Reviewerly/exHarmony huggingface dataset:
Computer Vision Based Automatic Margin Computation Model for Digital Document Images

Abstract Margin, in typography, is described as the space between the text content and the document edges and is often essential information for the consumer of the document, digital or physical. In the present age of digital disruption, it is customary to store and retrieve documents digitally and retrieve information automatically from the documents when necessary. Margin is one such non-textual information that becomes important for some business processes, and the demand for computing margins algorithmically mounts to facilitate RPA. We propose a computer vision-based text localization model, utilizing classical DIP techniques such as smoothing, thresholding, and morphological transformation to programmatically compute the top, left, right, and bottom margins within a digital document image. The proposed model has been experimented with different noise filters and structural elements of various shapes and size to finalize the bilateral filter and lines and structural elements for the removal of noises most commonly occurring due to scans. The proposed model is targeted towards text document images and not the natural scene images. Hence, the existing benchmark models developed for text localization in natural scene images have not performed with the expected accuracy. The model is validated with 485 document images of a real-time business process of a reputed TI company. The results show that $$91.34\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>91.34</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> of the document images have conferred more than $$90\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>90</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> IoU value which is well beyond the accuracy range determined by the company for that specific process.
Hybrid Techniques for the Diagnosis of Acute Lymphoblastic Leukemia Based on Fusion of CNN Features

Acute lymphoblastic leukemia (ALL) is one of the deadliest forms of leukemia due to the bone marrow producing many white blood cells (WBC). ALL is one of the most common types of cancer in children and adults. Doctors determine the treatment of leukemia according to its stages and its spread in the body. Doctors rely on analyzing blood samples under a microscope. Pathologists face challenges, such as the similarity between infected and normal WBC in the early stages. Manual diagnosis is prone to errors, differences of opinion, and the lack of experienced pathologists compared to the number of patients. Thus, computer-assisted systems play an essential role in assisting pathologists in the early detection of ALL. In this study, systems with high efficiency and high accuracy were developed to analyze the images of C-NMC 2019 and ALL-IDB2 datasets. In all proposed systems, blood micrographs were improved and then fed to the active contour method to extract WBC-only regions for further analysis by three CNN models (DenseNet121, ResNet50, and MobileNet). The first strategy for analyzing ALL images of the two datasets is the hybrid technique of CNN-RF and CNN-XGBoost. DenseNet121, ResNet50, and MobileNet models extract deep feature maps. CNN models produce high features with redundant and non-significant features. So, CNN deep feature maps were fed to the Principal Component Analysis (PCA) method to select highly representative features and sent to RF and XGBoost classifiers for classification due to the high similarity between infected and normal WBC in early stages. Thus, the strategy for analyzing ALL images using serially fused features of CNN models. The deep feature maps of DenseNet121-ResNet50, ResNet50-MobileNet, DenseNet121-MobileNet, and DenseNet121-ResNet50-MobileNet were merged and then classified by RF classifiers and XGBoost. The RF classifier with fused features for DenseNet121-ResNet50-MobileNet reached an AUC of 99.1%, accuracy of 98.8%, sensitivity of 98.45%, precision of 98.7%, and specificity of 98.85% for the C-NMC 2019 dataset. With the ALL-IDB2 dataset, hybrid systems achieved 100% results for AUC, accuracy, sensitivity, precision, and specificity.
A Novel Chaotic Image Encryption Scheme Armed with Global Dynamic Selection

Due to the equivalent keys revealed by a chosen-plaintext attack or a chosen-ciphertext attack, most of the existing chaotic image encryption schemes are demonstrated to be insecure. In order to improve security performance, some scholars have recently proposed the plaintext-related chaotic image encryption scheme. Although the equivalent effect of a one-time pad is achieved, an additional secure channel is required to transmit the hash values or other parameters related to the plaintext before the ciphertext can be decrypted at the receiving end. Its main drawback is that an absolutely secure channel is needed to transmit the information related to the plaintext, which is not feasible in practical applications. To further solve this problem, this paper proposes a chaotic image encryption scheme based on global dynamic selection of a multi-parallel structure. First, a chaotic sequence is employed to dynamically select DNA encoding rules. Secondly, the permutation with a multi-parallel structure is performed on the DNA-encoded matrix, and the DNA decoding rules are dynamically selected according to another chaotic sequence. Finally, the diffusion rules obtained by the ciphertext feedback mechanism are introduced to determine the dynamic diffusion. Compared with the existing local dynamic encryption schemes, the main advantage of this scheme is that it can realize global dynamic selection, so as to ensure that there is no equivalent key, and it can resist the chosen-ciphertext attack or chosen-plaintext attack and does not need an additional secure channel to transmit parameters related to plaintext, which is practical. A theoretical analysis and numerical experiments demonstrate the feasibility of the method.
STUDENT PROBLEMS IN ONLINE LEARNING: SOLUTIONS TO KEEP EDUCATION GOING ON

