Reviewerly/exHarmony huggingface dataset:
Adversarial Distillation Adaptation Model with Sentiment Contrastive Learning for Zero-Shot Stance Detection

Abstract Zero-shot stance detection is both crucial and challenging because it demands detecting the stances of previously unseen targets in the inference stage. Learning transferable target invariant features effectively from training data is crucial for zero-shot stance detection. This paper proposes an adversarial adaptation approach for zero-shot stance detection, which applies an adversarial discriminative domain adaptation network to transfer knowledge efficiently. Specifically, the proposed model applies knowledge distillation to prevent overfitting the destination data and forgetting the learned source knowledge. Moreover, stance contrastive learning is applied to enhance the quality of feature representation for superior generalization, and sentiment information is extracted to assist with stance detection. The experimental results indicate that our model performs competitively on two benchmark datasets.
A Novel Customized Crypto Token Based Approach on Blockchain

The world is moving from Web 2.0 to Web 3.0, leading to a rise in Blockchain technology and its applications like crypto-currencies, supply chains, and IOT. Tokenization of digital assets is necessary, but standards are hard to obtain due to their diverse nature. In this work, a custom crypto token has been created on an existing Internet Computer Blockchain. Each token has a unique Value, Owner, and Symbol. This token has been implemented using Motoko at backend and React at frontend. Using Motoko language, smart contract has been established to govern the behavior of our token on the IC blockchain. The decentralized application manages the tokens, serving as an important digital asset in online trading. The token's unique properties and seamless integration provide a more accessible and secure platform for transactions, making it an important advancement in the field.
Grouping Education Students at Pusdikjas Institutions of The TNI-AD's Disjasad Using the K-Means Clustering Method

The Military Physical Education Center, known by the abbreviation Pusdikjasmil, is an implementation body that is directly under the TNI AD. Pusdikjas has the task of providing education in the military physical field to support the duties of the Indonesian Army. At the Pusdikjas agency, student data is obtained from the addition of students attending education each year. The process of admitting students to Pusdikjas institutions produces abundant value data and is repeated every year. By using data mining techniques, abundant student data can be grouped and analyzed to find information that is useful for Pusdikjas agencies. The algorithm used in this research is K-means clustering. The data used from 2018 - 2022 was 1428 data on coaching education students, with the variables used being NRP, attitude &amp; and behavior, knowledge &amp; and skills, and physical. Processed using the JavaScript programming language, this research produces optimal clusters, namely 2 clusters. cluster 1 with a total data of 751 students in cluster 1 describes a very satisfactory category of students, and cluster 2 with total data of 676 students. Cluster 2 describes the satisfactory student category. As for testing the quality of the clusters contained in the system using silhouette with results reaching 0.6543347543126016.
Transparent Security Method for Automating IoT Security Assessments

People and businesses are dependent on the security of the Internet of Things (IoT). Vendor-independent security assessment and certification intends to provide an objective view of the security of an IoT product. Unfortunately, the assessment is often done for a single version and configuration of the product and usually does not yield data to reproduce the assessment. We present the Transparent Security Method, in which product security is described by a machine-readable security statement. A security statement can be verified using tools for automated assessment, which can be repeated for different product versions and configurations to cover the product life-cycle. As a case study, we create an entry-level security statement for a real IoT product and do the verification using common security tools. In the study, 12 out of 15 security claims are verified fully or partially by automation. A security statement can be used in certification or labeling to speed up security assessment, especially in re-certification. Tool-based verification discourages inflated security claims, as they can be scrutinized. Eventually, this should drive product security improvements, as products without security statements are less attractive.
Chosen Ciphertext Security for Blind Identity-Based Encryption with Certified Identities

Identity-based encryption (IBE) is one of the important extension of public-key encryption (PKE) which can use identities, such as email addresses or phone numbers, as public keys. Due to this aspect, IBE can reduce the cost of maintaining public key infrastructure. However, it has a special issue called the “key escrow problem”, which means that the key generation center (KGC) can decrypt all ciphertexts and get all messages in them since it inherently has all user public/private key pairs. To tackle this problem, Emura, Katsumata, and Watanabe recently proposed a variant of IBE called blind IBE with certified identities by introducing identity certifying authority (ICA). Their constructions are based on pairing or lattice and secure against chosen plaintext attacks. In this paper, we introduce security against chosen ciphertext attacks (CCA security) for blind identity-based Encryption with certified identities. More precisely, we formalize definitions of 3-types security against chosen ciphertext attacks (security against corrupted users, KGC, and ICA) for blind IBE with certified identities. Then, we propose the first blind IBE with certified identities schemes. Roughly, we provide a generic construction by applying the Fujisaki-Okamoto transformation to previous CPA secure Blind IBE with certified identities schemes. Through our generic construction, we obtain two instantiations based on pairing or lattice. Finally, we implement our pairing based scheme and demonstrate that it is practically efficient compared with existing schemes.
A Source Hiding Protocol for Cooperative Intelligent Transportation Systems (C-ITS)

