nils-herrmann/clean_scirepeval huggingface dataset:
BraMBLe: A Bayesian Multiple-Blob Tracker

Blob trackers have become increasingly powerful in recent years largely due to the adoption of statistical appearance models which allow effective background subtraction and robust tracking of deforming foreground objects. It has been standard, however, to treat background and foreground modelling as separate processes — background subtraction is followed by blob detection and tracking — which prevents a principled computation of image likelihoods. This paper presents two theoretical advances which address this limitation and lead to a robust multiple-person tracking system suitable for single-camera real-time surveillance applications. The first innovation is a multi-blob likelihood function which assigns directly comparable likelihoods to hypotheses containing different numbers of objects. This likelihood function has a rigorous mathematical basis: it is adapted from the theory of Bayesian correlation, but uses the assumption of a static camera to create a more specific background model while retaining a unified approach to background and foreground modelling. Second we introduce a Bayesian filter for tracking multiple objects when the number of objects present is unknown and varies over time. We show how a particle filter can be used to perform joint inference on both the number of objects present and their configurations. Finally we demonstrate that our system runs comfortably in real time on a modest workstation when the number of blobs in the scene is small.
Diabetes Disease Diagnosis Method based on Feature Extraction using K-SVM

Now-a-days, diabetes disease is considered one of the key reasons of death among the people in the world. The availability of extensive medical information leads to the search for proper tools to support physicians to diagnose diabetes disease accurately. This research aimed at improving the diagnostic accuracy and reducing diagnostic missclassification based on the extracted significant diabetes features. Feature selection is critical to the superiority of classifiers founded through knowledge discovery approaches, thereby solving the classification problems relating to diabetes patients. This study proposed an integration approach between the SVM technique and K-means clustering algorithms to diagnose diabetes disease. Experimental results achieved high accuracy for differentiating the hidden patterns of the Diabetic and Nondiabetic patients compared with the modern diagnosis methods in term of the performance measure. The T-test statistical method obtained significant improvement results based on KSVM technique when tested on the UCI Pima Indian standard dataset. K-means Clustering; Diabetes Patients; SVM;
A Survey on Visual Approaches for Analyzing Scientific Literature and Patents

The increasingly large number of available writings describing technical and scientific progress, calls for advanced analytic tools for their efficient analysis. This is true for many application scenarios in science and industry and for different types of writings, comprising patents and scientific articles. Despite important differences between patents and scientific articles, both have a variety of common characteristics that lead to similar search and analysis tasks. However, the analysis and visualization of these documents is not a trivial task due to the complexity of the documents as well as the large number of possible relations between their multivariate attributes. In this survey, we review interactive analysis and visualization approaches of patents and scientific articles, ranging from exploration tools to sophisticated mining methods. In a bottom-up approach, we categorize them according to two aspects: (a) data type (text, citations, authors, metadata, and combinations thereof), and (b) task (finding and comparing single entities, seeking elementary relations, finding complex patterns, and in particular temporal patterns, and investigating connections between multiple behaviours). Finally, we identify challenges and research directions in this area that ask for future investigations.
Fear bradycardia and activation of the human periaqueductal grey

Animal models of predator defense distinguish qualitatively different behavioral modes that are activated at increasing levels of predation threat. A defense mode observed at intermediate threat levels is freezing: a cessation of locomotion that is characterized by a parasympathetically dominated autonomic nervous system response that causes heart rate deceleration, or fear bradycardia. Studies in rodents have shown that freezing depends on amygdalar projections to the periaqueductal grey (PAG). In humans, freezing-like behaviors are implicated in development and maintenance of psychopathology, but neural mechanisms underlying freezing or its characteristic autonomic response profile have not been identified. Here, we combined event-related blood oxygenation level-dependent functional MRI (BOLD-fMRI) with autonomic response measures in a picture viewing paradigm to probe activity and interconnectivity within the amygdala-PAG pathway and test for an association with parasympathetic as opposed to sympathetic activation. In response to negatively arousing pictures, we observed parasympathetic (bradycardia) and sympathetic (pupil dilation) autonomic responses, BOLD responses in the amygdala and PAG, and effective connectivity between these regions. Critically, BOLD responses in the PAG to negative pictures correlated on a trial-by-trial basis with bradycardia but not pupil dilation. This correlation with bradycardia remained significant when partialling out pupil dilation. Additionally, activity in regions associated with motor planning and inhibition mirrored the PAG response. Thus, our findings implicate the human PAG in a parasympathetically dominated defense mode that subserves a state of attentive immobility. Mechanistic insight into this qualitatively distinct defense mode may importantly advance translational models of anxiety disorders.
Beyond flat surface computing: challenges of depth-aware and curved interfaces

