Reviewerly/exHarmony huggingface dataset:
Conditional Independence Test Based on Residual Similarity

Recently, many regression-based conditional independence (CI) test methods have been proposed to solve the problem of causal discovery. These methods provide alternatives to test CI of x,y given Z by first removing the information of the controlling set Z from x and y , and then testing the independence between the two residuals R x,Z and R y,Z . When the residuals are linearly uncorrelated, the independence test between them is nontrivial. With the ability to calculate inner product in high-dimensional space, kernel-based methods are usually used to achieve this goal, but they are considerably time-consuming. In this paper, we test the independence between two linear combinations under linear structural equation model. We show that the dependence between the two residuals can be captured by the difference between the similarity of R x,Z and R y,Z and that of R x,Z and R r ( R r is an independent copy of R y,Z ) in high-dimensional space. With this result, we provide a new way to test CI based on the similarity between residuals, which is called SCIT — the abbreviation of Similarity-based CI Testing. Furthermore, we develop two versions of the proposal, called Kernel-SCIT and Neural-SCIT, respectively. Kernel-SCIT calculates the similarity by using kernel functions, while Neural-SCIT approximates the upper bound of the similarity by using deep neural networks. In both algorithms, random permutation tests are performed to control Type I error rate. The proposed tests are evaluated on (conditional) independence test and causal discovery with both synthetic and real datasets. Experimental results show that Kernel-SCIT is simpler yet more efficient and effective than the typical existing kernel-based methods HSIC and KCIT in the cases of small sample size, and Neural-SCIT can significantly boost the performance of CI testing when sufficient samples are available. The source code is available at https://github.com/xyw5vplus1/SCIT .
Self-adaptive optical flow estimation in multiharsh outdoor scenes

Open-air optical flow estimation is crucial for autonomous driving and traffic monitoring. However, it is still challenging to obtain high-precision optical flow under severe imaging environments. To alleviate this problem, we propose a self-adaptive optical flow estimation framework for harsh outdoor scenes, which consists of a frame-adaptive learnable module (FAL-module), a streak-invariant feature extractor (SFE), and a feature matching and flow propagation network. In the framework, three components are jointly learned in an end-to-end manner. The input frames can be adaptively enhanced without sacrificing optical flow-related information by FAL-module. And the SFE decreases the influence of streak-like noise generated by rainy and snowy. Compared with the state-of-the-art methods, the experimental results show that our approach can significantly improve the optical flow estimation quality in multiple harsh outdoor scenes without performance penalties in normal scenes.
Secure Role Based Access Control Data Sharing Approach and Cloud Environment

the primary objective of cloud storage is to maintain data integrity, which involves implementing measures to prevent unauthorized access and ensuring that data can be regenerated if mishandled. To achieve this, a proxy server will be assigned the task of protecting and restoring data in case of unauthorized modifications. Users' data will be stored in both public and private areas of the cloud, with only public data being accessible to users and private data being kept more secure. Cloud storage offers users various redundancy configurations to balance performance and fault tolerance, with data availability being critical in distributed storage systems, especially when node failures are common in real-life scenarios. In this research, a proposed aes 128 encryption algorithm and role-based access control (rbac) scheme are explored to provide secure data storage and sharing, as well as a secure user access policy. Additionally, a backup server approach is used as a proxy storage server for ad hoc data recovery for all distributed data servers. The experiment's analysis is proposed in both public and private cloud environments, utilizing keywords such as rbac, elgamal encryption scheme, proxy key generation, advanced encryption standard (aes), and more
High-Order Correlation-Guided Slide-Level Histology Retrieval With Self-Supervised Hashing

Histopathological Whole Slide Images (WSIs) play a crucial role in cancer diagnosis. It is of significant importance for pathologists to search for images sharing similar content with the query WSI, especially in the case-based diagnosis. While slide-level retrieval could be more intuitive and practical in clinical applications, most methods are designed for patch-level retrieval. A few recently unsupervised slide-level methods only focus on integrating patch features directly, without perceiving slide-level information, and thus severely limits the performance of WSI retrieval. To tackle the issue, we propose a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</b> igh-Order Correlation-Guided <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> elf-Supervised <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</b> ashing-Encoding <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</b> etrieval (HSHR) method. Specifically, we train an attention-based hash encoder with slide-level representation in a self-supervised manner, enabling it to generate more representative slide-level hash codes of cluster centers and assign weights for each. These optimized and weighted codes are leveraged to establish a similarity-based hypergraph, in which a hypergraph-guided retrieval module is adopted to explore high-order correlations in the multi-pairwise manifold to conduct WSI retrieval. Extensive experiments on multiple TCGA datasets with over 24,000 WSIs spanning 30 cancer subtypes demonstrate that HSHR achieves state-of-the-art performance compared with other unsupervised histology WSI retrieval methods.
CYBER ATTACKS FOR DATA BREACH AND POSSIBLE DEFENSE STRATEGIES IN INTERNET OF HEALTHCARE THINGS ECOSYSTEM

