is from the head-motion image of a remote partner, and the other one is from the combination of voice signals of a local user and its remote partner. This paper presents how the combination of remote interaction and local interaction was implemented in ARM-COMS communication, and discusses the feaibility of this approach.
Fuzzy Expert System in Agriculture Domain

Agriculture is an important source of livelihood and economy of a country. Decision making plays an important role in various fields. Farmers are the backbone of agriculture. They need expert systems to make decisions during land preparation, sowing, fertilizer management, irrigation management, etc. for farming. Expert systems may suggest precisely suitable solutions to farmers for all the activities. Uncertainty deals with various situations during sowing, weed management, diagnosis of disease, insect, storage, marketing of product, etc. Uncertainty is compounded by many facts that many decision-making activities in agriculture are often vague or based on perception. Imprecision, vagueness, and insufficient knowledge are handled using the concept of fuzzy logic. Fuzzy logic with expert systems helps find uncertain data. Fuzzy expert systems are oriented with numerical processing.
Global structure of policy search spaces for reinforcement learning

Reinforcement learning is gaining prominence in the machine learning community. It dates back over three decades in areas such as cybernetics and psychology, but has more recently been applied widely in robotics, game playing and control systems. There are many approaches to reinforcement learning, most of which are based on the Markov decision process model. The goal of reinforcement learning is to learn the best strategy (referred to as a policy in reinforcement learning) of an agent interacting with its environment in order to reach a specified goal. Recently, evolutionary computation has been shown to be of benefit to reinforcement learning in some limited scenarios. Many studies have shown that the performance of evolutionary computation algorithms is influenced by the structure of the fitness landscapes of the problem being optimised. In this paper we investigate the global structure of the policy search spaces of simple reinforcement learning problems. The aim is to highlight structural characteristics that could influence the performance of evolutionary algorithms in a reinforcement learning context. Results indicate that the problems we investigated are characterised by enormous plateaus that form unimodal structures, resulting in a kind of needle-in-a-haystack global structure.
Two-Dimensional Algebraic Codes for Multiple Burst Error Correction

We construct native binary 2D binary cyclic codes capable of correcting multiple occurrences of multiple non-overlapping pre-defined 2D error shapes using frequency domain techniques. The starting location of each error shape is determined using a novel decoding algorithm based on a careful selection of the common zero set. The proposed code construction is generic and offers superior coding rates compared to the native 2D BCH code with the same error correction ability.
Application of Variable-Valued Logic to Correct Pattern Recognition Algorithms

In this work the logical analysis is carried out for the predetermined subject domain, that represents an object and its describing characteristics in terms of variable-valued logic, besides the set of algorithms operating at the given domain is studied. The study developed logical procedure for creation of the correct algorithms that analyze subject domain, model the knowledge base for the set objects, minimize it and allocate a unique feature set for each object.
Optimized Division of Exploration Areas in Multi-robot Systems Considering Static and Dynamic Charging Stations

The mobile robots popularization, in terrestrial, aerial or aquatic context, opens the possibility of a new research niche: the use of these robots for activities such as monitoring, search/rescue, cleaning services, as well their use in precision agriculture. As many times the area of operation of these robots is very large, it becomes unfeasible the fulfillment of the activity using a single robot, but even using many robots, there is the limitation of the battery autonomy. With the popularization of autonomous robots use, the search for mechanisms to make these robots recharge their battery autonomously are impulsed, however, the choice of the best place to insert the charging stations considering the displacement time saved and use of battery to move the robots is a challenge. The present work shows a Java Desktop graphical application that makes use of the JST library that requests data to the user, such as the map of the place to be explored, quantity of charging stations, if these stations must be in static places, perhaps due to electrical outlet locations restriction, or if they could be dynamically placed. Based on these data an optimal solution to insert the charging stations as well as the definition of each robot activity area are presented. Algorithms such as Voronoi, Viktor Grabarchuk and Centroid position are used in this process. The Voronoi Algorithm allowed the balanced division of the action area into a group of robots considering static recharge positions. The combination of the Viktor Grabarchk and Centroid Position algorithms allowed a balanced division of the operating area for different robots, and also, the definition of a central position to allocate the bases of recharges, which reduces the time of displacement of the robot to the base when necessary.
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Let R=GR(pe,m)[u]∕〈uk〉 be a finite commutative ring for a prime p and any positive integers e,m and k. In this paper, we derive the explicit representation of cyclic codes over the ring R of length n, where n and p are coprime. We also discuss the dual of such cyclic codes over the ring R and give a sufficient condition for the codes to be self-dual. Moreover, we study quasi-cyclic codes of length kn and index k over the ring R, and obtain some good codes satisfying the bound given in Dougherty and Shiromoto (2000) over the ring Z9 as an example.
Asymptotic expansions and saddlepoint approximations using the analytic continuation of moment generating functions

