Miracle-dz/UniFAR-FTUs huggingface dataset:
Bates' Pocket Guide to Physical Examination and History Taking

This concise pocket-sized guide presents the classic Bates approach to physical examination and history taking in a quick-reference outline format. It contains all the critical information needed to obtain a clinically meaningful health history and conduct a thorough physical assessment. Fully revised and updated to reflect current health care literature, the Seventh Edition will help health professionals elicit relevant facts from the patient's history, review examination procedures, highlight common findings, learn special assessment techniques, and sharpen interpretive skills. The book features a vibrant full-color art program and an easy-to-follow two-column format with step-by-step examination techniques on the left and abnormalities with differential diagnoses on the right.
Implicit learning of tonality: A self-organizing approach

Tonal music is a highly structured system that is ubiquitous in our cultural environment. We demonstrate the acquisition of implicit knowledge of tonal structure through neural self-organization resulting from mere exposure to simultaneous and sequential combinations of tones. In the process of learning, a network with fundamental neural constraints comes to internalize the essential correlational structure of tonal music. After learning, the network was run through a range of experiments from the literature. The model provides a parsimonious account of a variety of empirical findings dealing with the processing of tone, chord, and key relationships, including relatedness judgments, memory judgments, and expectancies. It also illustrates the plausibility of activation being a unifying mechanism underlying a range of cognitive tasks.
EEG Alpha Waves Dataset

We describe the experimental procedures for a dataset that we have made publicly available at https://doi.org/10.5281/zenodo.2348891 in both mat (Mathworks, Natick, USA) and csv formats. This dataset contains electroencephalographic recordings of 20 subjects in a simple resting-state eyes open/closed experimental protocol. The electroencephalographic headset consisted of 16 electrodes. Data were recorded during a pilot experiment taking place in the GIPSA-lab, Grenoble, France, in 2017 (1). Python code for manipulating the data is available at https://github.com/plcrodrigues/Alpha-Waves-Dataset . The ID of this dataset is ALPHA.EEG. 2017a-GIPSA.
Developing a collaborative MOOC learning environment utilizing video sharing with discussion summarization as added-value

Massive Open Online Courses (MOOC) platforms provide a rich environment for knowledge creation through its massiveness and inherited collaborative tools. However, it also restricts spontaneous knowledge sharing by the existing LMS barriers between the main multimedia content and the collaborative tools. None the less, the collaboration still massive due to the number of participants. The separation of the multimedia content and the discussion tools is the first focus point of this paper. Moreover, this article is presenting a new added value to the MOOC architecture so to link the learner’s discussions and its summary with the multimedia contents. The added-value component involves a summarization algorithm that summarizes the shared collaborative textual discussion collected from the various learners viewing relevant MOOC multimedia/video contents. The affectivity of the summarization component was tested using the popular ROUGE software package from University of Southern California. The new MOOC architecture represents an enhanced learning environment that enables learners to share the multimedia information along with its annotated collaborative information with the power of summarizing the final outcome of the presented annotations relevant to a specific shared multimedia content.
Electromagnetic worm-like locomotion system for in-pipe robots: novel design of magnetic subsystem

This paper describes a magnetic subsystem of an electromagnetic worm-like locomotion system (WLLS) with coupled electromagnets. WLLS simple-design consists of two elastically connected ring-like segments that form two magnetically coupled electromagnetic actuators. The actuators generate longitudinal and transverse displacements which lead to locomotion due to synchronized changes of inertial, normal pressure, and friction forces. In present paper, analysis of the magnetic circuit of the WLLS had been performed. From symmetry and general consideration of magnetic system effectiveness, a novel design of the coupled magnetic circuits had been developed. The novel WLLS has two-force component electromagnetic actuators that produce the transverse and longitudinal forces. The novel design of the electromagnetic WLLS is able to move an in-pipe robots and should have better specific pulling force.
Depression Detection from Bangla Facebook Status using Machine Learning Approach

Depression is a mood disorder. This mental illness is a silent killer. It badly affects how human feels, thinks, and acts. Every year, all over the world huge number of people commit suicide due to depression[1]. In Asia region, most of the Bengali speaking people use Facebook rather than Twitter for their social communication and share most of their emotions there. That is why Facebook is a potential source for our research. Although detecting depression is a psychological matter, in this paper there was an attempt to predict depression using machine learning. Here, a model is proposed to predict depressed or not depressed using machine learning from Bangla Facebook Status. The work is done with 50 Facebook users’ data, among them 17 people had committed suicide. Naive Bayes[2], Multinomial Naive Bayes[3], Logistic Regression[4], Linear SVC[5] and some other classifiers used for this work. The work achieved satisfying accuracy as the first work in Bangla language.
Chinese Semantic Role Labeling Based on Genetic Algorithm

