Miracle-dz/UniFAR-FTUs huggingface dataset:
Diagnosis of Schizophrenia using Multivariate Analysis of Brain MR Images

Current laboratory procedures for the diagnosis of Schizophrenia are mere subjective methods i.e. based on symptom profiles. Neural correlates do not provide sufficiently useful criteria. The existing univariate image analysis procedures are sub-optimal for the identification of specific neural correlates of schizophrenia. This paper aims at developing a mathematical tool for the diagnosis of schizophrenia using multivariable analysis of brain MR images. To identify the correlative pattern of gray matter differences between schizophrenics and healthy controls, Multivariate Linear Model (MLM) and Voxel based Morphometry (VBM) in Statistical Parametric Mapping (SPM) are used. Five images each of patients and controls are acquired and subject to multivariate analysis. The perceivable differences between the brain anatomies of the two groups are extracted and the regions of interest are recorded using the Talairach and Tournoux atlas. The pattern of the Eigen image is characterized by positive loadings indicating gray matter decline in the patients as well as the negative loadings reflecting gray matter increase in the patients. A learning algorithm using Support Vector Machines (SVM) is proposed and a classifier model is to be built. The differences extracted from the multivariate results have to be input to the model. When a random image is then input to the model, it has to accurately classify the incoming image as schizophrenic or a healthy one. The accuracy is predicted to be around 80%. These findings suggest that the multivariable analysis of brain images could be an effective method for the diagnosis of schizophrenia.
DroidEye: Fortifying Security of Learning-Based Classifier Against Adversarial Android Malware Attacks

To combat the evolving Android malware attacks, systems using machine learning techniques have been successfully deployed for Android malware detection. In these systems, based on different feature representations, various kinds of classifiers are constructed to detect Android malware. Unfortunately, as classifiers become more widely deployed, the incentive for defeating them increases. In this paper, we first extract a set of features from the Android applications (apps) and represent them as binary feature vectors; with these inputs, we then explore the security of a generic learning-based classifier for Android malware detection in the presence of adversaries. To harden the evasion, we first present count featurization to transform the binary feature space into continuous probabilities encoding the distribution in each class (either benign or malicious). To improve the system security while not compromising the detection accuracy, we further introduce softmax function with adversarial parameter to find the best trade-off between security and accuracy for the classifier. Accordingly, we develop a system named DroidEye which integrates our proposed method for Android malware detection. Comprehensive experiments on the real sample collection from Comodo Cloud Security Center are conducted to validate the effectiveness of DroidEye against adversarial Android malware attacks. Our proposed secure-learning paradigm is also applicable for other detection tasks, such as spammer detection in social media.
Remixing entity linking evaluation datasets for focused benchmarking

In recent years, named entity linking (NEL) tools were primarily developed in terms of a general approach, whereas today numerous tools are focusing on speci  c domains such as e.g. the mapping of persons and organizations only, or the annotation of locations or events in microposts. However, the available benchmark datasets necessary for the evaluation of NEL tools do not re  ect this focalizing trend. We have analyzed the evaluation process applied in the NEL benchmarking framework GERBIL [37,30] and all its benchmark datasets. Based on these insights we have extended the GERBIL framework to enable a more  ne grained evaluation and in depth analysis of the available benchmark datasets with respect to different emphases. This paper presents the implementation of an adaptive  lter for arbitrary entities and customized benchmark creation as well as the automated determination of typical NEL benchmark dataset properties, such as the extent of content-related ambiguity and diversity. These properties are integrated on different levels, which also enables to tailor customized new datasets out of the existing ones by remixing documents based on desired emphases. Besides a new system library to enrich provided NIF [11] datasets with statistical information, best practices for dataset remixing are presented, and an in depth analysis of the performance of entity linking systems on special focus datasets is presented.
Tractable Stochastic Model Predictive Control using Conditional Value at Risk Optimization

