Reviewerly/exHarmony huggingface dataset:
Patch-Based Backdoors Detection and Mitigation with Feature Masking

Pre-trained models have been employed by substantial downstream tasks, achieving remarkable achievements in transfer learning scenarios. However, poisoning the training samples guides the target model to make misclassification in the inference phase, backdoor attacks against pre-trained models represents a new security threat. In this paper, we propose two patch-based backdoors detection and mitigation methods via feature masking. Our approaches are motivated by the observation that, patch-based triggers induce abnormal feature distribution at the intermediate layer. By exploiting the feature importance extraction method and gradient-based threshold method, the backdoored samples can be detected and the abnormal feature values can be backward linked to the trigger position. Hence, masking the features within the trigger posed achieves the correct labels for those backdoored samples. Finally, we employ the unlearning technique to dramatically mitigate the negative effect of the backdoor attacks. The extensive experimental results show that our approaches perform better in defense effectiveness and model inference accuracy on clean examples than the state-of-the-art method.
PENGEMBANGAN REPRESENTASI VISUAL DALAM LINTASAN KEBAHASAAN PADA TEOREMA PYTHAGORAS

AbstractThis research was inspired by students' learning obstacles of visual representation in the cross-linguistic of Pythagorean Theorem material. The method used was a research and development based on ADDIE (Analyze Design Develop Evaluate). Analyze, the researcher analyzed the learning process to know students' learning obstacles in Pythagorean Theorem material. Design, the researcher designed an instrument to solve the problem. Develop, the researcher developed an instrument that has been designed based on student's needs. The students' learning obstacles has detected in the pre-test. Implementation, the researcher implemented the instrument that has been arranged before. Evaluate, the researcher evaluated the instrument based on the result. The subjects used in this research were ten 12th grade students in the Science department of SMA Negeri 1 Sandai. The result revealed the visual representation in the cross-linguistic that has been developed could solve students' learning obstacles. It was proved by the decreasing of students' learning obstacles index. Keywords: Cross-Linguistic, Pythagorean Theorem, Visual Representation
A cluster and label approach for classifying imbalanced data streams in the presence of scarcely labelled data

Classifying imbalanced data streams is often a challenging task primarily due to the continuous flow of infinite data and due to the unavailability of class labels. The problem is two-fold when the stream is imbalanced in nature. Due to the characteristics of data streams, it is impossible to store and process the data and deal with imbalance. There is a need to provide a solution that can consider the unavailability of class labels and classify the imbalanced data streams. This paper proposes a semi-supervised learning (SSL)-based model to classify scarcely labelled imbalanced data streams. A modified cluster and label SSL approach that uses expectation maximisation for clustering and similarity-based label propagation for labelling the unlabelled clusters is proposed. The model also employs a novel imbalance sensitive cluster merge technique to deal with the imbalance data. The results prove that the model outperforms standard stream classification algorithms.
The Melody Triangle: Exploring Pattern and Predictability in Music

The Melody Triangle is an interface for the discovery of melodic materials, where the input – positions within a triangle – directly map to information theoretic properties of the output. A model of human expectation and surprise in the perception of music, information dynamics, is used to ‘map out’ a musical generative system’s parameter space. This enables a user to explore the possibilities afforded by a generative algorithm, in this case Markov chains, not by directly selecting parameters, but by specifying the subjective predictability of the output sequence. We describe some of the relevant ideas from information dynamics and how the Melody Triangle is defined in terms of these. We describe its incarnation as a screen based performance tool and compositional aid for the generation of musical textures; the users control at the abstract level of randomness and predictability, and some pilot studies carried out with it. We also briefly outline a multi-user installation, where collabo- ration in a performative setting provides a playful yet informative way to explore expectation and surprise in music, and a forthcoming mobile phone version of the Melody Triangle.
Pengenalan Software Desain 3D Modelling Untuk Siswa SMK Bima Utomo Bs

One way to improve the quality of education in schools is to implement an information system based on computer technology, one of which is to provide an understanding of the development of software technology. In today's digital era, software technology is one of the necessities for creating and planning products. In designing interior and exterior buildings, software is needed to plan buildings which were originally from sheets of work paper in the form of sketches but can only be made into 3D modeling. By introducing the role of 3D Modeling software technology as a supporter of student interest in learning so that it can improve achievement, student understanding and student interest in 3D Modeling Software. The form of community service activities carried out is counseling by presenting material about the introduction of 3d Modeling software which is developing and widely used in the current digital era and practicing tools on the software.
Improving Nepali News Classification Using Bidirectional Encoder Representation from Transformers