To find the effective features from many syntactic features could improve the efficiency and accuracy of the semantic role labeling system. Based on the original semantic role labeling system, the genetic algorithm is used to optimize those syntactic features. From the experiment results, it could be concluded that the syntactic feature selection based on genetic algorithm is efficient. The system uses fewer features, but achieves almost the same F value as the whole extended model.
Exploiting compression in solving discretized linear systems

We propose a method for exploiting compression in computing the solution to a system of linear algebraic equations. The method is based on computing an approximate solution in a reduced space, and thus we seek a basis in which the solution has a compressed representation and can consequently be computed more efﬁciently. Although the method is completely general, it is especially effective for linear systems resulting from discretization of an underlying continuous problem, which will be our main focus. We address three primary issues: (1) how to compute an approximate solution to a given linear system using a given basis, (2) how to choose a basis that will yield signiﬁcant compression, and (3) how to detect when the chosen basis is of sufﬁcient dimension to provide a satisfactory approximation. While all three aspects have antecedents in previous ideas and methods, we combine, adapt, and extend them in a manner we believe to be novel for the purpose of solving discretized linear systems. We demonstrate that the resulting method can be competitive with—and often substantially outperforms—current standard methods and is effective for efﬁciently solving linear systems resulting from the discretization of major classes of continuous problems, including both differential equations and integral equations.
Compressed Sensing Image Mapping Spectrometer

This paper presents a novel snapshot imaging spectrometer based on the image mapping and compressed sensing concept named Compressed Sensing Image Mapping Spectrometer (CSIMS). The operation principle is to slice the input image to different directions and encode the strip pieces before dispersion by a prism. The detector obtains the mixture spatial-spectral data simultaneously. The datacube is reconstructed by the compressed sensing algorithm and combining all the pieces together. The mathematical model of CSIMS is established to describe the light wave propagation through the entire system based on the scalar diffraction theory. The simulations are conducted to prove the effectiveness of the CSIMS principle, and the results show that the reconstructed datacube reveals higher spatial resolution and more accurate spectral curves than that of the relative snapshot imaging spectrometer based on compressed sensing.
Communication in Emergency Management through Data Integration and Trust: an introduction to the CEM-DIT system

This paper discusses the development of the CEM-DIT (Communication in Emergency Management through Data Integration and Trust) system, which allows decision makers in crises to send out automated data requests to multiple heterogeneous and potentially unknown sources and interactively determine how reliable, relevant and trustworthy the responses are. We describe the underlying technology, which is based partially on data integration and matching, and partly on utilisation of provenance data. We describe our cooperation with the Urban Observatory (UO), which allows us to develop the system in collaboration with developers of the kind of crisis-relevant data which the system is designed for. The system is currently in development, and we describe which parts are fully implemented and which are currently being developed
Vehicle license plate recognition method based on deep convolution network in complex road scene

The license plate robust recognition algorithm in complex road scene has both theoretical and practical values. The existing license plate recognition algorithm can achieve better recognition results under ideal road scenes such as moderate light intensity, good shooting angle, and clear license plate target, but in complex road scenes such as fast speed, blurred aging of license plates, and low illumination such as rainy days, the effectiveness of the license plate recognition algorithm still needs to be improved. Based on the realistic requirements of license plate recognition algorithm and in-depth analysis of the principle of deep convolution network, we designed a deep convolution network for Chinese characters, letters, and numbers in the license plate to automatically learn the essential features of the image to make up for the limitation of the artificial feature recognition of the traditional license plate recognition algorithm. At the same time, according to the convolution kernel, downsampling, and nonlinear operation of the deep convolution network, the nonlinear abstraction ability of the license plate character feature is improved. The experimental results show that the proposed method can quickly and accurately identify the license plate character in complex road scenes. The recognition accuracy is better than the existing license plate recognition algorithm, which improves the accuracy of license plate recognition and achieves an ideal license plate recognition effect.
Automated Synthesis of Memristor Crossbar Networks

