Reviewerly/exHarmony huggingface dataset:
Establishing the National Cybersecurity (Resilience) Ecosystem

The goal and motivation of this paper is to present an approach on establishing National Cybersecurity (Resilience) Ecosystem in Western Balkan Countries which stems from the desire to provide a secure environment for public services to the citizens of the Western Balkans as much as possible. The paper deals with the system dependencies and cybersecurity trends, the need for standard compliances and infrastructure. Another part of the paper based on the results of the gap analysis of the current frameworks where we created a mapping table that helps the reader to understand the link between the several national stakeholders which will ensure the cybersecurity resilience. Finally, in the paper we explained the integrations of the current national cybersecurity ecosystem to the Global Cybersecurity Ecosystems and the European cybersecurity technical ecosystem which is followed with the summarizing the overall research work with findings and recommendations. These services at the same time must be easily accessible and based on international standards. As small countries as we are, the possibilities for implementing such models are enormous even as pilot projects in the beginning, continuing with their expansion. Therefore, we are convinced and motivated to push this model of a cybersecurity (resilience) ecosystem forward and to become a model of success for other countries.
Machine Learning for Dynamically Predicting the Onset of Renal Replacement Therapy in Chronic Kidney Disease Patients Using Claims Data

Chronic kidney disease (CKD) represents a slowly progressive disorder that can eventually require renal replacement therapy (RRT) including dialysis or renal transplantation. Early identification of patients who will require RRT (as much as 1 year in advance) improves patient outcomes, for example by allowing higher-quality vascular access for dialysis. Therefore, early recognition of the need for RRT by care teams is key to successfully managing the disease. Unfortunately, there is currently no commonly used predictive tool for RRT initiation. In this work, we present a machine learning model that dynamically identifies CKD patients at risk of requiring RRT up to one year in advance using only claims data. To evaluate the model, we studied approximately 3 million Medicare beneficiaries for which we made over 8 million predictions. We showed that the model can identify at risk patients with over 90% sensitivity and specificity. Although additional work is required before this approach is ready for clinical use, this study provides a basis for a screening tool to identify patients at risk within a time window that enables early proactive interventions intended to improve RRT outcomes.
Passwordless VPN using FIDO2 Security Keys: Modern authentication security for legacy VPN systems

This paper describes a method of secure authentication using FIDO2 security keys with VPN systems based on legacy protocols by leveraging a dedicated web-based VPN portal and allowing to implement a Passwordless access method.
HEATMAP MATRIX: USING REORDERING, DISCRETIZATION AND FILTERING RESOURCES TO ASSIST MULTIDIMENSIONAL DATA ANALYSIS

Visualization can help users to analyze multidimensional datasets and make better decisions related to them. The current literature presents a set of multidimensional data visualization techniques that deal with discrete variables, continuous ones,
Numerical Simulation of Quantum Dissipative Dynamics of a Superconducting Neuron

The process of dynamic state switching in a modified scheme of a superconducting quantum parametron in a heat reservoir is studied numerically. In the Born-Markov approximation, the system evolution is reduced to an adiabatic generalized master equation for the density matrix in the instantaneous basis. The numerical solution of the Redfield equation and the possibility of using secular approximation and random phase approximation (Pauli equation) is discussed. We provide a comparison of the efficiency of two numerical simulations based on the OpenMP technology in different representations of the system Hamiltonian: coordinate and occupation number.
Tiny Hands are Cute: Adaptive Virtual Hands to Accurately Select Nail-Size Arm’s Reach Virtual Objects in Dense Immersive VR

Object selection is an essential task in Virtual Reality applications. Current object selection techniques suffer from issues of hand jitter, accuracy, and higher errors in selecting nail-sized objects in a dense virtual environment. We present tiny hands, a technique to reduce the size of virtual hands to easily navigate and accurately select nail-sized object in a dense environment. We present 3 gesture based interaction techniques (i) Fisting non-dominant hand (ii) Palm downwards for 2 s and (iii) Pinch-in and Pinch-out non-dominant hand in this paper. Our initial findings indicate that tiny hands technique is a versatile approach for selection of small objects in dense VE. Users also found these techniques to be natural, intuitive, easy to use and accurate in selecting small targets in dense VEs.
Adaptive Multiparty NIKE

We construct adaptively secure multiparty non-interactive key exchange (NIKE) from polynomially-hard indistinguishability obfuscation and other standard assumptions. This improves on all prior such protocols, which required sub-exponential hardness. Along the way, we establish several compilers which simplify the task of constructing new multiparty NIKE protocols, and also establish a close connection with a particular type of constrained PRF.
Shapley Chains: Extending Shapley Values to Classifier Chains

