Reviewerly/exHarmony huggingface dataset:
SpeechFormer++: A Hierarchical Efficient Framework for Paralinguistic Speech Processing

Paralinguistic speech processing is important in addressing many issues, such as sentiment and neurocognitive disorder analyses. Recently, Transformer has achieved remarkable success in the natural language processing field and has demonstrated its adaptation to speech. However, previous works on Transformer in the speech field have not incorporated the properties of speech, leaving the full potential of Transformer unexplored. In this paper, we consider the characteristics of speech and propose a general structure-based framework, called SpeechFormer++, for paralinguistic speech processing. More concretely, following the component relationship in the speech signal, we design a unit encoder to model the intra- and inter-unit information ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , frames, phones, and words) efficiently. According to the hierarchical relationship, we utilize merging blocks to generate features at different granularities, which is consistent with the structural pattern in the speech signal. Moreover, a word encoder is introduced to integrate word-grained features into each unit encoder, which effectively balances fine-grained and coarse-grained information. SpeechFormer++ is evaluated on the speech emotion recognition (IEMOCAP & MELD), depression classification (DAIC-WOZ) and Alzheimer's disease detection (Pitt) tasks. The results show that SpeechFormer++ outperforms the standard Transformer while greatly reducing the computational cost. Furthermore, it delivers superior results compared to the state-of-the-art approaches.
Effects of using educational technology tools to enhance EFL students’ speaking performance

Educational speaking technology is a digital expertise used to enhance speaking performance. This research examined the effects of using educational speaking technology tools: FORVO, YouGlish, and OALD 8th ed. to enhance students’ speaking performance. A quasi-experimental pretest-posttest two groups design was used. Test, questionnaire, interview, and teacher-log were used to gather the data from 82 first-year Information communication and Technology (IT) students selected through comprehensive sampling. The experimental group students had learned speaking skills through educational speaking technology tools while the control group students learned using the conventional method. When the quantitative data were analyzed through independent samples T-test, the qualitative data were analyzed through thematic analysis. The findings of the study uncovered that there was a statistical difference between the experimental and control group students in their speaking performance. Accordingly, the learners who had learned through educational speaking technology have enhanced their speaking performance compared to the students that learned conventionally. Predominantly, students who learned through educational speaking technology were fluent, coherent, and accurate in their speech, rich in lexical resources, used a variety of grammatical ranges, and better in pronunciation. Besides, the students’ have positive perceptions towards using educational speaking technology tools. Consequently, this study recommends researchers, teachers, and students to make the use of educational technology and to go in line with the state of the art.
Acceptance and use of virtual reality games: an extension of HMSAM

Abstract Virtual reality (VR) is considered as one of the technological megatrends of 2020s, and today, VR systems are used in various settings, digital gaming being among the most popular ones. However, there has been a dearth of understanding regarding the central factors behind VR gaming acceptance and use. The present study therefore aimed to explain the factors that drive the use and acceptance of VR games. We extended the hedonic-motivation system acceptance model with utilitarian and inconvenience factors to capture the pertinent features of VR systems more holistically. We proposed a theoretical model and analyzed it through covariance-based structural equation modeling using an online survey sample of 473 VR gamers. Our findings help explain the role of different antecedents behind VR gaming acceptance and demonstrate that VR gaming is driven more by the hedonic gaming aspects than by the utilitarian health and well-being aspects of VR games, enjoyment being the strongest driver behind VR gaming intention and immersion. Moreover, findings also suggested that use intentions and immersion levels are not significantly diminished by physical discomfort and VR sickness. The findings, which potentially extend to other VR systems as well, also pose important implications for the providers of VR games. As the main contribution, based on our empirical findings, we provide a greater theoretical understanding on VR gaming acceptance and use.
Doctors ranking through heterogeneous information: The new score functions considering patients’ emotional intensity

With the popularity of the Internet and the growing complexity of COVID-19, more and more patients tend to consult doctors online. With the difficulty of doctor selection caused by a massive amount of information, this study proposes a hybrid multi-criteria decision-making framework, which can model patients’ emotional intensity through heterogeneous information and rank doctors. Firstly, online reviews (ORs) are transformed into probabilistic linguistic term sets through sentiment analysis. Then, new score functions are proposed considering the nonlinear influence of doctors’ information and the patients’ negative bias toward ORs. Next, a method of weight determination combining the Term Frequency Inverse Document Frequency and the Decision-making Trial and Evaluation Laboratory method is proposed. Finally, the proposed score functions are applied to the Combined Compromise Solution (CoCoSo) method to aggregate information and rank doctors. The proposed method is verified in a case study on haodf.com. The results show that considering the emotional intensity of heterogeneous information will make the recommendations more realistic. Comparative analysis and sensitivity analysis are further performed to illustrate the availability and effectiveness of the proposed method.
Performance Evaluation of Multiwavelet Transform for Single Image Dehazing

