nils-herrmann/clean_scirepeval huggingface dataset:
Visual Analytics Methods for Movement Data

All the power of computational techniques for data processing and analysis is worthless without human analysts choosing appropriate methods depending on data characteristics, setting parameters and controlling the work of the methods, interpreting results obtained, understanding what to do next, reasoning, and drawing conclusions. To enable effective work of human analysts, relevant information must be presented to them in an adequate way. Since visual representation of information greatly promotes man’s perception and cognition, visual displays of data and results of computational processing play a very important role in analysis. However, a simple combination of visualization with computational analysis is not sufficient. The challenge is to build analytical tools and environments where the power of computational methods is synergistically combined with man’s background knowledge, flexible thinking, imagination, and capacity for insight. This is the main goal of the emerging multidisciplinary research field of Visual Analytics (Thomas and Cook [45]), which is defined as the science of analytical reasoning facilitated by interactive visual interfaces. Analysis of movement data is an appropriate target for a synergy of diverse technologies, including visualization, computations, database queries, data transformations, and other computer-based operations. In this chapter, we try to define what combination of visual and computational techniques can support the analysis of massive movement data and how these techniques should interact. Before that, we shall briefly overview the existing computer-based tools and techniques for visual analysis of movement data.
A case of diprosopiasis in Trachemys scripta scripta.

This short communication describes a case of diprosopiasis in Trachemys scripta scripta imported from Florida (USA) and farmed for about 4 months by a private owner in Palermo, Sicily, Italy. The water turtle showed the morphological and radiological features characterizing such deformity. This communication aims to advance the knowledge of the reptile's congenital anomalies and suggests the need for more detailed investigations to better understand its pathogenesis.
Pavlov's Cockroach: Classical Conditioning of Salivation in an Insect

Secretion of saliva to aid swallowing and digestion is an important physiological function found in many vertebrates and invertebrates. Pavlov reported classical conditioning of salivation in dogs a century ago. Conditioning of salivation, however, has been so far reported only in dogs and humans, and its underlying neural mechanisms remain elusive because of the complexity of the mammalian brain. We previously reported that, in cockroaches Periplaneta americana, salivary neurons that control salivation exhibited increased responses to an odor after conditioning trials in which the odor was paired with sucrose solution. However, no direct evidence of conditioning of salivation was obtained. In this study, we investigated the effects of conditioning trials on the level of salivation. Untrained cockroaches exhibited salivary responses to sucrose solution applied to the mouth but not to peppermint or vanilla odor applied to an antenna. After differential conditioning trials in which an odor was paired with sucrose solution and another odor was presented without pairing with sucrose solution, sucrose-associated odor induced an increase in the level of salivation, but the odor presented alone did not. The conditioning effect lasted for one day after conditioning trials. This study demonstrates, for the first time, classical conditioning of salivation in species other than dogs and humans, thereby providing the first evidence of sophisticated neural control of autonomic function in insects. The results provide a useful model system for studying cellular basis of conditioning of salivation in the simpler nervous system of insects.
DiCE: The Infinitely Differentiable Monte-Carlo Estimator

The score function estimator is widely used for estimating gradients of stochastic objectives in stochastic computation graphs (SCG), e.g., in reinforcement learning and meta-learning. While deriving the first order gradient estimators by differentiating a surrogate loss (SL) objective is computationally and conceptually simple, using the same approach for higher order derivatives is more challenging. Firstly, analytically deriving and implementing such estimators is laborious and not compliant with automatic differentiation. Secondly, repeatedly applying SL to construct new objectives for each order derivative involves increasingly cumbersome graph manipulations. Lastly, to match the first order gradient under differentiation, SL treats part of the cost as a fixed sample, which we show leads to missing and wrong terms for estimators of higher order derivatives. To address all these shortcomings in a unified way, we introduce DICE, which provides a single objective that can be differentiated repeatedly, generating correct estimators of derivatives of any order in SCGs. Unlike SL, DICE relies on automatic differentiation for performing the requisite graph manipulations. We verify the correctness of DICE both through a proof and numerical evaluation of the DICE derivative estimates. We also use DICE to propose and evaluate a novel approach for multi-agent learning. Our code is available at github.com/alshedivat/lola.
Wideband printed dipole antenna for DTV signal reception