A numerically tractable Stochastic Model Predictive Control (SMPC) strategy using Conditional Value at Risk (CVaR) optimization for discrete-time linear time-invariant systems, with state and input constraints, subject to additive uncertainty, is presented. SMPC strategies make use of the probabilistic description of uncertainty to define chance constraints which allow a certain admissible level of constraint violation. SMPC strategies require the initial state of a system to be within a particular set, referred to as feasibility set, probabilistically, such that the derived control input, when applied to the system, gives rise to states that are also within the feasibility set satisfying all chance constraints on the system. This leads to recursive feasibility of the SMPC strategy. Such strategies are restrictive in nature when the uncertainty in the system is unbounded, as in the case of White Gaussian noise. In such a case, the feasibility set is very small and leads to a strategy that is very conservative. To reduce this conservatism, some constraint violations are permitted. However, such violations affect the closed-loop behaviour of the system leading to performance degradation. This performance degradation can be quantified as a penalty on the system for violating constraints, and intuitively, it can be thought of as a risk taken by the system in that undesirable state. An approach following the exact penalty method is proposed using the CVaR function to determine the penalty cost. The same optimal solution as the original constrained problem is obtained from a single unconstrained minimization. Since accurate computation of the expected value of risk using the CVaR function is not possible, a scenario-based approximation of the CVaR is used to obtain an overall tractable and computationally efficient SMPC strategy. An extensive simulation study of the double integrator system is provided to present the functionality of the proposed method.
The Use of Correspondence Analysis in Assessing the Antecedents of Innovativeness in Public Hospitals

Abstract The aim of the study presented in this article is to show correspondence analysis as a method useful in the diagnosis of coexistence of category variables in antecedents of innovativeness, with the positions of the respondents representing various medical professions in hospitals. Primary data obtained in the course of empirical research, carried out using a questionnaire study on a sample of 459 respondents representing 8 public hospitals in Poland, is used to this aim. To follow up on the achievements of the analysis, literature on the issue of innovativeness and its antecedents was also used. The results of the correspondence analysis allows one to confirm the thesis of the different opinions of doctors, nurses/midwives and managers regarding the level of significance of antecedents of innovativeness, where for doctors and managers in this context the most important is financial optimization, and for nurses the improvement of the quality of medical services. The results may provide an important clue to the chief executives of hospitals in the context of further changes and innovativeness necessary to achieve the desired efficiency of these organizations.
Persistence Parallelism Optimization: A Holistic Approach from Memory Bus to RDMA Network

Emerging non-volatile memories (NVM), such as phase change memory (PCM) and Resistive RAM (ReRAM), incorporate the features of fast byte-addressability and data persistence, which are beneficial for data services such as file systems and databases. To support data persistence, a persistent memory system requires ordering for write requests. The datapath of a persistent request consists of three segments: through the cache hierarchy to the memory controller, through the bus from the memory controller to memory devices, and through the network from a remote node to a local node. Previous work contributes significantly to improve the persistence parallelism in the first segment of the data path. However, we observe that the memory bus and the Remote Direct Memory Access (RDMA) network remain severely under-utilized because the persistence parallelism in these two segments is not fully leveraged during ordering. In this paper, we propose a novel architecture to further improve the persistence parallelism in the memory bus and the RDMA network. First, we utilize inter-thread persistence parallelism for barrier epoch management with better bank-level parallelism (BLP). Second, we enable intra-thread persistence parallelism for remote requests through RDMA network with buffered strict persistence. With these features, the architecture efficiently supports persistence through all three segments of the write datapath. Experimental results show that for local applications, the proposed mechanism can achieve 1.3x performance improvement, compared to the original buffered persistence work. In addition, it can achieve 1.93x performance improvement for remote applications serviced through the RDMA network.
An Integrated Approach of Fuzzy Quality Function Deployment and Fuzzy Multi-Objective Programming Tosustainable Supplier Selection and Order Allocation

