Reviewerly/exHarmony huggingface dataset:
Interacting with autonomous systems and intelligent algorithms – new theoretical considerations on the relation of understanding and trust

Contemporary technology increasingly relies on artificial intelligence and machine learning and is therefore able to act increasingly autonomously. This leads to systems that display agency instead of merely serving as tools that are utilized by a human user who is in charge. Given that these autonomous systems take roles that emulate human functions (e.g., decision making, tutoring, counseling), it is increasingly important to scrutinize how humans interact with these systems and to which extent they understand and trust these systems. While recent research has specifically addressed how to foster users’ understanding of algorithms, we argue that fostering calibrated trust, in the sense of trust that is warranted and calibrated to the actual reliability of the system, might be more fruitful. Building on these considerations we propose a theoretical research framework on the interrelation of human trust and understanding and discuss how they might be affected by design variables such as explanations, anthropomorphization and trust cues.
Interpreting Predictive Models through Causality: A Query-Driven Methodology

Machine learning algorithms have been widely adopted in recent years due to their efficiency and versatility across many fields. However, the complexity of predictive models has led to a lack of interpretability in automatic decision-making. Recent works have improved general interpretability by estimating the contributions of input features to the prediction of a pre-trained model. Despite these advancements, practitioners still seek to gain causal insights into the underlying data-generating mechanisms. To this end, some works have attempted to integrate causal knowledge into interpretability, as non-causal techniques can lead to paradoxical explanations. These efforts have provided answers to various queries, but relying on a single pre-trained model may result in quantification problems. In this paper, we argue that each causal query requires its own reasoning; thus, a single predictive model is not suited for all questions. Instead, we propose a new framework that prioritizes the query of interest and then derives a query-driven methodology accordingly to the structure of the causal model. It results in a tailored predictive model adapted to the query and an adapted interpretability technique. Specifically, it provides a numerical estimate of causal effects, which allows for accurate answers to explanatory questions when the causal structure is known.
Prioritizing Answer Sets Based on Conditional Expert Knowledge

Answer set programming (ASP) and conditional reasoning both are powerful and widely used methodologies from the field of knowledge representation and reasoning (KR) which are capable of formalizing default statements that usually hold but also leave room for exceptions. While ASP convinces with an intuitive rule-based syntax and fast solvers, conditionals come along with a sophisticated preference-based semantics. Here, we combine both approaches by calculating answer sets which we then prioritize based on conditional expert knowledge. We apply our hybrid approach to the task of planning warehouse layouts from the logistics domain which is predestinated for our approach because it involves, on the low-level, many variables and technical framework conditions (like rack positions) and, on the high-level, expert knowledge of the layout designer.
REVIEWING THE DEVELOPMENT OF INTELLIGENCE IN CREATING A PROSPEROUS COMMUNITY

Welfare is a measure of a society that is already in a state of prosperity. Welfare can be measured by health, economic condition, happiness and quality of life of the people. The role of intelligence in realizing the welfare of society is quite important. Because in carrying out the process of developing a community, objective data is needed that is valid to be used as material for problem analysis.Through a descriptive approach, this research examines phenomena through the discovery and description of data in a narrative manner. The data collection technique used is library research, namely collecting books, journals, and other relevant research and then reviewing them.To realize prosperity for the community, four elements are needed to conceptualize it. (1) the existence of information or knowledge; (2) the existence of an analytical strategy; (3) the existence of potential threats; and (4) the existence of safeguards against national interests and security. These four elements are owned by Intelligence and become the basic concept. The role of intelligence agencies in creating a prosperous society has a central position. So it can be said that the field of intelligence has a very large and much needed role in realizing a prosperous society.
An information entropy-based filter pruning method for efficient ConvNets

