Reviewerly/exHarmony huggingface dataset:
Some examples of privacy-preserving sharing of COVID-19 pandemic data with statistical utility evaluation

A considerable amount of various types of data have been collected during the COVID-19 pandemic, the analysis and understanding of which have been indispensable for curbing the spread of the disease. As the pandemic moves to an endemic state, the data collected during the pandemic will continue to be rich sources for further studying and understanding the impacts of the pandemic on various aspects of our society. On the other hand, naïve release and sharing of the information can be associated with serious privacy concerns.We use three common but distinct data types collected during the pandemic (case surveillance tabular data, case location data, and contact tracing networks) to illustrate the publication and sharing of granular information and individual-level pandemic data in a privacy-preserving manner. We leverage and build upon the concept of differential privacy to generate and release privacy-preserving data for each data type. We investigate the inferential utility of privacy-preserving information through simulation studies at different levels of privacy guarantees and demonstrate the approaches in real-life data. All the approaches employed in the study are straightforward to apply.The empirical studies in all three data cases suggest that privacy-preserving results based on the differentially privately sanitized data can be similar to the original results at a reasonably small privacy loss ([Formula: see text]). Statistical inferences based on sanitized data using the multiple synthesis technique also appear valid, with nominal coverage of 95% confidence intervals when there is no noticeable bias in point estimation. When [Formula: see text] and the sample size is not large enough, some privacy-preserving results are subject to bias, partially due to the bounding applied to sanitized data as a post-processing step to satisfy practical data constraints.Our study generates statistical evidence on the practical feasibility of sharing pandemic data with privacy guarantees and on how to balance the statistical utility of released information during this process.
Pelatihan Pemrograman Visual dengan Scratch untuk Siswa SD Negeri Sawah Panggang

Thinking critically about problems is one of the high goals of education. From a young age, we must learn to think logically about problems and find appropriate solutions to them. As an educational institution, primary school is not only a place where students acquire teaching materials, but also where they develop critical thinking and become accustomed to adapting to the fast-paced development of the world. One of his ways of developing logical thinking, especially in his SD Negeri Sawah, is by teaching his students basic programming skills. This allows students to approach issues in a more structured way. Therefore, Sanata Dharma University Faculty of Science and Engineering, considering the problems of the campus environment, is holding training for students to be able to visually understand the basics of programming as a concern of the academic world. Practice thinking logically. Training presentations are designed like his Scratch games, making students more interested in learning to solve problems. This activity he successfully carried out in two phases. Beginner and advanced classes at Sawah Panggang Primary School will be held offline. Through this activity, some of the students were able to explore more and be more courageous while working on the assignment.
Infrared Sea Surface Ship Target Detection Algorithm Based on Improved YOLOV5

Ship target detection on the sea surface is one of the most common scenes in the field of target detection. The accuracy of target detection plays an important role in the field of sea rescue and defense. However, complex infrared sea surface scenes, such as island shore, fish scale wave, sea surface bright band and other disturbances, bring great challenges to target detection. In this paper, we propose an improved YOLOV5 model by analyzing the characteristics of infrared image imaging and ship target on the sea surface. Aiming at the problem of information loss of small targets in the deep layer of the network, we redesigned the backbone network, which was composed of four Multi-scale residual blocks, and each block was connected by CBAM (Convolutional Block Attention Module) attention mechanism to improve information transmission and fusion between different feature layers. Aiming at the problem of complex sea surface scene interference, we use FPN and PAN (Feature Pyramid Network and Personal Area Network) mechanism to construct feature fusion network in neck, and add shallow feature fusion in FPN. Our model achieves good performance both in terms of latency and accuracy. In a self-collected sea surface scene dataset, multiple SOTAs for detection tasks are compared and the results demonstrate the superiority of the proposed method.
Application of English education big data system based on multi-source information fusion and machine learning

For administrators in the field of education, the data generated in the actual education process is difficult to achieve effective management due to its large amount of information, complex structure and multiple types. However, in the actual construction of education informatization, it is impossible to explore the laws and development trends of education information only by manual analysis, so it is difficult to obtain effective concepts and relevant views. College education administrators cannot fully optimize the process of teaching diagnosis and reform through education informatization. Based on this background, this research introduces machine learning technology to achieve technical support for the integrated application of educational data. This paper focuses on the design and construction of an English teaching management data analysis system for college teaching, which combines machine learning technology with multi-source information technology. The system is mainly composed of hardware layer, data layer, service layer and application layer, which can assist teaching and realize learning career planning, faculty authority management, curriculum data modification, teaching resource allocation and scientific research review. The simulation results show that this big data system has high processing efficiency, and is superior to other algorithms in terms of mean value and FVDF extreme value, so it can effectively assist English teaching management. This paper completes the design of English education big data system by introducing machine learning and multi-source information fusion into the field of education.
Mixed discrete-variable Gaussian states

