nils-herrmann/clean_scirepeval huggingface dataset:
Real time multi-object tracking using TLD framework

Today's state-of-the-art methods for object tracking perform adaptive tracking by detection, meaning that a detector estimates the position of an object and adjusts its parameters to the objects appearance at the same time. We propose a novel learning framework for tracking multiple unknown objects in a video stream by detection. Proposed system tracks multiple objects in presence of occlusion, clutter and scaling. The object is defined by its location and extent in a single frame. The tracker follows the object from frame to frame. The detector localizes all appearances that have been seen so far and corrects the tracker if required. The learning estimates detectors errors and updates that in the future to avoid these errors. A novel learning method (P-N learning) which estimates the errors by a pair of experts: (i) P-expert observes missed detections, and (ii) N-expert observes false alarms. First, instead of heuristically defining a tracking algorithm, we discovered that a discriminative structure prediction model from labeled video data and capture the interdependence of multiple influence factors. In every next frame the aim is to calculate the location and extent of object or indicate that object is not present. There are different algorithms which perceive the object in real-time. This system proposes a model which uses template matching algorithm with modifications based on SURF algorithm and squared difference error method. The template matching is done based on comparison of image features. We develop a novel method of tracking based upon template tracking algorithm which crops the region of interest(ROI) from the selected live object from a video stream from trained object database. Matching feature is found by applying principle component analysis.
SUSTAIN: a network model of category learning.

SUSTAIN (Supervised and Unsupervised STratified Adaptive Incremental Network) is a model of how humans learn categories from examples. SUSTAIN initially assumes a simple category structure. If simple solutions prove inadequate and SUSTAIN is confronted with a surprising event (e.g., it is told that a bat is a mammal instead of a bird), SUSTAIN recruits an additional cluster to represent the surprising event. Newly recruited clusters are available to explain future events and can themselves evolve into prototypes-attractors-rules. SUSTAIN's discovery of category substructure is affected not only by the structure of the world but by the nature of the learning task and the learner's goals. SUSTAIN successfully extends category learning models to studies of inference learning, unsupervised learning, category construction, and contexts in which identification learning is faster than classification learning.
M3R: Increased performance for in-memory Hadoop jobs

Main Memory Map Reduce (M3R) is a new implementation of the Hadoop Map Reduce (HMR) API targeted at online analytics on high mean-time-to-failure clusters. It does not support resilience, and supports only those workloads which can fit into cluster memory. In return, it can run HMR jobs unchanged – including jobs produced by compilers for higher-level languages such as Pig, Jaql, and SystemML and interactive front-ends like IBM BigSheets – while providing significantly better performance than the Hadoop engine on several workloads (e.g. 45x on some input sizes for sparse matrix vector multiply). M3R also supports extensions to the HMR API which can enable Map Reduce jobs to run faster on the M3R engine, while not affecting their performance under the Hadoop engine.
Psychometric measures of boredom: a review of the literature.

This research presents a review of the psychometric measures on boredom that have been developed over the past 25 years. Specifically, the author examined the Boredom Proneness Scale (BPS; ), the job boredom scales by E. A. Grubb (1975) and T. W. Lee (1986), a boredom coping measure (J. A. Hamilton, R. J. Haier, & M. S. Buchsbaum, 1984), 2 scales that assess leisure and free-time boredom (S. E. Iso-Ahola & E. Weissinger, 1990; M. G. Ragheb & S. P. Merydith, 2001), the Sexual Boredom Scale (SBS; J. D. Watt & J. E. Ewing, 1996), and the Boredom Susceptibility (BS) subscale of the Sensation Seeking Scale (M. Zuckerman, 1979a). Particular attention is devoted to discussing the literature regarding the psychometric properties of the BPS because it is the only full-scale measure on the construct of boredom.
Generating Holistic 3D Scene Abstractions for Text-Based Image Retrieval

