Reviewerly/exHarmony huggingface dataset:
Improved Plantard Arithmetic for Lattice-based Cryptography

This paper presents an improved Plantard’s modular arithmetic (Plantard arithmetic) tailored for Lattice-Based Cryptography (LBC). Based on the improved Plantard arithmetic, we present faster implementations of two LBC schemes, Kyber and NTTRU, running on Cortex-M4. The intrinsic advantage of Plantard arithmetic is that one multiplication can be saved from the modular multiplication of a constant. However, the original Plantard arithmetic is not very practical in LBC schemes because of the limitation on the unsigned input range. In this paper, we improve the Plantard arithmetic and customize it for the existing LBC schemes with theoretical proof. The improved Plantard arithmetic not only inherits its aforementioned advantage but also accepts signed inputs, produces signed output, and enlarges its input range compared with the original design. Moreover, compared with the state-of-the-art Montgomery arithmetic, the improved Plantard arithmetic has a larger input range and smaller output range, which allows better lazy reduction strategies during the NTT/INTT implementation in current LBC schemes. All these merits make it possible to replace the Montgomery arithmetic with the improved Plantard arithmetic in LBC schemes on some platforms. After applying this novel method to Kyber and NTTRU schemes using 16-bit NTT on Cortex-M4 devices, we show that the proposed design outperforms the known fastest implementation that uses Montgomery and Barrett arithmetic. Specifically, compared with the state-of-the-art Kyber implementation, applying the improved Plantard arithmetic in Kyber results in a speedup of 25.02% and 18.56% for NTT and INTT, respectively. Compared with the reference implementation of NTTRU, our NTT and INTT achieve speedup by 83.21% and 78.64%, respectively. As for the LBC KEM schemes, we set new speed records for Kyber and NTTRU running on Cortex-M4.
A Practical Micro Fringe Projection Profilometry for 3-D Automated Optical Inspection

As an important defect detection technology, automated Optical Inspection (AOI) is widely used in the quality control of Printed Circuit Board Assembly (PCBA) and precision electronic manufacturing. To meet the requirement of 3D AOI with large measurable volume, high speed and high precision, a practical structured light imaging system based on the micro fringe projection profilometry is proposed. To be compatible with the traditional 2D AOI system, an optical configuration with an axial bi-telecentric lens camera and a lateral Scheimpflug projector is adopted, which helps to achieve a large Depth of Field (DOF). Adapting to the constraint of limited space for calibration target movement, a simplified and precise telecentric camera calibration method is proposed, which requires no auxiliary device like the micro-positioning stage. Then, an improved Bundle-Adjustment method is introduced to globally optimize the calibration parameters. To accelerate the 3D reconstruction, we further propose a ray backtracking algorithm after the corresponding point matching by using a modified telecentric camera model. It is demonstrated in experiments using real PCBA and micro-structure components that the system DOF is 8 mm with a Field of View of 32 mm &#x00D7; 28 mm. The reconstruction time is shortened to 40% of the traditional method.
MiniKers

Automating operations of objects has made life easier and more convenient for billions of people, especially those with limited motor capabilities. On the other hand, even able-bodied users might not always be able to perform manual operations (e.g., both hands are occupied), and manual operations might be undesirable for hygiene purposes (e.g., contactless devices). As a result, automation systems like motion-triggered doors, remote-control window shades, contactless toilet lids have become increasingly popular in private and public environments. Yet, these systems are hampered by complex building wiring or short battery lifetimes, negating their positive benefits for accessibility, energy saving, healthcare, and other domains. In this paper we explore how these types of objects can be powered in perpetuity by the energy generated from a unique energy source - user interactions, specifically, the manual manipulations of objects by users who can afford them when they can afford them. Our assumption is that users' capabilities for object operations are heterogeneous, there are desires for both manual and automatic operations in most environments, and that automatic operations are often not needed as frequently - for example, an automatic door in a public space is often manually opened many times before a need for automatic operation shows up. The energy harvested by those manual operations would be sufficient to power that one automatic operation. We instantiate this idea by upcycling common everyday objects with devices which have various mechanical designs powered by a general-purpose backbone embedded system. We call these devices, MiniKers. We built a custom driver circuit that can enable motor mechanisms to toggle between generating powers (i.e., manual operation) and actuating objects (i.e., automatic operation). We designed a wide variety of mechanical mechanisms to retrofit existing objects and evaluated our system with a 48-hour deployment study, which proves the efficacy of MiniKers as well as shedding light into this people-as-power approach as a feasible solution to address energy needed for smart environment automation.
Hands-on interaction in the augmented reality (AR) chemistry laboratories enhances the learning effects of low-achieving students: a pilot study

