nils-herrmann/clean_scirepeval huggingface dataset:
The interpersonal theory of suicide.

Suicidal behavior is a major problem worldwide and, at the same time, has received relatively little empirical attention. This relative lack of empirical attention may be due in part to a relative absence of theory development regarding suicidal behavior. The current article presents the interpersonal theory of suicidal behavior. We propose that the most dangerous form of suicidal desire is caused by the simultaneous presence of two interpersonal constructs-thwarted belongingness and perceived burdensomeness (and hopelessness about these states)-and further that the capability to engage in suicidal behavior is separate from the desire to engage in suicidal behavior. According to the theory, the capability for suicidal behavior emerges, via habituation and opponent processes, in response to repeated exposure to physically painful and/or fear-inducing experiences. In the current article, the theory's hypotheses are more precisely delineated than in previous presentations (Joiner, 2005), with the aim of inviting scientific inquiry and potential falsification of the theory's hypotheses.
Calli Essential Oils Synergize with Lawsone against Multidrug Resistant Pathogens

The fast development of multi-drug resistant (MDR) organisms increasingly threatens global health and well-being. Plant natural products have been known for centuries as alternative medicines that can possess pharmacological characteristics, including antimicrobial activities. The antimicrobial activities of essential oil (Calli oil) extracted from the Calligonum comosum plant by hydro-steam distillation was tested either alone or when combined with lawsone, a henna plant naphthoquinone, against MDR microbes. Lawsone showed significant antimicrobial activities against MDR pathogens in the range of 200-300 µg/mL. Furthermore, Calli oil showed significant antimicrobial activities against MDR bacteria in the range of 180-200 µg/mL, Candida at 220-240 µg/mL and spore-forming Rhizopus fungus at 250 µg/mL. Calli oil's inhibition effect on Rhizopus, the major cause of the lethal infection mucormycosis, stands for 72 h, followed by an extended irreversible white sporulation effect. The combination of Calli oil with lawsone enhanced the antimicrobial activities of each individual alone by at least three-fold, while incorporation of both natural products in a liposome reduced their toxicity by four- to eight-fold, while maintaining the augmented efficacy of the combination treatment. We map the antimicrobial activity of Calli oil to its major component, a benzaldehyde derivative. The findings from this study demonstrate that formulations containing essential oils have the potential in the future to overcome antimicrobial resistance.
The testing effect in recognition memory: a dual process account.

The testing effect, or the finding that taking an initial test improves subsequent memory performance, is a robust and reliable phenomenon--as long as the final test involves recall. Few studies have examined the effects of taking an initial recall test on final recognition performance, and results from these studies are equivocal. In 3 experiments, we attempt to demonstrate that initial testing can change the ways in which later recognition decisions are executed even when no difference can be detected in the recognition hit rates. Specifically, initial testing was shown to enhance later recollection but leave familiarity unchanged. This conclusion emerged from three dependent measures: source memory, exclusion performance, and remember/know judgments.
Cuisine In and Out : A Distributed Search Engine with Attention Machine Translation and Doc 2 Vec

The project aims to give convenience for searching best matched cuisine in Yelp review dataset, and also translate English reviews to German by attention neural machine translation. It’s a great fun to explore Natural Language Processing applications in search engine, besides improving the distributed performance for large dataset. Our project and demo focus on the high performance of retrieval valuable Yelp reviews and direct real-time hyperlinks to business’ homepage with query terms of cuisine; our report will focus on illustrating zigzag discovery path on "the-state-of-the-art" neural machine translation. We have spent much time on acquiring new NLP knowledge, learnt tf-seq2seq by TensorFlow, trained translationmodels on GPU servers, and translated the reviewing dataset from English to German.
BotOrNot: A System to Evaluate Social Bots

While most online social media accounts are controlled by humans, these platforms also host automated agents called social bots or sybil accounts. Recent literature reported on cases of social bots imitating humans to manipulate discussions, alter the popularity of users, pollute content and spread misinformation, and even perform terrorist propaganda and recruitment actions. Here we present BotOrNot, a publicly-available service that leverages more than one thousand features to evaluate the extent to which a Twitter account exhibits similarity to the known characteristics of social bots. Since its release in May 2014, BotOrNot has served over one million requests via our website and APIs.
Learning Heuristics for Automated Reasoning through Deep Reinforcement Learning