Spatial relationships between objects provide important information for text-based image retrieval. As users are more likely to describe a scene from a real world perspective, using 3D spatial relationships rather than 2D relationships that assume a particular viewing direction, one of the main challenges is to infer the 3D structure that bridges images with users text descriptions. However, direct inference of 3D structure from images requires learning from large scale annotated data. Since interactions between objects can be reduced to a limited set of atomic spatial relations in 3D, we study the possibility of inferring 3D structure from a text description rather than an image, applying physical relation models to synthesize holistic 3D abstract object layouts satisfying the spatial constraints present in a textual description. We present a generic framework for retrieving images from a textual description of a scene by matching images with these generated abstract object layouts. Images are ranked by matching object detection outputs (bounding boxes) to 2D layout candidates (also represented by bounding boxes) which are obtained by projecting the 3D scenes with sampled camera directions. We validate our approach using public indoor scene datasets and show that our method outperforms baselines built upon object occurrence histograms and learned 2D pairwise relations.
Branched-Chain Amino Acids : Metabolism , Physiological Function , and Application Branched-Chain Amino Acids Activate Key Enzymes in Protein Synthesis after Physical Exercise 1 – 3

BCAAs (leucine, isoleucine, and valine), particularly leucine, have anabolic effects on protein metabolism by increasing the rate of protein synthesis and decreasing the rate of protein degradation in resting human muscle. Also, during recovery from endurance exercise, BCAAs were found to have anabolic effects in human muscle. These effects are likely to be mediated through changes in signaling pathways controlling protein synthesis. This involves phosphorylation of the mammalian target of rapamycin (mTOR) and sequential activation of 70-kD S6 protein kinase (p70 S6 kinase) and the eukaryotic initiation factor 4E-binding protein 1. Activation of p70 S6 kinase, and subsequent phopsphorylation of the ribosomal protein S6, is associated with enhanced translation of specific mRNAs. When BCAAs were supplied to subjects during and after one session of quadriceps muscle resistance exercise, an increase in mTOR, p70 S6 kinase, and S6 phosphorylation was found in the recovery period after the exercise with no effect of BCAAs on Akt or glycogen synthase kinase 3 (GSK-3) phosphorylation. Exercise without BCAA intake led to a partial phosphorylation of p70 S6 kinase without activating the enzyme, a decrease in Akt phosphorylation, and no change in GSK-3. It has previously been shown that leucine infusion increases p70 S6 kinase phosphorylation in an Akt-independent manner in resting subjects; however, a relation between mTOR and p70 S6 kinase has not been reported previously. The results suggest that BCAAs activate mTOR and p70 S6 kinase in human muscle in the recovery period after exercise and that GSK-3 is not involved in the anabolic action of BCAAs on human muscle.  2006.
Beyond intelligent interfaces: Exploring, analyzing, and creating success models of cooperative problem solving

Cooperative problem-solving systems are computer-based systems that augment a person's ability to create, reflect, design, decide, and reason. Our work focuses on supporting cooperative problem solving in the context of high-functionality computer systems. We show how the conceptual framework behind a given system determines crucial aspects of the system's behavior. Several systems are described that attempted to address specific shortcomings of prevailing assumptions, resulting in a new conceptual framework. To further test this resulting framework, we conducted an empirical study of a success model of cooperative problem solving between people in a large hardware store. The conceptual framework is instantiated in a number of new system-building efforts, which are described and discussed.
The Physical Activity Scale for Individuals with Physical Disabilities: test-retest reliability and comparison with an accelerometer.

The objective was to determine the test-retest reliability and criterion validity of the Physical Activity Scale for Individuals with Physical Disabilities (PASIPD).



Forty-five non-wheelchair dependent subjects were recruited from three Dutch rehabilitation centers. Subjects' diagnoses were: stroke, spinal cord injury, whiplash, and neurological-, orthopedic- or back disorders. The PASIPD is a 7-d recall physical activity questionnaire that was completed twice, 1 wk apart. During this week, physical activity was also measured with an Actigraph accelerometer.



