Reviewerly/exHarmony huggingface dataset:
DFDT: An End-to-End DeepFake Detection Framework Using Vision Transformer

The ever-growing threat of deepfakes and large-scale societal implications has propelled the development of deepfake forensics to ascertain the trustworthiness of digital media. A common theme of existing detection methods is using Convolutional Neural Networks (CNNs) as a backbone. While CNNs have demonstrated decent performance on learning local discriminative information, they fail to learn relative spatial features and lose important information due to constrained receptive fields. Motivated by the aforementioned challenges, this work presents DFDT, an end-to-end deepfake detection framework that leverages the unique characteristics of transformer models, for learning hidden traces of perturbations from both local image features and global relationship of pixels at different forgery scales. DFDT is specifically designed for deepfake detection tasks consisting of four main components: patch extraction &amp; embedding, multi-stream transformer block, attention-based patch selection followed by a multi-scale classifier. DFDT’s transformer layer benefits from a re-attention mechanism instead of a traditional multi-head self-attention layer. To evaluate the performance of DFDT, a comprehensive set of experiments are conducted on several deepfake forensics benchmarks. Obtained results demonstrated the surpassing detection rate of DFDT, achieving 99.41%, 99.31%, and 81.35% on FaceForensics++, Celeb-DF (V2), and WildDeepfake, respectively. Moreover, DFDT’s excellent cross-dataset &amp; cross-manipulation generalization provides additional strong evidence on its effectiveness.
Optimized URL Feature Selection Based on Genetic-Algorithm-Embedded Deep Learning for Phishing Website Detection

Deep learning models for phishing URL classification based on character- and word-level URL features achieve the best performance in terms of accuracy. Various improvements have been proposed through deep learning parameters, including the structure and learning strategy. However, the existing deep learning approach shows a degradation in recall according to the nature of a phishing attack that is immediately discarded after being reported. An additional optimization process that can minimize the false negatives by selecting the core features of phishing URLs is a promising avenue of improvement. To search the optimal URL feature set and to fully exploit it, we propose a combined searching and learning strategy that effectively models the URL classifier for recall. By incorporating the deep-learning-based URL classifier with the genetic algorithm to search the optimal feature set that minimizing the false negatives, an optimized classifier that guarantees the best performance was obtained. Extensive experiments on three real-world datasets consisting of 222,541 URLs showed the highest recall among the deep learning models. We demonstrated the superiority of the method by 10-fold cross-validation and confirmed that the recall improved compared to the latest deep learning method. In particular, the accuracy and recall were improved by 4.13%p and 7.07%p, respectively, compared to the convolutional–recurrent neural network in which the feature selection optimization was omitted.
Improved Security of E-Healthcare Images Using Hybridized Robust Zero-Watermarking and Hyper-Chaotic System along with RSA

With the rapid advancements of the internet of things (IoT), several applications have evolved with completely dissimilar structures and requirements. However, the fifth generation of mobile cellular networks (5G) is unable to successfully support the dissimilar structures and requirements. The sixth generation of mobile cellular networks (6G) is likely to enable new and unidentified applications with varying requirements. Therefore, 6G not only provides 10 to 100 times the speed of 5G, but 6G can also provide dynamic services for advanced IoT applications. However, providing security to 6G networks is still a significant problem. Therefore, in this paper, a hybrid image encryption technique is proposed to secure multimedia data communication over 6G networks. Initially, multimedia data are encrypted by using the proposed model. Thereafter, the encrypted data are then transferred over the 6G networks. Extensive experiments are conducted by using various attacks and security measures. A comparative analysis reveals that the proposed model achieves remarkably good performance as compared to the existing encryption techniques.
SpeechFormer: A Hierarchical Efficient Framework Incorporating the Characteristics of Speech

Transformer has obtained promising results on cognitive speech signal processing field, which is of interest in various applications ranging from emotion to neurocognitive disorder analysis. However, most works treat speech signal as a whole, leading to the neglect of the pronunciation structure that is unique to speech and reflects the cognitive process. Meanwhile, Transformer has heavy computational burden due to its full attention operation. In this paper, a hierarchical efficient framework, called SpeechFormer, which considers the structural characteristics of speech, is proposed and can be served as a general-purpose backbone for cognitive speech signal processing. The proposed SpeechFormer consists of frame, phoneme, word and utterance stages in succession, each performing a neighboring attention according to the structural pattern of speech with high computational efficiency. SpeechFormer is evaluated on speech emotion recognition (IEMOCAP & MELD) and neurocognitive disorder detection (Pitt & DAIC-WOZ) tasks, and the results show that SpeechFormer outperforms the standard Transformer-based framework while greatly reducing the computational cost. Furthermore, our SpeechFormer achieves comparable results to the state-of-the-art approaches.
FAIR Genomes metadata schema promoting Next Generation Sequencing data reuse in Dutch healthcare and research

