Miracle-dz/UniFAR-FTUs huggingface dataset:
Word Embeddings in place of Dictionary Lookup in the context of Academic Writing

Researchers who are non-native speakers of English always face some problems to compose a scientific article in this language. Most of the time, it is due to the lack of vocabulary or knowledge of alternate ways of expression. They can use machine translation systems to translate from their mother tongues to English when writing English articles. However, sometimes the translation output is not correct, or does not comply with the academic writing style. Some researchers may also use a bilingual dictionary or a thesaurus to search for suitable words. Word embeddings have been applied to many natural language processing tasks such as information retrieval, sentiment analysis, question answering and document classification. In this paper, we propose to use word embedding models as an alternative to dictionary lexicon or term bank look-up. As opposed to a thesaurus, which usually provides only semantically similar words or expressions, a word embedding model may not only show semantically similar words but also other words that have similar word vectors. Hopefully, a word embedding model trained on a collection of academic articles should comply with the academic writing style and contain terms which are similar in the domain considered. Such dictionary lookup can be of help to non-native speakers of English to search for vocabularies that are suitable for writing an article in style and in lexicon. For example, a lower level proficiency person may know the easy word “but”, but word vectors may propose “however” or “although” as alternative expressions. Following a trend in natural language processing (NLP), we first apply our methods to the NLP research field, i.e., we apply NLP methods on NLP data. This has been called NLP4NLP [2]. We use the ACL Anthology Reference Corpus (ACL-ARC hereafter) as our NLP domain corpus. ACL Anthology is a digital archive of research papers in the premium conferences in NLP and the English language quality of the papers is reputed. The goal of this paper is to run a preliminary experiment. We will use
Identification of a General Odorant Receptor for Repellents in the Asian Corn Borer Ostrinia furnacalis

Attractants and repellents are considered to be an environment-friendly approach for pest management. Odorant receptors (ORs), which are located on the dendritic membranes of olfactory sensory neurons in insects, are essential genes for recognizing attractants and repellents. In the Asian corn borer, Ostrinia furnacalis, ORs that respond to sex pheromones have been characterized, but general ORs for plant odorants, especially for repellents, have not been identified. Nonanal is a plant volatile of maize that could result in avoidance of the oviposition process for female adults in O. furnacalis. In this study, we identified a female-biased OR that responds to nonanal using a Xenopus oocyte expression system. In addition, we found that OfurOR27 was also sensitive to two other compounds, octanal and 1-octanol. Behavioral analysis showed that octanal and 1-octanol also caused female avoidance of oviposition. Our results indicated that OfurOR27 is an OR that is sensitive to repellents. Moreover, the two newly identified repellents may help to develop a chemical ecology approach for pest control in O. furnacalis.
Reconstruction method of gradient-index field with background-oriented schlieren

The refractive index fields of several media, say, transparent solids, transparent liquids, and air are often crucial when using these media as optical elements, probing optical properties, and deriving other physical information related to the refractive index. For example, in an optical system using a high-power laser, the thermal lens effect of optical elements is one of great issues because the laser focal position will be changed by the effect [1]. Thus, a method to measure a gradient-index of the optical element should be useful to take a step to reduce the thermal lens effect. Basically, light rays passing through a medium with an inhomogeneous refractive index field will be deflected toward the area of higher refractive index. This way, it should be possible to obtain information about the refractive index field from measurement of such deflection. Consequently, there have been several methods for visualizing gradient-index fields, including schlieren photography [2] and shadowgraphy [3]. Although these visualization methods are powerful tools for qualitatively analyzing the gradient-index field, quantitative analysis requires additional methods. Rainbow schlieren photography is one candidate. In this method, the strength of deflection of light rays is obtained by using a rainbow aperture [4-6]. The quantitative phase-contrast imaging has also developed with incoherent-light [7, 8], coherent-light [9, 10], and asymmetric illumination [11]. There is also background-oriented schlieren (BOS) technique for measuring gradient-index field quantitatively [12-20]. In the BOS technique, displacements of dot-patterns that are printed on a background are caused by deflection of light rays passing through a gradient-index field. The optical flow algorithm can be used to obtain the displacements of the background-dot-patterns. However, obtaining depth information of the gradient-index field with the BOS technique is often difficult because the dot-pattern displacement results from the light ray deflected all along its path, requiring integration over the path. To address this, a method of reconstructing the depth information of a gradient-index field is proposed here on the basis of the Lagrangian optics with the BOS technique.
Automated Gleason grading of prostate cancers via deep learning in label-free multiphoton microscopic images

