Reviewerly/exHarmony huggingface dataset:
Probing via Prompting

Probing is a popular approach to understand what linguistic information is contained in the representations of pre-trained language models. However, the mechanism of selecting the probe model has recently been subject to intense debate, as it is not clear if the probes are merely extracting information or modelling the linguistic property themselves. To address this challenge, this paper introduces a novel model-free approach to probing via prompting, which formulates probing as a prompting task. We conduct experiments on five probing tasks and show that PP is comparable or better at extracting information than diagnostic probes while learning much less on its own. We further combine the probing via prompting approach with pruning to analyze where the model stores the linguistic information in its architecture. Finally, we apply the probing via prompting approach to examine the usefulness of a linguistic property for pre-training by removing the heads that are essential to it and evaluating the resulting model’s performance on language modeling.
Parametric Timed Model Checking for Guaranteeing Timed Opacity

Information leakage can have dramatic consequences on systems security. Among harmful information leaks, the timing information leakage is the ability for an attacker to deduce internal information depending on the system execution time. We address the following problem: given a timed system, synthesize the execution times for which one cannot deduce whether the system performed some secret behavior. We solve this problem in the setting of timed automata (TAs). We first provide a general solution, and then extend the problem to parametric TAs, by synthesizing internal timings making the TA secure. We study decidability, devise algorithms, and show that our method can also apply to program analysis.
A Machine Learning Approach to Classify Anti-social Bengali Comments on Social Media

The growth of social media is causing the emergence of hate speech. Email extortion and cyberbullying are on the rise in Bangladesh, along with online sexual harassment of women. In order to prevent these crimes, studies on Bengali comments on social media have become progressively important. However, the requisite datasets are scarce for this kind of study. The motive of this research is to create a dataset of Bangla comments from social platforms and develop a classifier model as well as to detect whether the comments are social or anti-social quickly and efficiently. 2000 comments were gathered from Facebook and YouTube, two prominent platforms for social media. In our study, an artificial neural network model like Gated Recurrent Unit (GRU), and supervised machine learning classifiers like Logistic Regression (LR), Random Forest (RF), Multinomial Naive Bayes (MNB), and Support Vector Machine (SVM) were utilized in our study to distinguish between anti-social and socially acceptable comments. Finally, language models such as unigrams, bigrams, and trigrams have been implemented in our research. To the best of our knowledge, there are no studies regarding the anti-social classification in Bangla language. This work will help to prevent anti-social activities in Bangla community.
Security Research in Personnel Electronic File Management Based on Blockchain Technology

Compared with traditional files, electronic personnel files have the characteristics of the economy, environmental protection, convenience, and sharing and are gradually replacing traditional paper files. However, the development of electronic archives is still in its infancy, and there are still many problems, including the professional quality of personnel, information management, and the security of electronic archives storage. As an emerging technology, blockchain technology has the characteristics of decentralization, immutability, and traceability. This paper applies blockchain technology in the management of electronic archives, overcomes the internal distortion and insecurity of electronic archives, and designs an electronic archives management module based on blockchain technology. Through the application in some schools, the effectiveness and practicability of the algorithm are proved.
Causal graph extraction from news: a comparative study of time-series causality learning techniques

Causal graph extraction from news has the potential to aid in the understanding of complex scenarios. In particular, it can help explain and predict events, as well as conjecture about possible cause-effect connections. However, limited work has addressed the problem of large-scale extraction of causal graphs from news articles. This article presents a novel framework for extracting causal graphs from digital text media. The framework relies on topic-relevant variables representing terms and ongoing events that are selected from a domain under analysis by applying specially developed information retrieval and natural language processing methods. Events are represented as event-phrase embeddings, which make it possible to group similar events into semantically cohesive clusters. A time series of the selected variables is given as input to a causal structure learning techniques to learn a causal graph associated with the topic that is being examined. The complete framework is applied to the New York Times dataset, which covers news for a period of 246 months (roughly 20 years), and is illustrated through a case study. An initial evaluation based on synthetic data is carried out to gain insight into the most effective time-series causality learning techniques. This evaluation comprises a systematic analysis of nine state-of-the-art causal structure learning techniques and two novel ensemble methods derived from the most effective techniques. Subsequently, the complete framework based on the most promising causal structure learning technique is evaluated with domain experts in a real-world scenario through the use of the presented case study. The proposed analysis offers valuable insights into the problems of identifying topic-relevant variables from large volumes of news and learning causal graphs from time series.
Predicting Rigid Body Dynamics Using Dual Quaternion Recurrent Neural Networks With Quaternion Attention