In recent years, deep Convolutional Neural Networks (ConvNets) have achieved great success in many vision tasks. While larger and deeper networks perform better, deploying these models on resource-constrained devices makes it more challenging. CNN models can be effectively compressed and accelerated via filter pruning. The traditional filter pruning method is based on a pre-trained model, pruning, and then recovering model accuracy through fine-tuning. However, recent research had shown that using pre-trained models to prune a model reduces the optimization space of the pruned model. Obtaining a pre-trained model is also computationally intensive. A further weakness of the prior works is that the process for selecting filters is too simplistic and insufficient for identifying possibly more important filters. To address these weaknesses, we propose a pruning method that does not rely on a pre-trained model and instead uses the information entropy of the channels during training as a criterion of the filter’s importance. In each training step, we calculate the importance of each channel in each layer of the network based on information entropy. Since the channel in the current layer is calculated based on the filter in the previous layer, the importance of the filter can be equated with the importance of the channel. Then the least important filters are pruned according to the pre-defined pruning rate, and the next training step is resumed. Note that to guarantee the capacity of the pruned model, we still allow those pruned filters to be updated in the next training step. It is experimentally demonstrated that our proposed method outperforms the traditional weight-based magnitude pruning method largely on the CIFAR10 and CIFAR100 datasets using the ResNet series networks and the VGG16 network. On the ResNet56 network, there is only a 0.1% accuracy loss at 40% pruning using our pruning method on the CIFAR10 training set.
Hybrid Bayesian Networks for the Reliability Analysis of Systems with Continuous Variables

The standard way of dealing with continuous variablesinto reliability models is to discretize (or even binarise)them, resulting in discrete state models. The presentpaper proposes an approach where continuous systemvariables can be directly exploited by resorting to HybridBayesian Networks (HBN), where both continuousand discrete variables can be mixed in a general way.This allows one to: model the inter-dependencies betweendiscrete state components or subsystems, modelthe inter-dependencies between continuous system variables,model the influence of contextual information onsystem variables and components, model the definitionof specific system events or conditions given specificvalues of the system variables. We will show how theabove issues can be captured in a principled way bythe HBN formalism, by making the final analyses moregrounded on the actual values of every system variable.We finally present a case study where the model of agranule storage tank system of a petrochemical plant isconsidered, and we present the results of specific analysesimplemented as inference on the HBN model.
Weight-based multi-stream model for Multi-Modal Video Question Answering

There has been a tremendous success in individual domains of Computer Vision, Natural Language Processing, and Knowledge Representation. Videos are a rich source of information with the multi-modal data forms of images, audio, and optionally subtitles blended. Current research is going on in combining these individual domains which have given rise to topics such as image captioning, visual question answering, and video question answering. Video Question Answering is a model which combines research topics like object detection and recognition, temporal information processing, visual attention, and natural language processing. In this paper, we propose a model with Attention Mechanism for Video Question Answering that assigns varying weights to the many pieces of information the video encompasses. The model combines the question with 3 streams i.e., video's frames, subtitles, and objects to get the most probable answer. The model also receives the set of answer candidates as input and predicts one of them as the most probable answer since it has been trained and tested on the TVQA dataset.
An Interactive Modelling Environment for Designing Warehouse Layouts Based on ASP

In this paper, we apply answer set programming (ASP) to the task of planning warehouse layouts within the logistical domain. Warehouse layout planners have to take into account a vast number of aspects to come up with feasible layouts, especially the placement of logistical elements under specific conditions like the accessibility of warehouse elements. Also optimization criteria, e. g., minimizing the pathways, have to be considered. Out of a generally large number of feasible layouts, the layout planner has to decide which layout fits best. As this can be quite difficult and time-consuming, we propose an interactive modelling environment which creates all feasible layouts for a given planning problem by exploiting a logistical knowledge base as well as specific case data provided by the user, who can interactively review all optimal layouts and look for the most suited one. It allows for taking soft constraints and vague expert knowledge into account while leaving room for proposing novel ideas for layouts.
SBERTiment: A New Pipeline to Solve Aspect Based Sentiment Analysis in the Zero-Shot Setting

The field of Natural Language Processing is gaining increased attention for the Aspect Based Sentiment Analysis task due to its ability to provide fine-grained information. This paper introduces SBERTiment, a novel approach to perform Aspect Based Sentiment Analysis. The method extracts relevant topics along with their sentiments from the input text by using a 2-step pipeline. In the first step, a token classification model is used to identify the relevant aspect terms and their sentiments. In the second step, a Sentence-BERT embedding model maps each aspect term to a predefined aspect category. Our approach has been tested on benchmark datasets and has achieved scores that are comparable to the best-performing methods. The pipeline is also able to perform zero-shot classification, which means it can extract information in unseen domains without additional training. When evaluated on a dataset with unseen aspect categories, SBERTiment achieved the best score among benchmark approaches.
A Multimodal Analysis of a Key Opinion Leader’s Instagram Posts

