nils-herrmann/clean_scirepeval huggingface dataset:
Neural network prediction model for fine particulate matter (PM2.5) on the US-Mexico border in El Paso (Texas) and Ciudad Juárez (Chihuahua)

The daily average PM2.5 concentration forecast is a leading component nowadays in air quality research, which is necessary to perform in order to assess the impact of air on the health and welfare of every living being. The present work is aimed at analyzing and benchmarking a neural-network approach to the prediction of average PM2.5 concentrations. The model thus obtained will be indispensable, as a control tool, for the purpose of preventing dangerous situations that may arise. To this end we have obtained data and measurements based on samples taken during the early hours of the day. Results from three different topologies of neural networks were compared so as to identify their potential uses, or rather, their strengths and weaknesses: Multilayer Perceptron (MLP), Radial Basis Function (RBF) and Square Multilayer Perceptron (SMLP). Moreover, two classical models were built (a persistence model and a linear regression), so as to compare their results with the ones provided by the neural network models. The results clearly demonstrated that the neural approach not only outperformed the classical models but also showed fairly similar values among different topologies. Moreover, a differential behavior in terms of stability and length of the training phase emerged during testing as well. The RBF shows up to be the network with the shortest training times, combined with a greater stability during the prediction stage, thus characterizing this topology as an ideal solution for its use in environmental applications instead of the widely used and less effective MLP.
The PS-Battles Dataset - an Image Collection for Image Manipulation Detection

The boost of available digital media has led to a significant increase in derivative work. With tools for manipulating objects becoming more and more mature, it can be very difficult to determine whether one piece of media was derived from another one or tampered with. As derivations can be done with malicious intent, there is an urgent need for reliable and easily usable tampering detection methods. However, even media considered semantically untampered by humans might have already undergone compression steps or light post-processing, making automated detection of tampering susceptible to false positives. In this paper, we present the PSBattles dataset which is gathered from a large community of image manipulation enthusiasts and provides a basis for media derivation and manipulation detection in the visual domain. The dataset consists of 102’028 images grouped into 11’142 subsets, each containing the original image as well as a varying number of manipulated derivatives.
Understanding Fibroblasts in Order to Comprehend the Osteopathic Treatment of the Fascia

The osteopathic treatment of the fascia involves several techniques, each aimed at allowing the various layers of the connective system to slide over each other, improving the responses of the afferents in case of dysfunction. However, before becoming acquainted with a method, one must be aware of the structure and function of the tissue that needs treating, in order to not only better understand the manual approach, but also make a more conscious choice of the therapeutic technique to employ, in order to adjust the treatment to the specific needs of the patient. This paper examines the current literature regarding the function and structure of the fascial system and its foundation, that is, the fibroblasts. These connective cells have many properties, including the ability to contract and to communicate with one another. They play a key role in the transmission of the tension produced by the muscles and in the management of the interstitial fluids. They are a source of nociceptive and proprioceptive information as well, which is useful for proper functioning of the body system. Therefore, the fibroblasts are an invaluable instrument, essential to the understanding of the therapeutic effects of osteopathic treatment. Scientific research should make greater efforts to better understand their functioning and relationships.
Shedder status-An analysis of self and non-self DNA in multiple handprints deposited by the same individuals over time.

There are several studies that suggest that different people deposit different quantities of their own DNA on items they touch, i.e. some are good shedders and others are bad shedders. It is of interest to determine if individuals deposit consistent quantities of their own DNA, no matter the occasion, as well as the degree of variability among individuals. To investigate this, participants were tested for their ability to deposit DNA by placing right and left handprints on separate DNA-free glass plates at three set times during the day (morning, midday and afternoon) on four different days spaced over several weeks. Information regarding recent activities performed by the individual was recorded, along with information on gender, hand dominance and hand size. A total of 240 handprint deposits were collected from 10 individuals and analyzed for differences in DNA quantity and the type of the DNA profile obtained at different times of the day, on different days, between the two hands of the same individual, and between different individuals. Furthermore, the correlation between the deposit quantity and the ratio of self to non-self DNA in the mixed deposits was analyzed to determine if the amount of non-self DNA has an effect on overall DNA quantities obtained. In general, this study has shown that while there is substantial variation in the quantities deposited by individuals on different occasions, some clear trends were evident with some individuals consistently depositing significantly more or less DNA than others. Non-self DNA was usually deposited along with self DNA and, in most instances, was the minor component. Incidents where the non-self portion was the major component were very rare and, when observed, were associated with a poor depositor/shedder. Forensic DNA scientists need to consider the range and variability of DNA a person deposits when touching an object, the likelihood of non-self DNA being co-deposited onto the handled object of interest and the factors that may affect the relative quantity of this component within the deposit.
Extraction of digital elevation models from satellite stereo images through stereo matching based on epipolarity and scene geometry