In the past decade, multi-touch-sensitive interactive surfaces have transitioned from pure research prototypes in the lab, to commercial products with wide-spread adoption. One of the longer term visions of this research follows the idea of ubiquitous computing, where everyday surfaces in our environment are made interactive. However, most of current interfaces remain firmly tied to the traditional flat rectangular displays of the today's computers and while they benefit from the directness and the ease of use, they are often not much more than touch-enabled standard desktop interfaces.
 In this paper, we argue for explorations that transcend the traditional notion of the flat display, and envision interfaces that are curved, three-dimensional, or that cross the boundary between the digital and physical world. In particular, we present two research directions that explore this idea: (a) exploring the three-dimensional interaction space above the display and (b) enabling gestural and touch interactions on curved devices for novel interaction possibilities. To illustrate both of these, we draw examples from our own work and the work of others, and guide the reader through several case studies that highlight the challenges and benefits of such novel interfaces. The implications on media requirements and collaboration aspects are discussed in detail, and, whenever possible, we highlight promising directions of future research. We believe that the compelling application design for future non-flat user interfaces will greatly depend on exploiting the unique characteristics of the given form factor.
Beauty and Brains: Detecting Anomalous Pattern Co-Occurrences

Our world is filled with both beautiful and brainy people, but how often does a Nobel Prize winner also wins a beauty pageant? Let us assume that someone who is both very beautiful and very smart is more rare than what we would expect from the combination of the number of beautiful and brainy people. Of course there will still always be some individuals that defy this stereotype; these beautiful brainy people are exactly the class of anomaly we focus on in this paper. They do not posses rare qualities, but it is the unexpected combination of factors that makes them stand out. In this paper we define the above described class of anomaly and propose a method to quickly identify them in transaction data. Further, as we take a pattern set based approach, our method readily explains why a transaction is anomalous. The effectiveness of our method is thoroughly verified with a wide range of experiments on both real world and synthetic data.
ValueCharts: analyzing linear models expressing preferences and evaluations

In this paper we propose ValueCharts, a set of visualizations and interactive techniques intended to support decision-makers in inspecting linear models of preferences and evaluation. Linear models are popular decision-making tools for individuals, groups and organizations. In Decision Analysis, they help the decision-maker analyze preferential choices under conflicting objectives. In Economics and the Social Sciences, similar models are devised to rank entities according to an evaluative index of interest. The fundamental goal of building models expressing preferences and evaluations is to help the decision-maker organize all the information relevant to a decision into a structure that can be effectively analyzed. However, as models and their domain of application grow in complexity, model analysis can become a very challenging task. We claim that ValueCharts will make the inspection and application of these models more natural and effective. We support our claim by showing how ValueCharts effectively enable a set of basic tasks that we argue are at the core of analyzing and understanding linear models of preferences and evaluation.
Evolutionary consequences of antibiotic use for the resistome, mobilome and microbial pangenome

The widespread use and abuse of antibiotic therapy has evolutionary and ecological consequences, some of which are only just beginning to be examined. One well known consequence is the fixation of mutations and lateral gene transfer (LGT) events that confer antibiotic resistance. Sequential selection events, driven by different classes of antibiotics, have resulted in the assembly of diverse resistance determinants and mobile DNAs into novel genetic elements of ever-growing complexity and flexibility. These novel plasmids, integrons, and genomic islands have now become fixed at high frequency in diverse cell lineages by human antibiotic use. Consequently they can be regarded as xenogenetic pollutants, analogous to xenobiotic compounds, but with the critical distinction that they replicate rather than degrade when released to pollute natural environments. Antibiotics themselves must also be regarded as pollutants, since human production overwhelms natural synthesis, and a major proportion of ingested antibiotic is excreted unchanged into waste streams. Such antibiotic pollutants have non-target effects, raising the general rates of mutation, recombination, and LGT in all the microbiome, and simultaneously providing the selective force to fix such changes. This has the consequence of recruiting more genes into the resistome and mobilome, and of increasing the overlap between these two components of microbial genomes. Thus the human use and environmental release of antibiotics is having second order effects on the microbial world, because these small molecules act as drivers of bacterial evolution. Continued pollution with both xenogenetic elements and the selective agents that fix such elements in populations has potentially adverse consequences for human welfare.
A Review of Wireless Sensor Technologies and Applications in Agriculture and Food Industry: State of the Art and Current Trends

