nils-herrmann/clean_scirepeval huggingface dataset:
Fallen Person Detection for Mobile Robots Using 3D Depth Data

Falling down and not managing to get up again is one of the main concerns of elderly people living alone in their home. Robotic assistance for the elderly promises to have a great potential of detecting these critical situations and calling for help. This paper presents a feature-based method to detect fallen people on the ground by a mobile robot equipped with a Kinect sensor. Point clouds are segmented, layered and classified to detect fallen people, even under occlusions by parts of their body or furniture. Different features, originally from pedestrian and object detection in depth data, and different classifiers are evaluated. Evaluation was done using data of 12 people lying on the floor. Negative samples were collected from objects similar to persons, two tall dogs, and five real apartments of elderly people. The best feature-classifier combination is selected to built a robust system to detect fallen people.
Associations between owner personality and psychological status and the prevalence of canine behavior problems

Behavioral problems are a major source of poor welfare and premature mortality in companion dogs. Previous studies have demonstrated associations between owners' personality and psychological status and the prevalence and/or severity of their dogs' behavior problems. However, the mechanisms responsible for these associations are currently unknown. Other studies have detected links between the tendency of dogs to display behavior problems and their owners' use of aversive or confrontational training methods. This raises the possibility that the effects of owner personality and psychological status on dog behavior are mediated via their influence on the owner's choice of training methods. We investigated this hypothesis in a self-selected, convenience sample of 1564 current dog owners using an online battery of questionnaires designed to measure, respectively, owner personality, depression, emotion regulation, use of aversive/confrontational training methods, and owner-reported dog behavior. Multivariate linear and logistic regression analyses identified modest, positive associations between owners' use of aversive/confrontational training methods and the prevalence/severity of the following dog behavior problems: owner-directed aggression, stranger-directed aggression, separation problems, chasing, persistent barking, and house-soiling (urination and defecation when left alone). The regression models also detected modest associations between owners' low scores on four of the 'Big Five' personality dimensions (Agreeableness, Emotional Stability, Extraversion & Conscientiousness) and their dogs' tendency to display higher rates of owner-directed aggression, stranger-directed fear, and/or urination when left alone. The study found only weak evidence to support the hypothesis that these relationships between owner personality and dog behavior were mediated via the owners' use of punitive training methods, but it did detect a more than five-fold increase in the use of aversive/confrontational training techniques among men with moderate depression. Further research is needed to clarify the causal relationship between owner personality and psychological status and the behavioral problems of companion dogs.
EEG-Based Emotion Recognition in Music Listening

Ongoing brain activity can be recorded as electroen-cephalograph (EEG) to discover the links between emotional states and brain activity. This study applied machine-learning algorithms to categorize EEG dynamics according to subject self-reported emotional states during music listening. A framework was proposed to optimize EEG-based emotion recognition by systematically 1) seeking emotion-specific EEG features and 2) exploring the efficacy of the classifiers. Support vector machine was employed to classify four emotional states (joy, anger, sadness, and pleasure) and obtained an averaged classification accuracy of 82.29% ± 3.06% across 26 subjects. Further, this study identified 30 subject-independent features that were most relevant to emotional processing across subjects and explored the feasibility of using fewer electrodes to characterize the EEG dynamics during music listening. The identified features were primarily derived from electrodes placed near the frontal and the parietal lobes, consistent with many of the findings in the literature. This study might lead to a practical system for noninvasive assessment of the emotional states in practical or clinical applications.
The Nature of Posttraumatic Growth in Adult Survivors of Child Sexual Abuse.

Potential negative sequelae for survivors of child sexual abuse is well documented. However, growing evidence suggests that some individuals who actively cope with traumatic events can progress from a negative trajectory toward positive psychological change, often termed posttraumatic growth. Current posttraumatic growth theories may be of limited applicability to developmental considerations involved in child sexual abuse. This explorative study examines posttraumatic growth among adult female survivors of child sexual abuse. In-depth interviews were conducted with six participants who believed they had grown through coping with their abuse. Data was analyzed using interpretative phenomenological analysis. Three superordinate and nine subordinate themes were identified and explored. Some participants reported experiencing growth and distress simultaneously. Theoretical and clinical implications are examined in relation to the study's findings.
Study and analysis of various task scheduling algorithms in the cloud computing environment

Cloud computing is a novel perspective for large scale distributed computing and parallel processing. It provides computing as a utility service on a pay per use basis. The performance and efficiency of cloud computing services always depends upon the performance of the user tasks submitted to the cloud system. Scheduling of the user tasks plays significant role in improving performance of the cloud services. Task scheduling is one of the main types of scheduling performed. This paper presents a detailed study of various task scheduling methods existing for the cloud environment. A brief analysis of various scheduling parameters considered in these methods is also discussed in this paper.
Development and evaluation of consensus-based sediment quality guidelines for freshwater ecosystems.