Purpose The purpose of this study was to develop the augmented reality (AR) educational program combined with the instructional guidance for supportive learning, which enhanced the thinking process cooperative discussion and problem-solving skills in chemistry subject. Design/methodology/approach The method used the quasi-experimental research design. Of the 45 students who attended this experiment, only 25 with low achievement qualified in operating the AR learning system of saponification and transesterification environment (ARLS-STE) system. Findings These results confirmed that the AR educational program could have increased substantial benefits in improvements of students’ knowledge and the ability of the thinking process for the participants with the lowest score. In semi-structured interviews, most of participants enjoyed manipulating the ARLS-STE system, which was realistic, motived and interesting for learning science subjects. Originality/value The low-achieving students have often been known with a low learning capability, and they lack in developing constructional knowledge, despite being keen for learning. Regarding educational concerns for this population, providing orientated learning and supportive materials could increase their learning effects. Virtual worlds are an efficient learning tool in educational setting. The AR can offer visual concepts and physical interaction for students with low achievement in learning. Thus, this study investigates the acceptability of an educational program designed in the ARLS-STE, which involves the learning effects of academic knowledge and the capability of thinking process for students with low achievement. The ARLS-STE system was developed for this proposal, based upon the marker-based AR technologies combined with hands-on manipulation.
Curiosity-Driven Salient Object Detection With Fragment Attention

Recent deep learning based salient object detection methods with attention mechanisms have made great success. However, existing attention mechanisms can be generally separated into two categories. One part chooses to calculate weights indiscriminately, which yields computational redundancy. While one part focuses randomly on a small part of the images, such as hard attention, resulting in incorrectness owing to insufficiently targeted selection of a subset of tokens. To alleviate these problems, we design a Curiosity-driven Network (CNet) and a Curiosity-driven Learning Algorithm (CLA) based on fragment attention (FA) mechanism newly defined in this paper. FA imitates the process of cognition perception driven by human curiosity, and divides the degree of curiosity into three levels, i.e. curious, a little curious and not curious. These three levels correspond to five saliency degrees, including salient and non-salient, likewise salient and likewise non-salient, completely uncertain. With more knowledge gained by the network, CLA transforms the curiosity degree of each pixel to yield enhanced detail-enriched saliency maps. In order to extract more context-aware information of potential salient objects and make a better foundation for CLA, a high-level feature extraction module (HFEM) is further proposed. Based on the much better high-level features extracted by HFEM, FA can classify the curiosity degree for each pixel more reasonably and accurately. Extensive experiments on five popular datasets clearly demonstrate that our method outperforms the state-of-the-art approaches without any pre-processing operations or post-processing operations.
A Machine Translation System Between a Pair of Closely Related Languages

Machine translation between closely related languages is easier than between language pairs that are not related with each other. Having many parts of their grammars and vocabularies in common reduces the amount of effort needed to develop a translation system between related languages. A translation system that makes a morphological analysis supported by simpler translation rules and context dependent bilingual dictionaries may suffice most of the time. Usually a semantic analysis may not be needed. This paper presents a machine translation system from Turkish to Crimean Tatar that uses finite state techniques for the translation process. By developing a machine translation system between Turkish and Crimean Tatar, we propose a sample model for translation between close pairs of languages. The system we developed takes a Turkish sentence, analyses all the words morphologically, translates the grammatical and context dependent structures, translates the root words and finally morphologically generates the Crimean Tatar text. Most of the time, at least one of the outputs is a true translation of the input sentence.
Research on Morphology Detection of Metal Additive Manufacturing Process Based on Fringe Projection and Binocular Vision

This paper considers the three-dimensional (3D) shape measurement of metal parts during an additive manufacturing process in a direct energy deposition (DED) printing system with high temperature and strong light; a binocular measurement system based on ultraviolet light source projection is built using fringe projection and Fourier analysis. Firstly, ultraviolet light projection and an optical filter are used to obtain high-quality fringe patterns in an environment with thermal radiation. Then, Fourier analysis is carried out by using a single deformed fringe, and a spatial phase unwrapping algorithm is applied to obtain an unambiguous unwrapping phase, which is used as the guiding basis for the binocular matching process and 3D shape reconstruction. Finally, the accuracy of the measuring system is evaluated using a standard ball-bar gauge and the measurement error of this system is within 0.05 mm @ 100 × 100 mm. The results show that the system can measure 3D shape changes of metal parts in the additive manufacturing process. The proposed method and system have the potential to be used for online inspection and quality control of additive manufacturing.
MFFAMM: A Small Object Detection with Multi-Scale Feature Fusion and Attention Mechanism Module

