Reviewerly/exHarmony huggingface dataset:
HePCo: Data-Free Heterogeneous Prompt Consolidation for Continual Federated Learning

In this paper, we focus on the important yet understudied problem of Continual Federated Learning (CFL), where a server communicates with a set of clients to incrementally learn new concepts over time without sharing or storing any data. The complexity of this problem is compounded by challenges from both the Continual and Federated Learning perspectives. Specifically, models trained in a CFL setup suffer from catastrophic forgetting which is exacerbated by data heterogeneity across clients. Existing attempts at this problem tend to impose large overheads on clients and communication channels or require access to stored data which renders them unsuitable for real-world use due to privacy. In this paper, we attempt to tackle forgetting and heterogeneity while minimizing overhead costs and without requiring access to any stored data. We achieve this by leveraging a prompting based approach (such that only prompts and classifier heads have to be communicated) and proposing a novel and lightweight generation and distillation scheme to consolidate client models at the server. We formulate this problem for image classification and establish strong baselines for comparison, conduct experiments on CIFAR-100 as well as challenging, large-scale datasets like ImageNet-R and DomainNet. Our approach outperforms both existing methods and our own baselines by as much as 7% while significantly reducing communication and client-level computation costs.
SLACK: Stable Learning of Augmentations with Cold-start and KL regularization

Data augmentation is known to improve the generalization capabilities of neural networks, provided that the set of transformations is chosen with care, a selection often performed manually. Automatic data augmentation aims at automating this process. However, most recent approaches still rely on some prior information; they start from a small pool of manually-selected default transformations that are either used to pretrain the network or forced to be part of the policy learned by the automatic data augmentation algorithm. In this paper, we propose to directly learn the augmentation policy without leveraging such prior knowledge. The resulting bilevel optimization problem becomes more challenging due to the larger search space and the inherent instability of bilevel optimization algorithms. To mitigate these issues (i) we follow a successive cold-start strategy with a Kullback-Leibler regularization, and (ii) we parameterize magnitudes as continuous distributions. Our approach leads to competitive results on standard benchmarks despite a more challenging setting, and generalizes beyond natural images.
Rewriting the Script: Adapting Text Instructions for Voice Interaction

Voice assistants have sharply risen in popularity in recent years, but their use has been limited mostly to simple applications like music, hands-free search, or control of internet-of-things devices. What would it take for voice assistants to guide people through more complex tasks? In our work, we study the limitations of the dominant approach voice assistants take to complex task guidance: reading aloud written instructions. Using recipes as an example, we observe twelve participants cook at home with a state-of-the-art voice assistant. We learn that the current approach leads to nine challenges, including obscuring the bigger picture, overwhelming users with too much information, and failing to communicate affordances. Instructions delivered by a voice assistant are especially difficult because they cannot be skimmed as easily as written instructions. Alexa in particular did not surface crucial details to the user or answer questions well. We draw on our observations to propose eight ways in which voice assistants can ``rewrite the script'' -- summarizing, signposting, splitting, elaborating, volunteering, reordering, redistributing, and visualizing -- to transform written sources into forms that are readily communicated through spoken conversation. We conclude with a vision of how modern advancements in natural language processing can be leveraged for intelligent agents to guide users effectively through complex tasks.
Sistem Informasi Ekstrakurikuler Berbasis Web Pada SMK Muhammadiyah Somagede

Extracurricular is an activity aimed at developing potential, talents and abilities outside the academic field that must be followed by students.The extracurricular registration process at SMK Muhammadiyah Somagede is currently still using conventional methods in the form of paper forms.In addition to this, the process of recapitulating student data from each extracurricular activity, which is still done manually. This process takes a long time because you have to check each formula sheet and also have a lot of risk of errors. It should be more efficient if it is done online using an information system that can simplify, save time and cost in the extracurricular selection process and the student data recapitulation process. The purpose of this system is the creation of an Extracurricular Information System that can speed up and make it easier for grade 10 and 11 students to make selections and make it easier to manage registrant data for each extracurricular.The benefit of this system is that it helps the Student Staff of SMK Muhammadiyah Somagede to process extracurricular information data and process student data and recap data on extracurricular registrants, for Class 10 and 11 students of SMK Muhammadiyah Somagede it is easier to get extracurricular information and the extracurricular selection process, for those in charge of extracurricular activities at SMK Muhammadiyah Somagede Makes it easier to get information on the schedule of extracurricular activities that are taught and student registration data, for the Principal of SMK Muhammadiyah Somagede and PCM Somagede Staff Makes it easier to get information from extracurricular activities and find out the comparison graph of the extracurricular activities that are most chosen by students, and as a medium to introduce STIKOM Yos Sudarso Purwokerto to the general public. In this case the system development method uses a prototype model. Based on the results of the benefit test, the average value of the Correctness value is 99.37%, the Usability value is 97.5%, the Efficiency value is 97.5% and the Reliability value is 99.12%. From the results of the benefit test, it can be concluded that the Web-Based Extracurricular Information System at SMK Muhammadiyah Somagede can be used to speed up the recapitulation process of extracurricular registrants
CLIP2Protect: Protecting Facial Privacy using Text-Guided Makeup via Adversarial Latent Search