Numerical sediment quality guidelines (SQGs) for freshwater ecosystems have previously been developed using a variety of approaches. Each approach has certain advantages and limitations which influence their application in the sediment quality assessment process. In an effort to focus on the agreement among these various published SQGs, consensus-based SQGs were developed for 28 chemicals of concern in freshwater sediments (i.e., metals, polycyclic aromatic hydrocarbons, polychlorinated biphenyls, and pesticides). For each contaminant of concern, two SQGs were developed from the published SQGs, including a threshold effect concentration (TEC) and a probable effect concentration (PEC). The resultant SQGs for each chemical were evaluated for reliability using matching sediment chemistry and toxicity data from field studies conducted throughout the United States. The results of this evaluation indicated that most of the TECs (i.e., 21 of 28) provide an accurate basis for predicting the absence of sediment toxicity. Similarly, most of the PECs (i.e., 16 of 28) provide an accurate basis for predicting sediment toxicity. Mean PEC quotients were calculated to evaluate the combined effects of multiple contaminants in sediment. Results of the evaluation indicate that the incidence of toxicity is highly correlated to the mean PEC quotient (R(2) = 0.98 for 347 samples). It was concluded that the consensus-based SQGs provide a reliable basis for assessing sediment quality conditions in freshwater ecosystems.
Uncovering Distributional Differences between Synonyms and Antonyms in a Word Space Model

For many NLP applications such as Information Extraction and Sentiment Detection, it is of vital importance to distinguish between synonyms and antonyms. While the general assumption is that distributional models are not suitable for this task, we demonstrate that using suitable features, differences in the contexts of synonymous and antonymous German adjective pairs can be identified with a simple word space model. Experimenting with two context settings (a simple windowbased model and a ‘co-disambiguation model’ to approximate adjective sense disambiguation), our best model significantly outperforms the 50% baseline and achieves 70.6% accuracy in a synonym/antonym classification task.
Asynchronous Bidirectional Decoding for Neural Machine Translation

The dominant neural machine translation (NMT) models apply unified attentional encoder-decoder neural networks for translation. Traditionally, the NMT decoders adopt recurrent neural networks (RNNs) to perform translation in a left-toright manner, leaving the target-side contexts generated from right to left unexploited during translation. In this paper, we equip the conventional attentional encoder-decoder NMT framework with a backward decoder, in order to explore bidirectional decoding for NMT. Attending to the hidden state sequence produced by the encoder, our backward decoder first learns to generate the target-side hidden state sequence from right to left. Then, the forward decoder performs translation in the forward direction, while in each translation prediction timestep, it simultaneously applies two attention models to consider the source-side and reverse target-side hidden states, respectively. With this new architecture, our model is able to fully exploit sourceand target-side contexts to improve translation quality altogether. Experimental results on NIST Chinese-English and WMT English-German translation tasks demonstrate that our model achieves substantial improvements over the conventional NMT by 3.14 and 1.38 BLEU points, respectively. The source code of this work can be obtained from https://github.com/DeepLearnXMU/ABDNMT.
Application of Synchronous Text-Based Dialogue Systems in Mental Health Interventions: Systematic Review

Synchronous written conversations (or "chats") are becoming increasingly popular as Web-based mental health interventions. Therefore, it is of utmost importance to evaluate and summarize the quality of these interventions.



The aim of this study was to review the current evidence for the feasibility and effectiveness of online one-on-one mental health interventions that use text-based synchronous chat.



A systematic search was conducted of the databases relevant to this area of research (Medical Literature Analysis and Retrieval System Online [MEDLINE], PsycINFO, Central, Scopus, EMBASE, Web of Science, . There were no specific selection criteria relating to the participant group. Studies were included if they reported interventions with individual text-based synchronous conversations (ie, chat or text messaging) and a psychological outcome measure.



A total of 24 articles were included in this review. Interventions included a wide range of mental health targets (eg, anxiety, distress, depression, eating disorders, and addiction) and intervention design. Overall, compared with the waitlist (WL) condition, studies showed significant and sustained improvements in mental health outcomes following synchronous text-based intervention, and post treatment improvement equivalent but not superior to treatment as usual (TAU) (eg, face-to-face and telephone counseling).



