Reviewerly/exHarmony huggingface dataset:
A combined method based on CNN architecture for variation-resistant facial recognition

Identifying individuals from a facial image is a technique that forms part of computer vision and is used in various fields such as security, digital biometrics, smartphones, and banking. However, it can prove difficult due to the complexity of facial structure and the presence of variations that can affect the results. To overcome this difficulty, in this paper, we propose a combined approach that aims to improve the accuracy and robustness of facial recognition in the presence of variations. To this end, two datasets (ORL and UMIST) are used to train our model. We then began with the image pre-processing phase, which consists in applying a histogram equalization operation to adjust the gray levels over the entire image surface to improve quality and enhance the detection of features in each image. Next, the least important features are eliminated from the images using the Principal Component Analysis (PCA) method. Finally, the pre-processed images are subjected to a neural network architecture (CNN) consisting of multiple convolution layers and fully connected layers. Our simulation results show a high performance of our approach, with accuracy rates of up to 99.50% for the ORL dataset and 100% for the UMIST dataset.
Towards virtual machine scheduling research based on multi-decision AHP method in the cloud computing platform

Virtual machine scheduling and resource allocation mechanism in the process of dynamic virtual machine consolidation is a promising access to alleviate the cloud data centers of prominent energy consumption and service level agreement violations with improvement in quality of service (QoS). In this article, we propose an efficient algorithm (AESVMP) based on the Analytic Hierarchy Process (AHP) for the virtual machine scheduling in accordance with the measure. Firstly, we take into consideration three key criteria including the host of power consumption, available resource and resource allocation balance ratio, in which the ratio can be calculated by the balance value between overall three-dimensional resource (CPU, RAM, BW) flat surface and resource allocation flat surface (when new migrated virtual machine (VM) consumed the targeted host’s resource). Then, virtual machine placement decision is determined by the application of multi-criteria decision making techniques AHP embedded with the above-mentioned three criteria. Extensive experimental results based on the CloudSim emulator using 10 PlanetLab workloads demonstrate that the proposed approach can reduce the cloud data center of number of migration, service level agreement violation (SLAV), aggregate indicators of energy comsumption (ESV) by an average of 51.76%, 67.4%, 67.6% compared with the cutting-edge method LBVMP, which validates the effectiveness.
Psychometric Predictive Power of Large Language Models

Next-word probabilities from language models have been shown to successfully simulate human reading behavior. Building on this, we show that, interestingly, instruction-tuned large language models (LLMs) yield worse psychometric predictive power (PPP) for human reading behavior than base LLMs with equivalent perplexities. In other words, instruction tuning, which helps LLMs provide human-preferred responses, does not always make them human-like from the computational psycholinguistics perspective. In addition, we explore prompting methodologies in simulating human reading behavior with LLMs, showing that prompts reflecting a particular linguistic hypothesis lead LLMs to exhibit better PPP but are still worse than base LLMs. These highlight that recent instruction tuning and prompting do not offer better estimates than direct probability measurements from base LLMs in cognitive modeling.
Physics-informed deep neural network for image denoising

Image enhancement deep neural networks (DNN) can improve signal to noise ratio or resolution of optically collected visual information. The literature reports a variety of approaches with varying effectiveness. All these algorithms rely on arbitrary data (the pixels’ count-rate) normalization, making their performance strngly affected by dataset or user-specific data pre-manipulation. We developed a DNN algorithm capable to enhance images signal-to-noise surpassing previous algorithms. Our model stems from the nature of the photon detection process which is characterized by an inherently Poissonian statistics. Our algorithm is thus driven by distance between probability functions instead than relying on the sole count-rate, producing high performance results especially in high-dynamic-range images. Moreover, it does not require any arbitrary image renormalization other than the transformation of the camera’s count-rate into photon-number.
Pengukuran Kualitas Aplikasi E-Surat Menggunakan ISO 9126

Electronic mail is a web-based tool that is used to archive incoming and outgoing letters. This application was created to help and expedite the job of filing letters at SMK Muhammadiyah Banda Aceh. Defects are common in any new software development; development allows for errors to make the application heavier. As a result, testing software is critical; the purpose is to determine the quality of the information system that has been built or developed, because quality affects user happiness. A standard is required in the construction and development of a quality information system so that the feasibility of the information system can be measured. The International Organization for Standardization (ISO) 9126 standard is used in this study's quality assessment. The results reveal that the built web-based document archiving information system satisfied the ISO 9126 criteria in terms of functionality, usability, dependability, and efficiency.
Breast cancer classification based on convolutional neural network and image fusion approaches using ultrasound images

