Reviewerly/exHarmony huggingface dataset:
Novel Low-Complexity Polynomial Multiplication Over Hybrid Fields for Efficient Implementation of Binary Ring-LWE Post-Quantum Cryptography

Post-quantum cryptography (PQC) refers to the cryptosystem that can resist the attacks launched from mature quantum computers in the not far future and has recently gained intensive attention from the research community as most of the existing public-key cryptosystems are vulnerable to attacks from quantum computers. Ring-Learning-with-Errors (Ring-LWE)-based scheme is an essential type of the lattice-based PQC due to its strong security proof and ease of implementation. As the latest variant of the Ring-LWE, the binary Ring-LWE (BRLWE)-based scheme possesses even smaller computational complexity and thus is more suitable for resource-constrained applications. However, the existing works have not well covered various aspects related to this new scheme, especially on the low-complexity hardware implementation. In this paper, we aim to present a novel implementation of the BRLWE-based scheme on the hardware platform with very low-complexity with this point of view. To carry out the specified work in a successful manner, we have proposed mainly four layers of coherent interdependent efforts: (i) we have provided the necessary algorithmic derivation process in detail to formulate the desired algorithm for the polynomial multiplication over hybrid fields, which is the major arithmetic component of the BRLWE scheme; (ii) we have presented the corresponding hardware architecture in a thorough format with sufficient description of the internal structures; (iii) we have also provided the complexity analysis and implementation-based comparison to demonstrate the superior performance of the proposed polynomial multiplication over the state-of-the-art design; (iv) finally, we have extended the proposed low-complexity polynomial multiplication to the major operational phase of the BRLWE scheme. We have shown that the proposed BRLWE structure involves significantly lower area-time complexities over the existing design, e.g., the proposed design has at least 66.01% less area-delay product (ADP) than the newly reported (Straix V device). Overall, the proposed design and implementation strategies are highly efficient, and the proposed BRLWE structure is desirable for many emerging applications.
Design and development of a new strain measuring method based on smartphone and machine vision

• This paper presents a new strain measuring method based on smartphones. • A piston-type sensor to complement this method was designed and manufactured. • The automatic calibration of the pixel size of images was achieved. • The experimental results of the sensor matched well with those from FBG. In this study, a new strain measuring method was designed based on smartphone and machine vision algorithms, while the automatic calibration of the pixel size of images taken by the smartphone was achieved with the help of circle detection. A piston-type sensor to complement this method was designed, and the static and dynamic experimental results of the sensor matched well with those from FBG. Meanwhile, the sensor was tested with temperature compensation to obtain an empirical equation for the effect of temperature on the sensor. When compared with the FBG data, the static data of the sensor had the mean error of less than 4 με and the standard error of the mean (SEM) of 1.9 με, while the dynamic data had the mean error is less than 4.3 με and the SEM is 5.4 με. The results confirm that the sensor has great potential in structural health monitoring.
Performance evaluation of median-modified Wiener filter algorithm in high-resolution complementary metal–oxide–semiconductor radio-magnetic X-ray imaging system: An experimental study

Noise reduction is a fundamental early stage in X-ray image performance evaluation. In this study, the median-modified Wiener filter (MMWF) algorithm based on a nonlinear adaptive spatial filter is compared to two general noise-reduction techniques using median and Wiener filters to confirm an excellent denoising approach in X-ray images. To acquire images, a high-resolution complementary metal–oxide–semiconductor (CMOS) radio-magnetic X-ray imaging system (exposure conditions: 100, 400, and 700μA) and rat phantom were used. The performances of the denoising methods were evaluated in terms of contrast-to-noise ratio (CNR), coefficient of variation (COV), and no-reference image quality assessment using the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE). The average results for CNR, COV, and BRISQUE in the acquired X-ray image using the MMWF algorithm were 1.08, 1.10, and 1.03 times higher than those of the noisy image, respectively. On average, the MMWF algorithm provided better image restoration than general noise-reduction techniques and was found to be most effective in relatively lower exposure conditions.
Divide and Conquer: An Ensemble Approach for Hostile Post Detection in Hindi