This paper addresses the problem of generating digital elevation models from satellite images taken by linear pushbroom cameras. Since there exist unique geometric properties for linear pushbroom images, we argue that the conventional DEM generation schemes developed for perspective images are not suitable for satellite images. Using the geometric properties of linear pushbroom images, we design a new matching strategy optimized for linear pushbroom image in three aspects: conjugate search method, correlation patch design and match sequence determination. We will discuss in what aspect conventional approaches and our new approach differ and show how performance has improved by hiring proper techniques. A series of experiments using SPOT panchromatic stereo pairs showed that our approach outperformed conventional approaches in terms of accuracy and processing time. q 2003 Elsevier B.V. All rights reserved.
Independent-gate four-terminal FinFET SRAM for drastic leakage current reduction

An independent-gate four-terminal FinFET SRAM have been successfully fabricated for drastic leakage current reduction. The new SRAM is consisted of a four-terminal (4T-) FinFET which has a flexible Vth controllability. The 4T-FinFET with a TiN metal gate is fabricated by a newly developed gate separation etching process. By appropriately controlling the Vth of the 4T-FinFET, we have successfully demonstrated the reduction of the leakage current and power consumption of the SRAM cell.
Microalgae for biodiesel production and other applications : A review

Sustainable production of renewable energy is being hotly debated globally since it is increasingly understood that first generation biofuels, primarily produced from food crops and mostly oil seeds are limited in their ability to achieve targets for biofuel production, climate change mitigation and economic growth. These concerns have increased the interest in developing second generation biofuels produced from non-food feedstocks such as microalgae, which potentially offer greatest opportunities in the longer term. This paper reviews the current status of microalgae use for biodiesel production, including their cultivation, harvesting, and processing. The microalgae species most used for biodiesel production are presented and their main advantages described in comparison with other available biodiesel feedstocks. The various aspects associated with the design of microalgae production units are described, giving an overview of the current state of development of algae cultivation systems (photo-bioreactors and open ponds). Other potential applications and products from microalgae are also presented such as for biological sequestration of CO2, wastewater treatment, in human health, as food additive, and for aquaculture.
Effect of Mozart Music on Hippocampal Content of BDNF in Postnatal Rats

1. Cellular and Molecular Research Center, and Neuroscience Department, Tehran University of Medical Sciences, Tehran, Iran 2. Anatomy Department, Tehran University of Medical Science, Tehran, Iran. 3. Physiology Research Center (PRC), Tehran university of Medical Sciences, Tehran, Iran. 4. Institute for Cognitive Science studies (ICSS), Tehran, Iran. 5. Department of Material Science and Engineering, Sharif University of Technology, Tehran, Iran.
Optimization of a novel method for determination of benzene, toluene, ethylbenzene, and xylenes in hair and waste water samples by carbon nanotubes reinforced sol-gel based hollow fiber solid phase microextraction and gas chromatography using factorial experimental design.

A novel design of solid phase microextraction fiber containing carbon nanotube reinforced sol-gel which was protected by polypropylene hollow fiber (HF-SPME) was developed for pre-concentration and determination of BTEX in environmental waste water and human hair samples. The method validation was included and satisfying results with high pre-concentration factors were obtained. In the present study orthogonal array experimental design (OAD) procedure with OA(16) (4(4)) matrix was applied to study the effect of four factors influencing the HF-SPME method efficiency: stirring speed, volume of adsorption organic solvent, extraction and desorption time of the sample solution, by which the effect of each factor was estimated using individual contributions as response functions in the screening process. Analysis of variance (ANOVA) was employed for estimating the main significant factors and their percentage contributions in extraction. Calibration curves were plotted using ten spiking levels of BTEX in the concentration ranges of 0.02-30,000ng/mL with correlation coefficients (r) 0.989-0.9991 for analytes. Under the optimized extraction conditions, the method showed good linearity (0.3-20,000ng/L), repeatability, low limits of detections (0.49-0.7ng/L) and excellent pre-concentration factors (185-1872). The best conditions which were estimated then applied for the analysis of BTEX compounds in the real samples.
Breastfeeding and dummy use have a protective effect on sudden infant death syndrome

We conducted a literature review on the effect of breastfeeding and dummy (pacifier) use on sudden infant death syndrome (SIDS). From 4343 abstracts, we identified 35 relevant studies on breastfeeding and SIDS, 27 on dummy use and SIDS and 59 on dummy use versus breastfeeding.