A novel printed dipole antenna capable of generating a wide operating band for digital television (DTV) signal reception in the 470 ~ 806 MHz band is presented. The antenna is of a rectangular shape and has a size of 20 times 227 mm2, and the antenna comprises two asymmetric radiating portions of arm 1 and arm 2 separated by a step-shaped feed gap with its one open end at the center of antenna's one long side edge and the other open end at about L/4 away from the center of the opposite long side edge. By the use of the step-shaped feed gap, the proposed antenna can generate two adjacent resonant modes to form a wide operating band of larger than 50% in 2.5:1 VSWR bandwidth, which is much wider than that of the corresponding conventional center-fed dipole antenna with a straight feed gap. The antenna's first resonant mode is operated as a 0.5-wavelength mode controlled by the antenna length L, while the second resonant mode is a 1.0-wavelength mode controlled by the total effective length of arms 1 and 2, which is about 1.25L in the proposed design. Detailed design considerations and radiation characteristics of the two excited resonant modes for broadband operation of the proposed dipole antenna are discussed in the paper.
Genetics of human enteric neuropathies

Knowledge of molecular mechanisms that underlie development of the enteric nervous system has greatly expanded in recent decades. Enteric neuropathies related to aberrant genetic development are thus becoming increasingly recognized. There has been no recent review of these often highly morbid disorders. This review highlights advances in knowledge of the molecular pathogenesis of these disorders from a clinical perspective. It includes diseases characterized by an infantile aganglionic Hirschsprung phenotype and those in which structural abnormalities are less pronounced. The implications for diagnosis, screening and possible reparative approaches are presented.
Class representative autoencoder for low resolution multi-spectral gender classification

Gender is one of the most common attributes used to describe an individual. It is used in multiple domains such as human computer interaction, marketing, security, and demographic reports. Research has been performed to automate the task of gender recognition in constrained environment using face images, however, limited attention has been given to gender classification in unconstrained scenarios. This work attempts to address the challenging problem of gender classification in multi-spectral low resolution face images. We propose a robust Class Representative Autoencoder model, termed as AutoGen for the same. The proposed model aims to minimize the intra-class variations while maximizing the inter-class variations for the learned feature representations. Results on visible as well as near infrared spectrum data for different resolutions and multiple databases depict the efficacy of the proposed model. Comparative results with existing approaches and two commercial off-the-shelf systems further motivate the use of class representative features for classification.
Screening in New Credit Markets : Can Individual Lenders Infer Borrower Creditworthiness in Peer-to-Peer Lending ?

The current banking crisis highlights the challenges faced in the traditional lending model, particularly in terms of screening smaller borrowers. The recent growth in online peer-to-peer lending marketplaces offers opportunities to examine different lending models that rely on screening by multiple peers. This paper evaluates the screening ability of lenders in such peer-topeer markets. Our methodology takes advantage of the fact that lenders do not observe a borrower’s true credit score but only see an aggregate credit category. We find that lenders are able to use available information to infer a third of the variation in creditworthiness that is captured by a borrower’s credit score. This inference is economically significant and allows lenders to lend at a 140-basis-points lower rate for borrowers with (unobserved to lenders) better credit scores within a credit category. While lenders infer the most from standard banking “hard” information, they also use non-standard (subjective) information. Our methodology shows, without needing to code subjective information that lenders learn even from such “softer” information, particularly when it is likely to provide credible signals regarding borrower creditworthiness. Our findings highlight the screening ability of peer-to-peer markets and suggest that these emerging markets may provide a viable complement to traditional lending markets, especially for smaller borrowers.
The Firefighter Problem: a survey of results, directions and questions

