nils-herrmann/clean_scirepeval huggingface dataset:
Robot Faces that Follow Gaze Facilitate Attentional Engagement and Increase Their Likeability

Gaze behavior of humanoid robots is an efficient mechanism for cueing our spatial orienting, but less is known about the cognitive-affective consequences of robots responding to human directional cues. Here, we examined how the extent to which a humanoid robot (iCub) avatar directed its gaze to the same objects as our participants affected engagement with the robot, subsequent gaze-cueing, and subjective ratings of the robot's characteristic traits. In a gaze-contingent eyetracking task, participants were asked to indicate a preference for one of two objects with their gaze while an iCub avatar was presented between the object photographs. In one condition, the iCub then shifted its gaze toward the object chosen by a participant in 80% of the trials (joint condition) and in the other condition it looked at the opposite object 80% of the time (disjoint condition). Based on the literature in human-human social cognition, we took the speed with which the participants looked back at the robot as a measure of facilitated reorienting and robot-preference, and found these return saccade onset times to be quicker in the joint condition than in the disjoint condition. As indicated by results from a subsequent gaze-cueing tasks, the gaze-following behavior of the robot had little effect on how our participants responded to gaze cues. Nevertheless, subjective reports suggested that our participants preferred the iCub following participants' gaze to the one with a disjoint attention behavior, rated it as more human-like and as more likeable. Taken together, our findings show a preference for robots who follow our gaze. Importantly, such subtle differences in gaze behavior are sufficient to influence our perception of humanoid agents, which clearly provides hints about the design of behavioral characteristics of humanoid robots in more naturalistic settings.
Pathoanatomy and Associated Injuries of Posterior Malleolus Fracture of the Ankle.

We present a classification system that progresses in severity, indicates the pathomechanics that cause the fracture and therefore guides the surgeon to what fixation will be necessary by which approach.



The primary posterior malleolar fracture fragments were characterized into 3 groups. A type 1 fracture was described as a small extra-articular posterior malleolar primary fragment. Type 2 fractures consisted of a primary fragment of the posterolateral triangle of the tibia (Volkmann area). A type 3 primary fragment was characterized by a coronal plane fracture line involving the whole posterior plafond.



In type 1 fractures, the syndesmosis was disrupted in 100% of cases, although a proportion only involved the posterior syndesmosis. In type 2 posterior malleolar fractures, there was a variable medial injury with mixed avulsion/impaction etiology. In type 3 posterior malleolar fractures, most fibular fractures were either a high fracture or a long oblique fracture in the same fracture alignment as the posterior shear tibia fragment. Most medial injuries were Y-type or posterior oblique fractures. This fracture pattern had a low incidence of syndesmotic injury.



The value of this approach was that by following the pathomechanism through the ankle, it demonstrated which other structures were likely to be damaged by the path of the kinetic energy. With an understanding of the pattern of associated injuries for each category, a surgeon may be able to avoid some pitfalls in treatment of these injuries.
Application of Artificial Neural Networks in Weather Forecasting: A Comprehensive Literature Review

To recognize application of Artificial Neural Networks (ANNs) in weather forecasting, especially in rainfall forecasting a comprehensive literature review from 1923 to 2012 is done and presented in this paper. And it is found that architectures of ANN such as BPN, RBFN is best established to be forecast chaotic behavior and have efficient enough to forecast monsoon rainfall as well as other weather parameter prediction phenomenon over the smaller geographical region.
Interest-based personalized search

Web search engines typically provide search results without considering user interests or context. We propose a personalized search approach that can easily extend a conventional search engine on the client side. Our mapping framework automatically maps a set of known user interests onto a group of categories in the Open Directory Project (ODP) and takes advantage of manually edited data available in ODP for training text classifiers that correspond to, and therefore categorize and personalize search results according to user interests. In two sets of controlled experiments, we compare our personalized categorization system (PCAT) with a list interface system (LIST) that mimics a typical search engine and with a nonpersonalized categorization system (CAT). In both experiments, we analyze system performances on the basis of the type of task and query length. We find that PCAT is preferable to LIST for information gathering types of tasks and for searches with short queries, and PCAT outperforms CAT in both information gathering and finding types of tasks, and for searches associated with free-form queries. From the subjects' answers to a questionnaire, we find that PCAT is perceived as a system that can find relevant Web pages quicker and easier than LIST and CAT.
Comparison between relational and NOSQL databases

