Reviewerly/exHarmony huggingface dataset:
StructFormer: Joint Unsupervised Induction of Dependency and Constituency Structure from Masked Language Modeling

There are two major classes of natural language grammars -the dependency grammar that models one-to-one correspondences between words and the constituency grammar that models the assembly of one or several corresponded words.While previous unsupervised parsing methods mostly focus on only inducing one class of grammars, we introduce a novel model, StructFormer, that can simultaneously induce dependency and constituency structure.To achieve this, we propose a new parsing framework that can jointly generate a constituency tree and dependency graph.Then we integrate the induced dependency relations into the transformer, in a differentiable manner, through a novel dependency-constrained self-attention mechanism.Experimental results show that our model can achieve strong results on unsupervised constituency parsing, unsupervised dependency parsing, and masked language modeling at the same time.
Local-aware spatio-temporal attention network with multi-stage feature fusion for human action recognition

Abstract In the study of human action recognition, two-stream networks have made excellent progress recently. However, there remain challenges in distinguishing similar human actions in videos. This paper proposes a novel local-aware spatio-temporal attention network with multi-stage feature fusion based on compact bilinear pooling for human action recognition. To elaborate, taking two-stream networks as our essential backbones, the spatial network first employs multiple spatial transformer networks in a parallel manner to locate the discriminative regions related to human actions. Then, we perform feature fusion between the local and global features to enhance the human action representation. Furthermore, the output of the spatial network and the temporal information are fused at a particular layer to learn the pixel-wise correspondences. After that, we bring together three outputs to generate the global descriptors of human actions. To verify the efficacy of the proposed approach, comparison experiments are conducted with the traditional hand-engineered IDT algorithms, the classical machine learning methods (i.e., SVM) and the state-of-the-art deep learning methods (i.e., spatio-temporal multiplier networks). According to the results, our approach is reported to obtain the best performance among existing works, with the accuracy of 95.3% and 72.9% on UCF101 and HMDB51, respectively. The experimental results thus demonstrate the superiority and significance of the proposed architecture in solving the task of human action recognition.
Playing chunk-transferred DASH segments at low latency with QLive

More users have a growing interest in low latency over-the-top (OTT) applications such as online video gaming, video chat, online casino, sports betting, and live auctions. OTT applications face challenges in delivering low latency live streams using Dynamic Adaptive Streaming over HTTP (DASH) due to large playback buffer and video segment duration. A potential solution to this issue is the use of HTTP chunked transfer encoding (CTE) with the common media application format (CMAF). This combination allows the delivery of each segment in several chunks to the client, starting before the segment is fully available in real-time. However, CTE and CMAF alone are not sufficient as they do not address other limitations and challenges at the client-side, including inaccurate bandwidth measurement, latency control, and bitrate selection.
Chosen-Ciphertext Clustering Attack on CRYSTALS-KYBER Using the Side-Channel Leakage of Barrett Reduction

This study proposes a chosen-ciphertext sidechannel attack against a lattice-based key encapsulation mechanism (KEM), the third-round candidate of the national institute of standards and technology (NIST) standardization project.Unlike existing attacks that target operations, such as inverse NTT and message encoding/decoding, we target Barrett reduction in the decapsulation phase of CRYSTALS-KYBER to obtain a secret key.We show that a sensitive variable-dependent leakage of Barrett reduction exposes an entire secret key.The results of experiments conducted on the ARM Cortex-M4 microcontroller accomplish a success rate of 100%.We only need six chosen ciphertexts for KYBER512 and KYBER768 and eight chosen ciphertexts for KYBER1024.We also show that the m4 scheme of the pqm4 library, an implementation with the ARM Cortex-M4 specific optimization (typically in assembly), is vulnerable to the proposed attack.In this scheme, six, nine, and twelve chosen ciphertexts are required for KYBER512, KYBER768, and KYBER1024, respectively.Index Terms-Barrett reduction, chosen-ciphertext attack (CCA), key decapsulation mechanism, lattice-based cryptography, side-channel attack (SCA). I. INTRODUCTIONB Y 2025, it is expected that there will be more than 30 billion Internet of Things (IoT) connections, almost four IoT devices per person on average [1].In addition, the demand for IoT security is increasing, and the global IoT security market size is expected to increase to more than 20.8 billion by 2025 [2].Accordingly, establishing a trustworthy IoT infrastructure that ensures information protection is essential.Five areas, including cloud-based IoT security, have been reported
Human action recognition based on multi-scale feature maps from depth video sequences

