Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Review Text and External Knowledge for Explainable Recommendation

The current research of recommender system mainly focuses on improving the accuracy of the recommendation, but pays less attention to explainability. Explainability is essential to enhance users’ trust and satisfaction, which can even increase the likelihood of buying items. In the existing explainable recommender systems, the mining of explicit and implicit features is not comprehensive, and the interaction among these features is not considered plentifully. In addition, the generated recommended reason text is not personalized and content-rich enough. It is necessary to improve the quality of recommended reason text because it is difficult to meet the needs of different users by using low-quality text. In this paper, we propose a new method that fuses aspect sentiment from review text and external knowledge to predict rating and generate personalized, content-rich recommended reasons text, which applies fine-tuning BERT to solve aspect-based sentiment analysis and extends Transformer to generate recommended reason text. The experiment results on real-world datasets demonstrated that our method was effective, and our model was superior to the baseline models on various metrics. For rating prediction task, our model can achieve an improvement of 0.6% on average in terms of RMSE. For recommended reason generation task, our model can achieve an improvement of 9.2% to 11.3% over state-of-the-arts in terms of BLEU.
PREDICTING EMG ENVELOPES OF GRASPING MOVEMENTS FROM EEG RECORDINGS USING UNSCENTED KALMAN FILTERING

Electromyographic (EMG) control of prosthetics is well established both in research and cl inical settings. However, it remains unclear how much of the EMG information can be predicted from the electroencephalographic (EEG) signals, and used instead, for control. In this study, we used a dataset that contains simultaneously acquired EEG and EMG signals of 31 subjects performing 33 grasping conditions, and applied unscented Kalman filtering (UKF) to continuously predict the EMG grasping envelopes from the low-frequency (0.1-2 Hz) EEG. We achieved higher prediction accuracy for intermediate grasps compared to power or precision grasps. Our findings indicate the feasibility of continuously predicting EMG envelopes of grasping movements from EEG signals. Keywords EEG, EMG, grasping, UKF
Reliable Uncertainty Quantification of Deep Learning Models for a Free Electron Laser Scientific Facility

Particle accelerators are essential instruments for scientific experiments. They provide different experiments with particle beams of different parameters (e.g. beam energies or durations). This is accomplished by changing a wide variety of controllable settings, in a process called tuning. This is a challenging task, as many particle accelerators are complex machines with thousands of components, each of which contribute sources of uncertainty. Fast, accurate models of these systems could aid rapid customization of beams, but in order to accomplish this reliably, quantified uncertainties are essential. We address the problem of obtaining reliable uncertainties from learned models of a noisy, high-dimensional, nonlinear accelerator system: the X-ray free electron laser at the Linac Coherent Light Source, which is a scientific user facility. We examine the efficacy of Bayesian Neural Networks (BNNs) to reliably quantify predictive uncertainty and compare these with Quantile Regression Neural Networks (QRNNs). The QRNN models provide mean absolute error on predictions that are consistent with the noise of the measured data. We find the BNN is sensitive to outliers and is substantially more computationally expensive, but it still captures the general trend of the target data.
Image Captioning Using R-CNN & LSTM Deep Learning Model

:- Image Captioning is the process of creating a text description of an image. It uses both Natural Language Processing (NLP) and Computer Vision to generate the captions. The image captioning task is done by combining the detection process when the descriptions consist of a single word like cat, skateboard, etc. and Image Captioning when one predicted region covers the full image, for example cat riding a skateboard. To address the localization and description task together, we propose a Fully Convolution Localization Network that processes a picture with a single forward pass which can be consistently trained in a single round of optimization. To process an image, first the input image is processed using CNN. Then Localization Layer proposes regions and includes a region detection network adopted from faster R-CNN and captioning network. The model directly combines the faster R-CNN framework for region detection and long short-term memory (LSTM) for captioning.
THE STUDY OF CUSTOMER SATISFACTION AMONG GRAB USERS IN KUALA LUMPUR, MALAYSIA

The successful emergence of sharing economy has received so many attentions from both organizations and customers. This recent model of business is said by many to come in the nature of disruptive, especially within the tourism industry. From small to big and popular hotels suffer loss ever since its appearance. Moreover, the sudden boom of on-demand ride hailing services such as Grab becomes a threat to many traditional taxi companies that are believed to have operated and been successful for years before the footprints of Grab is vividly seen. It turned out that the culprit behind the success of this sharing economy business model is the utilization of technology to powerfully connect providers and customers seamlessly. Especially, when the scale of internet penetration and technology development is crawling in a lightning speed. Also, not only that, many claimed sharing economy to be more affordable, convenient and sustainable to the environment. These three just pack the right combo to succeed in the market in no time. As interesting as it is, this study will research the three prominent factors in sharing economy including economic, social and environmental factor to see if they influence customers satisfaction or if the increase of customers in sharing economy is just a form of customers’ curiosity towards a brand innovation.
BCTT_A_274514 1..13

