Reviewerly/exHarmony huggingface dataset:
A Unification of Heterogeneous Data Sources into a Graph Model in E-commerce

Abstract The incorporation of heterogeneous data models into large-scale e-commerce applications incurs various complexities and overheads, such as redundancy of data, maintenance of different data models, and communication among different models for query processing. Graphs have emerged as data modelling techniques for large-scale applications with heterogeneous, schemaless, and relationship-centric data. Models exist for mapping different types of data to a graph; however, the unification of data from heterogeneous source models into a graph model has not received much attention. To address this, we propose a new framework in this study. The proposed framework first transforms data from various source models into graph models individually and then unifies them into a single graph. To justify the applicability of the proposed framework in e-commerce applications, we analyse and compare query performance, scalability, and database size of the unified graph with heterogeneous source data models for a predefined set of queries. We also access some qualitative measures, such as flexibility, completeness, consistency, and maturity for the proposed unified graph. Based on the experimental results, the unified graph outperforms heterogeneous source models for query performance and scalability; however, it falls behind for database size.
Corpus based sentimenal movie review analysis using auto encoder convolutional neural network

In natural language processing, most prominent branch is sentiment analysis. People’s emotions and attitudes are analyzed using this sentiment analysis towards service, some product, etc. In prediction of the future scope of a product, some benefits are given by sentiment analysis. However, manual analysis of such a huge amount of documents is a highly tedious task, especially with limited time. Hence, for solving this problem, various attempts are made in literature and proposed different sentiment analysis methods. However, in generation of lexicon, popular NLP tools has some drawbacks. The accuracy of lexicons based on humans is less and they are limited too. On the other hand, lexicons based on dictionary are highly general and they are domain specific. So, a technique called Corpus Integrated Autoencoder Convolutional Neural Network based Sentiment Analysis (CI-AECNN) is proposed in this work for solving this issue. The sentiment lexicon generation based on corpus is performed in this work. Candidates sentiment orientation are computed using this and seed lexicon are added with recognized sentiment words and from seed lexicon, words with incorrect sentiment are removed. The long short-term memory (LSTM) is used for performing Word Sense Disambiguation. Conditional random fields are used for extracting features. At last, auto-encoder, convolutional neural network is used for performing classification. In MATLAB simulation environment, conducted this research work’s overall analysis and it indicates that better results are produced by proposed technique when compared with available techniques.
The Influence of Single-Clause Sentences on IELTS Writing Task 2 Band Score

This research aims to investigate the frequencies of single-clause sentences (SS) and multi-clause sentences (MS) used by IELTS test-takers in writing task 2 and the relationship between their use of single-clause sentences and band score in this section. The researcher used the descriptive comparative research design and the quantitative method design with convenient sampling with fifty (50) sample essays mainly sourced from the set of Cambridge English IELTS Academic books. After collecting the data, the researchers analyzed the data using statistical and descriptive analysis. The result shows that the percentages of single-clause and multi-clause sentences in the range between 0 and 6.0 bands are relatively equal. On the other hand, these percentages in the range of 6.5-9.0 have a significant difference. Additionally, the results of Pearson Correlation Analysis show that the frequency of single-clause sentences has an insignificant negative relationship with the IELTS writing task 2 band score. Therefore, IELTS writing task 2 compositions with more single-clause sentences are potentially graded with a lower band score.
Diagnostic and educational self-assessment of the digital competence of university teachers

The importance of promoting digital transformation processes in higher education has been increasing over in the last decade.Recently, the impact of the health crisis experienced in 2020 due to the COVID-19 pandemic on educational activity has further accelerated this transformation.In order to take advantage of the potential of digital tools in educational processes, as well as to manage these new learning situations, it is essential for university faculties to have an adequate level of digital teaching competence (DTC).This article aims to explore this competence in depth, by analysing the self-perceived level of DTC of university teaching staff and their assessment in each of the areas that comprise it.The research was carried out in a Spanish public university with a sample of 61 university teaching staff, following the methodological approach of educational design research (EDR), through quantitative and qualitative instruments for collecting information from a sample of key informants and potential users.The results suggest that the questionnaire has validity for its function, and detects those areas of digital competence in which participants need to improve, and those in which their level of competence is optimal.As for the evaluation of the university faculty, the results show a medium-to-high level of competence, and when evaluating the tool, they have highlighted its usefulness.In conclusion, the need to have DTC assessment tools in higher education that allow faculties to reflect on their own digital competence is essential today, in order to carry out quality teaching and learning processes adapted to the needs of students.
Certifying position-momentum entanglement at telecommunication wavelengths

