Reviewerly/exHarmony huggingface dataset:
Application of (AHP) Method in Ranking the Factors Affecting the Formation of Organizational Behavior of Employees

Organizations are unable to develop the effectiveness of their collective wisdom without the voluntary willingness of individuals to cooperate. To this end, the ever-changing conditions, increasing competition, and the need for organizations to be more efficient have highlighted the need for a valuable generation of human capital, a generation known as organizational soldiers. These human resources are undoubtedly the distinguishing features of effective from ineffective organizations, because without any expectations, they act in addition to their official role and do not spare any effort. They are involved in the formation of organizational citizenship behavior. Therefore, first, according to the research background, the components are identified and then, using the hierarchical analysis process technique, the identified components are ranked and the degree of importance is determined. Finally, the result is that organizational commitment and motivation in Work is of the utmost importance.
Inverted Classroom to Develop Self-regulated Learning in University Students in Times of Pandemic

The article presents a literature review on the theories of the flipped classroom as a didactic strategy to develop self-regulated learning in university students a new learning paradigm, where the student regulates his own learning. The connectivity provided by ICTs and especially the internet has brought with it new, unsuspected communication possibilities. These have caused a transformation in the distribution and access to knowledge with effects that we are just beginning to know. Mainly in the educational field, this ease of access gives us the possibility that human beings have the freedom to learn at our own pace from physical spaces without the need to travel to training centers or adjust to fixed schedules, the development of learning Self-regulation from the psychoeducational point of view allows regulating the capacity of the learning construction process itself, thus achieving the competence of learning to learn.
Automatic exploration of the property space of reservoirs

Reservoir Computing is an efficient implementation of a recurrent neural network for dealing with temporal/sequential data processing. However, in terms of matching reservoir dynamics to tasks, the precise balance of properties (kernel rank, generalization rank, memory capacity, size) of reservoirs will vary. To provide guidance for the generation of reservoirs, we use NSGA-II and MAP-Elites to explore the balance between those properties. We further provide three generation strategies for reservoirs: (a) the optimization of the properties of the random generator, (b) the direct optimization of general purpose reservoir, and (c) a combination of both approaches. We show that each approach can generate reservoirs with different ranges of characteristics, making them thus appropriate for different categories of tasks.
A Comparative Analysis of Multiple Biasing Techniques for $Q_{biased}$ Softmax Regression Algorithm

Over the past many years the popularity of robotic workers has seen a tremendous surge. Several tasks which were previously considered insurmountable are able to be performed by robots efficiently, with much ease. This is mainly due to the advances made in the field of control systems and artificial intelligence in recent years. Lately, we have seen Reinforcement Learning (RL) capture the spotlight, in the field of robotics. Instead of explicitly specifying the solution of a particular task, RL enables the robot (agent) to explore its environment and through trial and error choose the appropriate response. In this paper, a comparative analysis of biasing techniques for the Q-biased softmax regression (QBIASSR) algorithm has been presented. In QBIASSR, decision-making for un-explored states depends upon the set of previously explored states. This algorithm improves the learning process when the robot reaches unexplored states. A vector bias(s) is calculated on the basis of variable values of experienced states and added to the Q-value function for action selection. To obtain the optimized reward, different techniques to calculate bias(s) are adopted. The performance of all the techniques has been evaluated and compared for obstacle avoidance in the case of a mobile robot. In the end, we have demonstrated that the cumulative reward generated by the technique proposed in our paper is at least 2 times greater than the baseline.
Differentially Private Distance Learning in Categorical Data

Abstract Most privacy-preserving machine learning methods are designed around continuous or numeric data, but categorical attributes are common in many application scenarios, including clinical and health records, census and survey data. Distance-based methods, in particular, have limited applicability to categorical data, since they do not capture the complexity of the relationships among different values of a categorical attribute. Although distance learning algorithms exist for categorical data, they may disclose private information about individual records if applied to a secret dataset. To address this problem, we introduce a differentially private family of algorithms for learning distances between any pair of values of a categorical attribute according to the way they are co-distributed with the values of other categorical attributes forming the so-called context. We define different variants of our algorithm and we show empirically that our approach consumes little privacy budget while providing accurate distances, making it suitable in distance-based applications, such as clustering and classification.
Cartographic Analysis of the Geomorphological Hazards of the Eastern Part of Sheikh Saad Sub-District by Using GIS

