nils-herrmann/clean_scirepeval huggingface dataset:
Role of glucose in chewing gum-related facilitation of cognitive function

This study tests the hypothesis that chewing gum leads to cognitive benefits through improved delivery of glucose to the brain, by comparing the cognitive performance effects of gum and glucose administered separately and together. Participants completed a battery of cognitive tests in a fully related 2 x 2 design, where one factor was Chewing Gum (gum vs. mint sweet) and the other factor was Glucose Co-administration (consuming a 25 g glucose drink vs. consuming water). For four tests (AVLT Immediate Recall, Digit Span, Spatial Span and Grammatical Transformation), beneficial effects of chewing and glucose were found, supporting the study hypothesis. However, on AVLT Delayed Recall, enhancement due to chewing gum was not paralleled by glucose enhancement, suggesting an alternative mechanism. The glucose delivery model is supported with respect to the cognitive domains: working memory, immediate episodic long-term memory and language-based attention and processing speed. However, some other mechanism is more likely to underlie the facilitatory effect of chewing gum on delayed episodic long-term memory.
Force Control of Series Elastic Actuator: Implications for Series Elastic Actuator Design

The force controller of series elastic actuator (SEA) is an important issue, because it determines the output performance of SEA (the bandwidth and the resonant peak). However, the existing force controllers of SEA attach little importance to the determinants of the performance. This paper develops four different kinds of force controllers and selects the optimal controller by comparing their capabilities. The comparison demonstrates that the parameters of dynamical models are key factors affecting the output performance. Finally, the experiments reveal that the equivalent mass and the damping coefficient of SEA are determinants. The conclusion can provide theoretical implications to SEA design.
Human Social Interaction Modeling Using Temporal Deep Networks

We present a novel approach to computational modeling of social interactions based on modeling of essential social interaction predicates (ESIPs) such as joint attention and entrainment. Based on sound social psychological theory and methodology, we collect a new “Tower Game” dataset consisting of audio-visual capture of dyadic interactions labeled with the ESIPs. We expect this dataset to provide a new avenue for research in computational social interaction modeling. We propose a novel joint Discriminative Conditional Restricted Boltzmann Machine (DCRBM) model that combines a discriminative component with the generative power of CRBMs. Such a combination enables us to uncover actionable constituents of the ESIPs in two steps. First, we train the DCRBM model on the labeled data and get accurate (76%-49% across various ESIPs) detection of the predicates. Second, we exploit the generative capability of DCRBMs to activate the trained model so as to generate the lower-level data corresponding to the specific ESIP that closely matches the actual training data (with mean square error 0.01-0.1 for generating 100 frames). We are thus able to decompose the ESIPs into their constituent actionable behaviors. Such a purely computational determination of how to establish an ESIP such as engagement is unprecedented.
Stargazing: an integrative conceptual review, theoretical reconciliation, and extension for star employee research.

Stars--employees with disproportionately high and prolonged (a) performance, (b) visibility, and (c) relevant social capital--have garnered attention in economics, sociology, and management. However, star research is often isolated within these research disciplines. Thus, 3 distinct star research streams are evolving, each disconnected from the others and each bringing siloed theoretical perspectives, terms, and assumptions. A conceptual review of these perspectives reveals a focus on the expost effects that stars exert in organizations with little explanation of who a star is and how one becomes a star. To synthesize the stars literature across these 3 disciplines, we apply psychological theories, specifically motivation theories, to create an integrative framework for stars research. Thus, we present a unified stars definition and extend theory on the making, managing, and mobility of stars. We extend research about how and why employees may be motivated to become stars, how best to manage stars and their relationships with colleagues, and how to motivate star retention. We then outline directions for future research.
Arduino UNO and GSM based wireless health monitoring system for patients

In India, near about 20% of the total population loses their lives due to interrupted health monitoring system i.e. in most of the hospitals, doctor visits patients either in morning shift or in evening shift or in both shifts. What happens if patient's health becomes critical in between that interval or when a doctor is not available with a patient. The answer is; a patient may lose her\his life. So to avoid this critical situation; we are proposing a smart embedded system device which monitors patients health continuously. This system monitors patients heart rate, body temperature and saline liquid level (if any). If any of the above parameters goes beyond the threshold value, this smart device informs doctors or care taker and ask for corrective actions to save patients life.
Traction alopecia.