The success of deep learning based face recognition systems has given rise to serious privacy concerns due to their ability to enable unauthorized tracking of users in the digital world. Existing methods for enhancing privacy fail to generate naturalistic images that can protect facial privacy without compromising user experience. We propose a novel two-step approach for facial privacy protection that relies on finding adversarial latent codes in the low-dimensional manifold of a pretrained generative model. The first step inverts the given face image into the latent space and finetunes the generative model to achieve an accurate reconstruction of the given image from its latent code. This step produces a good initialization, aiding the generation of high-quality faces that resemble the given identity. Subsequently, user-defined makeup text prompts and identity-preserving regularization are used to guide the search for adversarial codes in the latent space. Extensive experiments demonstrate that faces generated by our approach have stronger black-box transferability with an absolute gain of 12.06% over the state-of-the-art facial privacy protection approach under the face verification task. Finally, we demonstrate the effectiveness of the proposed approach for commercial face recognition systems. Our code is available at https://github.com/fahadshamshad/Clip2Protect.
Sistem Informasi Administrasi Keuangan Siswa Berbasis Web Pada SMA Yadika Kota Jambi

Jambi City Yadika High School is a special institution to provide educational knowledge to students under the supervision of teachers located in Jambi City, having its address at Jalan Maya, South Lingkar, South Jambi. The process of processing school financial administration often experiences various obstacles. Constraints that often occur include student payments such as Education Development Donations as well as payment of building fees and school financial reporting. The benefits of the research carried out are improving the quality of service to students, managing student financial administration data to be relatively faster and accurate, and student finance administration can be known simply by accessing a web page, without having to come to the administration section. The purpose of the research is to design the Analysis and Design of a Web-Based Student Financial Administration Information System at Yadika High School Jambi City. The method uses a prototype. The results of the research are data processing applications that have been designed to facilitate data processing and report generation. Suggestions before using the application, it is better for the user to get training first so that it can be operated optimally.
Prediksi Masa Studi Mahasiswa Unama Jambi Menggunakan Metode Algoritma C4.5

Every year the number of students at The University of Dynamics of the Nation jambi is always increasing but the students who graduate are different from the number of students who enter. Therefore, the author conducts a data mining analysis on student data so that it can be used by academic supervisors to find out the graduation status of students and as a warning so that students can graduate on time so as to reduce the number of delays in graduation. The author uses student data in 2016 and 2017 as training data and 2020 as testing data as many as 120 training data and 109 testing data and has carried out the data cleaning and attribute selection process using the forward selection method. In conducting the analysis, the author used the Tools Weka tool. The author uses the C4.5 algorithm method with 12 attributes, but there are 4 attributes that are most influential after selecting data using the forward selection method on WEKA. In this case, the author uses 4 test options, namely 5 Fold Cross Validation, 10 Fold Cross Validation, 70% Percentage Split, and 80% Percentage Split. The C4.5 Algorithm method produces the largest accuracy value in the training data, namely 5 Fold Cross Validation by 92.5% and in the testing data by 100%.
Optimal decision for the satellite navigation system under navigation countermeasures based on spoofing effect evaluation results

Abstract This paper realizes the evaluation and optimal decision of the spoofing effect on the global navigation satellite system (GNSS) under navigation countermeasures based on the game perspective, to overcome the limitations of current relevant research findings and maintain the secure application of the GNSS and maximize its defensive benefit during countering. An evaluation system based on the perspective of the game between spoofing strategies and defense measures is created to build the benefit matrix, which can solve the limitation in building a benefit matrix (the vital element in a game) that conforms to the confrontation‐oriented GNSS spoofing properties. Based on the built benefit matrix, a method integrating the grey expert‐based system with the hybrid evolutionary method is developed to realize quantifying the matrix and further make the optimal decision stably, which can solve the uncertainties of qualitative indices during quantifying and no global optimal solution for benefit equations deduced from the quantified matrix. Finally, based on various judgment principles, simulation and testing results verify the effectiveness of the obtained results and the superiority of the proposed method by comparing the results solved by the method in this paper with the results obtained by other decision‐making processing methods.
RESILIENCE RESOURCES OF FUTURE PSYCHOLOGISTS VOLUNTEERING DURING MARTIAL LAW