The emergence of sustainability paradigm has influenced many research disciplines including supply chain management. It has drawn the attention of manufacturing companies’ CEOs to incorporate sustainability in their supply chain and manufacturing activities. Supplier selection problem, as one of the main problems in supply chain activities, is also combined with sustainable development where traditional procedures are now transformed to sustainable initiatives. Moreover, allocating optimal order quantities to sustainable suppliers has also attracted attention of many scholars and industrial practitioners, which has not been comprehensively addressed. Therefore, a practical model of supplier selection and order allocation based on the sustainability Triple Bottom Line (TBL) approach is presented in this research article. The proposed approach utilizes Fuzzy Analytical Hierarchy Process combined with Quality Function Deployment (FAHP-QFD) for reflecting buyer’s sustainability requirements into the preference weights that are then exerted by an efficient Fuzzy Assessment Method (FAM) to assess the suppliers to obtain their sustainability scores. Thereupon, these scores are utilized in a fuzzy multi-objective mix-integer non-linear programming model (MINLP) for allocating orders to suppliers based on the manufacturer’s sustainability preference. A real-world application of food industry is presented to show the practicality of the proposed approach.
Current Challenges in Orographic Flow Dynamics: Turbulent Exchange Due to Low-Level Gravity-Wave Processes

This paper examines current understanding of the influence of orographic flow dynamics on the turbulent transport of momentum and scalar quantities above complex terrain. It highlights three key low-level orographic flow phenomena governed by gravity-wave dynamics: Foehn flow, atmospheric rotors and gravity-wave modulation of the stable boundary layer. Recent observations and numerical simulations are used to illustrate how these flows can cause significant departures from the turbulent fluxes, which occur over flat terrain. Orographically forced fluxes of heat, moisture and chemical constituents are currently unaccounted for in numerical models. Moreover, whilst turbulent orographic drag parameterisation schemes are available (in some models), these do not represent the large gravity-wave scales associated with foehn dynamics; nor do they account for the spatio-temporal heterogeneity and non-local turbulence advection observed in wave-rotor dynamics or the gravity waves, which modulate turbulence in the boundary layer. The implications for numerical models, which do not resolve these flows, and for the parametrisation schemes, which should account for the unresolved fluxes, are discussed. An overarching need is identified for improved understanding of the heterogeneity in sub-grid-scale processes, such as turbulent fluxes, associated with orographic flows, and to develop new physically-based approaches for parameterizing these processes.
Sturgeon in the Sacramento–San Joaquin Watershed: New Insights to Support Conservation and Management

Author(s): Klimley, A. Peter; Chapman, Eric D. ; Cech, Jr., Joseph J.; Cocherell, Dennis E. ; Fangue, Nann A. ; Gingras, Marty; Jackson, Zachary; Miller, Emily A. ; Mora, Ethan A. ; Poletto, Jamilynn B. ; Schreier, Andrea M. ; Seesholtz, Alicia; Sulak, Kenneth J.; Thomas, Michael J.; Woodbury, David; Wyman, Megan T. | Abstract: The goal of a day-long symposium on March 3, 2015, Sturgeon in the Sacramento–San Joaquin Watershed: New Insights to Support Conservation and Management, was to present new information about the physiology, behavior, and ecology of the green (Acipenser medirostris) and white sturgeon (Acipenser transmontanus) to help guide enhanced management and conservation efforts within the Sacramento–San Joaquin watershed. This symposium identified current unknowns and highlighted new electronic tracking technologies and physiological techniques to address these knowledge gaps. A number of presentations, each reviewing ongoing research on the two species, was followed by a round-table discussion, in which each of the participants was asked to share recom-mendations for future research on sturgeon in the watershed. This article presents an in-depth review of the scientific information presented at the sympo-sium with a summary of recommendations for future research.
Insulin-Like Androgenic Gland Hormone Gene in the Freshwater Chinese Mitten Crab Eriocheir sinensis ：cDNA Cloning, Expression Pattern, and Interaction with EsIGFBP7

