Reviewerly/exHarmony huggingface dataset:
Automating Agential Reasoning: Proof-Calculi and Syntactic Decidability for STIT Logics

This work provides proof-search algorithms and automated counter-model extraction for a class of STIT logics. With this, we answer an open problem concerning syntactic decision procedures and cut-free calculi for STIT logics. A new class of cut-free complete labelled sequent calculi G3LdmL^m_n, for multi-agent STIT with at most n-many choices, is introduced. We refine the calculi G3LdmL^m_n through the use of propagation rules and demonstrate the admissibility of their structural rules, resulting in auxiliary calculi Ldm^m_nL. In the single-agent case, we show that the refined calculi Ldm^m_nL derive theorems within a restricted class of (forestlike) sequents, allowing us to provide proof-search algorithms that decide single-agent STIT logics. We prove that the proof-search algorithms are correct and terminate.
Centering-based Neural Coherence Modeling with Hierarchical Discourse Segments

Previous neural coherence models have focused on identifying semantic relations between adjacent sentences.However, they do not have the means to exploit structural information.In this work, we propose a coherence model which takes discourse structural information into account without relying on human annotations.We approximate a linguistic theory of coherence, Centering theory, which we use to track the changes of focus between discourse segments.Our model first identifies the focus of each sentence, recognized with regards to the context, and constructs the structural relationship for discourse segments by tracking the changes of the focus.The model then incorporates this structural information into a structure-aware transformer.We evaluate our model on two tasks, automated essay scoring and assessing writing quality.Our results demonstrate that our model, built on top of a pretrained language model, achieves state-of-the-art performance on both tasks.We next statistically examine the identified trees of texts assigned to different quality scores.Finally, we investigate what our model learns in terms of theoretical claims 1 .
Evolution equation for multi-photon states in turbulence

The recently developed Wigner functional theory is used to formulate an evolution equation for arbitrary multi-photon states, propagating through a turbulent atmosphere under arbitrary conditions. The resulting evolution equation, which is obtained from an infinitesimal propagation approach, is in the form of a Fokker-Planck equation for the Wigner functional of the state and therefore incorporates functional derivatives. We show consistency with previously obtained solutions from different approaches and consider possible ways to find additional solutions for this equation.
Language Generation via Combinatorial Constraint Satisfaction: A Tree Search Enhanced Monte-Carlo Approach

Generating natural language under complex constraints is a principled formulation towards controllable text generation.We present a framework to allow specification of combinatorial constraints for sentence generation.We propose TSMH 1 , an efficient method to generate high likelihood sentences with respect to a pre-trained language model while satisfying the constraints.Our approach is highly flexible, requires no task-specific training, and leverages efficient constraint satisfaction solving techniques.To better handle the combinatorial constraints, a tree search algorithm is embedded into the proposal process of the Markov chain Monte Carlo (MCMC) to explore candidates that satisfy more constraints.Compared to existing MCMC approaches, our sampling approach has a better mixing performance.Experiments show that TSMH achieves consistent and significant improvement on multiple language generation tasks.
A Neural Network Study of Blasius Equation

In this work we applied a feed forward neural network to solve Blasius equation which is a third-order nonlinear differential equation. Blasius equation is a kind of boundary layer flow. We solved Blasius equation without reducing it into a system of first order equation. Numerical results are presented and a comparison according to some studies is made in the form of their results. Obtained results are found to be in good agreement with the given studies.
Self-Supervised Visual Representation Learning from Hierarchical Grouping

We create a framework for bootstrapping visual representation learning from a primitive visual grouping capability. We operationalize grouping via a contour detector that partitions an image into regions, followed by merging of those regions into a tree hierarchy. A small supervised dataset suffices for training this grouping primitive. Across a large unlabeled dataset, we apply this learned primitive to automatically predict hierarchical region structure. These predictions serve as guidance for self-supervised contrastive feature learning: we task a deep network with producing per-pixel embeddings whose pairwise distances respect the region hierarchy. Experiments demonstrate that our approach can serve as state-of-the-art generic pre-training, benefiting downstream tasks. We additionally explore applications to semantic region search and video-based object instance tracking.
Improving Anomaly Detection Fusion Method of Rotating Machinery Based on ANN and Isolation Forest

