Reviewerly/exHarmony huggingface dataset:
Seeing Health with Eyes: Feature Combination for Image-Based Human BMI Estimation

Body Mass Index (BMI) is an important measurement of human obesity and health, which can provide useful information for plenty of practical purposes, such as monitoring, re-identification, and health care. Recently, some data-driven advances have been proposed to estimate BMI by 2D or 3D features from face images, frontal-body images and RGB-D images. However, due to the privacy issue or limitations of 3D cameras, the required data is hard to be obtained. More importantly, each of the previous works has only studied for a single type of features, hence it is worth investigating whether combinations of different features are more effective. To address this issue, we analyze the correlation of various features extracted from 2D body images with the estimated BMI, and then propose an accurate BMI estimation method with the optimal feature combination. Extensive experiments demonstrate that the proposed method outperforms these image-based BMI estimation methods which only utilize a single type feature in most cases. Code has been made available at : https://github.com/FVL2020/Features_for_BMI_estimation.
Evolution of Reading Comprehension and Question Answering Systems

Natural Language Processing (NLP) has witnessed considerable advances in textual understanding through statistical and rule-based techniques, which have now yielded to developments in the field of neural networks and deep learning. The paper surveys research relevant to the topic of Reading Comprehension and Question Answering (QA) implementations. The initial focus of the paper is on Attention and Transformer models. A brief description of the architectures is presented highlighting the essence of the ‘Attention is all you Need’ paper wherein the authors of Bidirectional Encoding Transformer (BERT) have elucidated the significant departure from the recurrence concept of Recurrent Neural Networks (RNN). Subsequently the trends in Open Domain Question Answering (ODQA) which mark the progression from the passage- based question answering is presented. Of particular interest is Haystack which is an end-to-end open-source framework for Question Answering & Neural search. This field seems a promising avenue for a more intelligent form of ‘search’. In a nutshell, the paper weaves through RNNs, Long Short-Term Memory (LSTM), and the currently trending Attention based Transformer models in NLP. Finally, we dwell on more contemporary pieces of research such as ODQA, Multi-Hop QA, evaluation using Adversarial Networks.
An encrypted multitone modulation method for physical layer security based on chaotic cryptography

This paper proposes an encrypted multitone modulation method to achieve low probability of being deciphered for physical layer security (PLS) of communication based on chaotic cryptography. The main objective of the proposed method is to map random, unpredictable, many-to-one relations between a multitone group (MTG) and baseband data. After the baseband data are mapped to several types of MTGs within the available frequency band with the mapping relation, the transmitter first determines the MTG type according to the data to be sent, and then the definite MTG is selected according to the MTG index sequence. Obviously, under this modulation scheme, the mapping relationship and MTG index sequence determine the distribution of frequency points. Inspired by the characteristics of this modulation, the long-term statistical undifferentiated features of different types of MTGs are obtained by encrypting the mapping relationships, and then the index sequence is designed to realize a roughly equal probability of being used for each MTG type. To accurately decipher the baseband data information, the cooperative receiver needs to master the mapping relationship and related secret keys. After establishing the communication system model under this modulation mode, the anti-non-cooperative reception deciphering ability of the system and its performance are analyzed theoretically. The reliability and concealment of the proposed method are experimentally verified and compared with the reliability and security of the orthogonal frequency-division multiplexing- (OFDM) quadrature phase-shift keying (QPSK) and differential frequency hopping (DFH) modulation methods under the same number of subcarriers and channel environments.
Handwriting Trajectory Recovery from Off-Line Multi-Stroke Characters by Deep Ordering Prediction and Heuristic Search

Stroke order recovery from off-line multi-stroke characters is a great challenge due to the ambiguity in intersection and connection among strokes. In this paper, we propose a novel framework for handwriting trajectory recovery from off-line handwritten characters by deep neural network (DNN) based ordering prediction and heuristic search, where several DNN modules are designed to extract stroke skeleton, ambiguous zones and starting points, respectively. Then, the ordering matrix M <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</inf> among all the stroke segments is calculated by a pointer network (Ptr-Net). Besides, a convolutional neural network (CNN) is used to measure the time adjacency between two arbitrary segments. Based on these necessary measurements, the final writing order is decided via searching for the optimal permutation by heuristic A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∗</sup> search. Experiments on handwriting images synthesized from the public online handwriting datasets CASIA-OLHWDB1.1, ICDAR13-Online and UNIPEN show that the proposed method yields superior performance on Chinese and English/Arabic hand-writing.
Deep Arbitrary HDRI: Inverse Tone Mapping With Controllable Exposure Changes