In the current clinical care, Gleason grading system based on the architectural pattern of cancerous epithelium in histological images is the most powerful prognostic predictor for prostate cancers (PCa). However, the standard procedure of histological examination often includes complicated tissue fixation and staining, which are time-consuming and may delay the diagnosis and surgery. In this study, the unstained prostate tissues were investigated with multiphoton microscopy (MPM) to produce subcellular-resolution images. And then, a deep learning network (AlexNet) was introduced for automated Gleason grading. We achieved an average accuracy of Gleason grading of 78.1%±3.4% for classification. And the area under the curve (AUC) in test set achieves 0.943 which indicates that the proposed model is effective in Gleason grading. At the end, the heat map was performed to visualize the Gleason score of tumour. Our results suggested that MPM, combined with deep learning method, holds the potential to be used as a real-time clinical diagnostic tool for PCa diagnosis.
FlexTensor: An Automatic Schedule Exploration and Optimization Framework for Tensor Computation on Heterogeneous System

Tensor computation plays a paramount role in a broad range of domains, including machine learning, data analytics, and scientific computing. The wide adoption of tensor computation and its huge computation cost has led to high demand for flexible, portable, and high-performance library implementation on heterogeneous hardware accelerators such as GPUs and FPGAs. However, the current tensor library implementation mainly requires programmers to manually design low-level implementation and optimize from the algorithm, architecture, and compilation perspectives. Such a manual development process often takes months or even years, which falls far behind the rapid evolution of the application algorithms. In this paper, we introduce FlexTensor, which is a schedule exploration and optimization framework for tensor computation on heterogeneous systems. FlexTensor can optimize tensor computation programs without human interference, allowing programmers to only work on high-level programming abstraction without considering the hardware platform details. FlexTensor systematically explores the optimization design spaces that are composed of many different schedules for different hardware. Then, FlexTensor combines different exploration techniques, including heuristic method and machine learning method to find the optimized schedule configuration. Finally, based on the results of exploration, customized schedules are automatically generated for different hardware. In the experiments, we test 12 different kinds of tensor computations with totally hundreds of test cases and FlexTensor achieves average 1.83x performance speedup on NVIDIA V100 GPU compared to cuDNN; 1.72x performance speedup on Intel Xeon CPU compared to MKL-DNN for 2D convolution; 1.5x performance speedup on Xilinx VU9P FPGA compared to OpenCL baselines; 2.21x speedup on NVIDIA V100 GPU compared to the state-of-the-art.
DeepSniffer: A DNN Model Extraction Framework Based on Learning Architectural Hints

As deep neural networks (DNNs) continue their reach into a wide range of application domains, the neural network architecture of DNN models becomes an increasingly sensitive subject, due to either intellectual property protection or risks of adversarial attacks. Previous studies explore to leverage architecture-level events disposed in hardware platforms to extract the model architecture information. They pose the following limitations: requiring a priori knowledge of victim models, lacking in robustness and generality, or obtaining incomplete information of the victim model architecture. Our paper proposes DeepSniffer, a learning-based model extraction framework to obtain the complete model architecture information without any prior knowledge of the victim model. It is robust to architectural and system noises introduced by the complex memory hierarchy and diverse run-time system optimizations. The basic idea of DeepSniffer is to learn the relation between extracted architectural hints (e.g., volumes of memory reads/writes obtained by side-channel or bus snooping attacks) and model internal architectures. Taking GPU platforms as a show case, DeepSniffer conducts model extraction by learning both the architecture-level execution features of kernels and the inter-layer temporal association information introduced by the common practice of DNN design. We demonstrate that DeepSniffer works experimentally in the context of an off-the-shelf Nvidia GPU platform running a variety of DNN models. The extracted models are directly helpful to the attempting of crafting adversarial inputs. Our experimental results show that DeepSniffer achieves a high accuracy of model extraction and thus improves the adversarial attack success rate from 14.6%$\sim$25.5% (without network architecture knowledge) to 75.9% (with extracted network architecture). The DeepSniffer project has been released in Github.
Learning with background music: a field experiment

Empirical evidence of how background music benefits or hinders learning becomes the crux of optimizing music recommendation in educational settings. This study aims to further probe the underlying mechanism through an experiment in naturalistic setting. 30 participants were recruited to join a field experiment which was conducted in their own study places for one week. During the experiment, participants were asked to conduct learning sessions with music in the background and collect music tracks they deemed suitable for learning using a novel mobile-based music discovery application. A set of participant-related, context-related, and music-related data were collected via a pre-experiment questionnaire, surveys popped up in the music app, and the logging system of the music app. Preliminary results reveal correlations between certain music characteristics and learners' task engagement and perceived task performance. This study is expected to provide evidence for understanding cognitive and emotional dimensions of background music during learning, as well as implications for the role of personalization in the selection of background music for facilitating learning.
Factors associated with nutrition information label use among undergraduate students in Chulalongkorn University