Aiming at the low detection accuracy and poor positioning for small objects of single-stage object detection algorithms, we improve the backbone network of SSD (Single Shot MultiBox Detector) and present an improved SSD model based on multi-scale feature fusion and attention mechanism module in this paper. Firstly, we enhance the feature extraction ability of the shallow network through the feature fusion method that is beneficial to small object recognition. Secondly, the RFB (Receptive Field block) is used to expand the object’s receptive field and extract richer semantic information. After feature fusion, the attention mechanism module is added to enhance the feature information of important objects and suppress irrelevant other information. The experimental results show that our algorithm achieves 80.7% and 51.8% mAP on the PASCAL VOC 2007 classic dataset and MS COCO 2017 dataset, which are 3.2% and 10.6% higher than the original SSD algorithm. Our algorithm greatly improves the accuracy of object detection and meets the requirements of real-time.
Identify Consistent Imaging Genomic Biomarkers for Characterizing the Survival-Associated Interactions Between Tumor-Infiltrating Lymphocytes and Tumors

The tumor-infiltrating lymphocytes (TILs) and its correlation with tumors play a critical role in the development and progression of breast cancer. Existing studies have demonstrated that the combination of the whole-slide pathological images (WSIs) and genomic data can better characterize the immunological mechanisms of TILs and assess the prognostic outcome in breast cancer. However, it is still very challenging to characterize the intersections between TILs and tumors in WSIs because of their large size and heterogeneity patterns, and the high dimensional genomic data also brings difficulty for the integrative analysis with WSIs data. To address the above challenges, in this paper, we propose an interpretable multi-modal fusion framework, IMGFN, that can fuse the interaction information between TILs and tumors with the genomic data via an attention mechanism for prognosis predictions of breast cancer. Specifically, for WSIs data, we use the graph attention network (i.e., GAT) to describe the spatial interactions of TILs and tumor regions across WSIs. As to genomic data, we use co-expression network analysis algorithms to cluster genes into co-expressed modules followed by applying the Concrete Autoencoders to select survival-associated modules. Finally, a self-attention layer is adopted to combine both the imaging and genomic features for the prognosis prediction of breast cancer. The experimental results on The Cancer Genome Atlas(TCGA) dataset suggest that the proposed IMGFN can not only achieve better prognosis results than the comparing methods but also identify consistent survival-associated imaging and genomic biomarkers correlated strongly with the interaction between TILs and tumors.
Evaluation System of Music Art Instructional Quality Based on Convolutional Neural Networks and Big Data Analysis

In order to speed up the process of high-quality education and improve the level of education quality among the general public, people have pushed for the use of music art education in recent years. In this respect, this study covers the CNN-based assessment of the quality of music art teaching and creates a set of evaluation indices for that quality. The model architecture, network topology, learning parameters, and learning algorithm are all determined using this information, which also acts as the basis for the NN assessment model. The MATLAB simulation tool uses the CNN assessment model to train and learn a predetermined quantity of instructional quality data. The training experiment shows that this system can outperform other comparative systems in prediction accuracy by roughly 95%. Additionally, both the training and prediction accuracy of the model are completely acceptable. The evaluation findings and analytical data of the music art instructional quality assessment system created in this study can be used as a guide for determining the music art instructional quality and for making judgments regarding it.
ContraReg: Contrastive Learning of Multi-modality Unsupervised Deformable Image Registration

Establishing voxelwise semantic correspondence across distinct imaging modalities is a foundational yet formidable computer vision task. Current multi-modality registration techniques maximize hand-crafted inter-domain similarity functions, are limited in modeling nonlinear intensity-relationships and deformations, and may require significant re-engineering or underperform on new tasks, datasets, and domain pairs. This work presents ContraReg, an unsupervised contrastive representation learning approach to multi-modality deformable registration. By projecting learned multi-scale local patch features onto a jointly learned inter-domain embedding space, ContraReg obtains representations useful for non-rigid multi-modality alignment. Experimentally, ContraReg achieves accurate and robust results with smooth and invertible deformations across a series of baselines and ablations on a neonatal T1-T2 brain MRI registration task with all methods validated over a wide range of deformation regularization strengths.
Quality enhanced framework through integration of blockchain with supply chain management