In promoting a product on social media, KOL mostly utilize semiotic resources in creating aesthetic contents to attract the audience.However, the credibility, attractiveness, product suitability, and meaning transfer from KOL to the audience also play important roles in the process.This study analyses social media contents posted by KOL and scrutinizes the semiotic resources in the contents to see what strategy the KOL employ to gain purchase interests based on multimodal analysis.Qualitative research methods with multimodality analysis using the social semiotic theory of Kress and Van Leeuwen is used to reveal the meaning behind the semiotic resources.Nine pictures from KOL Instagram account were analysed.The result showed that on ideational level, KOL built trustworthiness of the audience.On the interpersonal metafunction, KOL built familiarity and similarity to the audience.Lastly, on textual metafunction level, KOL brought product match-up and product transfer element to the audience.To sum up, the research found that KOL employed multimodality metafunctions as a strategy to build customer's purchase interest.
Customer List Protection in Avoiding Unfair Business Competition Based on Law Number 30 of 2000 concerning Trade Secrets and Law Number 5 of 1999 concerning Prohibition of Monopoly Practices and Unfair Business Competition

Trade secrets are part of intellectual property, because the information contains economic value, however, the Trade Secret Act does not specifically state what can be classified as information with economic value, such as customer lists. Customer lists can be classified as information that can be protected by trade secret laws in Indonesia. Therefore, legal efforts are needed to protect this confidential information from acts of violation that can lead to unfair business competition
IMPROVING THE MECHANISMS FOR PREPARING FUTURE MUSIC TEACHERS FOR PEDAGOGICAL ACTIVITIES ON THE BASIS OF RHYTHM

The "Rhythm Teaching Method" is a system for teaching music, created by the Swiss music educator Emil Dal Croze at the beginning of the last century. It helps students perceive the rhythm of music, perform regular rhythmic movements of music-rhythm and beat, and the emotional nature of music is the movement of melody. It helps students imitate and create their own either fun or soothing movements so that they understand the music.
Pengembangan Mobile Learning Management System Berbasis Blended Learning pada Pembelajaran Masa WFH Pandemic Covid-19

The spread of the Coronavirus (Covid-19) outbreak, which has spread so quickly throughout the world, has caused anxiety and sadness for humanity to grieve. Starting from Wuhan China since the end of January 2020. The World Health Organization (WHO) has determined the phenomenon of this incident as a pandemic. A pademic occurs when an infectious disease spreads easily from human to human in various places in all corners of the world. Until the last two weeks, cases of Covid-19 outside China have increased thirteen times, causing concern for the world community. This research tries to provide solutions to the problem of how to develop and access LMS for learning anywhere and anytime (ubiquitos learning) both online and offline, using mobile devices that have limited connections. The M-LMS application being developed will be in the form of a prototype that takes the open source Moodle LMS model, and will focus on only a few important modules, namely the Subject Description module, Assignment module, Quiz module, Subject matter module. In its implementation, this research will be divided into three stages, namely pre-development data collection, development and implementation, and post-development data collection. The pre-development data collection is intended to obtain provision for a preliminary study of the core problems being faced, while the development and implementation stages focus on modeling software designs into diagrams and creating programming code to implement the designs that have been made. While the post-development data collection stages are for improving the applications made, drawing conclusions, and suggestions for further research topics.
Implementasi Algoritma Advance Encryption Standard dan Caesar Cipher pada Pesan Terenkripsi

The development of information technology allows many people to communicate at any time with various media, one of which is the exchange of messages. However, many people do not realize that there are security holes that are used by irresponsible parties to commit crimes such as theft of messages, intercepting messages, and changing the contents of messages. One technique for securing messages is cryptography. Cryptography is the science and art of keeping messages secure when messages are sent from one place to another. Several cryptographic methods that can be used are Advanced Encryption Standard and Caesar Cipher, because the Advanced Encryption Standard method has high security for message security while the Caesar Cipher method has the advantage of being fast in calculations. The combination of these two algorithms in making secret messages is implemented in the form of a mobile-based application to make it easier for users to make secret messages. The results of the encrypted messages formed are difficult to decrypt because they go through 2 stages of the encryption process so that the contents of important messages can be guaranteed confidentiality.
Penerapan Algoritma Backpropagation Untuk Text Recognition Yang Ditranslate Ke Bahasa Daerah