In multi-user scenarios where requirements of privacy are high, we propose a modular, anonymous and source hiding protocol for sending signed messages ( $$\textsf{SHP}$$ ). By combining existing cryptographic building blocks, encryption and ring signature schemes, we are able to mitigate traceability and routing attacks on otherwise anonymous participants. Moreover, our protocol is secure without any trusted third party. Our construction is provably secure in our security model, where the adversary controls the communication channels. We illustrate our construction via a Vehicle Ad-Hoc Network (VANET) use-case, where the $$\textsf{SHP}$$ provide source anonymity to cluster of vehicles exchanging signed safety and security messages to road-side units within Cooperative Intelligent Transportation Systems (C-ITS).
Decision Support System For Determining Food Menu Using Analytical Hierarchy Process (Ahp) Method

Food menus in the culinary and restaurant industries often involve various complex features, such as food origin, food category, diet category, ingredients, and time. This study aims to develop a Decision Support System (DSS) based on the AHP method to assist chefs, restaurant managers and food business owners in compiling a diverse menu, meeting nutritional needs, taking into account certain preferences and limitations, and creating a pleasant dining experience. The Analytic Hierarchy Process (AHP) method can be used as a tool in making more effective and structured decisions. The results of this study indicate that the Analytic Hierarchy Process (AHP) method succeeded in producing relative weights for each criterion and sub-criteria, thus enabling priority in preparing food menus. In testing, this system is able to provide the best recommendations based on global priority values for certain types of food, which are expected to increase the variety and quality of food menus and meet consumer preferences and needs. Through experiments conducted using a Decision Support System, a decision model is formed that determines the priority for the weight of all criteria and alternatives. The results show preferences in the process of determining food menus by producing Cold Coffee with a value of 0.30 (29%), Biscuit Dough Donuts with a value of 0.25 (25%), Chicken Dimsum with a value of 0.21 (21%), Succotash with a a value of 0.15 (15%), and Toffee Banana with a value of 0.10 (10%).
Image Memory on Pyramidal Neural Networks of Fast Learning

the application of backward-oriented pyramidal neural networks of fast learning for the implementation of image memory elements is considered. Networks of the class under consideration are representable by linear operators, have a self-similar structure, and are a special case of the fast Fourier transform algorithm. Methods of topological construction of pyramidal networks are given. It is proved that pyramid networks belong to the category of networks with a deep degree of learning.
Foundation of the SuperHyperSoft Set and the Fuzzy Extension SuperHyperSoft Set: A New Vision

We introduce for the first time the SuperHyperSoft Set and the Fuzzy and Fuzzy Extension SuperHyperSoft Set. Through a theorem we prove that the SuperHyperSoft Set is composed from many HyperSoft Sets.
Application Deployment and Performance Measurement in Serverless Cloud for Microservices

The effectiveness of Cloud technology relies heavily on its ability to perform at a high level. To measure this performance, it is necessary to conduct a performance evaluation based on specific aims and applications and assess the capabilities of the cloud services. In the case of enterprise applications deployed on the cloud, the service provider must consider the application's deployment model, security, networking, and operational constraints. This evaluation involves identifying benchmarks, configuring the system, running tests, analyzing results, and providing recommendations. There are various performance metrics that can be applied to different aspects of the cloud services to evaluate their performance. The figures below display data on resource utilization and the impact of the load on the application. Microservices offer organizations the opportunity to deploy applications on the cloud by providing web service functions and an architecture that enables scaling and updating of applications with minimal inconsistency. Through public cloud technology such as Amazon Web Services, organizations can deploy secure and valuable applications to the cloud.
Smart Glasses for Blind People Using Obstacles Detection

