Reviewerly/exHarmony huggingface dataset:
A Novel Approach for Human Face Extraction and Detection using SAE-AFB-RFCN Framework

Research into Facial Recognition Technology (FRT), which uses a person's face to identify them, has become a hot topic among scientists. Face recognition relies heavily on feature extraction and classifiers. Occlusion, illuminations, and a complicated background provide the most difficult problems for face recognition systems to overcome. With the advent of Artificial Intelligence and Deep Learning techniques now it became easy to identify different features of an Image and to detect a face. In this paper, Stacked Auto Encoder (SAE), Artificial Feeding Bird (AFB), Region Based Fully Convolutional Network (RFCN), novel approach is developed for human face feature extraction and detection. Initially, the dataset is normalized using rescaling method. Then the Stacked Auto Encoder with Artificial Feeding Birds (SAE-AFB) optimization algorithm is used for facial feature extraction and Region based Fully Convolutional Networks (R-FCN) algorithm is used for detection and classification. The WIDER Face dataset is used for training and testing. Experimental results demonstrate that the proposed SAE-AFB-RFCN framework outperforms the existing algorithms in terms of accuracy, precision, recall and F1-score.
Limit setting using spacings in the presence of unknown backgrounds

Finding upper limits on the rate of events from a proposed process in the presence of unknown backgrounds is an often encountered problem in the search for rare processes. Methods based on unusually large ``gaps,'' or spacings, in the event distribution allow to set limits on the rate of the proposed signal distribution. In this paper, we present two novel spacings-based methods: the ``sum of sorted spacings'' and the ``product of complementary spacings'' as tests and compare these to existing tests on synthetic data as well as on a published dataset.
Ultra-compact optical full-adder based on directed logic and microring resonators

Photonic integrated circuits with compact design have opened possibilities for the development of optical computing systems; however, the overuse of photonic components in optical designs has slowed the progress of dense integration. In this paper, we propose an ultra-compact optical full-adder based on directed logic and microring resonators. To the best of our knowledge, the proposed structure requires fewer optical components than any other current designs, resulting in a significantly reduced footprint 59.2µm×29.2µm. Also, the proposed structure exhibits a maximum delay time of approximately 10 ps, implying a minimum date rate of 100 GHz. Simulation results by finite-difference time-domain (FDTD) demonstrate the effectiveness and feasibility of the proposed optical full-adder.
A Path-Planning Method to Significantly Reduce Local Oscillation of Manipulators Based on Velocity Potential Field

The robotics industry and associated technology applications are a vital support for modern production and manufacturing. With the intelligent development of the manufacturing industry, the application of collaboration robots and human-robot collaboration technology is becoming more and more extensive. In a human-robot collaboration scenario, there are uncertainties such as dynamic impediments, especially in the human upper limb, which puts forward a higher assessment of the manipulator’s route planning technology. As one of the primary branches of the artificial potential field (APF), the velocity potential field (VPF) offers the advantages of good real-time performance and convenient mathematical expression. However, the traditional VPF algorithm is prone to local oscillation phenomena near obstacles, which degrades the smoothness of the movement of the manipulators. An improved velocity potential field algorithm is proposed in this paper. This method solves the problem of sudden velocity change when the manipulator enters and departs the region of the potential field by setting new functions for attraction velocity and repulsion velocity functions. A virtual target point construction method is given to overcome the local oscillation problem of the manipulators near obstacles. The simulation and practical findings of the manipulators reveal that the improved VPF algorithm can not only avoid collision but also effectively reduce the local oscillation problem when dealing with the human upper limb as a dynamic obstacle. The implementation of this algorithm can increase the safety and real-time performance of the human-robot collaboration process and ensure that the collaborative robot is safer and smoother in the working process.
Tumor Segmentation in Colorectal Ultrasound Images Using an Ensemble Transfer Learning Model: Towards Intra-Operative Margin Assessment