1Department of Medical Oncology, DanaFarber Cancer Institute, Boston, MA, 02215, USA; 2Department of Hospice and Palliative Medicine, Montefiore Medical Center/ Albert Einstein College of Medicine, Bronx, NY, USA Abstract: It is estimated that approximately one-third of patients with triple-negative breast cancer (TNBC) will develop brain metastases. The prognosis for patients with breast cancer brain metastasis has improved in the recent past, especially for hormone receptor and human epidermal growth factor receptor 2 (HER) positive subtypes. However, the overall survival rate for patients with triple-negative subtype remains poor. The development of newer treatment options, including antibody-drug conjugates such as Sacituzumab govitecan, is particularly encouraging. This article reviews the clinical outcomes, challenges, and current approach to the treatment of brain metastasis in TNBC. We have also briefly discussed newer treatment options and ongoing clinical trials. The development of brain metastasis significantly decreases the quality of life of patients with TNBC, and newer treatment strategies and therapeutics are the need of the hour for this disease subgroup.
A literature survey on the Use of Convolutional Neural Networks in Agriculture

: Deep learning constitutes a modern technique for image processing with large potential. As it has been successfully applied in various areas, it has recently entered also the domain of agriculture. In this paper, we have conducted a survey of research efforts that employ convolutional neural networks (CNN), which constitute a specific class of deep learning, applied to various agricultural and food production challenges. We examine the agricultural problems under study, models employed, sources of data used and the overall precision achieved according to the performance metrics used by the authors. We compare CNN with other existing techniques, and we list advantages and disadvantages of using CNN in agriculture. Moreover, we discuss the future potential of this technique, and we describe our personal experiences after employing CNN to approximate a problem of identifying missing vegetation from a sugar cane plantation in Costa Rica. Our overall findings indicate that CNN constitute a promising technique with high performance in terms of precision and classification accuracy, outperforming existing commonly-used image processing techniques. However, the success of each CNN model is highly dependent on the quality of the dataset used for training.
Reliable circular disk pole placement with sensor failures

The reliable design problem of disk pole placement with sensor failures for linear systems is discussed. A more practical and general model of sensor failures is presented. A sufficient condition of pole placement in circular disk is given. A state feedback controller is designed by solving LMIs. A simulation example shows the efficiency and necessity of the design method.
Augmented Reality (AR) Generated Value-in-Use Experiences: Literature Review and Analysis

Augmented Reality (AR) research has sparked both academia and practice interest. However, despite the increasing interest to study this emergent topic, till now the growing body of knowledge is mainly from engineering and computer science disciplines. The aim of this study is to conduct a systematic literature review to identify, evaluate and synthesize (AR) emergent themes from business management publications in the last years. Hence, contribute to the literature with evidence-based results about the existing research progress, and classify into clusters with multidimensional interrelationships to help researchers build up on these find - ings and expand (AR) research from business standpoint. The study is exploratory and qualitative in nature; a structured hybrid-narrative review method is used to extract unstructured data within articles. Thus, discover relevant patterns, uncover hidden thematic clusters, relationships, and identify multidimensionality of dominant concepts from the existing (AR) business literature. It was evident from the stream of research, that most of researchers’ have a clear interest in exploring the interactive nature of Augmented Reality (AR) as new digital technology and defining its experiential characteristics with a hierarchical set of dimensions that will affect customers’ perceptions of service operations. Hence, in line with these findings, and grounded on the service literature body of knowledge, Augmented Reality can be defined as: “smart service based technology that generate value-in-use experiences”.
Preferential Begging Responses of Shiny Cowbirds to the Conspecific Chatter Call

