nils-herrmann/clean_scirepeval huggingface dataset:
Fully Distributed Scrum: The Secret Sauce for Hyperproductive Offshored Development Teams

Scrum was designed to achieve a hyperproductive state where productivity increases 5-10 times over industry averages and many collocated teams have achieved this effect. The question for this paper is whether distributed, offshore teams can consistently achieve the hyperproductive state. In particular, can a team establish a localized velocity and then maintain or increase that velocity when distributing teams across continents. Since 2006, Xebia started projects with half Dutch and half Indian team members. After establishing localized hyperproductivity, they move the Indian members of the team to India and show increasing velocity with fully distributed teams. After running XP engineering practices inside many distributed Scrum projects, Xebia has systematically productized a model very similar to the SirsiDynix model (J. Sutherland, 2006) for high performance, distributed, offshore teams with outstanding quality.
Annual research review: re-thinking the classification of autism spectrum disorders.

The nosology of autism spectrum disorders (ASD) is at a critical point in history as the field seeks to better define dimensions of social-communication deficits and restricted/repetitive behaviors on an individual level for both clinical and neurobiological purposes. These different dimensions also suggest an increasing need for quantitative measures that accurately map their differences, independent of developmental factors such as age, language level and IQ.



Psychometric measures, clinical observation as well as genetic, neurobiological and physiological research from toddlers, children and adults with ASD are reviewed.



The question of how to conceptualize ASDs along dimensions versus categories is discussed within the nosology of autism and the proposed changes to the DSM-5 and ICD-11. Differences across development are incorporated into the new classification frameworks.



It is crucial to balance the needs of clinical practice in ASD diagnostic systems, with neurobiologically based theories that address the associations between social-communication and restricted/repetitive dimensions in individuals. Clarifying terminology, improving description of the core features of ASD and other dimensions that interact with them and providing more valid and reliable ways to quantify them, both for research and clinical purposes, will move forward both practice and science.
Determinants of acute malnutrition among children aged 6-59 months in Public Hospitals, Oromia region, West Ethiopia:

Acute malnutrition is a recent and severe weight loss as a result of acute food shortage and/or illness. It is the leading causes of morbidity and mortality of children aged 6–59 months as risk of death is nine times higher than that of children without it. In Ethiopia despite recent economic progress; nutritional status of children is among the worst in the world and it remains major public health problem. According to recently published mini demographic & health survey 2014; 9 % of children are wasted (3 % severely wasted) in the country and in Oromia region 8.8 % of children are wasted (1.7 % severely wasted). However there is no information on determinants of acute malnutrition in the study area. The aim of this study was to identify determinants of acute malnutrition among children aged 6–59 months in the study area.  A facility based un-matched case control study was conducted on 339 (113 cases and 226 controls) children aged 6–59 months with their respective mothers from December 3, 2014 to March 25, 2015. The data were collected by using a pre-tested structured questionnaire and mid upper arm circumference (MUAC). Odds Ratio along with 95 % confidence interval was estimated to identify determinants of acute malnutrition (wasting) using the multivariable logistic regression.  Wasting was associated with diarrheal diseases in the previous two weeks [AOR (95 % CI) =3.94 (2.01–7.73)], mothers habit of less frequent hand washing [AOR (95 % CI) =14.39 (7.33–28.22)], did not exclusively breastfed [AOR (95 % CI) = 2.63 (1.29–4.82)], having large family sizes [AOR (95 % CI) =2.59 (1.34–5.0)], absence of latrine [AOR (95 % CI) =2.99(1.23–7.06)], illiterate mothers [AOR (95 % CI) =2.16(1.14–4.11)] and febrile illness in the previous two weeks [AOR (95 % CI) =1.89 (1.0–3.59)].  Wasting was significantly associated with diarrheal and febrile illness in the previous two weeks, lack of maternal education, hand washing & sub optimal breastfeeding practices, larger family size, and absence of latrine. Thus, an organized effort should be made at all levels to improve maternal and child health services, hand washing & breastfeeding practices, construction of latrines and education of mothers to restrain the problems of child acute malnutrition.
Understanding self-reflection: how people reflect on personal data through visual data exploration

