nils-herrmann/clean_scirepeval huggingface dataset:
Water conservation as a tool to support sustainable practices in a Brazilian public university

Rational use of water can be a powerful tool to promote sustainability on university campuses. Other than resource and financial savings, it aims to support technological and behavior innovation towards a more balanced relationship between human activities and nature. This work reports on a water saving program case study, led by a research group at a university in the northeast of Brazil. It describes and discusses methods used and results obtained. From 1999 to 2008 the program reduced per capita water use by half at the university. It has brought significant resource savings to the institution. Internal results foster the implementation of cooperative projects between the university and public and private partners. All these projects involve engineers, social workers and undergraduate students from different courses. However, internal and external results have been insufficient to guarantee the internalization of the program in routine activities of the university. The permanence of the program still depends on the research group that created and manages it. The paper also presents the difficulties faced in sustaining a program like this at a Brazilian university and discusses future action to be taken to achieve the pro-
Android application for sending SMS messages with speech recognition interface

Voice SMS is an application developed in this work that allows a user to record and convert spoken messages into SMS text message. User can send messages to the entered phone number or the number of contact from the phonebook. Speech recognition is done via the Internet, connecting to Google's server. The application is adapted to input messages in English. Used tools are Android SDK and the installation is done on mobile phone with Android operating system. In this article we will give basic features of the speech recognition and used algorithm. Speech recognition for Voice SMS uses a technique based on hidden Markov models (HMM - Hidden Markov Model). It is currently the most successful and most flexible approach to speech recognition.
Adversarial Image Perturbation for Privacy Protection A Game Theory Perspective

Users like sharing personal photos with others through social media. At the same time, they might want to make automatic identification in such photos difficult or even impossible. Classic obfuscation methods such as blurring are not only unpleasant but also not as effective as one would expect [28, 37, 18]. Recent studies on adversarial image perturbations (AIP) suggest that it is possible to confuse recognition systems effectively without unpleasant artifacts. However, in the presence of counter measures against AIPs [7], it is unclear how effective AIP would be in particular when the choice of counter measure is unknown. Game theory provides tools for studying the interaction between agents with uncertainties in the strategies. We introduce a general game theoretical framework for the user-recogniser dynamics, and present a case study that involves current state of the art AIP and person recognition techniques. We derive the optimal strategy for the user that assures an upper bound on the recognition rate independent of the recogniser’s counter measure. Code is available at https://goo.gl/hgvbNK.
Clinicians guide for cue‐based transition to oral feeding in preterm infants: An easy‐to‐use clinical guide

This article aims to provide evidence to guide multidisciplinary clinical practitioners towards successful initiation and long-term maintenance of oral feeding in preterm infants, directed by the individual infant maturity.



A comprehensive review of primary research, explorative work, existing guidelines, and evidence-based opinions regarding the transition to oral feeding in preterm infants was studied to compile this document.



Current clinical hospital practices are described and challenged and the principles of cue-based feeding are explored. "Traditional" feeding regimes use criteria, such as the infant's weight, gestational age and being free of illness, and even caregiver intuition to initiate or delay oral feeding. However, these criteria could compromise the infant and increase anxiety levels and frustration for parents and caregivers. Cue-based feeding, opposed to volume-driven feeding, lead to improved feeding success, including increased weight gain, shorter hospital stay, fewer adverse events, without increasing staff workload while simultaneously improving parents' skills regarding infant feeding. Although research is available on cue-based feeding, an easy-to-use clinical guide for practitioners could not be found. A cue-based infant feeding regime, for clinical decision making on providing opportunities to support feeding success in preterm infants, is provided in this article as a framework for clinical reasoning.



Cue-based feeding of preterm infants requires care providers who are trained in and sensitive to infant cues, to ensure optimal feeding success. An easy-to-use clinical guideline is presented for implementation by multidisciplinary team members. This evidence-based guideline aims to improve feeding outcomes for the newborn infant and to facilitate the tasks of nurses and caregivers.
Convolutional neural networks for malware classification

According to AV vendors malicious software has been growing exponentially last years. One of the main reasons for these high volumes is that in order to evade detection, malware authors started using polymorphic and metamorphic techniques. As a result, traditional signature-based approaches to detect malware are being insufficient against new malware and the categorization of malware samples had become essential to know the basis of the behavior of malware and to fight back cybercriminals. During the last decade, solutions that fight against malicious software had begun using machine learning approaches. Unfortunately, there are few opensource datasets available for the academic community. One of the biggest datasets available was released last year in a competition hosted on Kaggle with data provided by Microsoft for the Big Data Innovators Gathering (BIG 2015). This thesis presents two novel and scalable approaches using Convolutional Neural Networks (CNNs) to assign malware to its corresponding family. On one hand, the first approach makes use of CNNs to learn a feature hierarchy to discriminate among samples of malware represented as gray-scale images. On the other hand, the second approach uses the CNN architecture introduced by Yoon Kim  to classify malware samples according their x86 instructions. The proposed methods achieved an improvement of 93.86% and 98,56% with respect to the equal probability benchmark.
Measuring influence in online social network based on the user-content bipartite graph