The quantum systems with finite-dimensional Hilbert space have several applications and are intensively explored theoretically and experimentally. The mathematical description of these systems follows the analogy with the usual infinite-dimensional case. There exist finite versions for most of the elements used in the continuous case, but here we provide a finite version corresponding to the mixed Gaussian states. Our aim is to fill this gap. The definition we propose for the mixed discrete Gaussian states is based on the explicit formulas we have obtained in the case of pure discrete-variable Gaussian states.
Research on distributed relational database based on MySQL

With the rapid development of big data, cloud computing, artificial intelligence and other technologies, the expansion of database application requirements and the change of computer hardware environment, especially the rapid development of computer network and digital communication technology, satellite communications, cellular communications, computer local area networks, wide area networks, and the proliferation of Intranet and Internet have been widely used, resulting in the emergence of distributed database systems. Especially for basic industries such as telecommunications, databases often carry thousands of business systems, and millions of queries and tens of millions of bills are carried out every day. In the face of such massive scale, high concurrency, data security, real-time analysis and other multi scenario problems, the processing capacity of traditional centralized databases is far from meeting the needs. For this reason, this paper proposes a distributed database based on MySQL transformation. Through research and implementation, it can solve the above problems, meet the requirements of distributed transaction processing, ensure data backup, high availability and high performance, and improve efficiency. This paper will also describe its modeling model and sharding algorithm in detail.
TBFormer: Two-Branch Transformer for Image Forgery Localization

Image forgery localization aims to identify forged regions by capturing subtle traces from high-quality discriminative features. In this paper, we propose a Transformer-style network with two feature extraction branches for image forgery localization, and it is named as Two-Branch Transformer (TBFormer). Firstly, two feature extraction branches are elaborately designed, taking advantage of the discriminative stacked Transformer layers, for both RGB and noise domain features. Secondly, an Attention-aware Hierarchical-feature Fusion Module (AHFM) is proposed to effectively fuse hierarchical features from two different domains. Although the two feature extraction branches have the same architecture, their features have significant differences since they are extracted from different domains. We adopt position attention to embed them into a unified feature domain for hierarchical feature investigation. Finally, a Transformer decoder is constructed for feature reconstruction to generate the predicted mask. Extensive experiments on publicly available datasets demonstrate the effectiveness of the proposed model.
Convolutional Neural Networks for Face Detection and Face Mask Multiclass Classification

In recent years, due to the COVID-19 pandemic, there have been a large number of infections among humans, causing the virus to spread around the world. According to recent studies, the use of masks has helped to prevent the spread of the virus, so it is very important to use them correctly. Using masks in public places has become a common practice these days and if it is not used correctly the virus will continue to be transmitted. The contribution of this work is the development of a convolutional neural network model to detect and classify the correct use of face masks. Deep learning methods are the most effective method to detect whether a person is using a mask properly. The proposed model was trained using the MaskedFace-Net dataset and evaluated with different images of it. The Caffe model is used for face detection, after which the image is preprocessed to extract features. These images are the input of the new convolutional neural network model, where it is classified among incorrect mask, non-mask, and mask. The proposed model achieves an accuracy rate of 99.69% in the test percentage, which is higher compared to other authors.
Tokenization of Tunisian Arabic: A Comparison between Three Machine Learning Models

Tokenization represents the way of segmenting a piece of text into smaller units called tokens. Since Arabic is an agglutinating language by nature, this treatment becomes a crucial preprocessing step for many Natural Language Processing (NLP) applications such as morphological analysis, parsing, machine translation, information extraction, and so on. In this article, we investigate word tokenization task with a rewriting process to rewrite the orthography of the stem. For this task, we are using Tunisian Arabic (TA) text. To the best of the researchers’ knowledge, this is the first study that uses TA for word tokenization. Therefore, we start by collecting and preparing various TA corpora from different sources. Then, we present a comparison of three character-based tokenizers based on Conditional Random Fields (CRF), Support Vector Machines (SVM) and Deep Neural Networks (DNN). The best proposed model using CRF achieved an F-measure result of 88.9%.
Fine-Grained Video Retrieval With Scene Sketches