Deep convolutional neural networks (CNNs) have recently made significant advances in the inverse tone mapping technique, which generates a high dynamic range (HDR) image from a single low dynamic range (LDR) image that has lost information in over- and under-exposed regions. The end-to-end inverse tone mapping approach specifies the dynamic range in advance, thereby limiting dynamic range expansion. In contrast, the method of generating multiple exposure LDR images from a single LDR image and subsequently merging them into an HDR image enables flexible dynamic range expansion. However, existing methods for generating multiple exposure LDR images require an additional network for each exposure value to be changed or a process of recursively inferring images that have different exposure values. Therefore, the number of parameters increases significantly due to the use of additional networks, and an error accumulation problem arises due to recursive inference. To solve this problem, we propose a novel network architecture that can control arbitrary exposure values without adding networks or applying recursive inference. The training method of the auxiliary classifier-generative adversarial network structure is employed to generate the image conditioned on the specified exposure. The proposed network uses a newly designed spatially-adaptive normalization to address the limitation of existing methods that cannot sufficiently restore image detail due to the spatially equivariant nature of the convolution. Spatially-adaptive normalization facilitates restoration of the high frequency component by applying different normalization parameters to each element in the feature map according to the characteristics of the input image. Experimental results show that the proposed method outperforms state-of-the-art methods, yielding a 5.48dB higher average peak signal-to-noise ratio, a 0.05 higher average structure similarity index, a 0.28 higher average multi-scale structure similarity index, and a 7.36 higher average HDR-VDP-2 for various datasets.
Peculiarities of blockchain technology introduction in the field of healthcare: current situation and prospects

The current situation in the field of health care is considered and the key problems faced by this industry are described. Today, there are two main issues to be addressed in healthcare: data ownership and data security. The patient's medical data is preferably stored in centralized, isolated systems that are incompatible with each other. This situation creates difficulties in terms of timely exchange of medical data and access to them. The lack of data complicates further diagnosis and treatment of the patient. In addition, systems that store medical data are not completely reliable. Third parties can easily access and modify medical data. It is expected that blockchain technology can solve the problems that currently exist in the field of health care. Blockchain technology will create distributed, decentralized systems that will significantly improve the quality of care provided. The paper considers the areas in the field of health care, in which blockchain technology is beginning to develop, as well as related projects. All considered projects can be divided into four areas: supply chain surveillance and fight against counterfeit products, telemedicine, diagnostics, storage and management of medical data. The healthcare sector is developing rapidly and new areas are expected in which the blockchain will be used. Although there are still some problems that need to be overcome for the blockchain to be fully used.
Index Calculus Method for Solving Elliptic Curve Discrete Logarithm Problem Using Quantum Annealing

This paper presents an index calculus method for elliptic curves over prime fields using quantum annealing. The relation searching step is transformed into the QUBO (Quadratic Unconstrained Boolean Optimization) problem, which may be efficiently solved using quantum annealing, for example, on a D-Wave computer. Unfortunately, it is hard to estimate the complexity of solving the given QUBO problem using quantum annealing. Using Leap hybrid sampler on the D-Wave Leap cloud, we could break ECDLP for the 8-bit prime field. The most powerful general-purpose quantum computers nowadays would break ECDLP at most for a 6-bit prime using Shor's algorithm. In presented approach, the Semaev method of construction of decomposition base is used, where the decomposition base has a form $$\mathcal {B}=\left\{ x: 0 \le x \le p^{\frac{1}{m}} \right\} $$ , with m being a fixed integer.
MPCSL - A Modular Pipeline for Causal Structure Learning

The examination of causal structures is crucial for data scientists in a variety of machine learning application scenarios. In recent years, the corresponding interest in methods of causal structure learning has led to a wide spectrum of independent implementations, each having specific accuracy characteristics and introducing implementation-specific overhead in the runtime. Hence, considering a selection of algorithms or different implementations in different programming languages utilizing different hardware setups becomes a tedious manual task with high setup costs. Consequently, a tool that enables to plug in existing methods from different libraries into a single system to compare and evaluate the results is substantial support for data scientists in their research efforts.
Study on intelligent construction of English-Chinese bilingual context model based on CBR

The article briefly reviews the context-based view of the case-based reasoning mechanism and second language acquisition theories, and analyzes the feasibility of the application of CBR in bilingual context knowledge representation. Based on this, it proposes bilingualism. The computational model, algorithm and general implementation process of the system for contextual knowledge representation CBR system is a preliminary exploration of the application of machine learning and artificial intelligence theory in the study of second language acquisition theory. It is intended to provide a second language acquisition study to the new research methods and perspectives.
A Framework for developing Teamwork Enabled Services in Smart City Domains

