Reviewerly/exHarmony huggingface dataset:
Prediction of Unemployment Rates in Turkey by k-Nearest Neighbor Regression Analysis

Unemployment is a growing global problem. It provokes problems socially, psychologically and economically, considering it directly affects how people live in aspects of needs and hobbies, basically in the simplest meaning of living. Turkey is within the scope of economically growing countries and unemployment is one of the critical negative factors for targeted growth. In order to overcome this problem, unemployment rates can be estimated for the future with the available data. In this study, unemployment rates in Turkey are estimated by k-nearest neighbor regression (kNNR). For this purpose, a new dataset is created from the features that are likely to affect the unemployment rate. The performance of the kNNR algorithm is compared with two different machine learning algorithms which are ridge regression and linear regression. From the experimental results, it is observed that the kNNR method (coefficient of determination (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) = 0.7498) outperformed the compared methods. The results show that kNNR algorithm can be used effectively in this area.
Ensemble diversity enhancement based on parameters evolution of base learners

Ensemble learning shows very good performance when dealing with the problem of concept drift. Accuracy and diversity are two important characteristics of ensemble learning, and diversity is the key factor affecting the generalization ability of ensemble learning. In this paper, a modeling method is proposed to enhance diversity among base learners based on parameters evolution of base learners. The method uses online sequential extreme learning machine (OS_ELM) as base learner. The base learners are grouped according to their performance on the sliding window, and perform evolution operations. At the same time, the concept of gene flow is introduced, which increases the diversity among base learners and improves the prediction performance of the ensemble algorithm in dealing with the concept drift data streams. Finally, the rationality and effectiveness of the proposed algorithm are verified by using the synthetic data sets and real-world data sets.
An Enhanced Item-Based Collaborative Filtering Approach for Book Recommender System Design

The massive amounts of web-sourced data have made accessibility of precise (tailored) information for end-users a challenging task. If this personalized information-filtering technology is possibly thought to get automated at the machine level, this necessitates the design of an appropriate machine-assisted recommender system that suffices both system and end-user requirements. This paper attempts to make use of an innovative hybrid approach to build a prototype of a machine-assisted recommender that can be used as a tool in the physical library of universities, organizations, and institutions. In this manuscript, an Enhanced Item-Based Collaborative Filtering Approach for Book Recommender System Design is proposed to predict the popular books based on the transactions found in the issue/return transaction database. Demographic attributes of books are used for the precise calculation of item-item similarity with cosine/correlation coefficient by discovering all significant associations rules in the formulated item set and it provides the recommendations. The proposed algorithm’s performances are calculated based on accuracy, precision, and recall.
Complexity and the Place of Translation in Digital Humanities

Although a fundamentally humanistic discipline and an activity that takes its drive from human action, translation studies has only very recently started to consider complexity as a suitable paradigm. After four decades of reductionist thought, complexity signals the need for translation studies to acknowledge entropy—the tendency of the universe towards disorder—and the decentralization of translation activities as a characteristic of translation activities in more recent years. Perhaps more significantly, complexity raises the problem of new methodologies that are capable of revealing the n-dimensionality of any translation act. In this context, this chapter examines the possibilities of aligning translation studies to the latest developments in digital humanities and of seeing translation scholarship and scholarly collaboration in translation studies as profoundly non-linear.. As a case in point, I analyze the full corpus of abstracts presented at the 2019 EST congress by means of computational semantic analysis (more specifically, topic modelling and tf-idf). Capturing the multiplicity of translation discourses—in the plural—is essential for mapping out the complexity underpinning the discipline of translation studies, like many scholars before us have rightfully argued.
Self-shadow analysis of bifacial solar photovoltaic and its implication on view factor computation

A solar photovoltaic (PV) module is subject to self-shadow on the ground, requiring careful consideration during PV system design. The effect of self-shadow is more prominent for bifacial PV where a significant energy gain can be achieved from reflected radiation at the rear side. The self-shaded area blocks the global horizontal radiation underneath the module. It creates a variation between view factor from the shaded and non-shaded regions and causes non-uniformity of the reflected radiation which the module receives at the rear side. This reflected radiation depends on the view factor from solar PV to the ground. Therefore, to compute the reflected radiation at the rear side accurately, it is important to determine the view factor to the shaded and non-shaded regions separately.The unique contribution of this work is it applies the view factor computation model developed by the authors to compute view factor to the shaded and non-shaded ground. This paper applies a shadow projection algorithm to determine the coordinates of shadow on the available ground. Here, the view factor is computed at different ground clearance height (GCH) and tilt angle of the PV module. It is found with the increase of height; the effect of self-shading diminishes and the view factor to non-shaded region increases in comparison to the shaded region. However, beyond the ground clearance height of 1m and tilt angle of 45&#x00B0;, the view factor decreases significantly. Results are analysed on different days of the month. Finally using, Solaris Prospect data, the reflected irradiance at the rear side of the module is computed.
Estimate the Difference Threshold for Curvature Gain of Redirected Walking