The relational database is provided from the traditional DBMS, which ensures the integrity of data and consistency of transactions. For many software applications, these are the principles of a proper DBMS. But in the last few years, witnessing the velocity of data growth and the lack of support from traditional databases for this issue, as a solution to it, the NoSQL (Not Only SQL) databases have appeared. These two kinds while being used for the same purposes (create, retrieve, update and manage data) they both have their own advantages and disadvantages over each other. The purpose of this study is to try and compare the research question of what are the pros and cons for each of these database' features and characteristics? This paper is a qualitative research, based on detailed and intensive analysis of the two database types, through use and comparison of some published materials during last few years.
Happy, Nervous or Surprised? Classification of Human Affective States in Social Media

Sentiment classification has been a well investigated re search area in the computational linguistics community. However, most of the research is primarily focused on de tecting simply the polarity in text, often needing extensive manual labeling of ground truth. Additionally, little atten tion has been directed towards a finer analysis of human moods and affective states. Motivated by research in psy chology, we propose and develop a classifier of several hu man affective states in social media. Starting with about 200 moods, we utilize mechanical turk studies to derive natural istic signals from posts shared on Twitter about a variety of affects of individuals. This dataset is then deployed in an af fect classification task with promising results. Our findings indicate that different types of affect involve different emo tional content and usage styles; hence the performance of the classifier on various affects can differ considerably.
FPGA implementation of hand gesture recognition system using neural networks

Gesture recognition enables human to communicate with machine and interact naturally without any mechanical devices. The ultimate aim of gesture recognition system is to create a system which understands human gesture and use them to control various other devices. This research focuses on gesture recognition system with a radial basis function network. The radial basis function network is a 3 layer network and trained with a radial basis function algorithm to identify the classes. The complete system is implemented on a Field Programmable Gate Array with image processing unit. The system is design to identify 24 American sign-language hand signs and also real time hand gesture signs. This combination leads to maximum recognition rate. The proposed system is very small due to FPGA implementation which is highly suitable for control of equipments at home, by the handicapped people.
A tutorial on ferroresonance

Ferroresonance is a widely studied phenomenon but it is still not well understood because of its complex behavior. It is “fuzzy-resonance.” A simple graphical approach using fundamental frequency phasors has been presented to elevate the readers understanding. Its occurrence and how it appears is extremely sensitive to the transformer characteristics, system parameters, transient voltages and initial conditions. More efficient transformer core material has lead to its increased occurrence and it has considerable effects on system apparatus and protection. Power system engineers should strive to recognize potential ferroresonant configurations and design solutions to prevent its occurrence.
Control of Micro Mirrors for High Precision Performance

Microelectromechanical Systems (MEMS) technology has already led to advances in optical imaging, scanning, communications and adaptive applications. Many of these efforts have been approached without the use of feedback control techniques that are common in macro-scale operations to ensure repeatable and precise performance. This paper examines control techniques and related issues of precision performance as applied to a one-degree-of-freedom electrostatic MEMS micro mirror.
Chronic Physical Illness: A Psychophysiological Approach for Chronic
Physical Illness

Growing evidence demonstrates that psychological risk variables can contribute to physical disease. In an effort to thoroughly investigate potential etiological origins and optimal interventions, this broad review is divided into five sections: the stress response, chronic diseases, mind-body theoretical models, psychophysiological interventions, and integrated health care solutions. The stress response and its correlation to chronic disorders such as cardiovascular, gastrointestinal, autoimmune, metabolic syndrome, and chronic pain are comprehensively explored. Current mind-body theoretical models, including peripheral nerve pathway, neurophysiological, and integrative theories, are reviewed to elucidate the biological mechanisms behind psychophysiological interventions. Specific interventions included are psychotherapy, mindfulness meditation, yoga, and psychopharmacology. Finally, the author advocates for an integrated care approach as a means by which to blur the sharp distinction between physical and psychological health. Integrated care approaches can utilize psychiatric nurse practitioners for behavioral assessment, intervention, research, advocacy, consultation, and education to optimize health outcomes.
Hierarchical routing in ad hoc mobile networks

Clustering is a method by which nodes are hierarchically organized on the basis of their relative proximity to one another. Routes can be recorded hierarchically, across clusters, to increase routing flexibility. Hierarchical routing greatly increases the scalability of routing in ad hoc networks by increasing the robustness of routes. This paper presents the Adaptive Routing using Clusters (ARC) protocol, a protocol that creates a cluster hierarchy composed of cluster leaders and gateway nodes to interconnect clusters. ARC introduces a new algorithm for cluster leader revocation that eliminates the ripple effect caused by leadership changes. Further, ARC utilizes a limited broadcast algorithm for reducing the impact of network floods. The performance of ARC is evaluated by comparing it both with other clustering schemes and with an on-demand ad hoc routing protocol. It is shown that the cluster topology created by ARC is more stable than that created by other clustering algorithms and that the use of ARC can result in throughput increases of over 100%.
Effects of inspiratory muscle training on resistance to fatigue of respiratory muscles during exhaustive exercise.

