nils-herrmann/clean_scirepeval huggingface dataset:
Surveying Wearable Human Assistive Technology for Life and Safety Critical Applications: Standards, Challenges and Opportunities

In this survey a new application paradigm life and safety for critical operations and missions using wearable Wireless Body Area Networks (WBANs) technology is introduced. This paradigm has a vast scope of applications, including disaster management, worker safety in harsh environments such as roadside and building workers, mobile health monitoring, ambient assisted living and many more. It is often the case that during the critical operations and the target conditions, the existing infrastructure is either absent, damaged or overcrowded. In this context, it is envisioned that WBANs will enable the quick deployment of ad-hoc/on-the-fly communication networks to help save many lives and ensuring people's safety. However, to understand the applications more deeply and their specific characteristics and requirements, this survey presents a comprehensive study on the applications scenarios, their context and specific requirements. It explores details of the key enabling standards, existing state-of-the-art research studies, and projects to understand their limitations before realizing aforementioned applications. Application-specific challenges and issues are discussed comprehensively from various perspectives and future research and development directions are highlighted as an inspiration for new innovative solutions. To conclude, this survey opens up a good opportunity for companies and research centers to investigate old but still new problems, in the realm of wearable technologies, which are increasingly evolving and getting more and more attention recently.
Deep Ground Truth Analysis of Current Android Malware

To build effective malware analysis techniques and to evaluate new detection tools, up-to-date datasets reflecting the current Android malware landscape are essential. For such datasets to be maximally useful, they need to contain reliable and complete information on malware’s behaviors and techniques used in the malicious activities. Such a dataset shall also provide a comprehensive coverage of a large number of types of malware. The Android Malware Genome created circa 2011 has been the only well-labeled and widely studied dataset the research community had easy access to. But not only is it outdated and no longer represents the current Android malware landscape, it also does not provide as detailed information on malware’s behaviors as needed for research. Thus it is urgent to create a high-quality dataset for Android malware. While existing information sources such as VirusTotal are useful, to obtain the accurate and detailed information for malware behaviors, deep manual analysis is indispensable. In this work we present our approach to preparing a large Android malware dataset for the research community. We leverage existing anti-virus scan results and automation techniques in categorizing our large dataset (containing 24,650 malware app samples) into 135 varieties (based on malware behavioral semantics) which belong to 71 malware families. For each variety, we select three samples as representatives, for a total of 405 malware samples, to conduct in-depth manual analysis. Based on the manual analysis result we generate detailed descriptions of each malware variety’s behaviors and include them in our dataset. We also report our observations on the current landscape of Android malware as depicted in the dataset. Furthermore, we present detailed documentation of the process used in creating the dataset, including the guidelines for the manual analysis. We make our Android malware dataset available to the research community.
Unsupervised Word Segmentation and Lexicon Discovery Using Acoustic Word Embeddings

In settings where only unlabeled speech data is available, speech technology needs to be developed without transcriptions, pronunciation dictionaries, or language modelling text. A similar problem is faced when modeling infant language acquisition. In these cases, categorical linguistic structure needs to be discovered directly from speech audio. We present a novel unsu-pervised Bayesian model that segments unlabeled speech and clusters the segments into hypothesized word groupings. The result is a complete unsupervised tokenization of the input speech in terms of discovered word types. In our approach, a potential word segment (of arbitrary length) is embedded in a fixed-dimensional acoustic vector space. The model, implemented as a Gibbs sampler, then builds a whole-word acoustic model in this space while jointly performing segmentation. We report word error rates in a small-vocabulary connected digit recognition task by mapping the unsupervised decoded output to ground truth transcriptions. The model achieves around 20% error rate, outperforming a previous HMM-based system by about 10% absolute. Moreover, in contrast to the baseline, our model does not require a pre-specified vocabulary size.
Behavioral theories and the neurophysiology of reward.

The functions of rewards are based primarily on their effects on behavior and are less directly governed by the physics and chemistry of input events as in sensory systems. Therefore, the investigation of neural mechanisms underlying reward functions requires behavioral theories that can conceptualize the different effects of rewards on behavior. The scientific investigation of behavioral processes by animal learning theory and economic utility theory has produced a theoretical framework that can help to elucidate the neural correlates for reward functions in learning, goal-directed approach behavior, and decision making under uncertainty. Individual neurons can be studied in the reward systems of the brain, including dopamine neurons, orbitofrontal cortex, and striatum. The neural activity can be related to basic theoretical terms of reward and uncertainty, such as contiguity, contingency, prediction error, magnitude, probability, expected value, and variance.
THE APPROXIMATE AND EXACT SOLUTIONS OF THE FRACTIONAL-ORDER DELAY DIFFERENTIAL EQUATIONS USING LEGENDRE SEUDOSPECTRAL METHOD

