Reviewerly/exHarmony huggingface dataset:
Interaction Forests: Identifying and exploiting interpretable quantitative and qualitative interaction effects

Although interaction effects can be exploited to improve predictions and allow for valuable insights into covariate interplay, they are given little attention in analysis. We introduce interaction forests, which are a variant of random forests for categorical, continuous, and survival outcomes, explicitly considering quantitative and qualitative interaction effects in bivariable splits performed by the trees constituting the forests. The new effect importance measure (EIM) associated with interaction forests allows ranking of the covariate pairs with respect to their interaction effects' importance for prediction. Using EIM, separate importance value lists for univariable effects, quantitative interaction effects, and qualitative interaction effects are obtained. In the spirit of interpretable machine learning, the bivariable split types of interaction forests target well interpretable interaction effects that are easy to communicate. To learn about the nature of the interplay between identified interacting covariate pairs it is convenient to visualise their estimated bivariable influence. We provide functions that perform this task in the R package diversityForest that implements interaction forests. In a large-scale empirical study using 220 data sets, interaction forests tended to deliver better predictions than conventional random forests and competing random forest variants that use multivariable splitting. In a simulation study, EIM delivered considerably better rankings for the relevant quantitative and qualitative interaction effects than competing approaches. These results indicate that interaction forests are suitable tools for the challenging task of identifying and making use of well interpretable interaction effects in predictive modelling.
Is This Translation Error Critical?: Classification-Based Human and Automatic Machine Translation Evaluation Focusing on Critical Errors

This paper discusses a classification-based approach to machine translation evaluation, as opposed to a common regression-based approach in the WMT Metrics task. Recent machine translation usually works well but sometimes makes critical errors due to just a few wrong word choices. Our classification-based approach focuses on such errors using several error type labels, for practical machine translation evaluation in an age of neural machine translation. We made additional annotations on the WMT 2015-2017 Metrics datasets with fluency and adequacy labels to distinguish different types of translation errors from syntactic and semantic viewpoints. We present our human evaluation criteria for the corpus development and automatic evaluation experiments using the corpus. The human evaluation corpus will be publicly available upon publication.
Fair and Accurate Review in Publication Process: A learning-based Proactive Approach for Assigning Reviewers to Manuscripts

Peer review is one of the most crucial and important tasks that are associated with academic conferences, journals and grant proposals; and assignment of an appropriate reviewer plays vital role for accurate and fair review process. This paper presents a learning based proactive system that assigns reviewer(s) whose expertise matches with the domain(s) of the paper satisfying constraints. The assignment of reviewer to paper needs to satisfy various constraints such as maximum number of papers per reviewer, minimum number of reviewers per paper and conflict of interest. he core challenge in reviewer paper assignment is to make the computer understand the subject domain of experts and papers. In proposed system, features are extracted from title, abstract and introduction section of publications of reviewer and submitted papers. These features help the model learn the domain features of experts and submitted papers more accurately. Once the training set is built utilizing the inherent correlation between abstract and title, the model is trained and the similarity between reviewers and papers is predicted. The experimental results on test data set of AAAI 2014 and NIPS 2019 demonstrate the effectiveness of the proposed system.
Peculiarities of The Students’ Conceptual Structures and Basic Beliefs Correlation

The research is basically aimed at studying the students’ conceptual structure and basic beliefs being formed and correlated as well as at their classifying in accordance with the level of integration and coherence. The students’ conceptual structures and basic beliefs are analyzed in the article; special attention is paid to semantic, categoric and conceptual abilities coherence (homogeneity) and formedness. If speaking about methodology of the research, it includes theoretical and empirical methods. Theoretical, cluster and factor analyses as well as the Kruskal-Wallis rank sum test allowed us to define 5 clusters (groups) with low, medium-low, mean (average), high and extraordinary indices of the students’ conceptual structure and basic beliefs formedness. These clusters significantly differ at levels from p≤0.01 to p≤0.05. The students’ conceptual structure and basic beliefs correlations form the level systems classification characterized by different coherence of these levels. It is sufficient to conclude that different basic beliefs are the core of the students’ subjective inner world and are correlated with these beliefs conceptual structures specificity. The attained results prove the phenomenon of the intellectual resource “splitting”: the intellectual resource can split into productive and non-productive components in case of high-level intelligence, i.e. if the intelligence level is too high the intellectual resource and its basic components can be insufficiently formed and/or poorly developed.
Design of Intelligent Distance Music Education System Based on Pan-communication Technology