Object detection is a new technique that involves identifying specific instances of semantic objects (such as people, structures, or cars) in images and videos, and is the goal of object detection, a branch of computer vision and imaging. Deep learning has made great strides in object detection and is now widely used in practical applications including driving, safety and object tracking. Convolutional Neural Networks (CNNs) are often used to extract features from images and then use object detection techniques such as bounding box regression or unlimited maximum probability to find objects and determine their groups. Object detection can be used to assist blind people by providing them with audio feedback about the environment around them. This can be done through wearable devices equipped with cameras and object detection algorithms. The device can take images of the environment and use object detection to identify objects such as people, obstacles, and text. The device can then provide audio feedback to the blind person, such as “There is a person in front of you” or “There is a curb ahead”. This technology can help blind people navigate their environment more safely and independently.
Demystifying Hyperledger Fabric Framework for Distributed Ledgers and Approach to Evaluate Its Performance

Hyperledger Fabric is a highly advanced blockchain framework for enterprise level organizations. Hyperledger Fabric is designed to establish either permissioned or private blockchain networks. This means that only endorsed members and parties are able to join the network, and ledgers are only accessible to those parties that have been granted permission to access them. Such environment is ideal for organizations that needs a high degree of privacy and security in their ledgers. Another key feature of Fabric is its endorsement policy, which permits to define flexible and customizable transaction validation rules. As a result, organizations can define which members are required to sign off on transactions before they can be added to the blockchain. The Fabric framework also includes a numeral other features that make it suitable for enterprise-level use such as support for multiple consensus algorithms, modular architecture and private data collections that permits integration of external services. Its modular nature, support for several other consensus algorithms and customizable endorsement policy make it a suitable for businesses looking to setup their own blockchain networks. This research work is disclosing the practical approach to implement Hyperledger Fabric along with the approach to evaluate its performance primarily throughput and latency.
TelePi: an affordable telepathology microscope camera system anyone can build and use

Abstract Telepathology facilitates histological diagnoses through sharing expertise between pathologists. However, the associated costs are high and frequently prohibitive, especially in low-resource settings, where telepathology would paradoxically be of paramount importance due to a paucity of pathologists. We have constructed a telepathology system (TelePi) with a budget of &lt; €120 using the small, single-board computer Raspberry Pi Zero and its High-Quality Camera Module in conjunction with a standard microscope and open-source software. The system requires no maintenance costs or service contracts, has a small footprint, can be moved and shared across several microscopes, and is independent from other computer operating systems. TelePi uses a responsive and high-resolution web-based live stream which allows remote consultation between two or more locations. TelePi can serve as a telepathology system for remote diagnostics of frozen sections. Additionally, it can be used as a standard microscope camera for teaching of medical students and for basic research. The quality of the TelePi system compared favorable to a commercially available telepathology system that exceed its cost by more than 125-fold. Additionally, still images are of publication quality equal to that of a whole slide scanner that costs 800 times more. In summary, TelePi is an affordable, versatile, and inexpensive camera system that potentially enables telepathology in low-resource settings without sacrificing image quality.
SVFLC: Secure and Verifiable Federated Learning With Chain Aggregation

As many countries have promulgated laws to protect users’ data privacy, how to legally use users’ data has become a hot topic. With the emergence of federated learning (also known as collaborative learning), multiple participants can create a common, robust, and secure machine learning model while addressing key issues in data sharing such as privacy, security, accessibility, etc. Unfortunately, existing research shows that federated learning is not as secure as it claims, gradient leakage and the correctness of aggregation results are still key problems. Recently, some scholars try to address these security problems in federated learning by cryptography and verification techniques. However, there are some issues in this scheme that remain unsolved. First, some solutions cannot guarantee the correctness of the aggregation results. Second, existing state-of-the-art federated learning schemes have a costly computational and communication overhead. In this paper, we propose SVFLC, a secure and verifiable federated learning scheme with chain aggregation to solve these problems. We first design a privacy-preserving method that can solve the problem of gradient leakage and defend against collusion attacks by semi-honest users. Then, we create a verifiable method based on a homomorphic hash function, which can ensure the correctness of the weighted aggregation results. Besides, the SVFLC can also track users who encounter calculation errors during the aggregation process. Additionally, the extensive experiment results on real-world datasets demonstrate that the SVFLC is efficient, compared with other solutions.
Processing &amp; Production of Scientific Data: An historical marriage, now in a permanent divorce?