News classification is the process of grouping different news documents into specific categories. In natural language processing (NLP), one of the major problems is news classification. Day-by-day lots of news is generated from different news portals, and it is difficult to consign the specific types of news from that portal. As users want to read specific categories of news automatically, it is necessary to consign the news into respective classes. Text classification has been done by using different machine learning algorithms like support vector machine (SVM), long short-term memory (LSTM), and naive Bayes. However, for Nepali news classification tasks, bidirectional encoder representations from transformers (BERT) have not been thoroughly scrutinized these days. In this research, five separate news portals are used for news collection, and their classification accuracies are compared. A BERT-based deep neural network is used for the classification of the news and compares it with LSTM, BiLSTM, GRU, and BiGRU based on a neural network using parameter accuracy, precision, recall, and F1-score. BERT model outperforms other models with an accuracy of 95%.
Subject classification algorithm of Chinese materials in university libraries based on the Bert model

At present, the construction of first-class disciplines in colleges and universities in our country is in full swing. It is of far-reaching significance to classify book resources with discipline labels, which is not only conducive to discipline construction but also a prerequisite for the construction of intelligent libraries. The traditional way of classifying books through manual experience is time-consuming and laborious, and some studies have introduced machine learning methods, but they only classify books in small specialized areas and do not take into account the current urgent need for subject classification work in university libraries. In this study, by using the BERT class pretraining model, the advantages of deep learning in feature extraction are fully utilized to extract features and classify topics for book resources, better book representations are learned, and good results are achieved on the test dataset. The experiments showed that in the best case, 94.12% of books were accurately predicted with corresponding subject labels on the subject label dataset. This study helps to realize the disciplinary label classification of book resources and has a positive effect on the librarians of university libraries to supplement the book resources with reasonable disciplinary classification labels.
MRWM: A Multiple Residual Wasserstein Driven Model for Image Denoising

Residual histograms can provide valuable information for vision research. However, current image restoration methods have not fully exploited the potential of multiple residual histograms, especially their role as overall regularization constraints. In this paper, we propose a novel framework of multiple residual Wasserstein driven model (MRWM) that can organically combine multiple residual Wasserstein constraints and various natural image priors for image denoising. Specifically, by utilizing the Wasserstein distance derived from the optimal transmission theory, the multiple residual histograms of the observed images are forced to be as close as possible to the reference residual histogram, thereby improving the accuracy of residual estimation. Furthermore, the proposed concrete MRWM unifies the multiple residual Wasserstein distribution approximation and the image total variation prior knowledge to carry out image denoising. Alternating iterative algorithm of histogram matching and Chambolle dual projection has the characteristics of less parameters and easy implementation. Finally, our experiments confirm that compared with some representative image denoising algorithms, the MRWM can obtain better performance in objective evaluation, and can better preserve the details such as the image edges, making the image look more natural.
An Approach for Transparent Logistic Solution for Life Saving Medicines and Essential Goods using IoT and Blockchain

IoT devices are increasingly being used in real life to facilitate human life processes such as logistics, through a variety of applications. It has huge demand but the problem it faces security issues. The proposal has presented an autonomous system for the logistics of medicines, vaccines, and of other important and valuable items using Blockchain technology. This work is an amalgamation of IoT and Blockchain technology which is imperative in today’s world. The proposed system takes inputs from various sensors through the Raspberry Pi module IoT device. Afterward, the data are passed to the Blockchain smart contract by running a javascript file. These sensor inputs are then used to trigger the smart contract under certain conditions and also create a transaction after the smart contract is triggered. The smart contract has been deployed on the Goerli Ethereum Test Network. The sensors used in the proposed work are the Ultrasonic sensor and Flame sensor. Certain threshold value has been set on both the sensors for which the smart contract gets triggered. The threshold value is modeled based on a flammable and fragile object being transported from a sender to a receiver. Whenever the distance of the object from the container is less than the threshold or if a flame is detected by the sensor, a transaction of Goerli ETH tokens is automatically processed from the sender’s account to the receiver’s account in a form of compensation. Fig. 1 shows the Raspberry Pi IoT device which was used for the proposed work.
Are You Feeling Happy? the Effect of Emotions on People’s Interaction Experience Toward Empathetic Chatbots

