Reviewerly/exHarmony huggingface dataset:
ANALYSIS OF SUCCESS FACTOR SISTEM INFORMASI AKADEMIK (SIAKAD) USE THE DELONE AND MCLEAN MODEL (CASE STUDY STIE MUHAMMADIYAH PRINGSEWU LAMPUNG)

The purpose of this research is to analize factors that influance the successful implementation of Sistem Informasi Akademik (SIAKAD) at STIE Muhammadiyah Pringsewu Lampung. This research use the information system success model of Delon & McLean wich modified into STIE Muhammadiyah Pringsewu Lampung needed. The data analisys base on Structural Equation Modeling (SEM) with Analysis of Moment Structures (AMOS) as tools. The result of this research show that 4 of 5 hypotheses are significanly accepted which information quality give positive influential to user satisfaction, system quality give positive influential to user satisfaction, service quality give positive influential to user satisfaction and user satisfaction give positive influential to net benefits. Success factors generated from this research will be used as recommendations for the next development of SIAKAD so service quality of SIAKAD to STIE Muhammadiyah Pringsewu Lampung civitas akademica will improved.
Exploring Toxicity in the Workplace in a Saudi Arabian University from the Perspectives of Senior Personnel and Faculty Members

This study focuses on the context of a Saudi University to: (1) identify the behaviours and characteristics of toxic members in the workplace; (2) understand the impact of a toxic member within a workplace; (3) understand how leaders manage toxic individuals. The survey conducted comprised of seventy-five items developed based on four primary themes identified in the interview analysis, the key findings of the literature review, and some items used by Kusy and Holloway (2009), with some minor modifications. The survey was distributed to 500 faculty members at different colleges, including those in leadership positions, at MU. The return rate was 134 or 330. The results identified several behaviours and characteristics of toxic members in the workplace: prevalence of gossip, lack of confidence in others, and failure to accept others’ opinions were in the top three. Toxic members within a workplace were found to lower workers’ self-esteem, undermine confidence and even create financial crises. Leaders reacted by either forming a committee of professionals to treat the toxic individual, leaving the organisation, requesting the services of an external consultant or reducing the perceived causes of harmful behaviours. The research recommends creating a committee of professionals to deal with the behaviour of toxic individuals and identifying suitable external consultants. Key words: Toxicity, Workplace, Saudi Arabian, University, Faculty Members DOI: 10.7176/JEP/11-17-13 Publication date: June 30th 2020
Designing Cognitively Efficient Visualizations

A design methodology for producing cognitively efficient visualizations is introduced. The method involves seven steps (1) a high-level cognitive task description; (2) a data inventory; (3) cognitive task refinement; (4) identification of appropriate visualization types; (5) applying visual thinking design patterns (VTDPs); (6) prototype development; and (7) evaluation and design refinement. Most of the chapter is devoted to a set of VTDPs. These are descriptions of interactive visualization methods that have demonstrated value, together with a description of perceptual and cognitive issues relating to their use and guidelines for applicability. VDTPs provide a method for taking into account perceptual and cognitive issues in designing interaction especially with respect to key bottlenecks in the visual thinking process, such as limited visual working memory capacity. They also provide a way of reasoning about semiotic issues in perceptual terms via the concept of the visual query.
Recognizing Arrow Of Time In The Short Stories

Recognizing the arrow of time in the context of paragraphs in short stories is a challenging task. i.e., given only two paragraphs (excerpted from a random position in a short story), determining which comes first and which comes next is a difficult task even for humans. In this paper, we have collected and curated a novel dataset for tackling this challenging task. We have shown that a pre-trained BERT architecture achieves reasonable accuracy on the task, and outperforms RNN-based architectures.
A Survey on Software Applications based on Blockchain Technology

To ensure transparency, Blockchain technology is being implemented in the development of software applications along all the verticals like Banking and Finance, Healthcare, Governance, Education, Agriculture etc. Blockchain is an evolving and a very promising technology, as it is used to eliminate fraud, mitigate risk, makes falsification very hard, brings out transparency and authenticity for various uses. A Blockchain is also termed as Distributed Ledger Technology (DLT). The paper surveys various strategies and algorithms used in Blockchain based applications that were published in renowned journals. The survey also focuses on identifying the merits and demerits of Blockchain technology in almost every possible aspect and provides room for future development.
The Spiroplot App (Media Exposition)

We introduce an app for generating spiroplots, based on a new discrete-time, linear, dynamic system that repeatedly rotates a pair of points, and plots points where they land. The app supports easy definition of the initial situation and has various visualization settings. It can be accessed at https://spiroplot.sites.uu.nl.
Learning Gaussian Graphical Models via Multiplicative Weights

