Reviewerly/exHarmony huggingface dataset:
Development of Student’s Enrolment System Using Depth-first Search Algorithm

This paper presents a new electronic system that uses technology which is the student enrolment system (SES) used at the University of Technology (Baghdad). The work aims to save time and effort for both the direct admission staff and the student. Application (SES) was evaluated using several variables: (student score, school branch, student desire, role of success in the high school, admission channel, application date and number of seats allocated to each admission channel in each department). The depth-first search algorithm was used to search in data as a tree or graph data structure and speed up the admission process for first-year students at University of Technology (Baghdad). The results obtained from the (depth-first search) algorithm was tabulated using the SPSS statistical programme (statistical version 20) to analyse variance to determine which of the factors most affected the results of student enrolment to the departments. The results showed that the electronic system (SES) included the acceptance of the highest rates in the scientific departments according to the student's desire and within the qualitative capacity of each department. The results showed that the best average was present at the student model 3 (0.218567 ± 0.0044792), which means that the student made the best choices. The results of (ANOVA) showed that when the value of (P ≤ 0.05), there will be statistical significance, as it was found that the student score (p = 0.001), the role of success (p = 0.003) and the number of seats available for each department (p = 0.004) are among the most influential factors on student acceptance, followed by the student branch (p = 0.033) and application date (p = 0.060), while the student's desire (p = 0.549) and acceptance channel (p = 0.56) have the least influence on the student's acceptance.
Security Analysis of DBTRU Cryptosystem

DBTRU was proposed by Thang and Binh in 2015. As a variant of NTRU, the integer polynomial ring is replaced by two binary truncated polynomial rings GF(2)[x]/(xn+1). DBTRU has some advantages over NTRU in terms of security and performance. In this paper, we propose a polynomial-time linear algebra attack against the DBTRU cryptosystem, which can break DBTRU for all recommended parameter choices. The paper shows that the plaintext can be achieved in less than 1 s via the linear algebra attack on a single PC.
Document Layout Analysis with Variational Autoencoders: An Industrial Application

We present a novel method for Document Layout Analysis that detects documents that are not compliant with a given template. The major challenge we solve is dealing with a highly unbalanced dataset with only a few, hard-to-distinguish, non-compliant documents. Our model learns to detect inadequate documents based on localised non-compliant characteristics, including stamps, handwritten text, and misplaced signatures. Nevertheless, the model must not report documents containing other artefacts such as amendments or notes, which we deem acceptable. We address these challenges via generative modelling, using anomaly detection techniques to validate document layout. In particular, we first let the model learn the compliant document distribution. Then, we detect and report out-of-distribution samples for their automated rejection. In the paper, we investigate and compare two major approaches to anomaly detection: 1) classifying anomalies as those samples that cannot be accurately generated by the model; and 2) detecting samples whose mapping to a known proxy distribution is not possible. Both methods can be trained without annotations and obtain a classification accuracy of $$\sim $$ 90% on real-world documents, outperforming alternative supervised solutions.
TrueDetective 4.0: A Big Data Architecture for Real Time Anomaly Detection

Industry suffers from many machine-related problems, such as breakdown, failures, personnel safety, and management cost. Predictive maintenance is an industrial and research area that is permeating goods and services production systems, aimed at preventing critical issues in machinery and workplaces, and reducing the costs in terms of resources, time and money caused by incoming risk events that can slow or even stop the production. This paper presents TD4 a Big Data IoT architecture able to: (i) acquire huge amounts of data from real-time sensor streams, (ii) analyze and prepare the data, scaling over a network of distributed working nodes, (iii) perform real-time fault prediction. Experiments on well-known benchmarks show the applicability of the proposed architecture on different real scenarios.
Understanding Negative Calibration from Entropy Perspective

Deep neural network (DNN) based image classifiers have successfully applied to various scenes but suffered from severe overconfidence issues, essential to safety-critical applications. In recent years, plenty of research has focused on probabilistic calibration to reduce the risks from overconfident predictions. Recent work presents that it is harder to further calibrate the models calibrated by regularization techniques like label smoothing. We extend the above study to the corrupted dataset setting which is frequently encountered in the image capture processing, such as light condition, weather condition, or the quality of devices. Interestingly, we discover that post-hoc method like temperature scaling (TS) would hurt the calibration performance of original models under corrupted shift of CIFAR-10, CIFAR-100, and TinyImageNet, and we call this phenomenon Negative Calibration (NC). We observe that NC occurs in the decreasing of output entropy when the post-hoc method is applied to the pre-trained model, and we take ResNet-18 CIFAR-10 as an example to understand NC from the perspective of entropy.
A Comparative Study: Classification Vs. Matrix Factorization for Therapeutics Recommendation