We found ample evidence that both breastfeeding and dummy use reduce the risk of SIDS. There has been a general reluctance to endorse dummy use in case it has a detrimental effect of breastfeeding. However, recent evidence suggests that dummy use might not be as harmful to breastfeeding as previously believed.
Pre-fight Detection - Classification of Fighting Situations using Hierarchical AdaBoost

This paper investigates the detection and classification of fighting and pre and post fighting events when viewed from a video camera. Specifically we investigate normal, pre, post and actual fighting sequences and classify them. A hierarchical AdaBoost classifier is described and results using this approach are presented. We show it is possible to classify pre-fighting situations using such an approach and demonstrate how it can be used in the general case of continuous sequences.
The user-subjective approach to personal information management systems

In this paper we suggest a user-subjective approach to Personal Information Management (PIM) system design. This approach advocates that PIM systems relate to the subjective value-added attributes that the user gives to the data stored in the PIM system. These attributes should facilitate system use: help the user find the information item again, recall it when needed and use it effectively in the next interaction with the item. Driven from the user-subjective approach are three generic principles which are described and discussed: (a) The subjective classification principle stating that all information items related to the same subjective topic should be classified together regardless of their technological format; (b) The subjective importance principle proposing that the subjective importance of information should determine its degree of visual salience and accessibility; and (c) The subjective context principle suggesting that information should be retrieved and viewed by the user in the same context in which it was previously used. We claim that these principles are only sporadically implemented in operating systems currently available on personal computers, and demonstrate alternatives for interface design. USER-SUBJECTIVE APPROACH TO PIM SYSTEMS 3
Dining Cryptographers Revisited

Dining cryptographers networks (or DC-nets) are a privacypreserving primitive devised by Chaum for anonymous message publication. A very attractive feature of the basic DC-net is its non-interactivity. Subsequent to key establishment, players may publish their messages in a single broadcast round, with no player-to-player communication. This feature is not possible in other privacy-preserving tools like mixnets. A drawback to DC-nets, however, is that malicious players can easily jam them, i.e., corrupt or block the transmission of messages from honest parties, and may do so without being traced. Several researchers have proposed valuable methods of detecting cheating players in DC-nets. This is usually at the cost, however, of multiple broadcast rounds, even in the optimistic case, and often of high computational and/or communications overhead, particularly for fault recovery. We present new DC-net constructions that simultaneously achieve noninteractivity and high-probability detection and identification of cheating players. Our proposals are quite efficient, imposing a basic cost that is linear in the number of participating players. Moreover, even in the case of cheating in our proposed system, just one additional broadcast round suffices for full fault recovery. Among other tools, our constructions employ bilinear maps, a recently popular cryptographic technique for reducing communication complexity.
Tappan Zee (north) bridge: mining memory accesses for introspection

The ability to introspect into the behavior of software at runtime is crucial for many security-related tasks, such as virtual machine-based intrusion detection and low-artifact malware analysis. Although some progress has been made in this task by automatically creating programs that can passively retrieve kernel-level information, two key challenges remain. First, it is currently difficult to extract useful information from user-level applications, such as web browsers. Second, discovering points within the OS and applications to hook for active monitoring is still an entirely manual process. In this paper we propose a set of techniques to mine the memory accesses made by an operating system and its applications to locate useful places to deploy active monitoring, which we call tap points. We demonstrate the efficacy of our techniques by finding tap points for useful introspection tasks such as finding SSL keys and monitoring web browser activity on five different operating systems (Windows 7, Linux, FreeBSD, Minix and Haiku) and two processor architectures (ARM and x86).
SafeGPU: Contract- and library-based GPGPU for object-oriented languages

Using GPUs as general-purpose processors has revolutionized parallel computing by providing, for a large and growing set of algorithms, massive data-parallelization on desktop machines. An obstacle to their widespread adoption, however, is the difficulty of programming them and the low-level control of the hardware required to achieve good performance. This paper proposes a programming approach, SafeGPU, that aims to make GPU data-parallel operations accessible through high-level libraries for object-oriented languages, while maintaining the performance benefits of lower-level code. The approach provides data-parallel operations for collections that can be chained and combined to express compound computations, with data synchronization and device management all handled automatically. It also integrates the design-by-contract methodology, which increases confidence in functional program correctness by embedding executable specifications into the program text. We present a prototype of SafeGPU for Eiffel, and show that it leads to modular and concise code that is accessible for GPGPU non-experts, while still providing performance comparable with that of hand-written CUDA code. We also describe our first steps towards porting it to C#, highlighting some challenges, solutions, and insights for implementing the approach in different managed languages. Finally, we show that runtime contract-checking becomes feasible in SafeGPU, as the contracts can be executed on the GPU.
SmaCH: A Framework for Smart Cultural Heritage Spaces