Rapid advancements in consumer technologies enable people to collect a wide range of personal data. With a proper means for people to ask questions and explore their data, longitudinal data feeds from multiple self-tracking tools pose great opportunities to foster deep self-reflection. However, most self-tracking tools lack support for self-reflection beyond providing simple feedback. Our overarching goal is to support self-trackers in reflecting on their data and gaining rich insights through visual data exploration. As a first step toward the goal, we built a web-based application called Visualized Self, and conducted an in-lab think-aloud study (N = 11) to examine how people reflect on their personal data and what types of insights they gain throughout the reflection. We discuss lessons learned from studying with Visualized Self, and suggest directions for designing visual data exploration tools for fostering self-reflection.
A SENSITIVITY ANALYSIS APPROACH FOR SOME DETERMINISTIC MULTI-CRITERIA DECISION MAKING METHODS

Often data in multi-criteria decision making (MCDM) problems are imprecise and changeable. Therefore, an important step in many applications of MCDM is to perform a sensitivity analysis on the input data. This paper presents a methodology for performing a sensitivity analysis on the weights of the decision criteria and the performance values of the alternatives expressed in terms of the decision criteria. The proposed methodology is demonstrated on three widely used decision methods. These are the weighted sum model (WSM), the weighted product model (WPM), and the analytic hierarchy process (AHP). This paper formalizes a number of important issues on sensitivity analysis and derives some critical theoretical results. Also, a number of illustrative examples and computational experiments further illustrate the application of the proposed methodology.  Analytic Hierarchy Process, Deterministic Decision Making, Multi-Criteria Decision Making, Sensitivity Analysis, Weighted Product Model, and Weighted Sum Model.  The authors are very grateful to the two anonymous referees and the anonymous associate editor. Their thoughtful and constructive comments significantly enhanced the context and presentation of this paper.
Version-Aware Rating Prediction for Mobile App Recommendation

With the great popularity of mobile devices, the amount of mobile apps has grown at a more dramatic rate than ever expected. A technical challenge is how to recommend suitable apps to mobile users. In this work, we identify and focus on a unique characteristic that exists in mobile app recommendation—that is, an app usually corresponds to multiple release versions. Based on this characteristic, we propose a fine-grain version-aware app recommendation problem. Instead of directly learning the users’ preferences over the apps, we aim to infer the ratings of users on a specific version of an app. However, the user-version rating matrix will be sparser than the corresponding user-app rating matrix, making existing recommendation methods less effective. In view of this, our approach has made two major extensions. First, we leverage the review text that is associated with each rating record; more importantly, we consider two types of version-based correlations. The first type is to capture the temporal correlations between multiple versions within the same app, and the second type of correlation is to capture the aggregation correlations between similar apps. Experimental results on a large dataset demonstrate the superiority of our approach over several competitive methods.
Neural Network Model for Earthquake Prediction Using DMETER Data and Seismic Belt Information

The mechanism of the earthquake remains to be investigated, though some anomalies associated with earthquakes have been found by DEMETER satellite observations. The next step is to use the DEMETER data for earthquake prediction. It is a useful and feasible way to use the self-adaptive artificial neural network to construct relations between various symptom factors and earthquake occurrences. The back-propagation neural network is quite suitable to express the nonlinear relation between earthquake and various anomalies. In this paper a series of physical quantities measured by the DEMETER satellite including Electron density, Electron temperature, ions temperature and oxygen ion density, together with seismic belt information are used to form sample sets for a back-propagation neural network. The neural network model then can be used to conduct the prediction. In the end, validation tests are performed based on those important seismic events happened in 2008.
IoT-Based Smart Garbage System for Efficient Food Waste Management