Images captured in poor lighting conditions (such haze, fog, mist, or smog) have a lower level of visibility because air particles deflect light. Single picture dehazing techniques can restore clarity to a single hazy image. Even though Dark channel prior (DCP) has been the most used method for image dehazing algorithms, it has poor picture quality and requires a lot of adjustments for real time applications such as; computer vision, object detection. However, previous studies such as; Dark Channel Prior (DCP) on image dehazing agonises from a gigantic processing time and sleaze of the original image. In addition, the dimensions of the images have a significant effect on the performance of the dehazing algorithms. Hence, the main motive of this work is to minimize the computational overload either by reducing the size of the image or by accurate transmission map estimation without compromising the image quality. Hence, this work is focused on reducing the computational time for image dehazing through multilevel discrete (Haar) wavelet transform, in which the image dimensions has been reduced without degrading quality. The performance measures of proposed algorithm have been analysed in terms of PSNR, MMSE and SSIM and compared with existing DCP algorithm. The simulation results proven that, the computational time for proposed algorithm has been reduced by 90% when compared to DCP without degrading image quality.
Blockchain Enabled Internet of Things: Current Scenario and Open Challenges for Future

The modern world and its rapid progression have cemented the requirement of digitization and has started a revolution for automation. The fore runner for this is IoT or the Internet of Things. On a very basic level IoT can be explained as the interconnection of smart devices. The Internet of Things (IoT) describes the network of physical objects “things” that are embedded with sensors, software, and other technologies for the purpose of connecting and exchanging data with other devices and systems over the internet. These devices range from ordinary household objects to sophisticated industrial tools. With more than 7 billion connected IoT devices today, experts are expecting this number to grow to 10 billion by 2020 and 22 billion by 2025. IoT is quickly becoming one of the most important technologies as it allows us to connect everyday objects to the internet using embedded devices and can then be used to communicate with them. This forms a connection between the physical and digital world. But along with this huge advantage comes a lack of intrinsic security measures which make this more vulnerable and open to privacy as well as security threats. To overcome this weakness technologies such as big data and cloud computing have been tried in tandem with IoT which has provided average results. Blockchain is a system of recording data such that it becomes almost impossible to change it. It is a digital ledger of transactions that is duplicated and distributed across the entire network of computers. So, in a sense it allows us to record digitized data and distribute it but not edit it. Thus, most of the issues related to security and vulnerability can be solved quite easily. By integrating these technologies, it brings availability, security, and integrity to the applications. This research paper targets readers who have not been particularly invested in these topics and would like an overview of IoT, blockchain and their applications, uses and future prospects.
Unraveling a blockchain-based framework towards patient empowerment: A scoping review envisioning future smart health technologies

The COVID-19 pandemic shows us how crucial patient empowerment can be in the healthcare ecosystem. Now, we know that scientific advancement, technology integration, and patient empowerment need to be orchestrated to realize future smart health technologies. In that effort, this paper unravels the Good (advantages), Bad (challenges/limitations), and Ugly (lacking patient empowerment) of the blockchain technology integration in the Electronic Health Record (EHR) paradigm in the existing healthcare landscape. Our study addresses four methodically-tailored and patient-centric Research Questions, primarily examining 138 relevant scientific papers. This scoping review also explores how the pervasiveness of blockchain technology can help to empower patients in terms of access, awareness, and control. Finally, this scoping review leverages the insights gleaned from this study and contributes to the body of knowledge by proposing a patient-centric blockchain-based framework. This work will envision orchestrating three essential elements with harmony: scientific advancement (Healthcare and EHR), technology integration (Blockchain Technology), and patient empowerment (access, awareness, and control).
Comparison of the Hologic Genius Digital Diagnostics System with the ThinPrep Imaging System—A retrospective assessment