In spite of increased attention on explainable machine learning models, explaining multi-output predictions has not yet been extensively addressed. Methods that use Shapley values to attribute feature contributions to the decision making are one of the most popular approaches to explain local individual and global predictions. By considering each output separately in multi-output tasks, these methods fail to provide complete feature explanations. We propose Shapley Chains to overcome this issue by including label interdependencies in the explanation design process. Shapley Chains assigns Shapley values as feature importance scores in multi-output classification using classifier chains, by separating the direct and indirect influence of these feature scores. Compared to existing methods, this approach allows to attribute a more complete feature contribution to the predictions of multi-output classification tasks. We provide a mechanism to distribute the hidden contributions of the outputs with respect to a given chaining order of these outputs. Moreover, we show how our approach can reveal indirect feature contributions missed by existing approaches. Shapley Chains helps to emphasize the real learning factors in multi-output applications and allows a better understanding of the flow of information through output interdependencies in synthetic and real-world datasets.
Neighbor Collaboration-Based Secure Federated QoS Prediction for Smart Home Services

Smart homes IoT (SHIoT) deploys various devices that can achieve rich functionality by invoking cloud services for better in-home experiences. Recommending the quality services will allow the devices to provide greater comfort to the homeowner. Traditional service recommendation methods require collecting the quality of service (QoS) data to achieve better recommendations but also pose privacy problems. Inspired by federated learning (FL), some privacy-preserving federated recommendation methods have been proposed. However, studies have demonstrated that user preferences and even raw data can be inferred in FL. To implement service recommendations in SHIoT while protecting the edge privacy, we propose a secure federated QoS prediction method with neighbor collaboration (NCSF). By collaboration, users in NCSF upload perturbed updates, while the perturbations are offset during server aggregation without affecting the global model. Experiments on real-world datasets show that NCSF can achieve same recommendation quality and stronger privacy protection as the FL approach of plaintext aggregation.
Smart Glove for Translating Arabic Sign Language “SGTArSL”

Deaf and mute people who use sign language often face serious communication problems with others. The Smart Glove for Translating Arabic Sign Language (SGTArSL) tries to address this problem by providing them with means of communications, especially with their families. SGTArSL uses small components to build a communication device to enhance the communication between deaf /mute people with others who, do not necessarily, understand their language; thus improving their independence. The system includes a smart glove that translates the Arabic Sign Language (ArSL) alphabets using only one hand by building a Printed Circuit Board (PCB) on a textile glove. The PCB uses a microcontroller and sensors to detect and classify the hand gesture, then sends the observed alphabet via Bluetooth as a text to a mobile application. In this paper, SGTArSL is successfully tested to enable people who communicate with deaf/mute users to understand their sign language alphabets.
An Efficient Implementation of AES algorithm using Custom Key

Cryptography in the digital era plays an irreplaceable role in data security by providing integrity, authentication, and confidentiality to protect sensitive data from unauthorized access. Though Advanced Encryption Standard (AES) is an approved symmetric cryptographic algorithm, it needs improvement in terms of speed, area, and security. In the proposed work, unified AES 128-bit and 256-bit cipher text are implemented with iterative encryptor and decryptor core, to optimize area and delay. For this, compact and combined MixColumn / Inverse - MixColumn operations are introduced in the algorithm. Also, “The Transported Subbyte Operation” has been modified, where each byte in the matrix is swapped before S box substitution to enhance the security level. Simulation of the proposed work is carried out using ModelSim-Altera 6.3g_p1 (Quartus II 8.1). The enhanced modified algorithm resulted in an area reduction of 21.27% and a delay reduction of 16.99%.
Subregular linguistics: bridging theoretical linguistics and formal grammar

Abstract Subregular linguistics is a fairly new approach that seeks a deeper understanding of language by combining the rigor of formal grammar with the empirical sophistication of theoretical linguistics. The approach started in phonology but has since branched out to morphology and even syntax, unearthing unexpected parallels between these three domains of language. In this paper, I argue based on these results that subregular linguistics has a lot to offer to both fields. Subregular linguistics may be the ideal conduit for knowledge transfer between these two communities.
Combating Label Distribution Shift for Active Domain Adaptation

We consider the problem of active domain adaptation (ADA) to unlabeled target data, of which subset is actively selected and labeled given a budget constraint. Inspired by recent analysis on a critical issue from label distribution mismatch between source and target in domain adaptation, we devise a method that addresses the issue for the first time in ADA. At its heart lies a novel sampling strategy, which seeks target data that best approximate the entire target distribution as well as being representative, diverse, and uncertain. The sampled target data are then used not only for supervised learning but also for matching label distributions of source and target domains, leading to remarkable performance improvement. On four public benchmarks, our method substantially outperforms existing methods in every adaptation scenario.
Solar energy potential in Freiburg, Graz, Maribor, Banja Luka, Nis, and Athens

