Reviewerly/exHarmony huggingface dataset:
Prediction of cold chain logistics temperature using a novel hybrid model based on the mayfly algorithm and extreme learning machine

Purpose The transportation of fresh food requires cold chain logistics to maintain a low-temperature environment, which can reduce food waste and ensure product safety. Therefore, temperature control is a major challenge that cold chain logistics face. Design/methodology/approach This research proposes a prediction model of refrigerated truck temperature and air conditioner status (air speed and air temperature) based on hybrid mayfly algorithm (MA) and extreme learning machine (ELM). To prove the effectiveness of the proposed method, the mayfly algorithm–extreme learning machine (MA-ELM) is compared with the traditional ELM and the ELM optimized by classical biological-inspired algorithms, including the genetic algorithm (GA) and particle swarm optimization (PSO). The assessment is conducted through two experiments, including temperature prediction and air conditioner status prediction, based on a case study. Findings The prediction method is evaluated by five evaluation indicators, including the mean relative error (MRE), mean absolute error (MAE), mean squared error (MSE), root mean square error (RMSE) and coefficient of determination (R 2 ). It can be concluded that the biological algorithm, especially the MA, can improve the prediction accuracy. This result clearly proves the effectiveness of the proposed hybrid prediction model in revealing the nonlinear patterns of the cold chain logistics temperature. Research limitations/implications The case study illustrates the effectiveness of the proposed temperature prediction method, which helps to keep the product fresh. Even though the performance of MA is better than GA and PSO, the MA has the disadvantage of premature convergence. In the future, the modified MA can be designed to improve the performance of MA-ELM. Originality/value In prior studies, many scholars have conducted related research on the subject of temperature monitoring. However, this monitoring method can only identify temperature deviations that have occurred that harmed fresh food. As a countermeasure, research on the temperature prediction of cold chain logistics that can actively identify temperature changes has become the focus. Once a temperature deviation is predicted, temperature control measures can be taken in time to resolve the risk.
RC-MVSNet: Unsupervised Multi-View Stereo with Neural Rendering

Finding accurate correspondences among different views is the Achilles’ heel of unsupervised Multi-View Stereo (MVS). Existing methods are built upon the assumption that corresponding pixels share similar photometric features. However, multi-view images in real scenarios observe non-Lambertian surfaces and experience occlusions. In this work, we propose a novel approach with neural rendering (RC-MVSNet) to solve such ambiguity issues of correspondences among views. Specifically, we impose a depth rendering consistency loss to constrain the geometry features close to the object surface to alleviate occlusions. Concurrently, we introduce a reference view synthesis loss to generate consistent supervision, even for non-Lambertian surfaces. Extensive experiments on DTU and Tanks &Temples benchmarks demonstrate that our RC-MVSNet approach achieves state-of-the-art performance over unsupervised MVS frameworks and competitive performance to many supervised methods. The code is released at https://github.com/Boese0601/RC-MVSNet .
A Tale of Two Mice: Sustainable Electronics Design and Prototyping

Electronics have become integral to all aspects of life and form the physical foundation of computing; however electronic waste (e-waste) is among the fastest growing global waste streams and poses significant health and climate implications. We present a design guideline for sustainable electronics and use it to build a functional computer mouse with a biodegradable printed circuit board and case. We develop an end-to-end digital fabrication process using accessible maker tools to build circuits on biodegradable substrates that reduce embodied carbon and toxic waste. Our biodegradable circuit board sends data over USB at 800 kbps and generates 12 MHz signals without distortion. Our circuit board dissolves in water (in 5.5 min at 100 °C, 5 hrs at 20 °C) and we successfully recover and reuse two types of chips after dissolving. We also present an environmental assessment showing our design reduces the environmental carbon impact (kg CO2e) by 60.2% compared to a traditional mouse.
A Highly Unified Reconfigurable Multicore Architecture to Speed Up NTT/INTT for Homomorphic Polynomial Multiplication