Tumor boundary identification during colorectal cancer surgery can be challenging, and incomplete tumor removal occurs in approximately 10% of the patients operated for advanced rectal cancer. In this paper, a deep learning framework for automatic tumor segmentation in colorectal ultrasound images was developed, to provide real-time guidance on resection margins using intra-operative ultrasound. A colorectal ultrasound dataset was acquired consisting of 179 images from 74 patients, with ground truth tumor annotations based on histopathology results. To address data scarcity, transfer learning techniques were used to optimize models pre-trained on breast ultrasound data for colorectal ultrasound data. A new custom gradient-based loss function (GWDice) was developed, which emphasizes the clinically relevant top margin of the tumor while training the networks. Lastly, ensemble learning methods were applied to combine tumor segmentation predictions of multiple individual models and further improve the overall tumor segmentation performance. Transfer learning outperformed training from scratch, with an average Dice coefficient over all individual networks of 0.78 compared to 0.68. The new GWDice loss function clearly decreased the average tumor margin prediction error from 1.08 mm to 0.92 mm, without compromising the segmentation of the overall tumor contour. Ensemble learning further improved the Dice coefficient to 0.84 and the tumor margin prediction error to 0.67 mm. Using transfer and ensemble learning strategies, good tumor segmentation performance was achieved despite the relatively small dataset. The developed US segmentation model may contribute to more accurate colorectal tumor resections by providing real-time intra-operative feedback on tumor margins.
Heavy Metal Contamination Assessment in Sediments, Soils and Surface Waters in Agriculture-Based Rural Chhattisgarh, India, and Evaluation of Irrigation Water Quality

Regional geochemical mapping was carried out in Bilaspur and Korba Districts of Chhattisgarh, and stream sediments/slope wash, soil, and water samples were analyzed for concentration of heavy metals. The study contributes to understanding heavy metals contamination of sediments, soils, and water due to anthropogenic activity, mainly in agriculture-based rural areas. The study reveals that high geochemical anomalies observed for heavy metals like Ni, Cr, As, and Zn in sediments and soil samples are due to the extensive uses of phosphatic fertilizer and soil amendments in the form of poultry and swine manure. Water quality assessment of major streams in the study areas shows that the water is suitable for domestic and agricultural uses. Correlation analysis reveals that the chemical weathering of rock-forming minerals doesn’t control the surface water chemistry of the study area and is also an anthropogenic source of sodium in water. This study also shows the importance of the country’s geochemical mapping database, which will have much broader applications than conventional mineral exploration and geological mapping.
Modus Operandi of the Crime as a System-Forming Element of the Criminalistic Description of Crimes Connected with Altering Identification Marks on Vehicles

The system of actions of a person (or persons) involved in unlawful activities of preparing, committing and hiding a criminal act is traditionally called modus operandi in criminalistic publications, it is the basic and one of the most informative elements of the criminalistic description of crimes connected with altering vehicle identification marks (further — VIM). The author analyzed the practice of law enforcement, specialized publications and vehicle technical examinations of VIMs to find and describe 22 methods of altering VIMs, which are then used to single out key features testifying that the initial identifications marks of a vehicle have been altered. Besides, the author found an interconnection and interdependence between the modus operandi of the crime, the object of criminal infringement and the personality of the criminal, which will undoubtedly facilitate the process of proposing and verifying typical investigation leads.
Frequency and Predictors of Occult Cancer in Ischemic Stroke: A Systematic Review and Meta-Analysis (579)

Tuesday, April 28April 14, 2020Free AccessFrequency and Predictors of Occult Cancer in Ischemic Stroke: A Systematic Review and Meta-Analysis (579)Bastien Rioux, Lahoud Touma, Ahmad Nehme, Genevieve Gore, Laura Gioia, and Mark KeezerAuthors Info & AffiliationsApril 14, 2020 issue94 (15_supplement)https://doi.org/10.1212/WNL.94.15_supplement.579 Letters to the Editor
Optimal CBDC design for Ukraine through the lens of privacy and security

The growing popularity of cryptocurrencies has put on the agenda the need to develop an alternative and more regulated digital currency design, including a central bank digital currency (CBDC). The objective of the study is to formulate proposals for the development of the optimal design of the CBDC of Ukraine. The study is based on a review of the literature on digital currencies, cryptocurrencies and legislation on personal data protection, payment security and financial supervision. The implementation of the CBDC in Ukraine is associated with numerous technical and legal risks, including: privacy and data security risks; complications of financial monitoring; contradictions with national and international legislation; banks disintermediation risks. To prevent these risks, a CBDC design is proposed that is compatible with national and international legislation, ensures data privacy and security, facilitates financial tracking, and reduces the risks of disintermediation. The development of the CBDC in Ukraine could have a significant impact on the country's financial system. However, it is essential that the design of the CBDC is carefully considered to mitigate potential risks.
Factors Determining the Behavior of E-commerce Users According to the Demographic Aspects of College Students