The advancement of semiconductor device technology over the past decades has enabled the design of increasingly complex electrical and computational machines. Electronic design automation (EDA) has played a significant role in the design and implementation of transistor-based machines. However, as transistors move closer toward their physical limits, the speed-up provided by Moore’s law will grind to a halt. Once again, we find ourselves on the verge of a paradigm shift in the computational sciences as newer devices pave the way for novel approaches to computing. One of such devices is the memristor – a resistor with non-volatile memory. Memristors can be used as junctional switches in crossbar circuits, which comprise of intersecting sets of vertical and horizontal nanowires. The major contribution of this dissertation lies in automating the design of such crossbar circuits – doing a new kind of EDA for a new kind of computational machinery. In general, this dissertation attempts to answer the following questions: a. How can we synthesize crossbars for computing large Boolean formulas, up to 128-bit? b. How can we synthesize more compact crossbars for small Boolean formulas, up to 8-bit? c. For a given loop-free C program doing integer arithmetic, is it possible to synthesize an equivalent crossbar circuit? We have presented novel solutions to each of the above problems. Our new, proposed solutions resolve a number of significant bottlenecks in existing research, via the usage of innovative logic representation and artificial intelligence techniques. For large Boolean formulas (up to 128-bit), we have utilized Reduced Ordered Binary Decision Diagrams (ROBDDs) to automatically synthesize linearly growing crossbar circuits that compute them. This cutting edge approach towards flow-based computing has yielded state-of-the-art results. It is worth noting that this approach is scalable to n-bit Boolean formulas. We have made significant original contributions by leveraging
Dynamic matching algorithms

We study marketplaces in which participants arrive over time, looking to interact with each other. While such interactions have historically been decentralized, the past few years have seen a dramatic increase in the number of internet-enabled platforms which facilitate the process of connecting together, or matching, sets of two or more participants. We will focus mainly on centralized matching markets such as kidney exchange and carpooling platforms. In such platforms, the algorithm which determines whom to match and when to do so plays an important role in the efficiency of the marketplace. In the first part, we study the interface between the participant heterogeneity, the types of matchings that are allowed, and the frequency at which the platform computes the allocations. We provide an empirical analysis of the effect of match frequency based on data from major US Kidney exchange programs. We then study models that enable us to compare the participants’ match rates and waiting times under varying matching policies. We show both in theory and in practice that matching quickly can be beneficial, compared to policies which try to increase opportunities for optimization through artificial waiting. Until now, the theory of matching algorithms has focused mostly on static environments and little is known in the case where all participants arrive and depart dynamically. In our second part, we help bridge this gap by introducing a new theoretical problem for dynamic matching when anyone can arrive online. We provide new algorithms with state-of-the-art theoretical guarantees, both in the case of adversarial and random order inputs. Finally, we show that these algorithms perform well on kidney exchange and carpooling data. Thesis Supervisor: Itai Ashlagi Title: Assistant Professor of Management Science and Engineering Stanford University Thesis Supervisor: Patrick Jaillet Title: Dugald C. Jackson Professor of Electrical Engineering and Computer Science Co-director, Operations Research Center
A double screening orthogonal-matching-pursuit algorithm for compressed sensing receiver with high column correlation sensing matrix

Modulated wideband converter (MWC) is a multi-branch struc- ture for sub-Nyquist sampling the signals which spectra are sparse. With several band-limited sampling sequences, MWC can recover the signals by compressed sensing (CS) reconstruction algorithms. However, for hardware-implemented MWC, the hardware scale and imperfection of the physical devices lead to strong column correlations of the sensing matrix which may invalidate the classic CS algorithms. Combined with the characteristics of the sensing matrix of the CS receiver, this paper proposes the double screening orthogonal matching pursuit algorithm (DSOMP) to solve this problem. DSOMP updates the support based on the distance from the residual to the column vector subspace of the sensing matrix and adds a secondary screening mechanism to remove strong interference terms. With the strong column correlation sensing matrix of the CS receiver, DSOMP exhibits an excellent support set recovery rate.
A New Secure Lightweight Authentication Protocol for NFC mobile Payment

As mobile applications grow, securing these applications become an important factor for their success. Especially, when these applications are related to financial transactions. Nowadays, mobile payment that is based on NFC technology is considered one of these important topics. In this paper, we propose A New Secure and Lightweight Authentication Protocol for NFC mobile Payment (NSLA) protocol. NSLA protocol presents a new method to update the users’ identities and the valid session keys, which preserves the privacy and ensures the integrity of the system. The presented performance analysis shows that NSLA protocol satisfies low computation overhead. Moreover, the security analysis proves that NSLA protocol has an immunity against replay attack, brute force attack, denial of service attack, and others types of attacks.
CrashLocator : Locating the Faulty Functions by Analyzing the Crash Stack Information in Crash Reports