With the rising of online social networks, influence has been a complex and subtle force to govern users’ behaviors and relationship formation. Therefore, how to precisely identify and measure influence has been a hot research direction. Differentiating from existing researches, we are devoted to combining the status of users in the network and the contents generated from these users to synthetically measure the influence diffusion. In this paper, we firstly proposed a directed user-content bipartite graph model. Next, an iterative algorithm is designed to compute two scores: the users’ Influence and boards’ Reach. Finally, we conduct extensive experiments on the dataset extracted from the online community Pinterest. The experimental results verify our proposed model can discover most influential users and popular broads effectively and can also be expected to benefit various applications, e.g., viral marketing, personal recommendation, information retrieval, etc.
Microglia in neurodegenerative disease

Microglia, the resident macrophages of the CNS, are exquisitely sensitive to brain injury and disease, altering their morphology and phenotype to adopt a so-called activated state in response to pathophysiological brain insults. Morphologically activated microglia, like other tissue macrophages, exist as many different phenotypes, depending on the nature of the tissue injury. Microglial responsiveness to injury suggests that these cells have the potential to act as diagnostic markers of disease onset or progression, and could contribute to the outcome of neurodegenerative diseases. The persistence of activated microglia long after acute injury and in chronic disease suggests that these cells have an innate immune memory of tissue injury and degeneration. Microglial phenotype is also modified by systemic infection or inflammation. Evidence from some preclinical models shows that systemic manipulations can ameliorate disease progression, although data from other models indicates that systemic inflammation exacerbates disease progression. Systemic inflammation is associated with a decline in function in patients with chronic neurodegenerative disease, both acutely and in the long term. The fact that diseases with a chronic systemic inflammatory component are risk factors for Alzheimer disease implies that crosstalk occurs between systemic inflammation and microglia in the CNS.
Biometric identification via eye movement scanpaths in reading

This paper presents an objective evaluation of various eye movement-based biometric features and their ability to accurately and precisely distinguish unique individuals. Eye movements are uniquely counterfeit resistant due to the complex neurological interactions and the extraocular muscle properties involved in their generation. Considered biometric candidates cover a number of basic eye movements and their aggregated scanpath characteristics, including: fixation count, average fixation duration, average saccade amplitudes, average saccade velocities, average saccade peak velocities, the velocity waveform, scanpath length, scanpath area, regions of interest, scanpath inflections, the amplitude-duration relationship, the main sequence relationship, and the pairwise distance between fixations. As well, an information fusion method for combining these metrics into a single identification algorithm is presented. With limited testing this method was able to identify subjects with an equal error rate of 27%. These results indicate that scanpath-based biometric identification holds promise as a behavioral biometric technique.
THE ROBUSTNESS OF CRITICAL PERIOD EFFECTS IN SECOND LANGUAGE ACQUISITION

This study was designed to test the Fundamental Difference Hypothesis (Bley-Vroman, 1988), which states that, whereas children are known to learn language almost completely through (implicit) domain-specific mechanisms, adults have largely lost the ability to learn a language without reflecting on its structure and have to use alternative mechanisms, drawing especially on their problem-solving capacities, to learn a second language. The hypothesis implies that only adults with a high level of verbal analytical ability will reach near-native competence in their second language, but that this ability will not be a significant predictor of success for childhood second language acquisition. A study with 57 adult Hungarian-speaking immigrants confirmed the hypothesis in the sense that very few adult immigrants scored within the range of child arrivals on a grammaticality judgment test, and that the few who did had high levels of verbal analytical ability; this ability was not a significant predictor for childhood arrivals. This study replicates the findings of Johnson and Newport (1989) and provides an explanation for the apparent exceptions in their study. These findings lead to a reconceptualization of the Critical Period Hypothesis: If the scope of this hypothesis is lim-
Geo-level Bayesian Hierarchical Media Mix Modeling

