Reviewerly/exHarmony huggingface dataset:
Advanced Steganography: Key Pairing

Research for greater security of important data becomes more sophisticated day by day as all information is processed only through online mode or travels through Internet medium. Due to the online processing of the required work, the operation of the transformation of the data must be secure in every process of the moment. Papers have been proposed a key pair concept to provide data security. An improved version of the steganography concept is also offered by the paper, by hiding the data through multi-tech images. Two levels of processing are proposed such as embedding and extracting data by hiding its information by covering some envelope. The presence of both keys allows only the data to come in a readable form. The paper therefore presents a meaningful approach and a concept for further processing in the steganographic region.
Parallel Breadth-First Search and Exact Shortest Paths and Stronger Notions for Approximate Distances

We introduce stronger notions for approximate single-source shortest-path distances, show how to efficiently compute them from weaker standard notions, and demonstrate the algorithmic power of these new notions and transformations. One application is the first work-efficient parallel algorithm for computing exact single-source shortest paths graphs -- resolving a major open problem in parallel computing. Given a source vertex in a directed graph with polynomially-bounded nonnegative integer lengths, the algorithm computes an exact shortest path tree in $m \log^{O(1)} n$ work and $n^{1/2+o(1)}$ depth. Previously, no parallel algorithm improving the trivial linear depths of Dijkstra's algorithm without significantly increasing the work was known, even for the case of undirected and unweighted graphs (i.e., for computing a BFS-tree). Our main result is a black-box transformation that uses $\log^{O(1)} n$ standard approximate distance computations to produce approximate distances which also satisfy the subtractive triangle inequality (up to a $(1+\varepsilon)$ factor) and even induce an exact shortest path tree in a graph with only slightly perturbed edge lengths. These strengthened approximations are algorithmically significantly more powerful and overcome well-known and often encountered barriers for using approximate distances. In directed graphs they can even be boosted to exact distances. This results in a black-box transformation of any (parallel or distributed) algorithm for approximate shortest paths in directed graphs into an algorithm computing exact distances at essentially no cost. Applying this to the recent breakthroughs of Fineman et al. for compute approximate SSSP-distances via approximate hopsets gives new parallel and distributed algorithm for exact shortest paths.
Role of Multilingualism in developing spirit of tolerance and harmony

In the interconnected and globalized world, multilingualism has a great role to play in creating a peaceful global society based on mutual understanding, solidarity, tolerance, affection and compassion. We know that the world has witnessed two World Wars, and continues to witness international conflicts, regional disputes, local unrests and sectarian clashes. Have we contemplated why all these happen, here and there, now and then? If we ponder upon, we will find that most of these conflicts and disputes occur due to linguistic, ethnic, religious, ideological or economic differences. And the reason behind it is that everyone refuses to recognize the other, his language, race, religion, and ideology. On the other hand everyone – individual or community or state - wants to achieve his own goals and objectives at the expense of the other. This is where clashes and disputes emanate from. This is what called "unilateral theory" where one party does not recognize or respect the other leading to the marginalization of the weaker by the stronger, which further leads to sowing the seeds of hatred and hostility, clash and conflict. So, to my belief, the recognition of pluralism - linguistic, cultural, religious, ethnic and ideological - can prove to be the only solution to many conflicts and disputes. Hence, this paper will focus on why we should encourage and promote multilingualism and how it develops the spirit of tolerance and understanding in the multilingual personalities.
Transforming educational space under the impact of the pandemic: student and teacher well-being in higher education

The article presents the study of some areas of educational space transformation under the COVID-19 pandemic influence. The focus of attention is on such problems as the cognitive well-being of the student, the tutor model of teacherstudent relations in the digitalising educational space, the functioning of academic mobility institution in a pandemic and post-pandemic world. Cognitive well-being of students implies mastering digital literacy with strict adherence to the norms of digital ethics. A tutor in modern education plays the role of not only a developer of an individual educational trajectory, but also a controller and educator. For the successful implementation of tutoring functions, the teaching staff of universities needs additional training. The main trends in academic mobility are internal and virtual mobility. The authors conclude that educational organisations need for adequate infrastructural policy, and educational space – inevitable transformations in budget financing. The results of the article can be used in further research on the higher education transformation problems, in the development of proposals and recommendations for specialists in the of higher education management field for use in practice.
Universal Perturbation Attack on Differentiable No-Reference Image- and Video-Quality Metrics