We demonstrate how to learn efficient heuristics for automated reasoning algorithms through deep reinforcement learning. We consider search algorithms for quantified Boolean logics, that already can solve formulas of impressive size up to 100s of thousands of variables. The main challenge is to find a representation which lends to making predictions in a scalable way. The heuristics learned through our approach significantly improve over the handwritten heuristics for several sets of formulas.
Lip reading using a dynamic feature of lip images and convolutional neural networks

In this paper, a lip-reading method using a novel dynamic feature of lip images is proposed. The dynamic feature of lip images is calculated as the first-order regression coefficients using a few neighboring frames (images). It constiutes a better representation of the time derivatives to the basic static image. The dynamic feature is processed by using convolution neural networks (CNNs), which are able to reduce the negative influence caused by shaking of the subject and face alignment blurring at the feature-extraction level. Its effectiveness has been confirmed by word-recognition experiments comparing the proposed method with the conventional static (original) image.
An Architecture for a Text Simplification System

We present a pipelined architecture for a text simplification system and describe our implementation of the three stages— analysis, transformation and regeneration. Our architecture allows each component to be developed and evaluated independently. We lay particular emphasis on the discourse level aspects of syntactic simplification as these are crucial to the process and have not been dealt with by previous research in the field. These aspects include generating referring expressions, deciding determiners, deciding sentence order and preserving rhetorical and anaphoric structure.
A Survey on Sentiment Analysis Algorithms for Opinion Mining

Opinion mining and sentiment analysis is rapidly growing area. There are numerous e-commerce sites available on internet which provides options to users to give feedback about specific product. These feedbacks are very much helpful to both the individuals, who are willing to buy that product and the organizations. An accurate method for predicting sentiments could enable us, to extract opinions from the internet and predict customer’s preferences. There are various algorithms available for opinion mining. Before applying any algorithm for polarity detection, pre-processing on feedback is carried out. From these pre-processed reviews opinion words and object on which opinion is generated are extracted and any opinion mining technique is applied to find the polarity of the review. Opinion mining has three levels of granularities: Document level, Sentence level and Aspect level. In this paper various algorithms for sentiment analysis are studied and challenges and applications appear in this field are discussed.
Organizational Ambidexterity: Balancing Exploitation and Exploration for Sustained Performance

Organizational ambidexterity has emerged as a new research paradigm in organization theory, yet several issues fundamental to this debate remain controversial. We explore four central tensions here: Should organizations achieve ambidexterity through differentiation or through integration? Does ambidexterity occur at the individual or organizational level? Must organizations take a static or dynamic perspective on ambidexterity? Finally, can ambidexterity arise internally, or do firms have to externalize some processes? We provide an overview of the seven articles included in this special issue and suggest several avenues for future research.
Exploiting a Natural Network Effect for Scalable, Fine-grained Clock Synchronization

Nanosecond-level clock synchronization can be an enabler of a new spectrum of timingand delay-critical applications in data centers. However, the popular clock synchronization algorithm, NTP, can only achieve millisecond-level accuracy. Current solutions for achieving a synchronization accuracy of 10s-100s of nanoseconds require specially designed hardware throughout the network for combatting random network delays and component noise or to exploit clock synchronization inherent in Ethernet standards for the PHY. In this paper, we present HUYGENS, a software clock synchronization system that uses a synchronization network and leverages three key ideas. First, coded probes identify and reject impure probe data—data captured by probes which suffer queuing delays, random jitter, and NIC timestamp noise. Next, HUYGENS processes the purified data with Support Vector Machines, a widely-used and powerful classifier, to accurately estimate one-way propagation times and achieve clock synchronization to within 100 nanoseconds. Finally, HUYGENS exploits a natural network effect—the idea that a group of pair-wise synchronized clocks must be transitively synchronized— to detect and correct synchronization errors even further. Through evaluation of two hardware testbeds, we quantify the imprecision of existing clock synchronization across server-pairs, and the effect of temperature on clock speeds. We find the discrepancy between clock frequencies is typically 5-10μs/sec, but it can be as much as 30μs/sec. We show that HUYGENS achieves synchronization to within a few 10s of nanoseconds under varying loads, with a negligible overhead upon link bandwidth due to probes. Because HUYGENS is implemented in software running on standard hardware, it can be readily deployed in current data centers.
Bag of Visual Words and Fusion Methods for Action Recognition: Comprehensive Study and Good Practice