The ring learning with error (RLWE)-based fully homomorphic encryption (FHE) scheme has become one of the most promising FHE schemes. However, its performance is limited by the homomorphic multiplication, especially the polynomial multiplication which occupies major computing resources. Therefore, efficient implementation of polynomial multiplication is crucial for high-performance FHE applications. In this article, we present an area-efficient and highly unified reconfigurable multicore number theoretic transform (NTT)/inverse NTT (INTT) architecture (named MCNA), which employs NTT and INTT for polynomial multiplier with a variable number of reconfigurable processing elements. To reduce latency, MCNA merges the preprocessing and postprocessing into the constant-geometry NTT and INTT, respectively. Also, a reconfigurable modular multiplier based on digital signal processor (DSP) is proposed to speed up the modular multiplication. In order to avoid designing independent memory access pattern for INTT, a unified read/write structure of NTT/INTT is presented. Furthermore, a novel memory access pattern named “cyclic-sharing” is proposed to reduce 25% memory capacity. MCNA is evaluated on a Xilinx Virtex-7 field-programmable gate array (FPGA) platform. Running at 250-MHz clock frequency, the throughput of MCNA for NTT/INTT achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.78\times \sim 9.32\times $ </tex-math></inline-formula> improvements in comparison to prior works, while the area efficiency of lookup table (LUT) and flip-flop (FF) is improved by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.25\times \sim 4.79\times $ </tex-math></inline-formula> . For polynomial multiplication, the throughput of MCNA achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.73\times \sim 7.69\times $ </tex-math></inline-formula> enhancements, as well as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.13\times \sim 14.8\times $ </tex-math></inline-formula> area efficiency improvements.
Graph Pre-training for AMR Parsing and Generation

meaning representation (AMR) highlights the core semantic information of text in a graph structure.Recently, pre-trained language models (PLMs) have advanced tasks of AMR parsing and AMR-to-text generation, respectively.However, PLMs are typically pretrained on textual data, thus are sub-optimal for modeling structural knowledge.To this end, we investigate graph self-supervised training to improve the structure awareness of PLMs over AMR graphs.In particular, we introduce two graph auto-encoding strategies for graphto-graph pre-training and four tasks to integrate text and graph information during pre-training.We further design a unified framework to bridge the gap between pre-training and fine-tuning tasks.Experiments on both AMR parsing and AMR-to-text generation show the superiority of our model.To our knowledge, we are the first to consider pre-training on semantic graphs.
The concreteness advantage in lexical decision does not depend on perceptual simulations.

words are typically more difficult to identify than concrete words in lexical-decision, word-naming, and recall tasks. This behavioral advantage, known as the concreteness effect, is often considered as evidence for embodied semantics, which emphasizes the role of sensorimotor experience in the comprehension of word meaning. In this view, online sensorimotor simulations triggered by concrete words, but not by abstract words, facilitate access to word meaning and speed up word identification. To test whether perceptual simulation is the driving force underlying the concreteness effect, we compared data from early-blind and sighted individuals performing an auditory lexical-decision task. Subjects were presented with property words referring to abstract (e.g., "logic"), concrete multimodal (e.g., "spherical"), and concrete unimodal visual concepts (e.g., "blue"). According to the embodied account, the processing advantage for concrete unimodal visual words should disappear in the early blind because they cannot rely on visual experience and simulation during semantics processing (i.e., purely visual words should be abstract for early-blind people). On the contrary, we found that both sighted and blind individuals are faster when processing multimodal and unimodal visual words compared with abstract words. This result suggests that the concreteness effect does not depend on perceptual simulations but might be driven by modality-independent properties of word meaning. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Exploring Gaze for Assisting Freehand Selection-based Text Entry in AR

With eye-tracking increasingly available in Augmented Reality, we explore how gaze can be used to assist freehand gestural text entry. Here the eyes are often coordinated with manual input across the spatial positions of the keys. Inspired by this, we investigate gaze-assisted selection-based text entry through the concept of spatial alignment of both modalities. Users can enter text by aligning both gaze and manual pointer at each key, as a novel alternative to existing dwell-time or explicit manual triggers. We present a text entry user study comparing two of such alignment techniques to a gaze-only and a manual-only baseline. The results show that one alignment technique reduces physical finger movement by more than half compared to standard in-air finger typing, and is faster and exhibits less perceived eye fatigue than an eyes-only dwell-time technique. We discuss trade-offs between uni and multimodal text entry techniques, pointing to novel ways to integrate eye movements to facilitate virtual text entry.
A systematic review on forensic accounting and its contribution towards fraud detection and prevention