Services that collaborate alongside other services or systems for performing tasks, need to be aware of either predetermined or other abrupt and unexpected behaviors in other to adapt theirs. The service behaviors we consider are performed in smart city domains which are dynamic environments where continuously new services appear, that usually have a large variation in the way they perform the same task. We use roles having teamwork behavior to represent the composition procedure in such domains, and we model the team of services through the individual behavior of each participant as well as their group goal. In this paper, we present a framework, which consists of the approach and IT system in order to serve the choreography between a large number of heterogeneous services so as to achieve a seamless and cooperative environment suitable for a smart city. This enables composite city services to adapt their behavior during execution and themselves intervene from inside the team if a possible unexpected behavior happens during the service activity, in order to run proactively and avoid obstacles or collisions. A scenario from a smart city domain illustrates that, services having different teamwork abilities are composed to a new one which inherits teamwork features and combines them to something novel.
On the Exploration of College English Teaching Management Mode under Computer Multimedia Technology

Abstract The times are advancing, and there are more and more things that attract students’ attention, which makes the teaching content in the classroom boring, especially for students who like to try new things. If we do not change the teaching method, it will be difficult to mobilize the enthusiasm of students. The new college English teaching method is reformed on the basis of computer multimedia. At present, this measure has been implemented for some time. This paper focuses on putting forward some problems existing in the current college English teaching, and proposes some solutions to these problems. It is hoped that after the reform, computer multimedia can provide better help for college English teaching, which can cultivate the habit of autonomous learning. Use different methods at different times, it is possible to have a good result if everyone pays attention to it.
Fast laser cooling using optimal quantum control

Cooling down a trapped ion into its motional ground state is a central step for trapped ions based quantum information processing. State of the art cooling schemes often work under a set of optimal cooling conditions derived analytically using a perturbative approach, in which the sideband coupling is assumed to be the weakest of all the relevant transitions. As a result the cooling rate is severely limited. Here we propose to use quantum control technique powered with automatic differentiation to speed up the classical cooling schemes. We demonstrate the efficacy of our approach by applying it to find the optimal cooling conditions for classical sideband cooling and electromagnetically induced transparency cooling schemes, which are in general beyond the weak sideband coupling regime. Based on those numerically found optimal cooling conditions, we show that faster cooling can be achieved while at the same time a low average phonon occupation can be retained.
Exploring students’ attitudes toward university e-textbooks: Experiences, expectations, and preferences

Universities have been proactively exploring e-textbooks to resolve the issue of high textbook prices. The study examined students’ attitudes toward using e-textbooks through a questionnaire survey of 324 randomly sampled students from eight universities. The analysis highlighted students’ expectations from a library in terms of textbook provision and the preferred format, their intentions and behaviors toward the textbook purchase, perceived equitable discounts in price and ideal preview mechanisms offered by e-textbooks, assessment of e-textbooks, assessment of features in terms of usefulness and practicality, and the extrinsic and intrinsic motivators of adopting e-textbooks. Students in the West and East are similar in expecting libraries to underwrite their textbook access. The students prefer e-textbook over print for the library collection, but vice versa for individually owned textbooks. In the fast-growing virtual learning environment, direct library provision of e-textbooks has become essential, particularly during the pandemic and probably post-pandemic era. Big-screen mobile devices, such as laptops and tablets, are students’ most preferred options for reading e-textbooks. The students perceived e-textbooks as not yet mature and expected improvement. They also lack confidence in their self-control ability to read e-textbooks without distraction in the Internet environment. The findings suggest the design of motivator type of engagement features may focus on individual attainment.
Deep Visual Semantic Embedding with Text Data Augmentation and Word Embedding Initialization

Language and vision are the two most essential parts of human intelligence for interpreting the real world around us. How to make connections between language and vision is the key point in current research. Multimodality methods like visual semantic embedding have been widely studied recently, which unify images and corresponding texts into the same feature space. Inspired by the recent development of text data augmentation and a simple but powerful technique proposed called EDA (easy data augmentation), we can expand the information with given data using EDA to improve the performance of models. In this paper, we take advantage of the text data augmentation technique and word embedding initialization for multimodality retrieval. We utilize EDA for text data augmentation, word embedding initialization for text encoder based on recurrent neural networks, and minimizing the gap between the two spaces by triplet ranking loss with hard negative mining. On two Flickr-based datasets, we achieve the same recall with only 60% of the training dataset as the normal training with full available data. Experiment results show the improvement of our proposed model; and, on all datasets in this paper (Flickr8k, Flickr30k, and MS-COCO), our model performs better on image annotation and image retrieval tasks; the experiments also demonstrate that text data augmentation is more suitable for smaller datasets, while word embedding initialization is suitable for larger ones.
Artificial Intelligence in Personnel Management: Opportunities and Challenges to the Higher Education Sector (HES)