Avian brood parasites lay their eggs into the nests of other species, which incubate them and raise the chicks until their independence. Despite living their early weeks of life surrounded by heterospecifics, young brood parasites have the ability to recognize and associate to conspecifics after independence. It has been proposed that the initial conspecific recognition develops when a young parasite encounters a unique species-specific signal that triggers the learning of other aspects of the producer of the signal. For cowbirds (Molothrus spp.), this species-specific signal is hypothesized to be the chatter call. Young birds also could express auditory biases, which in some cases lead to discrimination in favor of conspecific songs. Therefore, the perceptual selectivity for chatters might be also present in nestlings. Our aim was to assess if nestlings of the shiny cowbird (M. bonariensis) present a preferential begging response to conspecific chatter calls. We evaluated if they respond more to the parasitic vocalization than host chicks and if they respond more to the chatter than to heterospecific nonhost calls. We tested shiny cowbird chicks reared by chalk-browed mockingbirds (Mimus saturninus) or house wrens (Troglodytes aedon) and host chicks, as control species. We randomly presented to 6-day-old chicks the following playback treatments: (1) conspecific chatter calls, (2) host calls, used as positive controls, and (3) nonhost calls, used as negative control. We measured if chicks begged during the playback treatments and the begging intensity. When responding to the playback of chatter calls, shiny cowbird chicks begged at a higher frequency and more intensively than host chicks. Shiny cowbird chicks reared by mockingbirds begged more intensively to playbacks of conspecific chatter calls than to host calls, while those reared by wrens begged with a similar intensity to playbacks of conspecific chatter and host calls. On the contrary, wren nestlings begged more intensively to playbacks of the wren call than to chatter calls. Mockingbird nestlings did not beg during any treatment. None of the three species begged during the playback of nonhost calls. Our results show that the chatter call produced a preferential begging response in cowbird nestlings, which may be the beginning of a process of conspecific recognition.
(How) Should I quantify my employees? An assessment of the risks and potentials of eHealth technologies for occupational health management

Employee health is increasingly important, as is the use of eHealth technologies in private and the organizational context. This paper examines which existing eHealth technologies are applied in occupational health management (OHM) and investigates the advantages and disadvantages of their application. To pursue this intention, we analyze the current state of research with a structured literature review and provide a comprehensive overview of relevant works in a concept matrix. The results show that numerous technologies such as smartphone applications or activity trackers are already being used in OHM. Thereby, positive outcomes such as an increase in employee health can be achieved, but it can also lead to negative outcomes such as a reduced work-life balance. Furthermore, the impact of the application depends on various mediators such as the work area (blueand white-collar work) or leadership support.
THIS DOCUMENT IS PART OF A NEW BOOK, IT IS BROUGHT TO YOU FREELY BY THE OPEN ACCESS JOURNAL MATERIALS AND DEVICES The book, “PEROVSKITES AND OTHER FRAMEWORK STRUCTURE MATERIALS (2D-perovskites, Aurivillius, Ruddlesden-Popper, Dion-

This article reviews Framework Structures (FWSs), defined as crystalline materials built of rigid AXn polyhedra sharing vertices (like perovskites, tungsten bronzes, Dion-Jacobson, Ruddlesden-Popper, and Aurivillius phases, quartz, silicates, and others), and their pecularities resulting from this linkage. The situation of rigid units linked by common vertices may allow the units to accomplish concordant rotations without deformation, which gives rise to soft phonon modes called “Rigid Unit Modes” (RUMs). The condensation of a RUM can trigger structural phase transitions to a structure of lower symmetry, with tilted polyhedra, at the origin of spontaneous ferroic or multiferroic properties. We overview results precedently obtained on RUMs in perovskites, tetragonal tungsten bronzes, and quartz, and detail new results on “maximally localized RUMs” (MLRUMs), a fundamental new concept in the physics of RUMs. We introduce also the related new concept of “skeletions” that allows to generate all ferroelastic phases found in these systems.
Corporate social responsibility and agency cost: Evidence from the UK retail industry

This dissertation aims to evaluate corporate social responsibility (CSR) impacts on the UK listed retailers' agency costs in 2016-2020 and is broken down into three main objectives – (1) describe the UK listed retailers' CSR performance level, (2) test impacts of CSR on agency costs of these firms, and (3) test the moderating role of industry for the CSR-agency costs relationship. By the application of the regressions with fixed effects and robust standard error type, this study has revealed that in this period, the UK listed retailers' CSR performance is at the moderate level on average. However, there is a large dispersion in CSR performance among retailers. CSR of the UK listed retailers significantly reduced their asset turnover (RTA), but it insignificantly affected selling and general administrative expenses (SGAE) ratio. This study finds no moderating effects of firm size for UK-listed retailers' CSR agency costs relationship. Finally, this study offers several implications associated with CSR investment and corporate governance mechanisms to shareholders and management of the UK-listed retailers and
Europan Molecular Indicators of Life Investigation (EMILI) for a Future Europa Lander Mission

