Reviewerly/exHarmony huggingface dataset:
REAL-TIME OBJECT DETECTION IN YAKSHAGANA IMAGES USING YOLO

The new method we introduced for object detection is called YOLO.In the past, classifiers were used to identify objects."Alternatively, we would treat object detection as a regression problem with spatially distinct bounding boxes and associated class probabilities.Direct prediction of salmon frames and class probabilities with a single neural network image in a single evaluation.Because the entire detection pipeline consists of a single network, detection performance can be optimized from start to finish."Our combined anchor moves very quickly.Our core YOLO algorithm performs real-time 45-frame image processing."Fast YOLO, a less sophisticated interpretation of the web, analyzes an astonishing 155 frames per second, but still outperforms other real-time sensors by a factor of two.Although YOLO does more locating crimes than state-of-the-art detection systems, it is less likely to predict false positives in the background.""After all, YOLO chooses truly universal representations of things.It performs better than other detection methods such as DPM and R-CNN when extrapolating from natural images to different domains such as art."
CRIME INTENTION DETECTION USING DEEP LEARNING

The Crime Rate and number of Criminals are increasing day by day, so there is great concern about the security issues, so to prevent and identify the crime before it occurs is the primary goal of the police and criminal officials.CCTV is normally deployed in every private and public area to control crime but it needs human supervision to monitor. it is very hard for a human to monitor many screens at the same time.It leads to many errors.To overcome these problems, we proposed Real-Time Crime Detection Technique using a Deep Learning Algorithm which monitors real-time videos and alerts the nearby Cybercrime admin about the occurrence of crime with current location.In this paper, we present YOLO as our object detection algorithm.Our architecture is extremely fast and process image in real-time at 45 frames per second.
ETHEREUM-BASED DECENTRALISED TOKEN EXCHANGE

In the world of modern technology, ensuring security is a top priority.To address this issue, blockchain technology has emerged as a promising solution by eliminating intermediaries and enhancing security.Cryptocurrencies are the first type of digital assets that have been successfully managed using blockchain technology.In recent years, financial institutions have been increasingly adding cryptocurrencies to their portfolios, leading to widespread adoption and interest among various stakeholders, including the banking sector, government, and individual investors.Cryptocurrency has the potential to become the future global currency, replacing fiat currency.This research project provides a comprehensive overview of the cryptocurrency market, including its origins, key features, price dynamics, market capitalization, and trading volumes.The project also explores important concepts such as Ethereum, smart contracts, tokens, and consensus algorithms that are critical to the functioning of the cryptocurrency market.
A survey of visualization techniques for comparing event sequences

Event sequence data is a special type of time-dependent data that captures information about the order in which discrete events occur. The time-dimension is one of the factors that makes event sequence data hard to understand. Other factors that contribute to this complexity are the dimensionality in terms of the amount of events and attributes, the frequency of events in terms of consistency and density, varying durations, and parallel occurrences. When end-users need to compare event sequences, all these characteristics need to be considered and justified. In this state-of-the-art report we review visualization techniques for the event sequence comparison task. We focus specifically on comparison, in contrast to general event sequence visualization, and review how different aspects, such as the data attributes or granularity, affect the comparison. We define a taxonomy based on five dimensions: Scale, Comparison Type, Size, Data, and Visualization & Interaction. Based on these dimensions, we provide an overview of the literature of the past 17 years. This overview is accompanied with an analysis of the strengths and weaknesses of the proposed techniques. Furthermore, we identify current gaps and research challenges in the literature.
Semantic Relation Transfer for Non-overlapped Cross-domain Recommendations

Although cross-domain recommender systems (CDRSs) are promising approaches to solving the cold-start problem, most CDRSs require overlapped users, which significantly limits their applications. To remove the overlap limitation, researchers introduced domain adversarial learning and embedding attribution alignment to develop non-overlapped CDRSs. Existing non-overlapped CDRSs, however, have several drawbacks. They ignore the semantic relations between source and target items, leading to noisy knowledge transfer. Moreover, they learn knowledge from both domain-shared and domain-specific preferences and are hence easily misled by the source-domain-specific preferences. To overcome these drawbacks, we propose a novel semantic relation-based knowledge transfer framework (SRTrans). We semantically cluster the source and the target items and calculate their similarities to extract relational knowledge between domains. To transfer the relational knowledge, we develop a new two-tier graph transfer network. Last, we introduce a task-oriented knowledge distillation supervision and combine it with a prediction loss to alleviate the negative impact of the source-domain-specific preferences. Our experimental results on real-world datasets demonstrate that SRTrans significantly outperforms state-of-the-art models.
ItrievalKD: An Iterative Retrieval Framework Assisted with Knowledge Distillation for Noisy Text-to-Image Retrieval