The genomes of thousands of individuals are profiled within Dutch healthcare and research each year. However, this valuable genomic data, associated clinical data and consent are captured in different ways and stored across many systems and organizations. This makes it difficult to discover rare disease patients, reuse data for personalized medicine and establish research cohorts based on specific parameters. FAIR Genomes aims to enable NGS data reuse by developing metadata standards for the data descriptions needed to FAIRify genomic data while also addressing ELSI issues. We developed a semantic schema of essential data elements harmonized with international FAIR initiatives. The FAIR Genomes schema v1.1 contains 110 elements in 9 modules. It reuses common ontologies such as NCIT, DUO and EDAM, only introducing new terms when necessary. The schema is represented by a YAML file that can be transformed into templates for data entry software (EDC) and programmatic interfaces (JSON, RDF) to ease genomic data sharing in research and healthcare. The schema, documentation and MOLGENIS reference implementation are available at https://fairgenomes.org .
Blockchain Applications in Forestry: A Systematic Literature Review

Blockchain applications have received a lot of attention in recent years. They provide enormous benefits and advantages to many different sectors. To date, there have not been any systematic studies comprehensively reviewing current blockchain-based applications in the forestry sector. This paper examines published work on blockchain-based applications in the forestry sector. A systematic review was conducted to identify, analyze, and discuss current literature on current blockchain applications deployed (and/or proposed) in the forestry sector, grouping results into three domains of forest management, traceability of forest-based products, and forest fire detection based on content analysis. The analyses highlight reported benefits, opportunities, and challenges of blockchain applications in the forestry sector. The study results show that blockchain has great potential in sustainable forestry, minimizing illegal logging, conserving biodiversity, and many other areas in forestry. It also shows that blockchain in forestry is still immature and complex, since it requires specialists to adopt. This paper contributes towards filling the existing research gap through this systematic review on blockchain applications in forestry. This review offers insights into a deep understanding of blockchain applications for managers, practitioners, and consultants interested in forestry. The paper identifies existing research gaps on related topics of blockchain applications in forestry and makes recommendations on potential future directions for research into blockchain in forestry.
Typomorphism of Native Gold (Geological-Industrial Types of Gold Deposits in the North-East of Russia)

This study presents the typomorphic features of native gold grains from three different geological-industrial types (GIT) of gold deposits in the North-East of Russia: (1) gold–arsenic-sulfide in black shale strata (Natalka, Degdekan, Karalveem, Maldyak deposits), (2) gold–quartz veins in granitoids (Dorozhnoye, Butarnoye, Shkolnoye, Maltan deposits), and (3) gold–silver adularia in volcanogenic strata (Kupol, Olcha, Kubaka, Burgali, Primorskoe, Dalnee deposits). The reliability of the geological interpretation is directly related to mineral associations, fineness variations, its internal structure and the content of microimpurities. Native gold is a reliable indicator for identifying various GIT of gold deposits at the early geological-prospecting stages of studying gold-bearing areas. Typomorphic features of native gold for each of the considered GIT are stable and do not depend on the age and scale of mineralization. It is shown that using an integrated approach obtains genetic information about a particular ore object, which makes it possible to predict the vertical range of mineralization and outline the technology for processing ores. The information obtained can also be effectively used in the search for placer deposits in nearby watercourses. Identification of typomorphic features of ore and placer native gold opens up wide opportunities for delineating the distribution areas of placer deposits.
Red and white blood cell morphology characterization and hands-on time analysis by the digital cell imaging analyzer DI-60

The Sysmex DI-60 digital morphology analyzer is a fully automated, cell-locating image analysis system. This study aimed to evaluate the analytical performance of DI-60.A total of 822 peripheral blood smears were used. The diagnostic performance of DI-60 in terms of red blood cell (RBC) morphology characterization, white blood cell (WBC) differentials, and the total assay time including hands-on time was evaluated.In comparison with manual slide review, DI-60 demonstrated acceptable accuracy in recognizing polychromasia, target cells, and ovalocytes. However, for schistocytes, DI-60 demonstrated low specificity (10.4%) despite the high sensitivity (97.2%). In the precision analysis of RBC morphology characterization, borderline samples harboring specific RBCs showed inconsistencies in the positive results among 20 replicates. Particularly, 6 of 10 samples showed inconsistencies in the precision for schistocytes. For WBC differentials, the overall agreement between pre-classification results and user-verified results was 89.4%. Except for basophils, normal WBCs showed a good correlation between DI-60 (after user verification) and manual counts. The sensitivities in detecting immature granulocytes, blasts, atypical lymphocytes, and normoblasts were 85.9%, 92.0%, 37.5%, and 77.6%, respectively. Although the total assay time of DI-60 was longer than that of manual review, the hands-on time was considerably shorter with a difference of 144.1 s/slide for abnormal samples.DI-60 demonstrated acceptable performance for normal samples. However, for abnormal WBC differentials and RBC morphology characterization, it should be utilized carefully. DI-60 may contribute to an improvement in laboratory efficiency with increased feasibility.
A Unified Vista and Juxtaposed Study on Sorting Algorithms