Abstract People may experience emotions before interacting with automated agents to seek information and support. However, existing literature has not well examined how human emotional states affect their interaction experience with agents or how automated agents should react to emotions. This study proposes to test how participants perceive an empathetic agent (chatbot) vs. a non-empathetic one under various emotional states (i.e., positive, neutral, negative) when the chatbot mediates the initial screening process for student advising. Participants are prompted to recall a previous emotional experience and have text-based conversations with the chatbot. The study confirms the importance of presenting empathetic cues in the design of automated agents to support human-agent collaboration. Participants who recall a positive experience are more sensitive to the chatbot’s empathetic behavior. The empathetic behavior of the chatbot improves participants’ satisfaction and makes those who recall a neutral experience feel more positive during the interaction. The results reveal that participants’ emotional states are likely to influence their tendency to self-disclose, interaction experience, and perception of the chatbot’s empathetic behavior. The study also highlights the increasing need for emotional acknowledgment of people who experience positive emotions so that design efforts need to be designated according to people’s dynamic emotional states.
BRIDGING THE DIGITAL COMPETENCE GAP: TELL US WHAT YOU NEED

Digital competence is one of the eight key competences for lifelong learning. In a generic way, it can be defined as the set of knowledge, skills, attitudes, abilities and strategies that make citizens capable of using digital technologies in a
Research on Pragmatic Value of English Fuzzy Language Based on Needs Analysis

In the process of college English teaching, students often lack interest and initiative in learning.At this time, teachers can try to introduce the concepts and contents of fuzzy language and enrich the means and methods of language learning, so as to improve students' learning enthusiasm.The pragmatic value of fuzzy language and fuzzy language in college English needs teachers' attention.According to students' actual learning situation, we should choose the most suitable fuzzy language teaching method to further improve the efficiency of college English teaching.This paper mainly explores the pragmatic value of fuzzy language in college English teaching from the perspective of needs analysis, and puts forward corresponding suggestions.Fuzzy language is uncertain and relative, and also has strong pragmatic value.Therefore, university teachers should attach importance to the pragmatic value of fuzzy language in teaching and deepen students' understanding of fuzzy language.
Recommendation Model Based on Probabilistic Matrix Factorization, Integrating User Trust Relationship, Interest Mining, and Item Correlation

Personalized recommendation has gained widespread attention in the academic and industrial fields to minimize information overload and has produced good benefits. Current research shows that social recommendations that effectively utilize user trust relationships can solve data sparsity and cold start problems common in traditional collaborative filtering algorithms. However, existing social recommendation models have focused only on direct trust relationships between users and have ignored indirect trust relationships and item correlations. To address these problems, we propose a probabilistic matrix factorization-based recommendation model based on trust relationships, interest mining, and item correlation. The proposed recommendation model considers the direct and indirect trust relationships between users, the similarities in users’ preferences for item attributes, and the correlations between items. Finally, the rating of the item is predicted by the target user and provides the target user with personalized item recommendations. We evaluate the recommendation performances of the proposed recommendation model on the FilmTrust and the CiaoDVD datasets and find that it alleviates the user’s cold start problem and provides higher recommendation accuracy and diversity than popular algorithms.
Personality and Emotion in Strong-Story Narrative Planning

Believable characters are core elements of a coherent story. Qualities that make story characters more believable include goals, beliefs, personality, and emotion. We propose computational models of emotion and personality by adapting the OCC model of emotion and the Five Factor personality model. Our models are formulated into multi-agent strong-story narrative planning with the promise of being highly reusable and domain independent. We evaluate these models using multiple human subject studies. We show that our model's reasoning about character emotions matches the expectations of human readers, and using our emotion model, we can generate a larger set of stories than precedent narrative planners. We also demonstrate that human readers can perceive and recognize the personalities of story characters through their consistent behavior generated by our model. Our final experiment supports that human readers significantly find the behavior generated by our models of emotion and personality more believable than behavior that lacks either or both.
NEED OF INTERNATIONAL LEGISLATION REGARDING CYBER CRIMES: PAKISTAN PERSPECTIVE

