Reviewerly/exHarmony huggingface dataset:
Pattern Recognition and Reconstruction: Detecting Malicious Deletions in Textual Communications

Digital forensic artifacts aim to provide evidence from digital sources for attributing blame to suspects, assessing their intents, corroborating their statements or alibis, etc. Textual data is a significant source of artifacts, which can take various forms, for instance in the form of communications. E-mails, memos, tweets, and text messages are all examples of textual communications. Complex statistical, linguistic and other scientific procedures can be manually applied to this data to uncover significant clues that point the way to factual information. While expert investigators can undertake this task, there is a possibility that critical information is missed or overlooked. The primary objective of this work is to aid investigators by partially automating the detection of suspicious e-mail deletions. Our approach consists in building a dynamic graph to represent the temporal evolution of communications, and then using a Variational Graph Autoencoder to detect possible e-mail deletions in this graph. Our model uses multiple types of features for representing node and edge attributes, some of which are based on metadata of the messages and the rest are extracted from the contents using natural language processing and text mining techniques. We use the autoencoder to detect missing edges, which we interpret as potential deletions; and to reconstruct their features, from which we emit hypotheses about the topics of deleted messages. We conducted an empirical evaluation of our model on the Enron e-mail dataset, which shows that our model is able to accurately detect a significant proportion of missing communications and to reconstruct the corresponding topic vectors.
Permeable Reactive Concrete Using Recycled Waste Materials for Nutrient Contamination Removal in Urban Surface Runoff

Abstract In recent years, stormwater control measures (SCMs) such as permeable concrete pavement have been experimentally investigated and used to manage hydrologic and water quality impacts of stormwater runoff. Research revealed the potential of permeable pavement in reducing and delaying peak flow rate, reducing runoff volume, and capturing heavy metals and other particulate-bound pollutants from stormwater runoff. However, few studies have evaluated the effects of permeable pavement on nutrients in stormwater runoff. This research aims to produce permeable reactive concrete (PRC) from waste fly ash, waste gypsum board and waste coco peat and to investigate its effectiveness in removing nutrient contamination present in stormwater or urban surface runoff. The raw materials underwent through granulation process to produce granulated filtering media (GFM). Cylindrical samples of PRC were then made and subjected to various physical and water quality tests. The use of GFM as partial coarse aggregates of PRC for urban surface runoff management and nutrient contamination removal has been tested and evaluated. After performing all the tests, the researchers concluded that GFM as partial coarse aggregates of PRC is effective due to the significant increase in infiltration rate of the entire sample compared to the traditional permeable concrete that has an average infiltration rate of 2-6 mm/s. The results in the water quality test revealed that PRC with GFM as partial coarse aggregates lessen the nitrate, phosphate, and ammonia that are present on urban surface runoff.
DocNER: A Deep Learning System for Named Entity Recognition in Handwritten Document Images

The extraction of relevant information from handwritten document images is still a challenging task. In this paper, we introduce a framework for named entities recognition from a collection of real handwritten marriage records. For this purpose, we perform an extensive evaluation of two different information extraction approaches to locate and recognize named entities from unstructured handwritten documents. The first one is based on an end-to-end neural network model that jointly performs transcription and semantic annotation of handwritten document images. While the second relies on two stages, the first one focuses on Handwritten Text Recognition (HTR) to transcribe documents into electronic texts, while the second seeks to identify semantic named entities using a state-of-the-art Natural Language Processing (NLP) model. This work is evaluated on a collection of real French handwritten marriage records, discussing the advantages and drawbacks of the explored approaches. The achieved results show the efficiency of the proposed framework even though it does not use any post-processing stage. Additional experiments are conducted using the Esposalles database to compare our methods to the participated systems in the ICDAR 2017 Information Extraction competition.
Data stream classification with ant colony optimisation