Abstract Transform inversions, in which density and survival functions are computed from their associated moment generating function $\mathcal{M}$ , have largely been based on methods which use values of $\mathcal{M}$ in its convergence region. Prominent among such methods are saddlepoint approximations and Fourier-series inversion methods, including the fast Fourier transform. In this paper we propose inversion methods which make use of values for $\mathcal{M}$ which lie outside of its convergence region and in its analytic continuation. We focus on the simplest and perhaps richest setting for applications in which $\mathcal{M}$ is either a meromorphic function in its analytic continuation, so that all of its singularities are poles, or else the singularities are isolated essential. Asymptotic expansions of finite- and infinite-orders are developed for density and survival functions using the poles of $\mathcal{M}$ in its analytic continuation. For finite-order expansions, the expansion error is a contour integral in the analytic continuation, which we approximate using the saddlepoint method based on following the path of steepest descent. Such saddlepoint error approximations accurately determine expansion errors and, thus, provide the means for determining the order of the expansion needed to achieve some preset accuracy. They also provide an additive correction term which increases accuracy of the expansion. Further accuracy is achieved by computing the expansion errors numerically using a contour path which ultimately tracks the steepest descent direction. Important applications include Wilks’ likelihood ratio test in MANOVA, compound distributions, and the Sparre Andersen and Cramér–Lundberg ruin models.
Open Information Extraction for Mongolian Language

In this paper, we describe MongoIE, an Open Information Extraction (Open IE) system for the Mongolian language. We present the characteristic of the language and, after analyzing the available preprocessing tools, we describe the features used for building the system. We have implemented two different approaches: (1) Rule-based and (2) Classification. Here, we describe them, analyze their errors and present their results. In the best of our knowledge, this is the first attempt in building Open IE systems for Mongolian. We conclude by suggesting possible future improvements and directions.
eHIFS

Securely deleting obsolete data is of significant importance, as reserving them may not only endanger data owners' privacy, but also violate data protection regulations like GDPR, SOX and HIPPA. However, completely eliminating data is extremely challenging in modern computer systems. One reason is the past existence of the deleted data may leave artifacts in the layout of a storage system at all layers, and such structural artifacts may be utilized by the adversary to derive sensitive information about the data having been deleted. A novel security notion, history independence, can ensure that memory representation of a data structure is independent of the operation sequences leading to it. Therefore, history independence can be utilized to remove the structural artifacts created by the deleted data, making it possible to achieve secure deletion guarantee. In this work, leveraging history independence, we build history independent systems. The existing history independent file system (HIFS) suffers from a significant degradation compared to the regular file system, rendering it impractical for real-world applications. A fundamental reason for such a degradation is, HIFS simply re-locates the entire data in a history independent manner for each single write. This is unfortunately unnecessary since a multi-snapshot adversary has observed a previous snapshot, and relocating data appearing in this old snapshot is vain and incurs unnecessary overhead. This will be exacerbated when the file system load factor is large. We thus design eHIFS, the first efficient History Independent File System, in which we smartly take advantage of knowledge on the adversary's observations and eliminate those unnecessary re-locations. Security analysis and experimental evaluation show that, compared to HIFS, eHIFS can achieve a similar history independence guarantee, with a 33X write throughput improvement when the file system load factor is 90%.
Document Image Retrieval Based on Convolutional Neural Network

With the rapid growth of digital documents, big data puts higher demands on document image retrieval. Document image retrieval is the domain between classical information retrieval and content-based retrieval. The traditional document image retrieval method relies on complex OCR-based text recognition and text similarity detection. This paper proposes a new content-based retrieval method for document graphics objects. This method focuses on feature extraction, feature fusion, and indexing. This paper uses the pretrained convolutional neural network model to learn the image representation of the retrieval task, extracts various features of the document image, and then performs the PCA reduction on the extracted high-dimensional features, and then uses the improved Rank fusion method based on Rank_avg to form new features matrix. Transfer learning is used to fine-tune the trained CNN model and apply it to the retrieval algorithm, which can effectively deal with the deficiency of training data. Finally, the similarity of the features is used to sort, and the query index is established based on the inverted indexing technique of visual vocabulary. Experiments with document image datasets containing charts and texts show that this method has better ability to retrieve document images with similar text contents. The fusion of dimension-reduced CNN features can effectively improve MAP of the retrieval system. The MAP of model fusion with good performance can reach 0.85. The reverse indexing technology based on visual vocabulary can effectively reduce the retrieval time by 27%.