Once upon a time, the same person generated and then processed data arising from an original scholarly pursuit. For example: an early modern astronomer predicted sky phenomena based on their observation; a ship's navigator plotted a course using readings from their instruments; and a natural philosopher uncovered laws of nature from basic physical measurements. These measuring and calculating skills would have been studied in tandem before being applied, thus ensuring their wedded deployment. And fittingly, when any results became known, "who got credit" would have been clear.
Non-Institutional and Grassroots Prescriptivism for Slovenian

The current SP 2001 with supplementary infrastructure (Fran portal, Jezikovna Svetovalnica) has the highest normative authority for the Slovenian speech community. Still, non-institutional usage guides have been appearing in Slovenia, addressing the issues of complicated rules and protracted searches. Since they do help speakers of Slovene resolve some usage problems, two forms of prescriptivism for Slovenian were surveyed: non-institutional prescriptivism and grassroots prescriptivism. Non-institutional prescriptivism is an anglophone invention appearing mostly in book form and is typically based on an author’s sense of style. Grassroots prescriptivism is a relatively novel phenomenon which consists of internet users interacting to solve one another’s usage problems without necessarily being language professionals. For Slovene, it was discovered that several books were published that resemble usage guides. Three have been researched and it was discovered that Pravipis by Aleksandra Kocmut most closely resembles a prototypical usage guide. Interestingly, the authors all cede normative authority to SP 2001, an institutional guide. Most of grassroots prescriptivism is carried out in specific Facebook groups in which their members rapidly respond to posted usage problems. Among other things, it was discovered that grassroots prescriptivism responds to usage problems more quickly while institutional guidace does so in a more reliable and informed manner.
Using Scramble Game To Develop English Vocabulary Of Grade Seven Students At SMP Negeri 1 Pematangsiantar

The objectives of this research was find out whether the using scramble game to develop English vocabulary of grade seven students at SMP Negeri 1 Pematangsiantar. This research used quantitative research with a quasi-experimental research design. The population of this study was the VII grade students of SMP Negeri 1 Pematangsiantar with a total of 352 students. The sample of this study was divided into two classes, class VII-7 as the control class consisting of 32 students who used the conventional media and class VII-8 as the experimental class consisting of 32 students who used scramble game. Data collection instruments used multiple choice for pre-test and post-test. The data analysis technique used in this research is SPSS version 21, by conducting normality test, homogeneity test and hypothesis test. In the post-test results for the experimental class, the average was 79.68, while in the control class the average was 74.53. Based on the hypothesis test using an independent test, it shows a sig (2-tailed) value of 0.016 &lt; 0.05, then Ha is accepted and Ho is rejected. Therefore, it can be concluded that the use of scramble game can improve the English vocabulary of grade seven students at SMP Negeri 1 Pematangsiantar.
Hibriditas Budaya pada Bangunan Masjid Azizi, Peninggalan Sejarah Kesultanan Langkat

&lt;p&gt;This article describes the historical heritage in the form of material during the Sultanate of Langkat with a case study of the Azizi Mosque. Historical heritage is an important instrument in tracing an event and as concrete evidence of the history that occurred as well as the Azizi Mosque. Writing and documenting historical relics is important to maintain the preservation and historical value as a form of strength and national identity. Azizi Mosque is a historical relic of the Sultanate of Langkat which was established on 12 Rabiul Awal year 1320 H. coinciding with June 13, 1902 AD. The method used is archaeological research with stages such as data collection, data processing, data analysis and interpretation or interpretation of data. This research uses archaeological analysis in the form of morphological, decorative and contextual analysis. The author's data collection technique is through interviews, literature studies and direct observation to the location of the object of research. Based on the research conducted by the author, it is found that the Azizi Mosque has a wealth of architectural art with an interesting and magnificent cultural hybridity that seems to be a new identity for the sultanate's mosque at that time. Various architectural arts from local and outside are integrated into this building. Among them are architectural arts from Europe, Turkey, Arabia, India, China and Malay. The Azizi Mosque is a true testament to the glory and existence of the sultanate.&lt;/p&gt;
Job crafting as a tool to increase motivation and employee engagement

The subject of the research in the article is the tools of motivation and involvement of personnel in the organization. The study examines the theoretical aspects of motivation and involvement of personnel, determines the relevance of self-development of the employees’ potential of modern organizations. The development perspective of personnel basic skills in the 21st century is shown, which includes, in addition to the traditionally necessary professional hard skills and soft skills, also existential skills containing the ability to self-development and self-learning. The concept of job crafting as a tool for staff self-development is proposed, the main idea of which is that in order for an employee to be involved in the workflow, it is necessary to reconsider the attitude to work according to the following criteria: the approach to completing tasks, the nature of interactions with colleagues, thinking when doing work. A comparative analysis of job crafting and the concept of kaizen (which is used when an organization needs changes to increase competitiveness) is carried out. Job crafting is relevant as a motivational tool when employees initiate changes themselves and form their own working environment in such a way that it meets their individual needs by adjusting the prevailing requirements of work and resources.