Owing to a paradigm shift toward Internet of Things (IoT), researches into IoT services have been conducted in a wide range of fields. As a major application field of IoT, waste management has become one such issue. The absence of efficient waste management has caused serious environmental problems and cost issues. Therefore, in this paper, an IoT-based smart garbage system (SGS) is proposed to reduce the amount of food waste. In an SGS, battery-based smart garbage bins (SGBs) exchange information with each other using wireless mesh networks, and a router and server collect and analyze the information for service provisioning. Furthermore, the SGS includes various IoT techniques considering user convenience and increases the battery lifetime through two types of energy-efficient operations of the SGBs: stand-alone operation and cooperation-based operation. The proposed SGS had been operated as a pilot project in Gangnam district, Seoul, Republic of Korea, for a one-year period. The experiment showed that the average amount of food waste could be reduced by 33%.
A quantitative study of irregular programs on GPUs

GPUs have been used to accelerate many regular applications and, more recently, irregular applications in which the control flow and memory access patterns are data-dependent and statically unpredictable. This paper defines two measures of irregularity called control-flow irregularity and memory-access irregularity, and investigates, using performance-counter measurements, how irregular GPU kernels differ from regular kernels with respect to these measures. For a suite of 13 benchmarks, we find that (i) irregularity at the warp level varies widely, (ii) control-flow irregularity and memory-access irregularity are largely independent of each other, and (iii) most kernels, including regular ones, exhibit some irregularity. A program's irregularity can change between different inputs, systems, and arithmetic precision but generally stays in a specific region of the irregularity space. Whereas some highly tuned implementations of irregular algorithms exhibit little irregularity, trading off extra irregularity for better locality or less work can improve overall performance.
A Model-Based Framework for Cloud API Testing

Following the Service-Oriented Architecture, a large number of diversified Cloud services are exposed as Web APIs (Application Program Interface), which serve as the contracts between the service providers and service consumers. Due to their massive and broad applications, any flaw in the cloud APIs may lead to serious consequences. API testing is thus necessary to ensure the availability, reliability, and stability of cloud services. The research proposes a model-based approach to automating API testing. The semi-structured API specifications, like XML/HTML specifications, are gathered from the Web sites using web crawlers, and translated into YAML-encoded standard representations. A scenario editor is designed to specify the dependencies among API operations. Test generators are built to derive test scripts from the specifications and scenarios, including test data, test cases for individual operations as well as operations sequences. Various algorithms can be used for test generation, such as combinatorial data generation, heuristic graph search, and optimization algorithms. The produced test scripts, together with a load model, can be deployed on Cloud and scheduled for execution. A prototype system, called ATCloud, was constructed to illustrate the process of API understanding, test scenario modeling using directed diagraph annotated with transfer probabilities between operations, cloud-based test resources management, distributed workload simulation, and performance monitoring.
A modified K-means clustering for mining of multimedia databases based on dimensionality reduction and similarity measures

With rapid innovations in digital technology and cloud computing off late, there has been a huge volume of research in the area of web based storage, cloud management and mining of data from the cloud. Large volumes of data sets are being stored, processed in either virtual or physical storage and processing equipments on a daily basis. Hence, there is a continuous need for research in these areas to minimize the computational complexity and subsequently reduce the time and cost factors. The proposed research paper focuses towards handling and mining of multimedia data in a data base which is a mixed composition of data in the form of graphic arts and pictures, hyper text, text data, video or audio. Since large amounts of storage are required for audio and video data in general, the management and mining of such data from the multimedia data base needs special attention. Experimental observations using well known data sets of varying features and dimensions indicate that the proposed cluster based mining technique achieves promising results in comparison with the other well-known methods. Every attribute denoting the efficiency of the mining process have been compared component wise with recent mining techniques in the past. The proposed system addresses effectiveness, robustness and efficiency for a high-dimensional multimedia database.
Paralinguistics in speech and language - State-of-the-art and the challenge