Human action recognition is an active research area in computer vision. Although great progress has been made, previous methods mostly recognize actions from depth video sequences at only one scale, and thus they often neglect multi-scale spatial changes that provide additional information in practical applications. In this paper, we present a novel framework with a multi-scale mechanism to improve scale diversity of motion features. We propose a multi-scale feature map called Laplacian pyramid depth motion images(LP-DMI). First, We employ depth motion images (DMI) as the templates to generate the multi-scale static representation of actions. Then, we caculate LP-DMI to enhance multi-scale dynamic information of motions and reduce redundant static information in human bodies. We further extract the multi-granularity descriptor called LP-DMI-HOG to provide more discriminative features. Finally, we utilize extreme learning machine (ELM) for action classification. The proposed method yeilds the recognition accuracy of 93.41%, 85.12%, 91.94% on the public MSRAction3D, UTD-MHAD and DHA dataset. Through extensive experiments, we prove that our method outperforms the state-of-the-art benchmarks.
Evidence of a new geothermal prospect in the Northern-Central trans-Mexican volcanic belt: Rancho Nuevo, Guanajuato, Mexico

Thermal manifestations are commonly found in central Mexico as result of the volcanic activity originating from the formation of the Trans-Mexican Volcanic Belt during the Quaternary. The Rancho Nuevo hot spring is one of them that has not been described before with a discharge temperature near 92 °C. The goal of the present study is to provide geothermal characteristics of thermal manifestations at Rancho Nuevo location based on geochemical and mineralogical results to explain deep-subsurface processes that occurred in the geothermal system. The presence of kaolinite, montmorillonite, opal, zeolite, barite, pyrite, and stibnite in altered soil sediments or around the hot springs identified by the techniques used in the present study, confirms the presence of hydrothermal activity. In addition, based on the X-ray diffraction, calcite precipitates at the surface of the thermal springs. This mineral association reflects deep geothermal processes and is eventually deposited in shallow zones. Fluid mixing processes and variations in redox conditions are suggested by mineral association and isotopic sulfur data. Finally, based on the physicochemical data provided by the water samples and the discharge conditions of the springs, stability diagrams were constructed for pyrite, barite, and zeolites using the Geochemist’s Work Bench program to corroborate these data with the mineralogical results. The mineralogical results and distribution, as well as the N-S trend of mineral associations suggest interaction processes between geothermal fluid and rocks of the stratigraphic sequence, and active major faults, enabling the upward flow of deep geothermal fluids. The approach to the conceptual model of the Rancho Nuevo geothermal prospect reveals an attractive potential for the exploration of a viable geothermal resource in central Mexico.
Learning and focusing strategies to improve ACO that solves CSP

Metaheuristics are powerful techniques for solving hard real-world problems in many application domains. Their behavior and performance strongly depend on their ability to efficiently explore and exploit the search space. A well-known metaheuristic is Ant Colony Optimization which has been successfully applied to solve many engineering problems. In this paper, we focus on ACO that solves Constraint Satisfaction Problems. In this context ACO has already shown to be able to solve many difficult CSPs, however, some problems are still very hard for this kind of technique. We introduce two strategies that allow improving ACO intensification and diversification process: one for learning in a pre-processing step and a second strategy to focus the search to a feasible space. Our results suggest that both the learning phase as well as the strategy to focus the search allow improving the ACO performance especially to solve hard CSPs. Moreover, these strategies can be applied to ACO for other application domains.
Threshold Schnorr with Stateless Deterministic Signing from Standard Assumptions

Schnorr’s signature scheme permits an elegant threshold signing protocol due to its linear signing equation. However each new signature consumes fresh randomness, which can be a major attack vector in practice. Sources of randomness in deployments are frequently either unreliable, or require state continuity, i.e. reliable fresh state resilient to rollbacks. State continuity is a notoriously difficult guarantee to achieve in practice, due to system crashes caused by software errors, malicious actors, or power supply interruptions (Parno et al., S&P ’11). This is a non-issue for Schnorr variants such as EdDSA, which is specified to derive nonces deterministically as a function of the message and the secret key. However, it is challenging to translate these benefits to the threshold setting, specifically to construct a threshold Schnorr scheme where signing neither requires parties to consume fresh randomness nor update long-term secret state. In this work, we construct a dishonest majority threshold Schnorr protocol that enables such stateless deterministic nonce derivation using standardized block ciphers. Our core technical ingredients are new tools for the zero-knowledge from garbled circuits (ZKGC) paradigm to aid in verifying correct nonce derivation: A proof per our scheme requires only a small constant number of exponentiations.
GANSpeech: Adversarial Training for High-Fidelity Multi-Speaker Speech Synthesis