The aim of this study was to assess the effect of inspiratory muscle training (IMT) on resistance to fatigue of the diaphragm (D), parasternal (PS), sternocleidomastoid (SCM) and scalene (SC) muscles in healthy humans during exhaustive exercise. Daily inspiratory muscle strength training was performed for 3 weeks in 10 male subjects (at a pressure threshold load of 60% of maximal inspiratory pressure (MIP) for the first week, 70% of MIP for the second week, and 80% of MIP for the third week). Before and after training, subjects performed an incremental cycle test to exhaustion. Maximal inspiratory pressure and EMG-analysis served as indices of inspiratory muscle fatigue assessment. The before-to-after exercise decreases in MIP and centroid frequency (fc) of the EMG (D, PS, SCM, and SC) power spectrum (P<0.05) were observed in all subjects before the IMT intervention. Such changes were absent after the IMT. The study found that in healthy subjects, IMT results in significant increase in MIP (+18%), a delay of inspiratory muscle fatigue during exhaustive exercise, and a significant improvement in maximal work performance. We conclude that the IMT elicits resistance to the development of inspiratory muscles fatigue during high-intensity exercise.
Nipah virus matrix protein: expert hacker of cellular machines.

Nipah virus (NiV, Henipavirus) is a highly lethal emergent zoonotic paramyxovirus responsible for repeated human outbreaks of encephalitis in South East Asia. There are no approved vaccines or treatments, thus improved understanding of NiV biology is imperative. NiV matrix protein recruits a plethora of cellular machinery to scaffold and coordinate virion budding. Intriguingly, matrix also hijacks cellular trafficking and ubiquitination pathways to facilitate transient nuclear localization. While the biological significance of matrix nuclear localization for an otherwise cytoplasmic virus remains enigmatic, the molecular details have begun to be characterized, and are conserved among matrix proteins from divergent paramyxoviruses. Matrix protein appropriation of cellular machinery will be discussed in terms of its early nuclear targeting and later role in virion assembly.
Efficient Recommendation of Aggregate Data Visualizations

Data visualization is a common and effective technique for data exploration. However, for complex data, it is infeasible for an analyst to manually generate and browse all possible visualizations for insights. This observation motivated the need for automated solutions that can effectively recommend such visualizations. The main idea underlying those solutions is to evaluate the utility of all possible visualizations and then recommend the top-k visualizations. This process incurs high data processing cost, that is further aggravated by the presence of numerical dimensional attributes. To address that challenge, we propose novel view recommendation schemes, which incorporate a hybrid multi-objective utility function that captures the impact of numerical dimension attributes. Our first scheme, Multi-Objective View Recommendation for Data Exploration (MuVE), adopts an incremental evaluation of our multi-objective utility function, which allows pruning of a large number of low-utility views and avoids unnecessary objective evaluations. Our second scheme, upper MuVE (uMuVE), further improves the pruning power by setting the upper bounds on the utility of views and allowing interleaved processing of views, at the expense of increased memory usage. Finally, our third scheme, Memory-aware uMuVE (MuMuVE), provides pruning power close to that of uMuVE, while keeping memory usage within a pre-specified limit.
Phonotactic language recognition based on DNN-HMM acoustic model

A recently introduced deep neural network (DNN) has achieved some unprecedented gains in many challenging automatic speech recognition (ASR) tasks. In this paper deep neural network hidden Markov model (DNN-HMM) acoustic models is introduced to phonotactic language recognition and outperforms artificial neural network hidden Markov model (ANN-HMM) and Gaussian mixture model hidden Markov model (GMM-HMM) acoustic model. Experimental results have confirmed that phonotactic language recognition system using DNN-HMM acoustic model yields relative equal error rate reduction of 28.42%, 14.06%, 18.70% and 12.55%, 7.20%, 2.47% for 30s, 10s, 3s comparing with the ANN-HMM and GMM-HMM approaches respectively on National Institute of Standards and Technology language recognition evaluation (NIST LRE) 2009 tasks.
Meta-analysis of psychological treatments for posttraumatic stress disorder in adult survivors of childhood abuse.