Background: Teenage students tend to consume more finished or semi-finished food in their daily life because of their time constraints. Nutrition information label plays its important role for selecting healthy semi-finished or finished food products. Conversely, teens do not pay much attention to the information on a label. Methods: The objective of this study was to assess influence factors relate to behaviors of reading nutrition information label among undergraduate students. Four hundred and thirty two students were selected by proportion to size and simple random sampling technique. Self-administered questionnaires were distributed to ascertain their knowledge and attitude on nutrition information label including their reading behavior. Results: This study revealed that only 18.3% and 4.4% of them had good knowledge and attitude on nutrition information label, respectively. More than 90% of students had poor reading behavior. The study also found that the level of students’ attitude on nutrition information label associated positively with their reading behavior ( p-value < 0.01). In addition, their age was negatively correlated with score of nutrition information reading behavior (r= -0.95, p-value < 0.05). Conclusion: Findings of this study suggested that university should encourage students to increasing attention to nutrition information label before consuming food products.
COIN Attacks: On Insecurity of Enclave Untrusted Interfaces in SGX

Intel SGX is a hardware-based trusted execution environment (TEE), which enables an application to compute on confidential data in a secure enclave. SGX assumes a powerful threat model, in which only the CPU itself is trusted; anything else is untrusted, including the memory, firmware, system software, etc. An enclave interacts with its host application through an exposed, enclave-specific, (usually) bi-directional interface. This interface is the main attack surface of the enclave. The attacker can invoke the interface in any order and inputs. It is thus imperative to secure it through careful design and defensive programming. In this work, we systematically analyze the attack models against the enclave untrusted interfaces and summarized them into the COIN attacks -- Concurrent, Order, Inputs, and Nested. Together, these four models allow the attacker to invoke the enclave interface in any order with arbitrary inputs, including from multiple threads. We then build an extensible framework to test an enclave in the presence of COIN attacks with instruction emulation and concolic execution. We evaluated ten popular open-source SGX projects using eight vulnerability detection policies that cover information leaks, control-flow hijackings, and memory vulnerabilities. We found 52 vulnerabilities. In one case, we discovered an information leak that could reliably dump the entire enclave memory by manipulating the inputs. Our evaluation highlights the necessity of extensively testing an enclave before its deployment.
Dynamic phase retrieval for fringe image processing in moiré tomography

The measurement and visualization of transient three-dimensional (3-D) physical parameters (density and temperature) distribution of complex flow fields are critical technologies for the characteristics studies of flow fields in modern energy engineering. Among the optical computed tomography (OCT) methods, Moiré tomography has the advantages of simple optical path structure, strong anti-interference ability and wide measurement range, which is especially suitable for complex flow field measurement in noisy environments. Acquiring the transient phase information from the moiré projection is of great importance for the dynamic 3-D parameters reconstruction of complex flow fields. In this paper, the dynamic phase retrieve methods including Fourier and spatial phase-shifting in moiré tomographic are studied, respectively. In the Fourier method, an adaptive first-order spectrum extraction algorithm for Fourier transform moiré fringe and a phase calculation method are proposed. Through this, the projection phase can be obtained directly by multiplying the inverse Fourier transform of the positive first-order spectrum of deformed fringe with the inverse Fourier transform of the negative first-order spectrum of reference fringe. In spatial phase-shifting method, a spatial phase-shifting- interferometry-based moiré volume computed tomography (MVCT) method was proposed to extract the radial shearing phase distribution of grid moiré fringe. The measured results for the first-order partial derivative of the phase projection of a propane flame both by Fourier and spatial phase-shifting methods in the experimental moiré computed tomography systems are presented. The research will be valuable for monitoring the combustion state in energy engineering.
Characterization and compensation of wavelength dependent aberrations in hyperspectral quantitative phase imaging with digital holographic microscopy

Multispectral quantitative phase imaging (QPI) with digital holographic microscopy (DHM) has been demonstrated to be a versatile tool to access wavelength dependent optical properties of technical and biological specimens as well as to reduce coherence induced image disturbances. However, when hyperspectral DHM is combined with microscopes and tunable lasers with a large spectral range, chromatic aberrations of the optical imaging system have to be considered. We have analyzed wavelength dependent defocusing effects and changes of the optical magnification in the related refocused holographic images in lens-based multi-wavelength DHM and show that both effects can be efficiently numerically compensated.
Impact of sociodemographic characteristics, nutritional status and perceived stress in body weight concern of female college students