TheThe successful employment of high-dimensional quantum correlations and its integration in telecommunication infrastructures is vital in cutting-edge quantum technologies for increasing robustness and key generation rate. Position-momentum Einstein-Podolsky-Rosen (EPR) entanglement of photon pairs are a promising resource of such high-dimensional quantum correlations. Here, we experimentally certify EPR correlations of photon pairs generated by spontaneous parametric down-conversion (SPDC) in a nonlinear crystal with type-0 phase-matching at telecommunication wavelength for the first time. To experimentally observe EPR entanglement, we perform scanning measurements in the near- and far-field planes of the signal and idler modes. We certify EPR correlations with high statistical significance of up to 45 standard deviations. Furthermore, we determine the entanglement of formation of our source to be greater than one, indicating a dimensionality of greater than 2. Operating at telecommunication wavelengths around 1550 nm, our source is compatible with today's deployed telecommunication infrastructure, thus paving the way for integrating sources of high-dimensional entanglement into quantum-communication infrastructures.
Chatbot as a Telehealth Intervention Strategy in the COVID-19 Pandemic

The COVID-19 pandemic and the need for social distancing have created a demand for new and innovative solutions in healthcare systems worldwide. One of the strategies that have been implemented are chatbots, which can be helpful in providing reliable health information and preventing people from seeking assistance in healthcare centers and being unnecessarily exposed to the virus. In this context, although a high number of chatbots have been implemented worldwide, little has been discussed about the process and challenges in developing and implementing this technology. This paper reports on an action research, which designed a novel chatbot as a prompt response to the COVID-19 pandemic. The chatbot is intended to be a first layer of interaction with the public, performing triage of patients and providing information about COVID-19 on a large scale and without human contact. Our contribution is twofold: (i) we reflected on the development process and discuss lessons learned and recommendations to support a multidisciplinary development and evolution process of the chatbot; and (ii) we identified some interactive and technological features that can be used as a reference framework for this kind of technology. These contributions can be useful to other researchers and multidisciplinary teams facing similar challenges.
Design and Implementation of a Smart Home in a Box to Monitor the Wellbeing of Residents With Dementia in Care Homes

There is a global challenge related to the increasing number of People with Dementia (PwD) and the diminishing capacity of governments, health systems, and caregivers to provide the best care for them. Cost-effective technology solutions that enable and ensure a good quality of life for PwD via monitoring and interventions have been investigated comprehensively in the literature. The objective of this study was to investigate the challenges with the design and deployment of a Smart Home In a Box (SHIB) approach to monitoring PwD wellbeing within a care home. This could then support future SHIB implementations to have an adequate and prompt deployment allowing research to focus on the data collection and analysis aspects. An important consideration was that most care homes do not have the appropriate infrastructure for installing and using ambient sensors. The SHIB was evaluated via installation in the rooms of PwD with varying degrees of dementia at Kirk House Care Home in Belfast. Sensors from the SHIB were installed to test their capabilities for detecting Activities of Daily Living (ADLs). The sensors used were: (i) thermal sensors, (ii) contact sensors, (iii) Passive Infrared (PIR) sensors, and (iv) audio level sensors. Data from the sensors were collected, stored, and handled using a 'SensorCentral' data platform. The results of this study highlight challenges and opportunities that should be considered when designing and implementing a SHIB approach in a dementia care home. Lessons learned from this investigation are presented in addition to recommendations that could support monitoring the wellbeing of PwD. The main findings of this study are: (i) most care home buildings were not originally designed to appropriately install ambient sensors, and (ii) installation of SHIB sensors should be adapted depending on the specific case of the care home where they will be installed. It was acknowledged that in addition to care homes, the homes of PwD were also not designed for an appropriate integration with ambient sensors. This study provided the community with useful lessons, that will continue to be applied to improve future implementations of the SHIB approach.
A Benchmark for Feature-injection Architectures in Image Captioning

Describing an image with a grammatically and semantically correct sentence, known as image captioning, has been improved significantly with recent advances in computer vision (CV) and natural language processing (NLP) communities.The integration of these communities leads to the development of feature-injection architectures, which define how extracted features are used in captioning.In this paper, a benchmark of feature-injection architectures that utilize CV and NLP techniques is reported for encoderdecoder based captioning.Benchmark evaluations include Inception-v3 convolutional neural network to extract image features in the encoder while the feature-injection architectures such as init-inject, pre-inject, par-inject and merge are applied with a multi-layer gated recurrent unit (GRU) to generate captions in the decoder.Architectures have been evaluated extensively on the MSCOCO dataset across eight performance metrics.It has been concluded that the init-inject architecture with 3-layer GRU outperforms the other architectures in terms of captioning accuracy.
Performance Analysis of Secure Elements for IoT