The test-retest reliability Spearman correlation of the PASIPD was 0.77. The criterion validity Spearman correlation was 0.30 when compared to the accelerometer.



The PASIPD had test-retest reliability and criterion validity that is comparable to well established self-report physical activity questionnaires from the general population.
A Blur-Robust Descriptor with Applications to Face Recognition

Understanding the effect of blur is an important problem in unconstrained visual analysis. We address this problem in the context of image-based recognition by a fusion of image-formation models and differential geometric tools. First, we discuss the space spanned by blurred versions of an image and then, under certain assumptions, provide a differential geometric analysis of that space. More specifically, we create a subspace resulting from convolution of an image with a complete set of orthonormal basis functions of a prespecified maximum size (that can represent an arbitrary blur kernel within that size), and show that the corresponding subspaces created from a clean image and its blurred versions are equal under the ideal case of zero noise and some assumptions on the properties of blur kernels. We then study the practical utility of this subspace representation for the problem of direct recognition of blurred faces by viewing the subspaces as points on the Grassmann manifold and present methods to perform recognition for cases where the blur is both homogenous and spatially varying. We empirically analyze the effect of noise, as well as the presence of other facial variations between the gallery and probe images, and provide comparisons with existing approaches on standard data sets.
Improved Anonymous Biometric-based Authentication with Key-Agreement Scheme for Multi-Server Environments

With the rapid growth in the number of spiraling network users and the increase in the use of communication technologies, the multi-server environment is the most common environment for widely deployed applications. Reddy et al. recently showed that Lu et al.'s biometric-based authentication scheme for multi-server environment was insecure, and presented a new authentication and key-agreement scheme for the multi-server. Reddy et al. continued to assert that their scheme was more secure and practical. After a careful analysis, however, their scheme still has vulnerabilities to well-known attacks. In this paper, the vulnerabilities of Reddy et al.'s scheme such as the privileged insider and user impersonation attacks are demonstrated. A proposal is then presented of a new biometric-based user authentication scheme for a key agreement and multi-server environment. Lastly, the authors demonstrate that the proposed scheme is more secure using widely accepted AVISPA (Automated Validation of Internet Security Protocols and Applications) tool, and that it serves to satisfy all of the required security properties.
Integrating Support for Collaboration in a Computer Science Intelligent Tutoring System

Calls for widespread Computer Science (CS) education have been issued from the White House down and have been met with increased enrollment in CS undergraduate programs. Yet, these programs often suffer from high attrition rates. One successful approach to addressing the problem of low retention has been a focus on group work and collaboration. This paper details the design of a collaborative ITS (CIT) for foundational CS concepts including basic data structures and algorithms. We investigate the benefit of collaboration to student learning while using the CIT. We compare learning gains of our prior work in a non-collaborative system versus two methods of supporting collaboration in the collaborative-ITS. In our study of 60 students, we found significant learning gains for students using both versions. We also discovered notable differences related to student perception of tutor helpfulness which we will investigate in subsequent work.
DNA – A General Energy System Simulation Tool

The paper reviews the development of the energy system simulation tool DNA (Dynamic Network Analysis). DNA has been developed since 1989 to be able to handle models of any kind of energy system based on the control volume approach, usually systems of lumped parameter components. DNA has proven to be a useful tool in the analysis and optimization of several types of thermal systems: Steam turbines, gas turbines, fuels cells, gasification, refrigeration and heat pumps for both conventional fossil fuels and different types of biomass. DNA is applicable for models of both steady state and dynamic operation. The program decides at runtime to apply the DAE solver if the system contains differential equations. This makes it easy to extend an existing steady state model to simulate dynamic operation of the plant. The use of the program is illustrated by examples of gas turbine models. The paper also gives an overview of the recent implementation of DNA as a Matlab extension (Mex).
Suicide by plastic bag suffocation combined with the mixture of citric acid and baking soda in an adolescent