Hospital information systems (HIS) hold various healthcare information of patients. Most of them are held as structural data by a database table. This information include history of diagnoses, medications, applied procedures and laboratory results of patients which can be used by machine learning methods to predict some useful information about patients. These predictions can be the progress of a disease, which is called prognosis, or it can also be therapeutics which includes medications and procedures. In this paper, we explain how to recommend therapeutics using various machine learning approaches, especially by comparing classification methods with matrix factorization (a recommender systems approach). In order to evaluate the performance of compared methods, we applied experiments on real patients' electronic health records (EHR). We observed that matrix factorization outperforms the compared classification approaches in terms of accuracy. Therefore, it is feasible to employ matrix factorization in clinical decision support systems to provide therapeutics recommendations which improves the daily performance of physicians, so the life quality of the patients.
Quantum Dots‐Functionalized Encryption Camera for Text Image Security Protection

With the wide utilization of mobile devices with a camera as online communication tools, there is a great need to develop image encryption technology for protecting confidential and sensitive information. Herein, quantum dots‐functionalized encryption (QDE) camera is proposed by integrating the fluorescence emission of quantum dots (QDs) with charge coupled device or complementary metal oxide semiconductor detector arrays during the imaging process. The variation of the photoluminescence emission from QDs controlled by UV365 nm illumination provides a convenient way to scramble the spatial distribution of RGB values in the captured image, which expands the information entropy of the captured image to realize in situ image encryption. The encrypted text images can be decrypted by a convolutional neural network‐based algorithm, resulting in high‐quality decrypted images with peak signal‐to‐noise ratio up to 45 dB. Compared with the previous reported image encryption techniques of chaotic cryptosystem, fluorescent material‐based encryption, and optical encryption, the proposed QDE camera shows advantages of high execution speed and easy miniaturization. It provides a promising in situ image encryption technique to guarantee information security in digitization process.
Context as a Distance Function in ConSQL

In conversational query answering systems, context plays a significant role to accurately and meaningfully carry it forward. In many chatbots, such as in Expedia, the discussion quickly degenerates into circling back to restarting the conversation or to inviting a live agent to intervene because the bot could not grasp the context. Contexts shorten interactions by way of implied query constraints to narrow search and to not repeat them in subsequent queries. In this paper, we introduce a novel way of viewing contexts as a distance function via the concept of query relaxation. We demonstrate that a typed domain distance function is sufficient to model context in a conversation. Our approach is based on the idea of non-monotonic constraint inheritance in a context hierarchy.
Granular Emotion Detection in Social Media Using Multi-Discipline Ensembles

A variety of applications across industry and society have started to adopt emotion detection in short written text as a key enabling component. However, the task of detecting fine-grained emotions (e.g. love, hate, sadness, happiness, etc.) in short texts such as social media remains both challenging and complex. Particularly for high-stakes applications such as health and public safety, there is a need for improved performance. To address the need for more accurate emotion detection in social media (EMDISM), we investigated the performance of ensemble classification approaches, which combine baseline models from machine learning, deep learning, and transformer learning. We evaluated a variety of ensemble approaches in comparison to the best individual component model using an EMDISM Twitter dataset with more than 1.2M samples. Results showed that the most accurate ensemble approaches performed significantly better than the best individual model.
Delving deep into pixelized face recovery and defense

Pixelization is arguably one of the most well-adopted deterministic obfuscation techniques for privacy preservation purposes. Although the recovery of pixelized faces is underexplored, the powerful deep neural networks might combat this problem in a data-driven manner. As a consequence, an unbreakable pixelization approach is desired. To achieve this goal, in this paper, we delve into two contradictory problems of unrecoverable pixelization and its counterpart, depixelization, by leveraging the best recovery to strengthen the robustness of the unrecoverable pixelized patterns. In particular, on the offensive end of recovery, we combat the large and continuous nature of pixelized regions by proposing two strategies, 1) an iterative depixelization network that progressively decomposes and predicts the pixelized regions and thus outer results are used to support inner inferences; 2) a dynamic dilated convolution operation is proposed to stride over the redundant identical pixels from the same pixelized region, enabling the network to adaptively extract valid feature representations. We show that our tailored depixelization method significantly outperforms several baselines or inpainting approaches by over 1.0 FID and 2% ID-SIM improvements on CelebA dataset which includes 182,732 human face images, and therefore we study how to defend this advanced recovery and produce unrecoverable pixelized patterns. To balance the visual perception and robustness of pixelization, we propose to generate two types of adversarial examples, pixel-wise and block-wise perturbations, which make different trade-offs between quality and robustness. By deploying our depixelization network in a semi-whitebox setting, our pixelization method can generate imperceptible perturbations while being robust to depixelization.
Optimising the Machine Translation Workflow