The aim of the present paper is to review the technical and scientific state of the art of wireless sensor technologies and standards for wireless communications in the Agri-Food sector. These technologies are very promising in several fields such as environmental monitoring, precision agriculture, cold chain control or traceability. The paper focuses on WSN (Wireless Sensor Networks) and RFID (Radio Frequency Identification), presenting the different systems available, recent developments and examples of applications, including ZigBee based WSN and passive, semi-passive and active RFID. Future trends of wireless communications in agriculture and food industry are also discussed.
Stability Analysis and Stabilization methods of DC Microgrid with Multiple Parallel-Connected DC-DC Converters loaded

-Constant power loads (CPLs) may yield instability due to the well-known negative impedance characteristic. This study analyzes the factors which cause instability of a DC microgrid with multiple DC-DC converters. Two stabilization methods are presented for two operation modes: constant voltage source mode and droop mode. And sufficient conditions for the stability of the DC microgrid are obtained by identifying the eigenvalues of the Jacobian matrix. The key is to transform the eigenvalue problem to a quadratic eigenvalue problem (QEP). When applying the methods in practical engineering, the salient feature is that the stability parameter domains can be estimated by the available constraints, such as the values of capacities, inductances, maximum load power and distances of the cables. Compared with some classical methods, the proposed methods have wider stability region. The simulation results based on MATLAB /simulink platform verify the feasibility of the methods.
Rainy weather recognition from in-vehicle camera images for driver assistance

We propose a weather recognition method from in-vehicle camera images that uses a subspace method to judge rainy weather by detecting raindrops on the windshield. "Eigendrops" represent the principal components extracted from raindrop images in the learning stage. Then the method detects raindrops by template matching. In experiments using actual video sequences, our method showed good detection ability of raindrops and promising results for rainfall judgment from detection results.
A bandwidth-tunable bioamplifier with voltage-controlled symmetric pseudo-resistors

This paper describes a bioamplifier that employs a voltage-controlled-pseudo-resistor to achieve tunable bandwidth and wide operating voltage range for biomedical applications. The versatile pseudo-resistor employed provides ultra-high resistance for ac coupling to cancel the dc offset from electrode–tissue interface. The voltage-controlled-pseudo-resistor consists of serial-connected PMOS transistors working at the subthreshold region and an auto-tuning circuit that makes sure the constant (time-invariant) control-voltage of the pseudo-resistor. This bandwidth-tunable bioamplifier is designed in a 0.18-μm standard CMOS process, achieving a gain of 40.2 dB with 10.35-μW power consumption. The designed chip was also used to develop the proof-of-concept prototype. An operation bandwidth of 9.5 kHz, inputreferred noise of 5.2 μVrms from 6.3 Hz to 9.5 kHz and 5.54 μVrms from 250 Hz to 9.5 kHz, and a tunable cutoff-frequency from 6.3–600 Hz were demonstrated to prove our design. & 2015
Linear-Time Joint Probabilistic Data Association for Multiple Extended Object Tracking

The Joint Probabilistic Data Association (JPDA) filter for multiple object tracking is based on the assumption that at most one measurement originates from a target object. However, with the development of high-resolution sensors, it is often the case that multiple spatially distributed detections are obtained from a single object. To tackle this emerging data association challenge, this paper presents a JPDA method based on the Poisson spatial measurement model for extended objects. As the constraint that one target gets at most one measurement is relaxed, the marginal association probabilities can be obtained with linear complexity in the number of measurements and targets. The proposed method is compared to a partition-based multiple extended object tracking algorithm.
SEQUEL: A Structured English Query Language

In this paper we present the data manipulation facility for a structured English query language (SEQUEL) which can be used for accessing data in an integrated relational data base. Without resorting to the concepts of bound variables and quantifiers SEQUEL identifies a set of simple operations on tabular structures, which can be shown to be of equivalent power to the first order predicate calculus. A SEQUEL user is presented with a consistent set of keyword English templates which reflect how people use tables to obtain information. Moreover, the SEQUEL user is able to compose these basic templates in a structured manner in order to form more complex queries. SEQUEL is intended as a data base sublanguage for both the professional programmer and the more infrequent data base user.
Using web security scanners to detect vulnerabilities in web services

Although web services are becoming business-critical components, they are often deployed with critical software bugs that can be maliciously explored. Web vulnerability scanners allow detecting security vulnerabilities in web services by stressing the service from the point of view of an attacker. However, research and practice show that different scanners have different performance on vulnerabilities detection. In this paper we present an experimental evaluation of security vulnerabilities in 300 publicly available web services. Four well known vulnerability scanners have been used to identify security flaws in web services implementations. A large number of vulnerabilities has been observed, which confirms that many services are deployed without proper security testing. Additionally, the differences in the vulnerabilities detected and the high number of false-positives (35% and 40% in two cases) and low coverage (less than 20% for two of the scanners) observed highlight the limitations of web vulnerability scanners on detecting security vulnerabilities in web services.
MRTouch: Adding Touch Input to Head-Mounted Mixed Reality