Recent advances in neural multi-speaker text-to-speech (TTS) models have enabled the generation of reasonably good speech quality with a single model and made it possible to synthesize the speech of a speaker with limited training data. Fine-tuning to the target speaker data with the multi-speaker model can achieve better quality, however, there still exists a gap compared to the real speech sample and the model depends on the speaker. In this work, we propose GANSpeech, which is a high-fidelity multi-speaker TTS model that adopts the adversarial training method to a non-autoregressive multi-speaker TTS model. In addition, we propose simple but efficient automatic scaling methods for feature matching loss used in adversarial training. In the subjective listening tests, GANSpeech significantly outperformed the baseline multi-speaker FastSpeech and FastSpeech2 models, and showed a better MOS score than the speaker-specific fine-tuned FastSpeech2.
Verification of piecewise deep neural networks: a star set approach with zonotope pre-filter

Abstract Verification has emerged as a means to provide formal guarantees on learning-based systems incorporating neural network before using them in safety-critical applications. This paper proposes a new verification approach for deep neural networks (DNNs) with piecewise linear activation functions using reachability analysis. The core of our approach is a collection of reachability algorithms using star sets (or shortly, stars), an effective symbolic representation of high-dimensional polytopes. The star-based reachability algorithms compute the output reachable sets of a network with a given input set before using them for verification. For a neural network with piecewise linear activation functions, our approach can construct both exact and over-approximate reachable sets of the neural network. To enhance the scalability of our approach, a star set is equipped with an outer-zonotope (a zonotope over-approximation of the star set) to quickly estimate the lower and upper bounds of an input set at a specific neuron to determine if splitting occurs at that neuron. This zonotope pre-filtering step reduces significantly the number of linear programming optimization problems that must be solved in the analysis, and leads to a reduction in computation time, which enhances the scalability of the star set approach. Our reachability algorithms are implemented in a software prototype called the neural network verification tool, and can be applied to problems analyzing the robustness of machine learning methods, such as safety and robustness verification of DNNs. Our experiments show that our approach can achieve runtimes twenty to 1400 times faster than Reluplex, a satisfiability modulo theory-based approach. Our star set approach is also less conservative than other recent zonotope and abstract domain approaches.
System of Additional Professional Education as a Basis of Lifelong Professional Education

The article considers the implementation of the principle of continuing education through the system of additional professional education at Orenburg State University. Additional professional education is the main element of the system of lifelong education. The authors analyze the current trends in the development of additional professional education in the Russian Federation and the role of higher education in it, as well as the opportunities and problems of implementing additional professional programs at the University. The analysis of the dynamics and structure of additional professional programs of the Orenburg State University is carried out. The mechanisms of implementing additional professional programs based on a practice-oriented integrated approach aimed at strengthening the practical orientation of programs based on the integration of University’s educational activities and strategic partnership with enterprises and organizations of the region are proposed. The authors analyze the possibilities and prospects of using e-learning and technologies of distance education in the implementation of additional professional programs. The article highlights the mandatory conditions for building an effective system of additional professional education at the University, based on close interaction with the program’s customers, the development of professional competencies of teaching staff engaged in the implementation of additional programs, and the active use of modern educational technologies.
A Hybrid Method to Predict Postoperative Survival of Lung Cancer Using Improved SMOTE and Adaptive SVM

Predicting postoperative survival of lung cancer patients (LCPs) is an important problem of medical decision-making. However, the imbalanced distribution of patient survival in the dataset increases the difficulty of prediction. Although the synthetic minority oversampling technique (SMOTE) can be used to deal with imbalanced data, it cannot identify data noise. On the other hand, many studies use a support vector machine (SVM) combined with resampling technology to deal with imbalanced data. However, most studies require manual setting of SVM parameters, which makes it difficult to obtain the best performance. In this paper, a hybrid improved SMOTE and adaptive SVM method is proposed for imbalance data to predict the postoperative survival of LCPs. The proposed method is divided into two stages: in the first stage, the cross-validated committees filter (CVCF) is used to remove noise samples to improve the performance of SMOTE. In the second stage, we propose an adaptive SVM, which uses fuzzy self-tuning particle swarm optimization (FPSO) to optimize the parameters of SVM. Compared with other advanced algorithms, our proposed method obtains the best performance with 95.11% accuracy, 95.10% G-mean, 95.02% F1, and 95.10% area under the curve (AUC) for predicting postoperative survival of LCPs.
Moat: Model Agnostic Defense against Targeted Poisoning Attacks in Federated Learning