Every area of the community there is information, news, announcements or notifications through print media such as pictures, banners, posters and paper. However, there are many foreign languages (English) in the information which makes people not understand the language, because people only understand their own regional language (Sundanese) and the lack of media to help people understand the regional language (Sundanese) to a foreign language (English). Therefore, the author takes research on android-based text detection. Text detection or called Optical Character Recognition (OCR) is a system that can detect text into data files that can be processed in such a way. The method of collecting data or information in this study uses qualitative, the method for developing this system uses the waterfall method which has the advantage of being able to accelerate system development and using a backpropagation algorithm for text recognition. Backpropagation algorithm is an algorithm that can minimize errors, so as to make text detection more accurate. The results of the text detection accuracy are 97% and the error accuracy results are 3%, the text accuracy results are taken from a total of 14 samples, and 68 words. The results of text detection are affected by several cases such as the type of text font, the level of light in the image and the direction in which the photo or image is taken.
PERANCANGAN FILM PENDEK BERJUDUL “MUDAHNYA MEMBACA” TENTANG GERAKAN LITERASI NASIONAL PADA REMAJA

Cultural changes in Indonesia make the literacy level of Indonesian adolescents low so that teenagers have less interest in reading. Due to the lack of short film media that raises literacy in Indonesia, the author designed a short film with an interesting story composition and good audio-visual delivery so that the message he wanted to convey was conveyed. However, this research aims to design short films in the form of entertainment media for teenagers to realize the low reading level in Indonesia. This research method uses descriptive qualitative methods by collecting data obtained from the method of observation, literature study, and questionnaires. The design method used in this paper is the stage of development, pre-production, production, post-production and distribution. The results of this study are an explanation of the creative strategies used in making short films, and a discussion of the pre-production, production, and post-production processes in a short film entitled "Mudahnya Membaca".
Social reality in communication in rural areas

Social reality is the construction that arises from the interaction of individuals in a specific context, many students from rural areas operate in environments that can affect their integral development, a situation where emphasis is placed on promoting communication as a support element. The objective of the study is to determine the relationship between social reality and communication in rural areas. The methodology is mixed, integrated by qualitative and quantitative methods. The technique used was the student survey and documentary analysis. The study population was made up of 105 high school students from the Dr. Carlos Romo Educational Unit. The results reflect that in the rural area there are risk factors that can affect the learning and integrity of the students. It is concluded in the importance of communication between school and family to face these situations.
Watermarking and Encryption Technique to Avoid Data Leakage Based on Block Chain

Abstract: For end users to access various systems, services, and applications, multimedia data sharing is increasingly becoming an essential part of their daily lives. Cloud storage services in the real world frequently disclose data. In secure data transfer media, authenticity and copyright protection of multimedia content have always been a concern. As more people use the Internet and digital technologies, the issue has become more serious. Creating copyright protection, on the other hand, is more difficult and complicated. In proposed approach both Watermarking and Intermediary Re-encryption (PRE) approach used for productive mixed media content sharing. Watermarking is accustomed to concealing the data, for example, conceal privileged intel in computerized media like pictures. Data security is provided by encryption methods. In proposed work, secret key can be encoded utilizing encryption calculation with the assistance of key. The user's private key is then combined with the encrypted key information and can be embedded using LSB (Least Significant Bit) in the image or audio. The ECC Encryption algorithm can be used to encrypt an image or audio after secret information has been embedded. Finally, the embedded data verification process enables authorized users to extract the decryption key. When user information does not correspond with embedded information, illegal or unauthorized access can be identified. Furthermore, Block chain technology is used to safeguard these transaction details.
AI Text Summarization System

Abstract: Our research paper presents an AI text summarization system that utilizes GPT, a powerful language model, to generate concise and meaningful summaries of lengthy text documents. The system consists of four modules: User, Android Application, GPT API, and GPT server. The User interacts with the system through the Android Application, which serves as the user interface. The GPT API acts as the intermediary between the Android Application and the GPT server, which hosts the GPT model and handles the text summarization process. The system employs state-of-the-art natural language processing techniques to generate summaries while preserving contextual coherence and salient information. The system's summarization capabilities are evaluated using metrics such as Rouge and F1 scores, demonstrating its effectiveness in capturing key information from different types of text documents. The system's integration with Android platforms provides convenient access for mobile users, making it suitable for applications such as news summarization, document summarization, and content curation. The modular architecture of the system allows for scalability and flexibility, enabling future enhancements and extensions. Our AI text summarization system utilizing GPT presents a promising approach for automatically summarizing large volumes of text, providing users with time-saving and meaningful summaries. The system has potential applications in various domains and can serve as a foundation for further research in the field of text summarization and natural language processing