Graphical model selection in Markov random fields is a fundamental problem in statistics and machine learning. Two particularly prominent models, the Ising model and Gaussian model, have largely developed in parallel using different (though often related) techniques, and several practical algorithms with rigorous sample complexity bounds have been established for each. In this paper, we adapt a recently proposed algorithm of Klivans and Meka (FOCS, 2017), based on the method of multiplicative weight updates, from the Ising model to the Gaussian model, via non-trivial modifications to both the algorithm and its analysis. The algorithm enjoys a sample complexity bound that is qualitatively similar to others in the literature, has a low runtime $O(mp^2)$ in the case of $m$ samples and $p$ nodes, and can trivially be implemented in an online manner.
Quality of Syntactic Implication of RL-Based Sentence Summarization

Work on summarization has explored both reinforcement learning (RL) optimization using ROUGE as a reward and syntax-aware models, such as models whose input is enriched with part-of-speech (POS)-tags and dependency information. However, it is not clear what is the respective impact of these approaches beyond the standard ROUGE evaluation metric. Especially, RL-based for summarization is becoming more and more popular. Using the standard Gigaword sentence summarization task, we compare an RL self-critical sequence training (SCST) method with syntax-aware models that leverage POS tags and Dependency information. We show that on all quantitative and qualitative evaluations, the combined model gives the best results, but also that only training with RL and without any syntactic information already gives nearly as good results as syntax-aware models with less parameters and faster training convergence.
A POMDP-based Method for Analyzing Blockchain System Security Against Long Delay Attack: (Extended Abstract)

Blockchain bears the long-delay attack which is challenging to be analyzed. In this study, we propose a blockchain security analysis model based on Partially Observable Markov Decision Process (POMDP) against long delay attack by capturing the dynamic network delay. In our model, an observation function about the network delay is learned and updated based on a clustering algorithm about the timely network status. With the support of the observation function, a POMDP model is constructed for attackers to maximize their expected rewards. To analyze the security of a blockchain system against long delay attack, the utility of the attackers and normal miners with the same mining power are calculated and compared. The system is then regarded secured as the utility of the normal miners is no less than that of the attackers.
DESIGN APPROCH OF IMAGE RETRIEVAL MECHANISM BASED ON REGIONAL NLP

NLP is taking the system at next level in the terms of intelligence and the decision making. On other hand education sector are growing by making different experiments in teaching methodologies but still systems are lacking with teaching to naive users or teenagers. Searching the image on the basis of its name is easier task rather searching it on the basis of the content was always big challenge. We have different dictionaries where individual words are represented using the statements and not the graphical presentation. Displaying the words through graphics has its own limitation it requires a NLP technique to understanding user input which may be at different aspects and it become more difficult if it’s in regional languages. This paper mainly focused on application and implementation methodologies of NLP.
Facial expression recognition based on feature fusion

In this article, an expression recognition algorithm based on feature fusion was proposed. First, 40 sets of Gabor filters were selected to perform filtering operations on the expression images to enhance the texture features of the expression images, and subsequently, Local Binary Patterns(LBP) operators were used to perform feature extraction on the filtered images output by each Gabor channel to obtain LBP feature maps. Then these characteristic graphs are taken as the input of the convolutional neural network and the convolutional neural network is trained.Finally, the input of the fully connected layer of the trained convolutional neural network was taken out separately as the features of the expression image, and these features are classified and identified using the extreme learning machine algorithm. The experimental results showed that the method in this paper was better than the method using a single feature and can effectively improve the recognition rate in expression recognition.
Can Agents Learn by Analogy?: An Inferable Model for PAC Reinforcement Learning

Model-based reinforcement learning algorithms make decisions by building and utilizing a model of the environment. However, none of the existing algorithms attempts to infer the dynamics of any state-action pair from known state-action pairs before meeting it for sufficient times. We propose a new model-based method called Greedy Inference Model (GIM) that infers the unknown dynamics from known dynamics based on the internal spectral properties of the environment. In other words, GIM can learn by analogy. We further introduce a new exploration strategy which ensures that the agent rapidly and evenly visits unknown state-action pairs. GIM is much more computationally efficient than state-of-the-art model-based algorithms, as the number of dynamic programming operations is independent of the environment size. Lower sample complexity could also be achieved under mild conditions compared against methods without inferring. Experimental results demonstrate the effectiveness and efficiency of GIM in a variety of real-world tasks.
Fuzzy C-Means Implementation to Classify Socio-Economic Status

Economic status is the level of prestige owned by someone in a society based on work or other material ownership that can indicate individual status. The application of data mining can help analyze data obtained from conditions of economic status. Data mining technique used is to use clustering techniques. One method of clustering is the Fuzzy C-Means algorithm. Fuzzy C-Means has a high degree of accuracy and fast computing time. Test the validity of the results of clustering to classify socioeconomic status by using the calculation of Partition Coefficient (PC) obtained 0.791, this means it can be said that the results of clustering are classified in either category. Based on the results of calculations that the Fuzzy C-Means method can classify socioeconomic status.
Longest Common Subsequence on Weighted Sequences.