Digital cytology (DC) with artificial intelligence (AI) is a new approach. The authors compared DC with liquid-based cytology (LBC) using computer assistance (CAS) in a retrospective, noninterventional study.In total, 1994 ThinPrep LBC slides (Hologic), which were previously analyzed in 2020 using an imaging system with CAS in routine cotesting for cytology/human papillomavirus, were reviewed in a blinded mode using the Genius Digital Diagnostics System (Hologic). In 555 cases, a histology result was available. The slides were digitally scanned (volumetric scan) at 14 levels integrated into one. AI algorithms were used to present a gallery of six tiles each (containing objects of interest) in five categories. Six additional tile rows were available, from which the diagnoses were made. All cases with a mismatch between DC and imaging system results were reviewed by an additional cytopathologist.In 86.56% of cases, a complete match between both systems was observed using the same cytology categories. When also considering the histology results, the match was 90.37%. In addition, when a cytology follow-up and/or a retrospective review was applied, the match reached 97.34%. In only 0.65% of cases was a major discrepancy observed (two grades of cytology or a low-grade squamous intraepithelial lesion/high-grade squamous intraepithelial lesion [LSIL/HSIL] shift), and none were identified by DC. Significantly more cases of higher severity (atypical squamous cells cannot exclude high grade [ASC-H], high-grade squamous intraepithelial lesion [HSIL]) were identified with DC, and its negative predictive value was higher. The screening time was significantly shorter with DC.With the Genius system for DC, the sensitivity for HSIL+/ASC-H and the specificity for LSIL and HSIL were superior to LBC and CAS. Screening time was significantly lower.
Leveraging natural language processing to augment structured social determinants of health data in the electronic health record

Abstract Objective Social determinants of health (SDOH) impact health outcomes and are documented in the electronic health record (EHR) through structured data and unstructured clinical notes. However, clinical notes often contain more comprehensive SDOH information, detailing aspects such as status, severity, and temporality. This work has two primary objectives: (1) develop a natural language processing information extraction model to capture detailed SDOH information and (2) evaluate the information gain achieved by applying the SDOH extractor to clinical narratives and combining the extracted representations with existing structured data. Materials and Methods We developed a novel SDOH extractor using a deep learning entity and relation extraction architecture to characterize SDOH across various dimensions. In an EHR case study, we applied the SDOH extractor to a large clinical data set with 225 089 patients and 430 406 notes with social history sections and compared the extracted SDOH information with existing structured data. Results The SDOH extractor achieved 0.86 F1 on a withheld test set. In the EHR case study, we found extracted SDOH information complements existing structured data with 32% of homeless patients, 19% of current tobacco users, and 10% of drug users only having these health risk factors documented in the clinical narrative. Conclusions Utilizing EHR data to identify SDOH health risk factors and social needs may improve patient care and outcomes. Semantic representations of text-encoded SDOH information can augment existing structured data, and this more comprehensive SDOH representation can assist health systems in identifying and addressing these social needs.
Ontology-driven and weakly supervised rare disease identification from clinical notes

Computational text phenotyping is the practice of identifying patients with certain disorders and traits from clinical notes. Rare diseases are challenging to be identified due to few cases available for machine learning and the need for data annotation from domain experts. We propose a method using ontologies and weak supervision, with recent pre-trained contextual representations from Bi-directional Transformers (e.g. BERT). The ontology-based framework includes two steps: (i) Text-to-UMLS, extracting phenotypes by contextually linking mentions to concepts in Unified Medical Language System (UMLS), with a Named Entity Recognition and Linking (NER+L) tool, SemEHR, and weak supervision with customised rules and contextual mention representation; (ii) UMLS-to-ORDO, matching UMLS concepts to rare diseases in Orphanet Rare Disease Ontology (ORDO). The weakly supervised approach is proposed to learn a phenotype confirmation model to improve Text-to-UMLS linking, without annotated data from domain experts. We evaluated the approach on three clinical datasets, MIMIC-III discharge summaries, MIMIC-III radiology reports, and NHS Tayside brain imaging reports from two institutions in the US and the UK, with annotations. The improvements in the precision were pronounced (by over 30% to 50% absolute score for Text-to-UMLS linking), with almost no loss of recall compared to the existing NER+L tool, SemEHR. Results on radiology reports from MIMIC-III and NHS Tayside were consistent with the discharge summaries. The overall pipeline processing clinical notes can extract rare disease cases, mostly uncaptured in structured data (manually assigned ICD codes). We discuss the usefulness of the weak supervision approach and propose directions for future studies.
A pointer meter reading recognition method based on YOLOX and semantic segmentation technology