<h2>Abstract</h2> Deep learning and image processing are used to classify and segment breast tumor images, specifically in ultrasound (US) modalities, to support clinical decisions and improve healthcare quality. However, directly using US images can be challenging due to noise and diverse imaging modalities. In this study, we developed a three-step image processing scheme involving speckle noise filtering using a block-matching three-dimensional filtering technique, region of interest highlighting, and RGB fusion. This method enhances the generalization of deep-learning models and achieves better performance. We used a deep learning model (VGG19) to perform transfer learning on three datasets: BUSI (780 images), Dataset B (162 images), and KAIMRC (5693 images). When tested on the BUSI and KAIMRC datasets using a fivefold cross-validation mechanism, the model with the proposed preprocessing step performed better than without preprocessing for each dataset. The proposed image processing approach improves the performance of the breast cancer deep learning classification model. Multiple diverse datasets (private and public) were used to generalize the model for clinical application.
A dehazing method for flight view images based on transformer and physical priori

&lt;abstract&gt;&lt;p&gt;Aiming at the problems of local dehazing distortion and incomplete global dehazing of existing algorithms in real airborne cockpit environments, a two-stage dehazing method PhysiFormer combining physical a priori with a Transformer oriented flight perspective was proposed. The first stage used synthetic pairwise data to pre-train the dehazing model. First, a pyramid pooling module (PPM) was introduced in the Transformer for multiscale feature extraction to solve the problem of poor recovery of local details, then a global context fusion mechanism was used to enable the model to better perceive global information. Finally, considering that combining the physical a priori needs to rely on the estimation of the atmosphere light, an encoding-decoding structure based on the residual blocks was used to estimate the atmosphere light, which was then used for dehazing through the atmospheric scattering model for dehazing. The second stage used real images combined with physical priori to optimize the model to better fit the real airborne environment. The experimental results show that the proposed method has better naturalness image quality evaluator (NIQE) and blind/referenceless image spatial quality evaluator (BRISQUE) indexes and exhibits the best dehazing visual effect in the tests of dense haze, non-uniform haze and real haze images, which effectively improves the problems of color distortion and haze residue.&lt;/p&gt;&lt;/abstract&gt;
Implementation of Elliptic Curve Cryptosystem with Bitcoin Curves on SECP256k1, NIST256p, NIST521p, and LLL

Very recent attacks like ladder leaks demonstrated the feasibility of recovering private keys with side-channel attacks using just one bit of secret nonce. ECDSA nonce bias can be exploited in many ways. Some attacks on ECDSA involve complicated Fourier analysis and lattice mathematics. This paper will enable cryptographers to identify efficient ways in which ECDSA can be cracked on curves NIST256p, SECP256k1, NIST521p, and weak nonce, kind of attacks that can crack ECDSA and how to protect yourself. Initially, we begin with an ECDSA signature to sign a message using the private key and validate the generated signature using the shared public key. Then we use a nonce or a random value to randomize the generated signature. Every time we sign, a new verifiable random nonce value is created, and a way in which the intruder can discover the private key if the signer leaks any one of the nonce values. Then we use Lenstra–Lenstra–Lovasz (LLL) method as a black box, we will try to attack signatures generated from bad nonce or bad random number generator (RAG) on NIST256p, SECP256k1 curves. The combination of nonce generation, post-message signing, and validation in ECDSA helps achieve Uniqueness, Authentication, Integrity, and Non-Repudiation. The analysis is performed by considering all three curves for the implementation of the Elliptic Curve Digital Signature Algorithm (ECDSA). The comparative analysis for each of the selected curves in terms of computational time is done with the leak of nonce and with the Lenstra–Lenstra–Lovasz method to crack ECDSA. The average computational costs to break ECDSA with curves NIST256p, NIST521p, and SECP256k1 are 0.016, 0.34,0.46 respectively which is almost zero depicting the strength of the algorithm. The average computational costs to break ECDSA with curves SECP256K1 and NIST256p using LLL are 2.9 and 3.4 respectively
Introducing forensic semiotics in criminal investigations