Machine Translation (MT) has now become an essential part of the localisation industry. New roles connected with it have emerged, and new technologies have been adopted. As Language Service Providers (LSPs) need to implement these systems in their workflows – because of client demand, to improve cost efficiency, or to meet the rising demand for translated content – the need for clear guidelines to be followed in the adoption process grows. In this paper, we describe in detail eight steps to integrate an MT workflow into the translation process. These steps have been identified by analysing existing literature and thoroughly validated through real-world MT implementations by STAR7. The first step is to identify appropriate use cases. Then we must select the most suitable MT engine. Testing and benchmarking using scoring systems to evaluate the performance of the system is essential, as is supplier engagement in order to involve and inform all stakeholders. As the operational stage begins, several different Post-Editing Machine Translation (PEMT) workflows can be adopted. Quality Assurance (QA) and Language Quality Assessment (LQA) steps can then take place. Finally, feedback from all stakeholders can be collected to improve both the workflow and/or the MT engine performance itself.
Multimodal Deep Learning and Fast Retrieval for Recommendation

We propose a retrieval architecture in the context of recommender systems for e-commerce applications, based on a multi-modal representation of the items of interest (textual description and images of the products), paired with a locality-sensitive hashing (LSH) indexing scheme for the fast retrieval of the potential recommendations. In particular, we learn a latent multimodal representation of the items through the use of CLIP architecture, combining text and images in a contrastive way. The item embeddings thus generated are then searched by means of different types of LSH. We report on the experiments we performed on two real-world datasets from e-commerce sites, containing both images and textual descriptions of the products.
Towards Polynomial Adaptive Local Explanations for Healthcare Classifiers

Local explanations aim to provide transparency for individual instances and their associated predictions. The need for local explanations is prominent for high-risk domains such as finance, law and health care. We propose a new model-agnostic framework for local explanations "Polynomial Adaptive Local Explanations (PALE)", to combat the lack of transparency of predictions through adaptive local models. We aim to explore explanations of predictions by assessing the impact of instantaneous rate of change in each feature and the association with the resulting prediction of the local model. PALE optimises a complex black-box model and the local explanation models for each instance, providing two forms of explanations, one provided by a localised derivative of an adapting polynomial, thus emphasising instance specificity, and the latter a core interpretable logistic regression model.
Performance analysis of coloured BIPV systems depending on reliability of irradiance data

In recent times, there has been a global increase in the building integrated photovoltaic (BIPV) system market. In addition, the capacity for aesthetic purposed coloured BIPV system integration has also increased. The performance of the coloured BIPV system is measured with Plane of Array (PoA) irradiance using the expected output power as a variable. In this paper, the output power was calculated based on the installation conditions of the sensor and the configuration of the BIPV system. The expected power of the PV system was calculated using the PV performance analysis program PVsyst, a simulation tool. To analyze the performance ratio relative to the irradiance data, the simulation results were compared with the monitoring data. The mean bias error (MBE) between the simulated data and the measured output power of the BIPV system was determined based on the installation condition of the input sensor and configuration of the BIPV system. The MBE values of output power dependent on sensor reliability showed a maximum of −19.44 % and a minimum of −1.4 %. Also, the calculated performance of the coloured BIPV system was 67.7 %, 71.6 %, and 67.7 %, respectively.
Maximum Stability Controller Synthesis for a System with an Interval Characteristic Polynomial Using Critical Root Diagrams

This article considers the application of critical root diagrams for the synthesis of a maximum stability robust controller. These diagrams describe the root arrangement variants where the maximum stability degree is achieved in stationary systems. The article solves the problem of providing the maximum stability degree for the one-parameter controller. This regulator is used in the system with interval-indefinite coefficients of the characteristic polynomial. For this, algebraic conditions are obtained. Under these conditions, the stability degree will be maximum. Also, the algorithm of regulator synthesis is given. Based on these conditions and algorithms, the limiting stability degree of the third-order interval system is found.
Mathematical Analysis of The Hash Functions as a Cryptographic Tools for Blockchain