Purpose This paper aims to determine how forensic accounting contributes to fraud detection and prevention and answer the following research questions: What are the standard techniques for fraud detection and prevention; and What are the significant challenges that hinder the application of forensic accounting in fraud prevention and detection? Design/methodology/approach The authors use the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method to carry out a systematic literature review (SLR) to identify and assess the existing literature on forensic accounting. Findings There exists a positive correlation between forensic accounting and fraud detection and prevention. Moreover, in both the empirical and non-empirical findings, the authors note that fraud is complex, and in carrying out fraud investigations, one must be aware of its complexity. Practical implications Although drug counterfeiting is a sector where forensic accountants have paid less attention, it is a rapidly expanding fraud area. This paper finds that to detect fraud at an early stage, one must increase consumer understanding of basic forensic accounting techniques by implementing accurate supply chain monitoring systems and inventory management controls and conducting adequate and effective regulatory, honest and legitimate customs inspections. Social implications The major factors that restrict forensic accounting are a lack of awareness and education. Hence, it is essential to incorporate forensic accounting in undergraduate and post-graduate courses. Originality/value From the existing literature, it has been observed that very few studies have been conducted in this field using the PRISMA and SLR techniques. Also, the authors carried out a holistic study that focuses on three different areas – fraud detection, fraud prevention and the challenges in forensic accounting.
Few shots are all you need: A progressive learning approach for low resource handwritten text recognition

Handwritten text recognition in low resource scenarios, such as manuscripts with rare alphabets, is a challenging problem. In this paper, we propose a few-shot learning-based handwriting recognition approach that significantly reduces the human annotation process, by requiring only a few images of each alphabet symbols. The method consists of detecting all the symbols of a given alphabet in a textline image and decoding the obtained similarity scores to the final sequence of transcribed symbols. Our model is first pretrained on synthetic line images generated from an alphabet, which could differ from the alphabet of the target domain. A second training step is then applied to reduce the gap between the source and the target data. Since this retraining would require annotation of thousands of handwritten symbols together with their bounding boxes, we propose to avoid such human effort through an unsupervised progressive learning approach that automatically assigns pseudo-labels to the unlabeled data. The evaluation on different datasets shows that our model can lead to competitive results with a significant reduction in human effort. The code will be publicly available in the following repository: https://github.com/dali92002/HTRbyMatching
Speech Recognition Based Robotic Arm Writing

The goal of this research is to develop a low-cost, easily controlled robotic arm system based on the Arduino Uno that is lightweight, inexpensive, and simple to use for those who are physically challenged. The arm’s unique feature is that it may be completely voice-operated, which means that it will only react to commands given by a human operator to the arm. Despite the fact that there are several approaches to allow the robot to work without the need to order it manually, the voice-based strategy provides superior precision and efficiency for the robot to carry out sensitive tasks than other alternatives. The mechanical design of the robot, the selection of appropriate motors, and the use of electrical devices to direct the robotic joints are all included in the hardware element. The software component consists of algorithms that convert required words into right sequence with target places, as well as algorithms that govern the robot’s movement to ensure that it moves in accordance with the requirements. The writing process is guided by the use of speech recognition software in this case. Either by implementing a system or by utilizing Android apps, this speech recognition is made available to the user.
Semantic Pre-alignment and Ranking Learning with Unified Framework for Cross-modal Retrieval

Cross-modal retrieval aims at retrieving highly semantic relevant information among multi-modalities. Existing cross-modal retrieval methods mainly explore the semantic consistency between image and text while rarely consider the rankings of positive instances in the retrieval results. Moreover, these methods seldom take into account the cross-interaction between image and text, which leads to the deficiency of learning their semantic relations. In this paper, we propose a Unified framework with Ranking Learning (URL) for cross-modal retrieval. The unified framework consists of three sub-networks, visual network, textual network, and interaction network. Visual network and textual network project the image feature and text feature into their corresponding hidden spaces respectively. Then, the interaction network forces the target image-text representation to align in the common space. For unifying both semantics and rankings, we propose a new optimization paradigm including pre-alignment for semantic knowledge transfer and ranking learning for final retrieval, which can decouple semantic alignment and ranking learning. The former focuses on the semantic pre-alignment optimized by semantic classification and the latter revolves around the retrieval rankings. For the ranking learning, we introduce a cross-AP loss which can directly optimize the retrieval metric average precision for cross-modal retrieval. We conduct experiments on four widely-used benchmarks, including Wikipedia dataset, Pascal Sentence dataset, NUS-WIDE-10k dataset, and PKU XMediaNet dataset respectively. Extensive experimental results show that the proposed method can obtain higher retrieval precision.
Quantum computing in power systems

