Reviewerly/exHarmony huggingface dataset:
Communication Reducing Quantization for Federated Learning with Local Differential Privacy Mechanism

As an emerging framework of distributed learning, federated learning (FL) has been a research focus since it enables clients to train deep learning models collaboratively without exposing their original data. Nevertheless, private information can still be inferred from the communicated model parameters by adversaries. In addition, due to the limited channel bandwidth, the model communication between clients and the server has become a serious bottleneck. In this paper, we consider an FL framework that utilizes local differential privacy, where the client adds artificial Gaussian noise to the local model update before aggregation. To reduce the communication overhead of the differential privacy-protected model, we propose the universal vector quantization for FL with local differential privacy mechanism, which quantizes the model parameters in a universal vector quantization approach. Furthermore, we analyze the privacy performance of the proposed approach and track the privacy loss by accounting the log moments. Experiments show that even if the quantization bit is relatively small, our method can achieve model compression without reducing the accuracy of the global model.
A Hyperparameter Search Approach Using BRKGA for Deep Feedforward Neural Network

This paper proposed to use the Biased Random-Key Genetic Algorithm (BRKGA) to optimize the main hyperparameters of a Deep Feedforward Neural Network (DFNN). The proposed methodology was detailed and compared with an approach based on the Tabu Search algorithm known in the literature and used for the same purpose in six datasets. Both algorithms showed high-performance results, but BRKGA resulted in better solution quality, faster convergence with fewer iterations, and better performance locally. Finally, a statistical test was used to prove the statistical difference between the two algorithms, thus guaranteeing the preference of the BRKGA regarding the results.
Quantum Token for Network Authentication

Classical token-based authentication can play a significant role in the web security check without accessing the database. For example, JSON Web Tokens (JWT) has been used to support scenarios such as single-sign-on. However, with the development of quantum computer, the security of JWT relying on the RSA algorithm would be compromised. Therefore, we propose a protocol to realize network authentication utilizing quantum token. Inspired by the structure of classical JWT, the structure of quantum token also consists of three parts: header, payload and quantum information. After the user logs in successfully, the quantum token can be issued by the server. If the user presents the quantum token to access again during the validity period, the server can verify whether the quantum token is valid. Our quantum token protocol can detect eavesdropping and achieve identity authentication. We also conduct a security analysis of the proposed protocol by addressing possible motives of an Eavesdropper and conclude the approach to be resilient against a broad range of attacks.
Proposing Recommendation System Using Bag of Word and Multi-label Support Vector Machine Classification

Currently, recommendation systems (RS) have attracted a great attention from researchers both in academic and industry. They help extracting useful information quickly to provide right services to users in accordance to there basic requests which are commonly not too details. In the field of higher education and academic research, proposing project/thesis title that has already been developed to the right students/researcher for reference is a new and challenging issue. Therefore, we propose to build a system to suggest title of graduation project in the paper. In proposal system, we use natural language processing (NLP) that is applied artificial neural network (ANN) to solve feature extraction and training problems. The simulation results show that the proposal system achieves an accuracy of 82% with 12 s. The results also show that proposal system is suitable for applying in real environment.
Complete Analysis of Implementing Isogeny-Based Cryptography Using Huff Form of Elliptic Curves

In this paper, we present the analysis of Huff curves for implementing isogeny-based cryptography. In this regard, we first investigate the computational cost of the building blocks when compression functions are used for Huff curves. We present a new formula for recovering the coefficient of the curve, from a given point on a Huff curve, which is essential for implementing SIDH. We also apply the square-root Vélu formula on Huff curves and further optimize Huff-CSIDH by exploiting Edwards curves for computing the coefficient of the image curve and present the Huff-Edwards hybrid model. From our implementation, the performance of Huff-SIDH and Montgomery-SIDH is almost the same, and the performance of Huff-CSIDH is 6% faster than Montgomery-CSIDH. The performance of Huff-Edwards CSIDH is almost the same as Montgomery-Edwards CSIDH. The result of our work shows that Huff curves can be quite practical for implementing isogeny-based cryptography but has some limitations.
A neural architecture generator for efficient search space

