Reviewerly/exHarmony huggingface dataset:
CONNECTING DESIGN ITERATIONS TO PERFORMANCE IN ENGINEERING DESIGN

Abstract No matter a system's size, complexity, or domain, iterations are fundamental to its design process. However, there is a duality: iterations are both signs of usefully exploring the system's design space and failure to find an appropriate solution. This ambiguity means that we have not been able to connect teams’ iterating behavior to their design's performance, potentially obscuring a way to influence the design process. As such, our exploratory study unpacks the relationship between design iterations and performance. We observed 88 teams in the 2020 Robots to the Rescue Competition in rich detail. Using logs of 7,956 iterations on a Computer-Aided Design platform, we analyzed how high- and low-performing teams revised their submissions, searching for consistent differences in their behavior. We found significant differences in the iterations’ number, scale, and cadence between these groups of teams. These findings emphasized the correlation between certain iteration patterns and the success of a design: the best teams will likely revise differently than the worst ones. It also showed the importance of a fine-grained, time- dependent view of the design process to resolve open questions in the literature.
VISUAL BEHAVIOUR IN THE EVALUATION OF PHYSICAL AND VIRTUAL PROTOTYPES

Abstract Product development stages are typically characterized by different forms of representations and degrees of specification, which potentially affect user's perception and evaluation. These effects are worth investigating more closely also because of the growing relevance of new technologies such as Virtual Reality (VR) in the design field. The objective of this paper is to elucidate the mutual relations between forms of representation, visual behaviour, and people's evaluations. The focus is on differences between virtual and physical prototypes. In the illustrated experiment, participants visited a tiny house in an immersive VR (360° images acquisition). The results were compared with a past experiment where the physical prototype of the same product was similarly evaluated. The dwell times on Areas of Interest (AOIs) pertaining to the tiny house were compared and correlated to variables concerning subjective evaluations. The results show just a few similarities of visual exploration in terms of gazed AOIs. Substantial differences in terms of how the duration of gazing affects evaluations have been found too. The larger number of significant correlations between observations and evaluations in the virtual exploration emerged.
Building Situational Awareness of GDPR

Because previous academic research does not comment sufficiently on how the relevant content of the European Union (EU) General Data Protection Regulation (GDPR has been properly communicated to the organisations, or how the situational awareness (SA) of GDPR has been built in the organisations, this qualitative empirical research was regarded as a valuable approach for gathering authentic research material on the practical bases of this phenomena. The aim of this empirical case study (CS) is to develop a picture of what processes organisations use to build SA of the GDPR requirements. To guide the CS, we asked how the SA for decision-making was constructed and how it was perceived in organisations. The experiences of eight Finnish organisations showed that the organisations’ practices of building SA and their experiences with the quality and adequacy of SA differed. However, building SA proved to be a critical step for organisations in the overall process of meeting GDPR requirements. Especially the data coming from inside the organisation became very relevant in the SA process, because it supported decision makers to determine how the GDPR requirements should be implemented in the organisation. As a main contribution of this article, based on best practices shared by organisations a model of building SA was built. The proposed model is threefold and was constructed by combining the findings of an empirical CS analysis, the steps of the intelligence process, and the essential elements of the model of creating information security SA. The result is potentially beneficial for building situational understanding of any complex or ambiguous issue, especially in complex and digitalised technological areas, where combining information management with accurate and efficient decision-making is a common challenge. The results can be used by any party who is looking to build SA of an abstract issue in a complex environment.
Spreading Lies Through the Cyber Domain

The expansion of Information Operations (IO) over the past ten years has allowed individuals and groups to increase their sphere of influence on a global scale. Nation-state cyber threat actors have increased their presence on social media, building out false personas to influence large populations. This type of activity is difficult to stop due to the availability of social networks on the internet and the ease of creating false personas that can’t be directly attributed to the actor. IO activity has been observed with the Russian cyber activity during the 2016 U.S. Presidential elections and from Russian social media campaigns provoking extremist groups and attempting to cause physical harm, such as the 2017 campaign on Facebook to start a rally and a simultaneous counter rally in front of the Islamic Da’wah Centre of Houston. Although Russia has been observed leveraging this capability, they are not the only global actor in the cyber domain taking advantage of IO. Global threat actors have leveraged social media platforms and blogs to influence the global population and spread propaganda. This type of activity has been seen within traditional warfare using propaganda techniques. With the introduction of the cyber domain into warfare, there is an increased ability to communicate not only to one population but to the global community with the intent to manipulate the masses using IO. This paper examines the Cybersecurity Operations (CO) that have been observed utilizing IO and the psychological impacts they have had in successful campaigns against the United States. This paper argues that with increased influence capabilities in the cyber domain, individuals and groups will continue using IO to support tactical and strategic objectives. Through the available literature, this paper examines the impacts that IO has had on the United States through attempts to manipulate elections and create divides in the nation over the last ten years. This paper leverages the psychology of group processes to analyze the literature involving social media campaigns and the influencing of groups through the lens of social identity theory to provide new insight into mitigating and countering IO.
Students’ Application of the MITRE ATT&amp;CK® Framework via a real-time Cybersecurity Exercise