This research study focused on computer and internet have benefitted man immensely but have also simultaneously begotten cybercrimes having serious implications on society. The international community is seriously concerned about the surge of cybercrimes. Inadequacy of domestic cyber laws and lack of international cooperation in the field hamper to fight this menace. The difficulty to establish jurisdiction in the ubiquitous and borderless cyberspace, while investigating and prosecuting cybercrimes, further adds to the issue. Current domestic laws governing cybercrimes in many states are weak to combat cyber offenses. The conventional legal mechanisms cannot cope with the adverse aftermath of the mushrooming Information Technology which renders the cyberspace a sanctuary for criminals who torment the unwary internet users. Prosecuting these anonymous and veiled offenders require appropriate legal framework at national and international level. Some international institutions like UN and other organs are striving to fight cybercrimes and likewise the Council of Europe has introduced an international treaty in this regard. It is the only available treaty about cybercrimes with many benefits for the member states. This research article has also focused on the grey areas in domestic criminal laws relating cybercrimes in a global perspective. The study concludes that Budapest Treaty is the best international legal framework to fight cybercrimes and needs the comity of nations to ally with. Keywords: Cybercrime, Cyberspace, Cyberlaws, Jurisdiction, Pakistan
Research on the Construction Strategy of the Theoretical Framework of Presence in Oral English Teaching Based on Augmented Reality Technology

Aiming at the common problems of "dumb foreign language", oral communication anxiety, low oral communication willingness and other common problems of oral English teaching effectiveness and low motivation.Taking the most advanced augmented reality technology as the starting point, starting from people's basic psychological needs, experience and exploration needs, we study how augmented reality oral English teaching can meet the needs of learners through functional and experiential design, and at the same time stimulate learners' intrinsic motivation and experience.Learn to invest.At present, research on augmented reality education generally lacks systematic research on the construction of the theoretical framework of augmented reality technology perception experience, teaching mode acceptance behavior research and teaching mode application research.Based on this, this paper first proposes the five-dimensional definitions of AR environmental presence, namely: spatial presence, immersion, reality, interaction and social presence.Further, a theoretical framework of augmented reality oral English teaching presence based on 3I characteristics and English teaching background is constructed.Finally, the correctness of the relationship among the proposed dimensions is systematically analyzed and discussed.This paper conducts in-depth research on the application of augmented reality technology in classroom teaching and online education from the aspects of perception, acceptance and application, which has great practical significance and theoretical value.
Predicted Stratigraphy - A New Approach to Reviewing Stratigraphic Data

Summary Stratigraphy is an essential building block in the Oil and Gas industry especially in exploration. As with all geological disciplines the development of new ideas and studies means that vintage datasets often become outdated as quickly as they are completed. However, these datasets could play an important role in improving the efficiency of future exploration conducted through a process of updating modern taxonomies and the use of large datasets in the generation of stratigraphic frameworks. This case study attempts to improve the efficiency of stratigraphic data reviews. This is achieved by utilising a taxonomic dataset from the Gulf of Mexico and data science approaches to predict a stratigraphic interpretation. This achieved by automatically deriving taxonomic events from biodata, removing outliers that are either unreliable or not in situ and finally using those reference points to create an age-depth profile for that well. Results show that this technique can be effective in wells with simple geology. The workflow also includes a confidence scoring module that estimates the data quality through each stratigraphic interval and therefore in areas of more complex geology, it is able to highlight areas requiring manual interpretation and thus improving the efficiency of the review.
ONTOLOGICAL MODEL OF THE EDUCATIONAL PORTAL OF THE UNIVERSITY

The article discusses the main aspects of building the ontology of the educational portal of the university from design to development. Examples of working with Protégé 4.3 and Sparql queries are given. In addition, the author talks about the importance of working on an educational portal in terms of developing the semantic core and the relevance of the portal for search engines. The author proposes a methodology for using Semantic Web technologies to form the structure and content of the university's educational portal. Develop an ontology of the educational portal to provide the possibility of semantic analysis of user search queries in order to organize a rational structure and content of the educational portal of the university and ensure the possibility of effective promotion of the portal in the Internet environment. In the ontological model, the author considers in the form of classes a set of educational programs of the university, and their constituent educational courses, as well as user profiles. Relations between classes will further help, when implementing user information requests, to determine the degree of compliance of each educational program (field of study) with the needs and preferences of a particular user, and to implement automatic selection of university educational programs recommended for applicants. An ontological model of the educational portal has been developed. The ontological model allows you to optimally structure the content of the educational portal, which further provides the possibility of issuing recommendations to users regarding the choice of the direction of training (applicants) or educational resources (students or teachers) in accordance with their needs and preferences. The developed ontology is used both at the stage of developing the educational portal of the university and its semantic core, and at the stage of functioning of the educational resource, as a knowledge base in the system for issuing recommendations to users on choosing the direction of training.