Recently the NLP community has started showing interest towards the challenging task of Hostile Post Detection. This paper presents our system for Shared Task @ Constraint2021 on "Hostile Post Detection in Hindi" (https://constraint-shared-task-2021.github.io/). The data for this shared task is provided in Hindi Devanagari script which was collected from Twitter and Facebook. It is a multi-label multi-class classification problem where each data instance is annotated into one or more of the five classes: fake, hate, offensive, defamation, and non-hostile. We propose a two level architecture which is made up of BERT based classifiers and statistical classifiers to solve this problem. Our team 'Albatross', scored 0.9709 Coarse grained hostility F1 score measure on Hostile Post Detection in Hindi subtask and secured 2nd rank out of 45 teams for the task (https://competitions.codalab.org/competitions/26654). Our submission is ranked 2nd and 3rd out of a total of 156 submissions with Coarse grained hostility F1 score of 0.9709 and 0.9703 respectively. Our fine grained scores are also very encouraging and can be improved with further finetuning. The code is publicly available (https://github.com/varadhbhatnagar/Hostile-Post-Detection-in-Hindi).
Multimodal Disentangled Representation for Recommendation

Discovering useful information formed by various hidden factors in multimodal data has been of great importance for recommender systems to improve both model performance and recommendation explainability. These factors hidden in multimodal data are highly entangled in a complex manner, posing great challenges in uncovering their entanglement during representation learning for recommendation. However, existing literature on disentangled representation learning only pays attention to unimodal data, failing to uncover the complex and entangled factors in multimodal data. In this paper, we study the problem of multimodal disentangled representation for recommendation in a weakly supervised manner, to the best of our knowledge, for the first time. We propose a multimodal disentangled recommendation (MDR) model that can learn well-disentangled representations carrying both complementary and common information from different modalities, such that both recommendation accuracy and representation explainability can be increased. Experimental results demonstrate the superiority of our MDR model in terms of both recommendation performance and explainabilty on various real-world datasets.
Perspective of anomaly detection in big data for data quality improvement

The period of Big Data examination has started in many businesses inside created nations. With expanding headway of Internet technology, expanding measures of data are spilling into contemporary associations. Data are getting bigger and more muddled because of the nonstop age of data from numerous gadgets and sources. In this investigation, we have examined the banking area's inconsistencies because of big data technology, inconsistencies in credit card and afterwards the path how to remove these inconsistencies.
Negative Knowledge for Open-world Wikidata

The Wikidata knowledge base (KB) is one of the most popular structured data repositories on the web, containing more than 1 billion statements for over 90 million entities. Like most major KBs, it is nonetheless incomplete and therefore operates under the open-world assumption (OWA) - statements not contained in Wikidata should be assumed to have an unknown truth. The OWA ignores however, that a significant part of interesting knowledge is negative, which cannot be readily expressed in this data model.
Classification of Attack Types and Analysis of Attack Methods for Profiling Phishing Mail Attack Groups

In recent years, there has been an increase in the number of phishing attacks targeting people in the fields of defense, security, and diplomacy around the world.In particular, hacking attack group Kimsuky has been conducting phishing attacks to collect key information from public institutions since 2013.The main feature of the attack techniques used by the Kimsuky attack group are to conceal malicious code in phishing e-mails disguised as normal e-mails to spread a document file that is vulnerable to security, such as a Hangul file, or to induce interest through a social engineering attack technique to collect account information.This study classified the types of phishing e-mail attacks into spoofed e-mails, e-mail body vulnerability use, and attached file spoofing, and detailed analyses of their attack methods, such as commonality and characteristic analyses, were performed to analyze the profile of this phishing e-mail attack group.Based on the results, the purpose of the attacking group was determined to be intelligence gathering because it focused on phishing attacks targeting Korean diplomatic and defense public institutions and related foreign institutions.Finally, a countermeasure that can be used by mail service providers and mail users to respond to phishing e-mails is suggested.
Live-Streaming Fraud Detection: A Heterogeneous Graph Neural Network Approach

Live-streaming platforms have recently gained significant popularity by attracting an increasing number of young users and have become a very promising form of online shopping. Similar to the traditional online shopping platforms such as Taobao, live-streaming platforms also suffer from online malicious fraudulent behaviors where many transactions are not genuine. The existing anti-fraud models proposed to recognize fraudulent transactions on traditional online shopping platforms are inapplicable on live-streaming platforms. This is mainly because live-streaming platforms are characterized by a unique type of heterogeneous live-streaming networks where multiple heterogeneous types of nodes such as users, live-streamers, and products are connected with multiple different types of edges associated with edge features. In this paper, we propose a new approach based on a heterogeneous graph neural network for LIve-streaming Fraud dEtection (called LIFE). LIFE designs an innovative heterogeneous graph learning model that fully utilizes various heterogeneous information of shopping transactions, users, streamers, and items from a given live-streaming platform. Moreover, a label propagation algorithm is employed within our LIFE framework to handle the limited number of labeled fraudulent transactions for model training. Extensive experimental results on a large-scale Taobao live-streaming platform demonstrate that the proposed method is superior to the baseline models in terms of fraud detection effectiveness on live-streaming platforms. Furthermore, we conduct a case study to show that the proposed method is able to effectively detect fraud communities for live-streaming e-commerce platforms.
Degree vs. approximate degree and Quantum implications of Huang’s sensitivity theorem