The MITRE ATT&amp;CK framework enables practitioners to understand and track cyber adversary behaviors. Concepts such as social engineering (SE) are not directly captured in current version of ATT&amp;CK as an individual technique, though the application of SE is relevant to many technical behaviors. Utilizing the ATT&amp;CK framework in an educational setting, specifically within a competition focused on SE, allows students to explore adversarial behavior through experiential learning and understand how SE is relevant within cybersecurity. The structure of the framework allows students to see and describe each behavior from the perspective of the adversary, motivating them to compile and question “why” and “how” each individual action contributes to the operational objectives. This paper shares students’ mappings of the ATT&amp;CK framework to playbooks they developed during a real-time SE penetration testing competition. Students were given numerous flags to pursue during the competition and this paper will share their playbooks and mappings to the ATT&amp;CK framework. This paper demonstrates that while someone with more knowledge and experience using the framework may map a SE case study differently than multidisciplinary students who are experiencing it for the first time, there is not a single correct way to map onto the matrix. Having students experience this mapping process allows them to understand the breakdown of an adversary’s behavior and interpret key tactics and techniques in a way that fits their mapping needs. This paper also demonstrates how a SE case study can be mapped onto the ATT&amp;CK framework despite SE not being the focus of the framework, and that SE uses tactics and techniques that are also relevant to technical cyberattacks. The authors hope to encourage more interdisciplinary cybersecurity education by sharing this experiential learning event.
Fast Video Classification based on unidirectional temporal differences based dynamic spatial selection with custom loss function and new class suggestion

With the new and emerging usages of faster video classification and identifications of new classes has pushed the research in this direction. Be it like similar video detection, percentage of similarity, anomaly detection or finding something trending in the current videos. Use of identification of objects in frames and features in surrounding has proven its advantages for video classifications specially for short video similarity detection. Use of temporally important objects and actions has also proved advantages for video classifications. However existing methods takes huge computation to train the model and does not detect the possibility of new classes. To address this scenario for faster video classification and reducing the training time and computation cost, we propose one directional temporal difference of frames and selectively selecting the spatial information with custom loss function. This allows faster training of the models and has a capability of detecting the new classes in the production videos. This new class detection will provide us new ways looking at video data and thus new kinds of platform conceptualization. Experiments were conducted in UCF and MSVD datasets. Validations were done using statistical methods like f-test etc. Validation for being faster in training are done using comparison of state of the art methods. The novelty of the work lies in the processing of video data for similarity detection in short video and new kinds of intelligence extraction. Which is generated from regression values for possible new classes of video.
Pengembangan Sistem Informasi Rekomendasi Wisata Kotawaringin Timur Berbasis Web MenggunakanMetode Item-Based Collaborative Filtering

Kotawaringin Timur Regency has many tourist objects and is rich in arts and culture spread in several sub-districts. Efforts to increase the promotion of KOTIM tourism continue to be carried out with the support of the application of technology, one of which is smart tourism. Prospective tourists tend to use internet technology to find information on tourist attractions they want to visit, rather than using the services of agents or travel agencies. Prospective tourists who want to visit East Kotawaringin need information and recommendations that are accurate, fast and economical. Recommendation systems have been widely used in the business world to help provide recommendations for various products. Therefore, a recommendation system is also used to generate tourist recommendations in East Kotawaringin using the Item-Based Collaborative Filtering method which utilizes rating data by visitors based on their travel experience. This system is in the form of a website where users can provide a rating for every tour in East Kotawaringin. Among the 35 tours in East Kotawaringin, as many as 7 tours are used in the recommendation system. These tours are Ujung Pandaran Beach, Red Kalap Gadur Waterfall, Jelawat Fish Tourism Icon, Balanga Roundabout, KOTIM Cultural Miniature Park, Satiruk Beach and Betang Tumbang Gagu. The result of the use of this recommendation system is the display of a tourist recommendation page that can be accessed by the user in the form of tours that have the highest rating equipped with detailed information on supporting tourism facilities.Keywords : Recommendation system, collaborative filtering method, tourism, East KotawaringinÂ
Penerapan Metode Forward Chaining Pada Sistem Pakar Identifikasi Penyakit Tanaman Kelapa Sawit

