Miracle-dz/UniFAR-FTUs huggingface dataset:
A High-Level Synthesis Implementation and Evaluation of an Image Processing Accelerator

Most frequently, an FPGA is used as an implementation platform in applications of graphics processing, as its structure can effectively exploit both spatial and temporal parallelism. Such parallelization techniques involve fundamental restrictions, namely being their dependence on both the processing model and the system’s hardware constraints, that can force the designer to restructure the architecture and the implementation. Predesigned accelerators can significantly assist the designer to solve this problem and meet his deadlines. In this paper, we present our accelerators for Grayscale and Sobel Edge Detection, two of the most fundamental algorithms used in digital image processing projects. We have implemented those algorithms with a “bare-metal” VHDL design, written purely by hand, as a portable USB accelerator device, as well as an HLS-based overlay of a similar implementation designed to be used by a Python interface. The comparisons of the two architectures showcase that the HLS generated design can perform equally to or even better than the handwritten HDL equivalent, especially when the correct compiler directives are provided.
EEG Classification in Brain Computer Interface (BCI): A Pragmatic Appraisal

Brain computer interface (BCI) is one of the technologies growing at an exponential rate with its applications extended to medical and non-medical fields. EEG is widely used in BCI for detection and analysis of abnormalities of the brain. EEG is characterized by inherently high temporal resolution and precision, low spatial resolution and specificity plus contains artifacts and redundant or noise information both from the subject and equipment interferences. Thus, feature extraction is a critical issue in translation algorithm development for BCI. Above all, BCI still faces a lot challenges that results in performance variation across and even within subjects. Thus, this work provides a concise but all encompassing review of methods that have been adopted in the recent time for development of an EEG classification in BCI.
On the Expressive Power of Scientific Manuscripts

Every research manuscript is appreciated in the form of citations. Citations are expected to carry the essence of the underlying base paper by some rhetorical means. However, this is not true in reality. Citation manipulations are equally possible which shall be identified using research semantics. This paper discusses machine learning based approaches for analyzing research citations with the aim of finding quality research citations. On analyzing the semantics of the research manuscript and the respective citations, this paper proposes various metrics for citation quality analysis including deep cite, raw expressive power, expressive power and normalized expressive power.
Capability of Modified SIFT to Match Stereo Imagery System

Sciences and Technology (CEST-2018), September 25-27, 2018, vol. 1. This is an open access article under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license, which permits any non-commercial use, distribution, adaptation, and reproduction in any medium, as long as the original work is properly cited. ISBN: 978-81-936820-5-0 Capability of Modified SIFT to Match Stereo Imagery System Omar Abusaeeda, Salah Naas , Nasar Aldian Shashoa 1 Department of Computer Networks, Faculty of Information Technology, Azzaytuna University, Libya 2, 3 Department of Electrical & Communication Engineering, Faculty of Engineering, Azzaytuna University, Libya
Intelligent Assessment of Sun Flower Seeds Using Machine Learning Approaches

Pakistan is an agricultural country. Sun flower is the major crop of Pakistan which is being sowing in many areas of country. It fulfills the requirement of edible oil. In this paper we are trying to identify the best quality from different sun flowers seeds verities by using machine learning approaches. We take the images of four kinds of sunflower seeds which names Top sun (A) , High Sun (B) ,US666 (C) and Seji (D) for classification. We get eight different images of each kind of sunflower. In this paper sun flowers seeds varieties were categorized by using Computer vision image processing tool (CVIP). The experience and knowledge of inspectors are required to perfectly perform this assessment process. We use the RST-Invariant Features, Histogram Features, Texture Features, and Pattern Classification and also use the nearest neighbor and k-nearest neighbor algorithms for final classification. We achieved the final results of four kinds of sunflower using nearest neighbor on distance one and two 89% and 72% average and on k-nearest neighbor 89% and 73% average percentage. These are the best percentage results using these algorithms for classification. In this way we can easily classify the sunflower seeds and also these methods provide opportunity to farmer and other people for identify and select the different better and healthy sunflower seeds for better benefits. Keywords: RST-Invariant Features, Histogram Features, Texture Features, Classification Algorithms
Using In-Situ Statistics and a Spatially-Aware Kernel for Longitudinal Neuroimaging Analysis

This paper introduces a framework for analyzing longitudinal neuroimaging datasets. We address the problem of detecting subtle, short-term changes in neural structure that are indicative of cognitive decline and correlate with risk factors for Alzheimer’s disease. Previous approaches have focused on separating populations with different risk factors based on gross changes, such as decreasing gray matter volume. In contrast, we introduce a new spatiallysensitive kernel that allows us to characterize individuals, as opposed to populations. We use this for both classification and regression, e.g., to predict changes in a subject’s cognitive test scores from neuroimaging data alone. In doing so, this paper presents the first evidence demonstrating that very small changes in white matter structure over a two year period can predict change in cognitive function in healthy adults.
Development of a deep-learning based tunnel incident detection system on CCTVs