Geographical information systems are the latest applied computer technologies that contribute to supporting contemporary geographical studies through the possibility of working on preparing a database of geographical phenomena and modeling them in a digital form by providing automated methods and a set of systems and programs for managing and processing data with spatial and non-spatial reference, which is one of the important functions in geographic information systems And the base on which it depends to reach the optimal decisions to reveal the spatial relationships and correlations between geographical phenomena and with high efficiency, to become the contemporary method in the method of processing and spatial analysis of geographical information instead of the old traditional methods of geographical analysis, and the system also allowed the geographical area to enter into the era of modern technologies to evaluate phenomena. Geographical forecasting. The research materials and methods are determined by adopting topographical and geological maps, land-sat satellite visuals, and DEM data to form the search database, and based on the GIS program (Arc Map 9.3) and the (Global Mepper 11) program and the extensions of the (Arc Map 9.3) program, which are (Spatial Analysis) And the three-dimensional analysis (3D analysis), and the outputs are the final process through which the results of the research emerge. These outputs show the type of information that will be processed and presented in the form of three-dimensional maps and shapes, studying the most important causes of geomorphological risks for the study area, and developing solutions and treatments through the conclusions and recommendations of the research.
What Drives Students’ Satisfaction With E-Learning Amid COVID-19 Pandemic?

This study aims to analyze the students’ satisfaction in the Faculty of Economics and Islamic Business, State Islamic Institute (IAIN) Fattahul Muluk Papua with the online learning process during the COVID-19 pandemic. It is necessary to have an evaluation of the online learning carried out during the COVID-19 pandemic so that the process can be measured and the learning quality may be improved. This study uses five Servqual dimensions, namely Tangible, Reliability, Responsiveness, Assurance, and Empathy to measure student satisfaction with the online learning process during the COVID-19 pandemic. This study uses quantitative methods with data analysis using multiple linear regression. Data were collected from 143 students of the Faculty of Economics and Islamic Business IAIN Fattahul Muluk Papua using a questionnaire that had been tested for validity and reliability. The results of this study indicate that the tangible variable and empathy variable has a significant effect on student satisfaction, while the Reliability, Responsiveness, and Assurance variables do not significantly influence students’ satisfaction with online learning during the COVID-19 pandemic.
Digit Character CAPTCHA recognition Based on Deep Convolutional Neural Network

With the developing of computer technology, Convolutional Neural Network (CNN) has made big development in both application region and research field. However, CAPTCHA (one Turing Test to tell difference between computer and human) technology is also widely used in many websites verification process and it has received great attention from researchers. In this essay, we introduced the CNN based on tensorflow framework and use the MINIST data set which is used in handwritten digit recognition to analyze the parameters and the structure of the CNN model. Moreover, we use different activation functions and compares them with different epochs. We also analyze many problems during the experiment to make the original data and the result more accurate.
Comparative Study for the Estimation of the Rays of Sun Incident on Inclined Plane and Flat Plane by Isotropic and Anisotropic Sky Models for Vadodara, Gujarat

The requirement to learn is to make comparison of various models for predicting the amount of the rays from the sun coming on an inclined plane and on the horizontal plane. To make this comparison, various models were used some of which are isotropic while others are anisotropic to calculate the total amount of rays in a month on Vadodara, Gujarat, situated in the western part of India. The inclined angle was assumed to be 22 °N of Vadodara. The results getting from various formulas were compared to the actual data. It was seen that Hay and Davies model (H.D.M) shows the maximum part of solar rays’ incident on an inclined plane throughout the year, whereas Badescu model (B.A.M) estimated the lowest portion of solar rays of sun among all the isotropic and anisotropic models. Finally, it is shown that BAM was preferred than other models due to less statistical error, and the predicted solar radiation is very near to the measured data.
WeatherNet: Nowcasting Net Radiation at the Edge

