Miracle-dz/UniFAR-FTUs huggingface dataset:
SUPPLIER DEVELOPMENT LITERATURE REVIEW AND

The purpose of this paper is to develop a Supplier Development (SD) literature framework and identify the main focus areas in SD research. To this end, a comprehensive review of the existing SD academic literature has been undertaken, which includes 62 research papers. These papers are classified according to their research content and the research methodology employed. A comprehensive list of future research areas is also presented. This review marks the beginning of a research project which aims at improving SD processes in practice in the manufacturing sector. Thus this paper will also briefly explore the proposed future research. The review of the SD presented here identifies the following main areas of focus: Supplier Development Activities, Practices and Success Factors; Direct or Indirect Supplier Development; Supplier Development as a Reactive or Strategic Process; Supplier Development in a Lean Six Sigma & SME context.
Voice Anonymization in Urban Sound Recordings

Monitoring health and noise pollution in urban environments often entails deploying acoustic sensor networks to passively collect data in public spaces. Although spaces are technically public, people in the environment may not fully realize the degree to which they may be recorded by the sensor network, which may be perceived as a violation of expected privacy. Therefore, we propose a method to anonymize and blur the voices of people recorded in public spaces–a novel, yet increasingly important task as acoustic sensing becomes ubiquitous in sensor-equipped smart cities. This method is analogous to Google’s face blurring in its Street View photographs, which arose from similar concerns in the visual domain. The proposed blurring method aims to anonymize voices by removing both the linguistic content and personal identity from voices, while preserving the rest of the acoustic scene.The method consists of a three-step process. First, voices are separated from non-voice content by a deep U-Net source separation model. Second, we evaluate two approaches to obscure the identity and intelligibility of the extracted voices: a low pass filter to remove most of the formants in the voices, and an inversion of Mel-Frequency Cepstral Coefficients (MFCC). Finally, the blurred vocal content is mixed with the separated non-vocal signal to reconstruct the acoustic scene. Using background recordings from a real urban acoustic sensor network in New York City, we present a complete evaluation of our method, with automatic speech recognition, speaker identification, sound event detection, and human perceptual evaluation.
Restoring public trust in scientific research by reducing conflicts of interest.

Clinician investigators and biomedical scientists share a fundamental obligation to produce new knowledge that can lead to better treatments or cures for human disease. Conflicts of interest (COIs) occur when professional responsibilities and altruistic goals are influenced by an outside interest that biases clinical judgement or interpretation of data. The public exposure of a failure to disclose COIs in clinical or translational research can lead to a loss of confidence in the medical profession and in scientific discovery, respectively. The Sunshine Act was seen as mitigating the lack of transparency in COI reporting by requiring the pharmaceutical and device industries to disclose all payments to physicians and teaching hospitals through the Open Payment program, a public and searchable database of industry payments. This database has certainly helped, but problems persist, as illustrated by front-page articles in the New York Times describing how undeclared COIs have roiled two major areas of medical research. The first New York Times reports detailed the failure of a top cancer researcher and senior administrator at Memorial Sloan Kettering Cancer Center to disclose over $3.0 million in compensation from industry (1). A follow-up report noted that this high level of remuneration was not unusual for influential physicians who serve on industry advisory boards (2). The second series of articles focused on a former Harvard physician-scientist and [. ] Viewpoint
Non-classical critical exponents at ideal Bose-Einstein condensation

We show that ideal Bose-Einstein condensation (BEC) in d = 3 dimensions is a non-classical critical second order phase transition with exponents α = −1, β = 1, γ = 1, δ = 2, η = 1 and ν = 1, obeying all the scaling equalities. These results are found with no approximations or assumptions. The previous exponents are a critical universality class on its own, different from the so-far accepted notion that BEC belongs to the Spherical Model universality class.
Controlled folding of 2 D materials : Grafin Printing