Data is the new fuel. With the expansion of the global technology, the in-creasing standards of living and with modernization, data values have caught a great height. Now a days, nearly all top MNCs feed on data. Now, to store all this data is a prime concern for all of them, which is relieved by the Data Structures, the systematic way of storing data. Now, once these data are stored and charged in secure vaults, it’s time to utilize them in the most efficient way. Now, there are a lot of operations that needs to be performed on these massive chunks of data, like searching, sorting, inserting, deleting, merging and so more. In this paper, we would be comparing all the major sorting algorithms that have prevailed till date. Further, work have been done and an inequality in dimension of time between the four Sorting algorithms, Bubble, Selection, Insertion, Merge, that have been discussed in the paper have been proposed.
VQTTS: High-Fidelity Text-to-Speech Synthesis with Self-Supervised VQ Acoustic Feature

The mainstream neural text-to-speech(TTS) pipeline is a cascade system, including an acoustic model(AM) that predicts acoustic feature from the input transcript and a vocoder that generates waveform according to the given acoustic feature. However, the acoustic feature in current TTS systems is typically mel-spectrogram, which is highly correlated along both time and frequency axes in a complicated way, leading to a great difficulty for the AM to predict. Although high-fidelity audio can be generated by recent neural vocoders from ground-truth(GT) mel-spectrogram, the gap between the GT and the predicted mel-spectrogram from AM degrades the performance of the entire TTS system. In this work, we propose VQTTS, consisting of an AM txt2vec and a vocoder vec2wav, which uses self-supervised vector-quantized(VQ) acoustic feature rather than mel-spectrogram. We redesign both the AM and the vocoder accordingly. In particular, txt2vec basically becomes a classification model instead of a traditional regression model while vec2wav uses an additional feature encoder before HifiGAN generator for smoothing the discontinuous quantized feature. Our experiments show that vec2wav achieves better reconstruction performance than HifiGAN when using self-supervised VQ acoustic feature. Moreover, our entire TTS system VQTTS achieves state-of-the-art performance in terms of naturalness among all current publicly available TTS systems.
Adoption of Blockchain Technology for Privacy and Security in the Context of Industry 4.0

Compared to centralized and decentralized models, distributed models have the potential to dramatically expand the scalability of existing IoT and Industry 4.0 solutions while maintaining participant organizations’ security and privacy. This is partly because participating firms are not required to rely on or trust other services or third parties to manage the data they gather and transfer, out of concern that these parties could misuse the data or, in the worst-case scenario, share it with mass surveillance programs. However, until blockchain technology (BCT) demonstrates its viability as a means of developing security solutions in decentralized, collaborative, and trustless environments, the vast majority of these use cases will struggle to meet the requirements for integrity, immutability, traceability, and notarization. By utilizing BCT, it is possible to eliminate intermediaries, enabling individuals and devices to manage their data independently of third parties and most significantly to achieve a high level of traceability with information flow harmony. This technology enables transaction, transparency, and traceability by enabling for the interchange of historical data. The fundamentals of blockchain are examined in this research paper, along with an investigation of its operation and a discussion of some of its most fundamental aspects and concepts. A concise overview of smart contracts enables us to completely reimagine how network members create and automate transactions. Finally, several IoT and Industry 4.0 application possibilities that leverage blockchain are investigated, as is the blockchain’s future trajectory.
Fast Overlapping Block Processing Algorithm for Feature Extraction

In many video and image processing applications, the frames are partitioned into blocks, which are extracted and processed sequentially. In this paper, we propose a fast algorithm for calculation of features of overlapping image blocks. We assume the features are projections of the block on separable 2D basis functions (usually orthogonal polynomials) where we benefit from the symmetry with respect to spatial variables. The main idea is based on a construction of auxiliary matrices that virtually extends the original image and makes it possible to avoid a time-consuming computation in loops. These matrices can be pre-calculated, stored and used repeatedly since they are independent of the image itself. We validated experimentally that the speed up of the proposed method compared with traditional approaches approximately reaches up to 20 times depending on the block parameters.
A Gesture Recognition Strategy Based on A-Mode Ultrasound for Identifying Known and Unknown Gestures