Data stream mining has recently emerged in response to the rapidly increasing continuous data generation. While the majority of Ant Colony Optimisation (ACO) rule induction algorithms have proved to be successful in producing both accurate and comprehensive classification models in nonstreaming (batch) settings, currently ACO-based algorithms for classification problems are not suited to be applied to data stream mining. One of the main challenges is the iterative nature of ACO algorithms, where many procedures—for example, heuristic calculation, selection of continuous attributes, pruning—require multiple passes through the data to create a model. In this paper, we present a new ACO-based algorithm for data stream classification. The proposed algorithm, called Stream Ant-Miner (sAnt-Miner), uses a novel hybrid pheromone model combining both a traditional construction graph and solution archives models to efficiently handle a large number of mixed-type (nominal and continuous) attributes directly without the need for additional procedures, reducing the computational time required to complete an iteration of the algorithm. Our results show that sAnt-Miner produces statistically significant concise models compared with state-of-the-art rule induction data stream algorithms, without negative effects on their predictive accuracy.
Leukocytes Segmentation and Classification in Digital Microscopic Images

Image processing and machine learning have recently gained positive contributions to various medical procedures. One of the diagnostic processes&#x0027; essential requirements in many diseases is laboratory tests, such as the Complete Blood Count (CBC) test. In CBC, various leukocytes, also known as White Blood Cells (WBC), are segmented, classified, and counted by a lab technician in microscopic slides. This process is very tiresome and requires a human technician with specialized skill sets. This research proposes a fully automatic algorithm for the segmentation and classification of white blood cells. The proposed method applies pre-processing techniques to digital microscopic images. White blood cells are then segmented based on color pallets. Hybrid features are extracted from the segmented images based on the fusion of local binary patterns and statistical features. Then various classifiers are used for the classification of WBC. Results suggest that the Support Vector Machine (SVM) and Artificial Neural Networks (ANN) outclass other classifiers. It was observed that the proposed methodology outperformed existing methods in terms of classification accuracy (97.5&#x0025;).
Unified Feature Fusion Network with Path Router for Multi-task Image Restoration

Image restoration is an important low-level vision task, which includes various sub-tasks such as deraining, dehazing, denoising, raindrop removal, etc. Although current researches have achieved significant results in various sub-tasks, only a few of them are designed for multiple degradation factors. However, in the actual natural environment, the weather is complex and changeable so the networks designed for a single task are usually inapplicable. In this paper, we propose an unified network that can effectively restore images in a variety of weather conditions (rain, haze, and raindrops). The network is mainly divided into three parts. The first part is the shared multi-expert feature extraction module. We introduce the multi-task learning with multi-gate mixture-of-experts(MMoE) architecture and propose the smooth dilated residual group to extract the low-level features. Furthermore, the gate fusion sub-network is proposed to weigh and sum the output of each expert, so the correlation and difference between tasks can be captured. The second part is the path router sub-network, which can select branches of different tasks, and only open one branch at a time. The third part is multi-gated feature fusion branch, which can extract high-level feature and fuse different levels of features. Finally, we add the output of the third part to the input image and get the clean image. Our model cam deal with a variety of weather conditions and the experiments show competitive results compared with state-of-the-art models for single tasks.
Review on Dec-POMDP Model for MARL Algorithms

With the various application and rapid development of deep reinforcement learning in multi-agent systems, the field of multi-agent reinforcement learning (MARL) continues to emerge with various practical application problems and corresponding solutions. Since single-agent decision-making tasks are difficult to meet the demands of complex application scenarios, most tasks need to be accomplished through cooperation or competition among multiple agents. This paper first introduces the characteristics of multi-agent systems, the basic concepts of reinforcement learning, and the methods for modeling multi-agent systems, then analyzes the strengths and weaknesses of two multi-agent reinforcement learning models for MARL algorithms in chronological order, which are communication-based learning model and collaborative-based learning model. Finally, based on the emerging opportunities and challenges in the field of MARL, possible research directions are proposed for the researchers' reference.
Zero-Day attack prevention Email Filter using Advanced Machine Learning

Preventing email spams continues to be a challenge as the attackers are using new techniques that circumvent the existing spam filters. Therefore, a smart email filter that can identify zero day attacks is necessary. In this paper, we propose an approach which not only looks at the text of the body of the email but also handles the embedded phishing URLs and attached spam images. The proposed approach uses several advanced Machine Learning algorithms to classify the emails and provides a structured process to identify the spams. We use lazyPredict library for selecting the best performing machine learning models. Our case studies using standard data sets show that these smart filters perform well in identifying spams and preventing zero-day attacks. Our analysis of results shows that Stacking classifier performs better with accuracy score of 0.97 for phishing URLs detection. Whereas, the perceptron classifier with accuracy of 0.97 the top performer in detecting email spams. The performances of other algorithms are also reported.
An Examination Application For Blind Students With Subjective Answer Evaluator