In this study, current status of Korean hazard mitigation guideline for tunnel operation is summarized. It shows that requirement for CCTV installation has been gradually stricted and needs for tunnel incident detection system in conjunction with the CCTV in tunnels have been highly increased. Despite of this, it is noticed that mathematical algorithm based incident detection system, which are commonly applied in current tunnel operation, show very low detectable rates by less than 50%. The putative major reasons seem to be (1) very weak intensity of illumination (2) dust in tunnel (3) low installation height of CCTV to about 3.5 m, etc. Therefore, an attempt in this study is made to develop an deep-learning based tunnel incident detection system, which is relatively insensitive to very poor visibility conditions. Its theoretical background is given and validating investigation are undertaken focused on the moving vehicles and person out of vehicle in tunnel, which are the official major objects to be detected. Two scenarios are set up: (1) training and prediction in the same tunnel (2) training in a tunnel and prediction in the other tunnel. From the both cases, targeted object detection in prediction mode are achieved to detectable rate to higher than 80% in case of similar time period between training and prediction but it shows a bit low detectable rate to
Output, collaboration and impact of e-learning research: Bibliometric analysis and visualizations at the country and institutional level (Scopus 2003-2016)

Bibliometric indicators of production and impact of e-learning research are analyzed to know the contribution of countries and institutions in the scientific development of this subject and to strengthen its characterization as knowledge domain. We extracted bibliometric indicators from 39,244 documents indexed in Scopus and SCImago Institutional Rankings , and generated maps of production and collaboration networks and graphics about the impact of e-learning research in countries and institutions. The results of this combined analysis showed that at country level the United States produce most of the works and generated the greatest international collaboration. At institutional level, the University of Hong Kong is the most productive and National Taiwan University of Science and Technology is the one with the greatest collaboration. In addition, the analysis showed that Taiwan ranks first in productivity and impact, which is why we linked these results to a brief analysis of its national policies. This study presents a new method to analyze both emerging and established knowledge domains.
Pulmonary Disease(COPD)

Chronic obstructive pulmonary disease(COPD) is greatly harmful to people,s health. Smoking is a important factor in development of chronic obstructive pulmonary disease. However, not all smokers are patients of COPD, only above 20% smokers can become patients of COPD. Recently, many researches indicate that smoking in development of COPD is associated with genetic suscepcitibility. To further know relationship between smoking and genetic suscepcitibility in COPD, we refer to a varieties of genetic suscepcitibility. It is important to try to provide theory base for prevention and treatment of COPD.
Domain-Based Sense Disambiguation in Multilingual Structured Data

Natural language text is pervasive in structured data sets—relational database tables, spreadsheets, XML documents, RDF graphs, etc.—requiring data processing operations to possess some level of natural language understanding capability. This, in turn, involves dealing with aspects of diversity present in structured data such as multilingualism or the coexistence of data from multiple domains. Word sense disambiguation is an essential component of natural language understanding processes. State-of-the-art WSD techniques, however, were developed to operate on single languages and on corpora that are considerably different from structured data sets, such as articles, newswire, web pages, forum posts, or tweets. In this paper we present a WSD method that is designed for short text typically present in structured data, applicable to multiple languages and domains. Our proof-of-concept implementation reaches an all-words F-score between 60% and 80% on both English and Italian data. We consider these as very promising ﬁrst results given the known difﬁculty of WSD and the particularity of the corpora targeted with respect to more conventional text.
Message Passing and Bulk Transport on Heterogeneous Multiprocessors

The decade of symmetric multiprocessors is about to end. Due to physical limitations, individual cores cannot be made any faster and adding cores won’t work anymore (dark silicon). Operating systems face the next big paradigm shift: systems are getting faster by exploiting specialized hardware leading to the era of asymmetric processors and heterogeneous systems. An ever growing count and the diversity of cores yields new challenges regarding scheduling and resource management. Multiple physical address spaces within single machines let them appear as a cluster-like system. Today’s commodity operating systems are not well designed to deal with heterogeneity and asymmetric processors. Many attempts have been made to tackle the challenges of heterogeneous hardware, most of which treat available co-processors like devices or independent execution environments rather than equivalent processors. Thus, true support of a multi-architecture system is rarely seen, despite the fact that such hardware already exists. Examples are, systems like the OMAP44xx SoC or Intel’s Xeon Phi co-processor. In this thesis we will elaborate the arising challenges introduced by heterogeneous system architectures. We ported the Barrelfish research operating system to the Xeon Phi execution environment and explored the impact on performance and system design imposed by the emerging hardware characteristics such as different instruction set architectures, multiple physical address spaces and asymmetric cores. Based on our proposed software framework, we demonstrate a parallel, heterogeneous execution of an OpenMP program using our implementation of a message passing based OpenMP library and show why more threads do not necessary imply a better performance.
Addressing Sparsity in Deep Neural Networks