Video based action recognition is one of the important and challenging problems in computer vision research. Bag of visual words model (BoVW) with local features has been very popular for a long time and obtained the state-of-the-art performance on several realistic datasets, such as the HMDB51, UCF50, and UCF101. BoVW is a general pipeline to construct a global representation from local features, which is mainly composed of five steps; (i) feature extraction, (ii) feature pre-processing, (iii) codebook generation, (iv) feature encoding, and (v) pooling and normalization. Although many effort s have been made in each step independently in different scenarios, their effects on action recognition are still unknown. Meanwhile, video data exhibits different views of visual patterns , such as static appearance and motion dynamics. Multiple descriptors are usually extracted to represent these different views. Fusing these descriptors is crucial for boosting the final performance of an action recognition system. This paper aims to provide a comprehensive study of all steps in BoVW and different fusion methods, and uncover some good practices to produce a state-of-the-art action recognition system. Specifically, we explore two kinds of local features, ten kinds of encoding methods, eight kinds of pooling and normalization strategies, and three kinds of fusion methods. We conclude that every step is crucial for contributing to the final recognition rate and improper choice in one of the steps may counteract the performance improvement of other steps. Furthermore, based on our comprehensive study, we propose a simple yet effective representation, called hybrid supervector , by exploring the complementarity of different BoVW frameworks with improved dense trajectories. Using this representation, we obtain impressive results on the three challenging datasets; HMDB51 (61.9%), UCF50 (92.3%), and UCF101 (87.9%).
Reviewing the socio-technical dynamics of low-carbon transitions

Effective mitigation of climate change will require far-reaching transformations of electricity, heat, agricultural, transport and other systems. The energy studies and modelling research that so often dominates academic and policy debates provide valuable insights into these transitions, but remain constrained by their focus on rational decision-making and their neglect of non-linear dynamics and broader social processes. This review paper describes insights from a complementary socio-technical approach that addresses the interdependent social, political, cultural, and technical processes of transitions. Focusing on the ‘Multi-Level Perspective’ (MLP), transitions are conceptualized as arising from the alignment of processes within and between three analytical levels: niche-innovations, socio-technical regimes and the socio-technical landscape. This analytical framework is illustrated with a case study of the German electricity transition and used to appraise lowcarbon transitions in several other sectors. We end by articulating four lessons for managing lowcarbon transitions.
Effect of Core Stabilizing Program on Balance in Spastic Diplegic Cerebral Palsy Children

Balance is a component of basic needs for daily activities and it plays an important role in static and dynamic activities. Core stabilization training is thought to improve balance, postural control, and reduce the risk of lower extremity injuries. The purpose of this study was to study the effect of core stabilizing program on balance in spastic diplegic cerebral palsy children.  Thirty diplegic cerebral palsy children from both sexes ranged in age from six to eight years participated in this study. They were assigned randomly into two groups of equal numbers, control group (A) children were received selective therapeutic exercises and study group (B) children were received selective therapeutic exercises plus core stabilizing program for eight weeks. Each patient of the two groups was evaluated before and after treatment by Biodex Balance System in laboratory of balance in faculty of physical therapy (antero posterior, medio lateral and overall stability). Patients in both groups received traditional physical therapy program for one hour per day and three sessions per week and group (B) were received core stabilizing program for eight weeks three times per week.  There was no significant difference between the two groups in all measured variables before wearing the orthosis (p>0.05), while there was significant difference when comparing pre and post mean values of all measured variables in each group (p<0.01). When comparing post mean values between both groups, the results revealed significant improvement in favor of group (B) (p<0.01).  core stabilizing program is an effective therapeutic exercise to improve balance in diplegic cerebral palsy children.
Technology integration in the classroom: A case of students experiences in using Edmodo to support learning in a blended classroom in a Kenyan University

Edmodo is simply a controlled online networking application that can be used by teachers and students to communicate and remain connected. This paper explores the experiences from a group of students who were using Edmodo platform in their course work. It attempts to use the SAMR (Substitution, