Reviewerly/exHarmony huggingface dataset:
EPCSAC - Extensible Platform for Cloud Scheduling Algorithm Comparison

When developing a new cloud scheduling algorithm, simulation is the most used approach to test the algorithm, mainly due to the impossibility of controlling all the cloud variables and also because of the costs involved. However, setting up the simulation environment can be a difﬁcult task and each environment can be conﬁgured its own way, resulting in no easy way of reproducing the results with other published algorithms neither comparing both under the same circumstances. To solve both of these problems, we propose the EPCSAC, an online open-source platform that allows researchers to worry only about the creation of their algorithm. They are able to create a set of simulation parameters, test their algorithm under these parameters, and compare their results with other algorithms on the platform. This way, there is an improved reproducibility and a reduced overhead of development. Researchers may have access to the platform at epcsac.lasdpc.icmc.usp.br/.
The use of English in Indonesia: Status and Influence

This research is a qualitative research which aims to describe the state of the use of English in Indonesia, in terms of language status and its influence on Indonesian society. To realize the purpose of this paper, the authors collected several news stories from national websites (such as Republika, Tribun News, detikNews, ANTARA, and Tempo) regarding the use of English in Indonesia as data that is explained and interpreted by descriptive qualitative analysis. The results of the study show that English is common and has massive use, even though it is still a foreign language. In fact, English has moved to improve its linguistic status, namely starting to act as a second language and the language of instruction.
EcoQBNs: First Application of Ecological Modeling with Quantum Bayesian Networks

A recent advancement in modeling was the development of quantum Bayesian networks (QBNs). QBNs generally differ from BNs by substituting traditional Bayes calculus in probability tables with the quantum amplification wave functions. QBNs can solve a variety of problems which are unsolvable by, or are too complex for, traditional BNs. These include problems with feedback loops and temporal expansions; problems with non-commutative dependencies in which the order of the specification of priors affects the posterior outcomes; problems with intransitive dependencies constituting the circular dominance of the outcomes; problems in which the input variables can affect each other, even if they are not causally linked (entanglement); problems in which there may be &gt;1 dominant probability outcome dependent on small variations in inputs (superpositioning); and problems in which the outcomes are nonintuitive and defy traditional probability calculus (Parrondo’s paradox and the violation of the Sure Thing Principle). I present simple examples of these situations illustrating problems in prediction and diagnosis, and I demonstrate how BN solutions are infeasible, or at best require overly-complex latent variable structures. I then argue that many problems in ecology and evolution can be better depicted with ecological QBN (EcoQBN) modeling. The situations that fit these kinds of problems include noncommutative and intransitive ecosystems responding to suites of disturbance regimes with no specific or single climax condition, or that respond differently depending on the specific sequence of the disturbances (priors). Case examples are presented on the evaluation of habitat conditions for a bat species, representing state-transition models of a boreal forest under disturbance, and the entrainment of auditory signals among organisms. I argue that many current ecological analysis structures—such as state-and-transition models, predator–prey dynamics, the evolution of symbiotic relationships, ecological disturbance models, and much more—could greatly benefit from a QBN approach. I conclude by presenting EcoQBNs as a nascent field needing the further development of the quantum mathematical structures and, eventually, adjuncts to existing BN modeling shells or entirely new software programs to facilitate model development and application.
An Advanced Image Captioning using combination of CNN and LSTM

The Captioning of Image now a days is gaining a lot of interest which generates an automated simple and short sentence describing the image content. Machines indeed are trained in a way that they can understand the Image content and generate captions which are almost accurate at a human level of knowledge is a very tedious and interesting task. There are various solutions used to solve this tedious task and generate simple sentences known as captions using neural network which still comes with problems such as inaccurate captions, generating captions only for the seen images, etc. In this paper, the proposed system model was able to generate more precise captions using a two staged model which consists of a combination of Deep Neural Network algorithms (Convolutional and Long Short-Term Memory). The proposed model was able to overcome the problems arise using Traditional CNN and RNN algorithms. The model is trained and tested using the Flicker8k Data set.
Synthesis Biometric Materials Based on Cooperative Among (DSA, WOA and gSpan-FBR) to Water Treatment

Starting from a training point in the chemical laboratory, whose events take place in the chemical laboratory in addition to wasting time and money, in addition to the revolution, the dream in the world of technology and electronic computing is intelligence and feasibility, so that they can be creative and innovative, in addition to that, reduce the material costs and efforts in this field. Therefore, our research aims to analyze the complex chemical materials to generate new materials from them and to verify them from a computerized validity before performing this experiment in a laboratory. The proposed model consists of three basic steps: First: Dismantling the chemical compound, which represents a complex network, and converting it into a group of subgraphs through the use of one of the graph mining algorithms. Second: After that, each subgraph is taken and the stimulating substance is added to it to speed up the work of the chemical reaction until it reaches a state of stability, by adding new nodes and connections to that subgraph which leads to the production of New subgraph. Third: One of the optimization techniques is applied to each new set of subgraphs to produce a set of patterns that represent the best materials that can be produced from the original spoilers. Thus, we have determined in advance to the researcher what materials can be added to accelerate the chemical reaction and what percentages are accurately mixed using electronic computing before this reaction is carried out in his laboratory.
THE IMPORTANCE OF ORGANIZATION AND MANAGEMENT INDEPENDENT EDUCATION IN THE LEARNING PROCESS

Training students to be competitive professionals in line with modern requirements through the credit-module system of education. Organize the learning process in a new way, based on independent learning. Requirements for students to organize and conduct independent work. Issues such as the creation of an independent education management system, its full digitization, monitoring of independent learning and analysis of the results will be discussed.
The Role of Intrinsic Motivation in the Relationship Between Psychological Capital and Innovative Performance: Empirical Evidence from Vietnam

This research examines the mediation of intrinsic motivation (IP) in the relationship between psychological capital (Psycap) and innovative performance (IP) in the educational environment of the transitioning economy A test was based on a convenient sample of 440 University lecturers participating in a hardcopy survey was collected from ten universities and colleges in Southern Vietnam, from April 2020 to December 2020, while social isolation is strictly enforced by the government (Covid-19) The hypotheses are then proposed and conducted using confirmatory factor analysis (CFA) and the structural equation modeling technique (SEM) The testing structural model results reveal that all the hypotheses are satisfied at the 5% significance level Intrinsic motivation is a partial mediator in the linkage between psychological capital and innovative performance These findings suggest that the importance of Psychological Capital (PsyCap) of workers promotes job performance in general, especially in individuals’ creativity in a transitioning market, Vietnam In addition, Based on the research results, several solutions are also proposed to promote innovative performance in the conditions of education in Vietnam Besides, the author also gave a few comments on the findings as well as the limitations that this study encountered, especially how the survey samples were collected © 2021 The Author(s) This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons org/licenses/by-nc/4 0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited
A Comparative Analysis of Emotion and Sentiment Analysis Method from Twitter Text

The Study of Sentiment is an area of science that specializes in the analysis of strong emotions expressed in texts. An opinion is a complete perception of a commodity, service, association, individual or some other form of entity about which a given text is conveyed. This work provides valuable knowledge of the roots of sentiment analysis and how sentiment evaluators can be configured. We demonstrated how to construct a basic classifier and use it as an example. These approaches will eventually change and there will still be the need for a more extensive assessment of emotions. Non-textual material has an important significance in analyses. Photos, photographs, animations and other visual material are also useful in performing social research. Of course, I can see that all these hyperlinks provide essential material. Some other ways of using social media are likes, retweets, reviews on posts and much more! It is hoped that common issues such as avoiding irony and sarcasm would be made less ambiguous. However, there will emerge other issues that will have to be tackled.
Robust Digital Watermarking Techniques for protecting copyright Applied to Digital Data: A Survey

The colossal prominence of the World Wide Web in the mid 1990's shown the business capability of offering media assets through the computerized networks. Since business intrigues look to utilize the advanced organizations to offer computerized media revenue driven, they have a solid interest in ensuring their proprietorship rights. Since the danger of utilizing media data, advanced fabrications, and unapproved sharing (robbery) of&#x0D; computerized content has expanded among content makers, merchants and clients. Today mixed media data theft alone has exposed all the enterprises to multi-billion income misfortunes. Customary advanced substance security methods, for example, encryption and scrambling, alone can't give satisfactory insurance of copyrighted substance, in light of the fact that these advances can't ensure computerized content whenever they are decoded. One approach to debilitate illicit duplication is to embed data known as watermark, into possibly weak information so that it is difficult to isolate the watermark from the information. Computerized watermarking is the way toward embedding’s an advanced sign or example inside a computerized picture, which gives proof of realness. This paper presents a study on different data concealing strategies and depicts characterization of advanced Watermarking procedures.
Altruism Extension of the Privacy Calculus