Early anomaly detection of rotating machinery, which is used to avoid major machine failures, has been gradually concerned. In many industrial scenarios, failure samples are difficult or expensive to obtain, so that anomaly detection methods show great advantages. In order to solve the problems caused by imbalanced samples in model training in industrial applications, the paper proposes an anomaly detection fusion method based on artificial neural network (ANN) and isolation forest (iForest) algorithm. The original data set is extended by upsampling to keep the number of different kinds of samples consistent. Then, the statistical features of each sample and a special energy feature are obtained through the Empirical Mode Decomposition (EMD) method. A feature data set will be combined. In order to better represent the intrinsic characteristics of the signal, the proposed method trains an ANN network to extract hidden features and reduce the dimensions of the feature data set. Finally, the extracted hidden features are fed to the isolated forest algorithm for anomaly detection. The proposed method is tested on the CWRU bearing datasets. The results show that the proposed method has better performance than the traditional method.
When international avatars meet – intercultural language learning in virtual reality exchange

Virtual exchange projects have become an effective pedagogical method to support students’ development of intercultural language competence. High-immersion experiences in Virtual Reality (VR) may offer an environment which is conducive to developing such competence. This paper reports on a pilot study carried out with two groups of university students (N=30) in the Netherlands and Germany. The students, involved in a virtual exchange using VR headsets, completed three tasks collaboratively. The aim of the study was to investigate participants’ perception regarding (1) their collaboration with foreign peers within the VR setting and (2) the perceived usefulness of the tool. The researchers employed questionnaires and conducted interviews and focus groups. The audio recording transcripts from the VR encounters and students’ reflective journals provide further data to triangulate the results. This pilot study provides first results with regard to virtual exchanges carried out in high-immersion VR.
Human activity recognition based on machine learning classification of smartwatch accelerometer dataset

This paper presents two Machine Learning models that classify time series data given from smartwatch accelerometer of observed subjects. For the purpose of classification we use Deep Neural Network and Random Forest classifier algorithms. The comparison of both models shows that they have similar performance with regard to recognition of subject's activities that are used in the test group of the dataset. Training accuracy reaches approximately 95% and 100% for Deep Learning and Random Forest model respectively. Since the validation and recognition, reached about 81% and 75% respectively, a tendency for improving accuracy as a function of number of participants is considered. The influence of data sample precision to the accuracy of the models is examined since the input data could be given from various wearable devices.
Reference and Document Aware Semantic Evaluation Methods for Korean Language Summarization

Text summarization refers to the process that generates a shorter form of text from the source document preserving salient information.Many existing works for text summarization are generally evaluated by using recall-oriented understudy for gisting evaluation (ROUGE) scores.However, as ROUGE scores are computed based on n-gram overlap, they do not reflect semantic meaning correspondences between generated and reference summaries.Because Korean is an agglutinative language that combines various morphemes into a word that express several meanings, ROUGE is not suitable for Korean summarization.In this paper, we propose evaluation metrics that reflect semantic meanings of a reference summary and the original document, Reference and Document Aware Semantic Score (RDASS).We then propose a method for improving the correlation of the metrics with human judgment.Evaluation results show that the correlation with human judgment is significantly higher for our evaluation metrics than for ROUGE scores.
Finding Sparse Structures for Domain Specific Neural Machine Translation

Neural machine translation often adopts the fine-tuning approach to adapt to specific domains. However, nonrestricted fine-tuning can easily degrade on the general domain and over-fit to the target domain. To mitigate the issue, we propose Prune-Tune, a novel domain adaptation method via gradual pruning. It learns tiny domain-specific sub-networks during fine-tuning on new domains. Prune-Tune alleviates the over-fitting and the degradation problem without model modification. Furthermore, Prune-Tune is able to sequentially learn a single network with multiple disjoint domain-specific sub-networks for multiple domains. Empirical experiment results show that Prune-Tune outperforms several strong competitors in the target domain test set without sacrificing the quality on the general domain in both single and multi-domain settings. The source code and data are available at https://github.com/ohlionel/Prune-Tune.
Meaningful ciphertext encryption algorithm based on bit scrambling, discrete wavelet transform, and improved chaos

This paper proposes a meaningful ciphertext encryption algorithm of good practicability and strong robustness. The plaintext undergoes pre-encryption of bit scrambling and secondary encryption of discrete wavelet transform (DWT) to obtain meaningful ciphertext. In the pre-encryption stage, only the higher three bit pages that are not all 0 or 1 are scrambled. The embedding method in the secondary encryption process is to add the hundreds and tens’ digit and ones’ digit of the middle ciphertext to the scrambled detail information obtained by DWT of the cover image. The information entropy of the intermediate ciphertext obtained by this method can reach 7.997, the correlation coefficient between the ciphertext and the cover image can reach 0.99, and the decrypted image of the ciphertext under high-intensity pollution can still be identified. It shows that the pre-encryption can conceal the plaintext information well, and the secondary encryption can eliminate the ciphertext texture characteristics while retaining the good robustness. Finally, the improved three one-dimensional chaos are used to generate random sequences, and the plaintext hash value is associated with the chaotic key. The improved chaos greatly improves the randomness of the sequence and broadens the range of parameters. The hash value SHA-256 of the plaintext is added to make the key stream adaptively change with the plaintext, which greatly improves the sensitivity of the plaintext and the ability to resist selected plaintext and ciphertext attacks. Experiments have also verified that the encryption algorithm can encrypt images of different types and shapes and has strong practicability, good bit scrambling effect, and high sensitivity to scrambling.
Revisiting the CompCars Dataset for Hierarchical Car Classification: New Annotations, Experiments, and Results