We consider the general problem of the Longest Common Subsequence (LCS) on weighted sequences. Weighted sequences are an extension of classical strings, where in each position every letter of the alphabet may occur with some probability. Previous results presented a PTAS and noticed that no FPTAS is possible unless P=NP. In this paper we essentially close the gap between upper and lower bounds by improving both. First of all, we provide an EPTAS for bounded alphabets (which is the most natural case), and prove that there does not exist any EPTAS for unbounded alphabets unless FPT=W[1]. Furthermore, under the Exponential Time Hypothesis, we provide a lower bound which shows that no significantly better PTAS can exist for unbounded alphabets. As a side note, we prove that it is sufficient to work with only one threshold in the general variant of the problem.
Is Press Freedom an Effective Tool to Empower Women? Empirical Evidence from Organisation of Islamic Cooperation (OIC) Countries

Feminists are concerned about the role of media in protecting rights of women. The available empirical studies are largely based on narrow case studies or anecdotal stories. This study empirically investigates the relationship of press freedom with women empowerment using a panel data of 52 Organisation of Islamic Cooperation (OIC) countries from 1996 to 2011. Ordered Logit method is applied to estimate the empirical model. The empirical findings show that press freedom is an effective tool to empower women. The role of press freedom matters more for political rights of women. Women are becoming more politically strong in all regions of the Muslim world. However, social empowerment of women is weak across the OIC countries. A comparative regional analysis indicates that women rights in Middle East and North African countries are rather weak.
Attend to the beginning: A study on using bidirectional attention for extractive summarization

Forum discussion data differ in both structure and properties from generic form of textual data such as news. Henceforth, summarization techniques should, in turn, make use of such differences, and craft models that can benefit from the structural nature of discussion data. In this work, we propose attending to the beginning of a document, to improve the performance of extractive summarization models when applied to forum discussion data. Evaluations demonstrated that with the help of bidirectional attention mechanism, attending to the beginning of a document (initial comment/post) in a discussion thread, can introduce a consistent boost in ROUGE scores, as well as introducing a new State Of The Art (SOTA) ROUGE scores on the forum discussions dataset. Additionally, we explored whether this hypothesis is extendable to other generic forms of textual data. We make use of the tendency of introducing important information early in the text, by attending to the first few sentences in generic textual data. Evaluations demonstrated that attending to introductory sentences using bidirectional attention, improves the performance of extractive summarization models when even applied to more generic form of textual data.
Curatr: A Platform for Exploring and Curating Historical Text Corpora

The 5th Digital Humanities in the Nordic Countries (DHN2020), Riga, Latvia, 21-23 October 2020 (cancelled due to Coronavirus outbreak)
A Generic Metaheuristic Approach to Sequential Security Games

The paper introduces a generic approach to solving Sequential Security Games (SGs) which utilizes Evolutionary Algorithms (EAs). Formulation of the method (named EASG) is general and largely game-independent, which allows for its application to a wide range of SGs with just little adjustments addressing game specificity. Experiments performed on $3$ different types of games (with 300 instances in total) demonstrate robustness and stability of EASG, manifested by repeatable achieving optimal or near-optimal solutions in the vast majority of the cases. The main advantage of EASG is time efficiency. The method scales better than state-of-the-art approaches and can be applied to sequential SGs with bigger numbers of steps compared to the existing methods. Due to anytime characteristics, EASG is very well suited for time-critical applications.
Hierarchical clustering on metric lattice

This work proposes a new clustering algorithm named 'fuzzy interval number hierarchical clustering' (FINHC) by converting original data into fuzzy interval number (FIN) firstly, then it proves F that denotes the collection of FINs is a lattice and introduces a novel metric distance based on the results from lattice theory, as well as combining them with hierarchical clustering. The relevant mathematical background about lattice theory and the specific algorithm which is used to construct FIN have been presented in this paper. Three evaluation indexes including compactness, recall and F1-measure are applied to evaluate the performance of FINHC, hierarchical clustering (HC) k-means, k-medoids, density-based spatial clustering of applications with noise (DBSCAN) in six experiments used UCI public datasets and one experiment used KEEL public dataset. The FINHC algorithm shows better clustering performance compared to other traditional clustering algorithms and the results are also discussed specifically.
Rules-Based Longitudinal Trajectory Planning Algorithm of a Solar Powered Communication/Remote-Sensing UAV

This paper proposes a simplified particle model to increase the efficiency of trajectory planning. The energy flow model including solar cells module, propulsion system module, and battery module are also proposed. From expert experiences, a rules-based longitudinal trajectory planning algorithm with better energy efficiency is developed. Simulations verify that the proposed algorithm basically satisfies the requirements, but it is hard to meet all the constraints and a globally optimization algorithm must be developed in the next step.
Mono-axial Solar Tracker with Equatorial Mount, for an Improved Model of a Photovoltaic Panel

