Reviewerly/exHarmony huggingface dataset:
Hybrid Algorithm for the detection of Pixel-based digital image forgery using Markov and SIFT descriptors

Today, image forgery is common due to the massification of low-cost/high-resolution digital cameras, along with the accessibility of computer programs for image processing. All media is affected by this issue, which makes the public doubt the news. Though image modification is a typical process in entertainment, when images are taken as evidence in a legal process, modification cannot be considered trivial. Digital forensics has the challenge of ensuring the accuracy and integrity of digital images to overcome this issue. This investigation introduces an algorithm to detect the main types of pixel-based alterations such as copy-move forgery, resampling, and splicing in digital images. For the evaluation of the algorithm, CVLAB, CASIA V1, Columbia, and Columbia Uncompressed datasets were used. Of 7100 images evaluated, 3666 were unaltered, 791 had resampling, 2213 had splicing, and 430 had copy-move forgeries. The algorithm detected all proposed forgery pixel methods with an accuracy of 91%. The main novelties of the proposal are the reduced number of features needed for identification and its robustness for the file format and image size.
Econometric Analysis of the Impact of Financial Structure on Innovation Based on the Fixed Effects Panel Model

This study addresses the impact of financial structure on innovation. The mechanism of the impact of financial structure on innovation at different levels of economic development is elucidated from the perspective of optimal financial structure decision theory and development economics, and empirical evidence is provided using manufacturing data from 59 countries or regions for the period 1996–2015. The study finds that financial structure has no significant effect on innovation at lower levels of economic development, but at higher levels of economic development, market-based financial structure significantly promotes innovation in the industry. The role of financial structure in innovation shifts in the interval where per capita income is greater than 9747 International dollar (hereinafter called I$9747) and less than I$17070. This paper verifies that the innovation approach of technological imitation requires the support of a bank-based financial structure, while the innovation approach of independent innovation requires the support of a market-based financial structure. The differences in innovation approaches at different stages of economic development lead to the evolution of the inherent demand for financial structures in the economy. Promoting the evolution of financial structures according to the stage of economic development is of great significance in building an innovative country and leading the sustainable and healthy development of the economy.
Prediction Method of College Students’ Psychological Pressure Based on Deep Neural Network

Aiming at the problems of low prediction accuracy and efficiency and poor prediction effect in the current psychological pressure prediction methods, a psychological pressure prediction method for college students based on deep neural network is proposed. The structure and algorithm of depth neural network and gray theory model are analyzed. Using the deep neural network, this paper establishes the sample set data of college students’ psychological pressure prediction and constructs the college students’ psychological pressure prediction model combined with the deep neural network algorithm of gray theory. The physical network information model is formed through the relationship between neurons. According to the dynamic changes of college students’ psychological pressure in each neuron of the physical network, the prediction of college students’ psychological pressure is completed. The experimental results show that the proposed method is effective in predicting college students’ psychological pressure and can effectively improve the accuracy and efficiency of college students’ psychological pressure prediction.
Three dimensional stabilization controller based on improved quaternion transformation for fixed-wing UAVs

To realize the three-dimensional stabilization control of fixed-wing unmanned aerial vehicles (UAVs), we analyze the nonholonomic characteristics, constraint non-integrability, and controllability of UAVs. To simplify the trigonometric function term in the dynamics of the UAV and avoid the singularity problem of the Euler angle in describing attitude, we use the quaternion theory to transform the dynamics of the UAV to avoid the complex trigonometric function derivation, which makes the dynamic matrix more concise. Based on this, a continuous periodic time-varying controller (CPTVC) is designed, and the effectiveness of the controller is proved using the homogeneous method. Finally, the results of the hardware in a loop simulation indicated that the exponential stability provided by the feedback controller can realize the three-dimensional stabilization of any initial position.
Foundations of Transaction Fee Mechanism Design