Redirected walking (RDW) allows users to navigate a large virtual world in a small physical space. At this time, if the applied redirection is below the detection threshold, the human hardly notice. However, some papers reported that users perceived changes in curvature gain even when redirections smaller than the detection threshold were applied. This means that the change in curvature gain caused human perception. Therefore, in this paper, we identified a threshold for the change in curvature gain, which was found to be 3.06°/m. Further experiments using different variation methods for variations in curvature gain will follow.
Maneuvering Digital Watermarking In Face Recognition

The challenges faced in the digital world are many, which could be resolved with some biometric recognition methods. These biometric recognition methods are encompassed within watermarking technology, steganography, cryptography, and many other schemes of security. These methods assist in securing digital images with the authentication of their owner. This paper briefly contextualizes the digital watermarking technique, which is referred to as Natural Preserving Transform (NPT) and Hartley Transform, which is endeavored in the face recognition process. The non-blind extraction and quasi-blind extraction techniques are used for extracting the watermark from the image in the proposed system. This paper articulates the application of this watermarking technique employed in face recognition through watermarking of various face images to multiple backgrounds encompassing gray-scale images. Natural Preserve Transform is employed as a part of the fuzzy logic watermarking. In the proposed system, NPT is employed for encoding a logo of grayscale watermarking text or logo image to a host image located anywhere. The robustness and performance of the proposed system are experimentally tested with the help of image processing operations like image compression, noise degradation, cropping. Due to its unique feature of uniform distribution of face images, this technique is selected among other methods in digital watermarking. The system is tested for its efficiency with experimental analyses, which could be confirmed with the results of the simulation. The above system is proposed for copyright protection, authentication, and security requirements.
A Commitment Scheme with Output Locality-3 Fit for the IoT Device

Low output locality is a property of functions, in which every output bit depends on a small number of input bits. In IoT devices with only a fragile CPU, it is important for many IoT devices to cooperate to execute a single function. In such IoT’s collaborative work, a feature of low output locality is very useful. This is why it is desirable to reconstruct cryptographic primitives with low output locality. However, until now, commitment with a constant low output locality has been constructed by using strong randomness extractors from a nonconstant-output-locality collision-resistant hash function. In this paper, we construct a commitment scheme with output locality-3 from a constant-output-locality collision-resistant hash function for the first time. We prove the computational hiding property of our commitment by the decisional <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mfenced open="(" close=")" separators="|"> <mrow> <mi>M</mi> <mo>,</mo> <mi>δ</mi> </mrow> </mfenced> </math> -bSVP assumption and prove the computational binding property by the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mfenced open="(" close=")" separators="|"> <mrow> <mi>M</mi> <mo>,</mo> <mi>δ</mi> </mrow> </mfenced> </math> -bSVP assumption, respectively. Furthermore, we prove that the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mfenced open="(" close=")" separators="|"> <mrow> <mi>M</mi> <mo>,</mo> <mi>δ</mi> </mrow> </mfenced> </math> -bSVP assumption can be reduced to the decisional <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mfenced open="(" close=")" separators="|"> <mrow> <mi>M</mi> <mo>,</mo> <mi>δ</mi> </mrow> </mfenced> </math> -bSVP assumption. We also give a parameter suggestion for our commitment scheme with the 128 bit security.
The practical role of context modeling in the elicitation of context-aware functionalities: a survey

