Reviewerly/exHarmony huggingface dataset:
Adaptive Smooth L1 Loss: A Better Way to Regress Scene Texts with Extreme Aspect Ratios

In recent years, scene text detection has experienced rapid development. Regression-based methods are currently a mainstream method for scene text detection, and the effect of bounding box regression is a major factor limiting their detection performance. The regression of bounding boxes is greatly affected by the aspect ratio of texts since the text in natural scenes varies greatly in height and width. However, the existing methods ignore the difference between the height and width of the text in the bounding box regression, which leads to an imperfect regression effect and thus suppresses the performance of the scene text detection. In this paper, we propose an Adaptive Smooth L1 Loss function (abbreviated as ASLL) for bounding box regression, which can adaptively determine the weight of each regression variable according to the current state of the model during the training process, so as to guide the bounding box to regress in a more critical direction. The experimental results demonstrate that ASLL achieves promising performance on scene text detection. Specially, an F-measure of 84.56&#x0025; is achieved on CTW-1500 dataset, surpassing the state-of-the-art detectors, and the detection results on TotalText and ICDAR2015 datasets are competitive to those of state-of-the-art methods.
Designing an Elderly Virtual Caregiver Using Dialogue Agents and WebRTC

An aging society has gradually become a worldwide topic. To assist elderly people in daily dialogue and aid memory, a virtual caregiver with dialogue agents has been actively developed. In most virtual caregivers, limitations of system environments or devices exist. Share-ability for elderly people who are so far apart also requires improvement urgently. The purpose of this paper is to design a novel architecture of an elderly virtual caregiver. Our key idea is to regard the online chat room as a virtual house for elderly people, integrating dialogue agents and Web real-time communication (WebRTC). In the proposed architecture, we first extend its useable places and devices by sharing its interface to an online chat room. Then, we regard the action of calling a partner to chat as one of the dialogue scenarios by integrating dialogue agents and WebRTC.
ExMed: An AI Tool for Experimenting Explainable AI Techniques on Medical Data Analytics

The recent explosion of demand for Explainable AI (XAI) techniques has encouraged the development of various algorithms such as the Local Interpretable Model-Agnostic Explanations (LIME) and the SHapley Additive exPlanations ones (SHAP).Although these algorithms have been widely discussed by the AI community, their applications to wider domains are rare, potentially due to the lack of easy-to-use tools built around these methods.In this paper, we present ExMed, a tool that enables XAI data analytics for domain experts without requiring explicit programming skills.In particular, it supports data analytics with multiple feature attribution algorithms for explaining machine learning classifications and regressions.We illustrate its domain of applications on two real world medical case studies, with the first one analysing COVID-19 control measure effectiveness and the second one estimating lung cancer patient life expectancy from the artificial Simulacrum health dataset.We conclude that ExMed can provide researchers and domain experts with a tool that both concatenates flexibility and transferability of medical sub-domains and reveal deep insights from data.
The influence of extreme dust events in the current and future 100% renewable power scenarios in Tenerife

In this paper, the influence of dust and extreme dust events in the renewable energy generation in the island of Tenerife has been studied. Two different models have been proposed: one model to estimate the influence of atmospheric Particulate Matter (PM) in the Photovoltaic (PV) generation, and a second model that captures the influence of a set of weather and temporal variables, including PM, in the renewable generation of the island. Additionally, the hypothetic situation of extreme dust events occurring in a future 100% renewable electric system in the island of Tenerife is analyzed. Results show the interconnections between PM2.5, PM10 and wind resources on the present and future power output in the Island, and how the future storage capabilities need to take into account these events for a necessary increase in resilience related to climate change and related extreme weather events.
Rule-Based Crime Information Extraction on Indonesian Digital News

Crime Information Extraction is a task to extract some entities in the crime domain. Previous researchers have studied this task using rules to extract some crime entities in the English dataset. However, the rules were not very precise, which made the system has miss-classification. The classification error is due to the inability to resolve the name entities. This study proposed a system that can extract crime-related information in the Indonesian language. Indonesian citizens said they need to know the crime information openly based on the Crime Information Need Survey. The extraction is done by creating rules that combine dependency parsing and Part-Of-Speech tagging. Two main methods will be implementing as Crime Classification using Ontology and Rule-Based Crime Argument Extraction. These methods will find five crime entities: crime type, victim, perpetrator, location, and time. The evaluation was conducted by comparing the system output with the data manual labeling. The evaluation results were 60.70 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> Fl-Measure, 62,43% precision, and 59,06% recall. The result shows that the proposed method still needed to be fixed in some areas, especially in creating a combination of rules. The system still hard to define the perpetrator entities, victim entities, and location entities
A Survey on User Authentication Factors