This paper presents a comparative analysis of solar energy potential for six different cities, in six different countries in Europe: Freiburg (Germany), Graz (Austria), Maribor (Slovenia), Banja Luka (Bosnia and Herzegovina), Nis (Serbia), and Athens (Greece). Data processed in this work are accessed from Photovoltaic Geographical Information System (PVGIS). Photovoltaic technology is crystalline silicon, and installed peak photovoltaic power is 5 kWp. The aim of the work is to find out whether there are statistically significant differences among the cities in relation to monthly energy production in regard to different types of photovoltaic system (fixed - free standing, fixed - building integrated, inclined, and two axis solar power plants). The work is based on four hypotheses. The estimation of solar energy production in different regions is very important for determination of potential regions suitable for generation of renewable and sustainable energy.
A Vision-based Hand-sign Language Teaching System using Deep Neural Network: Methodology and Experiments

In this paper, a real-time hand-sign language teaching system using deep neural network is proposed. Communication presents a significant barrier for persons who are impaired in hearing and speaking. There are various projects and studies have been conducted to create or improve smart systems for this rapid-growth population. Deep learning approaches became widely used to enhance the accuracy of sign language recognition models. However, most research has primarily concentrated on hand gestures for translation, not language self-learning for long-term development. This work aims to construct a complete system to assist deaf and mute people in studying and examining their performance. First, we designed and built a prosthetic arm equipped with a monocular camera using 3D printing. Second, the MediaPipe library was used to extract key points from collected videos of the hand gestures. Then, the Gated Recurrent Units model is trained to recognize words based on the data. The real-time experimental results demonstrate the system's effectiveness and potential with 97 percent accuracy.
Multi-Layered Safety-Critical Control Design for Robotic Systems via Control Barrier Functions

To satisfy the safety constraints, this paper proposes a multi-layered safety-critical control framework for robotic systems based on control barrier functions (CBFs), which is composed of the safe kinematic control layer and the inverse dynamics control layer. Both layers incorporate the CBF constraints to ensure safety, and the corresponding quadratic programs (QPs) are formulated. Through real-time optimization control, the robot can satisfy the safety constraints while tracking the desired trajectory. The proposed method is validated on the two-link planar manipulator with trajectory tracking and obstacle avoidance tasks.
TagTag: A Novel Framework for Service Tags Recommendation and Missing Tag Prediction

Currently, service tag recommendation plays an important role in the study of services. As a result, there have been many service tag recommendation studies that have achieved significant achievements. However, existing studies mainly have two problems: they only recommend one tag and cannot determine whether new tags are needed. To help solve the above problems, we propose a novel graph neural framework named TagTag to make multi-tag recommendations and missing tag prediction, which relies on the idea of tag collaboration graph. We conduct experiments on the real-world dataset from ProgrammableWeb, and the results show that TagTag performs better than existing studies. The code used in this paper is fully accessible at https://github.com/HIT-ICES/TagTag.
Single-Stream Multi-level Alignment for Vision-Language Pretraining

Self-supervised vision-language pretraining from pure images and text with a contrastive loss is effective, but ignores fine-grained alignment due to a dual-stream architecture that aligns image and text representations only on a global level. Earlier, supervised, non-contrastive methods were capable of finer-grained alignment, but required dense annotations that were not scalable. We propose a single stream architecture that aligns images and language at multiple levels: global, fine-grained patch-token, and conceptual/semantic, using two novel tasks: symmetric cross-modality reconstruction (XMM) and a pseudo-labeled key word prediction (PSL). In XMM, we mask input tokens from one modality and use cross-modal information to reconstruct the masked token, thus improving fine-grained alignment between the two modalities. In PSL, we use attention to select keywords in a caption, use a momentum encoder to recommend other important keywords that are missing from the caption but represented in the image, and then train the visual encoder to predict the presence of those keywords, helping it learn semantic concepts that are essential for grounding a textual token to an image region. We demonstrate competitive performance and improved data efficiency on image-text retrieval, grounding, visual question answering/reasoning against larger models and models trained on more data. Code and models available at zaidkhan.me/SIMLA .
Pengembangan Instrumen Tes Setipe PISA untuk Melatih Kemampuan Literasi Matematika Siswa Kelas V SD LAB UM