Cultural Heritage represents a world wide resource of inestimable value, attracting millions of visitors every year to monuments, museums and art exhibitions. A fundamental aspect of this resource is represented by its fruition and promotion. Indeed, to achieve a fruition of a cultural space that is sustainable, it is necessary to realize smart solutions for visitors' interaction to enrich their visiting experience. In this paper we present a service-oriented framework aimed to transform indoor Cultural Heritage sites in smart environments, which enforces a set of multimedia and communication services to support the changing of these spaces in an indispensable dynamic instrument for knowledge, fruition and growth for all the people. Following the Internet of Things paradigm, the proposed framework relies on the integration of a Wireless Sensor Network (WSN) with Wi-Fi and Bluetooth technologies to identify, locate and support visitors equipped with their own mobile devices.
Multidimensional Divide-and-Conquer

Most results in the field of algorithm design are single algorithms that solve single problems. In this paper we discuss multidimensional divide-and-conquer, an algorithmic paradigm that can be instantiated in many different ways to yield a number of algorithms and data structures for multidimensional problems. We use this paradigm to give best-known solutions to such problems as the ECDF, maxima, range searching, closest pair, and all nearest neighbor problems. The contributions of the paper are on two levels. On the first level are the particular algorithms and data structures given by applying the paradigm. On the second level is the more novel contribution of this paper: a detailed study of an algorithmic paradigm that is specific enough to be described precisely yet general enough to solve a wide variety of problems.
Toward a model of domain-specific search

We examine what makes a search system domain-specific and find that previous definitions are incomplete. We propose a new definition of domain specific search, together with a corresponding model, to assist researchers, systems designers and system beneficiaries in their analysis of their own domain. This model is then instantiated for two domains: intellectual property search (i.e. patent search) and medical or healthcare search. For each of the two we follow the theoretical model and identify outstanding issues. We find that the choice of dimensions is still an open issue, as linear independence is often absent and specific use-cases, particularly those related to interactive IR, still cannot be covered by the proposed model.
CHRONIC WOUND TISSUE CLASSIFICATION USING CONVOLUTIONAL NETWORKS AND COLOR SPACE REDUCTION

Chronic Wounds are ulcers presenting a difficult or nearly interrupted cicatrization process that increase the risk of complications to the health of patients, like amputation and infections. This research proposes a general noninvasive methodology for the segmentation and analysis of chronic wounds images by computing the wound areas affected by necrosis. Invasive techniques are usually used for this calculation, such as manual planimetry with plastic films. We investigated algorithms to perform the segmentation of wounds as well as the use of several convolutional networks for classifying tissue as Necrotic, Granulation or Slough. We tested four architectures: U-Net, Segnet, FCN8 and FCN32, and proposed a color space reduction methodology that increased the reported accuracies, specificities, sensitivities and Dice coefficients for all 4 networks, achieving very good levels.
Effects of steep high-frequency hearing loss on speech recognition using temporal fine structure in low-frequency region

The present study examined the effects of steep high-frequency sensorineural hearing loss (SHF-SNHL) on speech recognition using acoustic temporal fine structure (TFS) in the low-frequency region where the absolute thresholds appeared to be normal. In total, 28 participants with SHF-SNHL were assigned to 3 groups according to the cut-off frequency (1, 2, and 4 kHz, respectively) of their pure-tone absolute thresholds. Fourteen age-matched normal-hearing (NH) individuals were enrolled as controls. For each Mandarin sentence, the acoustic TFS in 10 frequency bands (each 3-ERB wide) was extracted using the Hilbert transform and was further lowpass filtered at 1, 2, and 4 kHz. Speech recognition scores were compared among the NH and 1-, 2-, and 4-kHz SHF-SNHL groups using stimuli with varying bandwidths. Results showed that speech recognition with the same TFS-speech stimulus bandwidth differed significantly in groups and filtering conditions. Sentence recognition in quiet conditions was better than that in noise. Compared with the NH participants, nearly all the SHF-SNHL participants showed significantly poorer sentence recognition within their frequency regions with "normal hearing" (defined clinically by normal absolute thresholds) in both quiet and noisy conditions. These may result from disrupted auditory nerve function in the "normal hearing" low-frequency regions.