Blockchain is one of the most interestingly developing technologies today, with its applications in many fields from smart contracts to cryptocurrencies. In this respect, blockchain is a hot modern topic nowadays. This study presents a mathematical analysis of cryptographic hash functions, which are one of the most important elements for understanding the security foundations of this technology. In this analysis presented; Hash functions, which are one of the building blocks of blockchain technology, used to ensure information integrity, have proven to be resistant to collision resistance, which is very important in data mining. Thus, a theory has been put forward that will contribute to time and energy saving, which is one of the important problems in data mining, in which blockchain technology is used.
The Tortoise and the Hare Algorithm for Finite Lists, Compositionally

In the tortoise-and-hare algorithm, when the fast pointer reaches the end of a finite list, the slow pointer points to the middle of this list. In the early 2000’s, this property was found to make it possible to program a palindrome detector for immutable lists that operates in one recursive traversal of the given list and performs the smallest possible number of comparisons, using the “There And Back Again” (TABA) recursion pattern. In this article, this palindrome detector is reconstructed in OCaml, formalized with the Coq Proof Assistant, and proved to be correct. More broadly, this article presents a compositional account of the tortoise-and-hare algorithm for finite lists. Concretely, compositionality means that programs that use a fast and a slow pointer can be expressed with an ordinary fold function for lists and reasoned about using ordinary structural induction on the given list. This article also contains a dozen new applications of the TABA recursion pattern and of its tail-recursive variant, “There and Forth Again”.
Evaluation of Organizational Performance based on Accounting Information Security

A study was conducted to examine the impact of the security of accounting information systems on the quality and management of information to evaluate organizational performance. Companies listed on the Iraq Stock Exchange were asked to complete a questionnaire. Sampling was the primary method of random sampling. A total of 336 survey questionnaires were distributed, but 316 correct answers were used in the review process, resulting in a 0.94 percent response rate. To verify the hypotheses, a general deductive and analytical description was derived using SPSS version 26. According to our empirical research, four critical situations exist in four key areas. First, information management (IM) and information quality (IQ) directly impact an organization's performance. Second, SMEs respond to the market's needs without a methodology or system. Using information effectively increases their competitive capability (financial performance, market share, innovation, customer satisfaction). Therefore, crucial to create a culture of efficient data, information management, and quality. A business's survival could also be adversely affected by rejecting innovation hypotheses in the medium term. Productivity is directly impacted by information management (IM) and quality of information (IQ). 36.5% of the results are explained by correlation. Hence, information can be viewed from many different perspectives: as the organization's final product, as a product created and used within the organization, as a corporate information product, or as a product originating from the environment.
A Novel RDA-Based Network to Conceal Image Data and Prevent Information Leakage

Image data play an important role in our daily lives, and scholars have recently leveraged deep learning to design steganography networks to conceal and protect image data. However, the complexity of computation and the running speed have been neglected in their model designs, and steganography security still has much room for improvement. For this purpose, this paper proposes an RDA-based network, which can achieve higher security with lower computation complexity and faster running speed. To improve the hidden image’s quality and ensure that the hidden image and cover image are as similar as possible, a residual dense attention (RDA) module was designed to extract significant information from the cover image, thus assisting in reconstructing the salient target of the hidden image. In addition, we propose an activation removal strategy (ARS) to avoid undermining the fidelity of low-level features and to preserve more of the raw information from the input cover image and the secret image, which significantly boosts the concealing and revealing performance. Furthermore, to enable comprehensive supervision for the concealing and revealing processes, a mixed loss function was designed, which effectively improved the hidden image’s visual quality and enhanced the imperceptibility of secret content. Extensive experiments were conducted to verify the effectiveness and superiority of the proposed approach.
Web Mining Using Improved Apriori Algorithm

In this study, we will be concentrating on one of the more recent advancements in data mining, specifically mining online usage. The purpose of web use mining is to gain usable knowledge from the data that web servers keep about the actions of its visitors by mining the data that is stored on such servers. By using the association rule generation in the Web domain, the pages that are most frequently referenced together can be combined into a single server session. This is possible because of the interconnected nature of the Web. In association rule mining, a technique known as frequent set mining is one of the methods that may be used to discover regular patterns from a web log file. When it comes to mining the usage of the web, the term association rules refers to groups of web pages that are accessed together and have a support value that is higher than a given threshold. The support can be expressed as a proportion of total transactions that match a particular pattern. With the aid of the presence or absence of association rules, web designers are able to effectively reconstruct the websites they have created for their clients. In this research, we have introduced a method called Aprior for the purpose of extracting frequent patterns from online log files. The findings of the experiments that were carried out on data relating to peoples use of the website indicate that general sequential patterns or frequent item sets are more suitable for use in Web customization and recommender systems.