Neural architecture search (NAS) has made significant progress in recent years. However, the existing methods usually search architectures in a small-scale, well-designed architecture space, discover only one architecture in a single search, and hardly rework, which severely limits their potential. In this paper, we propose a novel neural architecture generator (NAG) that can efficiently sample architectures in a large-scale architecture space. Like a generative adversarial network (GAN), our model consists of two components: (1) a generator that can generate directed acyclic graphs (DAGs) as cells or blocks of neural architectures and (2) a discriminator that can estimate the probability that a DAG comes from cells of real architectures rather than the generator. Furthermore, we employ a random search with NAG (RS-NAG) to discover the optimal architecture according to the customized requirements. Experimental results show that the NAG can generate diverse architectures with our customized requirements multiple times after one adversary training. Furthermore, compared with the existing methods, our RS-NAG achieves the competitive results with 2.50% and 25.5% top-1 accuracies on two benchmark datasets – CIFAR-10 and ImageNet.
Spinner: Automated Dynamic Command Subsystem Perturbation

Injection attacks have been a major threat to web applications. Despite the significant effort in thwarting injection attacks, protection against injection attacks remains challenging due to the sophisticated attacks that exploit the existing protection techniques' design and implementation flaws. In this paper, we develop Spinner, a system that provides general protection against input injection attacks, including OS/shell command, SQL, and XXE injection. Instead of focusing on detecting malicious inputs, Spinner constantly randomizes underlying subsystems so that injected inputs (e.g., commands or SQL queries) that are not properly randomized will not be executed, hence prevented. We revisit the design and implementation choices of previous randomization-based techniques and develop a more robust and practical protection against various sophisticated input injection attacks. To handle complex real-world applications, we develop a bidirectional analysis that combines forward and backward static analysis techniques to identify in-tended commands or SQL queries to ensure the correct execution of the randomized target program. We implement Spinner for the shell command processor and two different database engines(MySQL and SQLite) and in diverse programming languages including C/C++, PHP, JavaScript and Lua. Our evaluation results on 42real-world applications including 27 vulnerable ones show that it effectively prevents a variety of input injection attacks with low runtime overhead (around 5%).
Optimization Parameters Support Vector Regression using Grid Search Method

Bitcoin is a cryptocurrency known to have high price fluctuation. Investment depends on price fluctuations which have a high level of risk. Bitcoin investment has these principles. To avoid losses and gain profits, there needs a method that may be used to make forecasts of the price of bitcoin accurately. In this research, Bitcoin price predictions were deployed based on bitcoin price data obtained in the past (Time Series Forecasting) using the method Support Vector Regression. The data retrieved is weekly Bitcoin price data from January 2018 to March 2020. Bitcoin price data is nonlinear, so a kernel is used Radial Basis Function. Meanwhile, the variables of the Support Vector Regression method are optimized using Grid Search Method. The purpose of the research is to determine the accuracy of the Support Vector Regression method by looking at the result of the Mean Absolute Percentage Error value. The research showed that the Mean Absolute Percentage Error value obtained was equal to 10,74 % with parameter value <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{C=5,} \boldsymbol{\varepsilon=0.004}$</tex> , and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{\gamma=0.07}$</tex> . The Mean Absolute Percentage Error value indicates that the prediction results are categorized as a good prediction.
Design and Implementation of Immersive and Interactive System Based on Unreal Engine

The interactive experience of virtual scenes is a computer-generated virtual scene. This virtual scene allows users to temporarily detach from reality, immerse themselves in it and interact with the objects in the scene. This is conducive to mobilizing the user's enthusiasm to use, while adding a touch of comfort to the traveler in the tired journey, will experience a different fun from the real life. At the same times, this scenario will also allow the user to experience it in a way that will also inspire or connect with the mind. The two scenes selected for this project are "porthole" and "listening, moving in the sky". The first scene does not change the interaction between the user and the screen, and enables the user to experience the scenic spots of chinese distinctive cultural heritage and traditional culture, and in the age of the internet, it reflects chinese friendliness to people all over the world, and at the same time better spreads and protects chinese traditional culture. The second landscape allows children to experience the feeling of stepping into the sky in the game, reducing the anxiety brought to children by long waiting time, and the interaction of the connection between the speaker and the horn can effectively promote the communication between children.
Novel Augmented Reality Enhanced Solution towards Vocational Training for People with Mental Disabilities