Universal adversarial perturbation attacks are widely used to analyze image classifiers that employ convolutional neural networks. Nowadays, some attacks can deceive image- and video-quality metrics. So sustainability analysis of these metrics is important. Indeed, if an attack can confuse the metric, an attacker can easily increase quality scores. When developers of image- and video-algorithms can boost their scores through detached processing, algorithm comparisons are no longer fair. Inspired by the idea of universal adversarial perturbation for classifiers, we suggest a new method to attack differentiable no-reference quality metrics through universal perturbation. We applied this method to seven no-reference image- and video-quality metrics (PaQ-2-PiQ, Linearity, VSFA, MDTVSFA, KonCept512, Nima and SPAQ). For each one, we trained a universal perturbation that increases the respective scores. We also propose a method for assessing metric stability and identify the metrics that are the most vulnerable and the most resistant to our attack. The existence of successful universal perturbations appears to diminish the metric's ability to provide reliable scores. We therefore recommend our proposed method as an additional verification of metric reliability to complement traditional subjective tests and benchmarks.
SAR Image Water Extraction Based on Saliency Target Detection

Synthetic aperture radar (SAR) image water extraction has important research significance in water resources monitoring and other applications. In SAR images, the scattering properties of some land cover types with low backscattering coefficients, such as broad roads and mountain shadows, are very close to the scattering properties of water. Most water extraction methods are easy to identify those false water targets as water. Meanwhile, the water area in the scene is very small, and the proportion of water and background in the training concentration is extremely unbalanced. The imbalance of water samples affects the water extraction performance. Traditional machine learning water extraction methods require manual extraction of effective features, which are time-consuming and inefficient. Therefore, we decided to introduce the deep learning-based salient object detection network Pool-Net into SRA image water extraction. To overcome the effect of class imbalance, we introduce the focus loss function into the Pool-Net network. Experimental results show that the proposed method achieves good water extraction results on water class imbalanced SAR images. The water extraction accuracy of the proposed method is 2% higher than Pool-Net and also surpasses some well-known image segmentation methods.
Sensing Intrusion Detection for Automatic Driving System based on Scene Semantic Centroid

Sensing intrusion is a new threat to information security of automatic driving system, which employs digital noise like adversarial sample to show sensors fake information, aiming to mislead decision making and eventually achieve the hacker's illegal intention. Unfortunately, it is difficult for most traditional information security techniques to deal with this novel risk. Even if the methods like outlier detection can pick out the abnormal sensing data, they still hardly tell the samples containing adversarial noise. In this paper, a method based on semantic similarity check is proposed to address this issue. Scene semantic centroid is introduced to represent the core semantics of a category of driving scene. Correspondingly, each component of the centroid is used to depict the standard sensing semantics for each sensor in this kind of scene. On this basis, a straightforward algorithm is proposed to simultaneously detect both abnormal driving scene and the sensors that may be hacked. Considering the raw sensing semantic space is too high dimensional and too sparse to handle efficiently, a scene semantic autoencoder is developed to extract scene semantic centroid by semantic embedding. The experiments on Nuscenes dataset show that the proposed method is not only feasible to identify the suspicious intruded sensors, but also more effective and accurate than the traditional abnormal sensing data detection.
A Data-Centric Accelerator for High-Performance Hypergraph Processing

Hypergraph processing has emerged as a powerful approach for analyzing complex multilateral relationships among multiple entities. Past research on building hypergraph systems suggests that changing the scheduling order of bipartite edge tasks can improve the overlap-induced data locality in hypergraph processing. However, due to the complex intertwined connections between vertices and hyperedges, it is almost impossible to find a locality-optimal scheduling order. Thus, these task-centric hypergraph systems often suffer from substantial off-chip communications. In this paper, we first propose a novel data-centric Load-Trigger-Reduce (LTR) execution model to exploit fully the locality in hypergraph processing. Unlike a task-centric model that loads the required data along with a task, our LTR model invokes tasks as per the data used. Specifically, once the hypergraph data is loaded into the on-chip memory, all of its relevant computation tasks will be triggered simultaneously to output intermediate results, which are finally reduced to update the final results. Our LTR model enables all hypergraph data to be accessed once in each iteration. To fully exploit the LTR performance potential, we further architect an LTR-driven hypergraph accelerator, XuLin, which features with an adaptive data loading mechanism to minimize the loading cost via chunk merging at runtime. XuLin is also equipped with a priority-based differential data reduction scheme to reduce the impact of conflicting updates on performance. We have implemented XuLin both on a Xilinx Alveo U250 FPGA card and using a cycle-accurate simulator. The results show that XuLin outperforms the state-of-the-art hypergraph processing solutions Hygra and ChGraph by $20.47 \times$ and $8.77 \times$ on average, respectively.
Language Resource Construction of Multi-Domain Philippine English Text for Pre-training Objective