Based on the recent breakthrough of Huang (2019), we show that for any total Boolean function f,
Operation performance evaluation of green public buildings with AHP-fuzzy synthetic assessment method based on cloud model

At present, there always is a significant gap between the design and the actual operation performance. This study presents the application of AHP (Analytic Hierarchy Process) and fuzzy synthetic evaluation method based on cloud model theory to evaluate the overall operation performance of the green public buildings, considering indoor environment quality (IEQ) and energy consumption. The evaluation method contains three criteria as objective IEQ, subjective IEQ and annual energy consumption. Criteria of objective and subjective IEQ includes the same six sub-criteria of thermal environment in summer, in winter and in transition season, luminous environment, air quality and acoustic environment. The criteria annual energy consumption includes four sub-criteria of air conditioning electricity, lighting and socket electricity, power electricity and special electricity. Two cases of green and non-green library building are applied to be evaluated by this method, which is also validated with other method. Building managers can judge if the building performs efficiently or not with the evaluation method, and can also find which criteria or sub-criteria has poor operation performance. Furthermore, the evaluation method can reflect the relationship between sub-item of energy consumption and corresponding sub-criteria of IEQ, it can provide a direction for optimizing energy consumption. This evaluation method can not only be applied to evaluate the operation performance of different similar buildings, but also can be applied to evaluate the same building in different years. This study can provide a certain guiding significance for the development of evaluation the comprehensive operation performance of green buildings.
Qau-Net: Quartet Attention U-Net for Liver and Liver-Tumor Segmentation

U-Net and a large number of variants of U-Net have been successfully used for liver and liver-tumor segmentation. In this paper, we propose a novel network called quartet attention U-Net (QAU-Net). First, QAU-Net employs quartet attention including four branches to capture inner and cross-dimensional interactions between channels and spatial locations. Secondly, QAU-Net employs long-short skip-connection to instead of the vanilla skip-connection, which avoids the duplicate process of low-resolution information and improves the feature fusion of low-resolution and high-resolution information. We evaluate the proposed method on the public LITS dataset. Experiments demonstrate that QAU-Net has better feature representation and higher liver and liver-tumor segmentation accuracy. The available code of QAU-Net we proposed is opened at https://github.com/15029257158/QAU-Net.
Geochemical and Geothermometry study on hot-water springs for understanding prospectivity of low enthalpy reservoirs of Dholera Geothermal field, Gujarat, India

While performing various steps for exploration and exploitation of geothermal resources, the applications of geochemical methods are pivotal for reservoir characteristics evaluation. It is used concurrently with geological and hydro-geological appraisals to supplement available information regarding the prospectively of the region of investigation at the relatively low cost compared to geophysical methods or drilling. Temperature range of 40 °C to 47 °C is demonstrated by the study area comprising of several thermal water manifestations. Studies from existing wells and surveys have suggested the presence of a sizable low enthalpy geothermal resource in the field. One of the most prominent uses of geochemical sampling is the determination of subsurface temperatures using Geothermometry. In this study, commonly accepted concepts of cationic and Silica Geothermometry have been applied to understand conditions in the reservoir. Interpretation of the data has given us some input on the reservoir characteristics like reservoir temperature, mixing process and multiple fluid origins. In Dholera geothermal field for determination of variation in hydrochemical facies and understanding of the advancement of the hydrochemical forms, the current research additionally imagines the significance of graphical portrayals like Piper chart, Scholler and so forth individually. The seawater intrudes the composition of water shows the reservoir. The physio-chemical properties of the water also influence exploitation strategies.
Dynamic Class Queue for Large Scale Face Recognition In the Wild

Learning discriminative representation using large-scale face datasets in the wild is crucial for real-world applications, yet it remains challenging. The difficulties lie in many aspects and this work focus on computing resource constraint and long-tailed class distribution. Recently, classification-based representation learning with deep neural networks and well-designed losses have demonstrated good recognition performance. However, the computing and memory cost linearly scales up to the number of identities (classes) in the training set, and the learning process suffers from unbalanced classes. In this work, we propose a dynamic class queue (DCQ) to tackle these two problems. Specifically, for each iteration during training, a subset of classes for recognition are dynamically selected and their class weights are dynamically generated on-the-fly which are stored in a queue. Since only a subset of classes is selected for each iteration, the computing requirement is reduced. By using a single server without model parallel, we empirically verify in large-scale datasets that 10% of classes are sufficient to achieve similar performance as using all classes. Moreover, the class weights are dynamically generated in a few-shot manner and therefore suitable for tail classes with only a few instances. We show clear improvement over a strong baseline in the largest public dataset Megaface Challenge2 (MF2) which has 672K identities and over 88% of them have less than 10 instances. Code is available at https://github.com/bilylee/DCQ
Polygenic risk scores in psychiatry – Large potential but still limited clinical utility