Today, digital information decisively permeates all aspects of modern society. In such a connected world, one of the critical factors in maintaining the security of transmitted data is user authentication, which establishes confidence in the user identities presented to an information system. Over time, the definition of user authentication has not changed; however, a simple password is no longer the only factor used for authenticating a user. Two-factor Authentication (2FA) and Multi-factor Authentication (MFA) were proposed to provide a high level of security by adding more than one layer of authentication. For the most part, these layers of Authentication are based on knowledge, ownership, biometrics, and/or users' behaviors. This paper presents and analyzes authentication factors that are utilized to authenticate a user and prove his/her identity to a resource.
Progressive polarization based reflection removal via realistic training data generation

The reflection effect is unavoidable when taking photos through glasses or other transparent materials, which introduces undesired information into pictures. Hence, removing the influence of reflection becomes a key problem in computer vision. One of the main obstacles of recent learning based approaches is the lacking of realistic training data. To address this issue, we introduce a new dataset synthesis method as well as a novel neural network architecture for single image reflection removal. First, we make use of the polarization characteristics of light into the synthesis of datasets, so as to obtain more realistic and diversified training dataset POL. Then, we design a novel Progressive Polarization based Reflection Removal Network (P2R2Net), which preliminary estimates the coarse background layer to guide the final reflection removal. We demonstrate that our method performs better than the state-of-the-art single image reflection removal methods through quantitative and qualitative experimental comparisons. Specifically, the average PSNR of our restored images selected from three representative benchmark datesets: “Real20”, “SIR2” and “Nature” is improved at least 0.49 compared with existing methods and reaches to 24.52.
Bangladeshi Bangla speech corpus for automatic speech recognition research

This article reports the development of language resource for Bangladeshi Bangla spoken language (BBSL). Bangladeshi Bangla has inadequate large speech corpora for Large Vocabulary Continuous Speech Recognition (LVCSR) system. The accuracy of the automatic speech recognition (ASR) system rests on the quality of the speech corpus. This work discusses the common issues and activities related to the development of a large speech corpus named (SUBAK.KO). This corpus is designed to support ASR research in Bangladeshi Bangla. It has been labeled sentence-wise. We have trained this corpus with one of the well-known current End-to-End ASR algorithms, Recurrent Neural Networks (RNNs) with Connectionist Temporal Classification (CTC). To know the strengths and weaknesses, the CER (Character Error Rate) and the WER (Word Error Rate) of the trained RNNCTC model have been observed. Another open-source large Bangla ASR corpus has been trained using the same ASR algorithm. Both trained models have been compared to assess the quality of these corpora. It has been found that SUBAK.KO is a more balanced corpus and considered more regional accented speech variability for a LVCSR system compared to that open-source large Bangla ASR corpus.
Solving Tourism Management Challenges by Means of Mobile Augmented Reality Applications

The purpose of the present article was to examine the use of mobile augmented reality technologies in the process of planning and organizing tourist activities. The analysis of the attitude of TUI Showroom’s clients and managers of Russian travel agencies to the application of immersive technologies in travel consulting unveiled several possibilities for the practical use of mobile augmented reality applications in the travel business. The study concludes with the opinion that stimulation of the client's interest in the historical and cultural context of the tour by providing additional argumentation and high-quality information on the marketing proposal in a new, unusual manner forms the cultural, epistemic, and educational values of augmented reality, necessary in sales, personnel training and interaction with business partners.
Agile-CMMI V2.0 alignment: Bringing to light the agile artifacts pointed out by CMMI

Agile-CMMI alignment is becoming increasingly popular. The literature abounds with research and experience reports associating these two approaches. A new model, CMMI V2.0, and in particular CMMI-DEV V2.0, was published recently. This latest model only provides partial alignment guidelines with Agile, and the experiences reported in previous literature (referred to CMMI-DEV V1.3) are not directly applicable to CMMI-DEV V2.0. Therefore, agile organizations that want to align with CMMI-DEV 2.0, either from scratch or as a transition from CMMI-DEV 1.3, face a major challenge. In this paper, we apply the content analysis method to several CMMI Institute sources to bring to the light the Agile artifacts related to CMMI-DEV V2.0 Practices. As a result, we identify 31 Agile artifacts to address alignment with CMMI-DEV V2.0, passing from 0% to 41% coverage for Practices at Maturity Levels 2 and 3. These Agile artifacts can help to materialize the business value of CMMI-DEV V2.0 Practices and provide a starting point for agile organizations to define their roadmap. Consequently, the contribution of this paper is to present a detailed practice-level mapping based on which to design an Agile-CMMI V2.0 roadmap.
Construction and Analysis of Intelligent English Teaching Model Assisted by Personalized Virtual Corpus by Big Data Analysis