Aim: to estimate the contribution of nutritional status, perceived stress and sociodemographic variables in college students’ body weight concern. Method: participated 850 Brazilian students of one public university. Information such as age, area of study and schedule of the undergraduate course, body weight and height, economic status, household’s education level, household, studies funding and medication intake due to studies and labour activity were collected. Perceived stress and body weight concern were assessed, respectively, with the Portuguese versions of the Perceived Stress Scale (PSS) and the Weight Concerns Scale (WCS). The impact of several variables on body weight concern was verified by a causal model developed using Structural Equation Modelling. The goodness-of-fit of the model was evaluated using chi-square ratio index and degrees of freedom (χ/df), Comparative Fit Index (CFI), Normed of Fit Index (NFI), and Root Mean Square Error of Approximation (RMSEA). The contribution (β) and significance of causal pathways were evaluated using the z-test critical ratios (p < .05). Results: a significant contribution of the variables perceived stress, economic status and Corresponding author: Juliana Alvares Duarte Bonini Campos, Department of Food and Nutrition, Universidade Estadual Paulista – Unesp, Rodovia Araraquara Jau, km 1, 14807-902, Araraquara, São Paulo, Brazil. E-mail: jucampos@fcfar.unesp. br. Impact of Sociodemographic Characteristics, Nutritional Status and Perceived Stress in Body Weight Concern of Female College Students 325 nutritional status in college students’ body weight concern was verified. The fit of the final model was adequate (χ2/df = 4.38; CFI = .91; GFI = .93; RMSEA = .06) and explained 19% of the variability in body weight concern with a positive impact of nutritional status, perceived stress and economic status. Conclusion: the results should serve as an alert to researchers in this area of study and health professionals, highlighting the importance of performing the diagnosis of such manifestations and/or propose prevention strategies.
Perceptions and expectations about learning analytics from a brazilian higher education institution

Several tools to support learning processes based on educational data have emerged from research on Learning Analytics (LA) in the last few years. These tools aim to support students and instructors in daily activities, and academic managers in making institutional decisions. Although the adoption of LA tools is spreading, the field still needs to deepen the understanding of the contexts where learning takes place, and of the views of the stakeholders involved in implementing and using these tools. In this sense, the SHEILA framework proposes a set of instruments to perform a detailed analysis of the expectations and needs of different stakeholders in higher education institutions, regarding the adoption of LA. Moreover, there is a lacuna in research on stakeholders' expectations from LA outside the Global North. Therefore, this paper reports on the findings of the application of interviews and focus groups, based on the SHEILA framework, with students and teaching staff from a Brazilian public university, to investigate their perceptions of the potential benefits and risks of using LA in higher education in the country. Findings indicate that there is a high interest in using LA for improving the learning experience, in particular, being able to provide personalized feedback, to adapt teaching practices to students' needs, and to make evidence-based pedagogical decisions. From the analysis of these perspectives, we point to opportunities for using LA in Brazilian higher education.
Mojo vision nanoLEDs for invisible computing

Existing mobile and AR/VR headsets tether us to a display that creates a physical barrier to personal interactions. Next generation AR/VR solutions will need to fundamentally alter the way people receive and share information, a concept Mojo Vision calls “Invisible Computing”. NanoLEDs, a further shrinking of MicroLEDs to micron and sub-micron scale is expected to play a critical role in the future of Invisible Computing because of their ability to create the pixel densities required to get closer to the human eye while maintaining full visual acuity of the human visual system. Mojo Vision set the record for dynamic display NanoLED ppi at 14K (monochrome green) in May of 2019. A new record 20Kppi dynamic NanoLED display along with its application in Augmented Reality will be presented.
Volumetric technology: enabling near-work compatible AR displays

In this work we investigate design parameters of a stereoscopic head-worn augmented reality display that would facilitate a wider uptake of technology by enterprise and professional users. The emphasis is put on mimicking a way of how naturally the ambient world is perceived by human visual system. To solve this, we propose a solid-state multi-focal display architecture, which is tailored for near-work oriented tasks. The core of the proposed technology is a solid-state multi-plane volumetric screen, with four physical image depth planes which form the secondary image source. The volumetric screen utilizes electrically controllable liquid-crystal based diffuser elements, which receive the image information from the primary source – a pico projection unit. The volumetric screen is coupled with a bird-bath type optical image combiner/eyepiece to yield a 40-degree horizontal field of view covering a representable depth space of 0.35m to infinity where no effects of vergence-accommodation conflict are experienced.