The spread of the Covid-19 virus that has swept across the world has changed various aspects of human life, one of which is the change of learning process that commonly occurs face to face in the classroom become online learning. Online learning is chosen as the solution to keep teaching and learning during this pandemic and this is done by various levels of education starting from primary school, middle school, high school, to college levels. The existence of cases of learning difficulties experienced by students during the Covid-19 pandemic made students do not understand the subject matter delivered by the teacher. This study aimed to find out some solutions of students' problem in the online learning process. Participants in this study were 25 English education students from a private university in Bandar Lampung. The researcher distributed a close ended questionnaire of 9 questions in the form of Google to collect data, and used qualitative data to illustrate the results of the questionnaire in data findings. The results of the study show that there were some problems faced by students in online learning included (1) students difficult to understanding materials, (2) students encounter technical difficulties, (3) limited internet quota, (4) poor communication, and (5) learning becomes less effective. The solutions that researcher has found to overcome the problems are namely (1) teachers or lecturers must implement face to face class twice a week, (2) teachers should use platforms with simpler operations in online learning, (3) teachers or lecturers should provide a material that is interactive, dynamic and attractive and (4) the government must extend the quota assistance for students.
Evaluation of post-hoc interpretability methods in time-series classification

Abstract Post-hoc interpretability methods are critical tools to explain neural-network results. Several post-hoc methods have emerged in recent years but they produce different results when applied to a given task, raising the question of which method is the most suitable to provide accurate post-hoc interpretability. To understand the performance of each method, quantitative evaluation of interpretability methods is essential; however, currently available frameworks have several drawbacks that hinder the adoption of post-hoc interpretability methods, especially in high-risk sectors. In this work we propose a framework with quantitative metrics to assess the performance of existing post-hoc interpretability methods, particularly in time-series classification. We show that several drawbacks identified in the literature are addressed, namely, the dependence on human judgement, retraining and the shift in the data distribution when occluding samples. We also design a synthetic dataset with known discriminative features and tunable complexity. The proposed methodology and quantitative metrics can be used to understand the reliability of interpretability methods results obtained in practical applications. In turn, they can be embedded within operational workflows in critical fields that require accurate interpretability results for, example, regulatory policies.
Trustworthy Users: Using IOTA and IPFS for Attribute Validation in CP-ABE and dCP-ABE Schemes

Attribute spoofing is a major security threat in information exchange solutions based on Ciphertext-Policy Attribute-Based-Encryption (CP-ABE) and distributed CP-ABE (dCP-ABE), which can compromise privacy and security. This threat occurs when an attacker forces the Attribute Authorities to generate keys for attributes they do not possess. This paper analyzes the threat of attribute spoofing and identifies the primary attack vectors, including direct interference with the Attribute Authority and compromise of the shared attribute storage database. The authors propose a solution based on IOTA, a DAG-type DLT, and Interplanetary File System (IPFS) to prevent attribute spoofing. The solution requires distributed attribute storage, validation, and user authentication to counteract the two attack vectors effectively. The proposed solution mitigates the consequences of attribute spoofing, including privilege escalation and reduction, acquisition of private keys, and cutoff of data access. The authors also evaluate their proposal through a value-chain use case and conclude that it effectively mitigates the consequences of attribute spoofing.
Does ChatGPT resemble humans in language use?

Large language models (LLMs) and LLM-driven chatbots such as ChatGPT have shown remarkable capacities in comprehending and producing language. However, their internal workings remain a black box in cognitive terms, and it is unclear whether LLMs and chatbots can develop humanlike characteristics in language use. Cognitive scientists have devised many experiments that probe, and have made great progress in explaining, how people process language. We subjected ChatGPT to 12 of these experiments, preregistered and with 1,000 runs per experiment. In 10 of them, ChatGPT replicated the human pattern of language use. It associated unfamiliar words with different meanings depending on their forms, continued to access recently encountered meanings of ambiguous words, reused recent sentence structures, reinterpreted implausible sentences that were likely to have been corrupted by noise, glossed over errors, drew reasonable inferences, associated causality with different discourse entities according to verb semantics, and accessed different meanings and retrieved different words depending on the identity of its interlocutor. However, unlike humans, it did not prefer using shorter words to convey less informative content and it did not use context to disambiguate syntactic ambiguities. We discuss how these convergences and divergences may occur in the transformer architecture. Overall, these experiments demonstrate that LLM-driven chatbots like ChatGPT are capable of mimicking human language processing to a great extent, and that they have the potential to provide insights into how people learn and use language.
A dataset for medical instructional video classification and question answering