Purpose: E-commerce is a supply of needs typical of the information age. Based on contemporary technologies, it has represented an up-and-coming channel for multiplying the possibilities of supplying demands practically everywhere on the planet. So that it can be constantly improved, scientists and managers have been concerned with better understanding the behavior of online buyers. In this sense, this study aimed to identify the determining factors of e-commerce according to demographic aspects (purchase frequency, income, paid activity, age, and gender) of university students at an institution in the North of Brazil.&#x0D; &#x0D; Theoretical framework: The study was theoretically based on the factors and variables determining consumer purchasing behavior. Factors as analytical dimensions formed a theoretical architecture, each composed of their respective variables and analysis categories.&#x0D; &#x0D; Design/Methodology/Approach: The method used was a survey, whose population consisted of 51 students enrolled in the second semester of 2022, from which a sample of 47 respondents was extracted, given that the others dropped out of class. Data were collected with a questionnaire with closed questions, the validity of which was checked with a Cronbach's alpha test at 0.82, considered very compliant. The results were generated using the Mann-Whitney U test, given that the behavior of the variables was not regarded as normal by the Kolmogorov-Smirnov and Shapiro-Wilkens tests. The conclusion points to a kind of virtual democracy in which little differentiation can be seen in the factors determining purchases when taken from the point of view of the demographic variables studied.&#x0D; &#x0D; Findings: The results showed no differences in the determining factors of e-commerce regarding paid activity, frequency of purchases, and age. A difference in behavior was detected between men and women regarding price and between those with higher incomes and those with lower incomes regarding practicality.&#x0D; &#x0D; Research, Practical, and Social Implications: The practical implication of the study is that marketing strategies and new product launches offered in the virtual world can be mainly standardized because the demographic differences that impact the behavior of virtual consumers are statistically similar. From a theoretical point of view, the results indicate the need to study other demographic variables to confirm or refute these behavioral uniformities.&#x0D; &#x0D; Originality/Value: The contribution of this study to the science and practice of electronic commerce is the finding that consumers present similar behaviors when considering the demographic aspects of university students, who represent a large portion of virtual buyers.
Improving Seq2Seq Grammatical Error Correction via Decoding Interventions

The sequence-to-sequence (Seq2Seq) approach has recently been widely used in grammatical error correction (GEC) and shows promising performance. However, the Seq2Seq GEC approach still suffers from two issues. First, a Seq2Seq GEC model can only be trained on parallel data, which, in GEC task, is often noisy and limited in quantity. Second, the decoder of a Seq2Seq GEC model lacks an explicit awareness of the correctness of the token being generated. In this paper, we propose a unified decoding intervention framework that employs an external critic to assess the appropriateness of the token to be generated incrementally, and then dynamically influence the choice of the next token. We discover and investigate two types of critics: a pre-trained left-to-right language model critic and an incremental target-side grammatical error detector critic. Through extensive experiments on English and Chinese datasets, our framework consistently outperforms strong baselines and achieves results competitive with state-of-the-art methods.
Specialist or Generalist? Instruction Tuning for Specific NLP Tasks

The potential of large language models (LLMs) to simultaneously perform a wide range of natural language processing (NLP) tasks has been the subject of extensive research. Although instruction tuning has proven to be a data-efficient method for transforming LLMs into such generalist models, their performance still lags behind specialist models trained exclusively for specific tasks. In this paper, we investigate whether incorporating broadcoverage generalist instruction tuning can contribute to building a specialist model. We hypothesize that its efficacy depends on task specificity and skill requirements. Our experiments assess four target tasks with distinct coverage levels, revealing that integrating generalist instruction tuning consistently enhances model performance when the task coverage is broad. The effect is particularly pronounced when the amount of task-specific training data is limited. Further investigation into three target tasks focusing on different capabilities demonstrates that generalist instruction tuning improves understanding and reasoning abilities. However, for tasks requiring factual knowledge, generalist data containing hallucinatory information may negatively affect the model’s performance. Overall, our work provides a systematic guide for developing specialist models with general instruction tuning.
Lexical Entrainment for Conversational Systems

Conversational agents have become ubiquitous in assisting with daily tasks, and are expected to possess human-like features. One such feature is lexical entrainment (LE), a phenomenon in which speakers in human-human conversations tend to naturally and subconsciously align their lexical choices with those of their interlocutors, leading to more successful and engaging conversations. As an example, if a digital assistant replies “Your appointment for Jinling Noodle Pub is at 7 pm” to the question “When is my reservation for Jinling Noodle Bar today?”, it may feel as though the assistant is trying to correct the speaker, whereas a response of “Your reservation for Jinling Noodle Baris at 7 pm” would likely be perceived as more positive. This highlights the importance of LE in establishing a shared terminology for maximum clarity and reducing ambiguity in conversations. However, we demonstrate in this work that current response generation models do not adequately address this crucial human-like phenomenon. To address this, we propose a new dataset, named MultiWOZ-ENTR, and a measure for LE for conversational systems. Additionally, we suggest a way to explicitly integrate LE into conversational systems with two new tasks, a LE extraction task and a LE generation task. We also present two baseline approaches for the LE extraction task, which aim to detect LE expressions from dialogue contexts
Transitioning Representations between Languages for Cross-lingual Event Detection via Langevin Dynamics