Androgenic gland hormone has an insulin-like structure and it is the male-specific hormone found in crustaceans. Androgenic gland hormone controls male sexual differentiation and maintains male sexual characteristics. In this study, the full-length cDNA sequence of the androgenic gland hormone gene in Eriocheir sinensis (EsIAG) was cloned. The total length of the EsIAG cDNA was 1,495 bp, encoding 151 amino acids. EsIAG has the typical conserved structure of other reported IAGs but had low sequence similarity with other crustacean IAGs. EsIAG was expressed specifically in the androgenic gland. Insulin-like growth factor binding protein 7 from the Chinese mitten crab E. sinensis (EsIGFBP7) was highly expressed in the male sex-related organs among nine crab tissues. Interactions between EsIAG and EsIGFBP7 were verified using a yeast-two hybrid assay. These results obtained in this study provide basic information about the regulatory mechanism of male sex differentiation in E. sinensis.
Edge Fields for Robust Object Proposal

In this paper, we present a novel object proposal approach that can accurately detect and localize objects in an image. Our method searches for objects with the assumption that an object can be represented by a closed boundary. To search for closed boundaries in an image, the method employs edge features with the proposed Edge Fields (EFs) technique. With the EFs, our method can extract high quality of edges and can obtain good boundaries from the image. EFs consists of blurring and thresholding, in which blurring helps extract high quality of edges and thresholding prevents the method from losing image details during the blurring process. Experimental results demonstrate that our method is competitive with the state-of-the-art object proposal methods on the PASCAL VOC 2007 dataset.
Solid-phase extraction of antioxidant compounds from commercial cranberry extract and its antiradical activity

This study is concerned with the fractionation and determination of major antioxidant compounds (phenols, flavonoids, anthocyanins and vitamin C) in commercial cranberry extract. The total content of phenolics, flavonoids and total and monomers of anthocyanins, determined spectrophotometrically, was 1.67 mg/g, 0.41 mg/g, 5.12 mg/g and 3.32 mg/g. The content of vitamin C, determined volumetrically, was 121.74 mg/g. Commercial cranberry extract was dissolved in 80 % acetone and the solution was fractionated using solid phase extraction (SPE) in order to abstract vitamin C, neutral and acidic phenols. The free radical scavenging activity of the cranberry extract and its fractions was investigated on stable 2,2-diphenyl-1-picrylhydrazyl (DPPH) and reactive hydroxyl radicals employing electron spin resonance (ESR) spectroscopy. The most effective fractions were those containing vitamin C (AADPPH= AAOH=100%), neutral (AADPPH=89.50% and AAOH=43.11%) and acidic (AADPPH=83.98% and AAOH=38.58%) phenols. The presence of vitamin C, abstracted from cranberry extract, was determined by Fe(III)-mediated ascorbate oxidation which yields characteristic ESR doublet spectrum of ascorbyl radical.
Level of Information & Communication Technology (ICT) Usage Among ESL Teachers in Malaysia

The current study attempted to identify the level of (ICT) usage among ESL teachers, the difference between the level of ICT usage based on gender and age, the teachers’ perception towards ICT usage and the barriers faced by teachers. This study was a quantitative study using the descriptive survey approach. The total number of participants was 90 ESL student teachers. Data collection involved the use of a questionnaire and the results were analysed using descriptive and inferential statistics. Findings showed that the level of ICT among teachers was moderate to high. The findings also revealed that females used ICT more than males and younger teachers used ICT more than older teachers. In addition, the level of perceived ease of use and perceived usefulness was moderate to high and high respectively. The findings also indicated that the level of barrier among teachers was low.
Debin: Predicting Debug Information in Stripped Binaries