In blockchains such as Bitcoin and Ethereum, users compete in a transaction fee auction to get their transactions confirmed in the next block. A line of recent works set forth the desiderata for a “dream” transaction fee mechanism (TFM), and explored whether such a mechanism existed. A dream TFM should satisfy 1) user incentive compatibility (UIC), i.e., truthful bidding should be a user's dominant strategy; 2) miner incentive compatibility (MIC), i.e., the miner's dominant strategy is to faithfully implement the prescribed mechanism; and 3) miner-user side contract proofness (SCP), i.e., no coalition of the miner and one or more user(s) can increase their joint utility by deviating from the honest behavior. The weakest form of SCP is called 1-SCP, where we only aim to provide resilience against the collusion of the miner and a single user. Sadly, despite the various attempts, to the best of knowledge, no existing mechanism can satisfy all three properties in all situations.Since the TFM departs from classical mechanism design in modeling and assumptions, to date, our understanding of the design space is relatively little. In this paper, we further unravel the mathematical structure of transaction fee mechanism design by proving the following results:• Can we have a dream TFM? We prove a new impossibility result: assuming finite block size, no single­parameter, non-trivial, possibly randomized TFM can simultaneously satisfy UIC and 1-SCP. Consequently, no non-trivial TFM can satisfy all three desired properties simultaneously. This answers an important open question raised by Roughgarden in his recent work.• Rethinking the incentive compatibility notions. We observe that the prevalently adopted incentive compatibility notions may be too draconian and somewhat flawed. We rectify the existing modeling techniques, and suggest a relaxed incentive compatibility notion that captures additional hidden costs of strategic deviation. We construct a new mechanism called the “burning second-price auction”, and show that it indeed satisfies the new incentive compatibility notions. We additionally prove that the use of randomness is necessary under the new incentive compatibility notions for “useful” mechanisms that resist the coalitions of the miner and at least 2 users.• Do the new design elements make a difference? Unlike classical mechanisms, TFMs may employ a couple new design elements that are idiosyncratic to blockchains. For example, a burn rule (employed by Ethereum's EIP-1559) allows part to all of the payment from the users to be burnt rather than paid to the miner. Some mechanisms also allow unconfirmed transactions to be included in the block, to set the price for others. Our work unveils how these new design elements actually make a difference in TFM design, allowing us to achieve incentive compatible properties that would otherwise be impossible.
Locked And Tied: Locking Chain And Change Of Problems

Blockchains are typically named as democratisation technologies, however ever, their relationship with the law and general democratic establishments remains uncertain. First, it compares blockchain technology with the broader theory of transparency. Second, it examines the link between transparency and democracy, and asks how blockchain technology mediates these relationships. Finally, it studies blockchain however technology affects specific manifestations of transparency and freedom of information. The conclusion of this text is that the relationship between transparency and democratic ideals is complex, controversial, and extremely contextual; the “democratized” technical transparency embedded within the blockchain will simply be evidenced in its application. It's undemocratic. while not considering the political gatekeepers and also the legal, social, and cultural desires that support these goals, blockchain technology cannot bring home the bacon the broader goals of transparency.
Emotions for Virtual Learning Environments

Emotions are present and influence the learning process. One of the current challenges is how to get the emotions experienced by learners. Numerous mechanisms have been developed for this purpose. These mechanisms assess a number of basic emotions provided by the literature. A Virtual Learning Environment (VLE), on the other hand, is composed of several functionalities, in which not all emotions experienced by learners are always involved. This article presents a study conducted with experts in affective technologies and education, with the aim of obtaining the emotions that students may feel when using each of the functionalities of a VLE. The study counted with the participation of experts from several Latin American countries and concluded with a list of emotions associated with the functionalities of the VLE. The results obtained will allow the design of future mechanisms for the evaluation of emotions in VLEs, reducing the generic list used in the literature to only those emotions specific to each VLE component.
Graph Regularized Deep Sparse Representation for Unsupervised Anomaly Detection

Anomaly detection (AD) aims to distinguish the data points that are inconsistent with the overall pattern of the data. Recently, unsupervised anomaly detection methods have aroused huge attention. Among these methods, feature representation (FR) plays an important role, which can directly affect the performance of anomaly detection. Sparse representation (SR) can be regarded as one of matrix factorization (MF) methods, which is a powerful tool for FR. However, there are some limitations in the original SR. On the one hand, it just learns the shallow feature representations, which leads to the poor performance for anomaly detection. On the other hand, the local geometry structure information of data is ignored. To address these shortcomings, a graph regularized deep sparse representation (GRDSR) approach is proposed for unsupervised anomaly detection in this work. In GRDSR, a deep representation framework is first designed by extending the single layer MF to a multilayer MF for extracting hierarchical structure from the original data. Next, a graph regularization term is introduced to capture the intrinsic local geometric structure information of the original data during the process of FR, making the deep features preserve the neighborhood relationship well. Then, a L1-norm-based sparsity constraint is added to enhance the discriminant ability of the deep features. Finally, a reconstruction error is applied to distinguish anomalies. In order to demonstrate the effectiveness of the proposed approach, we conduct extensive experiments on ten datasets. Compared with the state-of-the-art methods, the proposed approach can achieve the best performance.
Eyeball Cursor Movement Detection Using Deep Learning