Paralinguistic analysis is increasingly turning into a mainstream topic in speech and language processing. This article aims to provide a broad overview of the constantly growing field by defining the field, introducing typical applications, presenting exemplary resources, and sharing a unified view of the chain of processing. It then presents the first broader Paralinguistic Challenge organised at INTERSPEECH 2010 by the authors including a historical overview of the Challenge tasks of recognising age, gender, and affect, a summary of methods used by the participants, and their results. In addition, we present the new benchmark obtained by fusion of participants’ predictions and conclude by discussing ten recent and emerging trends in the analysis of paralinguistics in speech and language.
Neurofeedback: A Comprehensive Review on System Design, Methodology and Clinical Applications

Neurofeedback is a kind of biofeedback, which teaches self-control of brain functions to subjects by measuring brain waves and providing a feedback signal. Neurofeedback usually provides the audio and or video feedback. Positive or negative feedback is produced for desirable or undesirable brain activities, respectively. In this review, we provided clinical and technical information about the following issues: (1) Various neurofeedback treatment protocols i.e. alpha, beta, alpha/theta, delta, gamma, and theta; (2) Different EEG electrode placements i.e. standard recording channels in the frontal, temporal, central, and occipital lobes; (3) Electrode montages (unipolar, bipolar); (4) Types of neurofeedback i.e. frequency, power, slow cortical potential, functional magnetic resonance imaging, and so on; (5) Clinical applications of neurofeedback i.e. treatment of attention deficit hyperactivity disorder, anxiety, depression, epilepsy, insomnia, drug addiction, schizophrenia, learning disabilities, dyslexia and dyscalculia, autistic spectrum disorders and so on as well as other applications such as pain management, and the improvement of musical and athletic performance; and (6) Neurofeedback softwares. To date, many studies have been conducted on the neurofeedback therapy and its effectiveness on the treatment of many diseases. Neurofeedback, like other treatments, has its own pros and cons. Although it is a non-invasive procedure, its validity has been questioned in terms of conclusive scientific evidence. For example, it is expensive, time-consuming and its benefits are not long-lasting. Also, it might take months to show the desired improvements. Nevertheless, neurofeedback is known as a complementary and alternative treatment of many brain dysfunctions. However, current research does not support conclusive results about its efficacy.
Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4

We conducted a combined genome-wide association study (GWAS) of 7,481 individuals with bipolar disorder (cases) and 9,250 controls as part of the Psychiatric GWAS Consortium. Our replication study tested 34 SNPs in 4,496 independent cases with bipolar disorder and 42,422 independent controls and found that 18 of 34 SNPs had P < 0.05, with 31 of 34 SNPs having signals with the same direction of effect (P = 3.8 × 10−7). An analysis of all 11,974 bipolar disorder cases and 51,792 controls confirmed genome-wide significant evidence of association for CACNA1C and identified a new intronic variant in ODZ4. We identified a pathway comprised of subunits of calcium channels enriched in bipolar disorder association intervals. Finally, a combined GWAS analysis of schizophrenia and bipolar disorder yielded strong association evidence for SNPs in CACNA1C and in the region of NEK4-ITIH1-ITIH3-ITIH4. Our replication results imply that increasing sample sizes in bipolar disorder will confirm many additional loci.
A case of autoimmune progesterone dermatitis characterized by grouped small vesicles.