In order to maximize the efficiency of a photovoltaic panel, a mechanical system known as a solar tracker is introduced. This paper offers a model of a solar tracker that allows the photovoltaic panel with a single tracking axis and a single motor to receive a quantity of solar radiation similar to those received by a normal surface. Which will reduce the cost of production, facilitate maintenance and minimize the consumption of the system, keeping the maximum performance throughout the year. Our solar tracker uses a modified equatorial mount, in order to follow the sun perfectly. Theoretical studies show that the yield of such a device varies according to the seasons between 91.7% at the winter and summer solstice, and 100% at the equinoxes compared to a normal surface to solar radiation. In order to compensate the loss at the solstices, we proposed to introduce mirrors at the top and bottom of the photovoltaic panel of a well-calculated size and angle. After carrying out the project, a real comparison made, showed us that our model offers an efficiency of about 43.46% compared to a fixed installation of the photovoltaic panel.
The psychological and pedagogical model of training in the educational space for the agricultural sector

The article discusses the issues of preparing a student for professional activities in the context of digitalization. Analyzed are pedagogical technologies focused on the implementation of scientific research and aimed at the most effective and rational methods of training, taking into account labor market trends. It describes in detail the integration into the educational space of interactive learning and the effectiveness of its application. The author proposes a theoretical psychological and pedagogical model, which includes blocks: interactive learning, positive factors (contributing to the development of positive educational motivation), negative factors (which impede the development of positive motivational activity), “I-image”, development of needs, basic properties of the nervous system. More attention is paid to stress resistance, as one of the individual and personal determinants that determine the demand for labor in the agriculturalsector.
Enhanced artificial bee colony approach for the enhancement and classification of underwater images

In an underwater view, wavelength-reliant light absorption in addition to scattering worsens the visibility of images that lead to lower contrast and also distorted color casts. To handle these problems, a proficient approach is proposed for the enhancement and also for the classification of UI. Originally, the underwater input image RGB is modified using Enhanced ABC algorithm. The subsequent phase is to extract the features. The extracted attributes are inputted to the Modified PCA approach, in this phase, the dimensionality of the features is reduced. Then, the classification operation is performed by utilizing ANFIS classifier. At last, the classified enhanced deeper water images along with the enhanced shallow water images are analyzed during the testing phase. The performance analysis is made for the proposed classifiers and existing techniques such as NN, SVM, and KNN. In addition, the performance of the proposed EABC model is compared over ABC, GA, and PSO in terms of correlation, Spearman rank correlation, sharpness, EME, Mutual information and NMI. The proposed classified method obtain the accuracy (0.9473), sensitivity (0.9230), specificity (0.9677) however, the existing methods provide only 0.8771 accuracy. Similarly, the proposed Enhanced ABC methods provide the improved performance while considering the other optimization algorithm.Abbreviations: ABC: artificial bee colony; ANFIS: adaptive neuro-fuzzy inference system; ANN: artificial neural network; CM: covariance matrix; DWT: discrete wavelet transform; EABC: enhanced artificial bee colony; EME: enhanced measurement error; FDR: false discovery rate; FLS: forward-looking sonar; FPR: false positive rate; FS: forward scattering; GA: genetic algorithm; KNN: k-nearest neighbors; MCC: Mathew’s correlation coefficient; MOE: measure of entropy; NMI: normalized mutual information; NN: neural network; NPV: negative prediction value; PCA: principal component analysis; PD: probability distribution; PSO: particle swarm optimization; RGB: red green blue; SD: standard deviation; SRC: Spearman rank correlation; SURF: speeded up robust feature; SVM: support vector machine; SWT: stationary wavelet transform; UIE: underwater image enhancement; WVD: Wigner-Ville distribution
Crypto based Multi-Variable Fingerprinting for Protecting DSP cores

In the design process of Consumer Electronics (CE) systems, there are several security challenges for a vendor or a buyer to ensure IP rights protection. This paper proposes a novel crypto framework based IP core protection methodology for DSP kernels that embeds a buyer fingerprint into a design during high level synthesis (HLS). The proposed signature encoding for fingerprint is multi-variable in nature offering stronger robustness, low embedding cost and low design overhead. It also makes use of a secure hashing technique to derive the fingerprint signature which provides an added level of security against counterfeiting. Upon testing on standard DSP benchmarks, it was found that the proposed approach satisfies all the major protection features of a fingerprint such as strong robustness to the buyer, low overhead, low run time and low embedding cost. Further with respect to the baseline design (with no fingerprint), the proposed approach offers robustness at 0% design cost overhead. Additionally on comparison with a related work, the proposed approach offers a lesser design cost and lesser area overhead without compromising robustness.