Computers have evolved ever since the beginning and reached a level where we can operate it remotely, wireless and in so many ways but these inventions are for people who are healthy and don’t have any physical disability. People with physical ailments require an alternative approach to make their computers more accessible and convenient .To develop an environment that is fair to everyone who is capable enough, we have designed a system that replaces the conventional method of cursor movement using mouse with eyeball movement. This will help people with body ailments to access computers thus contributing to the development in the field of technology and knowledge accusation. In this system we use the laptop camera to capture the images then identify the eyeball positions and movement by using face indexing and detecting the movement using the convolution neural network algorithms and OpenCV to perform the operations that a physical mouse could do. This system can be cost efficient and doesn’t require any pre installations so this will be quite effective and helpful for handicapped people
Design and Analysis of Adolescent Physical Health Monitoring System under the Background of Internet of Things and 5G

In recent years, with the rapid development of network communication, 5G technology has brought about higher network speed and more communication requirements. Internet of Things technology has been widely used in many fields. Therefore, this paper realizes the physical health monitoring system under the Internet of Things and 5G communication technology to detect the physical health of teenagers in real time. Based on the in-depth analysis of the theoretical research and application status of the healthy Internet of Things at home and abroad, this paper first studies the theories and methods of teenagers’ physical health information collection, collects human body temperature and movement steps, respectively, through wearable devices based on RFID anticollision algorithm, and further estimates human health and movement. The prototype system of the adolescent physical health monitoring system is realized. The system is divided into the terminal node and client information management system: (1) The terminal node is divided into information collection node and information collection node. The hardware circuit of the collection node mainly includes a microcontroller module, a sensor module, and a power circuit, and the collection node mainly includes a microcontroller module, a serial port module, and a power circuit. (2) The client information management system is divided into PC end and the mobile end. The PC end uses asp.net and SQL Server technology to design the database and user interface, and the mobile end uses WeChat public platform for development and design.
Characterizing Glanceable Visualizations: From Perception to Behavior Change

This chapter explores glanceability as a crucial requirement for several types of mobile visualizations, thereby integrating knowledge from the Vision Sciences, Visualization, Human-Computer Interaction, and Ubiquitous Computing. In mobile contexts, quick information needs are frequently occurring and differ from those in traditional visualizations that are designed for analyzing complex datasets. The chapter therefore discusses specific characteristics of glanceable mobile visualizations, explores different evaluation methodologies, and concludes with open challenges in the design of future glanceable visualizations.
Structural-Information-Based Robust Corner Point Extraction for Camera Calibration Under Lens Distortions and Compression Artifacts

Previous camera calibration methods often use a checkerboard to capture images and estimate the camera parameters from the correspondences between images and the checkerboard. The corner points in the checkerboard images are used as useful features for correspondence matching. Therefore, it is essential to precisely find the corner points in the checkerboard images. In many previous works, the corner points are extracted assuming that the checkerboard images are not distorted by its lens. Instead, image blurring and Gaussian noise on the images are usually considered, but other cases are not dealt with. However, the captured checkerboard images are often corrupted by lens distortions and compression artifacts, which leads to performance degradation of corner point extraction. Moreover, the corner points are extracted individually in the previous methods without considering their geometric relations. To better handle the corner point extraction problem under lens distortions, in our corner point extraction optimization, the distorted locations of the pixels on checkerboard images are corrected with the camera parameters, and the structural constraints for checkerboard image grids are then applied under the line-to-line mapping. Also, to robustly find the blurred edges between corner points due to JPEG compression, an edge surface model is newly proposed that models the transitions with over- and under-shoots around the blurred edges. Extensive experimental results show that our method significantly outperforms the state-of-the-art method with average 88.3% and 54.3% reduction in RMSE for corner point reprojection and camera parameter estimation, respectively under compression and lens distortions for synthetic and real data.
DEMO: A Secure Voting System for Score Based Elections

