nils-herrmann/clean_scirepeval huggingface dataset:
Affective Video Content Analysis: A Multidisciplinary Insight

In our present society, the cinema has become one of the major forms of entertainment providing unlimited contexts of emotion elicitation for the emotional needs of human beings. Since emotions are universal and shape all aspects of our interpersonal and intellectual experience, they have proved to be a highly multidisciplinary research field, ranging from psychology, sociology, neuroscience, etc., to computer science. However, affective multimedia content analysis work from the computer science community benefits but little from the progress achieved in other research fields. In this paper, a multidisciplinary state-of-the-art for affective movie content analysis is given, in order to promote and encourage exchanges between researchers from a very wide range of fields. In contrast to other state-of-the-art papers on affective video content analysis, this work confronts the ideas and models of psychology, sociology, neuroscience, and computer science. The concepts of aesthetic emotions and emotion induction, as well as the different representations of emotions are introduced, based on psychological and sociological theories. Previous global and continuous affective video content analysis work, including video emotion recognition and violence detection, are also presented in order to point out the limitations of affective video content analysis work.
A simple method of identifying the spinal accessory nerve in modified radical neck dissection: anatomic study and clinical implications for resident training

The major complication of neck dissection and surgery at the posterior triangle of the neck is the shoulder syndrome, which results from spinal accessory nerve injury. Erb’s point (the great auricular nerve) and the point where the spinal accessory nerve enters the trapezius muscle are used to identify the spinal accessory nerve in the posterior nerve triangle. Measurements were made during unilateral neck dissections in 30 patients to identify the relationship between the spinal accessory nerve and great auricular nerve and the distance between the entrance of the accessory nerve in the trapezious and clavicle. The distance between the spinal accessory nerve and Erb’s point was ranging from 0 to 3.8 cm (mean 1.53 cm). The distance between the spinal accessory nerve entering the trapezious muscle and the clavicle was between 2.5 and 7.3 cm (mean 4.8 cm). Since the great auricular nerve (Erb’s point) represents a constantly identifiable landmark, it allows simple and reliable identification of the course of the spinal accessory nerve. Also useful, but of secondary importance in our opinion, is identifying the nerve at the point where it enters the trapezius muscle.
A multifaceted investigation of the link between mental fatigue and task disengagement.

Mental fatigue is often characterized by reduced motivation for effortful activity and impaired task performance. We used subjective, behavioral (performance), and psychophysiological (P3, pupil diameter) measures during an n-back task to investigate the link between mental fatigue and task disengagement. After 2 h, we manipulated the rewards to examine a possible reengagement effect. Analyses showed that, with increasing fatigue and time-on-task, performance, P3 amplitude, and pupil diameter decreased. After increasing the rewards, all measures reverted to higher levels. Multilevel analysis revealed positive correlations between the used measures with time-on-task. We interpret these results as support for a strong link between task disengagement and mental fatigue.
EFSUMB guidelines and recommendations on the clinical use of ultrasound elastography. Part 1: Basic principles and technology.

The technical part of these Guidelines and Recommendations, produced under the auspices of EFSUMB, provides an introduction to the physical principles and technology on which all forms of current commercially available ultrasound elastography are based. A difference in shear modulus is the common underlying physical mechanism that provides tissue contrast in all elastograms. The relationship between the alternative technologies is considered in terms of the method used to take advantage of this. The practical advantages and disadvantages associated with each of the techniques are described, and guidance is provided on optimisation of scanning technique, image display, image interpretation and some of the known image artefacts.
Mul-T: A High-Performance Parallel Lisp

Mul-T is a parallel Lisp system, based on Multilisp's future construct, that has been developed to run on an Encore Multimax multiprocessor. Mul-T is an extended version of the Yale T system and uses the T system's ORBIT compiler to achieve “production quality” performance on stock hardware — about 100 times faster than Multilisp. Mul-T shows that futures can be implemented cheaply enough to be useful in a production-quality system. Mul-T is fully operational, including a user interface that supports managing groups of parallel tasks.
Extracting disease-symptom relationships from health question and answer forum