At present, a new round of scientific and technological revolution and industrial transformation with information technology at its core are accelerating. At present, a new round of scientific and technological revolution and industrial transformation with information technology at its core is accelerating. The challenge of new economy and new industry has put forward new requirements for the training of talents in China. The challenges of new economy and new industry have put forward new requirements for the cultivation of engineering talents in China. Based on corpus, this study constructed a model of intelligent English teaching assisted by virtual corpus. The traditional teaching of college English reading is based on, around, and for texts. Using DDL model, teachers can break the limitation of textbooks. On the basis of analyzing the general idea of the text, they can search out massive real corpus related to the general idea of the text by searching the core words in the text, so as to provide extensive reading resources for students in the maximum range. At the same time, teachers can rely on the corpus to design different types of teaching activities, realize student-centered task-based, inquiry-based, and autonomous learning and cultivate students’ critical thinking ability, practical ability, and cross-cultural communication ability. This model breaks the limitation of “classroom + textbook,” realizes student-centered task-based, exploratory, and autonomous learning, trains interdisciplinary new engineering talents needed by emerging industries and new economy in the future, and promotes the sustainable development of English teaching. Corpus-data-driven college English teaching mode breaks the limitation of “classroom + textbook,” changes the traditional college English teaching mode, and realizes student-centered task-oriented, inquiry-based, and autonomous learning.
A joint medical image compression and encryption using AMBTC and hybrid chaotic system

In wireless communications several imaging methods are developed. During this pandemic situation in worldwide due to COVID 19, telemedicine plays a vital role in maintaining social distancing. In telemedicine, there is demand for high compression and encryption approaches to overcome bandwidth and security issues. In this work, a joint compression and encryption (JCE) approach for images is proposed. The proposed model is more secure with faster computational time. For compression Absolute Moment Truncation Coding approach is used and furthermore for encryption two chaotic maps i.e., Arnold’s Cat and Henon Map are applied on the compressed image. Further, the experimental results were studied to measure the performance of the model for brute force, differential and statistical attack. The proposed schemes have shown good results for compression and even for security i.e., NPCR 99.86, UCAI=30.29, MSE= 108.91, PSNR=28.34, Correlation Coefficient = -0.0054 and Entropy 7.97 against the various other approaches.
Automatic Human Detection Using Reinforced Faster-RCNN for Electricity Conservation System

Electricity conservation systems are designed to conserve electricity to manage the bridge between the high raising demand and the production.Such systems have been so far using sensors to detect the necessity which adds an additional cost to the setup.Closed-circuit Television (CCTV) has been installed in almost everywhere around us especially in commercial places.Interpretation of these CCTV images is being carried out for various reasons to elicit the information from it.Hence a framework for electricity conservation that enables the electricity supply only when required, using existing resources would be a cost effective conservation system.Such a framework using a deep learning model based on Faster-RCNN is developed, which makes use of these CCTV images to detect the presence or absence of a human in a place.An Arduino microcontroller is embedded to this framework which automatically turns on/off the electricity based on human's presence/absence respectively.The proposed approach is demonstrated on CHOKE POINT dataset and two real time datasets which images from CCTV footages.F-measure, Accuracy scores (AUC score) and training time are the metrics for which the model is evaluated.An average accuracy rate of 82% is obtained by hyper-parameter tuning and using Adam optimization technique.This lays the underpinning for designing automatic frameworks for electricity conservation systems using existing resources.
A Geological-Geophysical Prospecting Model for Deep-Seated Gold Deposits in the Jiaodong Peninsula, China

The North China Craton is one of China’s major gold-producing areas. Breakthroughs have been continually made in deep prospecting at depths of 500–2000 m in the Jiaodong Peninsula, and geophysical methods have played an important role. Given that the geophysical signals of deep-seated gold deposits are difficult to detect, due to their thick overburden layers, conventional geophysical methods are not suitable for deep prospecting. Therefore, this study upgrades the geological-geophysical prospecting model, which is based on the deep metallogenic model and geophysical method of large exploration depths. Based on the analysis of the metallogenic geological factors of the altered-rock-type gold deposits in the fracture zones of the Jiaodong Peninsula, this study proposes that the gold deposits are controlled by large-scale faults, generally occur near the contact interfaces between the Early Precambrian metamorphic rock series and Mesozoic granitoids, and exhibit a stepped metallogenic model. This model then becomes the prerequisite and basic condition for deep prospecting by geophysical methods. For this reason, the traditional geophysical model, which focuses on the exploration of shallow mineralization anomalies, is transformed into a comprehensive multi-parameter geological-geophysical qualitative prospecting model highlighting the exploration of ore-controlling structural planes. The model adopts various frequency domain methods (e.g., CSAMT, AMT, WFEM), reflection seismology, and other methods to detect the deep geological structure. The characteristics of parameters such as gravity and magnetism, resistivity, polarizability, and the seismic reflection spectrum are applied to identify the ore-controlling fault location and dip angle change, and to estimate the ore-bearing location according to the stepped metallogenic model. The prospecting demonstration of deep-seated gold deposits in the Shuiwangzhuang mining area indicates the effectiveness of the comprehensive model. The comprehensive deep prospecting model effectively solves the problem of deep prospecting of gold deposits controlled by faults, promotes the great breakthrough of deep prospecting in the Jiaodong Peninsula, and provides an important technology demonstration for deep prospecting throughout China.
Self-supervised clarification question generation for ambiguous multi-turn conversation