New protocol stacks provide wireless IPv6 connectivity down to low power embedded IoT devices. From a security point of view, this leads to high exposure of such IoT devices. Consequently, even though they are highly resource-constrained, these IoT devices need to fulfil similar security requirements as conventional computers. The challenge is to leverage well-known cybersecurity techniques for such devices without dramatically increasing power consumption (and therefore reducing battery lifetime) or the cost regarding memory sizes and required processor performance. Various semiconductor vendors have introduced dedicated hardware devices, so-called secure elements that address these cryptographic challenges. Secure elements provide tamper-resistant memory and hardware-accelerated cryptographic computation support. Moreover, they can be used for mutual authentication with peers, ensuring data integrity and confidentiality, and various other security-related use cases. Nevertheless, publicly available performance figures on energy consumption and execution times are scarce. This paper introduces the concept of secure elements and provides a measurement setup for selected individual cryptographic primitives and a Datagram Transport Layer Security (DTLS) handshake over secure Constrained Application Protocol (CoAPs) in a realistic use case. Consequently, the paper presents quantitative results for the performance of five secure elements. Based on these results, we discuss the characteristics of the individual secure elements and supply developers with the information needed to select a suitable secure element for a specific application.
A Comparative Study of Homomorphic Encryption Schemes Using Microsoft SEAL

Abstract Fully homomorphic encryption (FHE) technology is a method of encrypting data that allows arbitrary calculations to be computed. Machine learning (ML) and many other applications are relevant to FHE such as Cloud Computing, Secure Multi-Party, and Data Aggregation. Only the authenticated user has the authority to decrypt the ciphertext and understand its meaning, as encrypted data can be computed and processed to produce an encrypted output. Homomorphic encryption uses arithmetic circuits that focus on addition and multiplication, allowing the user to add and multiply integers while encrypted. This paper discusses the performance of the Brakerski-Fan-Vercauteren scheme (BFV) and Cheon, Kim, Kim, and Song (CKKS) scheme using one of the most important libraries of FHE “Microsoft SEAL”, by applying certain arithmetic operations and observing the time consumed for every function applied in each scheme and the noise budget after every operation. The results obtained show the difference between the two schemes when applying the same operation and the number of sequential operations each can handle.
Conventional histological and cytological staining with simultaneous immunohistochemistry enabled by invisible chromogens

Conventional histological stains, such as hematoxylin plus eosin (H&E), and immunohistochemistry (IHC) are mainstays of histology that provide complementary diagnostic information. H&E and IHC currently require separate slides, because the stains would otherwise obscure one another. This consumes small specimen, limiting the total amount of testing. Additionally, performing H&E and IHC on different slides does not permit comparison of staining at the single cell level, since the same cells are not present on each slide, and alignment of tissue features can be problematic due to changes in tissue landscape with sectioning. We have solved these problems by performing conventional staining and IHC on the same slide using invisible IHC chromogens, such that the chromogens are not visible when viewing the conventional stain and the conventional stain is excluded from images of the IHC. Covalently deposited chromogens provided a convenient route to invisible chromogen design and are stable to reagents used in conventional staining. A dual-camera brightfield microscope system was developed that permits simultaneous viewing of both visible conventional stains and invisible IHC chromogens. Simultaneous staining was demonstrated on several formalin-fixed paraffin-embedded tissue specimens using single and duplex IHC, with chromogens that absorb ultraviolet and near infrared light, followed by H&E staining. The concept was extended to other conventional stains, including mucicarmine special stain and Papanicoulou stain, and further extended to cytology specimens. In addition to interactive video review, images were recorded using multispectral imaging and image processing to provide flexible production of color composite images and enable quantitative analysis.
Investigating the Impact of Cloud Computing Vendor on the Adoption of Cloud Computing

Cloud computing offers significant impacts on organization by changing how information systems are developed, deployed, operated, maintained, and paid for. Therefore, the adoption of cloud computing becomes the focus of relevant research; however, previous studies have mostly studied the factors affecting cloud computing adoption from the perspective of adopters, ignoring the influence of the vendors. This study defines cloud service capability and develops scale to measure it from the perspective of cloud computing vendors to empirically examine the impact of the supply-side of cloud computing. The initial scale of cloud service capability is constructed using qualitative research, and the formal scale is obtained after two rounds of pretest. The statistical results of matched data collected from 132 cloud computing vendors and their users show that cloud service capability significantly affects cloud computing adoption. This study shifts the research perspective on cloud adoption to make theoretical contributions and management insights from the perspective of cloud computing vendors.
Pedagogical situations: interpretation difference

Taking into account the need to clarify the terminological framework of modern pedagogy, the concept pedagogical situation in general and its place in the pedagogical discourse in a cross-cultural aspect is discussed. Today, the concept of situation gains a general methodological significance though lacks a clear definition. Based on the analysis of interdisciplinary research, we concluded that the integral concept of situation includes: presence of subjects, dynamism, external conditions, causal relations, chronotope. The analysis of scientific sources proved that in pedagogical discourse of Russian- and English-speaking authors the term pedagogical situation is used incorrectly, the concept of pedagogical situation is mistaken for other pedagogical categories at the theoretical level, there is no unified approach to its definition. Thus the pedagogical situation analysis in Russian language pedagogical discourse is centred on the formulation of a pedagogical task, and in the English-speaking pedagogical tradition it is based on the triangle diagram that includes a teacher, a student and content. In the conducted cross-cultural and partly interdisciplinary study, a pedagogical situation is conventionally understood as a process of interaction of subjects of pedagogical activity (educational process) with a certain task in a certain place and time.
A Novel Sparrow Particle Swarm Algorithm (SPSA) for Unmanned Aerial Vehicle Path Planning

Unmanned aerial vehicle (UAV) has been widely used in various fields, and meeting practical high-quality flight paths is one of the crucial functions of UAV. Many algorithms have the problem of too fast convergence and premature in UAV path planning. This study proposed a sparrow particle swarm algorithm for UAV path planning, the SPSA. The algorithm selects a suitable model for path initialization, changes the discoverer position update, and reinforces the influence of start-end line on path search, which can significantly reduce blind search. The number of target points reached is increased by adaptive variable speed escapes in areas of deadlock. In this case, the planned trajectory will fluctuate, and adaptive oscillation optimization can effectively reduce the fluctuation of the path. Finally, the optimal path is simplified, and the path nodes are interpolated with cubic splines to improve the smoothness of the path, which improves the smoothness of the UAV flight trajectory and makes it more suitable for use as the UAV real flight trajectory. By comparison, it can be concluded that the improved SPSA has good convergence speed and better search results and can avoid local optimality.
MAGIC: Multimodal relAtional Graph adversarIal inferenCe for Diverse and Unpaired Text-Based Image Captioning

Text-based image captioning (TextCap) requires simultaneous comprehension of visual content and reading the text of images to generate a natural language description. Although a task can teach machines to understand the complex human environment further given that text is omnipresent in our daily surroundings, it poses additional challenges in normal captioning. A text-based image intuitively contains abundant and complex multimodal relational content, that is, image details can be described diversely from multiview rather than a single caption. Certainly, we can introduce additional paired training data to show the diversity of images' descriptions, this process is labor-intensive and time-consuming for TextCap pair annotations with extra texts. Based on the insight mentioned above, we investigate how to generate diverse captions that focus on different image parts using an unpaired training paradigm. We propose the Multimodal relAtional Graph adversarIal InferenCe (MAGIC) framework for diverse and unpaired TextCap. This framework can adaptively construct multiple multimodal relational graphs of images and model complex relationships among graphs to represent descriptive diversity. Moreover, a cascaded generative adversarial network is developed from modeled graphs to infer the unpaired caption generation in image–sentence feature alignment and linguistic coherence levels. We validate the effectiveness of MAGIC in generating diverse captions from different relational information items of an image. Experimental results show that MAGIC can generate very promising outcomes without using any image–caption training pairs.
Trash Detection on Water Channels

Rivers and canals flowing through cities are often used illegally for dumping trash that contaminates freshwater channels, causes blockage in sewerage leading to urban flooding. The dumped trash is often found floating on the water surface. We propose to automatically identify this trash through visual inspection with the eventual goal of quantification, an early warning system to avoid blockages and urban flooding. The trash could be disfigured, partially submerged, or clumped together with other objects which obscure its shape and appearance. Thus, we consider surface trash as a blob detection problem that could either be solved as object detection or image segmentation or both. To this extent, we evaluate and compare several deep-learning-based object detection and segmentation algorithms. Unlike ocean trash, to the best of our knowledge, there is no large dataset on urban trash on water channels. Thus, using IoT-based camera nodes at multiple water channels, we collected a large dataset containing 48, 450 trash objects annotated for both bounding box and segmentation (the dataset will be made publicly available (Dataset is available at https://cvlab.lums.edu.pk/watertrash/)). In addition, we also propose modifications in state-of-the-art detection and segmentation algorithms to cater to an issue such as partially submerged, varying object sizes, and edge-based computing.