An otherwise healthy 22-year-old Caucasian woman was referred regarding a 6-year history of bifrontotemporal hair loss. Previously, she was treated with topical minoxidil with slight improvement. In addition, upon questioning, the patient described using a ponytail hair style daily since adolescence. Physical examination revealed two ovoid-shaped alopecic patches on both frontotemporal regions . Fringe sign was present . There were no pustules, erythema, or scale. Hair pull test was negative. On the basis of these findings, a clinical diagnosis of traction alopecia was made. Traction alopecia is a form of trauma-induced alopecia that results from prolonged or repeated pulling of hair shafts related to various types of hairstyles or occupational uniforms (nurses0caps or nuns0coifs).1–4 It is a reversible condition, but if the cause persists, permanent scarring alopecia may be developed. African-American women have a higher prevalence of traction alopecia than Caucasian women. In a pilot study, it was found in 37% of women who visited a primary care health center in Cape Town. Cornrows and other cultural hair care practices have been implicated as risk factors for traction alopecia in African-American patients. Traction alopecia is a diagnostic challenge when the external factor is not suspected or admitted, and it could be misdiagnosed as alopecia areata or trichotillomania. The fringe sign, persistence of residual hairs along the frontotemporal rim, may be a useful clinical marker of this condition. Dermoscopy can also be helpful in the diagnostic work-up process. Trichoscopy examination usually reveals white hair casts encircling the proximal portion of the hair shafts. The differential diagnosis of traction alopecia includes alopecia areata, trichotillomania, frontal fibrosing alopecia, and triangular temporal alopecia. Prompt investigation is important to avoid unnecessary treatments and to reverse hair loss. We recommended that the patient stop using the ponytail style. Partial regrowth was observed 6 months later.
Language identification with suprasegmental cues: a study based on speech resynthesis.

This paper proposes a new experimental paradigm to explore the discriminability of languages, a question which is crucial to the child born in a bilingual environment. This paradigm employs the speech resynthesis technique, enabling the experimenter to preserve or degrade acoustic cues such as phonotactics, syllabic rhythm, or intonation from natural utterances. English and Japanese sentences were resynthesized, preserving broad phonotactics, rhythm, and intonation (condition 1), rhythm and intonation (condition 2), intonation only (condition 3), or rhythm only (condition 4). The findings support the notion that syllabic rhythm is a necessary and sufficient cue for French adult subjects to discriminate English from Japanese sentences. The results are consistent with previous research using low-pass filtered speech, as well as with phonological theories predicting rhythmic differences between languages. Thus, the new methodology proposed appears to be well suited to study language discrimination. Applications for other domains of psycholinguistic research and for automatic language identification are considered.
Exploring Female Genital Cutting Among Survivors of Torture

Though the practice of female genital cutting (FGC) has been framed as a form of gender-based torture, few studies have examined the prevalence and impact of the practice among documented survivors of torture. This article presents a secondary analysis of data from 514 African-born women at an interdisciplinary clinic for survivors of torture. Results indicate few demographic differences between those who experienced FGC and those who had not, though a larger proportion of the FGC group were West African and identified as Muslim. Many with FGC were in the process of applying for asylum, reported sexual and psychological torture, and cited gender as a basis for their persecution. The FGC group evidenced unique correlates related to immigration status and psychological and sexual torture experiences that the non-FGC group did not. Findings indicate that female survivors of torture with FGC represent a distinct group with specific mental health needs.
Decoding Individual Finger Movements from One Hand Using Human EEG Signals

