Reviewerly/exHarmony huggingface dataset:
Modelling dynamic behaviour of agents in a multiagent world: Logical analysis of Wh-questions and answers

Abstract In a multiagent and multi-cultural world, the fine-grained analysis of agents’ dynamic behaviour, i.e. of their activities, is essential. Dynamic activities are actions that are characterized by an agent who executes the action and by other participants of the action. Wh-questions on the participants of the actions pose a difficult particular challenge because the variability of the types of possible answers to such questions is huge. To deal with the problem, we propose the analysis and classification of Wh-questions apt for agents’ communication in a multiagent system (MAS). Our proposal of such a system consists of agents who communicate with their fellow agents by messaging so that each autonomous agent, though resource-bounded, can make less or more rational decisions to meet its own and collective goals. In addition, by communicating with other fellow agents and their environment, agents can learn new concepts and enrich their ontology so that their behaviour is dynamic. We aim to make a general proposal of the system so that the ‘envelope’ of agents’ messages can be formalized in any MAS standard, be it The Foundation for Intelligent Physical Agents - Agent Communication Language (FIPA-ACL) or Knowledge Query and Manipulation Language (KQML). Yet, the content of messages is encoded in a formalized natural language. To this end, we apply Transparent Intensional Logic (TIL) with its procedural semantics which is particularly apt for a fine-grained analysis in which all the semantically salient features of natural language can be plausibly formalized. In this paper, we concentrate on analysing the content of query messages, particularly the content of those that encode Wh-questions and the answers to them. We also summarize TIL deduction system that makes it possible to answer such questions in an intelligent way. Linguists distinguish several subtypes of Wh-questions. Though the linguistic classification is helpful, it is not always suitable for agents’ communication and reasoning. We need the classification based on a logical analysis of Wh-questions so that the agents can infer possible answers to such questions rather than only looking for them by keywords. This paper aims to apply an appropriate classification of the logical types of Wh-questions and the analysis of such questions; we concentrate in particular on questions concerning the participants of activities. The application of these results to the analysis of processes and events based on verb valency frames is another novelty of the paper.
Improving Security of Internet of Vehicles Based on Post-quantum Signatures with Systolic Divisions

Internet of Things (IoT) techniques have been employed in many areas, e.g., vehicles, smart home, and medicine. Among the applications of IoTs, the Internet of Vehicles (IoV) is one of the most popular techniques. IoVs are protected by public key cryptographic systems, such as RSA and ECC. However, such systems are vulnerable to quantum computer attacks. Thus, we improve the security of IoV-based post-quantum signatures, which can resist quantum computer attacks. The key operations are divisions in a finite field. Hence, we improve the security of IoV-based post-quantum signatures with division by employing systolic architectures. We propose a systolic architecture for computing division in composite fields. After that, we improve the IoT security-based post-quantum signatures with systolic divisions. We test and verify our design on a Field-Programmable Gate Array (FPGA); the experimental results confirm our estimates. Furthermore, the optimized method proposed can be further applied to various applications like solving system of linear equations and cryptographic applications for IoT security.
Fully Projective Radical Isogenies in Constant-Time

At PQCrypto-2020, Castryck and Decru proposed CSURF (CSIDH on the surface) as an improvement to the CSIDH protocol. Soon after that, at Asiacrypt-2020, together with Vercauteren they introduced radical isogenies as a further improvement. The main improvement in these works is that both CSURF and radical isogenies require only one torsion point to initiate a chain of isogenies, in comparison to Vélu isogenies which require a torsion point per isogeny. Both works were implemented using non-constant-time techniques, however, in a realistic scenario, a constant-time implementation is necessary to mitigate risks of timing attacks. The analysis of constant-time CSURF and radical isogenies was left as an open problem by Castryck, Decru, and Vercauteren. In this work we analyze this problem. A straightforward constant-time implementation of CSURF and radical isogenies encounters too many issues to be cost effective, but we resolve some of these issues with new optimization techniques. We introduce projective radical isogenies to save costly inversions and present a hybrid strategy for integration of radical isogenies in CSIDH implementations. These improvements make radical isogenies almost twice as efficient in constant-time, in terms of finite field multiplications. Using these improvements, we then measure the algorithmic performance in a benchmark of CSIDH, CSURF and CRADS (an implementation using radical isogenies) for different prime sizes. Our implementation provides a more accurate comparison between CSIDH, CSURF and CRADS than the original benchmarks, by using state-of-the-art techniques for all three implementations. Our experiments illustrate that the speed-up of constant-time CSURF-512 with radical isogenies is reduced to about 3% in comparison to the fastest state-of-the-art constant-time CSIDH-512 implementation. The performance is worse for larger primes, as radical isogenies scale worse than Vélu isogenies.
Double compression detection in HEVC-coded video with the same coding parameters using picture partitioning information

