nils-herrmann/clean_scirepeval huggingface dataset:
Comparison of three one-question, post-task usability questionnaires

Post-task ratings of difficulty in a usability test have the potential to provide diagnostic information and be an additional measure of user satisfaction. But the ratings need to be reliable as well as easy to use for both respondents and researchers. Three one-question rating types were compared in a study with 26 participants who attempted the same five tasks with two software applications. The types were a Likert scale, a Usability Magnitude Estimation (UME) judgment, and a Subjective Mental Effort Question (SMEQ). All three types could distinguish between the applications with 26 participants, but the Likert and SMEQ types were more sensitive with small sample sizes. Both the Likert and SMEQ types were easy to learn and quick to execute. The online version of the SMEQ question was highly correlated with other measures and had equal sensitivity to the Likert question type.
Smart Indoor Positioning/Location and Navigation: A Lightweight Approach

— In this paper a new location indoor system is presented, which shows the position and orientation of the user in closed environments, as well as the optimal route to his destination through location tags. This system is called Labelee, and it makes easier the interaction between users and devices through QR code scanning or by NFC tag reading, because this technology is increasingly common in the latest smartphones. With this system, users could locate themselves into an enclosure with less interaction.
Oxytocin system dysfunction as a common mechanism underlying metabolic syndrome and psychiatric symptoms in schizophrenia and bipolar disorders

There is growing interest in using intranasal oxytocin (OT) to treat social dysfunction in schizophrenia and bipolar disorders (i.e., psychotic disorders). While OT treatment results have been mixed, emerging evidence suggests that OT system dysfunction may also play a role in the etiology of metabolic syndrome (MetS), which appears in one-third of individuals with psychotic disorders and associated with increased mortality. Here we examine the evidence for a potential role of the OT system in the shared risk for MetS and psychotic disorders, and its prospects for ameliorating MetS. Using several studies to demonstrate the overlapping neurobiological profiles of metabolic risk factors and psychiatric symptoms, we show that OT system dysfunction may be one common mechanism underlying MetS and psychotic disorders. Given the critical need to better understand metabolic dysregulation in these disorders, future OT trials assessing behavioural and cognitive outcomes should additionally include metabolic risk factor parameters.
Design of Compact High-Isolation Four-Way Power Combiners

In this paper, the design methods for four-way power combiners based on eight-port and nine-port mode networks are proposed. The eight-port mode network is fundamentally a two-stage binary four-way power combiner composed of three magic-Ts: two compact H-plane magic-Ts and one magic-T with coplanar arms. The two compact H-plane magic-Ts and the magic-T with coplanar arms function as the first and second stages, respectively. Thus, four-way coaxial-to-coaxial power combiners can be designed. A one-stage four-way power combiner based on a nine-port mode network is also proposed. Two matched coaxial ports and two matched rectangular ports are used to provide high isolation along the E-plane and the H-plane, respectively. The simulations agree well with the measured results. The designed four-way power combiners are superior in terms of their compact cross-sectional areas, a high degree of isolation, low insertion loss, low output-amplitude imbalance, and low phase imbalance, which make them well suited for solid-state power combination.
An Evolutionary Squeaky Wheel Optimization Approach to Personnel Scheduling

The quest for robust heuristics that are able to solve more than one problem is ongoing. In this paper, we present, discuss and analyze a technique called Evolutionary Squeaky Wheel Optimization and apply it to two different personnel scheduling problems.  for added evolution. In the evolutionary squeaky wheel optimization, a cycle of analysis-selection-mutation-prioritization-construction continues until stopping conditions are reached. The aim of the analysis step is to identify below average solution components by calculating a fitness value for all components. The selection step then chooses amongst these underperformers and discards some probabilistically based on fitness. The mutation step further discards a few components at random. Solutions can become incomplete and thus repairs may be required. The repair is carried out by using the prioritization step to first produce priorities that determine an order by which the following construction step then schedules the remaining components. Therefore, improvements in the evolutionary squeaky wheel optimization is achieved by selective solution disruption mixed with iterative improvement and constructive repair. Strong experimental results are reported on two different domains of personnel scheduling: bus and rail driver scheduling and hospital nurse scheduling.
Fuzzy Speed Control of Separately Excited DC Motor Using Armature Voltage Control Method

This paper presents a simulation of the speed control of a separately excited direct current motor (SEDM) using fuzzy logic control (FLC) in Matlab/Simulink environment. A fuzzy logic controller was designed to vary the motor’s speed by varying the armature voltage of the separately excited DC motor in the constant torque region (below the rated speed). The simulation results show that the armature voltage control method is better than field control method with regards to delay time and overshoot.
Transfer learning in multi-agent systems through parallel transfer