Electric power systems provide the backbone of modern industrial societies.Enabling scalable grid analytics is the keystone to successfully operating large transmission and distribution systems.However, today' s power systems are suffering from everincreasing computational burdens in sustaining the expanding communities and deep integration of renewable energy resources, as well as managing huge volumes of data accordingly.These unprecedented challenges call for transformative analytics to support the resilient operations of power systems.Recently, the explosive growth of quantum computing techniques has ignited new hopes of revolutionizing power system computations.Quantum computing harnesses quantum mechanisms to solve traditionally intractable computational problems, which may lead to ultra-scalable and efficient power grid analytics.This paper reviews the newly emerging application of quantum computing techniques in power systems.We present a comprehensive overview of existing quantum-engineered power analytics from different operation perspectives, including static analysis, transient analysis, stochastic analysis, optimization, stability, and control.We thoroughly discuss the related quantum algorithms, their benefits and limitations, hardware implementations, and recommended practices.We also review the quantum networking techniques to ensure secure communication of power systems in the quantum era.Finally, we discuss challenges and future research directions.This paper will hopefully stimulate increasing attention to the development of quantum-engineered smart grids.
Feature Learning-Based Generative Adversarial Network Data Augmentation for Class-Based Few-Shot Learning

As training deep neural networks enough requires a large amount of data, there have been a lot of studies to deal with this problem. Data augmentation techniques are basic solutions to increase training data using existing data. Geometric transformations and color space augmentations are well-known augmentation techniques, but they still require some manual work and can generate limited types of data only. Therefore, there are many interests in generative-model-based augmentation lately, which can learn the distribution of data. This study proposes a set of GAN-based data augmentation methods that can generate good quality training data. The proposed networks, f-DAGAN (data augmentation generative adversarial networks), have been motivated by the DAGAN that learns data distribution from two real data. The basic f-DAGAN uses dual discriminators handling both generated data and generated feature spaces for better learning the given data. The other versions of f-DAGANs have been proposed for generating hard or easy data that have additional dual classifiers for both generated data and feature spaces to control the generator. Hard data is useful for optimized training to increase the target performance such as classification accuracy. Easy data generation can be used especially in few-shot learning. The quality of generated data has been validated in two ways: using t-SNE visualization of generated data and classification accuracy by training with generated data using the MNIST data set. The t-SNE representations show that data generated by f-DAGAN are evenly distributed for every class better than the exiting generative model-based augmentation methods. The f-DAGAN also shows the best classification accuracy by training with generated data. The f-DAGAN version for easy and hard data generation generates data well from five-shot learning and performs well in sample data generation experiments.
Cognitive Internet of Things, Its Applications, and Its Challenges: A Survey

Internet-of-things (IoT) is a system of various things that can be devices, machines, objects, or people. Cognitive Internet of Things (CIoT) refers to the use of cognitive computing techniques in the various actions performed by the various self-configured machines connected to each other. Cognitive IoT is quite useful as it characterizes a smart behavior that may be pervaded into the various mechanisms and devices through cognitive computing and IoT. The data exchanged in the CIoT system may contain some critical information that should be secure and private. So, secure, and private protocols and mechanisms requiring the least communication and minimum overhead must be used against the threats. Machine learning techniques are the applications of Artificial Intelligence to build the statistical models in which decisions are made based on the training.
Fake profile recognition using big data analytics in social media platforms

Online social media platforms today have many more users than ever before. This increased fake profiles trends which is harming both social and business entities as fraudsters use images of people for creating new fake profiles. However, most of those proposed methods are out-dated and aren't accurate enough with an average accuracy of 83%. Our proposed solution, for this problem, is a Spark ML-based project that can predict fake profiles with higher accuracy than other present methods of profile recognition. Our project consists of Spark ML libraries including Random Forest Classifier and other plotting tools. We have described our proposed model diagram and tried to depict our results in graphical representations like confusion matrix, learning curve and ROC plot for better understanding. Research findings through this project illustrate that this proposed system has accuracy of 93% in finding fake profiles over social media platforms. While there is 7% false positive rate in which our system fails to correctly identify a fake profile.