Clarification Question Generation (CQG) aims to automatically generate clarification questions to avoid misunderstanding. In this paper, we focus on generating clarification questions in the scenario of ambiguous multi-turn conversation, which can be well applied to the interactive systems, e.g., dialogue systems and conversational recommendation systems. As a novel direction, limited manual-annotated samples are available for CQG. Moreover, existing approaches mainly ignore the representation of ambiguous semantics and cannot deal with the Out-of-Vocabulary (OOV) problem in a good manner. To address the above issues, we propose a Self-supervised Hierarchical Pointer-generator model (SHiP) for this task. In detail, similar to the backbone Coarse-to-fine process of CQG, we first formulate two self-supervised learning pretext tasks, i.e., Dialogue History Prediction and Entity Name Prediction. Then, we incorporate a hierarchical Transformer mechanism and a pointer-generator mechanism to understand the ambiguous multi-turn conversations and solve the OOV problem. Finally, we propose an end-to-end co-training paradigm to train the pretext tasks and downstream tasks. We quantify the improvements of SHiP against the competitive baselines on a publicly available dataset CLAQUA, showing a general improvement of 6.75% and 3.91% over state-of-the-art baseline in terms of BLEU and ROUGE-L, respectively.
Phonon-assisted entanglement between two quantum dots coupled to a plasmonic nanocavity

We theoretically investigate the influence of a phonon bath on entangled generation between two semiconductor quantum dots coupled to a plasmonic nanocavity and coherently driven by an external optical field. Our study reveals that phonons could be useful in a certain range of system parameters where a subradian state preparation is possible. We point out that this behavior is due to the fact that phonon environment modify the radiative properties of the two-qubit inducing different decay rates between the collective states of the system. The influence of the factors characteristic of quantum dot systems and plasmonic nanocavity such as energy mismatch in different dots, different plasmonic cavity losses as well as coupling to phonons is analyzed. The entanglement can be controlled via different external parameters such as the cavity detuning, the Rabi frequency of the driving field and the resonance frequency of the QDs.
Context-based extraction of concepts from unstructured textual documents

Summarizing a collection of unstructured textual documents, e.g., lecture slides or book chapters, by extracting the most relevant concepts helps learners realize connections among these concepts. However, to accomplish this goal existing methods neglect the context in which concepts are extracted - because a concept might be irrelevant in one context, but relevant in another one. To that end we propose a novel unsupervised method for extracting the relevant concepts from a collection of unstructured textual documents assuming that the documents are related to a certain topic. Our two-step method first identifies candidate concepts from the textual documents, then infers the context information for the input documents and finally ranks them with respect to the inferred context. In the second step this context information is enriched with more abstract information to improve the ranking process. In the experiments we demonstrate that our method outperforms seven supervised and unsupervised approaches on five datasets and is competitive on the other two. Furthermore, we release three new benchmark datasets that were created from books in the educational domain. Our code and datasets are available at: https://github.com/gulsaima/COBEC.
3D interacting hand pose and shape estimation from a single RGB image

Estimating 3D interacting hand poses and shapes from a single RGB image is challenging as it is difficult to distinguish the left and right-hands in interacting hand pose analysis. This paper proposes a network called GroupPoseNet using a grouping strategy to address this problem. GroupPoseNet extracts the left- and right-hand features respectively and thus avoids the mutual affection between the interacting hands. Empowered by a novel up-sampling block called MF-Block predicting 2D heat-maps in a progressive way by fusing image features, hand pose features, and multi-scale features, GroupPoseNet is effective and robust to severe occlusions. To achieve an effective 3D hand reconstruction, we design a transformer mechanism based inverse kinematics module(termed TikNet) to map 3D joint locations to hand shape and pose parameters of MANO hand model. Comprehensive experiments on the InterHand2.6M dataset show GroupPoseNet outperforms existing methods by a significant margin. Additional experiments also demonstrate it has a good generalization ability in the problems including left-hand, right-hand and interacting hand pose estimation from a single RGB image. We also show the efficiency of TikNet by the quantitative and qualitative results.