Transfer Learning(TL) has been shown to significantly accelerate the convergence of a reinforcement learning process. TL is the process of reusing learned information across tasks. Information is shared between a source and a target task. Previous work has required that the target task wait until the source task has finished learning before transferring information. The execution of the source task prior to the target task considerably extends the time required for the target task to complete. This paper proposes a novel approach allowing both source and target task to learn in parallel. This allows the transfer to be bi-directional, so processes can act as both source and target simultaneously. This, in consequence, allows tasks to learn from each other’s experiences and thereby reduces the learning time required. The proposed approach is evaluated on a multi-agent smartgrid scenario.
Video Desnowing and Deraining Based on Matrix Decomposition

The existing snow/rain removal methods often fail for heavy snow/rain and dynamic scene. One reason for the failure is due to the assumption that all the snowflakes/rain streaks are sparse in snow/rain scenes. The other is that the existing methods often can not differentiate moving objects and snowflakes/rain streaks. In this paper, we propose a model based on matrix decomposition for video desnowing and deraining to solve the problems mentioned above. We divide snowflakes/rain streaks into two categories: sparse ones and dense ones. With background fluctuations and optical flow information, the detection of moving objects and sparse snowflakes/rain streaks is formulated as a multi-label Markov Random Fields (MRFs). As for dense snowflakes/rain streaks, they are considered to obey Gaussian distribution. The snowflakes/rain streaks, including sparse ones and dense ones, in scene backgrounds are removed by low-rank representation of the backgrounds. Meanwhile, a group sparsity term in our model is designed to filter snow/rain pixels within the moving objects. Experimental results show that our proposed model performs better than the state-of-the-art methods for snow and rain removal.
Potential applications of plant based derivatives as fat replacers, antioxidants and antimicrobials in fresh and processed meat products.

Growing concern about diet and health has led to development of healthier food products. In general consumer perception towards the intake of meat and meat products is unhealthy because it may increase the risk of diseases like cardiovascular diseases, obesity and cancer, because of its high fat content (especially saturated fat) and added synthetic antioxidants and antimicrobials. Addition of plant derivatives having antioxidant components including vitamins A, C and E, minerals, polyphenols, flavanoids and terpenoids in meat products may decrease the risk of several degenerative diseases. To change consumer attitudes towards meat consumption, the meat industry is undergoing major transformations by addition of nonmeat ingredients as animal fat replacers, natural antioxidants and antimicrobials, preferably derived from plant sources.
Computation of total capacity for discrete memoryless multiple-access channels

The Arimoto-Blahut (1972) algorithm is generalized for computation of the total capacity of discrete memoryless multiple-access channels (MAC). In addition, a class of MAC is defined with the property that the uniform distribution achieves the total capacity. These results are based on the specialization of the Kuhn-Tucker condition for the total capacity of the MAC, and an extension of a known symmetry property for single-user channels.
On the Split-Block Realization of Millimeter-Wave Ridge Waveguide Components

A method to reduce RF leakage in split-block fabricated metallic ridged waveguide is presented. By placing a pin wall into the split-block seam, RF leakage and detrimental performance spikes are significantly reduced. Three different split-block double-ridge waveguide components of varying design complexity operating over 18–45-GHz bandwidth are fabricated and measured. It is shown that the addition of the pin wall improves the performance of each component such that results correlate well to simulated models with no split.
The Degradation of Polysorbates 20 and 80 and its Potential Impact on the Stability of Biotherapeutics

To study the potential impact of the degradation of Polysorbates (PS) 20 and 80 on the stability of therapeutic proteins in parenteral formulations. First, degradation products of PS20 and 80 were identified. Subsequently, the effect of degraded polysorbate on physical characteristics and long-term stability of protein formulations was assessed. Further, the impact of polysorbate degradation on protein stability was evaluated via shaking stress studies on formulations spiked with artificially degraded polysorbate or degradants like fatty acids. Additionally, aged formulations with reduced polysorbate content were shaken. The degradation of polysorbate leads to a buildup of various molecules, some of which are poorly soluble, including fatty acids and polyoxyethylene (POE) esters of fatty acids. Spiking studies showed that the insoluble degradants could potentially impact protein stability and that the presence of sufficient intact polysorbate was crucial to prevent this. End-of-shelf-life shaking of protein formulations showed that the stability of various monoclonal antibodies was, however, not affected. Although some degradants can potentially influence the stability of the protein (as discerned from spiking studies), degradation of polysorbates did not impact the stability of the different proteins tested in pharmaceutically relevant temperature and storage conditions.
A Survey on Arabic Character Recognition

Off-line recognition of text play a significant role in several application such as the automatic sorting of postal mail or editing old documents. It is the ability of the computer to distinguish characters and words. Automatic off-line recognition of text can be divided into the recognition of printed and handwritten characters. Off-line Arabic handwriting recognition still faces great challenges. This paper provides a survey of Arabic character recognition systems which are classified into the character recognition categories: printed and handwritten. Also, it examines the literature on the most significant work in handwritten text recognition without segmentation and discusses algorithms which split the words into characters.
The anatomy in relation to injury of the lateral collateral ligaments of the ankle: a current concepts review.