Augmented Reality is widely recognized as the next computing platform and has found applications in various sectors, including training, education, entertainment, engineering, etc. Although AR has been applied to cognitive training for people with neurological and psychiatric conditions, there is a huge potential in its use within the field of vocational rehabilitation for persons with psychiatric and neurodevelopmental disabilities. In this paper, we present a novel AR enhanced solution towards vocational rehabilitation for people with a range of cognitively functional levels. Multiple immersive training scenarios are designed and developed with a target of allowing users to develop their vocational skills which is evaluated by the Feasibility Evaluation Checklist. Proper user interface design approach was deployed with considerations given to the target users. The solution also supports multiplayer mode thus allowing the therapists to keep track of vital performance data of the users in the co-immersed AR environment. A user study was conducted with satisfying results achieved.
Motivations for and Initiatives on AI Engineering

There are evolving perspectives from a wide range of authors and documented sources that an urgency exists for defining and developing what will likely be an entirely new systems engineering paradigm for the engineering of artificially-intelligent (AI) systems. These perspectives evolve from many viewpoints related to complexities ranging from issues in data and data engineering to the challenges of realizing ethical and legal employment of autonomous system capabilities that are highly reliant on artificial intelligence technologies.
Distributed Variational Information Bottleneck for IOT Environments

Deep learning is becoming the latest trend in sensitive applications, such as healthcare, criminal justice, and finance. As these new applications emerge, adversaries are developing ways to circumvent them. In this paper, we investigate users&#x0027; revealing data to the public; parts of it are often sensitive when compactly represented. The representation should ensure that the target information is served accurately and reliably while simultaneously safeguarding sensitive information. In order to achieve that goal, we present a supervised deep learning framework based on the Information Bottleneck (IB) principle. The purpose of this was to maximize the mutual information between utility labels, and the learned compressing representation while minimizing the mutual information between the learned compressing representation and the original representation. Additionally, we examine a distributed learning framework to securely aggregate data from the Internet of Things (IoT) devices and create a utility model that is compatible with IoT devices. We apply the variational mutual information approximation to gain an accurate representation of bottlenecks. Through experiments with synthetic datasets, we demonstrate the efficiency and privacy-preserving capabilities of our framework.
Clubhouse Experience

Clubhouse is an invitation-only social networking application that differs from the usual social media platforms in that it is “audio only.” In this chapter, the sentiments in the social media messages about Clubhouse in the classic SMPs are examined by supervised learning (by using Hugging Face Transformer Library), and the user feelings are analyzed. Because Turkey is in the first ranks among European countries in terms of both the number of social media users and the number of messages, the analysis is conducted using the Turkish users. Mentions of Clubhouse have begun on Twitter and Sourtimes platforms in Turkey in early 2021. In this study, the aim is to demonstrate how Clubhouse, a new and different SMP, is evaluated by Twitter and Sourtimes users and to reveal user thoughts about this SMP along the timeline by using sentiment analysis.
The Imperative Role of Pervasive Data in Healthcare

Pervasive computing has to reflex the silent servant to monitor data automatically. This attracts caring for the patient smartly rather than traditionally. The current edge of smart technologies is composed of wireless sensor networks, ad hoc wireless networks and body sensor networks to gather patient data in every moment. The healthcare pervasive data can be utilized anytime and anywhere by the doctors, hospitals, as well as public sector organizations for planning and decision-making. The healthcare data depends on the status of data and how much the valid data is: accurate, continuous, and reliable. Therefore, smart healthcare management system is necessary to collaborate with wireless sensor networks, body sensor networks as well as wearable devices for integrations of data. These networks are resource-constraint devices and being a synchronous mode for gathering the complete data.
Progressive attention module for segmentation of volumetric medical images