Graphene and other two-dimensional materials exhibit an overwhelming abundance of novel electronic and mechanical properties. [1, 2] In the last few years significant inroads have been made on shaping, patterning, doping and processing of these materials to create novel technological applications. However, such approaches have generally ignored the ability for flat 2D materials to fold and crease, while typically considered any such deformations to be unwanted. [3] This is in fact surprising, since the ability to fold graphene is actually one of the few properties that are entirely unique to 2D materials; a truly new avenue that 3D materials are in no way capable of following.
Collaborative replication approach for Mobile-p 2 p networks

This paper proposes CADRE (Collaborative Allocation and Deallocation of Replicas with Efficiency), which is a dynamic replication scheme for improving the typically low data availability in dedicated and cooperative mobile ad-hoc peer-to-peer (M-P2P) networks. In particular, replica allocation and deallocation are collaboratively performed in tandem to facilitate effective replication. Such collaboration is facilitated by a hybrid super-peer architecture in which some of the mobile hosts act as the ‘gateway nodes’ (GNs) in a given region. GNs facilitate both search and replication. The main contributions of CADRE are as follows. First, it facilitates the prevention of ‘thrashing’ conditions due to its collaborative replica allocation and deallocation mechanism. Second, it considers the replication of images at different resolutions to optimize the usage of the generally limited memory space of the mobile hosts (MHs). Third, it addresses fair replica allocation across the MHs. Fourth, it facilitates the optimization of the limited energy resources of MHs during replication. Our performance evaluation demonstrates that CADRE is indeed effective in improving data availability in M-P2P networks with significant reduction in query response times and low communication traffic during replication as compared to a recent existing scheme as well as a baseline approach, which does not consider any replication.
Fluorescence Molecular Tomography Based on Group Sparsity Priori for Morphological Reconstruction of Glioma

Objective: Fluorescence molecular tomography (FMT) is an important tool for life science, which can noninvasive real-time three-dimensional (3-D) visualization for fluorescence source location. FMT is widely used in tumor research due to its high-sensitive and low cost. However, the reconstruction of FMT is difficult. Although the reconstruction methods of FMT have developed rapidly in recent years, the morphological reconstruction of FMT is still a challenge problem. Thus, the purpose of this study is to realize the morphological reconstruction performance of FMT in glioma research. Methods: In this study, group sparsity was used as a new priori information for FMT. Besides sparsity, group sparsity also takes the group structure of the fluorescent sources, which can maintain the morphological information of the sources. Fused LASSO method (FLM) was proved it can efficiently model the group sparsity prior. Thus, we utilize FLM to reconstruct the morphological information of glioma. Furthermore, to reduce the influence of the high scattering of skull, we modified the FLM for improving the accuracy of morphological reconstruction. Results: Glioma numerical simulation model and in vivo glioma model were established to evaluate the performance of morphological reconstruction of the proposed method. The results demonstrated that the proposed method was efficient to reconstruct the morphological information of glioma. Conclusion: Group sparsity priori can effectively improve the morphological accuracy of FMT reconstruction. Significance: Group sparsity can maintain the morphological information of fluorescent sources effectively, which has great application potential in FMT. The group sparsity based methods can realize the morphological reconstruction, which is of great practical significance in tumor research.
Perceived Value and Satisfaction towards Shariah-Compliant Hotel

In Malaysia, some businesses have introduced Shariah-Compliant Hotel to cater to the needs of Muslim travellers. However, only a limited number of empirical researches have examined the perceived value of Muslim tourists vis a vis Shariah-Compliant Hotel in Malaysia. Delivering value is very important to hotel operators, as it has been proven as a competitive advantage and could influence customers in making decisions. Therefore, this study examined the interrelationship between the perceived value of Muslim tourists and the effect of this value in their satisfaction. Six hypotheses were developed and tested using a sample of 136 Muslim tourists who have experienced staying at Shariah-Compliant Hotel. Data was analysed using the Partial Least Square algorithm (PLS-SEM). The finding reveals six dimensions of Muslim tourist perceived value (quality, price, emotional value, social value, Islamic physical attribute’s value and Islamic non-physical attribute’s value) that have positive effects on their satisfaction. These findings are useful to stakeholders, especially hoteliers, to gain a better understanding of how to increase Muslim tourist satisfaction as well as develop effective marketing strategies for Shariah-Compliant Hotel.
Participants responses to a stroke training simulator