This paper analyses the privacy calculus as an explanation of privacy thinking and behavior and extends its concepts with altruistic thinking which is becoming more relevant these days as companies create patterns of behavior, users' profiles and categories and one's behavior affects many others, e.g., concerning health issues.Privacy calculus attempts to explain the privacy paradox according to which people express their concern about privacy but behave differently.Our results show that students respect altruistic principles, are aware of both individual and social benefits and risks of privacy disclosure, but their real behavior in the online environment is influenced by these ideas a little only.The reason why the respondents' intention does not comply with their behavior may consist in their distrust and confusion concerning technology.The movement of explainable technology proves relevance of these ideas.Our results are relevant for companies formulating their privacy statements and respecting users' preferences regarding their privacy.
A hybrid ensemble deep learning approach for reliable breast cancer detection

Among the cancer diseases, breast cancer is considered one of the most prevalent threats requiring early detection for a higher recovery rate. Meanwhile, the manual evaluation of malignant tissue regions in histopathology images is a critical and challenging task. Nowadays, deep learning becomes a leading technology for automatic tumor feature extraction and classification as malignant or benign. This paper presents a proposed hybrid deep learning-based approach, for reliable breast cancer detection, in three consecutive stages: 1) fine-tuning the pre-trained Xception-based classification model, 2) merging the extracted features with the predictions of a two-layer stacked LSTM-based regression model, and finally, 3) applying the support vector machine, in the classification phase, to the merged features. For the three stages of the proposed approach, training and testing phases are performed on the BreakHis dataset with nine adopted different augmentation techniques to ensure generalization of the proposed approach. A comprehensive performance evaluation of the proposed approach, with diverse metrics, shows that employing the LSTM-based regression model improves accuracy and precision metrics of the fine-tuned Xception-based model by 10.65% and 11.6%, respectively. Additionally, as a classifier, implementing the support vector machine further boosts the model by 3.43% and 5.22% for both metrics, respectively. Experimental results exploit the efficiency of the proposed approach with outstanding reliability in comparison with the recent state-of-the-art approaches.
WANLP 2021 Shared-Task: Towards Irony and Sentiment Detection in Arabic Tweets using Multi-headed-LSTM-CNN-GRU and MaRBERT

Irony and Sentiment detection is important to understand people’s behavior and thoughts. Thus it has become a popular task in natural language processing (NLP). This paper presents results and main findings in WANLP 2021 shared tasks one and two. The task was based on the ArSarcasm-v2 dataset (Abu Farha et al., 2021). In this paper, we describe our system Multi-headed-LSTM-CNN-GRU and also MARBERT (Abdul-Mageed et al., 2021) submitted for the shared task, ranked 10 out of 27 in shared task one achieving 0.5662 F1-Sarcasm and ranked 3 out of 22 in shared task two achieving 0.7321 F1-PN under CodaLab username “rematchka”. We experimented with various models and the two best performing models are a Multi-headed CNN-LSTM-GRU in which we used prepossessed text and emoji presented from tweets and MARBERT.
FEATURES OF ANALOG-DIGITAL CONVERSION IN A LOGIC-TIME BASIS

The transition from analog information, in particular, in the form of visual picture, to its digital representation for the further processing is impossible without the use of analog-digital conversion. There are many effective methods and tools for analog-digital conversion nowadays. It is needed to admit the methods based on the principle of «golden ratio» with the formation of digital data in the Fibonacci codes form among them, as well as the method of transformation in the logic-time basis with the digital data formation in the form of unit codes. A feature of analog-digital conversion in the logic-time basis is the use of the method of time-pulse conversion. This conversion method is based on quantization by the unit duration τ of the input time duration of the optical signal. This paper reviews the features of analog-digital conversion in the logic-time basis, as well as the properties of unit codes, in the form of which the resulting digital signals are formed. Two types of unit codes, normal and positional, of their noise stability are analyzed. This property of unit codes allows them to be used for data transmission, state coding in control devices, content addressing in storage devices.
First the Worst: Finding Better Gender Translations During Beam Search

Generating machine translations via beam search seeks the most likely output under a model.However, beam search has been shown to amplify demographic biases exhibited by a model.We aim to address this, focusing on gender bias resulting from systematic errors in grammatical gender translation.Almost all prior work on this problem adjusts the training data or the model itself.By contrast, our approach changes only the inference procedure.We constrain beam search to improve gender diversity in n-best lists, and rerank n-best lists using gender features obtained from the source sentence.Combining these strongly improves WinoMT gender translation accuracy for three language pairs without additional bilingual data or retraining.We also demonstrate our approach's utility for consistently gendering named entities, and its flexibility to handle new gendered language beyond the binary.
Cluster Optimization for Boundary Points using Distributive Progressive Feature Selection Algorithm