The Firefighter Problem was introduced by Bert Hartnell in 1995 at the 25th Manitoba Conference on Combinatorial Mathematics and Computing [23]. Imagine that, at time 0, a fire breaks out at a vertex of a graph G. At each subsequent time, the firefighter “defends” a vertex of G and then the fire spreads from each “burning” vertex to all of its undefended neighbours. Once a vertex is burning or defended, it remains so from then onwards. The process terminates when the fire can no longer spread. An example is given in Figure 1. In the general form of the problem, more than one fire can break out, and the fire or fires are defended by one or more firefighters. There are a number of different objectives that can be pursued, including:
A 8–40 GHz, 8 mW LNA with 27 dB peak gain and 5.2 dB NF for multiband applications

An extremely-wideband, 8-40 GHz, low power Low Noise Amplifier (LNA) is presented in this paper. The LNA utilizes a novel combination of design techniques, namely resistive feedback, emitter degeneration, and shunt-peaked split-inductor to provide wideband input and noise matching. Furthermore, the LNA is designed as 3-stage amplifier to provide better gain control through stagger-tuning method, to meet gain specification across the entire band. The proposed LNA was implemented in 0.18 μm SiGe BiCMOS process with 150 GHz fT. The design achieves a peak gain of 27 dB with minimum Noise Figure (NF) of 5.2 dB. The gain of the LNA is greater than 15.2 dB and NF is less than 7.2 dB from 8-40 GHz. The LNA consumes only 6.7 mA from a 1.2 V supply and occupies an area of only 0.26 mm2. Using the proposed architecture, this work demonstrates LNA with extremely wide bandwidth and 3.7X improvement in FoM over comparable state-of-the-art designs.
Siamese Cascaded Region Proposal Networks for Real-Time Visual Tracking

Region proposal networks (RPN) have been recently combined with the Siamese network for tracking, and shown excellent accuracy with high efficiency. Nevertheless, previously proposed one-stage Siamese-RPN trackers degenerate in presence of similar distractors and large scale variation. Addressing these issues, we propose a multi-stage tracking framework, Siamese Cascaded RPN (C-RPN), which consists of a sequence of RPNs cascaded from deep high-level to shallow low-level layers in a Siamese network. Compared to previous solutions, C-RPN has several advantages: (1) Each RPN is trained using the outputs of RPN in the previous stage. Such process stimulates hard negative sampling, resulting in more balanced training samples. Consequently, the RPNs are sequentially more discriminative in distinguishing difficult background (i.e., similar distractors). (2) Multi-level features are fully leveraged through a novel feature transfer block (FTB) for each RPN, further improving the discriminability of C-RPN using both high-level semantic and low-level spatial information. (3) With multiple steps of regressions, C-RPN progressively refines the location and shape of the target in each RPN with adjusted anchor boxes in the previous stage, which makes localization more accurate. C-RPN is trained end-to-end with the multi-task loss function. In inference, C-RPN is deployed as it is, without any temporal adaption, for real-time tracking. In extensive experiments on OTB-2013, OTB-2015, VOT2016, VOT-2017, LaSOT and TrackingNet, C-RPN consistently achieves state-of-the-art results and runs in real-time.
A highly stretchable pH sensor array using elastomer-embedded laser carbonized patterns

This paper reports on a facile and low cost method to fabricate highly stretchable potentiometric pH sensor arrays for biomedical and wearable applications. The technique uses laser carbonization of a thermoset polymer followed by transfer and embedment of carbonized nanomaterial onto an elastomeric matrix. The process combines selective laser pyrolization/carbonization with meander interconnect methodology to fabricate stretchable conductive composites with which pH sensors can be realized. The stretchable pH sensors display a sensitivity of -51 mV/pH over the clinically-relevant range of pH 4-10. The sensors remain stable for strains of up to 50 %.
Adaptive image denoising using cuckoo algorithm