Detection of double compression, particularly in the high-efficiency video coding (HEVC) compressed domain, is one of the most operative and efficacious ways of authenticating the validity of videos in the field of forensic analysis. The strength of identifying abnormalities in the videos confides in diverse coding parameters (such as quantization parameters, size and structure of the group of pictures, and modes of compression). Many methods have been introduced to dig up HEVC double compression with different coding parameters. However, the revelation of the HEVC double compression under the same coding environments still remains a competitive task, as recompressions leave small footprints. In this paper, we introduce a novel method based on frame partitioning information to distinguish between single and double compressions with the same coding parameters. We propose extracting statistical and deep convolution neural network (DCNN) features from partition pictures and prediction modes, including coding unit, prediction unit, transform unit, and most probable modes information. Finally, machine learning technology is integrated to categorize videos into two classes, single and double compressions, by combining the statistical and DCNN features. We obtain the best experimental results by assembling the statistical and DCNN features for wide video graphics array (WVGA) and high-definition (HD) sequences with average accuracies of 99.66% and 99.60% in all-intra and 99.46% and 99.33% in low-delay P modes respectively. Experimental results of the proposed system show the effectiveness and efficiency over the state-of-the-art techniques in video forensic.
High-Accuracy RGB-D Face Recognition via Segmentation-Aware Face Depth Estimation and Mask-Guided Attention Network

Deep learning approaches have achieved highly accurate face recognition by training the models with very large face image datasets. Unlike the availability of large 2D face image datasets, there is a lack of large 3D face datasets available to the public. Existing public 3D face datasets were usually collected with few subjects, leading to the over-fitting problem. This paper proposes two CNN models to improve the RGB-D face recognition task. The first is a segmentation-aware depth estimation network, called DepthNet, which estimates depth maps from RGB face images by including semantic segmentation information for more accurate face region localization. The other is a novel mask-guided RGB-D face recognition model that contains an RGB recognition branch, a depth map recognition branch, and an auxiliary segmentation mask branch with a spatial attention module. Our DepthN et is used to augment a large 2D face image dataset to a large RGB-D face dataset, which is used for training an accurate RGB-D face recognition model. Furthermore, the proposed mask-guided RGB-D face recognition model can fully exploit the depth map and segmentation mask information and is more robust against pose variation than previous methods. Our experimental results show that DepthNet can produce more reliable depth maps from face images with the segmentation mask. Our mask-guided face recognition model outperforms state-of-the-art methods on several public 3D face datasets.
DA-STD: Deformable Attention-Based Scene Text Detection in Arbitrary Shape

Scene Text Detection (STD) is important for developing many popular technologies, such as Security and Automatic Driving. However, the existing text detection models are based on unified text shape and single background, which does not accord with the text characteristics in the natural scene. To detect arbitrarily shaped text with a complex background, we proposed a method based on deformable attention mechanism and named DA-STD. At first, a feature enhancement module named FPEM is applied to enhance the image’s ability of representation learning. In addition, unlike the attention in the vanilla Transformer, our method adopts the deformable attention module interested in the pixels around the sampling points rather than the global features to make relational modeling. Experiments show that not only can we effectively improve the performance of the model but also greatly save the computational cost in this way.
An Analysis of Requirement Engineering Practices in Pakistani Software Houses

Software Requirement Engineering (RE) has been considered as the most significant part of software development life cycle (SDLC). It impacts the overall project progress and quality of the final product. Spending insufficient amount of time and effort in requirement engineering phase can increase the cost of errors in next phases of SDLC. Many approaches and techniques have been proposed by researchers so far, but all of them have not been practiced very widely in the industry. In order to further refine and improve requirement engineering process, problems faced in contemporary practice needs to be understood. General objective of this research is to identify and analyze requirement engineering practices in Pakistani Software Industry. Survey is used to gather data from local software industry. The analysis presents how frequently a particular type of RE practice is being used by local industry experts. The ultimate goal is to suggest best practices in requirement engineering and give recommendations to upcoming researchers regarding methods and techniques in this research area.
AECNet: Attentive EfficientNet For Crowd Counting