Cross-lingual transfer learning (CLTL) for event detection (ED) aims to develop models in high-resource source languages that can be directly applied to produce effective performance for lower-resource target languages. Previous research in this area has focused on representation matching methods to develop a language-universal representation space into which source- and target-language example representations can be mapped to achieve cross-lingual transfer. However, as this approach modifies the representations for the source-language examples, the models might lose discriminative features for ED that are learned over training data of the source language to prevent effective predictions. To this end, our work introduces a novel approach for cross-lingual ED where we only aim to transition the representations for the target-language examples into the source-language space, thus preserving the representations in the source language and their discriminative information. Our method introduces Langevin Dynamics to perform representation transition and a semantic preservation framework to retain event type features during the transition process. Extensive experiments over three languages demonstrate the state-of-the-art performance for ED in CLTL.
Cue-CoT: Chain-of-thought Prompting for Responding to In-depth Dialogue Questions with LLMs

Large Language Models (LLMs), such as ChatGPT, greatly empower dialogue systems with strong language understanding and generation capabilities. However, most of the previous works prompt the LLMs to directly generate a response based on the dialogue context, overlooking the underlying linguistic cues about the user status exhibited in the context. Such in-depth dialogue scenarios are challenging for existing LLMs to figure out the user’s hidden needs and respond satisfactorily through a single-step inference. To this end, we propose a novel linguistic cue-based chain-of-thoughts (Cue-CoT), which enhances the LLMs inference with an intermediate reasoning step to find cues exhibited in the dialogue, aiming to provide a more personalized and engaging response. To evaluate the approach, we build a benchmark with in-depth dialogue questions, consisting of 6 datasets in both Chinese and English, targeting 3 major linguistic cues during the conversation: personality, emotion, and psychology. We conducted experiments on the proposed benchmark with 5 LLMs under both zero-shot and one-shot settings. Empirical results demonstrate our proposed Cue-CoT method outperforms standard prompting methods in terms of both helpfulness and acceptability on all datasets.
Simplified entanglement swapping protocol for the quantum Internet

Abstract In this study, a simplified version of the entanglement-swapping protocol, commonly used in the deployment of quantum networks, is presented. Quantum repeaters are essential in extending the range of quantum networks, especially when they are implemented through the laying of optical fiber. The simplified version of the entanglement-swapping protocol does not require the use of unitary transforms to finish characterizing the shared Bell state at both ends to be connected, as happens in the traditional version of the protocol, facilitating and reducing costs in quantum repeater implementations. Both a theoretical demonstration and an experimental one on an optical table, based on two revealing experiments, show the excellent performance of the presented protocol.
Future of Blockchain Technology in Internet of Things: Opportunities and Challenges

In the recent years, Internet of Things (IoT) has gained a significant recognition that allows for the interconnectivity of devices, machines, and humans. IoT is rapidly expanding, with numerous devices connected to the internet and generating vast amounts of data. However, traditional centralized data storage and processing methods are not sufficient enough to fulfil the requirements of the IoT. The fusion of Blockchain Technology and the Internet of Things (IoT) has the potential to transform the device interaction and exchange of data with one another. Blockchain's decentralized architecture can provide secure, transparent, and immutable data transactions between devices without the need for the intermediaries. The purpose of this research article is to analyze the present state of the blockchain-IoT ecosystem, explore the potential applications and challenges, and also focus on the discussion about some future directions and scope.&#x0D;
NEW REGULATION OF DIGITAL ASSETS FOR FUTURE BUSINESS – CASE OF SERBIA

In the past several years Serbia and surrounding countries became attractive environment for crypto currency hubs. Enterprises dealing with digital assets, which are present, dispose already over a 1 billion USD. Firstly, Serbian legislator adopted several regulations on this issue for the purpose of preventing money laundering, organised crime and terrorism. However, very soon it became necessary to create a legal framework for business, finance and attracting foreign investments. Serbia was among the early birds who adopted relatively complete regulation on digital assets in 2021. The purpose of this paper is to bring this very actual topic to the attention of the academic and broader public. The authors will analyse the legal framework in force and the use of digital assets in practice. New legislation represents one big step towards the modern business. Never the less there are some more actions to be done to include digital assets completely into the legal life in Serbia.