Injuries to the lateral ligament complex of the ankle are common problems in acute care practice. We believe that a well-developed knowledge of the anatomy provides a foundation for understanding the basic mechanism of injury, diagnosis, and treatment, especially surgical treatment, of lateral collateral ankle ligament injury. To address this issue we performed this review with regard to the anatomy of the lateral collateral ankle ligaments.
Detecting Stress Based on Social Interactions in Social Networks

Psychological stress is threatening people’s health. It is non-trivial to detect stress timely for proactive care. With the popularity of social media, people are used to sharing their daily activities and interacting with friends on social media platforms, making it feasible to leverage online social network data for stress detection. In this paper, we find that users stress state is closely related to that of his/her friends in social media, and we employ a large-scale dataset from real-world social platforms to systematically study the correlation of users’ stress states and social interactions. We first define a set of stress-related textual, visual, and social attributes from various aspects, and then propose a novel hybrid model - a factor graph model combined with Convolutional Neural Network to leverage tweet content and social interaction information for stress detection. Experimental results show that the proposed model can improve the detection performance by 6-9 percent in F1-score. By further analyzing the social interaction data, we also discover several intriguing phenomena, i.e., the number of social structures of sparse connections (i.e., with no delta connections) of stressed users is around 14 percent higher than that of non-stressed users, indicating that the social structure of stressed users’ friends tend to be less connected and less complicated than that of non-stressed users.
The Force of Law : Toward a Sociology of the Juridical Field by

Pierre Bourdieu holds the Chair in Sociology at the prestigious College de France, Paris. He is Directeur d'Etudes at l'Ecole des Hautes Etudes en Sciences Sociales, where he is also Director of the Center for European Sociology, and Editor of the influential journal Actes de la recherche en sciences sociales. Professor Bourdieu is the author or coauthor of approximately twenty books. A number of these have been published in English translation: The Algerians, 1962; Reproduction in Education, Society and Culture (with Jean-Claude Passeron), 1977; Outline of a Theory of Practice, 1977; Algeria I960, 1979; The Inheritors: French Students and their Relations to Culture, 1979;  A Social Critique of the Judgment of Taste, 1984. The essay below analyzes what Bourdieu terms the "juridical field." In Bourdieu's conception, a "field" is an area of structured, socially patterned activity or "practice," in this case disciplinarily and professionally defined. The "field" and its "practices" have special senses in
A vehicular collision warning algorithm based on the time-to-collision estimation under connected environment

Considering the traffic safety in the scenario of arterial road with on-ramp, this study proposes a time-to-collision (TTC) based vehicular collision warning algorithm under connected environment. In particular, the information of vehicles of interest, i.e., position, traveling direction and velocity, is assumed to be collected by the roadside device via the vehicle-to-infrastructure (V2I) communications. Then, the TTC of a pair of vehicles in arterial road and on-ramp is estimated based on the position, traveling direction and velocity difference of that pair of vehicles. Consequently, the TTC warning messages can be disseminated to vehicles within the communication range of the roadside device, so as to reduce the risk of collision. The proposed algorithm can be used in the cooperative vehicle infrastructure systems (CVIS) to improve the traffic safety.
RF-Sensing of Activities from Non-Cooperative Subjects in Device-Free Recognition Systems Using Ambient and Local Signals

We consider the detection of activities from non-cooperating individuals with features obtained on the radio frequency channel. Since environmental changes impact the transmission channel between devices, the detection of this alteration can be used to classify environmental situations. We identify relevant features to detect activities of non-actively transmitting subjects. In particular, we distinguish with high accuracy an empty environment or a walking, lying, crawling or standing person, in case-studies of an active, device-free activity recognition system with software defined radios. We distinguish between two cases in which the transmitter is either under the control of the system or ambient. For activity detection the application of one-stage and two-stage classifiers is considered. Apart from the discrimination of the above activities, we can show that a detected activity can also be localized simultaneously within an area of less than 1 meter radius.
Accidental genital trauma in the female children in Jordan and the role of forensic medicine.

To evaluate the frequency and the nature of genital trauma in female children in Jordan, and to stress the role of forensics.