Media mix modeling is a statistical analysis on historical data to measure the return on investment (ROI) on advertising and other marketing activities. Current practice usually utilizes data aggregated at a national level, which often suffers from small sample size and insufficient variation in the media spend. When sub-national data is available, we propose a geo-level Bayesian hierarchical media mix model (GBHMMM), and demonstrate that the method generally provides estimates with tighter credible intervals compared to a model with national level data alone. This reduction in error is due to having more observations and useful variability in media spend, which can protect advertisers from unsound reallocation decisions. Under some weak conditions, the geo-level model can reduce ad targeting bias. When geo-level data is not available for all the media channels, the geo-level model estimates generally deteriorate as more media variables are imputed using the national level data.
Compact multi-layer handset phone 13.56 MHz NFC antenna design by novel laser-induced thin-film antenna (LITA) technologies

This article presents a compact multi-layer handset phone 13.56 MHz NFC antenna design by novel LITA (Laser-Induced Integrated Thin-Film Antenna) technologies, which is jointly developed by ITRI and ACON in Taiwan. Through the proposed LITA technology, metal layouts of antennas can be formed on the internal surface of a smartwatch casing successfully with conformal, thin-film type, multi-layer and highly integrating characteristics. It is demonstrated that by designing a NFC antenna to become a two-layer overlapped coil structure by LITA technology, the required antenna layout area can be reduced about 40% compared to the co-planar NFC coil designs and also keep good inductive distance. The constructed antenna prototype integrated with a NFC reader chip (NXP NPC300) is analyzed and the read distance between the proposed antenna and type1 to type4 NFC tags classified by the NFC Forum are tasted and discussed in this paper.
Restful Design and Implementation of Smart Appliances for Smart Home

The world is changing and we are moving towards the day when everything will be network accessible, named 'Internet of Things (IoT)'. The growing popularity of IoT leads various services such as healthcare, connected car, smart education and smart home services. Among these services, smart home becomes commercialized most rapidly. We developed the Smart Home Protocol (SHP) to enable connectivity between various our smart home devices including smart appliances, LED bulbs, IP cameras, etc. In this paper, we present an overview of the SHP based on Restful architecture, followed by a detailed description of architecture and application profile for smart home devices. We also introduce our smart home services and describe the implementation of smart home protocol in smart appliances. To our understanding, this study is important, since it is a basis for enabling connectivity between our smart home products as well as those from other device and appliance manufacturers. We believe this work plays an important role for designing and implementing smart home devices such as smart appliances.
EVALUATION OF EFFECTIVENESS IN A NOVEL WOUND HEALING OINTMENT-CROCODILE OIL BURN OINTMENT

Crocodile oil and its products are used as ointments for burns and scalds in traditional medicines. A new ointment formulation - crocodile oil burn ointment (COBO) was developed to provide more efficient wound healing activity. The purpose of the study was to evaluate the burn healing efficacy of this new formulation by employing deep second-degree burns in a Wistar rat model. The analgesic and anti-inflammatory activities of COBO were also studied to provide some evidences for its further use.



The wound healing potential of this formulation was evaluated by employing a deep second-degree burn rat model and the efficiency was comparatively assessed against a reference ointment - (1% wt/wt) silver sulfadiazine (SSD). After 28 days, the animals were euthanized and the wounds were removed for transversal and longitudinal histological studies. Acetic acid-induced writhing in mice was used to evaluate the analgesic activity and its anti-inflammatory activity was observed in xylene -induced edema in mice.



COBO enhanced the burn wound healing (20.5±1.3 d) as indicated by significant decrease in wound closure time compared with the burn control (25.0±2.16 d) (P<0.01). Hair follicles played an importance role in the physiological functions of the skin, and their growth in the wound could be revealed for the skin regeneration situation. Histological results showed that the hair follicles were well-distributed in the post-burn skin of COBO treatment group, and the amounts of total, active, primary and secondary hair follicles in post-burn 28-day skin of COBO treatment groups were more than those in burn control and SSD groups. On the other hand, the analgesic and anti-inflammatory activity of COBO were much better than those of control group, while they were very close to those of moist exposed burn ointment (MEBO).



COBO accelerated wound closure, reduced inflammation, and had analgesic effects compared with SSD in deep second degree rat burn model. These findings suggest that COBO would be a potential therapy for treating human burns.  COBO, crocodile oil burn ointment; SSD, silver sulfadiazine; MEBO, moist exposed burn ointment; TCM, traditional Chinese medicine; CHM, Chinese herbal medicine; GC-MS, gas chromatography-mass spectrometry.
Review of Battery Charger Topologies, Charging Power Levels, and Infrastructure for Plug-In Electric and Hybrid Vehicles