The scientific investigation of crime comes under the general rubric of forensic science, which embraces a large range of related branches, from forensic psychology to computer forensics. In this chapter, it is argued that forensic semiotics belongs to this interdisciplinary paradigm, even though it is relatively unknown among both forensic scientists and semioticians themselves. The aim is to show how notions and methods developed in semiotics and cognate disciplines can be applied fruitfully to criminal investigations, focusing on the analysis of L-signs (signs of lying) and I-signs (signs of identity), which can help identify a perpetrator. Above all else, forensic semiotics implies the use of an analytical method based on abduction, namely the inference of how clues (signs) cohere into meaningful patterns—a mode of thinking that was exemplified by the classic fictional detectives C. Auguste Dupin and Sherlock Holmes, who are the ipso facto founders of forensic semiotic method.
Digital Healthcare in The Metaverse: Insights into Privacy and Security

In this article, metaverse healthcare systems are studied from the privacy and security perspectives. We address data communication security for the metaverse, and the privacy and security threats of employing machine learning and artificial intelligence (ML/AI) algorithms for metaverse healthcare. In addition, human-centric data privacy for the social interactions in the metaverse is studied. Our goal is to present new visions and approaches, including physical layer security, semantic metaverse communications, differential privacy, and adversarial machine learning. These approaches have shown promising results in the field of data communications and networking, as well as the computer science domain, showcasing a huge potential to be employed for the metaverse healthcare systems. As a case study, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">distributed differential privacy</i> for the metaverse healthcare systems, where each virtual clinic perturbs its medical model vector to safeguard privacy against malicious clients and honest-but-curious servers. Through our experiments on the Breast Cancer Wisconsin Dataset, we highlight the privacy-utility trade-off for different adjustable levels of privacy.
A Survey and Analysis of Evolutionary Operators for Permutations

There are many combinatorial optimization problems whose solutions are best represented by permutations. The classic traveling salesperson seeks an optimal ordering over a set of cities. Scheduling problems often seek optimal orderings of tasks or activities. Although some evolutionary approaches to such problems utilize the bit strings of a genetic algorithm, it is more common to directly represent solutions with permutations. Evolving permutations directly requires specialized evolutionary operators. Over the years, many crossover and mutation operators have been developed for solving permutation problems with evolutionary algorithms. In this paper, we survey the breadth of evolutionary operators for permutations. We implemented all of these in Chips-n-Salsa, an open source Java library for evolutionary computation. Finally, we empirically analyze the crossover operators on artificial fitness landscapes isolating different permutation features.
Skeleton-based Hand Gesture Recognition using Geometric Features and Spatio-Temporal Deep Learning Approach

Dynamic hand gesture recognition using a 3D skeleton dataset has become the most attractive research domain because of the multipurpose application. Although many researchers have been working to develop hand gesture systems, they are still facing challenges in achieving satisfactory performance and more generalizable properties because of the various complexities such as lacking effective features, computational complexity and slow execution speed etc. In the study, we proposed a selected joint skeleton feature selection approach along with CNN-based spatial and Multi-head attention network (MHAN) based temporal feature extraction to alleviate the problems. In the procedure, we selected the most effective skeleton point based on the visualized capability to extract geometric features and motion speed considering slow and faster motion speed. After enhancing the feature with CNN based spatial model, we produced a final joint skeleton-independent spatial feature vector. After that, we enhanced temporal contextual information by feeding them into MHAN; we applied a classification module to refine the feature with classification. We used two benchmark datasets to evaluate the model: DHGD and SHREC'17. The high performance of the proposed model proved the superiority of the proposed model.
Quantum approximated cloning-assisted density matrix exponentiation

Classical information loading is an essential task for many processing quantum algorithms, constituting a cornerstone in the field of quantum machine learning. In particular, the embedding techniques based on Hamiltonian simulation techniques enable the loading of matrices into quantum computers. A representative example of these methods is the Lloyd-Mohseni-Rebentrost protocol, which efficiently implements matrix exponentiation when multiple copies of a quantum state are available. However, this is a quite ideal set up, and in a realistic scenario, the copies are limited and the non-cloning theorem prevents from producing more exact copies in order to increase the accuracy of the protocol. Here, we propose a method to circumvent this limitation by introducing imperfect quantum copies that significantly enhance the performance of previous proposals.
Prompting Frameworks for Large Language Models: A Survey