This paper presents a novel denoising approach based on smoothing linear and nonlinear filters combined with an optimization algorithm. The optimization algorithm used was cuckoo search algorithm and is employed to determine the optimal sequence of filters for each kind of noise. Noises that would be eliminated form images using the proposed approach including Gaussian, speckle, and salt and pepper noise. The denoising behaviour of nonlinear filters and wavelet shrinkage threshold methods have also been analysed and compared with the proposed approach. Results show the robustness of the proposed filter when compared with the state-of-the-art methods in terms of peak signal-tonoise ratio and image quality index. Furthermore, a comparative analysis is provided between the said optimization algorithm and the genetic algorithm.
The Cilk++ concurrency platform

The availability of multicore processors across a wide range of computing platforms has created a strong demand for software frameworks that can harness these resources. This paper overviews the Cilk++ programming environment, which incorporates a compiler, a runtime system, and a race-detection tool. The Cilk++ runtime system guarantees to load-balance computations effectively. To cope with legacy codes containing global variables, Cilk++ provides a "hyperobject" library which allows races on nonlocal variables to be mitigated without lock contention or substantial code restructuring.
CLASSIFYING , PROFILING AND PREDICTING USER BEHAVIOR IN THE CONTEXT OF LOCATION BASED SERVICES

Motivated by the technology evolutions and the corresponding changes in user-consumer behavioral patterns, this study applies a Location Based Services (LBS) environmental determinants’ integrated theoretical framework by investigating its role on classifying, profiling and predicting user-consumer behavior. For that purpose, a laboratory LBS application was developed and tested with 110 subjects within the context of a field trial setting in the entertainment industry. Users are clustered into two main types having the “physical” and the “social density” determinants to best discriminate between the resulting clusters. Also, the two clusters differ in terms of their spatial and verbal ability and attitude towards the LBS environment. Similarly, attitude is predicted by the “location”, the “device” and the “mobile connection” LBS environmental determinants for the “walkers in place” (cluster #1) and by all LBS environmental determinants (i.e. those determinants of cluster #1 plus the “digital” and the “social environment” ones) for the “walkers in space” (cluster #2). Finally, the attitude of both clusters’ participants towards the LBS environment affects their behavioral intentions towards using LBS applications, with limited, however, predicting power observed in this relationship.
Ambulatory Assessment of Lifestyle Factors for Alzheimer's Disease and Related Dementias

Considering few treatments are available to slow or stop neurodegenerative disorders, such as Alzheimer’s disease and related dementias (ADRD), modifying lifestyle factors to prevent disease onset are recommended. The Voice, Activity, and Location Monitoring system for Alzheimer’s disease (VALMA) is a novel ambulatory sensor system designed to capture natural behaviours across multiple domains to profile lifestyle risk factors related to ADRD. Objective measures of physical activity and sleep are provided by lower limb accelerometry. Audio and GPS location records provide verbal and mobility activity, respectively. Based on a familiar smartphone package, data collection with the system has proven to be feasible in community-dwelling older adults. Objective assessments of everyday activity will impact diagnosis of disease and design of exercise, sleep, and social interventions to prevent and/or slow disease progression.
Essential oils: their antibacterial properties and potential applications in foods--a review.