This is a cross-sectional study conducted between March 2008 and December 2011 in Jordan University Hospital, Amman, Jordan. Sixty-three female children were examined for genital trauma after immediate admission. The mechanism of injury was categorized and reported by the examiners as either straddle, non-straddle blunt, or penetrating.



Straddle injury was the cause of injuries in 90.5% of patients, and contusions were the significant type of injury in 34% of patients, followed by abrasions in both labia majora and labia minora. Only one case suffered from non-intact hymen and 2 had hematuria. These 3 cases (4.7%) required surgical intervention and follow-up after 2 weeks.



Straddle injuries were the main cause of genital trauma and rarely affect the hymen; however, due to the sensitivity of the subject and the severity of the traumas, forensic physicians should provide consultation and cooperate with gynecologists to exclude or confirm hymenal injuries, where empathy is necessary to mitigate tension associated with such injuries for the sake of the child and the parents as well, along with good management of the injury type.
Application of Generative Autoencoder in De Novo Molecular Design

A major challenge in computational chemistry is the generation of novel molecular structures with desirable pharmacological and physiochemical properties. In this work, we investigate the potential use of autoencoder, a deep learning methodology, for de novo molecular design. Various generative autoencoders were used to map molecule structures into a continuous latent space and vice versa and their performance as structure generator was assessed. Our results show that the latent space preserves chemical similarity principle and thus can be used for the generation of analogue structures. Furthermore, the latent space created by autoencoders were searched systematically to generate novel compounds with predicted activity against dopamine receptor type 2 and compounds similar to known active compounds not included in the trainings set were identified.
Accelerating Large Scale Knowledge Distillation via Dynamic Importance Sampling

Knowledge distillation is an effective technique that transfers knowledge from a large teacher model to a shallow student. However, just like massive classification, large scale knowledge distillation also imposes heavy computational costs on training models of deep neural networks, as the softmax activations at the last layer involve computing probabilities over numerous classes. In this work, we apply the idea of importance sampling which is often used in Neural Machine Translation on large scale knowledge distillation. We present a method called dynamic importance sampling, where ranked classes are sampled from a dynamic distribution derived from the interaction between the teacher and student in full distillation. We highlight the utility of our proposal prior which helps the student capture the main information in the loss function. Our approach manages to reduce the computational cost at training time while maintaining the competitive performance on CIFAR100 and Market-1501 person re-identification datasets.
Chemoinformatic Classification Methods and their Applicability Domain.

Classification rules are often used in chemoinformatics to predict categorical properties of drug candidates related to bioactivity from explanatory variables, which encode the respective molecular structures (i.e. molecular descriptors). To avoid predictions with an unduly large error probability, the domain the classifier is applied to should be restricted to the domain covered by the training set objects. This latter domain is commonly referred to as applicability domain in chemoinformatics. Conceptually, the applicability domain defines the region in space where the "normal" objects are located. Defining the border of the applicability domain may then be viewed as detecting anomalous or novel objects or as detecting outliers. Currently two different types of measures are in use. The first one defines the applicability domain solely in terms of the molecular descriptor space, which is referred to as novelty detection. The second type defines the applicability domain in terms of the expected reliability of the predictions which is referred to as confidence estimation. Both types are systematically differentiated here and the most popular measures are reviewed. It will be shown that all common chemoinformatic classifiers have built-in confidence scores. Since confidence estimation uses information of the class labels for computing the confidence scores, it is expected to be more efficient in reducing the error rate than novelty detection, which solely uses the information of the explanatory variables.
Statistical Distance Estimation Algorithms With RSS Measurements for Indoor LTE-A Networks

An indoor base station (BS), such as a remote radio head or home eNodeB, is a cost-effective solution to achieve ubiquitous access and positioning functions in indoor Long-Term Evolution Advanced (LTE-A) networks. In this paper, two distance estimation algorithms adopt received signal strength (RSS) to estimate the corresponding distance between a BS and a mobile station. The statistical inference distance estimation (SIDE) algorithm is proposed to provide a consistent distance estimator when the particle number is larger than an inferential theoretic lower bound given a confidence level and an error constraint. Moreover, the particle-based distance estimation (PDE) algorithm is proposed to estimate distance information with the technique of particle filtering under mixed line-of-sight (LOS) and non-line-of-sight (NLOS) conditions in indoor LTE-A networks. Furthermore, the theoretic Cramér–Rao lower bound (CRLB), considering the variations from fading effects and time-variant channels, is derived as a benchmark to evaluate the precision of distance estimators. The performance of the proposed SIDE algorithm is verified through simulations, and the results fulfill the requirements of different confidence levels and error constraints. Furthermore, the proposed PDE algorithm outperforms other distance estimation schemes and reveals robustness against mixed-sight and time-variant indoor LTE-A networks.