We address the task of classifying car images at multiple levels of detail, ranging from the top-level car type, down to the specific car make, model, and year. We analyze existing datasets for car classification, and identify the CompCars as an excellent starting point for our task. We show that convolutional neural networks achieve an accuracy above 90% on the finest-level classification task. This high performance, however, is scarcely representative of real-world situations, as it is evaluated on a biased training/test split. In this work, we revisit the CompCars dataset by first defining a new training/test split, which better represents real-world scenarios by setting a more realistic baseline at 61% accuracy on the new test set. We also propagate the existing (but limited) type-level annotation to the entire dataset, and we finally provide a car-tight bounding box for each image, automatically defined through an ad hoc car detector. To evaluate this revisited dataset, we design and implement three different approaches to car classification, two of which exploit the hierarchical nature of car annotations. Our experiments show that higher-level classification in terms of car type positively impacts classification at a finer grain, now reaching 70% accuracy. The achieved performance constitutes a baseline benchmark for future research, and our enriched set of annotations is made available for public download.
Fractal sorting vector-based least significant bit chaotic permutation for image encryption*

The image’s least significant bit (LSB) covers lots of the details that have been commonly used in image encryption analysis. The newly proposed fractal sorting vector (FSV) and FSV-based LSB chaotic permutation (FSV-LSBCP) is a novel chaotic image encryption cryptosystem introduced in this article. The FSV-LSBCP effectively strengthens the security of the cryptographic scheme concerning the properties of the FSV. Key analysis, statistical analysis, resistance differential attack analysis, and resistance to cropping attacks and noise attacks are the focus of the suggested image encryption cryptosystem. The security experiment shows that the cryptosystem is adequate to achieve the desired degree of security.
Distributed Robust Dimensionality Reduction Fusion Estimation Under DoS Attacks and Uncertain Covariances

This paper is concerned with the networked distributed fusion estimation problem under denial-of-service (DoS) attacks, where the noise covariances are unknown but bounded, and the distribution information of DoS attacks is not required to be known. Based on the dimensionality reduction and compensation model, the local Kalman filter (LKF) with unknown covariances is designed by the maximum and minimum robust estimation criterion, while the distributed fusion Kalman filter (DFKF) is derived from the optimal weighted fusion criterion. Moreover, the robustness of the developed DFKF is also analyzed in the presence of DoS attacks. Finally, an illustrative example is exploited to demonstrate the effectiveness of the proposed methods.
Blockchain-Enabled EHR Framework for Internet of Medical Things

The Internet of Medical Things (IoMT) offers an infrastructure made of smart medical equipment and software applications for healthcare services.Through the internet, the IoMT is capable of providing remote medical diagnosis and timely health services.The patients can use their smart devices to create, store and share their electronic health records (EHR) with a variety of medical personnel including medical doctors and nurses.However, unless the underlying commination within IoMT is secured, malicious users can intercept, modify and even delete the sensitive EHR data of patients.Patients also lose full control of their EHR since most healthcare services within IoMT are constructed under a centralized platform outsourced in the cloud.Therefore, it is appealing to design a decentralized, auditable and secure EHR system that guarantees absolute access control for the patients while ensuring privacy and security.Using the features of blockchain including decentralization, auditability and immutability, we propose a secure EHR framework which is mainly maintained by the medical centers.In this framework, the patients' EHR data are encrypted and stored in the servers of medical institutions while the corresponding hash values are kept on the blockchain.We make use of security primitives to offer authentication, integrity and confidentiality of EHR data while access control and immutability is guaranteed by the blockchain technology.The security analysis and performance evaluation of the proposed framework confirms its efficiency.
HSPOG: An optimized target recognition method based on histogram of spatial pyramid oriented gradients