This application of an expert system for identification of oil palm plant diseases aims to make it easier for employees of the Oil Palm Research Center of Sungai Lilin Musi Banyuasin Business Unit to identify oil palm plant diseases based on the symptoms seen then this application provides solutions and appropriate control methods for plant diseases. the oil palm. The data used in this study are data on 10 diseases that exist in oil palm plants and 47 symptoms that appear. The method used in this expert system for identification of oil palm plant diseases uses the Forward Chaining method and the depth-first search tracking technique so that the tracking process will be tested from the first symptom to the last to get a conclusion in the form of the name of the disease along with its causes and how to control it. in this expert system, namely functional testing and expert system testing. Functional testing uses the Black Box method with the Equivalence Patitioning (EP) technique. System testing is carried out by system users, namely. Based on the results of testing with the Black Box method, the developed system functions as expected. Furthermore, expert testing is a test that compares the results of the system diagnosis with the results of expert diagnoses. Expertise test results also show good results. Furthermore, external testing, this test is by giving questionnaires to 35 respondents to get an assessment of the system. The results of the questionnaire (Expert) obtained a satisfaction value of 89.2%Keywords: Oil Palm Disease, Expert System, Forward Chaining Method, depth-first search
An analysis and a forecast of the cryptomarket based on the ARIMA model

The investment potential of the cryptocurrency market is examined through an analysis of the factors that lead to sharp fluctuations and a forecast of its volatility is made with the use of the CRIX index. This index is dynamic and its structure changes every three months, which provides detailed information on the volatility of the cryptocurrency market. The CRIX index includes the most liquid cryptocurrencies, which makes it a representative indicator of the cryptocurrency market as well as a reliable indicator when it comes to making market forecasts in the future. The Autoregressive Integrated Moving Average (ARIMA) model of forecasting dynamic rows used in the research is a barometer of the cryptocurrency market. The research follows the changes in the monthly price of the CRIX index from January 2015 to January 2019.
Artificial Intelligence Techniques in Egyptian Hotels: Current Status and Prospective

The pioneers of AI in the hotels when adoption artificial intelligence techniques, they will face different reaction concerns and questions from employees about this innovative artificial intelligence technique. This research aims to explore the current status and prospective to apply artificial intelligence techniques in Egyptian hotels, and their willingness to merge with employees' within Egyptian hotels and the challenges they face while using it, to achieve the research aim, an electronic questionnaire was designed and distributed to a random sample of employees' in Egyptian hotels, 400 valid responses were received and analyzed by SPSS V.29. The results indicated that Egyptian employees' do not have enough information about artificial intelligence techniques to accept the experience of artificial intelligence techniques in the future. This information includes advantages, risks, and artificial intelligence techniques substitution for the human element, Therefore, maybe employees' no acceptance and confidence in artificial intelligence techniques is the major obstacles to the adoption process of this innovative techniques in Egyptian hotels. Based on the results, some recommendations were suggested and directed to artificial intelligence techniques (AIT) professionals, hotel managers, hotels owners and the government and institutions responsible for revitalizing tourism and hotels in Egypt. One of the main recommendations was that the work in the concerned departments must be restructured and developed in terms of digital transformation, digitization, and service automation in terms of speed, goal achievement, ease of use, prices, and the institutional and intellectual readiness of department managers to provide their services in this context. Employees in the hotels industry should receive training in entrepreneurship, business administration, and innovation to link changes in smart technology and artificial intelligence techniques to the international hotel industry.
Improving the Performance of CNN by Using Dominant Patterns of CNN for Hand Detection

Many applications have used hand gestures for software interaction, image- and video-based action analysis, and behavioral monitoring. Hand detection is an essential step in the pipeline of these applications, and Convolutional Neural Networks (CNN) has provided superior solutions. However, CNN has similar features between hand and non-hand images, called non-dominant features. These features affect miss-classifications and long-time computation. Therefore, this paper focuses on the selection of dominant CNN features for hand detection, and it is our proposed method (DP-CNN) that selects the dominant feature patterns (DP) from the trained CNN features and classifies them using the Extreme Learning Machine (ELM) method. Evaluation results show the proposed method (DP-CNN-ELM), which can increase the accuracy and the F1-score of CNN. In addition, the proposed method can reduce the time computation of CNN in training and testing.
Spatio-temporal prediction for distributed PV generation system based on deep learning neural network model