We present in this paper an examination application for blind students with a subjective answer evaluator. In the present scenario, Blind students need a volunteer to give exams, but we have proposed a solution to that by developing a completely voice-controlled website that also records answers given by the students. This will help increase the number of literates who are visually impaired giving as they can independently give exams. The current way of checking subjective answers is adverse. Whenever a human being evaluates papers, the quality is affected by emotion. In this paper, we are testing four different models for subjective answers evaluation using machine language. These models include Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbours. After testing Random Forest proved to be the best giving 83%accuracy.
A flexible 3D scanning system using convergent stereo vision integrated with an industrial manipulator

This paper proposes a flexible three-dimensional (3D) scanning system based on an active stereo scanner mounted on a six degrees of freedom (6-DOF) industrial robot arm. In addition, we reduce the blind area of sensors by adding one more DOF to adjust the convergent angle of the two cameras. This configuration is suitable for scanning items with different sizes, complex shapes, and sophisticated structures due to the flexibility in approaching the object. Then, the research develops a robot-based iterative closest point method for point cloud registration. This 3D reconstruction strategy enhances the accuracy as well as the computational time of the point cloud processing. After that, we conduct several real-world experiments to examine the effectiveness of the proposed system and algorithm.
M-A-R: A Dynamic Symbol Execution Detection Method for Smart Contract Reentry Vulnerability

The original intention of smart contract design is to execute every transaction in the blockchain spontaneously, efficiently and fairly, meanwhile, smart contract plays an important role in the blockchain activities. With the development of blockchain, the vulnerability of smart contract becomes more and more obvious. The security vulnerability detection of smart contract is very important. This paper proposes M-A-R, a dynamic symbol execution method focusing on efficient detection for reentry vulnerability, realizes the security detection of the source code of smart contract, optimizes the design of its implementation method, then compares it with the existing related tools. The results show that M-A-R approach can detect the reentry vulnerability efficiently and has good universality and scalability.
FiLMing Multimodal Sarcasm Detection with Attention

Sarcasm detection identifies natural language expressions whose intended meaning is different from what is implied by its surface meaning. Today, social media has given rise to an abundant amount of multimodal data where users express their opinions through text and images. Our paper aims to leverage multimodal data to improve the performance of the existing systems for sarcasm detection. We propose a novel architecture that uses the RoBERTa model with a co-attention to incorporate context incongruity between input text and image attributes. Further, we integrate feature-wise affine transformation (FiLM) by conditioning the input image through FiLMed ResNet blocks with the textual features to capture the multimodal information. The output from both the models and CLS token from RoBERTa is concatenated for the final prediction. Our results demonstrate that our proposed model outperforms the existing state-of-the-art methods by 6.14% F1 score on the public Twitter multimodal sarcasm detection dataset (Our code+data is available at https://tinyurl.com/kp2ruj7c).
Limited Times of Data Access Based on SGX in Cloud Storage

It is straightforward to encrypt the outsourced data to protect its confidentiality using symmetric cryptography in cloud storage. How to control and restrict the use of the encryption key in data users&#x2019; devices becomes one of the critical issues. In most of existing time-based and policy-based schemes, the key cannot be stored locally in data users&#x2019; devices, making the traffic cost linear with the data access times as the key should be retrieved in each data access. In this paper, we propose a times-based scheme with Intel SGX to restrict the times the key can be used in the data user&#x2019;s device to restrict the times of data access. The basic idea is to compare the current used times with the specified maximal times to determine whether the key can be used or not. To this end, we use a monotonic counter to count the times the key has been used. When the use condition is not satisfied, we destroy the key securely and generate public proof so that (i) the encrypted data cannot be accessed anymore, i.e., the data is deleted assuredly, (ii) the deletion can be verified. In addition, a hash-based integrity check approach is utilized to detect and prevent replay attacks. The experimental results on the implemented prototype show our scheme is feasible in practice.
Practical Pose Trajectory Splines With Explicit Regularization

