nils-herrmann/clean_scirepeval huggingface dataset:
REAL TIME HAND DETECTION BY DEPTH IMAGES: A SURVEY

Human hand detection can enable human to communicate with a machine and interact without any external device. Human hands play an important role in different applications such as medical image processing, sign language translator, gesture recognition and augmented reality. A human hand has different length and breadth for male and female. So, it is a complex articulated object consisting many connected parts and joints. Traditional methods for hand detection and tracking used color and shape information from RGB camera. Using a depth camera for hand detection and tracking is a challenging and interesting domain in computer vision. Some research has shown that using depth data for hand detection can improve human computer interaction. Recently, researchers used depth data in different hand detection and tracking methods in real time application. This paper explains different types of methods which are used for human hand detection. Various techniques and methods are explored and analyzed in this survey to determine the shortfalls and future directions in the field of hand detection from depth data.
A Container Based Edge Cloud PaaS Architecture Based on Raspberry Pi Clusters
Venue: 2016 IEEE 4th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW)

Cloud technology is moving towards multi cloud environments with the inclusion of various devices. Cloud and IoT integration resulting in so called edge cloud and fog computing has started. This requires the combination of data centre technologies with much more constrained devices, but still using virtualised solutions to deal with scalability, flexibility and multi tenancy concerns. Lightweight virtualisation solutions do exist for this architectural setting with smaller, but still virtualised devices to provide application and platform technology as services. Containerisation is a solution component for lightweight virtualisation solution. Containers are furthermore relevant for cloud platform concerns dealt with by Platform as a Service (PaaS) clouds like application packaging and orchestration. We demonstrate an architecture for edge cloud PaaS. For edge clouds, application and service orchestration can help to manage and orchestrate applications through containers. In this way, computation can be brought to the edge of the cloud, rather than data from the Internet of Things (IoT) to the cloud. We show that edge cloud requirements such as cost efficiency, low power consumption, and robustness can be met by implementing container and cluster technology on small single board devices like Raspberry Pis. This architecture can facilitate applications through distributed multi cloud platforms built from a range of nodes from data centres to small devices, which we refer to as edge cloud. We illustrate key concepts of an edge cloud PaaS and refer to experimental and conceptual work to make that case.
Learning Text Similarity with Siamese Recurrent Networks
Venue: Rep4NLP@ACL

This paper presents a deep architecture for learning a similarity metric on variablelength character sequences. The model combines a stack of character level bidirectional LSTM's with a Siamese architecture. It learns to project variablelength strings into a fixed dimensional embedding space by using only information about the similarity between pairs of strings. This model is applied to the task of job title normalization based on a manually annotated taxonomy. A small data set is incrementally expanded and augmented with new sources of variance. The model learns a representation that is selective to differences in the input that reflect semantic differences (e.g. "Java developer" vs. "HR manager" but also invariant to nonsemantic string differences (e.g. "Java developer" vs. "Java programmer"
Scour Development Around Bucket Foundations

A wind turbine foundation design without scour protection, especially for wind turbines in the North Sea, is a sensitive topic for most offshore developers. However, recent research shows that this concept is realistic if there is extended comprehension of the scour and backfill phenomena. The present paper shows the comparative results for monopile and bucket foundations through the use of experimental analysis and field scour surveys of bucket foundations installed offshore. The main findings show that the bucket foundation is a candidate for a design without scour protection that behaves like scour protected monopiles in a variety of environmental conditions.
Financial risk identification and control of cross border merger and acquisition enterprises

Mergers and acquisitions (M&A) are basic channels for modern companies' growth. With globalization speeding up, multinational companies increasingly take on M&A activities to strengthen global market positions and raise competitiveness. In recent years, M&A activities have played an important role in Chinese companies. Financial risk is inherent to M&A processes in cross border companies. Also, more than 50% of Chinese companies did not achieve their M&A goals. Therefore, recognizing and controlling financial risk is essential. This paper analyses the financial risk from different perspectives and then provides suggestions by analysing a typical M&A case: Bohai Leasing merger with Seaco Company. Complete due diligence and clear M&A strategies, combination of various financing instrument, strategic paying methods and finance integration are some ways for controlling and decreasing finance risk.
Security Issues in Service Model of Cloud Computing Environment

Cloud computing is becoming increasingly fashionable in distributed computing environment. Processing and Data storage use cloud environment is becoming a movement universal. Software as a Service (SaaS) has on many business applications as well as in our day to day life, we can simply say that this disruptive technology. Cloud computing can be seen since Internet based computing, in which shared resources, software, and information are made available to devices on demand. It allows resources towards leveraged on per use basis. It diminishes cost and complexity of service providers by means of assets and operational costs. It allows users to access applications tenuously. On behalf of user, this construct directs cloud service provider to feel software updates and cost of servers etc. For both, cloud providers and consumers; availability, integrity, authenticity, confidentiality, and privacy are important concern. Infrastructure as a Service (IaaS) serves as base layer for many other release models and Platform as a Service (PaaS) clouds. Security of PaaS clouds is considered from multiple perspective including access control, service continuity and privacy while protecting together the service provider and the user. Security problems of PaaS clouds are explored and classified. In this paper we are going to some major security issues of current cloud computing environments.
Using LSTM and GRU neural network methods for traffic flow prediction
Venue: 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC)

Accurate and real time traffic flow prediction is important in Intelligent Transportation System (ITS) especially for traffic control. Existing models such as ARMA, ARIMA are mainly linear models and cannot describe the stochastic and nonlinear nature of traffic flow. In recent years, deep learning based methods have been applied as novel alternatives for traffic flow prediction. However, which kind of deep neural networks is the most appropriate model for traffic flow prediction remains unsolved. In this paper, we use Long Short Term Memory (LSTM) and Gated Recurrent Units (GRU) neural network (NN) methods to predict short term traffic flow, and experiments demonstrate that Recurrent Neural Network (RNN) based deep learning methods such as LSTM and GRU perform better than auto regressive integrated moving average (ARIMA) model. To the best of our knowledge, this is the first time that GRU is applied to traffic flow prediction.
Ask the GRU: Multi task Learning for Deep Text Recommendations
Venue: RecSys

In a variety of application domains the content to be recommended to users is associated with text. This includes research papers, movies with associated plot summaries, news articles, blog posts, etc. Recommendation approaches based on latent factor models can be extended naturally to leverage text by employing an explicit mapping from text to factors. This enables recommendations for new, unseen content, and may generalize better, since the factors for all items are produced by a compactly parametrized model. Previous work has used topic models or averages of word embeddings for this mapping. In this paper we present a method leveraging deep recurrent neural networks to encode the text sequence into a latent vector, specifically gated recurrent units (GRUs) trained end to end on the collaborative filtering task. For the task of scientific paper recommendation, this yields models with significantly higher accuracy. In cold start scenarios, we beat the previous state of the art, all of which ignore word order. Performance is further improved by multi task learning, where the text encoder network is trained for a combination of content recommendation and item metadata prediction. This regularizes the collaborative filtering model, ameliorating the problem of sparsity of the observed rating matrix.
Lipid oxidation in base algae oil and water in algae oil emulsion: Impact of natural antioxidants and emulsifiers.
Venue: Food research international

The impact of natural hydrophilic antioxidants, metal chelators, and hydrophilic antioxidant/metal chelator mixture on the oxidative stability of base algae oil and water in algae oil emulsion was investigated. The results showed that green tea extract and ascorbic acid had greatest protective effect against algae oil oxidation and generated four day lag phase, whereas rosmarinic acid, grape seed extract, grape seed extract polymer, deferoxamine (DFO) and ethylenediaminetetraacetic acid (EDTA) had no significant protective effect. Besides, there was no synergistic effect observed between natural antioxidants and ascorbic acid. The emulsifiers are critical to the physicochemical stability of water in algae oil emulsions. Polyglycerol polyricinoleate (PGPR) promoted the oxidation of emulsion. Conversely, the protective effect on algae oil oxidation was appreciated when defatted soybean lecithin (PC 75) or defatted lyso lecithin (Lyso PC) was added. The role of hydrophilic antioxidants in emulsion was similar to that in algae oil except EDTA which demonstrated strong antioxidative effect in emulsion. The results could provide information to build up stable food products containing polyunsaturated fatty acids (PUFA)
Robust design of a 3D and inkjet printed capacitive force/pressure sensor
Venue: 2016 17th International Conference on Thermal, Mechanical and Multi Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE)

We present a method to robust design of a capacitive force/pressure sensor, manufactured in rapid prototyping manner, by adaptation of the electrode structure. Rapid prototyping technologies, such as the considered 3D and inkjet printing processes, suffer from major uncertainties in geometry and material properties. The presented methodology consequently aims at minimizing the influence of topology and geometry variations as well as measurement noise on the sensor read out. It is outlined as a consequence of the beneficial combination of a Latin Hypercube design of experiments and the subsequent Linear Bayesian Minimum Mean Square Error (BMSE) estimator. Even though the additional effort in hardware and signal processing is low, we demonstrate a significant improvement of the measurement uncertainty when we move from a single capacitor to an optimized capacitor array.
Quantifying Aggressive Behavior in Zebrafish.
Venue: Methods in molecular biology

Aggression is a complex behavior that influences social relationships and can be seen as adaptive or maladaptive depending on the context and intensity of expression. A model organism suitable for genetic dissection of the underlying neural mechanisms of aggressive behavior is still needed. Zebrafish has already proven to be a powerful vertebrate model organism for the study of normal and pathological brain function. Despite the fact that zebrafish is a gregarious species that forms shoals, when allowed to interact in pairs, both males and females express aggressive behavior and establish dominance hierarchies. Here, we describe two protocols that can be used to quantify aggressive behavior in zebrafish, using two different paradigms: (1) staged fights between real opponents and (2) mirror elicited fights. We also discuss the methodology for the behavior analysis, the expected results for both paradigms, and the advantages and disadvantages of each paradigm in face of the specific goals of the study.
Emotion Socialization in the Context of Risk and Psychopathology: Maternal Emotion Coaching Predicts Better Treatment Outcomes for Emotionally Labile Children with Oppositional Defiant Disorder.
Venue: Social development

We examined whether maternal emotion coaching at pre treatment predicted children's treatment response following a 12 week program addressing children's Oppositional Defiant Disorder (ODD) symptoms. Eighty nine mother child dyads participated. At pre treatment, mothers and children engaged in an emotion talk task. Mothers also reported their beliefs about emotions at pre treatment and their child's disruptive behavior symptoms, emotion regulation, and emotion lability/negativity at pre mid and post treatment. Clinicians reported children's symptom severity at pre and post treatment. Children's emotion lability/negativity moderated effects of maternal emotion coaching on children's post treatment ODD symptoms, with stronger benefits of emotion coaching for children high in emotion lability/negativity. Results suggest that emotion coaching may promote treatment response for children with ODD who are especially at risk due to their emotionality.
Associations between allergic diseases and attention deficit hyperactivity/oppositional defiant disorders in children
Venue: Pediatric Research

We aim to investigate the detailed associations between allergic diseases with attention deficit hyperactivity disorder (ADHD) and oppositional defiant disorder (ODD) among children.Clinical information from 2,896 children enrolled in the Taiwan Children Health Study was obtained for analyses. Allergic diseases, including atopic dermatitis, asthma, and allergic rhinitis, have been evaluated based on the questions adjusted from International Study of Asthma and Allergies in Childhood. The Swanson, Nolan, and Pelham questionnaire was used to assess symptoms of ADHD and ODD. Symptoms of depression, stress, and poor sleep quality were evaluated as the interactive risk factors.Children having symptoms of allergic diseases within the past 1 y were associated with having all dimensions of symptoms of ADHD and ODD. Children with ever having a physician diagnosed atopic dermatitis were associated with inattentive and hyperactive impulsive symptoms of ADHD. Ever diagnosed asthma was associated with ADHD and ODD. Ever diagnosed allergic rhinitis was associated with inattentive and combined symptoms of ADHD and ODD.Conclusion:Children with allergic diseases, such as atopic dermatitis, asthma, and allergic rhinitis, were associated with exhibiting ADHD and ODD.
Child maltreatment and interpersonal relationship among Chinese children with oppositional defiant disorder.
Venue: Child abuse neglect

Child maltreatment negatively affects children's development and wellbeing. This study investigated the associations between child maltreatment (i.e. emotional neglect, emotional abuse, and physical abuse) and interpersonal functioning, including parent child relationship, teacher student relationship, and peer relationships among children with oppositional defiant disorder (ODD) A total of 256 children with ODD and their parents and class master teachers from Mainland China completed questionnaires. Results showed a negative correlation between emotional abuse (parent reported) and children's interpersonal relationships with parents, teachers, and peers. Emotional neglect and physical abuse were related to poor parent child relationships. Latent profile analysis revealed three profiles of child maltreatment among children with ODD. ODD children with more severe levels of one type of maltreatment were also more likely to have experienced severe levels of other types of maltreatment. Children with ODD who were in the group of high maltreatment had the poorest quality of interpersonal relationships. Our findings highlight the urgent need to prevent child maltreatment and promote more positive parenting in families with ODD children.
Trajectories of Oppositional Defiant Disorder Irritability Symptoms in Preschool Children
Venue: Journal of abnormal child psychology