The Europan Molecular Indicators of Life Investigation (EMILI) is an instrument concept being developed for the Europa Lander mission currently under study. EMILI will meet and exceed the scientific and technical/resource requirements of the organic composition analyzer identified as a core instrument on the Lander. EMILI tightly couples two complementary analytical techniques, based on 1) liquid extraction and processing with capillary electrophoresis and 2) thermal and chemical extraction with gas chromatography, to robustly detect, structurally characterize, and quantify the broadest range of organics and other Europan chemicals over widely-varying concentrations. Dual processing and analysis paths enable EMILI to perform a thorough characterization of potential molecular biosignatures and contextual compounds in collected surface samples. Here we present a summary of the requirements, design, and development status of EMILI with projected scientific opportunities on the Europa Lander as well as on other potential life detection missions seeking potential molecular biosignatures in situ.
DeepGen Network based Voice Conversion

A DeepGen network is proposed for voice conversion, which is the process of modification of a speech utterance by a source or - ator to that of a target orator, preserving the linguistic contents. Automatic dubbing is an application of speech processing which facilitates the modelling of the large variability of pitch. The proposed DeepGen network automizes voice conversion by taking blocks of consecutive frame-wise linguistic and fundamental frequency features. This block-wise approach models temporal dependencies within the features of the input block. Voice conversion, which is also called as voice cloning has always required significant amount of recorded speech but the proposed DeepGen network intends to convert one voice to another using a rela - tively smaller set of speech samples by bootstrapping samples from a larger speech dataset. The proposed generative model is a variant of the convolutional generative model with encoder and decoder blocks that bring into line the hidden structures of the feature spaces from the source and target voices on a two-stage training process. An attractive and efficient voice conversion is thus obtained in the real-world scenario using the proposed DeepGen network. The novelty of the work lies in the induction of a deep learning generative network for cloning voice in Tamil language.
Anti-SARS-CoV-2 IgG against the S Protein: A Comparison of BNT162b2, mRNA-1273, ChAdOx1 nCoV-2019 and Ad26.COV2.S Vaccines

Background: COVID-19 vaccines induce a differentiated humoral and cellular response, and one of the comparable parameters of the vaccine response is the determination of IgG antibodies. Materials and Methods: Concentrations of IgG anti-SARS-CoV-2 antibodies were analyzed at three time points (at the beginning of May, at the end of June and at the end of September). Serum samples were obtained from 954 employees of the Nicolaus Copernicus University in Toruń (a total of three samples each were obtained from 511 vaccinated participants). IgG antibody concentrations were determined by enzyme immunoassay. The statistical analysis included comparisons between vaccines, between convalescents and COVID-19 non-patients, between individual measurements and included the gender, age and blood groups of participants. Results: There were significant differences in antibody levels between mRNA and vector vaccines. People vaccinated with mRNA-1273 achieved the highest levels of antibodies, regardless of the time since full vaccination. People vaccinated with ChAdOx1 nCoV-2019 produced several times lower antibody levels compared to the mRNA vaccines, while the antibody levels were more stable. In the case of each of the vaccines, the factor having the strongest impact on the level and stability of the IgG antibody titers was previous SARS-CoV-2 infection. There were no significant correlations with age, gender and blood type. Summary: mRNA vaccines induce a stronger humoral response of the immune system with the fastest loss of antibodies over time.
Augmented Humanity: A Systematic Mapping Review

Augmented humanity (AH) is a term that has been mentioned in several research papers. However, these papers differ in their definitions of AH. The number of publications dealing with the topic of AH is represented by a growing number of publications that increase over time, being high impact factor scientific contributions. However, this terminology is used without being formally defined. The aim of this paper is to carry out a systematic mapping review of the different existing definitions of AH and its possible application areas. Publications from 2009 to 2020 were searched in Scopus, IEEE and ACM databases, using search terms “augmented human”, ”human augmentation” and “human 2.0”. Of the 16,914 initially obtained publications, a final number of 133 was finally selected. The mapping results show a growing focus on works based on AH, with computer vision being the index term with the highest number of published articles. Other index terms are wearable computing, augmented reality, human–robot interaction, smart devices and mixed reality. In the different domains where AH is present, there are works in computer science, engineering, robotics, automation and control systems and telecommunications. This review demonstrates that it is necessary to formalize the definition of AH and also the areas of work with greater openness to the use of such concept. This is why the following definition is proposed: “Augmented humanity is a human–computer integration technology that proposes to improve capacity and productivity by changing or increasing the normal ranges of human function through the restoration or extension of human physical, intellectual and social capabilities”.