The primary goal of this research was to study a virtual environments (VE) possibility to influence empathy on caregiver personal. In the present explorative study, 9 subjects from Norrlands University Hospital (NUS) completed a test consistent of three everyday tasks, reading a newspaper, filling a glass of water and putting toothpaste on a toothbrush. The procedure was done twice first from a non-stroke perspective and secondly from a perspective of a patient with stroke handicaps. The VE looked like a normal apartment and could be experienced with or without different perceptual disorders of stroke. Data from interviews and observations was analyzed via methods inspired by Grounded Theory. Results from observations and interviews indicate that the simulator in spite of problems of usability were effective in influencing caregivers empathy.
Some hypothetical anatomical stages in the evolution of avian flight

it is obvious that Confuciusornis was neither terrestrial nor arboreal in the sense of clambering around in trees. The very long pointed wings and highly asymmetrical vanes of the remiges are those of an aerially adapted bird such as a tropicbird, tern, falcon, nightjar, or swallow. The two extremely long central rectrices of some individuals make sense only if used in aerial display, as are the long rectrices of tropicbirds and some nightjars, or in arboreal displays as in some birds-of-paradise. As Chiappe et al. note (p. 79), the presence of many individuals in a single layer over a small area of lake deposit suggests colonial (or at least flocking) behavior and a catastrophic dieoff, perhaps associated with volcanic activity. I would suggest hat the reason Confuciusornis is the most abundant bird in the deposit is because flocks of them were flying over the lake when disaster struck. There can be little doubt
Designing In-Headset Authoring Tools for Virtual Reality Video

Virtual Reality (VR) video is emerging as a new art form. Viewing VR video requires wearing the VR headset to fully experience the immersive surrounding of the content. However, the novel viewing experience of VR video creates new challenges and requirements for conventional video authoring tools, which were designed mainly for working with normal video on a desktop display. Designing effective authoring tools for VR video requires intuitive video interfaces specific to VR. This dissertation develops new workflows and systems that enable filmmakers to create and improve VR video while fully immersed in a VR headset. We introduce a series of authoring tools that enables filmmakers to work with video in VR: 1) Vremiere, an in-headset video editing application that enables editors to edit VR video entirely in the headset, 2) CollaVR, a networked system that enables multiple users to collaborate and review video together in VR, and 3) a set of techniques to assist filmmakers in managing and accessing interfaces in stereoscopic VR video without suffering depth conflicts. The design of these applications is grounded in existing practices and principles learned in interviews with VR professionals. A series of studies is conducted to evaluate these systems, which demonstrate the potential of in-headset video authoring.
Near-optimal coherent-state receivers and multi-copy quantum state discrimination

Coherent states of light, and methods for distinguishing between them, are central to all applications of laser light. We obtain the ultimate quantum limit on the error probability exponent for discriminating among any M multimode coherent-state waveforms via the quantum Chernoff exponent in M -ary multi-copy state discrimination. A receiver, i.e., a concrete realization of a quantum measurement, called the Sequential Waveform Nulling (SWN) receiver, is proposed for discriminating an arbitrary coherent-state ensemble using only auxiliary coherent-state fields, beam splitters, and non-number-resolving single photon detectors. An explicit error probability analysis of the SWN receiver is used to show that it achieves the quantum limit on the error probability exponent, which is shown to be a factor of four greater than the error probability exponent of an ideal heterodyne-detection receiver on the same ensemble. We generalize the philosophy of the SWN receiver, which is itself adapted from some existing coherent-state receivers, and propose a receiver – the Sequential Testing (ST) receiver– for discriminating n copies of M pure quantum states from an arbitrary Hilbert space. The ST receiver is shown to achieve the quantum Chernoff exponent in the limit of a large number of copies, and is remarkable in requiring only local operations and classical communication (LOCC) to do so. In particular, it performs adaptive copy-by-copy binary projective measurements. Apart from being of fundamental interest, these results are relevant to communication, sensing, and imaging systems that use laser light and to photonic implementations of quantum information processing protocols in general.
The role of arc migration in the development of the Lesser Antilles: A new tectonic model for the Cenozoic evolution of the eastern Caribbean