To obtain higher accuracy of PV prediction to enhance PV power generation technology. This paper proposes a spatio-temporal prediction method based on a deep learning neural network model. Firstly, spatio-temporal correlation analysis is performed for 17 PV sites. Secondly, we compare CNN-LSTM with a single CNN or LSTM model trained on the same dataset. From the evaluation indexes such as loss map, regression map, RMSE, and MAE, the CNN-LSTM model that considers the strong correlation of spatio-temporal correlation among the 17 sites has better performance. The results show that our method has higher prediction accuracy.
Blockchain-based Trust, Transparent, Traceable Modeling on Learning Recognition System Kampus Merdeka

Kampus Merdeka is an evolution of education in Indonesia that accommodates various changes. The existence of a mechanism that includes various actors in it makes Kampus Merdeka have many new outcomes which must be recognized by all stakeholders who need it. Blockchain technology and smart contract offer the ability to build trust between all actors in the Kampus Merdeka activities with their transparent nature and reliable, immutable data storage capabilities. Every stage that occurs in it can be traced from upstream to downstream. This study aims to design an architectural model of a blockchain system for the learning recognition system Kampus Merdeka. It uses the analyticalstudy to identify the possible problems and the stakeholders involved and design the model solution proposed. As a result, it proposed the type of blockchain and the most suitable architecture for use in the learning recognition system Kampus Merdeka. In this study, the blockchain model is proposed as a mechanism for identifying and recognizing learning outcomes in the Kampus Merdeka environment more securely, challenging to modify, and traceable by all parties to ensure the authenticity of the learning outcomes. Furthermore, it can be recognized by all parties in it.
Using GPT-4 in Parameter Selection of Materials Informatics: Improving Predictive Accuracy Amidst Data Scarcity and 'Ugly Duckling' Dilemma

Materials informatics and cheminformatics struggle with data scarcity, hindering the extraction of significant relationships between structures and properties. The "Ugly Duckling" theorem, suggesting the difficulty of data processing without assumptions or prior knowledge, exacerbates this problem. Current methodologies don't entirely bypass this theorem and may lead to decreased accuracy with unfamiliar data. We propose using Open AI GPT-4 language model for explanatory variable selection, leveraging its extensive knowledge and logical reasoning capabilities to embed domain knowledge in tasks predicting structure-property correlations, such as the refractive index of polymers. This can partially overcome challenges posed by the "Ugly Duckling" theorem and limited data availability.
Zero-knowledge succinct non-interactive arguments of knowledge based on sets of polynomials

The paper discusses the basic principles of construction and the main types of zeroknowledge succinct non-interactive argument of knowledge (zk-SNARK) which is used in the model of a three-way insecure computing environment and based on sets of polynomials. A number of zk-SNARK cryptographic protocols with different algorithms for generating public parameters (Trusted Setup) are given, constructing succinct proofs of reliability calculations (Prover) and public/designated verification of proofs (Verifier). The cases of satisfying the feasibility of discrete functions (arithmetic/ Boolean circuits) using different polynomial sets are presented in quadratic arithmetic programs (QAP), square arithmetic programs (SAP), quadratic span programs (QSP), square span programs (SSP), quadratic polynomial programs (QPP), etc., also the use of authenticated data are described. The cryptographic transformations needed to build zk-SNARKs based on symmetric and asymmetric hash functions, exponential knowledge problems, digital signatures, homomorphic encryption, bilinear pairings based on elliptic curves, etc. are presented. Examples of multilateral verifiable calculations based on zk-SNARK are given.
Ontology, Oniontology, and Creativity

This short chapter introduces first the global architecture of ontology of music, which this book is going to use extensively as an initial classifying perspective.
Sistem Pendukung Keputusan Menggunakan Fuzzy Logic Tahani Untuk Penentuan Golongan Obat Sesuai Dengan Penyakit Diabetes

Abstract: Consuming foods and drinks that contain a lot of Glucose, allows the risk of developing Diabetes. Diabetes is a disorder of the metabolic system of carbohydrates, proteins, and fats in the body due to disturbances that occur in insulin secretion that cause a decrease in insulin performance. Please be aware that Diabetes can lead to death, blindness, heart disease and kidney failure. According to data from the International Diabetes Federation in 2019, Indonesia is ranked 7th, where 10.7% of the total population suffers from Diabetes. For this reason, we are interested in making fuzzy logic on determining drug classes in diabetes based on the patient's blood glucose levels. The purpose of this study is to prove that fuzzy logic can be a solution for classifying drugs in diabetic patients. We'll create a fuzzy Logic that uses The Hard Way graphic model on each variable membership function. For the fuzzy manufacturing process toolbox we used a Jupyter Notebook on anaconda Navigator.The limitation of the study is the use of doses on drugs. This research can contribute to the field of health.&#x0D; Keywords: Fuzzy Logic, Artificial Intelligence, Diabetes, Jupyter Notebook, Tahani Method.