Design of the intelligent distance music education system based on Pan-communication technology is presented in this paper. This paper uses the different principles of the size of new information in the signal sequence of speech and music and their change range to realize the speech/music recognition, and achieve the desired effect. Novelties are focused on two aspects. (1) Baseband in the general software radio method commonly used software way, using the simplified channel model to simulate the reality, the channel coding theory is applied to provide the robust model. (2) In the program design, first design the general class of the serial data communication, aiming to give full play to the advantages of some object-oriented programming and improve the versatility. The designed system is simulated on the different data sets. The results are satisfactory.
Solar Radiation on a Parabolic Concave Surface

Curved structures are used in buildings and may be integrated with photovoltaic modules. Self-shading occurs on non-flat (curved) surface collectors resulting in a non-uniform distribution of the direct beam and the diffuse incident solar radiation along the curvature the surface. The present study uses analytical expressions for calculating and analyzing the incident solar radiation on a general parabolic concave surface. Concave surfaces facing north, south and east/west are considered, and numerical values for the annual incident irradiations (in kWh) are demonstrated for two locations: 32° N (Tel Aviv, Israel) and 52.2° N (Lindenberg, Germany). The numerical results show that the difference in the incident global irradiation for the different surface orientations is not very wide. At 32° N, the irradiation difference between the south and north-oriented surface is about 15 percent, and between the south and east surface orientation it is about 9.6 percent. For latitude 52.2° N, the global irradiation difference between the south and north-oriented surface is about 16 percent, and between the south and east orientation it is about 3 percent.
Least Afflicted Load Balancing Algorithm (LALBA) for Performance Improvement of Multi-Scale Applications in Cloud Environment

Cloud computing is new image of Information Technology and the way applications are available to end-users. Also, the applications available are gaining popularity as being able to demonstrate the flexibility of use of resources as in Multi-Scale Cloud-Based Applications. These applications on the cloud are hosted on remote virtual machines with multi-tenancy architecture. Multi-tenant cloud computing architecture helps cloud users and customers to share computing resources and still the individual tenant's data is stored separately so as to make it is invisible to other tenants. With this multi-tenancy architecture, the application is being used by multiple people at the same time. Also, the application resides in various copies across various servers situated at different geographical locations. Virtual Machines that are housed in multiple Data Centers are coupled by way of load balancers. These load balancers are responsible for the optimal distribution of load/demand/tenant requests and the size of the data held within the request. So in short, two areas need to be focused on when architecting such systems. Firstly, the load must be balanced optimally across data centers and virtual machines for seamless operation at the back end. Secondly, at the front end, it is imperative that every tenant enjoys optimal service speeds without any degradation of service request speed. In case the back-end virtual machines are unbalanced in their load then there is bound to be degradation of services. For this very purpose, various researchers/academicians, and practitioners in the cloud space have been working tirelessly towards developing optimal algorithms to get the best load balancing architecture to ensure that the objective of seamless back end and front end quality of service in terms of average response times. Multi-Scale applications consist of variable payload for time and often unpredictable i.e. load can be increased/decreased suddenly or gradually. Hence to balance these loads equally at any time, a new algorithm is proposed. This algorithm considers the characteristics of multiscale applications in terms of data size and runtime characteristics of VM in terms of capacity and availability. Finally, the performance of this method is analyzed qualitatively as well as quantitatively by performing simulation experiments of multiscale applications.
Enhancing the competitive advantage for tourist souvenirs in Egypt: a marketing approach

Purpose: The main aim of this research is to study the relationship between innovative marketing - in its various dimensions (innovation in product, innovation in price, innovation in promotion, innovation in distribution) - and enhancing the competitive advantage of tourist souvenirs in Egypt. Research methodology: The study adopted the descriptive-analytical approach for accurate and detailed descriptions of the study’s subject quantitatively and qualitatively and analyzed data available on this subject. The field study included surveying a random sample of tourists who frequented the souvenirs and handicrafts stores in various tourist cities in Egypt. Results: There is a statistically significant effect of innovative marketing in enhancing tourist souvenirs’ competitive advantage in Egypt. Overall findings highlighted that all the hypotheses were accepted. Limitations: The repercussions of the COVID-19 virus crisis reduced the scope of the study to only tourists in South Sinai and Red sea Governorates; whereas, tourist souvenirs stores were allowed to operate in accordance with the precautionary measures in only three main governorates: the Red Sea, South Sinai, and Marsa Matrouh, in addition to some gift shops in major hotels. Contribution: The study findings provide guidelines for practitioners within the tourist souvenirs and handicrafts sector to undertake the results and adapt them to assess and enhance their performance. Thus, they could improve their competitiveness. Keywords: Tourist souvenirs, Innovative marketing, Competitive advantage
Quaternion Security Using Modifying Vernam Cipher With Image Steganography