In this paper, we address the problem of automatically extracting disease-symptom relationships from health question-answer forums due to its usefulness for medical question answering system. To cope with the problem, we divide our main task into two subtasks since they exhibit different challenges: (1) disease-symptom extraction across sentences, (2) disease-symptom extraction within a sentence. For both subtasks, we employed machine learning approach leveraging several hand-crafted features, such as syntactic features (i.e., information from part-of-speech tags) and pre-trained word vectors. Furthermore, we basically formulate our problem as a binary classification task, in which we classify the "indicating" relation between a pair of Symptom and Disease entity. To evaluate the performance, we also collected and annotated corpus containing 463 pairs of question-answer threads from several Indonesian health consultation websites. Our experiment shows that, as our expected, the first subtask is relatively more difficult than the second subtask. For the first subtask, the extraction of disease-symptom relation only achieved 36% in terms of F1 measure, while the second one was 76%. To the best of our knowledge, this is the first work addressing such relation extraction task for both "across" and "within" sentence, especially in Indonesia.
Evaluation of healthcare software from a usability perspective

This paper provides a framework for evaluating healthcare software from a usability perspective. The framework is based on a review of both the healthcare software literature and the general literature on software usability and evaluation. The need for such a framework arises from the proliferation of software packages in the healthcare field, and from an historical focus on the technical and functional aspects, rather than on the usability, of these packages. Healthcare managers are generally unfamiliar with usability concepts, even though usability differences among software can play a significant role in the acceptance and effectiveness of systems. Six major areas of usability are described, and specific criteria which can be used in the software evaluation process are also presented.
WADE: Writeback-aware dynamic cache management for NVM-based main memory system

Emerging Non-Volatile Memory (NVM) technologies are explored as potential alternatives to traditional SRAM/DRAM-based memory architecture in future microprocessor design. One of the major disadvantages for NVM is the latency and energy overhead associated with write operations. Mitigation techniques to minimize the write overhead for NVM-based main memory architecture have been studied extensively. However, most prior work focuses on optimization techniques for NVM-based main memory itself, with little attention paid to cache management policies for the Last-Level Cache (LLC).
 In this article, we propose a Writeback-Aware Dynamic CachE (WADE) management technique to help mitigate the write overhead in NVM-based memory.<sup;>1</sup;> The proposal is based on the observation that, when dirty cache blocks are evicted from the LLC and written into NVM-based memory (with PCM as an example), the long latency and high energy associated with write operations to NVM-based memory can cause system performance/power degradation. Thus, reducing the number of writeback requests from the LLC is critical.
 The proposed WADE cache management technique tries to keep highly reused dirty cache blocks in the LLC. The technique predicts blocks that are frequently written back in the LLC. The LLC sets are dynamically partitioned into a frequent writeback list and a nonfrequent writeback list. It keeps a best size of each list in the LLC. Our evaluation shows that the technique can reduce the number of writeback requests by 16.5% for memory-intensive single-threaded benchmarks and 10.8% for multicore workloads. It yields a geometric mean speedup of 5.1% for single-thread applications and 7.6% for multicore workloads. Due to the reduced number of writeback requests to main memory, the technique reduces the energy consumption by 8.1% for single-thread applications and 7.6% for multicore workloads.
Practical considerations in medical cannabis administration and dosing.