Fractional differential equations have recently been applied in various area of engineering, science, finance, applied mathematics, bio-engineering and others. However, many researchers remain unaware of this field. In this paper, an efficient numerical method for solving the fractional delay differential equations (FDDEs) is considered. The fractional derivative is described in the Caputo sense. The method is based upon Legendre approximations. The properties of Legendre polynomials are utilized to reduce FDDEs to linear or nonlinear system of algebraic equations. Numerical simulation with the exact solutions of FDDEs is presented.
Order-Embeddings of Images and Language

Hypernymy, textual entailment, and image captioning can be seen as special cases of a single visual-semantic hierarchy over words, sentences, and images. In this paper we advocate for explicitly modeling the partial order structure of this hierarchy. Towards this goal, we introduce a general method for learning ordered representations, and show how it can be applied to a variety of tasks involving images and language. We show that the resulting representations improve performance over current approaches for hypernym prediction and image-caption retrieval.
An Artificial Neural Network Model to Forecast Exchange Rates

For the purposes of this research, the optimal MLP neural network topology has been designed and tested by means the specific genetic algorithm multi-objective Pareto-Based. The objective of the research is to predict the trend of the exchange rate Euro/USD up to three days ahead of last data available. The variable of output of the ANN designed is then the daily exchange rate Euro/Dollar and the frequency of data collection of variables of input and the output is daily. By the analysis of the data it is possible to conclude that the ANN model developed can largely predict the trend to three days of exchange rate Euro/USD.
Review on Extraction and Isolation of Plant Secondary Metabolites

The use of plant metabolites for medicinal and cosmetic purpose is today gaining popularity. The most important step in this exploitation of metabolites is extraction and isolation of compound of interest. These day we can identified two group of extraction technique called conventional technique using cheaper equipment, high amount of solvent and takes long extracting time, and new or green technique using costly equipment, elevated pressure and / or temperatures with short extracting time. After extracting secondary metabolites a step of purification and isolation are required using Chromatographic or NonChromatographic techniques. This paper reviews the different technique of extraction and identification of plant metabolites.
Attitudes toward genetic testing and personalised nutrition in a representative sample of European consumers.

Negative consumer opinion poses a potential barrier to the application of nutrigenomic intervention. The present study has aimed to determine attitudes toward genetic testing and personalised nutrition among the European public. An omnibus opinion survey of a representative sample aged 14-55+ years (n 5967) took place in France, Italy, Great Britain, Portugal, Poland and Germany during June 2005 as part of the Lipgene project. A majority of respondents (66 %) reported that they would be willing to undergo genetic testing and 27 % to follow a personalised diet. Individuals who indicated a willingness to have a genetic test for the personalising of their diets were more likely to report a history of high blood cholesterol levels, central obesity and/or high levels of stress than those who would have a test only for general interest. Those who indicated that they would not have a genetic test were more likely to be male and less likely to report having central obesity. Individuals with a history of high blood cholesterol were less likely than those who did not to worry if intervention foods contained GM ingredients. Individuals who were aware that they had health problems associated with the metabolic syndrome appeared particularly favourable toward nutrigenomic intervention. These findings are encouraging for the future application of personalised nutrition provided that policies are put in place to address public concern about how genetic information is used and held.
Impact of 10-Minute Interval Roller Massage on Performance and Active Range of Motion.

Hodgson, DD, Quigley, PJ, Whitten, JHD, Reid, JC, and Behm, DG. Impact of 10-minute interval roller massage on performance and active range of motion.  2017-Roller massage (RM) has been shown to increase range of motion (ROM) without subsequent performance deficits. However, prolonged static stretching (SS) can induce performance impairments. The objective of this study was to examine the effects of combining SS and RM with and without subsequent RM on ROM and neuromuscular performance. Subjects (n = 12) participated in 5 sessions: (a) SS only (SS_rest), (b) SS + RM (SS + RM_rest), (c) SS with RM at 10 and 20 minutes after stretch (SS_RM), (d) SS + RM with RM at 10 and 20 minutes after stretch (SS + RM_RM), and (e) control. For the SS conditions, the quadriceps and hamstrings received passive SS for 2 × 30 seconds each. For the SS + RM conditions, SS was applied to the quadriceps and hamstrings for 30 seconds each, and RM was performed for 30 seconds per muscle. SS_RM and SS + RM_RM conditions received an additional 30-second RM at 10 and 20 minutes after warm-up, whereas sessions without additional RM rested for the same duration. Testing measures included hip flexion (HF) and knee flexion (KF) active and passive ROM, hurdle jump height and contact time, countermovement jump height, and maximal voluntary isometric contraction force. Initial KF and HF ROM improvements provided by SS_RM and SS + RM_RM were sustained up to 30 minutes after intervention. Furthermore, SS_RM exhibited greater ROM compared with sessions lacking additional RM in active and passive HF as well as active and passive KF. Similarly, SS + RM_RM elicited greater KF and HF ROM improvements than SS_rest. In conclusion, active KF and HF ROM improvements were prolonged by additional RM, whereas neuromuscular performance remained relatively unaffected.
Change Detection in Hierarchically Structured Information