Benefiting from the superiority of the pretraining paradigm on large-scale multi-modal data, current cross-modal pretrained models (such as CLIP) have shown excellent performance on text-to-image retrieval. However, the current research mainly focuses on the scenarios with strong matching of images and texts, which is not always available in practice. For example, in social media content or daily communication, the text is not always completely related to the image and may also contain some irrelevant content, which introduces non-negligible noise to text-to-image retrieval. The noisy multi-modal setting is significantly different from the current cross-modal pretraining corpus, which may lead to significant degradation of the retrieval performance of the general image-text retrieval models. In this paper, we focus on the task of noisy text-to-image retrieval and propose an iterative retrieval framework which firstly retrieves the key-semantic information from the noisy text with knowledge distillation, followed by retrieving the relevant image from the image pool with the key-semantic clue. Experiments on Noisy-MSCOCO and PhotoChat datasets confirm the superiority of the proposed iterative retrieval framework in the task of noisy text-to-image retrieval compared with the general retrieval models.
Modelling an Efficient Approach to Analyse Clone Phishing and Predict Cyber-Crimes

Clone phishing is one of the most common social engineering attacks that organizations, governments, and the general public have to deal with. The threat posed by clone emails has increased dramatically over the past few years and necessitates the development of an appropriate clone detection system. To combat this threat, this research study utilizes machine learning strategies to predict clone phishing by detecting clone emails. This article discusses the deep learning-based clone phishing prevention prototype, a multi-modal deep neural network (DNet) to recognize and avoid clone phishing attacks. The database is split to train the detection model and then use test data to confirm the findings in order to collect inherent characteristics of the email text and other parameters that can be classified as clone and non-clone. While comparing the proposed model with the other existing techniques, the results show that the proposed DNet model is comparatively more accurate and successful.
Discourse-Aware Graph Networks for Textual Logical Reasoning

Textual logical reasoning, especially question-answering (QA) tasks with logical reasoning, requires awareness of particular logical structures. The passage-level logical relations represent entailment or contradiction between propositional units (e.g., a concluding sentence). However, such structures are unexplored as current QA systems focus on entity-based relations. In this work, we propose logic structural-constraint modeling to solve the logical reasoning QA and introduce discourse-aware graph networks (DAGNs). The networks first construct logic graphs leveraging in-line discourse connectives and generic logic theories, then learn logic representations by end-to-end evolving the logic relations with an edge-reasoning mechanism and updating the graph features. This pipeline is applied to a general encoder, whose fundamental features are joined with the high-level logic features for answer prediction. Experiments on three textual logical reasoning datasets demonstrate the reasonability of the logical structures built in DAGNs and the effectiveness of the learned logic features. Moreover, zero-shot transfer results show the features' generality to unseen logical texts.
Adversarial Active Learning with Guided BERT Feature Encoding

Recent advances in BERT-based models has significantly improved the performance of many applications on text data, such as text classification, question answering, e-commerce search and recommendation system, etc. However, the labelling of text data is often complex and time-consuming. While active learning can interactively query and label the data, the effectiveness of existing active learning methods is mostly limited by static text embedding approaches and by the insufficiency of training data. To address this critical problem, in this research we propose a BERT-based adversarial semi-supervised active learning (B-ASAL) model. In our approach, we use generative adversarial modelling and semi-supervised learning to guide the fine-tuning of the BERT and to optimize its corresponding text embeddings and feature encodings. The adversarial generator paired with a semi-supervised classifier guided the BERT model to adjust its feature encoding to best fit the distribution of not only class labels but also the discrimination of labeled and unlabeled data. Moreover, our B-ASAL model selects data points with high uncertainty and high diversity to be labeled using minimax entropy regularization. To our best knowledge, this is the first work that uses adversarial semi-supervised learning joined with active learning to guide and optimize feature encoding. We evaluate our method on various real-world text classification datasets and show that our model outperforms state-of-the-art approaches.
Semi-Supervised Text Classification via Self-Paced Semantic-Level Contrast

Semi-Supervised Text Classification (SSTC) aims to explore discriminative information from unlabeled texts in a self-training manner. These methods pre-train the deep classifier on labeled texts. Recent works further fine-tune the model on the combination of labeled texts and pseudo-labeled texts generated by the pre-trained deep classifier. However, the model’s performance largely depends on the quality of pseudo-labels. To tackle such an issue, we propose a novel approach, namely Self-paced Semantic-level Contrastive Learning (S $$^2$$ CL) for SSTC. S $$^2$$ CL imposes a self-paced pseudo-label generator to improve the quality of pseudo-labels. We innovatively propose robust supervised learning and semantic-level contrastive learning modules to alleviate the model’s over-sensitivity to pseudo-labels’ quality. Empirically, S $$^2$$ CL significantly outperforms the state-of-the-art methods on benchmark datasets with 0.3% - 4.6% improvements on Micro-F1 and 0.3% - 11.1% improvements on Macro-F1. Furthermore, we establish a practical dataset, i.e., Events39, to provide a benchmark for evaluating the robustness against domain-shift of SSTC methods. The experimental results demonstrate the effectiveness of S $$^2$$ CL on Events39.
ConvBoost