In addition to natural processes such as photosynthesis and evapotranspiration, net radiation affects industrial applications such as photovoltaic energy management and solar thermal collection. We propose a deep learning approach for nowcasting net radiation within subhourly and intrahour horizons to better understand and control processes influenced by net radiation. Specifically, we developed a deep-learning-based CNN-LSTM, named WeatherNet, that combines multiple local ground-based cameras and weather sensor data to predict net radiation. Unlike previous methodologies, our approach involves images from three different cameras: a sky-facing RGB camera, a horizon-facing RGB camera, and a horizon-facing forward-looking infrared camera. Further, WeatherNet was designed to run "at the edge" using the Waggle edge computing framework to reduce the data bandwidth, improve the latency of predictions, and eliminate centralized data collection. With our proposed dataset and model, WeatherNet, we present a novel methodology using relatively inexpensive equipment for nowcasting net radiation precisely between a 15- and 90-minute horizon.
Robust Bayesian Transfer Learning between Autoregressive Inference Tasks

Bayesian transfer learning typically relies on a complete stochastic dependence specification between source and target learners. We advocate a solution to the Bayesian transfer learning paradigm which adopts Fully Probabilistic Design (FPD) to search for an optimal choice of distribution constrained by probabilistic source knowledge. Using this optimal decision-making strategy, an algorithm for accepting source knowledge is identified but is found to be effectively insensitive to source uncertainty. Therefore, we propose an adaptation of the FPD framework which results in a robust transfer learning algorithm.Experimental evidence gathered via synthetic data shows enhanced performance when employing both optimal algorithms in a low source data predictor variance regime. In a high source data predictor variance setting, only our adapted FPD-optimal algorithm achieves robustness.
Age-related Differences in the Effects of Working Memory Capacity and Cognitive Load on Word Recognition: An Eye Tracking Study

Objectives: The purpose of the study was to identify the impact of age-related working memory capacity and cognitive load to word recognition using an eye tracker.Methods: A total of 48 participants (24 young, 24 elderly adults) who were divided according to working memory capacity (high, low) participated in the study. This study consisted of a digit load and word recognition task.Results: First, the elderly group had lower accuracy and slower response times than the young group. There was a significant difference between working memory capacity and cognitive load in response time. In the group with low working memory capacity, the response time increased under lower cognitive load, which was different from the group with a high working memory capacity. Next, the elderly group had a lower proportion of fixation than the young group by time interval. In addition, there was a significant interaction between group and cognitive load by time interval. In particular, the proportion of fixation decreased over time under a high cognitive load while the proportion of fixation increased under a low cognitive load in the elders, because the inhibition and switching abilities were weakened which made the elders unable to process information efficiently.Conclusion: The study results showed significant effects between working memory capacity and cognitive load in response time and proportion of fixation. This implies that complex factors influence language processing, which makes measuring accuracy insufficient. Thus, this research confirms the correlation among aging, working memory, and cognitive load through both online and offline measurement.
Decision support system for handling intervention on toddlers stunting cases in Indonesia using the certainty factor method

Abstract Stunting is a condition of failure to thrive in children under five caused by lack of nutritional intake for a long time and the occurrence of repeated infections. The causative factor is influenced by inadequate parenting, especially in the first 1,000 days of life. This study applies a decision support system with an expert system as an intervention for handling stunting by providing solutions using the Certainty Factor method. This expert system is implemented using the PHP programming language, and the use of expert systems will be easier when implemented into web-based applications. The test results are acceleration in reducing the number of stunting cases, thus providing the right percentage and solution in handling and increasing supervision of government efforts to combat stunting. Stunting intervention research can see the results of the percentage value of sensitive and specific interventions for pregnant women, nursing mothers, and adolescent girls. Testing uses black-box validation and accuracy. The validation results are in the form of 100%, which shows the system functionality can run well and according to the list of requirements. The accuracy result is 83% which shows that the expert system can function quite well according to the Certainty Factor method.
Is Artificial Intelligence Digital?

The models of thinking and behavior of human population, using regular meta-descriptors of IT procedures, show that to increase the computing power will not be the shortest way to the artificial intelligence with ability to solve even simple task of the management. Human nature, human mental ability together with intuition, abstraction and association skills has to be described more in analog then in digital world. From analog point of view, we have connection with many analog signal levels encumbered by distortion, interference or crosstalk’s. The main role will be played by signal to noise ratio at the input to the destination neuron. Furthermore, we are ready to state that interference caused by crosstalk’s would create a part of human association abilities and imagination. Some views of these approaches are addressed in the paper.
Offline Handwritten Dogra Script Recognition Using Convolutional Neural Network