Pre-trained language models (PLMs) have gained significant attention in NLP because of its effectiveness in improving the performance of several downstream tasks. Pre-training these PLMs requires benchmark datasets to create universal language representation and to generate robust models. This paper established the first linguistic resource for Philippine English language to help future researchers in language modeling and other NLP tasks. We used NLP approach to prepare and build our data and transformers paradigm to generate small PLMs. The PHEnText corpus is composed of multi-domain Philippine English text data in formal language scraped from different sources. Tokenization process was performed using BPE and WordPiece tokenizer algorithms. Using a subset of the PHEnText, we generated four small versions of transformer-based language models. Cross-validation during the pre-training reported that a RoBERTa-base model outperformed all other variants in terms of training loss, evaluation loss and accuracy. This work introduced the PHEnText benchmark corpus composed of 2.6B tokens primarily intended for pre-training objective. The corpus provides starting point and opportunities for current and future NLP researches and once trained, can be used more efficiently via fine-tuning. Additionally, the dataset was prepared to be pre-training compatible with different transformer models. Furthermore, the generated PLMs using a subset of PHEnText rendered notable results in terms of minimal loss and nearly acceptable accuracy. Next step for this undertaking is to train PLMs using the entire PHEnText dataset and to test the models' effectiveness by fine-tuning them to NLP downstream tasks.
VOGUE: Secure User Voice Authentication on Wearable Devices using Gyroscope

Voice assistants are popular to wearable devices with limited input and output capabilities, however vulnerable to voice attacks, which cheat a voice assistant by playing forged voice commands without user awareness. In this paper, we propose VOGUE, which captures unique yet stable pattern of speech movement sequences of speakers with embedded gyroscope in wearable devices, to distinguish between registered legal user and malicious attackers (human or machines). The design of VOGUE is based on two key observations. First, speech, as a type of highly complex motor task, inherently requires coordinated actions of many orofacial, laryngeal, pharyngeal, and respiratory muscles, and the collective movements of muscles propagate to distant body segments. Second, to generate a certain word, the speech movement sequence of a speaker is known to be distinctive, and can be captured by inertial sensors. We implement VOGUE on three kinds of COTS android devices including smart glasses, watches and phones, and conduct comprehensive evaluation on the performances. Experimental results show that VOGUE achieves a mean false-acceptance rate (FAR) and false- rejection rate (FRR) of 2.23% and 2.48%, respectively, even under sophisticated voice impersonation attacks.
Towards Fast GPU-based Sparse DNN Inference: A Hybrid Compute Model

As the model scale of Deep Neural Networks (DNNs) increases, the memory and computational cost of DNNs become overwhelmingly large. Sparse Deep Neural Networks (SpDNNs) are promising to cope with this challenge by using fewer weights while preserving the accuracy. However, the sparsity nature of SpDNN models makes it difficult to run efficiently on GPUs. To stimulate technical advances for improving the efficiency of SpDNN inference, the MIT/IEEE/Amazon GraphChallenge proposes the SpDNN Challenge in 2019. In this paper, we present a hybrid compute model to improve the efficiency of Sparse Matrix Multiplications (SpMMs), the core computation of SpDNN inference. First, the given sparse weight matrix will be divided to generate many (sparse and dense) submatrices. For sparse submatrices, we leverage compile-time data embedding to compile the sparse data together with their corresponding computations into instructions and hence the number of random accesses can be reduced significantly. For dense submatrices, we follow the traditional computing mode where the data is obtained from the memory to exploit the high memory bandwidth of GPU. This hybrid compute model effectively balances the memory and instruction bottlenecks, and offers more scheduling opportunities to overlap computing operations and memory accesses on GPU. To determine whether a sub matrix is sparse, we present a cost model to estimate its time cost under the traditional computing mode and the data-embedded computing mode in an accurate and efficient manner. Once the computing mode for all submatrices is determined, customized codes will be generated for the SpDNN inference. Experimental results on the SpDNN Challenge benchmarks show that our approach achieves up to 197.86 tera-edges per second inference throughput on a single NVIDIA A100 GPU. Compared to the 2021 and 2020 champions, our approach offers up to 6.37x and 89.94x speedups on a single GPU, respectively. We also implement a 16-GPU version, showing up to 9.49x and 80.11x speedups over the former 16-GPU baselines of the 2021 and 2020 champions.
Triple DES Cryptography Based on Hash Function and DSA for Digital Certificate Authentication