The Internet is the essential wellspring of data in the present life where it offers the trade of data to the clients.The exchange of such data prompts an incredible security danger. Cryptography and steganography are two issues in security systems. Cryptography jumbles the message to be incomprehensible While Steganography shroud the message To be invisible. Therefore, Encryption any private data before concealing in the cover object will provide twofold security. This paper presents a technique for disguise message with four levels of security where the message first encrypt using modifying vernam cipher, in which the initial key originate automatically from random pixel of camouflage cover and alter continuously along message length then embedded cipher message in grayscale cover image, after that encrypt this cover using modifying vernam cipher also then embedded it in RGB color cover image. The simulation consequence illustrates that the scheme provides better protection.
Disentangling Document Topic and Author Gender in Multiple Languages: Lessons for Adversarial Debiasing

Text classification is a central tool in NLP. However, when the target classes are strongly correlated with other textual attributes, text classification models can pick up “wrong” features, leading to bad generalization and biases. In social media analysis, this problem surfaces for demographic user classes such as language, topic, or gender, which influence the generate text to a substantial extent. Adversarial training has been claimed to mitigate this problem, but thorough evaluation is missing. In this paper, we experiment with text classification of the correlated attributes of document topic and author gender, using a novel multilingual parallel corpus of TED talk transcripts. Our findings are: (a) individual classifiers for topic and author gender are indeed biased; (b) debiasing with adversarial training works for topic, but breaks down for author gender; (c) gender debiasing results differ across languages. We interpret the result in terms of feature space overlap, highlighting the role of linguistic surface realization of the target classes.
ANALISIS DAMPAK APLIKASI E-TOURISM SEBAGAI MEDIA PROMOSI PARIWISATA TERHADAP KUNJUNGAN WISATAWAN KABUPATEN TANGGAMUS

The world of tourism is not new for everyone. Tourism has become an industry in every tourist destination. Various facilities and conveniences and entertainment provided to support various tourism activities have been provided by the government or other tourism supporting components. One of the facilities that can be developed is e-tourism. E-tourism can provide tourist destination information with various needs and supporting facilities for users, especially tourists. Services are provided through the website as a form of convenience that can be obtained by visitors. Implementation of e-torism applications has a very good impact on tourists in the process of obtaining information. In addition to having an impact on tourists, this application has an impact on local governments in an effort to increase the number of visitors. Based on the results of the study the number of tourists rose to 40%. Thus this application has a very good impact on the development of tourism potential in Tanggamus Regency.
Improving Highest Security Lightweight block cipher (HISEC) Algorithm Using Key Dependent S-box

Information security is considered as a very critical issue in the transmission of information. Therforelossing or threatening the information transmission will therefore be a great loss in the process of transmitting the information. Recently, Lightweight block cipher Algorithms have gained wide acceptance and it is used in restricted applications, such as electronic passport, smart card, etc. In this study, a modified HISEC algorithm is proposed to enhance and improve the original HISEC algorithm by introducing the concept of a key dependent S-box. This proposal algorithm aims to generate a safer block of code and solve the problem of the fixed structure of the used S-box that was a vulnerability for the attacker. It was an impenetrable barrier facing the attacks of the (Linear cryptanalysis) and (Differential Cryptanalysis). The proposed algorithm showed some improvements when comparing it to the orginal algorithm.
Private Data Classification Using Deep Learning

As society is becoming increasingly digital in its interactions, the issue of data privacy is becoming more and more severe and of public concern too. Data or information can be personal, non personal or sensitive personal information of an individual. The organizations that are collecting and storing such data have the responsibility of ensuring protection to this data. They must separate the personal data and protect it which is a time consuming process. The literature reviewed for this explores a number of machine learning techniques used to classify the information into three classes, namely, non-personal, personal and sensitive personal information. This paper express that a machine learning model trained using a larger and diverse set of documents can yield a most accurate result.
3D ultrasound guided navigation system with hybrid image fusion