We describe a case of suicidal asphyxiation using a plastic bag combined with carbon dioxide (CO2) gas. A 20-year-old male, whose head was covered with a plastic bag, was found dead in his room. In the plastic bag, there were two glass-made cups containing liquid-like substance. Through crime scene investigation by police staffs, a bottle of citric acid and a box of baking soda were also discovered in his room. The forensic autopsy revealed that there were neither lesions nor injuries in all of the organs. Moreover, any drugs and poisons could not be detected in blood samples. Based on autopsy findings and crime scene investigation, the cause of death was diagnosed as acute asphyxia due to CO2 intoxication by the mixture of citric acid with baking soda in the plastic bag. To the best of our knowledge, there are no medical literatures describing plastic bag suffocation combined with CO2 gas generated from citric acid and baking soda, which has been widely distributed as suicidal means through websites. This case report promotes forensic pathologists and medical coroners to emphasize that the Internet has a crucial role on a source of suicidal information or a promoter of suicide all over the world.
Adversarial Attacks Beyond the Image Space

Generating adversarial examples is an intriguing problem and an important way of understanding the working mechanism of deep neural networks. Most existing approaches generated perturbations in the image space, i.e., each pixel can be modified independently. However, in this paper we pay special attention to the subset of adversarial examples that are physically authentic – those corresponding to actual changes in 3D physical properties (like surface normals, illumination condition, etc.). These adversaries arguably pose a more serious concern, as they demonstrate the possibility of causing neural network failure by small perturbations of real-world 3D objects and scenes. In the contexts of object classification and visual question answering, we augment state-of-the-art deep neural networks that receive 2D input images with a rendering module (either differentiable or not) in front, so that a 3D scene (in the physical space) is rendered into a 2D image (in the image space), and then mapped to a prediction (in the output space). The adversarial perturbations can now go beyond the image space, and have clear meanings in the 3D physical world. Through extensive experiments, we found that a vast majority of image-space adversaries cannot be explained by adjusting parameters in the physical space, i.e., they are usually physically inauthentic. But it is still possible to successfully attack beyond the image space on the physical space (such that authenticity is enforced), though this is more difficult than image-space attacks, reflected in lower success rates and heavier perturbations required.
Lobar holoprosencephaly with a median cleft: case report.

Holoprosencephaly is a congenital abnormality of the prosencephalon associated with median facial defects. Its frequency is 1 in 250 pregnancies and 1 in 16,000 live births. The degree of facial deformity usually correlates with the severity of brain malformation. Early mortality is prevalent in severe forms. This report presents a child with lobar holoprosencephaly accompanied by median cleft lip and palate. The treatment and 9 months' follow-up are presented. This unique case shows that holoprosencephaly may present different manifestations of craniofacial malformations, which are not always parallel to the severity of brain abnormalities. Patients with mild to moderate brain abnormalities may survive into childhood and beyond.
New Paradigm of 5G Wireless Internet

5G network is anticipated to meet the challenging requirements of mobile traffic in the 2020's, which are characterized by super high data rate, low latency, high mobility, high energy efficiency, and high traffic density. This paper provides an overview of China Mobile's 5G vision and potential solutions. Targeting a paradigm shift to user-centric network operation from the traditional cell-centric operation, 5G radio access network (RAN) design considerations are presented, including RAN restructure, Turbo charged edge, core network (CN) and RAN function repartition, and network slice as a service. Adaptive multiple connections in the user-centric operation is further investigated, where the decoupled downlink and uplink, decoupled control and data, and adaptive multiple connections provide sufficient means to achieve a 5G network with “no more cells.” Software-defined air interface (SDAI) is presented under a unified framework, in which the frame structure, waveform, multiple access, duplex mode, and antenna configuration can be adaptively configured. New paradigm of 5G network featuring user-centric network (UCN) and SDAI is needed to meet the diverse yet extremely stringent requirements across the broad scope of 5G scenarios.
Dictionary Learning for Fast Classification Based on Soft-thresholding