Human-machine interface(HMI) technology has gradually become a research hotspot with the continuous development of computer technology and Internet of Things (IOT) technology. Hand gesture recognition (HGR) as an important part of HMI technology has been widely concerned. Among the many technical routes of HGR technology, the wearable HGR technology based on A-mode ultrasound shows great application potential due to its advantages such as lightweight device, free from sensors’ constraint etc. However, in the process of processing A-mode ultrasonic signal, the amplitude of the signal at the main position may vary due to muscle fatigue and strength etc. when the same gesture is repeated some time later. In this paper, we design a method to overcome this problem and accomplish HGR in offline state by setting the threshold and use normalization method for the energy feature of ultrasonic signal according to the threshold. Three machine learning algorithms, including support vector machine (SVM), linear discriminant analysis (LDA) and Naive Bayes (NB) were used to verify the feasibility of this method, and the average recognition accuracy in the experiment reached 95.26%. Signal stability is also verified to be improved. In addition, We design a recognition strategy based on NB algorithm in this paper so that the model can identify the unknown gestures (i.e. gestures that have not been trained by the model). Five known gestures and five unknown gestures were set in the experiment, the average recognition accuracy of the known gestures is 91.2%, and the unknown gestures is 81.8%. This method can be used to optimize the user experience of HGR system.
Effective Local and Global Search for Fast Long-Term Tracking

Compared with short-term tracking, long-term tracking remains a challenging task that usually requires the tracking algorithm to track targets within a local region and re-detect targets over the entire image. However, few works have been done and their performances have also been limited. In this paper, we present a novel robust and real-time long-term tracking framework based on the proposed local search module and re-detection module. The local search module consists of an effective bounding box regressor to generate a series of candidate proposals and a target verifier to infer the optimal candidate with its confidence score. For local search, we design a long short-term updated scheme to improve the target verifier. The verification capability of the tracker can be improved by using several templates updated at different times. Based on the verification scores, our tracker determines whether the tracked object is present or absent and then chooses the tracking strategies of local or global search, respectively, in the next frame. For global re-detection, we develop a novel re-detection module that can estimate the target position and target size for a given base tracker. We conduct a series of experiments to demonstrate that this module can be flexibly integrated into many other tracking algorithms for long-term tracking and that it can improve long-term tracking performance effectively. Numerous experiments and discussions are conducted on several popular tracking datasets, including VOT, OxUvA, TLP, and LaSOT. The experimental results demonstrate that the proposed tracker achieves satisfactory performance with a real-time speed. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/difhnp/ELGLT</uri> .
A Machine Learning Model to Identify Duplicate Questions in Social Media Forums

In recent years, digital platform forums where question and answers are being discussed are attracting more number of users. Many discussions on these forums would be repetitive nature. Such duplicate questions were provided by Quora as a competition on Kaggle. It is observed that the dataset provided by Quora, requires many modifications before training machine learning models to obtain a good accuracy. These modifications include feature extraction, vectorization and tokenization after which the data is ready for training desired models. While analyzing each model after prediction, it gives plenty of information about its efficiency and many other factors. Later, these information of different models are compared and helps to choose the best model. These models later can be combined and used as a single model with best accuracy. In this paper, a Machine Learning model which will predict duplicate questions is proposed.
Regulating Libra

Abstract Libra is the first private cryptocurrency with the potential to change the landscape of global payment and monetary systems. Due to the scale and reach provided by its affiliation with Facebook, the question is not whether, but how, to regulate it. This article introduces the Libra project and analyses the potential responses open to regulators worldwide. We conclude that perhaps the greatest impact will come not from Libra itself, but rather from reactions to it, particularly by other BigTechs, incumbent financial institutions and governments around the world.
Parameterized Hardware Accelerators for Lattice-Based Cryptography and Their Application to the HW/SW Co-Design of qTESLA

This paper presents a set of efficient and parameterized hardware accelerators that target post-quantum lattice-based cryptographic schemes, including a versatile cSHAKE core, a binary-search CDT-based Gaussian sampler, and a pipelined NTT-based polynomial multiplier, among others. Unlike much of prior work, the accelerators are fully open-sourced, are designed to be constant-time, and can be parameterized at compile-time to support different parameters without the need for re-writing the hardware implementation. These flexible, publicly-available accelerators are leveraged to demonstrate the first hardware-software co-design using RISC-V of the post-quantum lattice-based signature scheme qTESLA with provably secure parameters. In particular, this work demonstrates that the NIST’s Round 2 level 1 and level 3 qTESLA variants achieve over a 40-100x speedup for key generation, about a 10x speedup for signing, and about a 16x speedup for verification, compared to the baseline RISC-V software-only implementation. For instance, this corresponds to execution in 7.7, 34.4, and 7.8 milliseconds for key generation, signing, and verification, respectively, for qTESLA’s level 1 parameter set on an Artix-7 FPGA, demonstrating the feasibility of the scheme for embedded applications.