Abstract A prototype of a navigation system to fuse two image modalities is presented. The standard inter-modality registration is replaced with a tracker-based image registration of calibrated imaging devices. Intra-procedure transrectal US (TRUS) images were merged with pre-procedure magnetic resonance (MR) images for prostate biopsy. The registration between MR and TRUS images was performed by an additional abdominal 3D-US (ab-3D-US), which enables replacing the inter-modal MR/TRUS registration by an intra-modal ab-3D-US/3D-TRUS registration. Calibration procedures were carried out using an optical tracking system (OTS) for the pre-procedure image fusion of the ab-3D-US with the MR. Inter-modal ab-3D-US/MR image fusion was evaluated using a multi-cone phantom for the target registration error (TRE) and a prostate phantom for the Dice score and the Hausdorff distance of lesions . Finally, the pre-procedure ab- 3D-US was registered with the TRUS images and the errors for the transformation from the MR to the TRUS were determined. The TRE of the ab-3D-US/MR image registration was 1.81 mm. The Dice-score and the Hausdorff distance for ab-3D-US and MR were found to be 0.67 and 3.19 mm. The Dice score and the Hausdorff distance for TRUS and MR were 0.67 and 3.18 mm. The hybrid navigation system showed sufficient accuracy for fusion guided biopsy procedures with prostate phantoms. The system might provide intra-procedure fusion for most US-guided biopsy and ablation interventions.
Student Testing Activity Dataset from Data Structures Course

E-learning platforms became more and more popular not only for distance learning but also for learning in full-time education.As this popularity grows, we can use the data extracted from them to complement the professor's work and make predictions regarding students' performance.In this paper, we present a dataset extracted from our e-Learning platform, which is based on the logs collected from testing activity.The focus of this paper is to present the dataset; the experiments presented in the paper are meant to explore the dataset along with its capabilities.The dataset consists of attributes relevant to the testing activity and provides labels which consist of average test grade and final exam grade.Our focus when building the dataset was to keep only the attributes relevant for the learning activity and to provide means to analyse and predict the student's final grade or failure.The paper presents the structure of the dataset, the methodology of collecting the data and experiments using several popular algorithms.The experimental results reveal that the actions performed by the users correlate with the results of the tests and the exam failure can be predicted with a pretty good accuracy using the default set of tuning parameters for our algorithms.As feature work, we can extend the set of experiments with other algorithms, and we can also use parameter tuning for each algorithm for a slight increase in performance.
Understanding Influence Operations in Social Media: A Cyber Kill Chain Approach

Bergh, Arild.Understanding Influence Operations in Social Media: A Cyber Kill Chain Approach. Journal of Information Warfare 2020 ;Volum 19.(4) s. 110-131
Guidance on Writing Literary Works for Students as Support for The School Literacy Movement

Community empowerment program in literacy aims to provide training and coaching in writing literary works for students. This activity is an effort to foster students' critical literacy skills, primarily through writing skills. Also, this activity is a way to bring schools and students closer to literature. The implementation method, Community Service, is carried out by providing training and mentoring on an ongoing basis. The training is conducted for three days for students. This training is carried out in two ways, namely in-class and outclass. In-class training is conducted to provide students with the same understanding of writing literary works. Simultaneously, outclass training is conducted to hone students' sensitivity, critical power, and intuition to capture any existing problems around them to be used as a material in writing literary works. Likewise, with students' writing skills increasing and better, This is evidenced by the results of literary works that they make in the form of an anthology of poetry. The poems they made was varied in typography, theme, and contents. The students were enthusiastic and enthusiastic in participating in the activity. The activities of fostering literary writing in schools can help students and school parties better recognize literacy, especially in literary works' authorship. On the other hand, fostering the writing of literary works in schools can help teachers form the School Literacy Programme in their respective schools, bearing in mind is still very important in the school environment, so there is a need for sweet activities to increase student competency and love in reading and writing.
Keystroke dynamics Based Technique to Enhance the Security in Smart Devices

Nowadays, smart devices have become a part of ourlives, hold our data, and are used for sensitive transactions likeinternet banking, mobile banking, etc. Therefore, it is crucial tosecure the data in these smart devices from theft or misplacement.The majority of the devices are secured with password/PINbaseduser authentication methods, which are already proveda less secure or easily guessable user authentication method.An alternative technique for securing smart devices is keystrokedynamics. Keystroke dynamics (KSD) is behavioral biometrics,which uses a natural typing pattern unique in every individualand difficult to fake or replicates that pattern. This paperproposes a user authentication model based on KSD as an additionalsecurity method for increasing the smart devices’ securitylevel. In order to analyze the proposed model, an android-basedapplication has been implemented for collecting data from fakeand genuine users. Six machine learning algorithms have beentested on the collected data set to study their suitability for usein the keystroke dynamics-based authentication model.