Classifiers based on sparse representations have recently been shown to provide excellent results in many visual recognition and classification tasks. However, the high cost of computing sparse representations at test time is a major obstacle that limits the applicability of these methods in large-scale problems, or in scenarios where computational power is restricted. We consider in this paper a simple yet efficient alternative to sparse coding for feature extraction. We study a classification scheme that applies the soft-thresholding nonlinear mapping in a dictionary, followed by a linear classifier. A novel supervised dictionary learning algorithm tailored for this low complexity classification architecture is proposed. The dictionary learning problem, which jointly learns the dictionary and linear classifier, is cast as a difference of convex (DC) program and solved efficiently with an iterative DC solver. We conduct experiments on several datasets, and show that our learning algorithm that leverages the structure of the classification problem outperforms generic learning procedures. Our simple classifier based on soft-thresholding also competes with the recent sparse coding classifiers, when the dictionary is learned appropriately. The adopted classification scheme further requires less computational time at the testing stage, compared to other classifiers. The proposed scheme shows the potential of the adequately trained soft-thresholding mapping for classification and paves the way towards the development of very efficient classification methods for vision problems.
Improving the Performance of OTDOA Based Positioning in NB-IoT Systems

In this paper, we consider positioning with observed-time-difference-of-arrival (OTDOA) for a device deployed in long-term-evolution (LTE) based narrow-band Internet-of-things (NB-IoT) systems. We propose an iterative expectation- maximization based successive interference cancellation (EM-SIC) algorithm to jointly consider estimations of residual frequency- offset (FO), fading-channel taps and time-of- arrival (ToA) of the first arrival-path for each of the detected cells. In order to design a low complexity ToA detector and also due to the limits of low-cost analog circuits, we assume an NB-IoT device working at a low-sampling rate such as 1.92 MHz or lower. The proposed EM-SIC algorithm comprises two stages to detect ToA, based on which OTDOA can be calculated. In a first stage, after running the EM-SIC block a predefined number of iterations, a coarse ToA is estimated for each of the detected cells. Then in a second stage, to improve the ToA resolution, a low-pass filter is utilized to interpolate the correlations of time-domain PRS signal evaluated at a low sampling-rate to a high sampling-rate such as 30.72 MHz. To keep low-complexity, only the correlations inside a small search window centered at the coarse ToA estimates are upsampled. Then, the refined ToAs are estimated based on upsampled correlations. If at least three cells are detected, with OTDOA and the locations of detected cell sites, the position of the NB-IoT device can be estimated. We show through numerical simulations that, the proposed EM-SIC based ToA detector is robust against impairments introduced by inter-cell interference, fading-channel and residual FO. Thus significant signal-to-noise (SNR) gains are obtained over traditional ToA detectors that do not consider these impairments when positioning a device.
Systematic optimization of 1 Gbit perpendicular magnetic tunnel junction arrays for 28 nm embedded STT-MRAM and beyond

This paper demonstrates the co-optimization of all critical device parameters of perpendicular magnetic tunnel junctions (pMTJ) in 1 Gbit arrays with an equivalent bitcell size of 22 F2 at the 28 nm logic node for embedded STT-MRAM. Through thin-film tuning and advanced etching of sub-50 nm (diameter) pMTJ, high device performance and reliability were achieved simultaneously, including TMR = 150 %, Hc > 1350 Oe, Heff <; 100 Oe, Δ = 85, Ic (35 ns) = 94 μA, Vbreakdown = 1.5 V, and high endurance (> 1012 write cycles). Reliable switching with small temporal variations (<; 5 %) was obtained down to 10 ns. In addition, tunnel barrier integrity and high temperature device characteristics were investigated in order to ensure reliable STT-MRAM operation.