The widespread use of the Internet and the increase in Internet of Things (IoT) equipment paved the way for the development of user-friendly systems. The inclusion of many electronic systems in the IoT ecosystem, especially in homes, has improved remote control and monitoring features. In general, within the concept of smart home, voice, control and joint movements are used as command center and WIFI, Bluetooth, Zigbee and GSM etc. technologies are used for communication. Regular monitoring of some health problems of individuals occurs in some situations that require instant intervention. In this context, individuals in need of home care or under surveillance at home are checked with Internet of Healthcare Things equipment in the company of experts. The rapid spread of the IoT ecosystem has also increased data production. Especially sensitive health data is at the beginning of critical data and requires security measures to be taken. In this study, the IoT devices used for home patient care have been evaluated for the sources of data leaks and possible security measures that may be experienced in the process from the data owner to the data storage stage. In order to identify possible risks and threats, 4 different target scenarios were created. These scenarios include home internet connection resources, data transfer, data storage and access. 8 different attacks (Deauth, DDOS, brute force, hashcat, Man-in-the-middle, Injection, Short Address Attack, Smart Contract Overflow) were applied to these possible scenarios where data leakage could occur. In addition, recently, blockchain applications and smart contract transmissions are preferred for data security. Among the attack scenarios, Short Address Attack and Smart Contract Overflow are attack methodologies used for blockchain security. In particular, denial of service was encountered in all attacks on wireless networks. Configuration errors, wrong product selection, use of weak passwords and default configurations in the IOT ecosystem seem to be the main sources of data leaks. As a result, the study includes possible attacker scenarios and possible vulnerabilities have been extracted within the scope of real scenarios. In addition, the measures to be taken against these vulnerabilities were evaluated and recommendations were given to take maximum security measures to prevent data leaks from within the IoT ecosystem.
Applying Lean to Improve Software Project Management Education

This article describes how different Lean principles and techniques have been adapted and applied to identify, analyze, and improve the value chain of a Software Project Management module in a computer science curriculum. This research is part of an initiative to improve the processes related to higher education teaching management, at a university, with the main goal of maximizing the value that is provided to students when taking a module. To represent and visualize the whole value chain of the module, the value stream mapping Lean management tool has been used. The essential module activities and the necessary elements to teach each one of these activities, from the first session to the final mark, were detected, collected, and represented using diagrams. The value chain analysis and review were performed by applying Lean principles and techniques. This article details several improvements to be applied to the module to eliminate the waste that did not add value to students.
Human activity recognition with the support of deep learning and machine learning-survey

Human Activity Recognition (HAR) is a method of automatically recognizing human actions based on streaming data from a variety of sensors, including inertial sensors, physiological sensors, position sensors, camera, time, and a variety of other ambient sensors. HAR has been shown to be beneficial in a variety of fields of study, including healthcare, elderly care, ambient living, personal care, social science, rehabilitation engineering, and a variety of other fields. Deep learning-based algorithms have become the most effective and efficient choice of algorithms for recognizing and solving HAR problems, thanks to recent advances in processing power. In this survey, we use deep learning algorithms to categories recent research work according to numerous variables and measurements in order to analyses recent trends in HAR. The articles are examined in terms of HAR, time series analysis, machine learning models, dataset construction methods, and the application of various other emerging trends such as transfer learning, active learning, and so on.
Linkage of Knowledge Sharing Platform and SDGs Implementation Mechanism and for North Korean Development Cooperation