Cannabis has been employed medicinally throughout history, but its recent legal prohibition, biochemical complexity and variability, quality control issues, previous dearth of appropriately powered randomised controlled trials, and lack of pertinent education have conspired to leave clinicians in the dark as to how to advise patients pursuing such treatment. With the advent of pharmaceutical cannabis-based medicines (Sativex/nabiximols and Epidiolex), and liberalisation of access in certain nations, this ignorance of cannabis pharmacology and therapeutics has become untenable. In this article, the authors endeavour to present concise data on cannabis pharmacology related to tetrahydrocannabinol (THC), cannabidiol (CBD) et al., methods of administration (smoking, vaporisation, oral), and dosing recommendations. Adverse events of cannabis medicine pertain primarily to THC, whose total daily dose-equivalent should generally be limited to 30mg/day or less, preferably in conjunction with CBD, to avoid psychoactive sequelae and development of tolerance. CBD, in contrast to THC, is less potent, and may require much higher doses for its adjunctive benefits on pain, inflammation, and attenuation of THC-associated anxiety and tachycardia. Dose initiation should commence at modest levels, and titration of any cannabis preparation should be undertaken slowly over a period of as much as two weeks. Suggestions are offered on cannabis-drug interactions, patient monitoring, and standards of care, while special cases for cannabis therapeutics are addressed: epilepsy, cancer palliation and primary treatment, chronic pain, use in the elderly, Parkinson disease, paediatrics, with concomitant opioids, and in relation to driving and hazardous activities.
Validation of the Social Networking Activity Intensity Scale among Junior Middle School Students in China

Online social networking use has been integrated into adolescents' daily life and the intensity of online social networking use may have important consequences on adolescents' well-being. However, there are few validated instruments to measure social networking use intensity. The present study aims to develop the Social Networking Activity Intensity Scale (SNAIS) and validate it among junior middle school students in China.



A total of 910 students who were social networking users were recruited from two junior middle schools in Guangzhou, and 114 students were retested after two weeks to examine the test-retest reliability. The psychometrics of the SNAIS were estimated using appropriate statistical methods.



Two factors, Social Function Use Intensity (SFUI) and Entertainment Function Use Intensity (EFUI), were clearly identified by both exploratory and confirmatory factor analyses. No ceiling or floor effects were observed for the SNAIS and its two subscales. The SNAIS and its two subscales exhibited acceptable reliability (Cronbach's alpha = 0.89, 0.90 and 0.60, and test-retest Intra-class Correlation Coefficient = 0.85, 0.87 and 0.67 for Overall scale, SFUI and EFUI subscale, respectively, p<0.001). As expected, the SNAIS and its subscale scores were correlated significantly with emotional connection to social networking, social networking addiction, Internet addiction, and characteristics related to social networking use.



The SNAIS is an easily self-administered scale with good psychometric properties. It would facilitate more research in this field worldwide and specifically in the Chinese population.
Fungal silver nanoparticles: synthesis, application and challenges.

This paper aims to summarize recent developments regarding the synthesis, application and challenges of fungal AgNPs. Possible methods to overcome the challenge of synthesis and reduce the toxicity of AgNPs have been discussed.



This review consults and summary a large number of papers.



Silver nanoparticles (AgNPs) have great potential in many areas, as they possess multiple novel characteristics. Conventional methods for AgNPs biosynthesis involve chemical agents, causing environmental toxicity and high energy consumption. Fungal bioconversion is a simple, low-cost and energy-efficient biological method, which could successfully be used for AgNPs synthesis. Fungi can produce enzymes that act as both reducing and capping agents, to form stable and shape-controlled AgNPs.



AgNPs have great potential in the medical and food industries, due to their antimicrobial, anticancer, anti-HIV, and catalytic activities. However, the observed in vitro and in vivo toxicity poses considerable challenges in the synthesis and application of AgNPs.
Psychiatric Axis I Comorbidities among Patients with Gender Dysphoria