Abstract: This study aims to produce valid PISA-type test instruments according to material experts, media experts, practical according to users, and to know the quality of test instruments. This study uses the Borg and Gall model. Based on the results of the study, the test instruments were very valid according to material experts (97.22 percent), media experts (95.83 percent), very practical according to the teacher (92.59 percent), and students (88.09 percent). The validity analysis of the questions obtained 16 valid questions and 14 invalid questions. Reliability is 0.52. Difficulty level analysis obtained 13 difficult questions, 13 moderate questions, and 4 easy questions. The analysis of differentiation was obtained 8 very good questions, 6 questions were quite good, and 16 questions were lacking.&#x0D; Abstrak: Penelitian ini bertujuan untuk menghasilkan instrumen tes setipe PISA yang valid menurut ahli materi, ahli media, praktis menurut pengguna, serta mengetahui kualitas instrumen tes. Penelitian ini menggunakan model Borg and Gall. Berdasarkan hasil penelitian, instrumen tes sangat valid menurut ahli materi (97,22 persen), ahli media (95,83 persen), sangat praktis menurut guru (92,59 persen), serta siswa (88,09 persen). Analisis validitas butir soal diperoleh 16 soal valid dan 14 soal tidak valid. Reliabilitas sebesar 0,52. Analisis tingkat kesukaran diperoleh 13 soal sukar, 13 soal sedang, dan 4 soal mudah. Analisis daya pembeda diperoleh 8 soal sangat bagus, 6 soal cukup bagus, dan 16 soal kurang.
A Knowledge-Guided Method for Disease Prediction Based on Attention Mechanism

Disease prediction, aimed at predicting possible future diseases of patients, is a fundamental research problem in medical informatics. Many studies have proposed the introduction of external knowledge to enhance existing models with some effect, but since most of these studies only consider entities directly related to the patient, they fail to take full advantage of the correlation between entities in the knowledge graph. To this end, we propose a new approach, which uses medical knowledge graphs for multi-hop reasoning to guide the self-attention based transformer model for disease prediction. Specifically, our approach design a reinforcement learning algorithm to perform path reasoning in the knowledge graph to obtain explicit disease progression paths. Since there is a semantic gap between the Electronic Health Records (EHR) data and the knowledge path data, we feed them into two separate transformer encoders to obtain the embedding representation. In order to measure the importance of the different knowledge information in relation to the patient information, an attention module is introduced to obtain a global attention representation. Experimental results on the real-world medical dataset MIMIC-III show the superiority of the proposed approach compared to a series of state-of-the-art baselines. At the same time, multi-hop knowledge paths bring stronger interpretability for disease prediction.
Smart Garbage Classification

Garbage recycling is a key aspect of maintaining our environment in good condition. Poor waste management has the potential to have a significant negative effect on the environment, on public health, and on the economy of the nation. The garbage must be separated into groups with similar recycling processes to make the recycling process much more effective and faster. Recycling, needless to say, is a very important task for all the countries. Garbage categorization is the most basic stage in enabling cost-effective recycling among the tasks required for recycling. Some of the well-known deep learning models for trash categorization include Densenet121, DenseNet169, InceptionResnetV2, MobileNet and Xception architecture. In this article, we propose a procedure to recognize single trash objects in images and categorize them into one of the recycling categories. A Convolutional Neural Network (CNN) with transfer learning is used. An analysis of the results obtained from the study shows that the EfficientNet-b0 CNN performs well under this scenario. The trash categorization problem for the target database can be efficiently handled using deep learning approaches as they offer a reliable foundation for image recognition with high consistency. Once a waste is put into the bin, the top compartment scans and predicts which type of waste it is, and then the respective lid of the bottom compartment opens, pushing the waste down. This prototype model of the system is proposed, which can be used in real-time implementation.
AI for Social Good: Sentiment Analysis to Detect Social Challenges in South Africa

Sentiment analysis has the potential to help analyse people’s opinions and emotions on social issues [1]. We believe that in multilingual communities sentiment analysis systems should be even used to quickly discover what social challenges exist. This would help government departments target those issues more precisely and effectively. Consequently, in this paper we describe our experiments to apply cross-lingual sentiment analysis on South African tweets to detect social challenges described in English, Sepedi (i.e. Northern Sotho) and Setswana tweets. We investigated the polarities of the 10 most emerging topics in the tweets that fall within jurisdictional areas of 10 South African government departments. Our AI-driven systems indicate that the topics of employment, police service, education, and health are particularly problematic for the investigated multilingual communities since more than 50% of the tweets are categorised as negative, whereas the mood regarding the topics of agriculture and rural development is rather positive. Our developed systems can be easily extended to other topics and languages.