This paper introduces a new challenge and datasets to foster research toward designing systems that can understand medical videos and provide visual answers to natural language questions. We believe medical videos may provide the best possible answers to many first aid, medical emergency, and medical education questions. Toward this, we created the MedVidCL and MedVidQA datasets and introduce the tasks of Medical Video Classification (MVC) and Medical Visual Answer Localization (MVAL), two tasks that focus on cross-modal (medical language and medical video) understanding. The proposed tasks and datasets have the potential to support the development of sophisticated downstream applications that can benefit the public and medical practitioners. Our datasets consist of 6,117 fine-grained annotated videos for the MVC task and 3,010 questions and answers timestamps from 899 videos for the MVAL task. These datasets have been verified and corrected by medical informatics experts. We have also benchmarked each task with the created MedVidCL and MedVidQA datasets and propose the multimodal learning methods that set competitive baselines for future research.
Novel secure data protection scheme using Martino homomorphic encryption

Abstract Globally, data maintenance and its security are vital tasks due to the rapid development of advanced technologies. Users now utilise Cloud computing technique and security techniques to preserve their data securely from intruders and hackers. Even yet, because of the technology's rapid advancement and inherent insecurity, attackers are conducting assaults on the cloud data. Hence, a homomorphic encryption technique was proposed based on Matrix Transformations with shifts, rotations, and transpositions of Binary converted ASCII values of each character in the plain text. For both encryption and decryption, symmetric cryptography employs the same secret key. The “avalanche effect” is a desirable feature of symmetric encryption in which two distinct keys generate separate cipher texts for the same message. As there are different conditions for the key, it helps to achieve this effect in this technique. The suggested algorithm's cryptanalysis reveals that it is more powerful than the existing encryption methods and resistant to a variety of attacks. So that an attacker cannot easily predict a plaintext through a statistical analysis.
Using composite structural equation modeling to examine high school teachers' acceptance of E-learning after Covid-19

Electronic learning (e-learning) is a broader approach to learning that brings new opportunities for teaching and learning in many fields of education compared to the face-to-face classroom environment. The purpose of this study is to explore high school teachers’ acceptance to continue teaching through e-learning platforms after COVID-19. Based on the framework of a unified theory of technology acceptance and use of technology, this study investigates the research questions in the context of Lesotho. The study adopted a quantitative approach, with a total number of 300 questionnaires collected from high school teachers. The result was analyzed using composite structural equation modeling. According to the study, instructors' acceptance of e-learning is influenced by several factors, which in turn has a significant impact on teachers' behavioral intentions in Lesotho. As a result of the study, individual challenges associated with using e-learning platforms were identified.&#x0D; Keywords: Composite-SEM; educational quality; e-learning; self-efficacy; technology acceptance.
A Critical Analysis of Vulgar Language on OTT Platforms: A Systematic Review

There is a serious concern regarding framing rules and guidelines to regulate the content on OTT platforms that needs urgent attention. In the Current Scenario, the use of vulgar language in the public domain on OTT platforms needs to take seriously. Some OTT platforms heavenly use foul language, which is open to children that cause a huge psychological impact on them. The use of vulgar language, including profanity and bad words, has to be regulated strictly. Web series uses obscene, abusive words and foul language with excessive usage. This review paper highlights the excessive use of swear words, profane language, and vulgar expletives on OTT platforms.
Not what u expect: Effects of prediction errors on item memory.