Cooccurring psychiatric disorders influence the outcome and prognosis of gender dysphoria. The aim of this study is to assess psychiatric comorbidities in a group of patients.  Eighty-three patients requesting sex reassignment surgery (SRS) were recruited and assessed through the Persian Structured Clinical Interview for DSM-IV Axis I disorders (SCID-I).  Fifty-seven (62.7%) patients had at least one psychiatric comorbidity. Major depressive disorder (33.7%), specific phobia (20.5%), and adjustment disorder (15.7%) were the three most prevalent disorders.  Consistent with most earlier researches, the majority of patients with gender dysphoria had psychiatric Axis I comorbidity.
CLASSIFICATION BASED ON ASSOCIATION-RULE MINING TECHNIQUES : A GENERAL SURVEY AND EMPIRICAL COMPARATIVE EVALUATION

In this paper classification and association rule mining algorithms are discussed and demonstrated. Particularly, the problem of association rule mining, and the investigation and comparison of popular association rules algorithms. The classic problem of classification in data mining will be also discussed. The paper also considers the use of association rule mining in classification approach in which a recently proposed algorithm is demonstrated for this purpose. Finally, a comprehensive experimental study against 13 UCI data sets is presented to evaluate and compare traditional and association rule based classification techniques with regards to classification accuracy, number of derived rules, rules features and processing time.
Temporally Identity-Aware SSD With Attentional LSTM.

Temporal object detection has attracted significant attention, but most popular detection methods cannot leverage rich temporal information in videos. Very recently, many algorithms have been developed for video detection task, yet very few approaches can achieve real-time online object detection in videos. In this paper, based on the attention mechanism and convolutional long short-term memory (ConvLSTM), we propose a temporal single-shot detector (TSSD) for real-world detection. Distinct from the previous methods, we take aim at temporally integrating pyramidal feature hierarchy using ConvLSTM, and design a novel structure, including a low-level temporal unit as well as a high-level one for multiscale feature maps. Moreover, we develop a creative temporal analysis unit, namely, attentional ConvLSTM, in which a temporal attention mechanism is specially tailored for background suppression and scale suppression, while a ConvLSTM integrates attention-aware features across time. An association loss and a multistep training are designed for temporal coherence. Besides, an online tubelet analysis (OTA) is exploited for identification. Our framework is evaluated on ImageNet VID dataset and 2DMOT15 dataset. Extensive comparisons on the detection and tracking capability validate the superiority of the proposed approach. Consequently, the developed TSSD-OTA achieves a fast speed and an overall competitive performance in terms of detection and tracking. Finally, a real-world maneuver is conducted for underwater object grasping.
Artificial bee colony metaheuristic for energy-efficient clustering and routing in wireless sensor networks

Swarm intelligence (SI)-based metaheuristics are well applied to solve real-time optimization problems of efficient node clustering and energy-aware data routing in wireless sensor networks. This paper presents another superior approach for these optimization problems based on an artificial bee colony metaheuristic. The proposed clustering algorithm presents an efficient cluster formation mechanism with improved cluster head selection criteria based on a multi-objective fitness function, whereas the routing algorithm is devised to consume minimum energy with least hop-count for data transmission. Extensive evaluation and comparison of the proposed approach with existing wellknown SI-based algorithms demonstrate its superiority over others in terms of packet delivery ratio, average energy consumed, average throughput and network life.
Accurate and robust face recognition from RGB-D images with a deep learning approach

Face recognition from RGB-D images utilizes 2 complementary types of image data, i.e. colour and depth images, to achieve more accurate recognition. In this paper, we propose a face recognition system based on deep learning, which can be used to verify and identify a subject from the colour and depth face images captured with a consumer-level RGB-D camera. To recognize faces with colour and depth information, our system contains 3 parts: depth image recovery, deep learning for feature extraction, and joint classification. To alleviate the problem of the limited size of available RGB-D data for deep learning, our deep network is firstly trained with colour face dataset, and later fine-tuned on depth face images for transfer learning. Our experiments on some public and our own RGB-D face datasets show that the proposed face recognition system provides very accurate face recognition results and it is robust against variations in head rotation and environmental illumination.
Tissue-resident macrophages