We present MRTouch, a novel multitouch input solution for head-mounted mixed reality systems. Our system enables users to reach out and directly manipulate virtual interfaces affixed to surfaces in their environment, as though they were touchscreens. Touch input offers precise, tactile and comfortable user input, and naturally complements existing popular modalities, such as voice and hand gesture. Our research prototype combines both depth and infrared camera streams together with real-time detection and tracking of surface planes to enable robust finger-tracking even when both the hand and head are in motion. Our technique is implemented on a commercial Microsoft HoloLens without requiring any additional hardware nor any user or environmental calibration. Through our performance evaluation, we demonstrate high input accuracy with an average positional error of 5.4 mm and 95% button size of 16 mm, across 17 participants, 2 surface orientations and 4 surface materials. Finally, we demonstrate the potential of our technique to enable on-world touch interactions through 5 example applications.
SNAP, Small-world Network Analysis and Partitioning: An open-source parallel graph framework for the exploration of large-scale networks

We present SNAP (small-world network analysis and partitioning), an open-source graph framework for exploratory study and partitioning of large-scale networks. To illustrate the capability of SNAP, we discuss the design, implementation, and performance of three novel parallel community detection algorithms that optimize modularity, a popular measure for clustering quality in social network analysis. In order to achieve scalable parallel performance, we exploit typical network characteristics of small-world networks, such as the low graph diameter, sparse connectivity, and skewed degree distribution. We conduct an extensive experimental study on real-world graph instances and demonstrate that our parallel schemes, coupled with aggressive algorithm engineering for small-world networks, give significant running time improvements over existing modularity-based clustering heuristics, with little or no loss in clustering quality. For instance, our divisive clustering approach based on approximate edge betweenness centrality is more than two orders of magnitude faster than a competing greedy approach, for a variety of large graph instances on the Sun Fire T2000 multicore system. SNAP also contains parallel implementations of fundamental graph-theoretic kernels and topological analysis metrics (e.g., breadth-first search, connected components, vertex and edge centrality) that are optimized for small- world networks. The SNAP framework is extensible; the graph kernels are modular, portable across shared memory multicore and symmetric multiprocessor systems, and simplify the design of high-level domain-specific applications.
Asynchronous Byzantine Agreement Protocols

A consensus protocol enables a system of n asynchronous processes, some of them faulty, to reach agreement. Both the processes and the message system are capable of cooperating to prevent the correct processes from reaching decision. A protocol is t-resilient if in the presence of up to t faulty processes it reaches agreement with probability 1. Byzantine processes are faulty processes that can deviate arbitrarily from the protocol; Fail-Stop processes can just stop participating in it. In a recent paper, f-resilient randomized consensus protocols were presented for t < n/S. We improve this to f <n/3, thus matching the known lower bound on the number of correct processes necessary for consensus. The protocol uses a general technique in which the behavior of the Byzantine processes is restricted by the use of a broadcast protocol that filters some of the messages. The apparent behavior of the Byzantine processes, filtered by the broadcast protocol, is similar to that of Fail-Stop processes. Plugging the broadcast protocol as a communicating primitive into an agreement protocol for Fail-Stop processes gives the result. This technique, of using broadcast protocols to reduce the power of the faulty processes and then using them as communication primitives in algorithms designed for weaker failure models, was used succesfully in other contexts. ‘!:I 1987
Stylometric Analysis of Scientific Articles

We present an approach to automatically recover hidden attributes of scientific articles, such as whether the author is a native English speaker, whether the author is a male or a female, and whether the paper was published in a conference or workshop proceedings. We train classifiers to predict these attributes in computational linguistics papers. The classifiers perform well in this challenging domain, identifying non-native writing with 95% accuracy (over a baseline of 67%). We show the benefits of usingsyntactic featuresin stylometry; syntax leads to significant improvements over bag-of-words models on all three tasks, achieving 10% to 25% relative error reduction. We give a detailed analysis of which words and syntax most predict a particular attribute, and we show a strong correlation between our predictions and a paper’s number of citations.
The Jalview Java alignment editor

Multiple sequence alignment remains a crucial method for understanding the function of groups of related nucleic acid and protein sequences. However, it is known that automatic multiple sequence alignments can often be improved by manual editing. Therefore, tools are needed to view and edit multiple sequence alignments. Due to growth in the sequence databases, multiple sequence alignments can often be large and difficult to view efficiently. The Jalview Java alignment editor is presented here, which enables fast viewing and editing of large multiple sequence alignments.