Benefiting from the intuitiveness and naturalness of sketch interaction, sketch-based video retrieval (SBVR) has received considerable attention in the video retrieval research area. However, most existing SBVR research still lacks the capability of accurate video retrieval with fine-grained scene content. To address this problem, in this paper we investigate a new task, which focuses on retrieving the target video by utilizing a fine-grained storyboard sketch depicting the scene layout and major foreground instances’ visual characteristics (e.g., appearance, size, pose, etc.) of video; we call such a task “fine-grained scene-level SBVR”. The most challenging issue in this task is how to perform scene-level cross-modal alignment between sketch and video. Our solution consists of two parts. First, we construct a scene-level sketch-video dataset called SketchVideo, in which sketch-video pairs are provided and each pair contains a clip-level storyboard sketch and several keyframe sketches (corresponding to video frames). Second, we propose a novel deep learning architecture called Sketch Query Graph Convolutional Network (SQ-GCN). In SQ-GCN, we first adaptively sample the video frames to improve video encoding efficiency, and then construct appearance and category graphs to jointly model visual and semantic alignment between sketch and video. Experiments show that our fine-grained scene-level SBVR framework with SQ-GCN architecture outperforms the state-of-the-art fine-grained retrieval methods. The SketchVideo dataset and SQ-GCN code are available in the project webpage https://iscas-mmsketch.github.io/FG-SL-SBVR/.
Cloud Computing-based Disaster Recovery

Cloud-based disaster recovery (DR) solutions help organisations recover from natural disasters and cyberattacks quickly and affordably. Cloud-based disaster recovery solutions improve recovery times, capital costs, scalability, and security. Yet, data security and DR strategy are still barriers to adoption. Only two issues. Besides these challenges, cloud-based catastrophe recovery looks promising. Cloud-based disaster recovery solutions will continue to grow as cloud technology improves. Hybrid cloud disaster recovery solutions, which integrate public and private cloud environments, will continue to grow in popularity, as will DRaaS, a cost-effective and flexible disaster recovery solution. As cloud-based disaster recovery solutions grow more prevalent, organisations must understand their disaster recovery needs, carefully analyse their cloud service providers' security and compliance procedures, and regularly test and update their disaster recovery plans and solutions. If organisations follow these measures, they may maximise the benefits of cloud-based disaster recovery solutions to safeguard their critical data and apps and ensure business continuity in a disaster.
Constructing Low-Redundant and High-Accuracy Knowledge Graphs for Education

Motivated by the successful applications of commonsense knowledge graphs (KGs) and encyclopedia KGs, many KG-based applications have been developed in education, such as course content visualization and learning path/material recommendations. While KGs for education are often constructed manually, attempts have been made to leverage machine learning algorithms to extract triples from teaching materials. However, education-related KGs learned by existing algorithms contain significant amounts of redundancy and noise. It is because the entities and relations in teaching materials are often instructional, abstract, and implicit, while textbooks often contain detailed explanations, examples, and illustrations. Off-the-shelf KG construction algorithms are designed for concrete entities. To this end, we propose an effective framework to construct low-redundant and high-accuracy KGs for education. First, we design an ontology that is tailored for education. By choosing related Wikidata items, we construct an instructional entity set. We avoid using traditional methods such as named-entity recognition to extract entities from textbooks, aiming to reduce redundancy. Then, we add subtopic relations among our selected instructional entities based on the corresponding hierarchy in Wikidata, and form a backbone. Second, we design a machine reading comprehension model with pre-defined questions to extract other types of relations, such as equivalent to, applied to, and inventor of. Third, we apply active KG error detection to further refine the KG with minimal human effort. In the experiments, we take the artificial intelligence domain as an example and demonstrate the effectiveness of the proposed framework. Our KG achieves an accuracy of around 80% scored by domain experts.
TSI-GAN: Unsupervised Time Series Anomaly Detection Using Convolutional Cycle-Consistent Generative Adversarial Networks