Tissue-resident macrophages are a heterogeneous population of immune cells that fulfill tissue-specific and niche-specific functions. These range from dedicated homeostatic functions, such as clearance of cellular debris and iron processing, to central roles in tissue immune surveillance, response to infection and the resolution of inflammation. Recent studies highlight marked heterogeneity in the origins of tissue macrophages that arise from hematopoietic versus self-renewing embryo-derived populations. We discuss the tissue niche-specific factors that dictate cell phenotype, the definition of which will allow new strategies to promote the restoration of tissue homeostasis. Understanding the mechanisms that dictate tissue macrophage heterogeneity should explain why simplified models of macrophage activation do not explain the extent of heterogeneity seen in vivo.
Generative Adversarial Training for MRA Image Synthesis Using Multi-contrast MRI

Magnetic Resonance Angiography (MRA) has become an essential MR contrast for imaging and evaluation of vascular anatomy and related diseases. MRA acquisitions are typically ordered for vascular interventions, whereas in typical scenarios, MRA sequences can be absent in the patient scans. This motivates the need for a technique that generates inexistent MRA from existing MR multi-contrast, which could be a valuable tool in retrospective subject evaluations and imaging studies. In this paper, we present a generative adversarial network (GAN) based technique to generate MRA from T1-weighted and T2-weighted MRI images, for the first time to our knowledge. To better model the representation of vessels which the MRA inherently highlights, we design a loss term dedicated to a faithful reproduction of vascularities. To that end, we incorporate steerable filter responses of the generated and reference images inside a Huber function loss term. Extending the wellestablished generator-discriminator architecture based on the recent PatchGAN model with the addition of steerable filter loss, the proposed steerable GAN (sGAN) method is evaluated on the large public database IXI. Experimental results show that the sGAN outperforms the baseline GAN method in terms of an overlap score with similar PSNR values, while it leads to improved visual perceptual quality.
Does child abuse cause crime ?

Does Child Abuse Cause Crime? Child maltreatment, which includes both child abuse and child neglect, is a major social problem. This paper focuses on measuring the effects of child maltreatment on crime using data from the National Longitudinal Study of Adolescent Health (Add Health). We focus on crime because it is one of the most socially costly potential outcomes of maltreatment, and because the proposed mechanisms linking maltreatment and crime are relatively well elucidated in the literature. Our work addresses many limitations of the existing literature on child maltreatment. First, we use a large national sample, and investigate different types of abuse in a similar framework. Second, we pay careful attention to identifying the causal impact of abuse, by using a variety of statistical methods that make differing assumptions. These methods include: Ordinary Least Squares (OLS), propensity score matching estimators, and twin fixed effects. Finally, we examine the extent to which the effects of maltreatment vary with socio-economic status (SES), gender, and the severity of the maltreatment. We find that maltreatment approximately doubles the probability of engaging in many types of crime. Low SES children are both more likely to be mistreated and suffer more damaging effects. Boys are at greater risk than girls, at least in terms of increased propensity to commit crime. Sexual abuse appears to have the largest negative effects, perhaps justifying the emphasis on this type of abuse in the literature. Finally, the probability of engaging in crime increases with the experience of multiple forms of maltreatment as well as the experience of Child Protective Services (CPS) investigation.
Animal models of atopic dermatitis.

Atopic dermatitis (AD) is characterized by allergic skin inflammation. A hallmark of AD is dry itchy skin due, at least in part, to defects in skin genes that are important for maintaining barrier function. The pathogenesis of AD remains incompletely understood. Since the description of the Nc/Nga mouse as a spontaneously occurring model of AD, a number of other mouse models of AD have been developed. They can be categorized into three groups: (1) models induced by epicutaneous application of sensitizers; (2) transgenic mice that either overexpress or lack selective molecules; (3) mice that spontaneously develop AD-like skin lesions. These models have resulted in a better understanding of the pathogenesis of AD. This review discusses these models and emphasizes the role of mechanical skin injury and skin barrier dysfunction in eliciting allergic skin inflammation.