We propose a novel neural network architecture based on dual quaternions which allow for a compact representation of informations with a main focus on describing rigid body movements. To cover the dynamic behavior inherent to rigid body movements, we propose recurrent architectures in the neural network. To further model the interactions between individual rigid bodies as well as external inputs efficiently, we incorporate a novel attention mechanism employing dual quaternion algebra. The introduced architecture is trainable by means of gradient based algorithms. We apply our approach to a parcel prediction problem where a rigid body with an initial position, orientation, velocity and angular velocity moves through a fixed simulation environment which exhibits rich interactions between the parcel and the boundaries.
Deep learning for size and microscope feature extraction and classification in Oral Cancer: enhanced convolution neural network

Background and Aim: Deep learning technology has not been implemented successfully in oral cancer images classification due to the overfitting problem. Due to the network arrangement and lack of proper data set for training, the network might not produce the required feature map with dimension reduction which result in overfitting problems. This research aims to reduce the overfitting by producing the required feature map with dimension reduction through using Convolutional Neural Network. Methodology: The proposed system uses the Enhanced Convolutional Neural Network and the autoencoder technique to increase the efficiency of feature extraction process and compresses the information. In this technique, unpooling and deconvolution is done to generate the input data to minimize the difference between input and output data. Furthermore, it extracts characteristic features from the input data set which regenerates the input data from those features by learning a network to reduce the overfitting problem. Results: Different value of accuracy and processing time is achieved using different sample group of Confocal Laser Endomicroscopy (CLE) images. Based on result, it shows that the proposed solution is better than the current system. Also, the proposed system has improved the classification accuracy by 5 ~ 5.5% in average and reduced the processing time by 20 ~ 30 milliseconds in average. Conclusion: The proposed system is focused on accurately classifying the oral cancer cells of different anatomical locations from the CLE images. Finally, this study enhances the accuracy and processing time using autoencoder method and solve the problem of overfitting.
JuryGCN: Quantifying Jackknife Uncertainty on Graph Convolutional Networks

Graph Convolutional Network (GCN) has exhibited strong empirical performance in many real-world applications. The vast majority of existing works on GCN primarily focus on the accuracy while ignoring how confident or uncertain a GCN is with respect to its predictions. Despite being a cornerstone of trustworthy graph mining, uncertainty quantification on GCN has not been well studied and the scarce existing efforts either fail to provide deterministic quantification or have to change the training procedure of GCN by introducing additional parameters or architectures. In this paper, we propose the first frequentist-based approach named JuryGCN in quantifying the uncertainty of GCN, where the key idea is to quantify the uncertainty of a node as the width of confidence interval by a jackknife estimator. Moreover, we leverage the influence functions to estimate the change in GCN parameters without re-training to scale up the computation. The proposed JuryGCN is capable of quantifying uncertainty deterministically without modifying the GCN architecture or introducing additional parameters. We perform extensive experimental evaluation on real-world datasets in the tasks of both active learning and semi-supervised node classification, which demonstrate the efficacy of the proposed method.
Faster search of clustered marked states with lackadaisical quantum walks

The nature of discrete-time quantum walks in the presence of multiple marked states can be found in the literature. An exceptional configuration of clustered marked states, which is a variant of multiple marked states, may be defined as a cluster of k marked states arranged in a $$\sqrt{k} \times \sqrt{k}$$ array within a $$\sqrt{N} \times \sqrt{N}$$ grid, where $$k=n^{2}$$ and n an odd integer. In this article, we establish through numerical simulation that for lackadaisical quantum walks, which is the analogue of a three-state discrete-time quantum walks on a line, the success probability to find a vertex in the marked region of this exceptional configuration is nearly 1 with smaller run-time. We also show that the weights of the self-loop suggested for multiple marked states in the state-of-the-art works are not optimal for this exceptional configuration of clustered marked states. We propose a weight of the self-loop which gives the desired result for this configuration.
Multiqudit interactions in molecular spins