Abstract: the article is devoted to researching the resources of resilience of future psychologists engaged in volunteer activities in the field of providing psychological assistance to victims of military aggression. The research is focused on studying the level of resilience and coping mechanisms that ensure the ability of future psychologists to adapt in conditions of constant emotional stress. 50 future psychologists aged 18-22 took part in the study, who were divided into two groups depending on the presence or absence of volunteer experience during martial law. It was found that future psychologists who are engaged in volunteer activities mainly rely on cognitive and behavioral coping and do not use emotional coping enough, which may indicate their unwillingness to work with their own emotions.
Theory-Based Mandarin Learning Anthropolinguistics in Indonesia

Language is the most important element to explore the deepest consciousness contained in a culture. Some Mandarin learning is still lacking in exploring the culture. If we learn a language not only in theoretical learning, but it is also important to learn the culture of the language because these two things can not be separated so that both go hand in hand. All that is contained in a language certainly contains the culture of origin of the language, on the contrary, culture is one of the forming elements of language. One of the scientific trends of macrolinguistics in studying the issue of Mandarin learning in Indonesia is anthropolinguistics. In anthropolinguistics it is the result of the integration between the sciences of culture and language. Anthropolinguistics has three theories: performance, indexicality, and certainty. This article examines how anthropolinguistic theory plays a role in Chinese language learning. The research method used by researchers in this study is a qualitative approach that is presented descriptively with articles and books that have relevance to the discussion. The research design uses the systematic literature review method with the analysis step starting with identifying, evaluating, and interpreting all relevant research results related to anthropolinguistic phenomena in learning Mandarin in Indonesia. Then the library results are processed systematically, objectively, and comprehensively. Based on the literature review with anthropolinguistic theory that has been done, it can be concluded that learning Mandarin in anthropolinguistics should consist of grammatical, communicative and pragmatic competencies and language skills.
Study on Subject Knowledge Building Based on Discipline Discourse Corpus: From the Perspective of Grammatical Metaphor

The purpose of this paper is to explore the distribution characteristics of different grammatical metaphor types in different types of knowledge. Six textbooks of geophysics, writing, geography, management, biochemistry, and history were selected as samples to build their own electronic textbook corpus, and by means of word suffix search and related word form search, it was found that: (1) there are certain distribution characteristics of grammatical metaphor usage types in subject textbooks of different knowledge types. Experiential grammatical metaphors are all used most frequently; interpersonal grammatical metaphors are used more than logical grammatical metaphors in the process of constructing technical knowledge; logical grammatical metaphors are used more than interpersonal grammatical metaphors in the process of constructing educational or daily knowledge. (2) The hierarchical nature of the representation, presentation, and organization of knowledge differs among knowledge types and it is inferred that technical knowledge is highest hierarchy knowledge type. This study also has some implications for the development and evaluation of teaching materials.
Enhancing the Security of Cryptographic Systems by Pseudo-Random Number Generation Algorithms

Pseudo-random numbers play an indispensable role in the design of encryption systems, such as public and private key flow. The efficiency of crypto systems is directly proportional to the quality of the secret key generated using a random number generation algorithm. In this paper, the efficiency and applicability of a modified Linear Congruential Generator (LCG) type algorithm will be presented to increase the rate of occurrence of numbers and tend as much as possible to a truly random number. Moreover, it will be integrated into a graphical interface, which can later be integrated into the security of a larger application or even a website.
Artificial Intelligence to Counter Cyber-Terrorism

This paper discusses the role of disruptive and innovative technologies for countering the spread of terrorist online content (TCO). In particular, it focuses on the use of Artificial Intelligence (AI) in support to Host Service Providers (HSPs) and Law and Enforcement Agencies (LEAs). The violent and terrorist content is more and more disseminated online taking advantages of the opportunities offered by Internet. The diffusion of terrorist propaganda has a negative impact on the civil society and poses several risks. For this reason, the European institutions published in 2021 the Regulation (EU) 2021/784 to address the misuse of hosting services for the dissemination to the public of TOC. It regulates the measures to be applied by HSPs and Member States’ authorities in order to identify and ensure the quick TOC removal and to facilitate cooperation with each other and Europol. In order to be compliant with these dispositions, AI-based disruptive technologies can provide LEAs and HSPs, especially the small and micro-ones, a concreate support. The implementation of the Regulation and the use of AI technologies have legal and ethical implications that have to be considered. The paper is based on the work and preliminary research conducted in the framework of the European funded project ALLIES, “AI based framework for supporting micro and small Hosting Service Providers (HSPs) on the report and removal of online terrorist content”, Grant Number 101080090.
A Signal Theory Model for Security Monitoring using CheckMK