Anomaly detection is widely used in network intrusion detection, autonomous driving, medical diagnosis, credit card frauds, etc. However, several key challenges remain open, such as lack of ground truth labels, presence of complex temporal patterns, and generalizing over different datasets. This paper proposes TSI-GAN, an unsupervised anomaly detection model for time-series that can learn complex temporal patterns automatically and generalize well, i.e., no need for choosing dataset-specific parameters, making statistical assumptions about underlying data, or changing model architectures. To achieve these goals, we convert each input time-series into a sequence of 2D images using two encoding techniques with the intent of capturing temporal patterns and various types of deviance. Moreover, we design a reconstructive GAN that uses convolutional layers in an encoder-decoder network and employs cycle-consistency loss during training to ensure that inverse mappings are accurate as well. In addition, we also instrument a Hodrick-Prescott filter in post-processing to mitigate false positives. We evaluate TSI-GAN using 250 well-curated and harder-than-usual datasets and compare with 8 state-of-the-art baseline methods. The results demonstrate the superiority of TSI-GAN to all the baselines, offering an overall performance improvement of 13% and 31% over the second-best performer MERLIN and the third-best performer LSTM-AE, respectively.
Aggregately Diversified Bundle Recommendation via Popularity Debiasing and Configuration-Aware Reranking

How can we expose diverse items across all users while satisfying their needs in bundle recommendations? Diversified bundle recommendation is a crucial task since it leads to great benefits for both sellers and users. However, there have been no studies on aggregate diversity in bundle recommendation, while they have been intensively studied in item recommendation. Moreover, existing methods of aggregately diversified item recommendation are not fully suitable for bundle recommendation. In this paper, we propose PopCon (Popularity Debiasing and Configuration-aware Reranking), an accurate method for aggregately diversified bundle recommendation. PopCon mitigates the popularity bias of a recommendation model by a popularity-based negative sampling in training process, and maximizes accuracy and aggregate diversity by a configuration-aware reranking algorithm. We show that PopCon provides state-of-the-art performance on real-world datasets, achieving up to $$60.5\%$$ higher Entropy@5 and $$3.92\times $$ higher Coverage@5 with comparable accuracies compared to the best competitor.
A Comprehensive Survey on Internet of Things Security: Challenges and Solutions

Internet of Things (IoT) is a group of self-contained objects, which is a brand-new pattern that incorporates the current existence of various devices. It is one of the most recent technologies that offer worldwide connectivity, user, sensor, and information management. Devices can become ubiquitously connected, thanks to connectivity. IoT has a number of problems, including fading, energy use, data security, network security, etc. Security emerges as one of the biggest issues among these. In this paper, a survey on IoT security solutions is presented to illustrate the various IoT security procedures. The security protocols that are divided into four categories based on technologies used to provide security such as machine learning, trust, blockchain, and cryptography have been elaborated. The major purpose of these protocols is to address the issue of network routing assaults in IoT. Each protocol’s benefits and drawbacks are examined together with the performance indicators that were used.
Neuromorphic high-frequency 3D dancing pose estimation in dynamic environment

As a beloved sport worldwide, dancing is getting integrated into traditional and virtual reality-based gaming platforms nowadays. It opens up new opportunities in the technology-mediated dancing space. These platforms primarily rely on passive and continuous human pose estimation as an input capture mechanism. Existing solutions are mainly based on RGB or RGB-Depth cameras for dance games. The former suffers in low-lighting conditions due to the motion blur and low sensitivity, while the latter is too power-hungry, has a low frame rate, and has limited working distance. With ultra-low latency, energy efficiency, and wide dynamic range characteristics, the event camera is a promising solution to overcome these shortcomings. We propose YeLan, an event camera-based 3-dimensional high-frequency human pose estimation(HPE) system that survives low-lighting conditions and dynamic backgrounds. We collected the world's first event camera dance dataset and developed a fully customizable motion-to-event physics-aware simulator. YeLan outperforms the baseline models in these challenging conditions and demonstrated robustness against different types of clothing, background motion, viewing angle, occlusion, and lighting fluctuations.
Layered Binary Templating

We present a new generic cache template attack technique, LBTA, layered binary templating attacks. LBTA uses multiple coarser-grained side channels to speed up cache-line granularity templating, ranging from 64 B to 2 MB in practice and in theory beyond. We discover first-come-first-serve data placement and data deduplication during compilation and linking as novel security issues that introduce side-channel-friendly binary layouts. We exploit this in inter-keystroke timing attacks and, depending on the target, even full keylogging attacks (Demo: The user first announces via Signal messenger to send money to a friend, then switches to Chrome to visit a banking website and enters the credentials there. All keystrokes are correctly leaked. https://streamable.com/dgnuwk ), e.g., on Chrome, Signal, Threema, Discord, and the passky password manager, indicating that all Chromium-based apps are affected.