The handwritten optical character recognition is a challenging and active branch of pattern recognition. The recognition of Indian scripts is potentially a complex problem due to several challenging issues like complex shapes of characters, similar shaped characters, positioning of diacritics, dots, etc. This paper focuses on the recognition of handwritten Dogra script. This paper also contributes to the construction of a handwritten Dogra script dataset. The handwritten document images are manually collected from several sources like archival departments, libraries, and museums and then pre-processed and segmented in order to obtain the final handwritten character dataset. The resultant handwritten Dogra dataset is evaluated using a convolutional neural network classifier, reporting a promising recognition accuracy of 88.95%.
A data recovery technique for Redis using internal dictionary structure

Since database systems are vulnerable to attacks the research in database forensics is gaining more importance. Database systems are used to store sensitive and confidential information. The loss of vital information from database can prove to be costly for any organization which may happen due to accidental or purposeful data deletion. Data recovery research in NoSQL databases is extremely important due to rapid growth of big data applications. The key-value database Redis, is one of the popular NoSQL database systems used by several applications. This article presents a data recovery technique for Redis database using internal dictionary structure. Redis is in-memory database and hence it does not leave any kind of signature of data deletion on the disk except log file. The proposed technique is based on tracing the memory addresses of dictionary data structure where key-value pairs are stored. The performance of this technique is evaluated by conducting several experiments on a sample dataset. The technique is found to be effective for the recovery of string, list, hash, set and sorted set data types.
High-Speed Multi-person Tracking Method Using Bipartite Matching

The article proposes a new multi-person pose tracking-by-detection method capable of very fast execution without use of GPU acceleration. Starting from a common online approach to object tracking, based on graph bipartite matching, we further improve the method with several novel additions. First, linear regression is used to estimate the movement of a pose root point, allowing the tracker to better preserve identity recognition despite missing detections. It is combined with a new cost function term based on per-joint distance to improve accuracy in scenes with high densities of individuals. Additionally, an alternative for visual feature matching without the use of neural networks is provided. Experiments on PoseTrack dataset have shown that proposed method outperforms other current tracking-by-detection methods based on graph bipartite matching and achieves tracking quality close to the state-of-the-art in general.
Advancing Automation in Digital Forensic Investigation

This paper represents the thoroughly technical approach to carry out forensics investigation in web applications or computer systems which combines and provided digital evidence from the particular computing device. The main objective is to recover and investigate the material found in digital devices related to cybercrime and maintain the integrity of the evidence collected. The main motive of the scanner is to investigate the system or application and process a stronger result/report of each vulnerable system or application effectively. This tool is the Open source that is used to perform some forensics investigation tasks which is helpful to the investigator to do their job and generate digital evidence which can be used by a court of law.
Effects of Interpupillary Distance and Visual Avatar’s Shape on the Perception of the Avatar’s Shape and the Sense of Ownership

Owing to the spread of virtual reality (VR) content, avatars that can be used have become more diverse. Several studies have been conducted on the effects of avatars on users. The investigation on the effects of avatar shape on the perception of object size and the sense of ownership of the avatar has been limited; however the effects on the perception of the avatar's shape have not been investigated. Studies focusing on visual effects have reported that the interpupillary distance (IPD) influences object-size perception and distance perception. It has also been reported that lowering the height of the camera and shortening the IPD can make the surrounding space seem larger. This suggests that the IPD may affect the shape perception and the sense of ownership of avatars. In this study, we focused on changing both the shape of the avatar and the IPD. We prepared avatars with a shape similar to that of the participant's body and avatars with extremely long/short limbs. Then, we examined the changes in the shape perception and the sense of ownership of the avatar by manipulating the prepared avatar based on one of the three IPDs (short, normal, and long). The results confirmed that the length of arms and legs and IPD may affect the perception of length and thickness of arms and legs and the sense of ownership of avatars.
Anomaly Detection for Cognitive Intelligence

Today’s industrial organizations are often tasked with objectives that are seemingly at odds with each other. They need to increase productivity while reducing machine failure or enhance product quality while speeding up time to market. Achieving these goals simultaneously can be incredibly challenging — if not impossible.How does one work around this dilemma? The trick lies in cognitive anomaly detection and prediction, which is a process that leverages unsupervised learning (cognitive computing) and pattern recognition to quickly and accurately identify the anomalies hidden in your Industrial Internet of Things data. The use of machine learning algorithms minimizes the appearances of false alarms.