Digital certificates are digital files that are conventionally used as proof of participation or a sign of appreciation owned by someone. Cryptographic authentication method using digital signatures that are used as confidential evidence and validation of digital certificate ownership. Digital signatures use cryptographic algorithms such as one-way hashes, non-symmetric key cryptography, and symmetric key cryptography. So this study uses the proposed algorithm used, namely SHA-256, DSA & 3DES. The SHA-256 algorithm is used because of its high security, while the DSA algorithm is a special standard for digital signatures with 2 (two) functions, namely signing & verifying, and the last is the 3DES algorithm as double security which guarantees 3 (three) times the encryption process and description. By implementing a digital signature using the SHA256 hash method, the DSA signing & verifying method, and the 3DES cryptographic algorithm, digital certificates can avoid editing threats, and ensure authentication security and validation of the validity of the file, so that it can detect the original digital certificate. In 10 experiments, it is known that the digital signature method with the SHA-256, DSA and 3DES algorithms has an average run time speed of 0.0518186331 seconds for the signing method and 0.1321175146 seconds for the verify method.
Improved Image Registration Algorithm for Aoggy Sky Based on Dark Channel Prior

The SIFT (Scale Invariant Feature Transform) operator is widely used in image registration because of its good invariant characteristics such as scale, rotation, and illumination. However, the images collected in fog and haze weather have low contrast, color distortion and texture. Defects such as missing, it is difficult to extract effective matching points when dealing with foggy blurred images, and the feature extraction ability of its images is very limited. From the perspective of recovering feature information, this paper optimizes the atmospheric light value of the traditional dark channel prior algorithm, and performs HSV space color compensation on the restored image, combining the advantages of the SIFT algorithm with the improved single image dark channel. The dehazing algorithm performs algorithm fusion instead of generating the dehazing image itself, but directly performs feature point detection and descriptor generation. The new algorithm can effectively restore the characteristics of foggy images and increase the number of feature points.
Image Captioning Generator Text-to-Speech

A model is created for blind people that can guide and support them while traveling on the highways just with the help of a smartphone application. This can be accomplished by first converting the scene in front of the user into text and then converting text into voice output. Then a method for the generation of image legends based on deep neural networks. With an image as an entry, the method can display an English sentence describing the contents of the image. The user first provides a voice command, then a quick snapshot is captured by the camera or webcam. This image is then fed as input to the image caption generator template that generates a caption for the image. Next, this caption text is converted to speech, which gives rise to a voice message on the description of the image.
Double-Attention YOLO: Vision Transformer Model Based on Image Processing Technology in Complex Environment of Transmission Line Connection Fittings and Rust Detection

Transmission line fittings have been exposed to complex environments for a long time. Due to the interference of haze and other environmental factors, it is often difficult for the camera to obtain high quality on-site images, and the traditional image processing technology and convolution neural networks find it difficult to effectively deal with the dense detection task of small targets with occlusion interference. Therefore, an image processing method based on an improved dark channel defogging algorithm, the fusion channel spatial attention mechanism, Vision Transformer, and the GhostNet model compression method is proposed in this paper. Based on the global receptive field of the saliency region capture and enhancement model, a small target detection network Double-attention YOLO for complex environments is constructed. The experimental results show that embedding a multi-head self-attention component into a convolutional neural network can help the model to better interpret the multi-scale global semantic information of images. In this way, the model learns more easily the distinguishable features in the image representation. Embedding an attention mechanism module can make the neural network pay more attention to the salient region of image. Dual attention fusion can balance the global and local characteristics of the model, to improve the performance of model detection.
An Easy-to-use and Robust Approach for the Differentially Private De-Identification of Clinical Textual Documents

Unstructured textual data is at the heart of healthcare systems. For obvious privacy reasons, these documents are not accessible to researchers as long as they contain personally identifiable information. One way to share this data while respecting the legislative framework (notably GDPR or HIPAA) is, within the medical structures, to de-identify it, i.e. to detect the personal information of a person through a Named Entity Recognition (NER) system and then replacing it to make it very difficult to associate the document with the person. The challenge is having reliable NER and substitution tools without compromising confidentiality and consistency in the document. Most of the conducted research focuses on English medical documents with coarse substitutions by not benefiting from advances in privacy. This paper shows how an efficient and differentially private de-identification approach can be achieved by strengthening the less robust de-identification method and by adapting state-of-the-art differentially private mechanisms for substitution purposes. The result is an approach for de-identifying clinical documents in French language, but also generalizable to other languages and whose robustness is mathematically proven.
Introduction and Overview of V2G Security

Vehicle to grid (V2G) technology can realize energy interaction and information interaction between electric vehicles and smart grid. Although the V2G network can realize the intelligent charging of electric vehicles and provide the service of fast storage and release of electric energy for the smart grid, the V2G network is facing severe security and privacy problems. This chapter lists the harm of network attacks to smart grid and users in V2G, and mainly classifies the network attacks in V2G. At the same time, corresponding protection strategies are proposed to mitigate and defend against these attacks.