Dery et al. recently proposed a secure voting protocol for score-based elections, where independent talliers perform the tallying procedure. The protocol offers perfect ballot secrecy: it outputs the identity of the winner(s), but keeps all other information secret, even from the talliers. This high level of privacy, which may encourage voters to vote truthfully, and the protocol's extremely lightweight nature, make it a most adequate and powerful tool for democracies of any size. We have implemented that system and in this work we describe the system's components - election administrators, voters and talliers - and its operation. Our implementation is in Python and is open source. We view this demo as an essential step towards convincing decision makers in communities that practice score-based elections to adopt it as their election platform.
An Empirical Investigation of Word Alignment Supervision for Zero-Shot Multilingual Neural Machine Translation

Zero-shot translations is a fascinating feature of Multilingual Neural Machine Translation (MNMT) systems.These MNMT models are usually trained on English-centric data, i.e.English either as the source or target language, and with a language label prepended to the input indicating the target language.However, recent work has highlighted several flaws of these models in zero-shot scenarios where language labels are ignored and the wrong language is generated or different runs show highly unstable results.In this paper, we investigate the benefits of an explicit alignment to language labels in Transformer-based MNMT models in the zero-shot context, by jointly training one cross attention head with word alignment supervision to stress the focus on the target language label.We compare and evaluate several MNMT systems on three multilingual MT benchmarks of different sizes, showing that simply supervising one cross attention head to focus both on word alignments and language labels reduces the bias towards translating into the wrong language, improving the zero-shot performance overall.Moreover, as an additional advantage, we find that our alignment supervision leads to more stable results across different training runs.
Intriguing Properties of Vision Transformers

Vision transformers (ViT) have demonstrated impressive performance across various machine vision problems. These models are based on multi-head self-attention mechanisms that can flexibly attend to a sequence of image patches to encode contextual cues. An important question is how such flexibility in attending image-wide context conditioned on a given patch can facilitate handling nuisances in natural images e.g., severe occlusions, domain shifts, spatial permutations, adversarial and natural perturbations. We systematically study this question via an extensive set of experiments encompassing three ViT families and comparisons with a high-performing convolutional neural network (CNN). We show and analyze the following intriguing properties of ViT: (a) Transformers are highly robust to severe occlusions, perturbations and domain shifts, e.g., retain as high as 60% top-1 accuracy on ImageNet even after randomly occluding 80% of the image content. (b) The robust performance to occlusions is not due to a bias towards local textures, and ViTs are significantly less biased towards textures compared to CNNs. When properly trained to encode shape-based features, ViTs demonstrate shape recognition capability comparable to that of human visual system, previously unmatched in the literature. (c) Using ViTs to encode shape representation leads to an interesting consequence of accurate semantic segmentation without pixel-level supervision. (d) Off-the-shelf features from a single ViT model can be combined to create a feature ensemble, leading to high accuracy rates across a range of classification datasets in both traditional and few-shot learning paradigms. We show effective features of ViTs are due to flexible and dynamic receptive fields possible via the self-attention mechanism.
Characterizing the Adversarial Vulnerability of Speech self-Supervised Learning

A leaderboard named Speech processing Universal PERformance Benchmark (SUPERB), which aims at benchmarking the performance of a shared self-supervised learning (SSL) speech model across various downstream speech tasks with minimal modification of architectures and a small amount of data, has fueled the research for speech representation learning. The SUPERB demonstrates speech SSL upstream models improve the performance of various downstream tasks through just minimal adaptation. As the paradigm of the self-supervised learning upstream model followed by downstream tasks arouses more attention in the speech community, characterizing the adversarial robustness of such paradigm is of high priority. In this paper, we make the first attempt to investigate the adversarial vulnerability of such paradigm under the attacks from both zero-knowledge adversaries and limited-knowledge adversaries. The experimental results illustrate that the paradigm proposed by SUPERB is seriously vulnerable to limited-knowledge adversaries, and the attacks generated by zero-knowledge adversaries are with transferability. The XAB test verifies the imperceptibility of crafted adversarial attacks.
Assessing the Fractal Characteristics of Signals in Ultrasound Cavitation

Ultrasound cavitation has been studied by different authors for understanding the mechanism of producing the cavitation bubbles and the signals that appear in the cavitation field in different liquids. Although it has been proved that the properties of the signals depend on the liquid, their characteristics are not entirely known. In this context, our study aims at determining the fractal dimension of a signal collected in water and analyze the existence of a fractal pattern. It is shown that the signal presents a multifractal behavior.