Human activity recognition (HAR) is one of the core research themes in ubiquitous and wearable computing. With the shift to deep learning (DL) based analysis approaches, it has become possible to extract high-level features and perform classification in an end-to-end manner. Despite their promising overall capabilities, DL-based HAR may suffer from overfitting due to the notoriously small, often inadequate, amounts of labeled sample data that are available for typical HAR applications. In response to such challenges, we propose ConvBoost -- a novel, three-layer, structured model architecture and boosting framework for convolutional network based HAR. Our framework generates additional training data from three different perspectives for improved HAR, aiming to alleviate the shortness of labeled training data in the field. Specifically, with the introduction of three conceptual layers--Sampling Layer, Data Augmentation Layer, and Resilient Layer -- we develop three "boosters" -- R-Frame, Mix-up, and C-Drop -- to enrich the per-epoch training data by dense-sampling, synthesizing, and simulating, respectively. These new conceptual layers and boosters, that are universally applicable for any kind of convolutional network, have been designed based on the characteristics of the sensor data and the concept of frame-wise HAR. In our experimental evaluation on three standard benchmarks (Opportunity, PAMAP2, GOTOV) we demonstrate the effectiveness of our ConvBoost framework for HAR applications based on variants of convolutional networks: vanilla CNN, ConvLSTM, and Attention Models. We achieved substantial performance gains for all of them, which suggests that the proposed approach is generic and can serve as a practical solution for boosting the performance of existing ConvNet-based HAR models. This is an open-source project, and the code can be found at https://github.com/sshao2013/ConvBoost
Estimation of drinking water volume of laboratory animals based on image processing

This paper describes an image processing-based technique used to measure the volume of residual water in the drinking water bottle for the laboratory mouse. This technique uses a camera to capture the bottle's image and then processes the image to calculate the volume of water in the bottle. Firstly, the Grabcut method separates the foreground and background to avoid the influence of background on image feature extraction. Then Canny operator was used to detect the edge of the water bottle and the edge of the liquid surface. The cumulative probability Hough detection identified the water bottle edge line segment and the liquid surface line segment from the edge image. Finally, the spatial coordinate system is constructed, and the length of each line segment on the water bottle is calculated by using plane analytical geometry. Then the volume of water is calculated. By comparing image processing time, the pixel number of liquid level, and other indexes, the optimal illuminance and water bottle color were obtained. The experimental results show that the average deviation rate of this method is less than 5%, which significantly improves the accuracy and efficiency of measurement compared with traditional manual measurement.
Evaluation of a Smart Intercom Microservice System Based on the Cloud of Things

This research migrates a monolithic smart intercom system to a microservice architecture, making the system more secure, stable, and scalable. The security mechanisms of the instant messaging platform are combined with microservices to improve the security of the system. The stability and performance of microservices are shown to be better than those of monolithic services through experimental tests. Residents can use different instant messaging software instead of the handset to improve the convenience of using this system. This system also implements community broadcasts, platform broadcasts, unit broadcasts, and family broadcasts to exchange messages across different instant messaging platforms. This paper proposes OpenAPI for other smart intercoms to integrate this system’s services and resources. In addition, this study deploys two microservice architectures using a native load balancer with a kube proxy and a service mesh load balancer with an istio proxy. Experiments were conducted during which residents tested the kube proxy and the istio proxy using stress testing tools on two microservice architectures, and the results showed that the kube proxy was slightly better than the istio proxy.
CeFlow: A Robust and Efficient Counterfactual Explanation Framework for Tabular Data Using Normalizing Flows

Counterfactual explanation is a form of interpretable machine learning that generates perturbations on a sample to achieve the desired outcome. The generated samples can act as instructions to guide end users on how to observe the desired results by altering samples. Although state-of-the-art counterfactual explanation methods are proposed to use variational autoencoder (VAE) to achieve promising improvements, they suffer from two major limitations: 1) the counterfactuals generation is prohibitively slow, which prevents algorithms from being deployed in interactive environments; 2) the counterfactual explanation algorithms produce unstable results due to the randomness in the sampling procedure of variational autoencoder. In this work, to address the above limitations, we design a robust and efficient counterfactual explanation framework, namely CeFlow, which utilizes normalizing flows for the mixed-type of continuous and categorical features. Numerical experiments demonstrate that our technique compares favorably to state-of-the-art methods. We release our source code ( https://github.com/tridungduong16/fairCE.git ) for reproducing the results.
Error Analysis on Reading Comprehension at Vocational School