Precision psychiatry is an emerging field with transformative opportunities for mental health. However, the use of clinical prediction models carries unprecedented ethical challenges, which must be addressed before accessing the potential benefits of precision psychiatry. This critical review covers multidisciplinary areas, including psychiatry, ethics, statistics and machine-learning, healthcare and academia, as well as input from people with lived experience of mental disorders, their family, and carers. We aimed to identify core ethical considerations for precision psychiatry and mitigate concerns by designing a roadmap for research and clinical practice. We identified priorities: learning from somatic medicine; identifying precision psychiatry use cases; enhancing transparency and generalizability; fostering implementation; promoting mental health literacy; communicating risk estimates; data protection and privacy; and fostering the equitable distribution of mental health care. We hope this blueprint will advance research and practice and enable people with mental health problems to benefit from precision psychiatry.
Deep Enhanced Weakly-Supervised Hashing With Iterative Tag Refinement

On image-sharing websites, images are usually associated with user-generated tags which contain semantic information and are more easily accessible than accurate labels. It is beneficial to utilize such tags as supervised information to learn image feature representation. However, some tags are not related with the image content and disturb the feature learning process. In this paper, we are dedicated to refining such noisy tags and upgrading the image feature learning. To this end, we propose a novel deep enhanced weakly-supervised hashing method, in which tags are adaptively refined according to image content. In our approach, we first map the deep image feature representation into the tag embedding space, and learn the discriminative as well as compact feature representations with the corresponding tags. After that, by referring to the learned feature representation in the first step, we refine the tags to become consistent with image content. The above two steps are alternated until convergence. Finally, we can obtain more content-relevant tags, better image features and binary hashing functions. The experiments on two image datasets prove that the proposed method outperforms the state-of-the-art weakly-supervised deep hashing methods on image retrieval task.
Semi-Supervised Anomaly Detection in Video-Surveillance Scenes in the Wild

Surveillance cameras are being installed in many primary daily living places to maintain public safety. In this video-surveillance context, anomalies occur only for a very short time, and very occasionally. Hence, manual monitoring of such anomalies may be exhaustive and monotonous, resulting in a decrease in reliability and speed in emergency situations due to monitor tiredness. Within this framework, the importance of automatic detection of anomalies is clear, and, therefore, an important amount of research works have been made lately in this topic. According to these earlier studies, supervised approaches perform better than unsupervised ones. However, supervised approaches demand manual annotation, making dependent the system reliability of the different situations used in the training (something difficult to set in anomaly context). In this work, it is proposed an approach for anomaly detection in video-surveillance scenes based on a weakly supervised learning algorithm. Spatio-temporal features are extracted from each surveillance video using a temporal convolutional 3D neural network (T-C3D). Then, a novel ranking loss function increases the distance between the classification scores of anomalous and normal videos, reducing the number of false negatives. The proposal has been evaluated and compared against state-of-art approaches, obtaining competitive performance without fine-tuning, which also validates its generalization capability. In this paper, the proposal design and reliability is presented and analyzed, as well as the aforementioned quantitative and qualitative evaluation in-the-wild scenarios, demonstrating its high sensitivity in anomaly detection in all of them.
Retrieval &amp; Interaction Machine for Tabular Data Prediction

Prediction over tabular data is an essential task in many data science applications such as recommender systems, online advertising, medical treatment, etc. Tabular data is structured into rows and columns, with each row as a data sample and each column as a feature attribute. Both the columns and rows of the tabular data carry useful patterns that could improve the model prediction performance. However, most existing models focus on the cross-column patterns yet overlook the cross-rowpatterns as they deal with single samples independently. In this work, we propose a general learning framework named Retrieval & Interaction Machine (RIM) that fully exploits both cross-row and cross-column patterns among tabular data. Specifically, RIM first leverages search engine techniques to efficiently retrieve useful rows of the table to assist the label prediction of the target row, then uses feature interaction networks to capture the cross-column patterns among the target row and the retrieved rows so as to make the final label prediction. We conduct extensive experiments on 11 datasets of three important tasks, i.e., CTR prediction (classification), top-n recommendation (ranking) and rating prediction (regression). Experimental results show that RIM achieves significant improvements over the state-of-the-art and various baselines, demonstrating the superiority and efficacy of RIM.