We present a novel approach for predicting debug information in stripped binaries. Using machine learning, we first train probabilistic models on thousands of non-stripped binaries and then use these models to predict properties of meaningful elements in unseen stripped binaries. Our focus is on recovering symbol names, types and locations, which are critical source-level information wiped off during compilation and stripping. Our learning approach is able to distinguish and extract key elements such as register-allocated and memory-allocated variables usually not evident in the stripped binary. To predict names and types of extracted elements, we use scalable structured prediction algorithms in probabilistic graphical models with an extensive set of features which capture key characteristics of binary code. Based on this approach, we implemented an automated tool, called Debin, which handles ELF binaries on three of the most popular architectures: x86, x64 and ARM. Given a stripped binary, Debin outputs a binary augmented with the predicted debug information. Our experimental results indicate that Debin is practically useful: for x64, it predicts symbol names and types with 68.8% precision and 68.3% recall. We also show that Debin is helpful for the task of inspecting real-world malware -- it revealed suspicious library usage and behaviors such as DNS resolver reader.
ACOUSTIC SCENE CLASSIFICATION USING MFCC AND MP FEATURES

This paper, clearly describes our experiments for efficient acoustic scene classification task as a part of ”Detection and Classification of Acoustic Scenes and Events-2016 (DCASE-2016)” IEEE Audio and Acoustic Signal Processing (AASP) challenge. Identification of features from given audio clips to appropriate acoustic scene classification is a challenging task because of heterogeneity by thier nature. In order to identify such features, in this paper we have implemented few methods using Matching Pursuit (MP) algorithm in order to extract Time-Frequency (TF) based features. MP algorithm is used to select atoms iteratively among the set of parameterized waveforms in the dictionary that best correlates the original signal structure. Using these selected set of atoms mean and standard deviation of amplitude and frequency parameters of first few (n) atoms are calculated separately, resulting into four MP feature sets. Combination of twenty MFCCs along with four MP features enhanced the recognition accuracy of acoustic scenes using GMM classifier.
LiDAR-Video Driving Dataset: Learning Driving Policies Effectively

Learning autonomous-driving policies is one of the most challenging but promising tasks for computer vision. Most researchers believe that future research and applications should combine cameras, video recorders and laser scanners to obtain comprehensive semantic understanding of real traffic. However, current approaches only learn from large-scale videos, due to the lack of benchmarks that consist of precise laser-scanner data. In this paper, we are the first to propose a LiDAR-Video dataset, which provides large-scale high-quality point clouds scanned by a Velodyne laser, videos recorded by a dashboard camera and standard drivers' behaviors. Extensive experiments demonstrate that extra depth information help networks to determine driving policies indeed.
A Collision Probability Model of Portal Vein Tumor Thrombus Formation in Hepatocellular Carcinoma

Hepatocellular carcinoma is one of the most common malignancies worldwide, with a high risk of portal vein tumor thrombus (PVTT). Some promising results have been achieved for venous metastases of hepatocellular carcinoma; however, the etiology of PVTT is largely unknown, and it is unclear why the incidence of PVTT is not proportional to its distance from the carcinoma. We attempted to address this issue using physical concepts and mathematical tools. Finally, we discuss the relationship between the probability of a collision event and the microenvironment of the PVTT. Our formulae suggest that the collision probability can alter the tumor microenvironment by increasing the number of tumor cells.
Asynchronous Iterative Water-Filling for Gaussian Frequency-Selective Interference Channels

This paper considers the maximization of information rates for the Gaussian frequency-selective interference channel, subject to power and spectral mask constraints on each link. To derive decentralized solutions that do not require any cooperation among the users, the optimization problem is formulated as a static noncooperative game of complete information. To achieve the so-called Nash equilibria of the game, we propose a new distributed algorithm called asynchronous iterative water-filling algorithm. In this algorithm, the users update their power spectral density (PSD) in a completely distributed and asynchronous way: some users may update their power allocation more frequently than others and they may even use outdated measurements of the received interference. The proposed algorithm represents a unified framework that encompasses and generalizes all known iterative water-filling algorithms, e.g., sequential and simultaneous versions. The main result of the paper consists of a unified set of conditions that guarantee the global converge of the proposed algorithm to the (unique) Nash equilibrium of the game.