Continuous monitoring of intelligent systems is used to analyze data and text from various sources. They usually monitor things such as risk, controls, opportunities, competition, and other concerns. While there exists literature that provides information on the capabilities of this kind of system, there has been a limited theoretical development in this field. The information sources monitored by these systems provide signals related to events, activities, or issues. However, selecting the appropriate information sources is not a simple task, because it is influenced by factors such as time, cost, redundancy, reliability, or weak signals. Furthermore, for the monitored signals, it is recommended to generate some analytics to study the flow and have a traceability of the issue we are dealing with. In this paper, a signal theory model is introduced and applied to address some of these issues regarding the SSH brute-force attacks. I will use a tool called CheckMK and its capabilities to implement a signal theory model used for monitoring security of a system.
Tripartite Real-time Semantic Segmentation Network with Scene Commonality

Abstract The two-branch real-time semantic segmentation network can quickly acquire low-level details and high-level semantics. However, the large contextual gap between them results in adverse impact on their fusion, and limits the further improvement of real-time segmentation accuracy. This paper proposes a Tripartite Real-time Semantic Segmentation Network with Scene Commonality (TriSCNet) to address this problem. Firstly, we add a parallel Scene Commonality Branch (SCB) based on the current two-branch architecture to learn intrinsic common features in similar street scene images, such as the spatial location distribution of various objects and the internal connections between them at the semantic level. Further, with the guidance of commonality, we propose an External Branch Attention Module (EBAM) to enrich and enhance the feature information of traditional two branches. Lastly, we utilize an Alignment and Selective Fusion Module (ASFM) to correct the misaligned context in the semantic branch and highlight the essential spatial information in the detailed branch. Our proposed TriSCNet achieves an excellent trade-off between accuracy and speed, yielding 79.6% mIOU at 67.2 FPS on Cityscapes test set and 76.8% mIOU at 127.4 FPS on CamVid test set, respectively.
Deliverable D8.3 Web interface for ELIXIR Contextual Data ClearingHouse

This deliverable report includes description of the work steps towards building a web interface for the reporting of errors and gaps in sequenced material source annotations as part of the Task 8.3 of BiCIKL. Beta version of the web interface has been published and is available for the registered users of PlutoF platform.
A Computer Abusive Access Case Study Solved with Windows Registry Analysis

This article has the aim to describe a real forensics investigation case. An employee is accused of revealing confidential company information related to a project he was working on using a company computer registered to the company domain. The accused defends himself, insinuating the doubt that it could have been anyone because his office is always open. After the seizure and acquisition of a company hard drive, the investigators want to find some evidences related the Windows system registry. In particular, the analysis will be aimed at identifying what were the energy and standby settings at the time of the seizure and if upon reactivation of the screen, the password was requested and needed to access the system.
Encrypted Documents and Cipher Keys From the 18th and 19th Century in the Archives of Aristocratic Families in Slovakia

In this article, we present encrypted documents and cipher keys from the 18th and 19th century, related to central-European aristocratic families Amade-Üchtritz, Esterházy, and Pálffy-Daun.In the first part of the article, we present an overview and analysis of the available documents from the archives with examples.We provide a short historical overview of the people related to the analyzed documents to provide a context for the research.In the second part of the article, we focus on the digital processing of these historical manuscripts.We developed new tools based on machine learning that can automate the transcription of encrypted parts of the documents, which contain only digits as cipher text alphabet.Our digit detection and segmentation are based on YOLOv7.YOLOv7 provided good detection precision and was able to cope with problems like noisy paper background and areas where digits collided with the text from the reverse side of the paper.
Innovation Pedagogy of 21st Century: Challenges and Opportunities

Since its coinage, the term pedagogy has been defined variedly by a number of educationists. With passing time and expanding knowledge, our understanding about pedagogy has become more comprehensive and inclusive. In the 21st century, the educational practices are undergoing massive changes to meet the needs and expectations of an ever-changing society. Innovations and improvements are becoming constant practices to adapt with the transformative societal requirements and expectations. Therefore, Constant research is necessary in the educational sector to improve the conventional teaching methods, strategies, learning-teaching materials and overall classroom management. Innovation pedagogy is that continuous research process. Innovation pedagogy makes the students future-ready by providing them the ideal environment for developing innovation competence.