In vitro studies have demonstrated antibacterial activity of essential oils (EOs) against Listeria monocytogenes, Salmonella typhimurium, Escherichia coli O157:H7, Shigella dysenteria, Bacillus cereus and Staphylococcus aureus at levels between 0.2 and 10 microl ml(-1). Gram-negative organisms are slightly less susceptible than gram-positive bacteria. A number of EO components has been identified as effective antibacterials, e.g. carvacrol, thymol, eugenol, perillaldehyde, cinnamaldehyde and cinnamic acid, having minimum inhibitory concentrations (MICs) of 0.05-5 microl ml(-1) in vitro. A higher concentration is needed to achieve the same effect in foods. Studies with fresh meat, meat products, fish, milk, dairy products, vegetables, fruit and cooked rice have shown that the concentration needed to achieve a significant antibacterial effect is around 0.5-20 microl g(-1) in foods and about 0.1-10 microl ml(-1) in solutions for washing fruit and vegetables. EOs comprise a large number of components and it is likely that their mode of action involves several targets in the bacterial cell. The hydrophobicity of EOs enables them to partition in the lipids of the cell membrane and mitochondria, rendering them permeable and leading to leakage of cell contents. Physical conditions that improve the action of EOs are low pH, low temperature and low oxygen levels. Synergism has been observed between carvacrol and its precursor p-cymene and between cinnamaldehyde and eugenol. Synergy between EO components and mild preservation methods has also been observed. Some EO components are legally registered flavourings in the EU and the USA. Undesirable organoleptic effects can be limited by careful selection of EOs according to the type of food.
Divide and Grow: Capturing Huge Diversity in Crowd Images with Incrementally Growing CNN

Automated counting of people in crowd images is a challenging task. The major difficulty stems from the large diversity in the way people appear in crowds. In fact, features available for crowd discrimination largely depend on the crowd density to the extent that people are only seen as blobs in a highly dense scene. We tackle this problem with a growing CNN which can progressively increase its capacity to account for the wide variability seen in crowd scenes. Our model starts from a base CNN density regressor, which is trained in equivalence on all types of crowd images. In order to adapt with the huge diversity, we create two child regressors which are exact copies of the base CNN. A differential training procedure divides the dataset into two clusters and fine-tunes the child networks on their respective specialties. Consequently, without any hand-crafted criteria for forming specialties, the child regressors become experts on certain types of crowds. The child networks are again split recursively, creating two experts at every division. This hierarchical training leads to a CNN tree, where the child regressors are more fine experts than any of their parents. The leaf nodes are taken as the final experts and a classifier network is then trained to predict the correct specialty for a given test image patch. The proposed model achieves higher count accuracy on major crowd datasets. Further, we analyse the characteristics of specialties mined automatically by our method.
Measuring IPv6 DNS Reconnaissance Attacks and Preventing Them Using DNS Guard

Traditional address scanning attacks mainly rely on the naive 'brute forcing' approach, where the entire IPv4 address space is exhaustively searched by enumerating different possibilities. However, such an approach is inefficient for IPv6 due to its vast subnet size (i.e., 2^64). As a result, it is widely assumed that address scanning attacks are less feasible in IPv6 networks. In this paper, we evaluate new IPv6 reconnaissance techniques in real IPv6 networks and expose how to leverage the Domain Name System (DNS) for IPv6 network reconnaissance. We collected IPv6 addresses from 5 regions and 100,000 domains by exploiting DNS reverse zone and DNSSEC records. We propose a DNS Guard (DNSG) to efficiently detect DNS reconnaissance attacks in IPv6 networks. DNSG is a plug and play component that could be added to the existing infrastructure. We implement DNSG using Bro and Suricata. Our results demonstrate that DNSG could effectively block DNS reconnaissance attacks.
Application of Data Mining Techniques for Financial Accounting Fraud Detection Scheme 1

Data mining techniques are providing great aid in financial accounting fraud detection, since dealing with the large data volumes and complexities of financial data are big challenges for forensic accounting. The implementation of data mining techniques for fraud detection follows the traditional information flow of data mining, which begins with feature selection followed by representation, data collection and management, pre processing, data mining, post-processing, and performance evaluation. Data mining methods have the capability of detecting fraud because these techniques can use past cases of fraud to build models, which identify and detect the risk of fraud. Financial statement fraud, one of the financial frauds, has reached the epidemic proportion globally. Collapses of high profile companies have left a dirty smear on the effectiveness of corporate governance, quality of financial reports, and credibility of audit functions. Financial statement fraud has become a critical issue in the businesses around the world. The aim of this contribution is to show some data mining techniques for fraud detection and prevention with applications in credit card and telecommunications, within a business of mining the data to achieve higher cost savings, and also in the interests of determining potential legal evidence.