Medical image segmentation is critical for many medical image analysis applications. 3D convolutional neural networks (CNNs) have been widely adopted in the segmentation of volumetric medical images. The recent development of channelwise and spatialwise attentions achieves the state-of-the-art feature representation performance. However, these attention strategies have not explicitly modeled interdependencies among slices in 3D medical volumes. In this work, we propose a novel attention module called progressive attention module (PAM) to explicitly model the slicewise importance for 3D medical image analysis.The proposed method is composed of three parts: Slice Attention (SA) block, Key-Slice-Selection (KSS) block, and Channel Attention (CA) block. First, the SA is a novel attention block to explore the correlation among slices for 3D medical image segmentation. SA is designed to explicitly reweight the importance of each slice in the 3D medical image scan. Second, the KSS block, cooperating with the SA block, is designed to adaptively emphasize the critical slice features while suppressing the irrelevant slice features, which helps the model focus on the slices with rich structural and contextual information. Finally, the CA block receives the output of KSS as input for further feature recalibration. Our proposed PAM organically combines SA, KSS, and CA, progressively highlights the key slices containing rich information for the relevant tasks while suppressing those irrelevant slices.To demonstrate the effectiveness of PAM, we embed it into 3D CNNs architectures and evaluate the segmentation performance on three public challenging data sets: BraTS 2018 data set, MALC data set, and HVSMR data set. We achieve 80.34%, 88.98%, and 84.43% of the Dice similarity coefficient on these three data sets, respectively. Experimental results show that the proposed PAM not only boosts the segmentation accuracy of the standard 3D CNNs methods consistently, but also outperforms the other attention mechanisms with slight extra costs.We propose a new PAM to identify the most informative slices and recalibrate channelwise feature responses for volumetric medical image segmentation. The proposed method is evaluated on three public data sets, and the results show improvements over other methods. This proposed technique can effectively assist physicians in many medical image analysis. It is also anticipated to be generalizable and transferable to assist physicians in a wider range of medical imaging applications to produce greater value and impact to health.
Zero-watermarking algorithm based on DC component in DCT domain

In recent years, with the rapid development of digital media technology, the number of Internet users has been increasing explosively while pictures play important roles in a network information carrier, which provides a good application background for the hidden communication. However, nearly all websites will conduct a transcoding process on uploaded pictures, which can cause unpredictable effects to carrier image's code rate, GOP (Group of Pictures) structure, QP (Quantization Parameters) and other key compression parameters. Obviously, it greatly limits most of the present information steganography based on compressed domain embedded applications. To solve this problem, this paper proposes an adaptive robust watermark method for watermark coding spread spectrum, combining the DCT transform domain and DC zero component. This method is based on image watermark encoding and achieves the purpose of embedding messages by selecting a certain number of DC values and expressing their mutual relations. Theoretically, watermarks embedded in DC components based on DCT transform domain have such advantages as strong robustness and good concealment, which are more conducive to resist compression or geometric attack. The experimental results show that this method improves the watermark information recovery rate of images with watermark carrier after different degrees of watermark attack as well as maintains good quality and robustness.
Towards Interpretability of Speech Pause in Dementia Detection Using Adversarial Learning

Speech pause is an effective biomarker in dementia detection. Recent deep learning models have exploited speech pauses to achieve highly accurate dementia detection, but have not exploited the interpretability of speech pauses, i.e., what and how positions and lengths of speech pauses affect the result of dementia detection. In this paper, we will study the positions and lengths of dementia-sensitive pauses using adversarial learning approaches. Specifically, we first utilize an adversarial attack approach by adding the perturbation to the speech pauses of the testing samples, aiming to reduce the confidence levels of the detection model. Then, we apply an adversarial training approach to evaluate the impact of the perturbation in training samples on the detection model. We examine the interpretability from the perspectives of model accuracy, pause context, and pause length. We found that some pauses are more sensitive to dementia than other pauses from the model's perspective, e.g., speech pauses near to the verb "is". Increasing lengths of sensitive pauses or adding sensitive pauses leads the model inference to Alzheimer's Disease (AD), while decreasing the lengths of sensitive pauses or deleting sensitive pauses leads to non-AD.
Proactive Ontology-based Cyber Threat Intelligence Analytic

Exploiting Cyber Threat Intelligence (CTI) as a valuable, updated, and structured source of information on threats and vulnerabilities can be a strong support for providing effective cybersecurity solutions. CTIs are shared across dedicated online platforms via a machine-readable format, such as Structured Threat Information eXpression (STIX). Meanwhile, ontology-based semantic knowledge modeling has become a promising solution that provides a machine-readable language for downstream work to address cybersecurity issues. Hence, by incorporating STIX concepts we propose in this paper an ontological-based CTI analysis that provides valuable threats information according to the security alerts reported by an analyzer. To test our ontology, we developed a set of reasoning rules to infer new knowledge on cyber threats. The experimental results show that such knowledge can be inferred by applying our approach for an ongoing and effective monitoring of cyber threats.