This study traces the developmental course of irritability symptoms in oppositional defiant disorder (ODD) from ages 3 5 and examines the psychopathological outcomes of the different trajectories at age 6.  A sample of 622 3 year old preschoolers (311 were boys) followed up until age 6, was assessed yearly with a semi structured diagnostic interview with parents and at age 6 with questionnaires answered by parents, teachers and children.  Growth Mixture Modeling yielded five trajectories of irritability levels for the whole sample (high persistent 3.5 decreasing 3.8 increasing 2.6 low persistent 44.1 and null 46.0 Among the children who presented with ODD during preschool age, three trajectories of irritability symptoms resulted (high persistent 31.9 decreasing 34.9 and increasing 33.2 Null, low persistent and decreasing irritability courses in the sample as a whole gave very similar discriminative capacity for children's psychopathological state at age 6, while the increasing and high persistent categories involved poorer clinical outcomes than the null course. For ODD children, the high persistent and increasing trajectories of irritability predicted disruptive behavior disorders, comorbidity, high level of functional impairment, internalizing and externalizing problems and low anger control at age 6.  Irritability identifies a subset of ODD children at high risk of poorer longitudinal psychopathological and functional outcomes. It might be clinically relevant to identify this subset of ODD children with a high number of irritability symptoms throughout development with a view to preventing comorbid and future adverse longitudinal outcomes.
Oral health status, dental anxiety, and behavior management problems in children with oppositional defiant disorder.
Venue: European journal of oral sciences

Mental disorders have been shown to affect children's oral health. This study was carried out to investigate the oral health status, dental anxiety (DA) and behavior management problems (BMPs) during dental treatment in 6 to 9 yr old children with oppositional defiant disorder (ODD)/attention deficit hyperactivity disorder (ADHD) The study and control groups included 40 children with ODD/ADHD and 80 normal children, respectively. All participants received an amalgam restoration. During the procedure, the children's behavior was assessed using the Frankl Rating Scale and the Verbal Skill Scale. Parents rated their children's DA using the parental version of the Children's Fear Survey Schedule Dental subscale (CFSS DS) Comorbid anxiety disorders were assessed using the Kiddie Sads Present and Lifetime Version questionnaire. Oral health status was assessed using the gingival index and the decayed, missing, and filled teeth score for permanent (DMFT) and primary (dmft) teeth. The findings showed that DA and BMPs were significantly higher in children with ODD/ADHD than in the controls. Furthermore, the frequency of DA and BMPs was higher in children with both ODD/ADHD and a comorbid anxiety disorder than in those without comorbid anxiety disorder. Children with ODD/ADHD had significantly higher DMFT/dmft scores than those in the control group, whereas the difference in gingival index was not statistically significant. In conclusion, children with ODD/ADHD had higher levels of DA, BMP and poorer oral health status.
Maltreatment and Emotional and Behavioral Problems in Chinese Children With and Without Oppositional Defiant Disorder
Venue: Journal of interpersonal violence

Maltreatment has negative effects on the parent child relationship and the emotional and behavioral development of children. The current study aimed to examine the associations among maltreatment, parent child relationship, and emotional and behavioral problems in Chinese children with or without oppositional defiant disorder (ODD) Participants in the study included 259 children with ODD and their 269 non ODD counterparts from northern, eastern, and southwestern China. We also collected data from their teachers and fathers or mothers. The results showed that ODD children suffered more maltreatment and had more emotional and behavioral problems than their non ODD peers. For all children (both ODD and non ODD children) emotional abuse predicted emotional problems but not behavioral problems. Physical abuse predicted behavioral problems but not emotional problems. Parent child relationship mediated the effects of emotional abuse and physical abuse on emotional problems among ODD children but not among non ODD children. Implications for prevention of emotional and physical abuse and ODD in the Chinese cultural context are discussed.
Extreme learning machine approach for sensorless wind speed estimation

Precise predictions of wind speed play important role in determining the feasibility of harnessing wind energy. In fact, reliable wind predictions offer secure and minimal economic risk situation to operators and investors. This paper presents a new model based upon extreme learning machine (ELM) for sensor less estimation of wind speed based on wind turbine parameters. The inputs for estimating the wind speed are wind turbine power coefficient, blade pitch angle, and rotational speed. In order to validate authors compared prediction of ELM model with the predictions with genetic programming (GP) artificial neural network (ANN) and support vector machine with radial basis kernel function (SVM RBF) This investigation analyzed the reliability of these computational models using the simulation results and three statistical tests. The three statistical tests includes the Pearson correlation coefficient, coefficient of determination and root mean square error. Finally, this study compared predicted wind speeds from each method against actual measurement data. Simulation results, clearly demonstrate that ELM can be utilized effectively in applications of sensor less wind speed predictions. Concisely, the survey results show that the proposed ELM model is suitable and precise for sensor less wind speed predictions and has much higher performance than the other approaches examined in this study.