Federated learning has migrated data-driven learning to a model-centric approach. As the server does not have access to the data, the health of the data poses a concern. The malicious participation injects malevolent gradient updates to make the model maleficent. They do not impose an overall ill-behavior. Instead, they target a few classes or patterns to misbehave. Label Flipping and Backdoor attacks belong to targeted poisoning attacks performing adversarial manipulation for targeted misclassification. The state-of-the-art defenses based on statistical similarity or autoencoder credit scores suffer from the number of attackers or ingenious injection of backdoor noise. This paper proposes a universal model-agnostic defense technique (Moat) to mitigate different poisoning attacks in Federated Learning. It uses interpretation techniques to measure the marginal contribution of individual features. The aggregation of interpreted values for important features against a baseline input detects the presence of an adversary. The proposed solution scales in terms of attackers and is also robust against adversarial noise in either homogeneous or heterogeneous distribution. The most appealing about Moat is that it achieves model convergence even in the presence of 90% attackers. We ran experiments for different combinations of settings, models, and datasets, to verify our claim. The proposed technique is compared with the existing state-of-the-art algorithms and justified that Moat outperforms them.
Using Ontology for Revealing Authorship Attribution of Arabic Text

Authorship attribution analysis is a research field that assigns an author to an unknown text based on writing features. These features reflect the author’s gender, age, religion, education, job, motivation or ideology. It has several types of features such as character, lexical, Syntactic, Structural and Semantic. This research proposed using Arabic ontology as a semantic feature in authorship attribution through a proposed new model. In the Islamic society, there is a problem in detecting unknown fatwa to specific jurisprudence doctrine so this research proposed a new model for detecting unknown fatwa to specific jurisprudence doctrine. This model depends on a new corpus which is manually collected and annotated fatwas from books of Islamic jurisprudence doctrines. This corpus is called ElWafaa LlFokahaa. It includes the fatwas of traveller's prayer for main Islamic doctrines (Hanfi, Shafie, Malki, and Hanbali). The proposed model used Arabic ontology for traveller's prayer in each Islamic doctrine which is established with protégé framework. It is divided into a training set in 70% of fatwas (known fatwas the owing Islamic jurisprudence doctrines) and 30% testing set (unknown fatwas of Islamic jurisprudence doctrines). For evaluating the proposed model, it is used the proposed evaluated method which is 90% with final experiments.
Hierarchical visualization of geographical areal data with spatial attribute association

Geographical areal data usually presents hierarchical structures, and its characteristics vary at different scales. At the higher scales, the visualization of geographical areal data is abstract and the detailed features are easily missed. As a difference, more detailed information is presented at the lower scales while the visual perception of global features is easily disturbed due to the overdrawing of visual elements. As the geographical areal data is visualized at a single scale at the same time, it seems impossible to balance the visual perception of both the global features and detailed characteristics. In this paper, we propose a multi-scale geographical areal data visualization method based on spatial attribute association to enhance the visual perception of both the global features and detailed characteristics. Firstly, the geographical areal data is aggregated into hierarchical clusters based on the spatial similarity. Then, the coefficient of variation is applied to estimate the attribute distribution of each cluster in the hierarchy, and a novel geographical areal data visualization scheme is proposed to adaptively present the multi-scale clusters with lower variation coefficients at the same time. In addition, a rich set of visual interfaces and user-friendly interactions are provided enabling users to specify those clusters of interest at different scales and compare multi-scale visualizations with different hierarchies. Finally, we implement a geographical areal data visualization framework, allowing users to visually explore the global features and detailed characteristics at the same time and get deeper insights into the potential features in the geographical areal data. Case studies and quantitative comparisons based on real-world datasets have been conducted to demonstrate the effectiveness of the proposed multi-scale visualization method for in-depth visual exploration of geographical areal data.
Opposing hypotheses of the reflection of light applied to the Michelson interferometer

The derivation of light paths in the Michelson interferometer is based on the hypothesis that the incident speed and reflected speed of the wavefront of a ray of light are equal in the frame at absolute rest. In this case, the Michelson‐Morley experiment predicts a fringe shift of 0.40. With the hypothesis that the incident speed and reflected speed of the wavefront of a ray of light are equal in the inertial frame of a mirror at the instance of collision, the Michelson‐Morley experiment predicts a fringe shift of 0.40 × 10 −4 , which is in agreement with the experimental result.
Occlusion Handling in Augmented Reality: Past, Present and Future

One of the main goals of many augmented reality applications is to provide a seamless integration of a real scene with additional virtual data. To fully achieve that goal, such applications must typically provide high-quality real-world tracking, support real-time performance and handle the mutual occlusion problem, estimating the position of the virtual data into the real scene and rendering the virtual content accordingly. In this survey, we focus on the occlusion handling problem in augmented reality applications and provide a detailed review of 161 articles published in this field between January 1992 and August 2020. To do so, we present a historical overview of the most common strategies employed to determine the depth order between real and virtual objects, to visualize hidden objects in a real scene, and to build occlusion-capable visual displays. Moreover, we look at the state-of-the-art techniques, highlight the recent research trends, discuss the current open problems of occlusion handling in augmented reality, and suggest future directions for research.