Neural networks (NNs) have been demonstrated to be useful in a broad range of applications, such as image recognition, automatic translation, and advertisement recommendation. State-of-the-art NNs are known to be both computationally and memory intensive, due to the ever-increasing deep structure, i.e., multiple layers with massive neurons and connections (i.e., synapses). Sparse NNs have emerged as an effective solution to reduce the amount of computation and memory required. Though existing NN accelerators are able to efficiently process dense and regular networks, they cannot benefit from the reduction of synaptic weights. In this paper, we propose a novel accelerator, Cambricon-X, to exploit the sparsity and irregularity of NN models for increased efficiency. The proposed accelerator features a processing element (PE)-based architecture consisting of multiple PEs. An indexing module efficiently selects and transfers needed neurons to connected PEs with reduced bandwidth requirement, while each PE stores irregular and compressed synapses for local computation in an asynchronous fashion. With 16 PEs, our accelerator is able to achieve at most 544 GOP/s in a small form factor (6.38 mm2 and 954 mW at 65 nm). Experimental results over a number of representative sparse networks show that our accelerator achieves, on average, $7.23\times$ speedup and $6.43\times$ energy saving against the state-of-the-art NN accelerator. We further investigate possibilities of leveraging activation sparsity and multi-issue controller, which improve the efficiency of Cambricon-X. To ease the burden of programmers, we also propose a high efficient library-based programming environment for our accelerator.
Boosting image captioning with an attentional mechanism = Boosting image captioning using diverse beam search

The task of automatically generating captions for arbitrary digital images involves both Computer Vision and Natural Language Processing. Popular approaches tackle the challenge by implementing neural networks based on frameworks capable of generating English captions of query images. Those architectures can be split into an image processing convolution neural network (CNN) encoder component transforming images to embedded vectors and a recurrent neural network (RNN) as a language model decoder component transforming embedded data to natural English sentences. In this undergraduate final project we implement and evaluate state-of-the-art image captioning algorithm upon reviewing how various architectures are used to generate captions for images. We propose the addition of an attentional based mechanism to the Long Short Term Memory (LSTM) network component of the captioning framework. In addition, we also propose the use of a Diverse Beam Search algorithm for the RNN inference module of the caption algorithm. Then the performance of the baseline implementation is compared with the new tunned captioning algorithm.
An Algorithm for Generating Invisible Data Poisoning Using Adversarial Noise That Breaks Image Classification Deep Learning

Today, the main two security issues for deep learning are data poisoning and adversarial examples. Data poisoning consists of perverting a learning system by manipulating a small subset of the training data, while adversarial examples entail bypassing the system at testing time with low-amplitude manipulation of the testing sample. Unfortunately, data poisoning that is invisible to human eyes can be generated by adding adversarial noise to the training data. The main contribution of this paper includes a successful implementation of such invisible data poisoning using image classification datasets for a deep learning pipeline. This implementation leads to significant classification accuracy gaps.
Diphthong Synthesis using the Three-Dimensional Dynamic Digital Waveguide Mesh

The human voice is a complex and nuanced instrument, and despite many years of research, no system is yet capable of producing natural-sounding synthetic speech. This affects intelligibility for some groups of listeners, in applications such as automated announcements and screen readers. Furthermore, those who require a computer to speak - due to surgery or a degenerative disease - are limited to unnatural-sounding voices that lack expressive control and may not match the user's gender, age or accent. It is evident that natural, personalised and controllable synthetic speech systems are required. A three-dimensional digital waveguide model of the vocal tract, based on magnetic resonance imaging data, is proposed here in order to address these issues. The model uses a heterogeneous digital waveguide mesh method to represent the vocal tract airway and surrounding tissues, facilitating dynamic movement and hence speech output. The accuracy of the method is validated by comparison with audio recordings of natural speech, and perceptual tests are performed which confirm that the proposed model sounds significantly more natural than simpler digital waveguide mesh vocal tract models. Control of such a model is also considered, and a proof-of-concept study is presented using a deep neural network to control the parameters of a two-dimensional vocal tract model, resulting in intelligible speech output and paving the way for extension of the control system to the proposed three-dimensional vocal tract model. Future improvements to the system are also discussed in detail. This project considers both the naturalness and control issues associated with synthetic speech and therefore represents a significant step towards improved synthetic speech for use across society.
Effect of Gardeniae Fructus on Cardiovascular System