This research was conducted to know the students’ errors in reading comprehension from 25students as the sample, there are 15 students have difficulties in identifying main idea, 5 students havedifficulties in identifying name, 22 students have difficulties in identifying spesific word, 7 students havedifficulties in finding reference, and 22 students have difficulties in understanding the structure of thetext. It can be seen from the high number of percentage of participants who answered wrong on everyquestion of the test.This research is a case study and it uses descriptive quantitative method. The subjectsof the research are fifty fife students of the ten grade students at SMK SWASTA Palapa, Pancur Batu,Pancur Batu who are detected as having difficulties in understanding English reading descriptive textand an English teacher who handles the classes. The test were used as the instruments of this study. Thetest was used to obtain the data about the students’ difficulties in understanding English readingdescriptive text.The finding shows that the majority of the students find difficulties in identifying mainidea of the text, more than half of students made mistake in identifying specific word in the text andmany students cannot understand the structure of the text. Whereas, the factors causing the students’difficulties in understanding English reading descriptive text are learners’ background, teachingtechnique,.and the learners’ environment.
A Relational Instance-Based Clustering Method with Contrastive Learning for Open Relation Extraction

Unsupervised text representations significantly narrow the gap with supervised pretraining, and relation clustering has gradually become an important method of open relational extraction (OpenRE). However, different relational categories generally overlap in the high-dimensional representation space, and distance-based clustering is difficult to separate different categories. In this work, we propose a relational instance-based clustering method with contrastive learning (RICL) - a framework to leverage similarity distribution information and contrastive method to promote better aggregation and relational representation. Specifically, to enable the model to better represent relation instances with word-level features, we construct an augmented dataset using only standard dropout as noise and iteratively optimize the vector representation of relation instances by fully using self-supervised signals. Experiments on real-world datasets show that RICL can achieve excellent performance compared with previous state-of-the-art methods.
Total Probability Theorem, Independence, Combinatorial

In Chap. 1 , we explained fundamental definitions, such as experiment, sample space, event, probability, conditional probability, etc. In this chapter, we study the applications and uses of these definitions for real problems. The applications of fundamental definitions to real problems create some new concepts such as total probability theorem, Bayes’ rule, independent trials, counting principle, etc. We explain these new concepts with solved examples.
Continuous Random Variables

In the previous chapters, we focused on the discrete experiments and for the discrete experiments we defined discrete sample spaces, events, and discrete random variables. Discrete sample spaces include a countable number of simple events. In this chapter, we study continuous experiments and define continuous sample spaces, continuous events, and continuous random variables. Continuous sample spaces include an uncountable number of simple events. For this reason, real number intervals are used to define the sample spaces and events for continuous experiments. Probability density function, cumulative distribution function, and mean and variance concepts for continuous random variables are explained by solved examples. Besides, conditional mean and variance calculations will be visited shortly.
Predicate Private Set Intersection with Linear Complexity

Private Set Intersection (PSI) enables two parties to learn the intersection of their input sets without exposing other items that are not within the intersection. However, real-world applications often require more complex computations than just obtaining the intersection. In this paper, we consider the setting where each item in the input set has an associated payload, and the desired output is a subset of the intersection obtained by evaluating certain conditions over the payload. We call this new primitive Predicate Private Set Intersection (PPSI) and show its applicability in many different scenarios. While a PPSI protocol can be obtained by combining existing circuit-PSI and generic circuit-based secure computation, this naive approach is not efficient. Therefore, we also provide a specially designed PPSI protocol with linear complexity and good concrete efficiency. We implemented the protocol and evaluated it with extensive experiments. The results validated the efficacy of our PPSI protocol.
DMA’n’Play: Practical Remote Attestation Based on Direct Memory Access

Remote attestation allows validating the trustworthiness of a remote device. Existing attestation schemes either require hardware changes, trusted computing components, or rely on strict timing constraints. In this paper, we present a novel remote attestation approach, called DMA’n’Play, that tackles these practical limitations by leveraging DMA (direct memory access). Since DMA does not require CPU time, DMA’n’Play even allows attestation of devices with real-time constraints. To prevent the exploitation of side-channels which potentially could determine if the attestation is running, we developed DMA’n’Play To-Go, a small, mobile attestation device that can be plugged into the attested device. We evaluated DMA’n’Play on two real-world devices, namely a syringe pump and a drone. Our evaluation shows that DMA’n’Play adds negligible performance overhead and prevents data-only attacks, by validating critical data in memory.