Drawing on multi-disciplinary literature, this conceptual article provides an integrative review of AI’s applicability in personnel management. Prior studies indicate that AI has the ability to change the ownership and responsibility for managerial decision making, information accumulation and strategic management. Following this line of research, we review the literature of AI and discuss its application in personnel management, particularly from the context of Higher Education Sector (HES). We draw insights from three theoretical perspectives (job replacement, psychological contract and demands-resources), aiming to understand the influence of AI-driven management on the organizations, managers and employees. Specifically, two questions are proposed and critically discussed. First, what might be the opportunities and challenges of AI-driven management? Second, how AI-driven policies could be implemented to the personnel management? Based on the analysis of theoretical perspectives and empirical research findings, we have learnt that managers’ attitude is crucial to the implementation of AI in personnel management, leading to different organizational outcomes. If not handled well, AI-driven management may cause job insecurity and dissatisfaction to the employees. Research findings has brought new insights into the AI-management literature and clarified AI’s applicability in HES. Implications for future studies are discussed.
Improving Weakly Supervised Object Localization by Uncertainty Estimation of pseudo supervision

Pseudo bounding box supervision is a promising approach for weakly supervised object localization (WSOL) with only image-level labels. However, the generated pseudo bounding boxes may be inaccurate or even completely non-overlapped with the objects of interest. In this paper, we propose to estimate the uncertainty of pseudo bounding boxes such that the negative impact caused by inaccurate estimation of pseudo supervision could be alleviated for better WSOL. The refined bounding boxes and corresponding variance uncertainties are learned by training a neural network regressor to penalize the erroneous estimations. To the best of our knowledge, this is the first work to incorporate uncertainty information of pseudo bounding boxes for WSOL. Experimental results show that our method not only outperforms previous state-of-the-art methods in CUB-200-2011 and ILSVRC datasets but also gives more precise bounding box prediction when the IoU threshold is higher.
An AI Integrated Face Detection System for Biometric Attendance Management

Fair attendance processing and management is a critical process to organizations and institution. The biometric system also results the false positive and false negative. People do not prefer biometric due to exposure of sensors imaging to the certain part of body. This paper discusses the efficient biometric attendance management through integrating Artificial Intelligence sub systems. The proposed system discusses an integrated technological framework to control and manage the attendance of employee and staff members. Face recognition system is approached with many of the algorithms but in this integrated approach of Artificial Intelligence and biometric systems with LBPH algorithm provides the precise output. This LBPH algorithm processes the pixels of a detected image and threshold value is determined with binary pattern. A regular database is created to manage and preserve the attendance to execute the future processing. This integrated technological system produces efficient results and also reduces the false negative and false positive response of the biometric attendance management system.
Learning deep relevance couplings for ad-hoc document retrieval

Modeling the relevance matching between a query and a document is challenging in ad-hoc document retrieval due to diverse semantic matching aspects. Both explicit and implicit semantic matching aspects exist, corresponding to the query term occurrences in a document and the term transformations between queries and documents respectively. While most of existing neural retrieval models involve both explicit and implicit matching aspects, they do not clearly and effectively distinguish and couple explicit and implicit matching signals, resulting in either only capturing the local knowledge of query topics or lacking of in-depth understanding of user query need. In this work, we propose a deep relevance coupling model (DRC) for document retrieval to capture the diverse semantic matching aspects. DRC effectively learns and couples three aspects: explicit semantic learning, implicit semantic learning, and relevance coupling. The explicit semantic learning employs a frequency layer and a probability layer to obtain the exact term-level relevance signals between queries and documents. The implicit semantic learning adopts a transformation layer and a dependency layer to automatically capture the implicit term-level relevance signals between queries and documents. The relevance coupling transforms the term-level signals to the query-document matching signals by using a term weight layer and then integrates all the query-document matching signals into the final relevance scores between queries and documents. Experiments conducted on the TREC collections demonstrate that the DRC model delivers better retrieval accuracy and robustness than the baseline methods.