Context-aware functionalities are functionalities that consider the context to produce a certain system behavior, typically an adaptation or recommendation. As contextual elements such as time, location, weather, user activity, device characteristics, network status, and countless others are becoming increasingly more accessible, the potential for adding context awareness to applications is enormous. Identifying novel, unexpected, and even delightful context-aware functionalities in practice can be challenging, though: What context information is relevant for a given user task? How can contextual elements be combined? What if there is a large number of contextual elements? Context modeling has been described in the literature as an essential aspect in the elicitation of context-aware functionalities; however, reports on the state of the practice are rare. In this study, we conducted a survey with industrial practitioners, mostly experienced professionals from large enterprises, to investigate how context models and context-modeling activities have been used to support the elicitation of context-aware functionalities. The results indicate a gap between research and industry: Context models are rarely used in practice, and context-modeling activities such as analysis of relevance and especially analysis of combinations of contextual elements have been overlooked due to their high complexity, despite practitioners recognizing their importance.
Interpreting Classification Models Using Feature Importance Based on Marginal Local Effects

Machine learning models are widespread in many different fields due to their remarkable performances in many tasks. Some require greater interpretability, which often signifies that it is necessary to understand the mechanism underlying the algorithms. Feature importance is the most common explanation and is essential in data mining, especially in applied research. There is a frequent need to compare the effect of features over time, across models, or even across studies. For this, a single metric for each feature shared by all may be more suitable. Thus, analysts may gain better first-order insights regarding feature behavior across these different scenarios. The \(\beta\)-coefficients of additive models, such as logistic regressions, have been widely used for this purpose. They describe the relationships among predictors and outcomes in a single number, indicating both their direction and size. However, for black-box models, there is no metric with these same characteristics. Furthermore, even the \(\beta\)-coefficients in logistic regression models have limitations. Hence, this paper discusses these limitations together with the existing alternatives for overcoming them, and proposes new metrics of feature importance. As with the coefficients, these metrics indicate the feature effect’s size and direction, but in the probability scale within a model-agnostic framework. An experiment conducted on openly available breast cancer data from the UCI Archive verified the suitability of these metrics, and another on real-world data demonstrated how they may be helpful in practice.
Egyptian Students Difficulties in Learning the Italian Verbal System

This essay aims to analyse errors made by Egyptian students in learning the Italian verbal system. We try to explain the most frequent errors highlighting the causes that generated them. This thesis is based on the analysis of compositions written by 60 Egyptian learners enrolled in the second year of the Italian degree course at the Faculty of Al Alsun. The analysis revealed that the verb is a part of the speech in which almost all students have made errors. The causes of the errors are numerous: the influence of the Arabic language; the difference between the structure of the tense in the Italian language and in the Arabic language and the intrinsic difficulties of the Italian language.
Gaussian Image Denoising Method Based on the Dual Channel Deep Neural Network with the Skip Connection

In the era of rapid development of artificial intelligence technology, image denoising methods based on deep learning have achieved better and better results, and some deeper networks have also been proposed. However, with the increasing number of network layers, gradient explosion and over fitting problems also appear in the training process. In this paper, a new Gaussian image denoising method based on dual channel deep neural network with skip connection is proposed. The network is composed by the first layer network and the second layer network in parallel, so as to widen the width of the network. It not only improves the denoising effect, but also reduces the problems in the training process. The first layer uses dilated convolution to expand the receptive field of the network, and the second layer is composed of skip connection modules. The method is tested on the data set68 and achieves good results.
CANVA AND GENIALLY AS TOOLS TO GENERATE INTERACTIVE EDUCATIONAL RESOURCES

New technologies are taking a leading role today, giving rise to the generation of online tools that allow users to create communicational products of increasingly better quality and in a simpler way. Various platforms in the technology market are contributing to make this a reality, since they are promoting the tools either for free or with special discounts so that it is easily accessible to the education sector. In the case of free tools, they have some limitations, but even with those limitations, if the objective to be fulfilled in the communicational process can be achieved when a class is taught.In this research work we will show some techniques to generate educational resources with the help of the tools canva and genially and that generate value to the content of the classes that are taught. Immediacy and practicality are important factors for the creation of educational material and thanks to these tools we can achieve it, obtaining quality designs and presentations in record time. Finally, the results of resources created for computer engineering classes will be shown.
Optimizing Hadoop parameter for speedup using Q-Learning Reinforcement Learning

Hadoop is the most popular open source big data processing platform which is being used in many big data analytics applications. The performance of Hadoop can be fine-tuned for application performance requirements by adjusting the value of the some of the configuration parameters. Various methods have been proposed in literature for fine tuning the configuration parameters of Hadoop. The relation between the Hadoop performance tuning parameters and speed up is dependent on the nature of the applications and environment dynamics. Tuning the parameters without consideration of these dynamics results in sub optimal configurations and lower performance.. Adaptive reinforcement learning using Q-Learning is proposed in this work to fine tune the configuration parameters with the objective of reducing the error between desired and achieved service level agreement (SLA).
Exploring and categorising the Arabic copula and auxiliary<i>kāna</i>through enhanced part-of-speech tagging