A group of different data objects is classified as similar objects is known as clusters. It is the process of finding homogeneous data items like patterns, documents etc. and then group the homogenous data items togetherothers groupsmay have dissimilar data items. Most of the clustering methods are either crisp or fuzzy and moreover member allocation to the respective clusters is strictly based on similarity measures and membership functions.Both of the methods have limitations in terms of membership. One strictly decides a sample must belong to single cluster and other anyway fuzzy i.e probability. Finally, Quality and Purity like measure are applied to understand how well clusters are created. But there is a grey area in between i.e. ‘Boundary Points’ and ‘Moderately Far’ points from the cluster centre. We considered the cluster quality [18], processing time and relevant features identification as basis for our problem statement and implemented Zone based clustering by using map reducer concept. I have implemented the process to find far points from different clusters and generate a new cluster, repeat the above process until cluster quantity is stabilized. By using this processwe can improve the cluster quality and processing time also.
A (Non)-Perfect Match: Mapping plWordNet onto PrincetonWordNet.

The paper reports on the methodology and final results of a large-scale synset mapping between plWordNet and Princeton WordNet. Dedicated manual and semi-automatic mapping procedures as well as interlingual relation types for nouns, verbs, adjectives and adverbs are described. The statistics of all types of interlingual relations are also provided.
N-Shot Learning for Augmenting Task-Oriented Dialogue State Tracking

Augmentation of task-oriented dialogues has followed standard methods used for plain-text such as back-translation, word-level manipulation, and paraphrasing despite its richly annotated structure.In this work, we introduce an augmentation framework that utilizes belief state annotations to match turns from various dialogues and form new synthetic dialogues in a bottom-up manner.Unlike other augmentation strategies, it operates with as few as five examples.Our augmentation strategy yields significant improvements when both adapting a DST model to a new domain, and when adapting a language model to the DST task, on evaluations with TRADE and TOD-BERT models.Further analysis shows that our model performs better on seen values during training, and it is also more robust to unseen values.We conclude that exploiting belief state annotations enhances dialogue augmentation and results in improved models in n-shot training scenarios.
Klasifikasi American Sign Language Menggunakan Ekstraksi Fitur Histogram of Oriented Gradients dan Jaringan Syaraf Tiruan

Sign languages have various types, one of which is American Sign Language (ASL). In this study, ASL images from the handshape alphabet were extracted using Histogram of Oriented Gradient (HOG) then these features were used for the classification of Artificial Neural Networks (ANN) with various training functions using 3 variations of multi-layer network architecture where ANN architecture consists of one hidden layer. Based on ANN training, trainbr test results have a higher success rate than other training functions. In architecture with 15 neurons in the hidden layer get an accuracy value of 99.29%, a precision of 91.84%, and a recall of 91.47%. The test results show that using the HOG feature and ANN classification method for ASL recognition gives a good level of accuracy, with an overall accuracy of 5 neurons 95.38%, 10 neurons 96.64%, and 15 neurons with 97.32%.&#x0D; &#x0D; Keywords— Artificial Neural Network; American Sign Language; Histogram of Oriented Gradient; Training Function
Watermarking in 3D Models Using Depth Path

This paper presents a 3D watermarking model based on the nearest distance between two vertices in mesh, where the embedding path moves as deep as possible until the embedding data is completed. The proposed algorithm achieved good results according to invisibility and robustness. The visibility was measured in termof Root Mean Square Error (RMSE) and Hausdroff Distance (HD), which obtained good results. As related to robustness, the proposed method showed resistance to geometrical attack (translation, scaling and rotation) as well as acceptable resistance to signal processing attacks like noise addition and simplification.
Private Trajectory Intersection Testing: Is Garbled Circuit Better than Custom Protocols?

In this paper, two protocols are presented for private intersection detection of two moving objects’ trajectories. Assuming that the movement trajectory of an object can be described by a time-dependent polynomial function, the problem of finding the intersection points is simplified to the problem of finding the common roots of the corresponding polynomials. Thereafter, GrÖbner Basis is used to design a novel secure protocol of finding the common roots of the polynomials. Another protocol is also designed based on the distance computation of two trajectories’ curves. Moreover, we present the complexity analysis of the protocol for private trajectory intersection testing of two moving objects, which is based on GrÖbner Basis. Then, we compare its complexity by the garbled circuit-based protocol for Euclidean Distance Computation of l points. We also prove the security of our proposed protocol, which is based on the distance computation of two curves. Keywords: Private Trajectory Intersection Testing, GrÖbner Basis, Distance Computation, Complexity , Garbled Circuit, Euclidean Distance