Due to its multifunctional properties,it generated much attention in both the communities of research and application.To review the progress on effect of gardeniae Fructus(geniposide,genipin,crocin,crocetin) on cardiovascular system,so as to provide more information on gardeniae Fructus for further research and application.The literatures published home and abroad in recent years were consulted and summarized.Modern studies show that gardeniae Fructus not only have protective effects on endothelial cells,antioxygen,anti-inflammatory,anticoagulation and other effects,but also have protective effects against smooth muscle cells proliferation.Currently there are more studies about gardeniae Fructus iridoid glycosides against ischemic injury,and the mechanism is multifaceted.More studies are needed on the mechanism of gardeniae Fructus for treatment of cardiovascular system injury.This review expects to provide reference for preventing atherosclerosis and developing new drugs.
The nature and function of content in computational models

Much of computational cognitive science construes human cognitive capacities as representational capacities, or as involving representation in some way. Computational theories of vision, for example, typically posit structures that represent edges in the distal scene. Neurons are often said to represent elements of their receptive fields. Despite the ubiquity of representational talk in computational theorizing there is surprisingly little consensus about how such claims are to be understood. The point of this chapter is to sketch an account of the nature and function of representation in computational cognitive models. A commitment to representation presupposes a distinction between representational vehicle and representational content. The vehicle is a physically realized state or structure that carries or bears content. Insofar as a representation is causally involved in a cognitive process, it is in virtue of the representational vehicle. A state or structure has content just in case it represents things to be a certain way; it has a ‘satisfaction condition’ – the condition under which it represents accurately. The representational vehicles in socalled ‘classical’ computational systems are symbols, physical structures characterized by a combinatorial syntax, over which computational processes are defined. Symbols are tailormade for semantic interpretation, for ‘hanging’ contents on, so to speak. But not all computational systems are symbolmanipulating systems. For example, connectionist models explain cognitive phenomena as the propagation of activation among units in highly connected networks; dynamical models characterize cognitive processes by a set of differential equations describing the behavior of the system over time. The systems so described do not operate on symbols in any obvious sense. There is a good deal of controversy about whether these systems are genuinely representational. For the most part, the dispute concerns whether such systems have representational vehicles, that is, states or structures causally involved in cognitive processes that are plausibly construed as candidates for semantic interpretation. In this chapter we will put this issue aside, abstracting away from questions about the bearers of content, and focus on the nature and function of representational content itself.
Study on effect of SARS on mental health of medical staffs in fever clinic of military hospital

Objective To Study on effect of SARS on mental health of medical staffs in fever clinic of military hospital. Methods During breakout of SARS, the medical staffs in fever clinic and common ward in 8 selected military hospitals were investigated by the psychological questionnaire and psychology table designed by Psychology academe of Chinese Academy of Science. The results were analyzed statistically. Results Being examined by t-test, the factor score of somatization and depression of medical staffs in fever clinic were significantly higher than those in common ward (P0.05). The factor score of anxiety and compulsion of the former were slightly higher than that of the latter, but there was no significant difference. The factor score of anxiety and compulsion of female staffs were significantly higher than that of male (P0.05). Conclusion It is important to promote the mental health of medical staffs in fever clinic and take some corresponding measures to maintain their mental health and improve efficiency in the case of sudden breakout of public hygienic affairs in the future.
Comparing the Antiarrhythmia Action of the Extracts from Choerospondias Axillaris (Roxb.) Burtt et Hill

Objective:To compare the antiarrhythmia actions of 3 kinds of extracts by different methods from Choerospondias Axillaris(Roxb.) Burtt et Hill.Methods:(1)i.v aconitine was used to induce arrhythmia in the rats;(2)i.v ouabain was used to induce guinea pig arrhythmia;(3)Arrhythmia was induced by ligation of coronary artery in the rats.Results: Compared with the NS group,both sample 1 and sample 2 significantly prolonged the lasting time of ventricular ectopic beats(VE),ventricular tachycardia(VT),ventricular fibrillation(VF) and heart arrest(HA) induced by using i.v aconitine in the rats,significantly prolonged the lasting time of arrhythmias induced by using i.v ouabain in guinea pig,and greatly reduced ectopic beats,delay the beginning time of arrhythmias,shortened the persisting time of ventricular tachycardia and decreased frequency of ventricular fibrillation in the rats of ligation of coronary artery.The actions showed a concentration-dependent tendency.Sample 3 produced opposite effect.Conclusion: Sample 1 and Sample 2 both resisted the occurrence of arrhythmias induced by aconitine,ouabain and ligation of coronary artery.Sample 3,however,promoted the occurrence of arrhythmias.