Posttraumatic stress disorder (PTSD) is highly prevalent in adult survivors of childhood sexual and/or physical abuse. However, intervention studies focusing on this group of patients are underrepresented in earlier meta-analyses on the efficacy of PTSD treatments. The current meta-analysis exclusively focused on studies evaluating the efficacy of psychological interventions for PTSD in adult survivors of childhood abuse. Sixteen randomized controlled trials meeting inclusion criteria could be identified that were subdivided into trauma-focused cognitive behavior therapy (CBT), non-trauma-focused CBT, eye movement desensitization and reprocessing, and other treatments (interpersonal, emotion-focused). Results showed that psychological interventions are efficacious for PTSD in adult survivors of childhood abuse, with an aggregated uncontrolled effect size of g=1.24 (pre- vs. post-treatment), and aggregated controlled effect sizes of g=0.72 (post-treatment, comparison to waitlist control conditions) and g=0.50 (post-treatment, comparison with TAU/placebo control conditions), respectively. Effect sizes remained stable at follow-up. As the heterogeneity between studies was large, we examined the influence of two a priori specified moderator variables on treatment efficacy. Results showed that trauma-focused treatments were more efficacious than non-trauma-focused interventions, and that treatments including individual sessions yielded larger effect sizes than pure group treatments. As a whole, the findings are in line with earlier meta-analyses showing that the best effects can be achieved with individual trauma-focused treatments.
Blockchain And Its Applications

Blockchain innovation was initially presented as the innovation behind the Bitcoin decentralized virtual currency, yet there is the desire that its qualities of precise and irreversible information move in a decentralized P2P system could make different applications conceivable. Blockchain an apparently unassuming information structure, and a suite of related conventions, have as of late taken the universes of Finance and Technology by tempest through its earth shattering application in the present day crypto-currency Bitcoin, and all the more so due to the problematic advancements it guarantees. blockchain, bitcoin, security, public ledger.
A Review : DoS and DDoS Attacks

Wireless sensor networks (WSNs) are a special type of Ad-hoc network. WSNs are easily subjected to intentional or unintentional attacks as compared to wired based networks. Denial of Service (DoS) attack is one of the main threats that the network is facing. DoS attack makes use of many hosts to send a lot of useless packets to the target in short time of invalid access which will consume the targets resources and causes outage of server operations. In this paper we will discuss various types of attacks on WSNs and emphasize on DoS attack. And will discuss smurf attack which is one of the DoS attack type.
Seq2Seq2Sentiment: Multimodal Sequence to Sequence Models for Sentiment Analysis

Multimodal machine learning is a core research area spanning the language, visual and acoustic modalities. The central challenge in multimodal learning involves learning representations that can process and relate information from multiple modalities. In this paper, we propose two methods for unsupervised learning of joint multimodal representations using sequence to sequence (Seq2Seq) methods: a Seq2Seq Modality Translation Model and a Hierarchical Seq2Seq Modality Translation Model. We also explore multiple different variations on the multimodal inputs and outputs of these seq2seq models. Our experiments on multimodal sentiment analysis using the CMU-MOSI dataset indicate that our methods learn informative multimodal representations that outperform the baselines and achieve improved performance on multimodal sentiment analysis, specifically in the Bimodal case where our model is able to improve F1 Score by twelve points. We also discuss future directions for multimodal Seq2Seq methods.
Do we only dream in colour? A comparison of reported dream colour in younger and older adults with different experiences of black and white media

This study aimed to find out whether differences in the reported colour of dreams can be attributed to the influence of black and white media or to methodological issues. Two age groups, with different media experience, were compared on questionnaire and diary measures of dream colour. Analysis revealed that people who had access to black and white media before colour media experienced more greyscale dreams than people with no such exposure, and there were no differences between diary and questionnaire measures of dream colour. Moreover, there were inter-group differences in the recall quality of colour and black and white dreams that point to the possibility that true greyscale dreams occur only in people with black and white media experience.
Absorptive Capacity : A New Perspective on Learning and Innovation

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Endoscopic pilonidal sinus treatment (E.P.Si.T.)

We present a new video-assisted minimally invasive technique for the treatment of pilonidal disease (E.P.Si.T: endoscopic pilonidal sinus treatment). Between March and November 2012, we operated on 11 patients suffering from pilonidal disease. Surgery is performed under local or spinal anesthesia using the Meinero fistuloscope. The external opening is excised and the fistuloscope is introduced through the small hole. Anatomy is identified, hair and debris are removed and the entire area is ablated under direct vision. There were no significant complications recorded in the patient cohort. The pain experienced during the postoperative period was minimal. At 1 month postoperatively, the external opening(s) were closed in all patients and there were no cases of recurrence at a median follow-up of 6 months. All patients were admitted and discharged on the same day as surgery and commenced work again after a mean time period of 4 days. Aesthetic results were excellent. The key feature of the E.P.Si.T. technique is direct vision, allowing a good definition of the involved area, removal of debris and cauterization of the inflamed tissue.