Brain computer interface (BCI) is an assistive technology, which decodes neurophysiological signals generated by the human brain and translates them into control signals to control external devices, e.g., wheelchairs. One problem challenging noninvasive BCI technologies is the limited control dimensions from decoding movements of, mainly, large body parts, e.g., upper and lower limbs. It has been reported that complicated dexterous functions, i.e., finger movements, can be decoded in electrocorticography (ECoG) signals, while it remains unclear whether noninvasive electroencephalography (EEG) signals also have sufficient information to decode the same type of movements. Phenomena of broadband power increase and low-frequency-band power decrease were observed in EEG in the present study, when EEG power spectra were decomposed by a principal component analysis (PCA). These movement-related spectral structures and their changes caused by finger movements in EEG are consistent with observations in previous ECoG study, as well as the results from ECoG data in the present study. The average decoding accuracy of 77.11% over all subjects was obtained in classifying each pair of fingers from one hand using movement-related spectral changes as features to be decoded using a support vector machine (SVM) classifier. The average decoding accuracy in three epilepsy patients using ECoG data was 91.28% with the similarly obtained features and same classifier. Both decoding accuracies of EEG and ECoG are significantly higher than the empirical guessing level (51.26%) in all subjects (p<0.05). The present study suggests the similar movement-related spectral changes in EEG as in ECoG, and demonstrates the feasibility of discriminating finger movements from one hand using EEG. These findings are promising to facilitate the development of BCIs with rich control signals using noninvasive technologies.
Non-Intrusive Appliance Load Identification with the Ensemble of Classifiers

The paper presents the application of the classifier fusion to identify electrical appliances present in the household. The analysis is based on the processing of features extracted from the current signal, recorded by the dedicated data acquisition hardware, installed in the vicinity of the energy meter. The selected features are based on the medium frequency measurements. The proposed identification module uses three classifiers of the same type: decision trees, rules and random forest. Experimental results prove the effectiveness of combining various classifiers to the same task and show their advantages and drawbacks. NIALM; artificial intelligence; classification; medium frequency measurements
Prefetch Side-Channel Attacks: Bypassing SMAP and Kernel ASLR

Modern operating systems use hardware support to protect against control-flow hijacking attacks such as code-injection attacks. Typically, write access to executable pages is prevented and kernel mode execution is restricted to kernel code pages only. However, current CPUs provide no protection against code-reuse attacks like ROP. ASLR is used to prevent these attacks by making all addresses unpredictable for an attacker. Hence, the kernel security relies fundamentally on preventing access to address information. We introduce Prefetch Side-Channel Attacks, a new class of generic attacks exploiting major weaknesses in prefetch instructions. This allows unprivileged attackers to obtain address information and thus compromise the entire system by defeating SMAP, SMEP, and kernel ASLR. Prefetch can fetch inaccessible privileged memory into various caches on Intel x86. It also leaks the translation-level for virtual addresses on both Intel x86 and ARMv8-A. We build three attacks exploiting these properties. Our first attack retrieves an exact image of the full paging hierarchy of a process, defeating both user space and kernel space ASLR. Our second attack resolves virtual to physical addresses to bypass SMAP on 64-bit Linux systems, enabling ret2dir attacks. We demonstrate this from unprivileged user programs on Linux and inside Amazon EC2 virtual machines. Finally, we demonstrate how to defeat kernel ASLR on Windows 10, enabling ROP attacks on kernel and driver binary code. We propose a new form of strong kernel isolation to protect commodity systems incuring an overhead of only 0.06-5.09%.
Towards analysing the rationale of information security non-compliance: Devising a Value-Based Compliance analysis method

Employees’ poor compliance with information security policies is a perennial problem. Current information security analysis methods do not allow information security managers to capture the rationalities behind employees’ compliance and non-compliance. To address this shortcoming, this design science research paper suggests: (a) a Value-Based Compliance analysis method and (b) a set of design principles for methods that analyse different rationalities for information security. Our empirical demonstration shows that the method supports a systematic analysis of why employees comply/do not comply with policies. Thus we provide managers with a tool to make them more knowledgeable about employees’ information security behaviours.
Lower protein content in infant formula reduces BMI and obesity risk at school age: follow-up of a randomized trial.