We study photon-mediated interactions between molecular spin qudits in the dispersive regime of operation. We derive from a microscopic model the effective interaction between molecular spins, including their crystal field anisotropy (i.e., the presence of nonlinear spin terms) and their multilevel structure. Finally, we calculate the long-time dynamics for a pair of interacting molecular spins using the method of multiple-scale analysis. This allows us to find a set of two-qudit gates that can be realized for a specific choice of molecular spins and to determine the time required for their implementation. Our results are relevant for the implementation of logical gates in general systems of qudits with unequally spaced levels or to determine an adequate computational subspace to encode and process the information.
Experimental demonstration of quantum transport enhancement using time-reversal symmetry breaking on a silicon photonic chip

The continuous-time quantum walk is a basic model for studying quantum transport and developing quantum-enhanced algorithms. Recent studies show that by introducing a phase into the standard continuous-time quantum walk model, the time-reversal symmetry can be broken without changing the Hermitian property of the Hamiltonian. The time-reversal symmetry breaking quantum walk shows advantages in quantum transport, such as perfect state transfer, directional control, transport speedup, and quantum transport efficiency enhancement. In this work, we implement the time-reversal symmetry breaking quantum walks on a reconfigurable silicon photonic chip and demonstrate the enhancement introduced by breaking time-reversal symmetry. Perfect state transfer on a three-site ring, a quantum switch implemented on a six-site graph, and transport speedup using a linear chain of triangles are demonstrated with high fidelity. Time-reversal asymmetry has also been used in a simplified light-harvesting model, implying the potential of time-reversal symmetry breaking in photosynthesis investigations.
Effect of chaos on information gain in quantum tomography

Does chaos in the dynamics enable or impede information gain in quantum tomography? We address this question by considering continuous measurement tomography in which the measurement record is obtained as a sequence of expectation values of a Hermitian observable evolving under the repeated application of the Floquet map of the quantum kicked top. For a given dynamics and Hermitian observables, we observe completely opposite behavior in the tomography of well-localized spin coherent states compared to random states. As the chaos in the dynamics increases, the reconstruction fidelity of spin coherent states decreases. This contrasts with the previous results connecting information gain in tomography of random states with the degree of chaos in the dynamics that drives the system. The rate of information gain and hence the fidelity obtained in tomography depends not only on the degree of chaos in the dynamics and to what extent it causes the initial observable to spread in various directions of the operator space, but, more importantly, how well these directions are aligned with the density matrix to be estimated. Our study also gives an operational interpretation for operator spreading in terms of fidelity gain in an actual quantum information tomography protocol.
GCA-Net : Utilizing Gated Context Attention for Improving Image Forgery Localization and Detection

Forensic analysis of manipulated pixels requires the identification of various hidden and subtle features from images. Conventional image recognition models generally fail at this task because they are biased and more attentive towards the dominant local and spatial features. In this paper, we propose a novel Gated Context Attention Network (GCA-Net) that utilizes non-local attention in conjunction with a gating mechanism in order to capture the finer image discrepancies and better identify forged regions. The proposed framework uses high dimensional embeddings to filter and aggregate the relevant context from coarse feature maps at various stages of the decoding process. This improves the network’s understanding of global differences and reduces false-positive localizations. Our evaluation on standard image forensic benchmarks shows that GCA-Net can both compete against and improve over state-of-the-art networks by an average of 4.7% AUC. Additional ablation studies also demonstrate the method’s robustness against attributions and resilience to false-positive predictions.
Multiclass classification by Min–Max ECOC with Hamming distance optimization

Abstract Two questions often arise in the field of the ensemble in multiclass classification problems, (i) how to combine base classifiers and (ii) how to design possible binary classifiers. Error-correcting output codes (ECOC) methods answer these questions, but they focused on only the general goodness of the classifier. The main purpose of our research was to strengthen the bottleneck of the ensemble method, i.e., to minimize the largest values of two types of error ratios in the deep neural network-based classifier. The research was theoretical and experimental, the proposed Min–Max ECOC method suggests a theoretically proven optimal solution, which was verified by experiments on image datasets. The optimal solution was based on the maximization of the lowest value in the Hamming matrix coming from the ECOC matrix. The largest ECOC matrix, the so-called full matrix is always a Min–Max ECOC matrix, but smaller matrices generally do not reach the optimal Hamming distance value, and a recursive construction algorithm was proposed to get closer to it. It is not easy to calculate optimal values for large ECOC matrices, but an interval with upper and lower limits was constructed by two theorems, and they were proved. Convolutional Neural Networks with Min–Max ECOC matrix were tested on four real datasets and compared with OVA (one versus all) and variants of ECOC methods in terms of known and two new indicators. The experimental results show that the suggested method surpasses the others, thus our method is promising in the ensemble learning literature.
Commentary: Metaphors We Live By