Feasibility studies indicate substantial innovation in this area of mental health intervention with studies utilizing trained volunteers and chatbot technologies to deliver interventions. While studies of efficacy show positive post-intervention gains, further research is needed to determine whether time requirements for this mode of intervention are feasible in clinical practice.
Ubiquitous Computing: Smart Devices, Environments and Interactions

This book provides an introduction to the complex field of ubiquitous computing Ubiquitous Computing (also commonly referred to as Pervasive Computing) describes the ways in which current technological models, based upon three base designs: smart (mobile, wireless, service) devices, smart environments (of embedded system devices) and smart interaction (between devices), relate to and support a computing vision for a greater range of computer devices, used in a greater range of (human, ICT and physical) environments and activities. The author details the rich potential of ubiquitous computing, the challenges involved in making it a reality, and the prerequisite technological infrastructure. Additionally, the book discusses the application and convergence of several current major and future computing trends.-Provides an introduction to the complex field of ubiquitous computing-Describes how current technology models based upon six different technology form factors which have varying degrees of mobility wireless connectivity and service volatility: tabs, pads, boards, dust, skins and clay, enable the vision of ubiquitous computing-Describes and explores how the three core designs (smart devices, environments and interaction) based upon current technology models can be applied to, and can evolve to, support a vision of ubiquitous computing and computing for the future-Covers the principles of the following current technology models, including mobile wireless networks, service-oriented computing, human computer interaction, artificial intelligence, context-awareness, autonomous systems, micro-electromechanical systems, sensors, embedded controllers and robots-Covers a range of interactions, between two or more UbiCom devices, between devices and people (HCI), between devices and the physical world.-Includes an accompanying website with PowerPoint slides, problems and solutions, exercises, bibliography and further reading Graduate students in computer science, electrical engineering and telecommunications courses will find this a fascinating and useful introduction to the subject. It will also be of interest to ICT professionals, software and network developers and others interested in future trends and models of computing and interaction over the next decades.
Too proud to let go: narcissistic entitlement as a barrier to forgiveness.

Narcissistic entitlement impedes forgiveness in ways not captured by other robust predictors (e.g., offense severity, apology, relationship closeness, religiosity, Big Five personality factors), as demonstrated in 6 studies. Narcissistic entitlement involves expectations of special treatment and preoccupation with defending one's rights. In Study 1, entitlement predicted less forgiveness and greater insistence on repayment for a past offense. Complementary results emerged from Study 2, which used hypothetical transgressions, and Study 3, which assessed broad forgiveness dispositions. Study 4 examined associations with the Big Five, and Study 5 extended the findings to a laboratory context. Study 6 demonstrated that entitlement predicted diminished increases in forgiveness over time. Taken together, these results suggest that narcissistic entitlement is a robust, distinct predictor of unforgiveness.
Corporate Brand Trust as a Mediator in the Relationship between Consumer Perception of CSR , Corporate Hypocrisy , and Corporate Reputation

The aim of this research is to investigate the relationship between consumer perception of Corporate Social Responsibility (CSR), corporate brand trust, corporate hypocrisy, and corporate reputation. Based on the one-to-one interview method using a structured questionnaire of 560 consumers in South Korea, the proposed model was estimated by structural equation modeling analysis. The model suggests that consumer perception of CSR influences consumer attitudes toward a corporation (i.e., perceived corporate hypocrisy and corporate reputation) by developing corporate brand trust. This in turn further enhances corporate reputation while decreasing corporate hypocrisy. The findings of our study demonstrate that consumer perception of CSR is an antecedent to corporate brand trust, which fully mediates the relationship between consumer perception of CSR and corporate reputation. In addition, corporate brand trust has the role of partial mediator in the relationship between consumer perception of CSR and corporate hypocrisy. These results imply that to better understand the relationship between consumer perception of CSR and consumer attitudes toward a corporation, it is necessary to consider corporate brand trust as an important mediating variable. The theoretical and practical implications of this study are discussed, together with its limitations and potential for future research.
Patientory : A Healthcare Peer-to-Peer EMR Storage Network v 1 . 0