This paper reviews the current status and implementation of battery chargers, charging power levels, and infrastructure for plug-in electric vehicles and hybrids. Charger systems are categorized into off-board and on-board types with unidirectional or bidirectional power flow. Unidirectional charging limits hardware requirements and simplifies interconnection issues. Bidirectional charging supports battery energy injection back to the grid. Typical on-board chargers restrict power because of weight, space, and cost constraints. They can be integrated with the electric drive to avoid these problems. The availability of charging infrastructure reduces on-board energy storage requirements and costs. On-board charger systems can be conductive or inductive. An off-board charger can be designed for high charging rates and is less constrained by size and weight. Level 1 (convenience), Level 2 (primary), and Level 3 (fast) power levels are discussed. Future aspects such as roadbed charging are presented. Various power level chargers and infrastructure configurations are presented, compared, and evaluated based on amount of power, charging time and location, cost, equipment, and other factors.
Classification of Histopathology Images of Breast into Benign and Malignant using a Single-layer Convolutional Neural Network

Breast cancer is known as the second most prevalent cancer among women worldwide, and an accurate and fast diagnosis of it requires a pathologist to go through a time-consuming process examining different captured images under varying magnifications. Computer vision and machine learning techniques are used by many scholars to automate this process and provide a faster and more accurate diagnosis of such cancers, but most of them have utilized handengineered feature descriptors to classify the type of images (whether benign or malignant) based upon. Deep learning techniques have made a significant progress in the world of pattern recognition, image classification, object detection, etc. Convolutional Neural Networks (CNN) -- a special kind of deep learning methods -- are best known for identifying patterns in the images; they try to represent an abstract form of images containing the most salient information needed for distinguishing them from different similar-looking images. The main aim of this paper is to employ CNN for the task of breast cancer classification given an unknown image of the patient for an accurate diagnosis. A new network design is proposed to extract the most informative features from a collection of histopathology images provided by BreakHis database of microscopic breast tumor images. The experimental results carried on 1,995 histopathological images (with a 40× magnifying factor), demonstrated an improved accuracy compared to some prior works, and a comparable performance regarding one of the previous works.
Tackling Youth Unemployment in Sub-Saharan Africa : Is there a role for Artisanal and Small-Scale Mining ?

This paper argues that a formalized and supported artisanal and small-scale mining (ASM) sector could assist immeasurably with reducing the intensity of the youth unemployment crisis which now plagues sub-Saharan Africa. Over the past decade, hundreds of thousands of the region's youth have pursued work in the ASM economy in various capacities, decisions which have brought much-needed stability in a landscape devoid of formal sector job opportunities. Host governments and donors, however, have often condemned these moves, failing to fully appreciate why youth have pursued work in ASM in the first place and the impact it has had on their livelihoods. Whilst by no means the solution to the youth unemployment problem in sub-Saharan Africa, heightened emphasis on formalizing and supporting ASM could certainly buy policymakers and donors some valuable time – at least in the short term – to sufficiently 're-think' their ineffective approaches to tackling the crisis. 3  One of the most pressing problems in sub-Saharan Africa today is youth unemployment. The data compiled by a range of organizations in recent years paint an extremely bleak picture. At present, of the estimated 296.9 million people aged 10-24 in the region, fewer than 50 per cent are employed in the formal economy . Some countries, such as Mozambique and Ghana, have youth unemployment/underemployment rates as high as 80 per cent [2]. The growing concern, however, is that despite presently being the most youthful area of the world, sub-Saharan Africa is on course to become even younger: due to its high fertility rate, the number of youth inhabiting the region, as a proportion of the population, is projected to exceed 75 per cent by 2015. The region will be home to nearly one quarter of the world's young people by 2025 .
30Music Listening and Playlists Dataset

We introduce the 30Music dataset, a collection of listening and playlists data retrieved from Internet radio stations through Last.fm API. In this paper we describe the creation process, its content, and its possible uses. Attractive features of the 30Music dataset that differentiate it from existing public datasets include, among the others, (i) the user listening sessions complete of contextual time information, (ii) the user playlists, and (iii) the positive user ratings, key information to experiment with the task of modeling user taste and recommending playlists.
Design of Experiments: Statistical Principles of Research Design and Analysis

Introducing a new hobby for other people may inspire them to join with you. Reading, as one of mutual hobby, is considered as the very easy hobby to do. But, many people are not interested in this hobby. Why? Boring is the reason of why. However, this feel actually can deal with the book and time of you reading. Yeah, one that we will refer to break the boredom in reading is choosing design of experiments statistical principles of research design and analysis as the reading material.
Foundations of Language: Brain, Meaning, Grammar, Evolution

Bargaining with reading habit is no need. Reading is not kind of something sold that you can take or not. It is a thing that will change your life to life better. It is the thing that will give you many things around the world and this universe, in the real world and here after. As what will be given by this foundations of language brain meaning grammar evolution, how can you bargain with the thing that has many benefits for you?