Early nutrition is recognized as a target for the effective prevention of childhood obesity. Protein intake was associated with more rapid weight gain during infancy-a known risk factor for later obesity.



We tested whether the reduction of protein in infant formula reduces body mass index (BMI; in kg/m(2)) and the prevalence of obesity at 6 y of age.



The Childhood Obesity Project was conducted as a European multicenter, double-blind, randomized clinical trial that enrolled healthy infants born between October 2002 and July 2004. Formula-fed infants (n = 1090) were randomly assigned to receive higher protein (HP)- or lower protein (LP)-content formula (within recommended amounts) in the first year of life; breastfed infants (n = 588) were enrolled as an observational reference group. We measured the weight and height of 448 (41%) formula-fed children at 6 y of age. BMI was the primary outcome.



HP children had a significantly higher BMI (by 0.51; 95% CI: 0.13, 0.90; P = 0.009) at 6 y of age. The risk of becoming obese in the HP group was 2.43 (95% CI: 1.12, 5.27; P = 0.024) times that in the LP group. There was a tendency for a higher weight in HP children (0.67 kg; 95% CI: -0.04, 1.39 kg; P = 0.064) but no difference in height between the intervention groups. Anthropometric measurements were similar in the LP and breastfed groups.



Infant formula with a lower protein content reduces BMI and obesity risk at school age. Avoidance of infant foods that provide excessive protein intakes could contribute to a reduction in childhood obesity.
Learning to predict where to look in interactive environments using deep recurrent q-learning

Bottom-Up (BU) saliency models do not perform well in complex interactive environments where humans are actively engaged in tasks (e.g., sandwich making and playing the video games). In this paper, we leverage Reinforcement Learning (RL) to highlight task-relevant locations of input frames. We propose a soft attention mechanism combined with the Deep Q-Network (DQN) model to teach an RL agent how to play a game and where to look by focusing on the most pertinent parts of its visual input. Our evaluations on several Atari 2600 games show that the soft attention based model could predict fixation locations significantly better than bottom-up models such as Itti-Kochs saliency and Graph-Based Visual Saliency (GBVS) models.
Faking Sandy: characterizing and identifying fake images on Twitter during Hurricane Sandy

In today's world, online social media plays a vital role during real world events, especially crisis events. There are both positive and negative effects of social media coverage of events, it can be used by authorities for effective disaster management or by malicious entities to spread rumors and fake news. The aim of this paper, is to highlight the role of Twitter, during Hurricane Sandy (2012) to spread fake images about the disaster. We identified 10,350 unique tweets containing fake images that were circulated on Twitter, during Hurricane Sandy. We performed a characterization analysis, to understand the temporal, social reputation and influence patterns for the spread of fake images. Eighty six percent of tweets spreading the fake images were retweets, hence very few were original tweets. Our results showed that top thirty users out of 10,215 users (0.3%) resulted in 90% of the retweets of fake images; also network links such as follower relationships of Twitter, contributed very less (only 11%) to the spread of these fake photos URLs. Next, we used classification models, to distinguish fake images from real images of Hurricane Sandy. Best results were obtained from Decision Tree classifier, we got 97% accuracy in predicting fake images from real. Also, tweet based features were very effective in distinguishing fake images tweets from real, while the performance of user based features was very poor. Our results, showed that, automated techniques can be used in identifying real images from fake images posted on Twitter.
From The Cover: A role for Arabidopsis cryptochromes and COP1 in the regulation of stomatal opening.