Since the launch of ChatGPT, a powerful AI Chatbot developed by OpenAI, large language models (LLMs) have made significant advancements in both academia and industry, bringing about a fundamental engineering paradigm shift in many areas. While LLMs are powerful, it is also crucial to best use their power where "prompt'' plays a core role. However, the booming LLMs themselves, including excellent APIs like ChatGPT, have several inherent limitations: 1) temporal lag of training data, and 2) the lack of physical capabilities to perform external actions. Recently, we have observed the trend of utilizing prompt-based tools to better utilize the power of LLMs for downstream tasks, but a lack of systematic literature and standardized terminology, partly due to the rapid evolution of this field. Therefore, in this work, we survey related prompting tools and promote the concept of the "Prompting Framework" (PF), i.e. the framework for managing, simplifying, and facilitating interaction with large language models. We define the lifecycle of the PF as a hierarchical structure, from bottom to top, namely: Data Level, Base Level, Execute Level, and Service Level. We also systematically depict the overall landscape of the emerging PF field and discuss potential future research and challenges. To continuously track the developments in this area, we maintain a repository at https://github.com/lxx0628/Prompting-Framework-Survey, which can be a useful resource sharing platform for both academic and industry in this field.
Pearl's and Jeffrey's Update as Modes of Learning in Probabilistic Programming

The concept of updating a probability distribution in the light of new evidence lies at the heart of statistics and machine learning. Pearl's and Jeffrey's rule are two natural update mechanisms which lead to different outcomes, yet the similarities and differences remain mysterious. This paper clarifies their relationship in several ways: via separate descriptions of the two update mechanisms in terms of probabilistic programs and sampling semantics, and via different notions of likelihood (for Pearl and for Jeffrey). Moreover, it is shown that Jeffrey's update rule arises via variational inference. In terms of categorical probability theory, this amounts to an analysis of the situation in terms of the behaviour of the multiset functor, extended to the Kleisli category of the distribution monad.
Advancing Semi-Supervised Task Oriented Dialog Systems by JSA Learning of Discrete Latent Variable Models

Developing semi-supervised task-oriented dialog (TOD) systems by leveraging unlabeled dialog data has attracted increasing interests. For semi-supervised learning of latent state TOD models, variational learning is often used, but suffers from the annoying high-variance of the gradients propagated through discrete latent variables and the drawback of indirectly optimizing the target log-likelihood. Recently, an alternative algorithm, called joint stochastic approximation (JSA), has emerged for learning discrete latent variable models with impressive performances. In this paper, we propose to apply JSA to semi-supervised learning of the latent state TOD models, which is referred to as JSA-TOD. To our knowledge, JSA-TOD represents the first work in developing JSA based semi-supervised learning of discrete latent variable conditional models for such long sequential generation problems like in TOD systems. Extensive experiments show that JSA-TOD significantly outperforms its variational learning counterpart. Remarkably, semi-supervised JSA-TOD using 20% labels performs close to the full-supervised baseline on MultiWOZ2.1.
Is this Dialogue Coherent? Learning from Dialogue Acts and Entities

In this work, we investigate the human perception of coherence in open-domain dialogues. In particular, we address the problem of annotating and modeling the coherence of next-turn candidates while considering the entire history of the dialogue. First, we create the Switchboard Coherence (SWBD-Coh) corpus, a dataset of human-human spoken dialogues annotated with turn coherence ratings, where next-turn candidate utterances ratings are provided considering the full dialogue context. Our statistical analysis of the corpus indicates how turn coherence perception is affected by patterns of distribution of entities previously introduced and the Dialogue Acts used. Second, we experiment with different architectures to model entities, Dialogue Acts and their combination and evaluate their performance in predicting human coherence ratings on SWBD-Coh. We find that models combining both DA and entity information yield the best performances both for response selection and turn coherence rating.
AI vs. Human Translators: Navigating the Complex World of Religious Texts and Cultural Sensitivity.

This article explores the growing landscape of machine and AI-powered translation, explicitly focusing on religious text translation. The objective is to assess how AI-powered translation tools, such as ChatGPT and Google Translate, can replace human translation in handling complex religious content. The analysis considers word choice, word count, readability, and overall translation quality. This article uses qualitative and comparative data analysis to evaluate translations of seven English to Arabic religious texts by ChatGPT, Google Translate, and human translators. The texts were chosen randomly in different religious contexts, and a systematic coding framework was employed. Through Nvivo software, we examined word placement, vocabulary diversity, fluency, and accuracy. The analysis concludes that ChatGPT and Google Translate provide fairly accurate translations, yet the quality is questioned. Human translation consistently outperforms machine translations, maintaining depth, cultural relevance, and nuanced understanding. Word count analysis shows that machine translations are more concise and missing significant elements. While AI-powered translation tools have made significant advancements, they still need to be capable of entirely replacing human expertise, especially in handling complex and culturally rich texts. Human translators continue to excel in conveying complex ideas and preserving the richness of language and culture.