Pointer meters are extensively employed in power, chemical, automotive, and other modern production processes. To improve the accuracy and robustness of the existing reading recognition algorithms for pointer meters, this research proposed a novel approach based on YOLOX convolutional network and semantic segmentation technology. Firstly, the dial of the target meter is detected from the background image using the YOLOX network. Secondly, the meter’s main tick marks, dial center, and pointer are determined by the proposed semantic segmentation solution using the attention U-Net network and then corrected the tilt through perspective transformation. Finally, the accurate reading is achieved by using the enhanced angle method that calculates the reading value using the angles between the pointer and the two most adjacent main tick marks. A series of experiments have been conducted to evaluate the presented approach’s feasibility and robustness. The results indicated that the fiducial errors of its reading values are no more than 0.31%.
ReSel: N-ary Relation Extraction from Scientific Text and Tables by Learning to Retrieve and Select

We study the problem of extracting N -ary relation tuples from scientific articles.This task is challenging because the target knowledge tuples can reside in multiple parts and modalities of the document.Our proposed method RESEL decomposes this task into a two-stage procedure that first retrieves the most relevant paragraph/table and then selects the target entity from the retrieved component.For the highlevel retrieval stage, RESEL designs a simple and effective feature set, which captures multilevel lexical and semantic similarities between the query and components.For the low-level selection stage, RESEL designs a cross-modal entity correlation graph along with a multi-view architecture, which models both semantic and document-structural relations between entities.Our experiments on three scientific information extraction datasets show that RESEL outperforms state-of-the-art baselines significantly. 1
Integrating Data on Early Medieval Graves: Mapping the THANADOS database to the ARIADNE infrastructure with the Mortuary Data Application Profile

This article discusses the creation of an Application Profile (AP) for mortuary data and how it was used to integrate the THANADOS anthropological and archaeological database of sepultures in the ARIADNEplus infrastructure. Ontologies increase data interoperability and their potential for being reused. The CIDOC CRM is a formal ontology designed to facilitate the integration, mediation, and interchange of heterogeneous cultural heritage information. APs provide exemplary mappings that other researchers can use as an orientation. For creating and testing the ARIADNE mortuary data AP, data from early medieval cemeteries provided an ideal case study because of the high degree of similarity between sites. The mapping of early medieval cemetery data has shown that mapping to an ontology contributes to data interoperability in several ways. Firstly, it may reveal inconsistencies in the native dataset and improve data organisation for future projects. Secondly, it enables the querying of information concerning graves and cemeteries from different sources across the ARIADNE infrastructure. Finally, this article provides an example of how archaeological data can be aggregated across multiple providers.
Proceedings of 27th International Conference

It is our great pleasure to welcome you to ACM CoNEXT 2010, the 6th International COnference on emerging Networking EXperiments and Technologies. The conference is hosted by Drexel University and is held in the City of Brotherly Love, Philadelphia. We hope that the vibe of the city in combination with an outstanding technical and social program will lead to stimulating discussions and exchange of ideas among the attending members of our world-spanning community.This year's edition continues the tradition of CoNEXT to foster the scientific and technological intersection between different research communities in networking from both academia and industry. The main conference is preceded by two interesting workshops that reflect the communities' renewed interest in fundamentally rethinking network architecture (ReArch) and what networking elements should and can do (PRESTO). In addition to those, we also have a student workshop to give the next generation of networking researchers a platform to discuss their work in an open and informal setting. The main conference is again organized along a single track of thematically grouped sessions to foster the interaction and discussions among all participants and across different points of views.CoNEXT strives to be an affordable conference and we managed to keep registration fees at the same level for the third consecutive year, despite a tough climate for attracting supporters. We are especially grateful to CISCO, for its outstanding commitment to research and education by becoming a Gold Supporter of ACM CoNEXT 2010; many thanks to our Bronze Supporter AT&T, and also to our Patrons: INTEL, NICTA and Drexel University. We thank the National Science Foundation and SIGCOMM for providing generous support for student travel grants.
Digital libraries and web 2.0

With the changing scenario in information generation, need, availability and dissemination the libraries' perspective is changing very rapidly. In pandemic situation the need and methods are also changed at large scale at ground level. The different meanings of the phrase “Library 2.0” as it is used in discussions on librarianship, and its relationship to Web 2.0; and the potential uses of social software (e.g. blogs, news feeds, social bookmarking) to enhance interaction with library users are discussed in this paper by the authors.
Forum Chemie Ingenieur Technik 11/2019

Chemie Ingenieur TechnikVolume 91, Issue 11 p. 1504-1508 Forum Forum Chemie Ingenieur Technik 11/2019 First published: 23 October 2019 https://doi.org/10.1002/cite.201971105AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Volume91, Issue11Special Issue: Herrn Professor Dr. techn. Hans-Jörg Bart zum 65. Geburtstag gewidmetNovember 2019Pages 1504-1508 RelatedInformation