In the COVID pandemic situation, crowd counting became one of the tools to monitor if the social-distancing norms are being followed or not. However, in designing crowd counting algorithm, there are several challenges such as background noise, camera-to-objects distance, occlusion, and variations due to illumination, scale, and viewpoint. In this research, we propose a novel pipeline for density estimation in crowd counting. The proposed pipeline makes use of an encoder-decoder-based architecture in which we explore the family of EfficientN ets for the encoder architecture. For the decoder, we propose a deeper attention network to assist the model in a better distinction between foreground and background pixels. We empirically show that for a crowd counting dataset, the use of average pooling operation for any backbone architecture of encoder gives a significant improvement in performance. In terms of Mean Absolute Error, the proposed pipeline outperforms existing state-of-the-art techniques by a large margin on large-scale and small-scale counting datasets, UCF-QNRF and UCF _CC_50 dataset. We also achieve state-of-the-art results on the ShanghaiTech and Mall datasets. We additionally propose a crowd counting dataset captured using drones. We perform benchmark experiments on this dataset with existing and the proposed methods. The proposed dataset can be found at http://www.iab-rubric.org/resources/CrowdUAV.html.
The Borderless Integration of Financial Management Innovation Using Big Data Analysis of Social Media

This article mainly studies the innovation of borderless integrated financial management in big data social media in the era of wireless communication networks. For evaluating company A’s financial sharing capabilities, five first-level indicators are divided into four dimensions: quality, efficiency, cost, and safety, and are allowed to score the financial sharing center capabilities. First of all, system integration takes performance appraisal as the core, and through the establishment of a reasonable appraisal system, performance appraisal for the members of the financial team is carried out. Secondly, team members need to accept two-way leadership, two-way assessment, and two-way incentives from the finance and business departments. The experimental factors were analyzed, and the results showed that the cumulative variance contribution rate with common factors reached 89.339%. The importance of the first-level indicators is in order of process reengineering (27.08%), operation management (22.33%), information system (20.24%), personnel management (17.178%), and strategic planning (12.46%).
Secure Key Generation from Speech signal Using Enhanced MFCC Algorithm

Authentication is the crucial process of verifying and permitting the right user to access cloud services. The cloud service security proves to be fragile by the brute force efforts of hackers. To revamp the durability of authentication process, integrating the security factors has been exhorted. One such factor is the secure key extracted from the authenticated user’s voiceprint. This paper proposes a model for enhancing the signal to noise ratio of the MFCC algorithm to extract the secure key from the user voiceprint. The proposed method is highly rigid against the voice duplication attacks preventing unauthorized user to access by duplicating the authorized user’s voice print.
Feature Selection Approach for Phishing Detection Based on Machine Learning

Phishing is a kind of cybercrime that uses disguised websites to trick people into providing personally sensitive information. Phishing detection with high accuracy has attracted enormous interest in Cyber Security. Many website-based features have been applied for phishing detection, however, useless features can lead to extra feature extraction and phishing detection time costs. This paper analyzes 111 features of the latest published phishing websites dataset to investigate the obvious differences and correlations between phishing and legitimate websites. By applying eleven commonly used Machine Learning algorithms and evaluating their performances, we choose Random Forest as our phishing detection algorithm. Based on feature importance methods, we proposed a framework to reduce the number of features while maintaining high detection accuracy. The model training time and the usage of memory can also be saved. With the combination of the proposed feature selection framework, the important findings indicate that by using only 14 features, the accuracy of phishing detection can achieve 97.0%.
TaGSim

Computing similarity between graphs is a fundamental and critical problem in graph-based applications, and one of the most commonly used graph similarity measures is graph edit distance (GED), defined as the minimum number of graph edit operations that transform one graph to another. Existing GED solutions suffer from severe performance issues due in particular to the NP-hardness of exact GED computation. Recently, deep learning has shown early promise for GED approximation with high accuracy and low computational cost. However, existing methods treat GED as a global, coarse-grained graph similarity value, while neglecting the type-specific transformative impacts incurred by different types of graph edit operations, including node insertion/deletion, node relabeling, edge insertion/deletion, and edge relabeling. In this paper, we propose a type-aware graph similarity learning and computation framework, TaGSim (T ype -a ware G raph Sim ilarity), that estimates GED in a fine-grained approach w.r.t. different graph edit types. Specifically, for each type of graph edit operations, TaGSim models its unique transformative impacts upon graphs, and encodes them into high-quality, type-aware graph embeddings, which are further fed into type-aware neural networks for accurate GED estimation. Extensive experiments on five real-world datasets demonstrate the effectiveness and efficiency of TaGSim, which significantly outperforms state-of-the-art GED solutions.
RExACtor: Automatic Regular Expression Signature Generation for Stateless Packet Inspection