We investigate spline-based continuous-time pose trajectory estimation using non-linear explicit motion priors. Current regularization priors either linearize the orientation, rely on the implicit regularization obtained from the used spline basis function, or use sampling based regularization schemes. The latter is a special case of a Riemann sum approximation, and we demonstrate when and why this can fail, and propose a way to avoid these issues. In addition we provide a number of novel practically useful theoretical contributions, including requirements on knot spacing for orientation splines, new basis functions for constant velocity extrapolation, and a generalization of the popular P-Spline penalty to orientation. We analyze the properties of the proposed approach using synthetic data. We validate our system using the standard task of visual-inertial calibration, and apply it to stereo visual odometry where we demonstrate real-time performance on KITTI.
Massive Remote School Trips: A Case Study

During the height of the CoViD-19 pandemic in 2020 and early 2021, schools throughout Europe have been closed for several months, leaving teachers with the responsibility of providing distance learning through video conferencing and remote-presence systems, while parents were scrambling for appropriate tools and support. School outings have also suffered from the limited mobility of students given by stay-at-home orders and other restrictions. In this paper we present a set of technologies developed to reproduce the school trip experience, allowing students to stay at home or in school and requiring only a Web browser and Internet access, while integrating communication tools that allow participants to actively be engaged in interactive lessons and educational experiences. In 2020 the tool has been used during the “CodyTrip” event, a two-day visit to the town of Urbino, attended by more than 15.000 students, followed up with a series of events in 2021 with over 115.000 participants. Results from the pilot events show very high engagement and demonstrate the feasibility of organizing online visiting experiences with massive participation without compromising the perceived interactivity of the proposed activities, which can be equally engaging for different audience demographics. Findings also suggest that this solution may be adopted not only as a contingent substitute for traveling during the pandemic, but as an effective tool to widen the scope and appeal of cultural tourism.
Comparison of Voice Based Virtual Assistants fostering Indian Higher Education – A Technical Perspective

To meet expectations of stakeholders in Education 4.0, the inclination towards acceptance, testing, and implementation of Voice Assistants has increased at a faster pace in western universities. This study has been done to understand the present level of performance and capability of voice assistants in answering the basic queries of users from Indian Higher education sector. Three voice assistants namely Amazon Alexa, Microsoft Cortana, and Google Assistant have been considered for a survey in which 100 students participated and tested the performance of these three voice assistants by asking basic questions related to admission and examination. A set of total 14 questions has been selected to be asked by user in both the categories. Category 1 related to Admissions contained 7 little complex questions while category 2 related to Examination had 7 direct and simpler questions. Responses of the users were noted down and after careful analysis, the explanations have been deduced about performance of these voice assistants in factors considered: understanding of voice and language of user, understanding of basic meaning of the question, request to repeat the question, whether the answer was given by voice assistants and quality of the answer received. As per the results, various observations have been noted that may further help Artificial Intelligence developers and programmers to understand the success and failure of voice assistants and accordingly bring improvements in their performance level. This study also helps in knowing about huge applications, contributions and benefits these voice assistants offer to the stakeholders. Once the improvements are incorporated by the developers, marketing professionals can boost the absorption and use of voice assistants in massive higher education sector of India by convincing the important stakeholders that also paves a way to tap into more undiscovered possibilities about use of Artificial Intelligence in higher education system at global level.
Bayesian Computation in Dynamic Latent Factor Models

Bayesian computation for filtering and forecasting analysis is developed for a broad class of dynamic models. The ability to scale-up such analyses in non-Gaussian, nonlinear multivariate time series models is advanced through the introduction of a novel copula construction in sequential filtering of coupled sets of dynamic generalized linear models. The new copula approach is integrated into recently introduced multiscale models in which univariate time series are coupled via nonlinear forms involving dynamic latent factors representing cross-series relationships. The resulting methodology offers dramatic speed-up in online Bayesian computations for sequential filtering and forecasting in this broad, flexible class of multivariate models. Two examples in nonlinear models for very heterogeneous time series of non-negative counts demonstrate massive computational efficiencies relative to existing simulation-based methods, while defining similar filtering and forecasting outcomes.