Detecting and representing changes to data is important for active databases, data warehousing, view maintenance, and version and configuration management. Most previous work in change management has dealt with flat-file and relational data; we focus on hierarchically structured data. Since in many cases changes must be computed from old and new versions of the data, we define the hierarchical change detection problem as the problem of finding a "minimum-cost edit script" that transforms one data tree to another, and we present efficient algorithms for computing such an edit script. Our algorithms make use of some key domain characteristics to achieve substantially better performance than previous, general-purpose algorithms. We study the performance of our algorithms both analytically and empirically, and we describe the application of our techniques to hierarchically structured documents.
Mindfulness-Based Interventions : An Emerging Phenomenon

I offer an overview of the rapidly growing field of mindfulness-based interventions (MBIs). A working definition of mindfulness in this context includes the brahma viharas, sampajanna and appamada, and suggests a very particular mental state which is both wholesome and capable of clear and penetrating insight into the nature of reality. The practices in mindfulness-based stress reduction (MBSR) that apply mindfulness to the four foundations are outlined, along with a brief history of the program and the original intentions of the founder, Jon Kabat-Zinn. The growth and scope of these interventions are detailed with demographics provided by the Center for Mindfulness, an overview of salient research studies and a listing of the varied MBIs that have grown out of MBSR. The question of ethics is explored, and other challenges are raised including teacher qualification and clarifying the “outer limits,” or minimum requirements, of what constitutes an MBI. Current trends are explored, including the increasing number of cohort-specific interventions as well as the publication of books, articles, and workbooks by a new generation of MBI teachers. Together, they form an emerging picture of MBIs as their own new “lineage,” which look to MBSR as their inspiration and original source. The potential to bring benefit to new fields, such as government and the military, represent exciting opportunities for MBIs, along with the real potential to transform health care. Sufficient experience in the delivery of MBIs has been garnered to offer the greater contemplative community valuable resources such as secular language, best practices, and extensive research.
Complexityof cyanobacterial exopolysaccharides : composition , structures , inducing factors andputative genes involved in their biosynthesis andassembly

Complexityof cyanobacterial exopolysaccharides: composition, structures, inducing factors andputative genes involved in their biosynthesis andassembly Sara Pereira, Andrea Zille, Ernesto Micheletti, Pedro Moradas-Ferreira, Roberto De Philippis & Paula Tamagnini IBMC – Instituto de Biologia Molecular e Celular, Universidade do Porto, Porto, Portugal; Departamento de Botânica, Faculdade de Ciências, Universidade do Porto, Porto, Portugal; Department of Agricultural Biotechnology, University of Florence, Florence, Italy; and Instituto de Ciências Biomédicas Abel Salazar (ICBAS), Universidade do Porto, Porto, Portugal
Bank Liquidity Creation

Although the modern theory of financial intermediation portrays liquidity creation as an essential role of banks, comprehensive measures of bank liquidity creation do not exist. We construct four measures and apply them to data on U.S. banks from 1993-2003. We find that bank liquidity creation increased every year and exceeded $2.8 trillion in 2003. Large banks, multibank holding company members, retail banks, and recently merged banks create the most liquidity. Bank liquidity creation is positively correlated with bank value. Testing recent theories of the relationship between capital and liquidity creation, we find that the relationship is positive for large banks and negative for small banks.
The basic science of wound healing.