Recently, there has been significant growth in the consumption of the most widely diversified Internet of Things (IoT) technological knowledge, and devices, which has resulted in an impact on not only electrical items and the agricultural and food industries (Agri-Food) supply chain networks. This has sparked intense curiosity about the development of information sharing that is reliable, traceable, and transparent, and also increased significant research and advancement efforts. Existing IoT-based trace & authenticity methods for agri-food distribution networks are constructed on top of centralized architectures, which creates the potential for significant issues such as data security, manipulation, and standard points of weakness. A creative and scraping methodological approach to implementing decentralized trust-free networks is represented by blockchain technologies, the decentralized blockchain technologies that underpin cryptocurrencies. The fault tolerance, data integrity, visibility, and complete tracing of saved transactional data, along with cohesive digital information of property resources and independent transactions implementations, are in fact features built into this digitalization. This study introduces Agri-BlockIoT, a completely decentralized blockchain-based traceable platform for managing a global agro-food distribution network that can seamlessly connect IoT systems that produce and consume digital information all along the distribution chain. We implemented a use caseto achieve transparency and traceability. Lastly, we analyzed and contrasted the implementations' capability in terms of delay, CPU, or network utilization.
Toward a Li‐Ion Battery Ontology Covering Production and Material Structure

An ontology for the structured storage, retrieval, and analysis of data on lithium‐ion battery materials and electrode‐to‐cell production is presented. It provides a logical structure that is mapped onto a digital architecture and used to visualize, correlate, and make predictions in battery production, research, and development. Materials and processes are specified using a predetermined terminology; a chain of unit processes (steps) connects raw materials and products (items) of battery cell production. The ontology enables the attachment of analytical methods (characterization methods) to items. Workshops and interviews with experts in battery materials and production processes are conducted to ensure that the structure is conformable both for industrial‐scale and laboratory‐scale data generation and implementation. Raw materials and intermediate products are identified and defined for all steps to the final battery cell. Steps and items are defined based on current standard materials and process chains using terms that are in common use. Alternative structures and the connection of the ontology to other existing ontologies are discussed. The contribution provides a pragmatic, accessible way to unify the storage of materials‐oriented lithium‐ion battery production data. It aids the linkage of such data with domain knowledge and the automation of data analysis in production and research.
Nonlinear Fourier transform receiver based on a time domain diffractive deep neural network

A diffractive deep neural network (D2NN) is proposed to distinguish the inverse nonlinear Fourier transform (INFT) symbols. Different from other recently proposed D2NNs, the D2NN is fiber based, and it is in the time domain rather than the spatial domain. The D2NN is composed of multiple cascaded dispersive elements and phase modulators. An all-optical back-propagation algorithm is proposed to optimize the phase. The fiber-based time domain D2NN acts as a powerful tool for signal conversion and recognition, and it is used in a receiver to recognize the INFT symbols all optically. After the symbol conversion by the D2NN, simple phase and amplitude measurement will determine the correct symbol while avoiding the time-consuming NFT. The proposed device can not only be implemented in the NFT transmission system, but also in other areas which require all optical time domain signal transformation and recognition, like sensing, signal coding and decoding, beam distortion compensation and image recognition.
Digital Audit During Covid-19 in Jordanian Audit Firms a Study of the Reality and Outlook the Future

This study aims to investigate the effect of the Covid-19 on audit procedures, through studying the availability of digital audit requirements of modern technologies and artificial intelligence techniques in Jordanian audit firms during the pandemic, as well as outlook the future impact of applying digital audit on the auditing profession, through studying the impact of the application of digital audit on the auditor's personal qualities. The study population comprises Jordanian audit firms, and the study sample consists of 60 firms, randomly selected. A questionnaire was distributed to an auditor in each of the study sample firms, where (60) questionnaires were distributed, (52) restored, representing a percentage of (87%). The results of the study showed the availability of digital audit requirements of modern technologies and artificial intelligence techniques in Jordanian audit firms. Also, applying digital audit affects the auditor’s personal qualities.
The Sentiment is in the Details

Determining the sentiment in the individual sentences of a newspaper article in an automated fashion is a major challenge. Manually created sentiment dictionaries often fail to meet the required standards. And while computer-generated dictionaries show promise, they are often limited by the availability of suitable linguistic resources. I propose and test a novel, language-agnostic and resource-efficient way of constructing sentiment dictionaries, based on word embedding models. The dictionaries are constructed and evaluated based on four corpora containing two decades of Danish, Dutch (Flanders and the Netherlands), English, and Norwegian newspaper articles, which are cleaned and parsed using Natural Language Processing. Concurrent validity is evaluated using a dataset of human-coded newspaper sentences, and compared to the performance of the Polyglot sentiment dictionaries. Predictive validity is tested through two long-standing hypotheses on the negativity bias in political news. Results show that both the concurrent validity and predictive validity is good. The dictionaries outperform their Polyglot counterparts, and are able to correctly detect a negativity bias, which is stronger for tabloids. The method is resource-efficient in terms of manual labor when compared to manually constructed dictionaries, and requires a limited amount of computational power.