Continental arc systems often show evidence of large-scale migration both toward and away from the incoming plate. In oceanic arc systems, however, while slab roll-back and the associated processes of backarc spreading and arc migration toward the incoming plate are commonplace, arc migration away from the incoming plate is rarely observed. We present a new compilation of marine magnetic anomaly and seismic data in order to propose a new tectonic model for the eastern Caribbean region that includes arc migration in both directions. We synthesized new evidence to show two phases of backarc spreading and eastward arc migration toward the incoming Atlantic. A third and final phase of arc migration to the west subdivided the earlier backarc basin on either side of the present-day Lesser Antilles arc. This is the first example of regional multidirectional arc migration in an intra-oceanic setting, and it has implications for along-arc structural and geochemical variations. The back and forth arc migrations were probably due to the constraints imposed by the neighboring American plates on this isolated subduction system, rather than variations in subducting slab buoyancy.
Segmenting and Tracking Extreme Climate Events using Neural Networks

Predicting extreme weather events in a warming world is one of the most pressing and challenging problems that humanity faces today. Deep learning and advances in the ﬁeld of computer vision provide a novel and powerful set of tools to tackle this demanding task. However, unlike images employed in computer vision, climate datasets present unique challenges. The channels (or physical variables) in a climate dataset are manifold, and unlike pixel information in computer vision data, these channels have physical properties. We present preliminary work using a convolutional neural network and a recurrent neural network for tracking cyclonic storms. We also show how state-of-the-art segmentation algorithms can be used to segment atmospheric rivers and tropical cyclones in global climate model simulations. We show how the latest advances in machine learning and computer vision can provide solutions to important problems in weather and climate sciences, and we highlight unique challenges and limitations
Digital Business Engineering : Findings from the Install 4 Schenker case

The development of digital business models is a key field of research in the age of digitization. As companies embark on the journey to develop digital business models, the need for a methodology which supports their journey is imminent. This paper contributes to sharpening the methodological foundation of Digital Business Engineering by conducting a single case study exemplified by Install4Schenker. Install4Schenker is a digital service coordinating, controlling and validating external craftsmen with the goal of avoiding two-menhandling while delivering products requiring installation such as heaters. Based on this case study, this paper aims to identify the potential for further research, optimizations, and clarifications for the Digital Business Engineering methodology.
Sampling the GGX Distribution of Visible Normals

Importance sampling microfacet bidirectional scattering distribution functions (BSDFs) using their distribution of visible normals (VNDF) yields significant variance reduction in Monte Carlo rendering. In this article, we describe an efficient and exact sampling routine for the VNDF of the GGX microfacet distribution. This routine leverages the property that GGX is the distribution of normals of a truncated ellipsoid, and sampling the GGX VNDF is equivalent to sampling the 2D projection of this truncated ellipsoid. To do that, we simplify the problem by using the linear transformation that maps the truncated ellipsoid to a hemisphere. Since linear transformations preserve the uniformity of projected areas, sampling in the hemisphere configuration and transforming the samples back to the ellipsoid configuration yields valid samples from the GGX VNDF.
Molecular Marker Development for the Discrimination of Atlantic and Pacific Blue Marlin (Makaira nigricans)

viii Chapter I: An Introduction. 2 Fisheries forensics. 2 Blue marlin: species history and genetics. 5 Assignment tests.8 Objectives o f this study. 10 Chapter II: Microsatellite Loci Development.12 Introduction. 12 Materials and methods. 12 Microsatellite loci development. 12 Screening o f microsatellite loci.15 Results.17 Discussion. 18
ArSEL: A Large Scale Arabic Sentiment and Emotion Lexicon