A 44-year-old woman presented with a 20-year history of pruritic, grouped vesicular eruptions. Multiple skin biopsies revealed nonspecific results. On further questioning, the patient stated that the skin lesions usually occurred 5 days before menstruation and improved 3 days after menstrual flow started. Dermatological examination showed small grouped vesicles localized on erythematous plaques approximately 20 9 30 mm in size, on the patient’s neck, shoulders, back, waist, upper arms and medial aspect of the legs (Fig. 1a–d). She had never received exogenous progesterone. Histological examination revealed multifocal small vesicles due to subepidermal separation, with neutrophil-rich exudates . Direct immunofluorescence (DIF) staining was negative. Using normal saline as negative control, intradermal skin test (IDT) with 0.1 mL saline solutions, each containing progesterone at concentrations of 1 : 1, 1 : 100 and 1 : 1000, was performed. An erythematous and indurated plaque was observed only with 1 : 1 concentration of progesterone after 30 min . The patient was diagnosed with autoimmune progesterone dermatitis (APD), which was confirmed by the occurrence of multiple urticarial plaques 24 h after intramuscular challenge test with micronized progesterone 50 mg/mL. Oral contraceptive therapy was recommended, but the patient refused it. APD is a rare dermatosis characterized by cyclical cutaneous eruptions. It usually affects premenopausal women, and occurs 3–10 days prior to the onset of menstrual flow, and resolves 2–3 days into menses when progesterone levels decrease. The pathogenesis of APD still remains unknown. It is supposed that the skin lesions occur via a type 1 hypersensitivity reaction, due to occurrence of APD in some patients after taking exogenous progesterone. However, this mechanism is not enough to explain the pathogenesis of patients such as our case, who have never taken exogenous progesterone. Antibodies against progesterone may stimulate dermatitis, leading to a type 3 hypersensitivity reaction. This mechanism may not be valid for some patients such as our case, because DIF staining is negative. IDT with progesterone may result with immediate (within 30 min) and delayed (24–48 h later) reactions. These two
Audio-Visual Spontaneous Emotion Recognition

Automatic multimodal recognition of spontaneous emotional expressions is a largely unexplored and challenging problem. In this paper, we explore audio-visual emotion recognition in a realistic human conversation setting—the Adult Attachment Interview (AAI). Based on the assumption that facial expression and vocal expression are at the same coarse affective states, positive and negative emotion sequences are labeled according to Facial Action Coding System. Facial texture in visual channel and prosody in audio channel are integrated in the framework of Adaboost multi-stream hidden Markov model (AdaMHMM) in which the Adaboost learning scheme is used to build component HMM fusion. Our approach is evaluated in AAI spontaneous emotion recognition experiments.
Imaging Cognition II: An Empirical Review of 275 PET and fMRI Studies

Positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) have been extensively used to explore the functional neuroanatomy of cognitive functions. Here we review 275 PET and fMRI studies of attention (sustained, selective, Stroop, orientation, divided), perception (object, face, space/motion, smell), imagery (object, space/ motion), language (written/spoken word recognition, spoken/ no spoken response), working memory (verbal/numeric, object, spatial, problem solving), semantic memory retrieval (categorization, generation), episodic memory encoding (verbal, object, spatial), episodic memory retrieval (verbal, nonverbal, success, effort, mode, context), priming (perceptual, conceptual), and procedural memory (conditioning, motor, and nonmotor skill learning). To identify consistent activation patterns associated with these cognitive operations, data from 412 contrasts were summarized at the level of cortical Brodmann's areas, insula, thalamus, medial-temporal lobe (including hippocampus), basal ganglia, and cerebellum. For perception and imagery, activation patterns included primary and secondary regions in the dorsal and ventral pathways. For attention and working memory, activations were usually found in prefrontal and parietal regions. For language and semantic memory retrieval, typical regions included left prefrontal and temporal regions. For episodic memory encoding, consistently activated regions included left prefrontal and medial-temporal regions. For episodic memory retrieval, activation patterns included prefrontal, medial-temporal, and posterior midline regions. For priming, deactivations in prefrontal (conceptual) or extrastriate (perceptual) regions were consistently seen. For procedural memory, activations were found in motor as well as in non-motor brain areas. Analysis of regional activations across cognitive domains suggested that several brain regions, including the cerebellum, are engaged by a variety of cognitive challenges. These observations are discussed in relation to functional specialization as well as functional integration.