The Histograms of Oriented Gradients (HOG) can produce good results in an image target recognition mission, but it requires the same size of the target images for classification of inputs. In response to this shortcoming, this paper performs spatial pyramid segmentation on target images of any size, gets the pixel size of each image block dynamically, and further calculates and normalizes the gradient of the oriented feature of each block region in each image layer. The new feature is called the Histogram of Spatial Pyramid Oriented Gradients (HSPOG). This approach can obtain stable vectors for images of any size, and increase the target detection rate in the image recognition process significantly. Finally, the article verifies the algorithm using VOC2012 image data and compares the effect of HOG.
Analysis of trends in modern library and information infrastructure in the ongoing pandemics. (Review of international professional publications). (Part 2)

The paper reviews some international online library and information professional publications including the websites of library associations, academic research charities and foundations and research groups. The paper is divided into the following sections: pandemics and open science; coronavirus and library materials and spaces; new impetus for digitization; copyright: latest news. These topics have not been predefined but taken shape as a result of analytical work and research in the body of professional publications. Analysis of leading library experts’ discussions in social networks has also helped to reveal current trends in library and information infrastructure development. COVID-19 pandemics brought about significant changes to the life and projects of the whole library and information space. A major step to open science has been taken when academic journal publishers, leading universities, research charities have taken responsibility to work in a collaborative manner and share coronavirus infection research data and results speedily and freely. Transition to digital collections in library holdings has accelerated, collections of e-books are growing, and the effect of prior digitization projects has manifested itself. The importance of libraries as a trustworthy source of information does not fade; caution is needed when using information from preprints published on preprint servers; the increasing need of informing digital materials users of attached copyright is highlighted.
Workload, Presence and Task Performance of Virtual Object Manipulation on WebVR

WebVR technology is widely used as a visualization approach to display virtual objects on 2D webpages. Much of the current literature on virtual object manipulation on the 2D screen pays particular attention to task performance, but few studies focus on users' psychological feedback and no literature aims at its relationship with task performance. This paper compares manipulation modes with different degrees of freedom (DoF) in translation and rotation on WebVR to explore users' workload and presence by self-reported data, and task performance by measuring completion time and error rate. The experiment results present that the increase of DoF is associated with lower perceived workload, while people may feel a higher level of presence during tasks. Additionally, this study only finds a positive correlation between workload and efficiency, and a negative correlation between presence and efficiency, which means that when feeling less workload or more presence, people tend to spend less time to complete translation and rotation tasks on WebVR.
Integrating of Remote Sensing and Airborne Magnetic Data to Outline the Geologic Structural Lineaments That Controlled Mineralization Deposits for the Area around Gabal El-Niteishat, Central Eastern Desert, Egypt

The Gabal (G.) El-Niteishat area lies in the Central Eastern Desert of Egypt which is known for various mineral resources and geological structures.Umm Gheig, Umm Naggat, Umm Shaddad, Wadi (W.) Zeidun and Sigdit represent some important regions that contain mineral deposits in the study area.Various filters such as first vertical derivative (FVD), horizontal gradient magnitude (HGM), tilt derivative (TDR) and near-surface were applied to the airborne magnetic data for the study area to deduce the structural lineaments and magnetic source edges which were controlled by the presence of mineral deposits.Processed Landsat ETM+ images are used for delineating the rock unit boundaries that are exposed in the study area such as serpentinite, metagabbro, metavolcanics and metasediments.Also, band ratios, principal component analysis (PCA) and false-color composite image (Crosta alteration image) were applied to get specific results about the alteration zones.The structural lineaments analysis illustrated that the common trends that affected the study area were NW-SE, NE-SW, E-W and N-S.Integration of remote sensing and airborne magnetic data exhibited the relation between mineralization and structural lineaments.
Real-Time Mobile Acceleration of DNNs

With the growth of mobile vision applications, there is a growing need to break through the current performance limitation of mobile platforms, especially for computationally intensive applications, such as object detection, action recognition, and medical diagnosis. To achieve this goal, we present our unified real-time mobile DNN inference acceleration framework, seamlessly integrating hardware-friendly, structured model compression with mobile-targeted compiler optimizations. We aim at an unprecedented, realtime performance of such large-scale neural network inference on mobile devices. A fine-grained block-based pruning scheme is proposed to be universally applicable to all types of DNN layers, such as convolutional layers with different kernel sizes and fully connected layers. Moreover, it is also successfully extended to 3D convolutions. With the assist of our compiler optimizations, the fine-grained block-based sparsity is fully utilized to achieve high model accuracy and high hardware acceleration simultaneously. To validate our framework, three representative fields of applications are implemented and demonstrated, object detection, activity detection, and medical diagnosis. All applications achieve real-time inference using an off-the-shelf smartphone, outperforming the representative mobile DNN inference acceleration frameworks by up to 6.7x in speed. The demonstrations of these applications can be found in the following link: https://bit.ly/39lWpYu.