A blockchain powered health information exchange (HIE) can unlock the true value of interoperability and cyber security. This system has the potential to eliminate the friction and costs of current third party intermediaries, when considering population health management. There are promises of improved data integrity, reduced transaction costs, decentralization and disintermediation of trust. Being able to coordinate patient care via a blockchain HIE essentially alleviates unnecessary services and duplicate tests with lowering costs and improvements in efficiencies of the continuum care cycle, while adhering to all HIPAA rules and standards. A patient-centered protocol supported by blockchain technology, Patientory is changing the way healthcare stakeholders manage electronic medical data and interact with clinical care teams.
Fabrication of Metallic Biomedical Scaffolds with the Space Holder Method: A Review

Bone tissue engineering has been increasingly studied as an alternative approach to bone defect reconstruction. In this approach, new bone cells are stimulated to grow and heal the defect with the aid of a scaffold that serves as a medium for bone cell formation and growth. Scaffolds made of metallic materials have preferably been chosen for bone tissue engineering applications where load-bearing capacities are required, considering the superior mechanical properties possessed by this type of materials to those of polymeric and ceramic materials. The space holder method has been recognized as one of the viable methods for the fabrication of metallic biomedical scaffolds. In this method, temporary powder particles, namely space holder, are devised as a pore former for scaffolds. In general, the whole scaffold fabrication process with the space holder method can be divided into four main steps: (i) mixing of metal matrix powder and space-holding particles; (ii) compaction of granular materials; (iii) removal of space-holding particles; (iv) sintering of porous scaffold preform. In this review, detailed procedures in each of these steps are presented. Technical challenges encountered during scaffold fabrication with this specific method are addressed. In conclusion, strategies are yet to be developed to address problematic issues raised, such as powder segregation, pore inhomogeneity, distortion of pore sizes and shape, uncontrolled shrinkage and contamination.
Multi-evidence Filtering and Fusion for Multi-label Classification, Object Detection and Semantic Segmentation Based on Weakly Supervised Learning

Supervised object detection and semantic segmentation require object or even pixel level annotations. When there exist image level labels only, it is challenging for weakly supervised algorithms to achieve accurate predictions. The accuracy achieved by top weakly supervised algorithms is still significantly lower than their fully supervised counterparts. In this paper, we propose a novel weakly supervised curriculum learning pipeline for multi-label object recognition, detection and semantic segmentation. In this pipeline, we first obtain intermediate object localization and pixel labeling results for the training images, and then use such results to train task-specific deep networks in a fully supervised manner. The entire process consists of four stages, including object localization in the training images, filtering and fusing object instances, pixel labeling for the training images, and task-specific network training. To obtain clean object instances in the training images, we propose a novel algorithm for filtering, fusing and classifying object instances collected from multiple solution mechanisms. In this algorithm, we incorporate both metric learning and density-based clustering to filter detected object instances. Experiments show that our weakly supervised pipeline achieves state-of-the-art results in multi-label image classification as well as weakly supervised object detection and very competitive results in weakly supervised semantic segmentation on MS-COCO, PASCAL VOC 2007 and PASCAL VOC 2012.
Object Recognition in 3D Point Clouds Using Web Data and Domain Adaptation

Over the last years, object detection has become a more and more active field of research in robotics. An important problem in object detection is the need for sufficient labeled training data to learn good classifiers. In this paper we show how to significantly reduce the need for manually labeled training data by leveraging data sets available on the World Wide Web. Specifically, we show how to use objects from Google’s 3D Warehouse to train an object detection system for 3D point clouds collected by robots navigating through both urban and indoor environments. In order to deal with the different characteristics of the web data and the real robot data, we additionally use a small set of labeled point clouds and perform domain adaptation. Our experiments demonstrate that additional data taken from the 3D Warehouse along with our domain adaptation greatly improves the classification accuracy on real-world environments.
Prediction of Bitcoin Price using Data Mining

Bitcoin is a computerized digital money and exchange network, represents an essential change in financial sectors, an interesting number of customers and excellent evaluation of channel inspection. In this research, dataset related to ten cryptocurrencies are used and created a new dataset by taking the closing price of each cryptocurrency for the research goal to ascertain how the direction and accuracy of price of the Bitcoin can be predicted by using data mining methods. Features engineering evaluated that all the ten cryptocurrencies are strongly correlated with each other. The task is achieved by implementation of supervised learning method in which random forest, support vector classifier, gradient boosting classifier, and neural network classifier are used under classification category and linear regression, recurrent neural network, gradient boosting regressor are used under regression category. In the classification category, support vector classifier achieved the highest accuracy of 62.31% and precision value 0.77. In regression category, gradient boosting regressor got the highest R-squared value 0.99.