Cryptochromes (CRY) are blue light photoreceptors that mediate various light-induced responses in plants and animals. Arabidopsis CRY (CRY1 and CRY2) functions through negatively regulating constitutive photomorphogenic (COP) 1, a repressor of photomorphogenesis. Water evaporation and photosynthesis are regulated by the stomatal pores in plants, which are closed in darkness but open in response to blue light. There is evidence only for the phototropin blue light receptors (PHOT1 and PHOT2) in mediating blue light regulation of stomatal opening. Here, we report a previously uncharacterized role for Arabidopsis CRY and COP1 in the regulation of stomatal opening. Stomata of the cry1 cry2 double mutant showed reduced blue light response, whereas those of the CRY1-overexpressing plants showed hypersensitive response to blue light. In addition, stomata of the phot1 phot2 double mutant responded to blue light, but those of the cry1 cry2 phot1 phot2 quadruple mutant hardly responded. Strikingly, stomata of the cop1 mutant were constitutively open in darkness and stomata of the cry1 cry2 cop1 and phot1 phot2 cop1 triple mutants were open as wide as those of the cop1 single mutant under blue light. These results indicate that CRY functions additively with PHOT in mediating blue light-induced stomatal opening and that COP1 is a repressor of stomatal opening and likely acts downstream of CRY and PHOT signaling pathways.
Integrating the Internet of Things with Business Process Management: A Process-aware Framework for Smart Objects

Due to the achievements in the Internet of Things (IoT) field, Smart Objects are often involved in business processes. However, the integration of IoT with Business Process Management (BPM) is far from mature: problems related to process compliance and Smart Objects configuration with respect to the process requirements have not been fully addressed yet; also, the interaction of Smart Objects with multiple business processes that belong to different stakeholders is still under investigation. My PhD thesis aims to fill this gap by extending the BPM lifecycle, with particular focus on the design and analysis phase, in order to explicitly support IoT and its requirements.
Bag of Local Convolutional Triplets for Script Identification in Scene Text

The increasing interest in scene text reading in multilingual environments raises the need to recognize and distinguish between different writing systems. In this paper, we propose a novel method for script identification in scene text using triplets of local convolutional features in combination with the traditional bag-of-visual-words model. Feature triplets are created by making combinations of descriptors extracted from local patches of the input images using a convolutional neural network. This approach allows us to generate a more descriptive codeword dictionary for the bag-of-visual-words model, as the low discriminative power of weak descriptors is enhanced by other descriptors in a triplet. The proposed method is evaluated on two public benchmark datasets for scene text script identification and a public dataset for script identification in video captions. The experiments demonstrate that our method outperforms the baseline and yields competitive results on all three datasets.
Boosting XML filtering through a scalable FPGA-based architecture

The growing amount of XML encoded data exchanged over the Internet increases the importance of XML based publish-subsc ribe (pub-sub) and content based routing systems. The input in su ch systems typically consists of a stream of XML documents and a set of user subscriptions expressed as XML queries. The pubsub system then filters the published documents and passes them t o the subscribers. Pub-sub systems are characterized by very hig h input ratios, therefore the processing time is critical. In this p aper we propose a “pure hardware” based solution, which utilizes XP ath query blocks on FPGA to solve the filtering problem. By utiliz ing the high throughput that an FPGA provides for parallel pr ocessing, our approach achieves drastically better through put t an the existing software or mixed (hardware/software) archit ectures. The XPath queries (subscriptions) are translated to regula r expressions which are then mapped to FPGA devices. By introducing stacks within the FPGA we are able to express and process a wid e range of path queries very efficiently, on a scalable environ me t. Moreover, the fact that the parser and the filter processing a re performed on the same FPGA chip, eliminates expensive communication costs (that a multi-core system would need) thus enab ling very fast and efficient pipelining. Our experimental evalua tion reveals more than one order of magnitude improvement compared to traditional pub/sub systems.