In recent years, studies have been dedicated mainly in the analysis, of crashes in real-world related to large-scale software systems. A crash in terms of computing can be termed as a computer program such as a software application that stops functioning properly. Software crash is a serious problem in production environment. When crash happens, the crash report with the stack trace of software at time of crash is sent to the developer team. Software development team may receive hundreds of stack traces from all deployment sites and many stack traces may be due to same problem. If the developer starts analyzing each trace, it may take a longer duration of time and redundancy many happen in terms of two developers fixing the same problem. This motivates us to present the solution to analyze the stack traces and find the important functions responsible for crash and rank them, so that development resources can be optimized. In this paper we have proposed the solution to solve the problem by developing Software CrashLocator. Keywords— Crash Locator, Windows Error Reporting, Crash Report, Mozilla Crash Reporter.
Test Score Gap Robustness to Scaling: The Scale Transformation Command

Social scientists frequently rely on the cardinal comparability of test scores to assess achievement gaps between population subgroups and their evolution over time. This approach has been criticized due to the ordinal nature of test scores and the sensibility of results to order-preserving transformations, which are theoretically plausible. Bond and Lang (2013) document the sensitivity of measured ability to scaling choices and develop a method to assess the robustness of changes in ability over time to scaling choices. This paper presents the scale transformation command, which expands the Bond and Lang method to more general cases and optimizes their algorithm to work with large data sets. The program assesses the robustness of an achievement gap between two subgroups to any arbitrary choice of scale by finding bounds for the original gap estimation. Additionally, the program finds scale transformations that are very likely and unlikely to benchmark against the results obtained. Finally, the program also allows the user to measure how much gap growth coefficients change when including controls in their specifications.
VLC-CDMA Systems Based on Optical Complementary Codes

VLC performs the dual-functions of communication and lighting and will play an important role in next-generation wireless communications. Proper multiple access methods should be identified for VLC in multi-user scenarios. This article proposes a VLC-CDMA system using OCCs, which are unipolar codes with a multiple sub-codes structure. OCCs offer good autocorrelation and cross-correlation functions. The issues on encoding criteria and application of OCCs in VLC-CDMA systems are discussed. The transmitters use LEDs with different wavelengths to distinguish different sub-codes. Compensator circuits are used to achieve equal gain combining at receivers to exploit the good correlation properties of OCCs. The receivers use the filters with different center wavelengths to distinguish different sub-codes. The performance of VLC-CDMA systems with OCC is compared to OOCs and multi-carrier CDMA systems, to verify that the proposed system is robust for its applications in multi-user scenarios.
Broadcast Performance Analysis and Improvements of the LTE-V2V Autonomous Mode at Road Intersection

An autonomous V2V communication mode (also known as side-link mode 4), which facilitates V2V communication in out of eNB coverage areas, has recently been introduced into the Long term evolution (LTE) standard. Recent research has studied the performance of this LTE-V2V autonomous mode for a highway use case. However, performance analysis for a highway use case cannot be easily applied to an intersection use case as it may contain non-line-of-sight (NLOS) communication links. In this paper, we analyze and evaluate the safety message broadcasting performance of LTE-V2V autonomous mode in an urban intersection scenario. Considering practical path loss models, we present the impact of NLOS communication link on the overall message dissemination performance. Through the analytical and simulation results, we show that the overall message dissemination performance degrades drastically with increasing vehicle density and increasing distance of the transmitting vehicle from the intersection. To improve the performance, we propose a vehicle-assisted relaying scheme in which the relaying vehicle is selected in an autonomous manner. We also present two resource allocation strategies for the relaying vehicle. For low to medium vehicle density along the street, we observe significant improvement in message dissemination through relaying compared to the scheme without relaying.
Lightweight and Expressive Fine-Grained Access Control for Healthcare Internet-of-Things

Healthcare Internet-of-Things (IoT) is an emerging paradigm that enables embedded devices to monitor patients vital signals and allows these data to be aggregated and outsourced to the cloud. The cloud enables authorized users to store and share data to enjoy on-demand services. Nevertheless, it also causes many security concerns because of the untrusted network environment, dishonest cloud service providers and resource-limited devices. To preserve patients’ privacy, existing solutions usually apply cryptographic tools to offer access controls. However, fine-grained access control among authorized users is still a challenge, especially for lightweight and resource-limited end-devices. In this paper, we propose a novel healthcare IoT system fusing advantages of attribute-based encryption, cloud and edge computing, which provides an efficient, flexible, secure fine-grained access control mechanism with data verification in healthcare IoT network without any secure channel and enables data users to enjoy the lightweight decryption. We also define the formal security models and present security proofs for our proposed scheme. The extensive comparison and experimental simulation demonstrate that our scheme has better performance than existing solutions.