Purpose: This study seeks to link the knowledge sharing platform to the SDGs implementation mechanism as a way to reduce the gap between theoretical discussions and reality on knowledge sharing with North Korea and improve the effectiveness of knowledge sharing.&#x0D; Originality: While the theoretical approach to knowledge sharing has been discussed mainly on the grand discourse on the premise of regime change, the projects implemented in reality were mainly simple technology transfers or short-term one-time economic knowledge transfer. This study has significance as a practical policy study that can narrow the gap between knowledge sharing theory and North Korea's reality and enhance development capacity by seeking ways to connect the knowledge platform to North Korea's SDGs implementation mechanism.&#x0D; Methodology: This study confirmed North Korea's perception of knowledge sharing through literature reviews and interviews, and analyzed existing technical assistance and knowledge sharing project data and preceding studies to derive its characteristics. In particular, three closed interviews were conducted and the contents of the closed presentations were referred to. In addition, reports and previous studies of major international organizations related to knowledge sharing were reviewed.&#x0D; Result: In order for the transferred technology and knowledge to be accumulated and reproduced in North Korea, horizontal and vertical exchanges between donors in each field and North Korea's internal experts, ministries, and specialized organizations are required, and a knowledge reproduction hub must be formed internally. This paper presents an alternative that the technical committee (TC) of North Korea's SDGs implementation mechanism is a hub institution that integrates sectoral information, knowledge, and project evaluations accumulated through business channels and can be linked to donor knowledge sharing platforms.&#x0D; Conclusion and Implication: This study has policy implications as a new approach that can create synergies by strengthening North Korea's development capabilities under various realistic constraints.
Net-track: Generic Web Tracking Detection Using Packet Metadata

While third-party trackers breach users’ privacy by compiling large amounts of personal data through web tracking techniques, combating these trackers is still left at the hand of each user. Although network operators may attempt a network-wide detection of trackers through inspecting all web traffic inside the network, their methods are not only privacy-intrusive but of limited accuracy as these are susceptible to domain changes or ineffective against encrypted traffic. To this end, in this paper, we propose Net-track, a novel approach to managing a secure web environment through platform-independent, encryption-agnostic detection of trackers. Utilizing only side-channel data from network traffic that are still available when encrypted, Net-track accurately detects trackers network-wide, irrespective of user’s browsers or devices without looking into packet payloads or resources fetched from the web server. This prevents user data from leaking to tracking servers in a privacy-preserving manner. By measuring statistics from traffic traces and their similarities, we show distinctions between benign traffic and tracker traffic in their traffic patterns and build Net-track based on the features that fully capture trackers’ distinctive characteristics. Evaluation results show that Net-track is able to detect trackers with 94.02% accuracy and can even discover new trackers yet unrecognized by existing filter lists. Furthermore, Net-track shows its potential for real-time detection, maintaining its performance when using only a portion of each traffic trace.
Crime Record Management System

The Crime Record Management System is a web-based application designed to improve the management of crime records across all police stations in the country. Effective crime prevention, detection, and conviction rely heavily on a responsive information management system. Therefore, centralizing information management in crime is proposed for the efficient sharing of crucial information among police stations. Initially, the system will be implemented in cities and towns and later interconnected to enable police staff to access information from all records in the state, ultimately helping to close cases faster. The system will also generate information for proactive and preventive measures to fight crime. The project will adopt a distributed architecture with a centralized database storage system, with high priority given to security and data protection mechanisms. The application is designed to handle various modules and their associated reports, produced in line with the administrative staff's applicable strategies and standards. In summary, the Crime Record Management System is a web-based application that aims to improve the management of crime records across police stations in the country. The system is designed to centralize information management, generate crucial information for crime prevention, and provide efficient and fast access to records. The project adopts a distributed architecture with centralized database storage, prioritizing data protection and security mechanisms while producing reports that align with applicable administrative strategies and standards
Digital heritage and archaeology in practice

Digital heritage and archaeology in practice comprises two companion volumes: Presentation, teaching, and engagement (from here on, PTE) and Data, ethics, and professionalism (DEP). The chapters present some of the outputs of the Institute for Digital Archaeology Method and Practice, based at Michigan State University and funded by the National Endowment for Humanities Institutes for Advanced Topics in Digital Humanities programme.
Multilingual BERT-based Word Alignment By Incorporating Common Chinese Characters