With the advancement of Web 2.0, social networks experienced a great increase in the number of active users reaching 2 billion active users on Facebook at the end of 2017. Consequently, the size of text data on the Internet increased tremendously. This textual data is rich in knowledge, which attracted many data scientists as well as computational linguists to develop resources and models to automatically process the data and extract useful information. One major interest is sentiment and emotion classification from text. In fact, learning the opinion and emotions of people is important for businesses, marketers, government, politicians, etc. While focus had been given to sentiment analysis, recently emotion analysis has captured great interest as well. Several resources were developed for emotion analysis from text for English, however, very few targeted Arabic text. We present in this paper, ArSEL, the first large scale Arabic Sentiment and Emotion Lexicon. ArSEL is built in a way to augment the publicly available Arabic Sentiment Lexicon, ArSenL, and to generate a large scale lexicon that includes emotion and sentiment labels for almost every lemma in ArSenL. We also show the efficiency of using ArSEL in emotion regression and classification tasks using an Arabic translated version of annotated data from SemEval 2007 “Affective Task” as well as SemEval 2018 Task1 “Affect in Tweets” Arabic dataset. Coverages of 91% and 84% are achieved on the two datasets respectively. An improvement of 30% compared to majority baseline is achieved in terms of average F1 measure for emotion classification on SemEval 2018 Arabic dataset. ArSEL is publicly available on http://oma-project.com.
Lock 3 DFace : A Large-Scale Database of Low-Cost Kinect 3 D Faces

In this paper, we present a large-scale database consisting of low cost Kinect 3D face videos, namely Lock3DFace, for 3D face analysis, particularly for 3D Face Recognition (FR). To the best of our knowledge, Lock3DFace is currently the largest low cost 3D face database for public academic use. The 3D samples are highly noisy and contain a diversity of variations in expression, pose, occlusion, time lapse, and their corresponding texture and near infrared channels have changes in lighting condition and radiation intensity, allowing for evaluating FR methods in complex situations. Furthermore, based on Lock3DFace, we design the standard experimental protocol for low-cost 3D FR, and give the baseline performance of individual subsets belonging to different scenarios for fair comparison in the future.
Analysis of depression and anxiety in patients with post-stroke epilepsy

Objective: The present study aimed to assess the depression and anxiety syndromes in patients with post-stroke epilepsy and explore the association of these disorders with epilepsy-related variables. Methods: 86 patients with brain stroke were collected in this study, including 46 post-stroke epilepsy patients in case group and 40 patients without epilepsy seizure as control group. The general data of the patients were recorded. Moreover, Beck Depression Inventory (BDI) and State-Trait Anxiety Inventory (STAI) were used to measure the depression and anxiety state of the participators. What’s more, numerical variables between two groups were compared with Chi-square and Fisher. Logistic regression was performed to assess the multivariate analysis. Results: Patients with post-stroke epilepsy showed high scores of depression and anxiety, and compared with the control group the difference of the two group were statistically significant (P<0.05). In the epilepsy group, 54.3% had depression and 43.5% had anxiety. Besides, a relationship was found between high scores of anxiety and depression and occurrence of epilepsy. In addition, factors associated with depression and anxiety were age, and duration of disease. Logistic regression analysis showed that both depression and anxiety were significantly associated with epilepsy (P=0.003, and P=0.031, respectively). Conclusion: Depression and anxiety could be useful syndromes for post-stroke epilepsy, and could be used to distinguish from patients without epilepsy. High scores of anxiety and depression were associated with occurrence of epilepsy in post-stroke.