Arabic syntax has yet to be studied in detail from a corpus-based perspective. The Arabic copula kāna (‘be’), functions also as an auxiliary, creating periphrastic tense–aspect constructions; but the literature on these functions is far from exhaustive. To analyse kāna within the one-million word Corpus of Contemporary Arabic, part-of-speech tagging (using novel, targeted enhancements to a previously described program which improves the accessibility for linguistic analysis of the output of Habash et al.’s [2012] mada disambiguator for the Buckwalter Arabic morphological analyser) is applied to disambiguate copula and auxiliary at a high rate of accuracy. Concordances of both are extracted, and 10 percent samples (499 instances of copula kāna and 387 of auxiliary kāna) are analysed manually to identify surface-level grammatical patterns and meanings. This raw analysis is then systematised according to the more general patterns’ main parameters of variation; special descriptions are developed for specific, apparently fixed-form expressions (including two phraseologies which afford expression of verbal and adjectival modality). Overall, we uncover substantial new detail, not mentioned in existing grammars (e.g., the quantitative predominance of the past imperfect construction over other uses of auxiliary kāna). There exists notable potential for these corpus-based findings to inform and enhance not only grammatical descriptions but also pedagogy of Arabic as a first or second/foreign language.
Towards a Hybrid-ASP Planning Approach With Adjoint Observation for Incomplete Task-Relevant Information

In the real world, robot task plans may easily become invalid due to unexpected state dynamics, preventing the robot from accessing the complete task-relevant information. The possible occurrence of information incompleteness during robot plan execution expects the robot to sense the environment and obtain the missing information actively. A set of planning and reasoning approaches has focused on robotic tasks in incomplete domains. However, the sensing action operators are generally abstracted as passively receiving sensory information and formulating a finite set of observations. The strict constraint of solely sensing without actively acting on the environment greatly reduces the chances of revealing more hidden information. This letter proposes a novel observation plan that couples an atomic sensing action with parallel actuating effects, and an adjoint observation scheme for actively run-time information gathering. To concretely realize the adjoint observation scheme, we present a Hybrid-ASP planning approach that integrates a deterministic task planner for generating an initially valid task plan for task achievement and a logic-based Answer Set Planner to make observation plans. Compared with the baseline planning approaches available, the experimental results have validated improved planning efficiency and plan effectiveness of information gathering from the Hybrid-ASP approach.
Privacy-Preserving Top-k Spatial Keyword Queries in Fog-Based Cloud Computing

With the popularity of location based services, spatial keyword query has become an important application. In order to save the storage and computational costs, most data owners will outsource the data to the cloud server, but this will lead to two problems, such as privacy leakage and heavy network bandwidth burden. To solve above problems, we propose a Privacy-preserving top-k Spatial Keyword queries based on Fog computing, namly PSKF. To further improve the search efficiency, we use the IR-tree to build the index and store it in the cloud server. Each fog server also saves a different subtree of the IR-tree, so that we can decide which fog server to participate in the query by pruning. Formal security analysis shows that our proposed PSKF achieves Indistinguishability under Known-Plaintext Attacks (IND-KPA), and extensive experiments demonstrate that our proposed scheme is efficient and feasible in practical applications.
Pedagogical Opportunities Of Distance Education In A New Reality

The article identifies one of the tendencies in the information pedagogy development as a leading direction for improving the distance learning quality, which consists in shifting the emphasis on the interaction between the teacher's training activity and the students' learning self-educational activity.The problem of forming new teacher's functions in the context of a distance educational process is considered.The distance learning pedagogical equipment is proposed, in which the teacher's function is to manage students' independent work, create the distance learning toolkit, and determine methods and forms for feedback and control.This is due to the fact that in distance learning a significant place is given to the students' independent work, which prepares them for active self-development.The aspect of subjects' pedagogical interaction, associated with their temporal and spatial remoteness, when creating innovative educational products and the corresponding internal students' increments is characterized.It is concluded that the effectiveness of distance learning in the education system is manifested in the focus on revealing the inner potential of a person, enhancing his independence qualities in solving educational and cognitive tasks.