Word alignment is an important task of detecting translation equivalents between a sentence pair. Although word alignment is no longer necessarily needed for neural machine translation, it’s still useful in a wealth of applications, e.g., bilingual lexicon induction, constraint decoding, and so on. However, the most well-known word aligners are still Giza++ and fastAlign, both of which are implementations of traditional IBM models. To keep pace with the advance in NMT, there has been a surge of interest in replacing the IBM models with neural models. We follow this trend but aim to boost performance of word alignment between Japanese and Chinese, which share a large portion of Chinese characters. Our key idea is to leverage these common Chinese characters in both languages as an indicator for inferring alignment; i.e., the source and target words with the common Chinese characters should be most likely aligned. Following this idea, we propose three methods that leverage common Chinese characters to boost the mBERT-based word alignment, including reward factor, representation alignment, and contrastive training. Furthermore, we annotate and release a golden dataset for Japanese-Chinese word alignment. Experiments on the dataset show that our methods outperform several strong baselines in terms of AER score and verify the effectiveness of exploiting common Chinese characters.
Feature Fusion and NRML Metric Learning for Facial Kinship Verification

Features extracted from facial images are used in various fields such as kinship verification. The kinship verification system determines the kin or non-kin relation between a pair of facial images by analysing their facial features. In this research, different texture and color features have been used along with the metric learning method, to verify the kinship for the four kinship relations of father-son, father-daughter, mother-son and mother-daughter. First, by fusing effective features, NRML metric learning used to generate the discriminative feature vector, then SVM classifier used to verify to kinship relations. To measure the accuracy of the proposed method, KinFaceW-I and KinFaceW-II databases have been used. The results of the evaluations show that the feature fusion and NRML metric learning methods have been able to improve the performance of the kinship verification system. In addition to the proposed approach, the effect of feature extraction from the image blocks or the whole image is investigated and the results are presented. The results indicate that feature extraction in block form, can be effective in improving the final accuracy of kinship verification.
Towards improving automatic speech recognition for underrepresented dialects with data augmentation

Speech technology harnessing information from a speaker's voice promises to enhance security and assist in everyday tasks. Automated speech recognition (ASR) converts spoken words into text, facilitating interaction with electronic devices. ASR is also increasingly used in education in programs that assess students' learning through interaction with computers. However, ASR may not work equally well for underrepresented accent groups. Multiple studies over the last several years (e.g., Tatman 2017, Koenecke et al., 2020) have shown that ASR performs particularly poorly on African American English (AAE). This performance drop is likely due to imbalances in accent representation in training data. Here, we assess vocal tract length adjustment as a data augmentation method for increasing representation of AAE speech in the training data, with the aim of improving ASR performance on AAE. We compare this data augmentation method to standard data augmentation methods (e.g., environmental).
Method Fragments for Service Oriented Architecture Realization: Review and Future Research Directions

As an architectural style, service-oriented architecture (SOA) improves the efficiency of developing information systems and enables a rapid response to ever-changing business needs. SOA adoption is a complex process, with different levels of maturity that require know-how in terms of practices. Organizations require methods to engineer services and redesign the existing business processes. The business processes have to be managed in real-time to provide better services. A business process is composed of one or more individual services. Besides the business process composition steps, various supporting activities are required to realize business process services. This research explores methods or a part of the method which supports the organization to achieve the promised benefits of SOA. This paper also discusses the future research directions in the context of adopting SOA to provide heterogeneous IoT services and automated business processes using blockchain technologies.
Cleaner Categories Improve Object Detection and Visual-Textual Grounding

Object detectors are core components of multimodal models, enabling them to locate the region of interest in images which are then used to solve many multimodal tasks. Among the many extant object detectors, the Bottom-Up Faster R-CNN [39] (BUA) object detector is the most commonly used by the multimodal language-and-vision community, usually as a black-box visual feature generator for solving downstream multimodal tasks. It is trained on the Visual Genome Dataset [25] to detect 1600 different objects. However, those object categories are defined using automatically processed image region descriptions from the Visual Genome dataset. The automatic process introduces some unexpected near-duplicate categories (e.g. “watch” and “wristwatch”, “tree” and “trees”, and “motorcycle” and “motorbike”) that may result in a sub-optimal representational space and likely impair the ability of the model to classify objects correctly. In this paper, we manually merge near-duplicate labels to create a cleaner label set, which is used to retrain the object detector. We investigate the effect of using the cleaner label set in terms of: (i) performance on the original object detection task, (ii) the properties of the embedding space learned by the detector, and (iii) the utility of the features in a visual grounding task on the Flickr30K Entities dataset. We find that the BUA model trained with the cleaner categories learns a better-clustered embedding space than the model trained with the noisy categories. The new embedding space improves the object detection task and also presents better bounding boxes features representations which help to solve the visual grounding task.