GENERAL COMMENTARY article Front. Comput. Sci., 29 June 2022Sec. Theoretical Computer Science Volume 4 - 2022 | https://doi.org/10.3389/fcomp.2022.890531
Slow Motion Matters: A Slow Motion Enhanced Network for Weakly Supervised Temporal Action Localization

Weakly supervised temporal action localization (WTAL) aims to localize actions in untrimmed videos with only weak supervision information (e.g. video-level labels). Most existing models handle all input videos with a fixed temporal scale. However, such models are not sensitive to actions whose pace of the movements is different from the ``normal" speed, especially slow-motion action instances, which complete the movements with a much slower speed than their counterparts with a normal speed. Here arises the slow-motion blurred issue: It is hard to explore salient slow-motion information from videos at ``normal" speed. In this paper, we propose a novel framework termed Slow Motion Enhanced Network (SMEN) to improve the ability of a WTAL network by compensating its sensitivity on slow-motion action segments. The proposed SMEN comprises a Mining module and a Localization module. The mining module generates mask to mine slow-motion-related features by utilizing the relationships between the normal motion and slow motion; while the localization module leverages the mined slow-motion features as complementary information to improve the temporal action localization results. Our proposed framework can be easily adapted by existing WTAL networks and enable them be more sensitive to slow-motion actions. Extensive experiments on three benchmarks are conducted, which demonstrate the high performance of our proposed framework.
An Environment-Specific Prioritization Model for Information-Security Vulnerabilities Based on Risk Factor Analysis

Vulnerabilities represent a constant and growing risk for organizations. Their successful exploitation compromises the integrity and availability of systems. The use of specialized tools facilitates the vulnerability monitoring and scanning process. However, the large amount of information transmitted over the network makes it difficult to prioritize the identified vulnerabilities based on their severity and impact. This research aims to design and implement a prioritization model for detecting vulnerabilities based on their network environment variables and characteristics. A mathematical prioritization model was developed, which allows for calculating the risk factor using the phases of collection, analysis, and extraction of knowledge from the open information sources of the OSINT framework. The input data were obtained through the Shodan REST API. Then, the mathematical model was applied to the relevant information on vulnerabilities and their environment to quantify and calculate the risk factor. Additionally, a software prototype was designed and implemented that automates the prioritization process through a Client–Server architecture incorporating data extraction, correlation, and calculation modules. The results show that prioritization of vulnerabilities was achieved with the information available to the attacker, which allows evaluating the overexposure of information from organizations. Finally, we concluded that Shodan has relevant variables that assess and quantify the overexposure of an organization’s data. In addition, we determined that the Common Vulnerability Scoring System (CVSS) is not sufficient to prioritize software vulnerabilities since the environments where they reside have different characteristics.
Interactive Exploration of Virtual Heritage by Means of Natural Gestures

This paper is situated at the intersection of using Virtual Reality as a tool for cultural heritage preservation and using gesture interaction-based technology in order to achieve touchless, distant interaction of users with reconstructed artifacts. Various studies emphasize the positive effect on the cultural experience brought on by the use of Virtual Reality in a museum context. We build our approach on this idea, by modeling and reconstructing museum exhibits, both small artifacts and large architectural edifices. We propose and design navigation and interaction scenarios, at the same time taking into account present day limitations regarding social interaction, imposed during the COVID-19 pandemic. By considering the user in the center of the experience and focusing on enabling him/her to adjust the perspective on the visualized artifacts and to freely interact with them through natural gestures, we allow the user to immerse in the virtual environment and interact with the reconstructed artifacts by means of simple hand gestures, with no touch. Finally, we assess the usability and utility of the Virtual Reality system in a questionnaire-based study with 137 participants over a period of 6 months, whose results we discuss in the paper.