Monitoring, security, and network management systems require packet classification solutions to identify particular application-layer protocols. In real-time cases, traffic must sometimes be identified per-packet, and thus cannot rely on traffic flow data or statistical methods for identification. Payload-based signature matching is utilized for clear text data, but the manual creation of regular expression signatures is inefficient and humans can overlook key repeated patterns in unknown traffic. In this paper, we present our system, a Regular Expression Apriori Constructor (RExACtor), which combines sequential pattern data mining using the Apriori algorithm, frequency distribution tables from natural language processing, and pairwise sequence alignment from the Needleman-Wunsch scoring algorithm to automatically generate regular expression signatures for targeted protocols. This solution takes as input traffic PCAPs and uses a modular framework to output signatures or common substrings based on user configurations. Our encoding algorithm creates more specific signatures than previous systems and our results show an 11.8% reduction in heap allocation, making our solution more space efficient than the state of the art. We also provide an implementation of the Hyperscan scanning framework for regular expressions which outperforms packet filtering libraries like Snort used in previous work. RExACtor advances the state of the art as both an automatic signature generator and knowledge discovery toolbox by reducing overall dynamic memory allocation and providing a solution for per-packet, signature-based analysis.
Word-Level Confidence Estimation for RNN Transducers

Confidence estimate is an often requested feature in applications such as medical transcription where errors can impact patient care and the confidence estimate could be used to alert medical professionals to verify potential errors in recognition. In this paper, we present a lightweight neural confidence model tailored for Automatic Speech Recognition (ASR) system with Recurrent Neural Network Transducers (RNN-T). Compared to other existing approaches, our model utilizes: (a) the time information associated with recognized words, which reduces the computational complexity, and (b) a simple and elegant trick for mapping between sub-word and word sequences. The mapping addresses the non-unique tokenization and token deletion problems while amplifying differences between confusable words. Through extensive empirical evaluations on two different long-form test sets, we demonstrate that the model achieves a performance of 0.4 Normalized Cross Entropy (NCE) and 0.05 Expected Calibration Error (ECE). It is robust across different ASR configurations, including target types (graphemes vs. morphemes), traffic conditions (streaming vs. non-streaming), and encoder types. We further discuss the importance of evaluation metrics to reflect practical applications and highlight the need for further work in improving Area Under the Curve (AUC) for Negative Precision Rate (NPV) and True Negative Rate (TNR).
Certificateless Reliable and Privacy-Preserving Auditing of Group Shared Data for FOG-CPSs

FOG-enabled cyber-physical systems (FOG-CPSs) open new security challenges as the local edge devices are easier to compromise than a traditional cloud server. Remote data integrity checking (RDIC) plays an important role in safeguarding against data corruption from a storage server. Certificateless cryptography (CLPKC)-based RDIC schemes do not suffer from the drawbacks of the public key infrastructure (PKI)-based RDIC protocols. Most of the CLPKC-based RDIC schemes proposed in the literature deal with personal data. However, in a FOG-CPS, it is also important to audit a data file shared by a group of edge devices. Most of the existing group shared data auditing schemes lack mechanisms to defend against a semi-trusted data auditor applicable for a FOG-CPS scenario. In order to address these issues, in this paper, we propose a novel CLPKC-based group shared data auditing protocol tailored to the specific security requirements of a FOG-CPS. Besides, we perform a detailed cryptanalysis of two existing CLPKC-based privacy-preserving group shared data auditing schemes. The formal security analysis of our proposed protocol establishes metadata and data integrity proof unforgeability and claimed zero-knowledge privacy and reliability properties through rigorous proofs in the random oracle model setting. Performance evaluations establish the efficiency of our proposed protocol.
FreeSia: A Cyber-physical System for Cognitive Assessment through Frequency-domain Indoor Locomotion Analysis

Thanks to the seamless integration of sensing, networking, and artificial intelligence, cyber-physical systems promise to improve healthcare by increasing efficiency and reducing costs. Specifically, cyber-physical systems are being increasingly applied in smart-homes to support independent and healthy aging. Due to the growing prevalence of noncommunicable diseases in the senior population, a key application in this domain is the detection of cognitive issues based on sensor data. In this article, we propose a novel cyber-physical system for cognitive assessment in smart-homes. Cognitive evaluation relies on clinical indicators characterizing symptoms of dementia based on the individual’s movement patterns. However, recognizing these patterns in smart-homes is challenging, because movement is constrained by the home layout and obstacles. Since different abnormal patterns are characterized by undulatory-like trajectories, we conjecture that frequency-based locomotion features may more effectively capture these patterns with respect to traditional features in the spatio-temporal domain. Based on this intuition, we introduce novel feature extraction techniques and adopt state-of-the-art machine learning algorithms for short- and long-term cognitive evaluation. Our system includes a user-friendly interface that enables clinicians to inspect the data and predictions. Extensive experiments carried out with a real-world dataset acquired from both cognitively healthy seniors and people with dementia show the superiority of our frequency-based features. Moreover, further experiments with an ensemble method show that prediction accuracy can be enhanced by combining features in the frequency and time domains.