Understanding wound healing today involves much more than simply stating that there are three phases: "inflammation, proliferation, and maturation." Wound healing is a complex series of reactions and interactions among cells and "mediators." Each year, new mediators are discovered and our understanding of inflammatory mediators and cellular interactions grows. This article will attempt to provide a concise report of the current literature on wound healing by first reviewing the phases of wound healing followed by "the players" of wound healing: inflammatory mediators (cytokines, growth factors, proteases, eicosanoids, kinins, and more), nitric oxide, and the cellular elements. The discussion will end with a pictorial essay summarizing the wound-healing process.
Automatic Online Evaluation of Intelligent Assistants

Voice-activated intelligent assistants, such as Siri, Google Now, and Cortana, are prevalent on mobile devices. However, it is challenging to evaluate them due to the varied and evolving number of tasks supported, e.g., voice command, web search, and chat. Since each task may have its own procedure and a unique form of correct answers, it is expensive to evaluate each task individually. This paper is the first attempt to solve this challenge. We develop consistent and automatic approaches that can evaluate different tasks in voice-activated intelligent assistants. We use implicit feedback from users to predict whether users are satisfied with the intelligent assistant as well as its components, i.e., speech recognition and intent classification. Using this approach, we can potentially evaluate and compare different tasks within and across intelligent assistants ac-cording to the predicted user satisfaction rates. Our approach is characterized by an automatic scheme of categorizing user-system interaction into task-independent dialog actions, e.g., the user is commanding, selecting, or confirming an action. We use the action sequence in a session to predict user satisfaction and the quality of speech recognition and intent classification. We also incorporate other features to further improve our approach, including features derived from previous work on web search satisfaction prediction, and those utilizing acoustic characteristics of voice requests. We evaluate our approach using data collected from a user study. Results show our approach can accurately identify satisfactory and unsatisfactory sessions.
Comparison of dietary assessment methods in nutritional epidemiology: weighed records v. 24 h recalls, food-frequency questionnaires and estimated-diet records.

Women (n 160) aged 50 to 65 years were asked to weigh their food for 4 d on four occasions over the period of 1 year, using the PETRA (Portable Electronic Tape Recorded Automatic) scales. Throughout the year, they were asked to complete seven other dietary assessment methods: a simple 24 h recall, a structured 24 h recall with portion size assessments using photographs, two food-frequency questionnaires, a 7 d estimated record or open-ended food diary, a structured food-frequency (menu) record, and a structured food-frequency (menu) record with portion sizes assessed using photographs. Comparisons between the average of the 16 d weighed records and the first presentation of each method indicated that food-frequency questionnaires were not appreciably better at placing individuals in the distribution of habitual diet than 24 h recalls, due partly to inaccuracies in the estimation of frequency of food consumption. With a 7 d estimated record or open-ended food diary, however, individual values of nutrients were most closely associated with those obtained from 16 d weighed records, and there were no significant differences in average food or nutrient intakes.
Adaptive Cruise Control Design Based on Fuzzy-PID

Adaptive cruise control (ACC) system is a Driver assistant system, which can provide safe and comfortable driving. Based on the analysis of ACC system, a parameter self-tuning fuzzy-PID algorithm is developed to design the ACC control strategy. According to the traffic condition, the parameters of PID controller are adapted on-line by fuzzy rule. The simulation results show that the parameter self- tuning fuzzy-PID controller integrates the advantages of PID controller and fuzzy controller. It improves driving safety of ACC, behaves higher speed of system response; performs smaller fluctuations and provides more comfortable traveling.
Sample Size in Qualitative Interview Studies: Guided by Information Power.

Sample sizes must be ascertained in qualitative studies like in quantitative studies but not by the same means. The prevailing concept for sample size in qualitative studies is "saturation." Saturation is closely tied to a specific methodology, and the term is inconsistently applied. We propose the concept "information power" to guide adequate sample size for qualitative studies. Information power indicates that the more information the sample holds, relevant for the actual study, the lower amount of participants is needed. We suggest that the size of a sample with sufficient information power depends on (a) the aim of the study, (b) sample specificity, (c) use of established theory, (d) quality of dialogue, and (e) analysis strategy. We present a model where these elements of information and their relevant dimensions are related to information power. Application of this model in the planning and during data collection of a qualitative study is discussed.
Neural Relation Extraction Within and Across Sentence Boundaries

Past work in relation extraction mostly focuses on binary relation between entity pairs within single sentence. Recently, the NLP community has gained interest in relation extraction in entity pairs spanning multiple sentences. In this paper, we propose a novel architecture for this task: inter-sentential dependency-based neural networks (iDepNN). iDepNN models the shortest and augmented dependency paths via recurrent and recursive neural networks to extract relationships within (intra-) and across (inter-) sentence boundaries. Compared to SVM and neural network baselines, iDepNN is more robust to false positives in relationships spanning sentences. We evaluate our models on four datasets from newswire (MUC6) and medical (BioNLP shared task) domains that achieve state-of-the-art performance and show a better balance in precision and recall for inter-sentential relationships. We perform better than 11 teams participating in the BioNLP shared task 2016 and achieve a gain of 5.2% (0.587 vs 0.558) in F1 over the winning team. We also release the crosssentence annotations for MUC6.