The characterization of the relationship between predictions and one-shot episodic encoding poses an important challenge for memory research. On the one hand, events that are compatible with our previous knowledge are thought to be remembered better than incompatible ones. On the other hand, unexpected situations, by virtue of their novelty, are known to cause enhanced learning. Several theoretical accounts try to solve this apparent paradox by conceptualizing prediction error (PE) as a continuum ranging from low PE (for expectation-matching events) to high PE (for expectation-mismatching ones). Under such a framework, the relationship between PE and memory encoding would be described by a U-shape function with higher memory performance for extreme levels of PE and lower memory for middle levels of PE. In this study, we tested the framework by using a gradual manipulation of the strength of association between scenes and objects to render different levels of PE and then tested for item memory of the (mis)matching events. In two experiments, in contrast to what was anticipated, recognition memory for object identity followed an inverted U-shape as a function of PE, with higher performance for intermediate levels of PE. Furthermore, in two additional experiments, we showed the relevance of explicit predictions at encoding to reveal such an inverted U pattern, thus providing the boundary conditions of the effect. We discussed our findings in light of existing literature relating PE and episodic memory, pointing out the potential roles of uncertainty in the environment, and the importance of the cognitive operations underlying encoding tasks. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Efficient Memory-Enhanced Transformer for Long-Document Summarization in Low-Resource Regimes

Long document summarization poses obstacles to current generative transformer-based models because of the broad context to process and understand. Indeed, detecting long-range dependencies is still challenging for today's state-of-the-art solutions, usually requiring model expansion at the cost of an unsustainable demand for computing and memory capacities. This paper introduces Emma, a novel efficient memory-enhanced transformer-based architecture. By segmenting a lengthy input into multiple text fragments, our model stores and compares the current chunk with previous ones, gaining the capability to read and comprehend the entire context over the whole document with a fixed amount of GPU memory. This method enables the model to deal with theoretically infinitely long documents, using less than 18 and 13 GB of memory for training and inference, respectively. We conducted extensive performance analyses and demonstrate that Emma achieved competitive results on two datasets of different domains while consuming significantly less GPU memory than competitors do, even in low-resource settings.
Logical Blocks for Fault-Tolerant Topological Quantum Computation

Logical gates constitute the building blocks of fault-tolerant quantum computation. While quantum error-corrected memories have been extensively studied in the literature, explicit constructions and detailed analyses of thresholds and resource overheads of universal logical gate sets have so far been limited. In this paper, we present a comprehensive framework for universal fault-tolerant logic motivated by the combined need for platform-independent logical gate definitions, flexible and scalable tools for numerical analysis, and exploration of novel schemes for universal logic that improve resource overheads. Central to our framework is the description of logical gates holistically in a way which treats space and time on a similar footing. Focusing on schemes based on surface codes, we introduce explicit, but platform-independent representations of topological logic gates -- called logical blocks -- and generate new, overhead-efficient methods for universal quantum computation. As a specific example, we propose fault-tolerant schemes based on surface codes concatenated with more general low-density parity check (LDPC) codes. The logical blocks framework enables a convenient mapping from an abstract description of the logical gate to a precise set of physical instructions for both circuit-based and fusion-based quantum computation (FBQC). Using this, we numerically simulate a surface-code-based universal gate set implemented with FBQC, and verify that their thresholds are consistent with the bulk memory threshold. We find that boundaries, defects, and twists can significantly impact the logical error rate scaling, with periodic boundary conditions potentially halving the resource requirements. Motivated by the favorable logical error rates for boundaryless computation, we introduce a novel computational scheme based on the teleportation of twists that may offer further resource reductions.
Density-Matrix Renormalization Group Algorithm for Simulating Quantum Circuits with a Finite Fidelity

We develop a density-matrix renormalization group (DMRG) algorithm for the simulation of quantum circuits. This algorithm can be seen as the extension of the time-dependent DMRG from the usual situation of Hermitian Hamiltonian matrices to quantum circuits defined by unitary matrices. For small circuit depths, the technique is exact and equivalent to other matrix product state–based techniques. For larger depths, it becomes approximate in exchange for an exponential speed up in computational time. Like an actual quantum computer, the quality of the DMRG results is characterized by a finite fidelity. However, unlike a quantum computer, the fidelity depends strongly on the quantum circuit considered. For the most difficult possible circuit for this technique, the so-called “quantum supremacy” benchmark of Google LLC [Arute et al., Nature 574, 505 (2019)], we find that the DMRG algorithm can generate bit strings of the same quality as the seminal Google experiment on a single computing core. For a more structured circuit used for combinatorial optimization (quantum approximate optimization algorithm), we find a drastic improvement of the DMRG results with error rates dropping by a factor of 100 compared with random quantum circuits. Our results suggest that the current bottleneck of quantum computers is their fidelities rather than the number of qubits.9 MoreReceived 12 July 2022Revised 15 December 2022Accepted 13 February 2023DOI:https://doi.org/10.1103/PRXQuantum.4.020304Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum computationQuantum entanglementQuantum simulationTechniquesTensor network methodsQuantum InformationCondensed Matter, Materials & Applied Physics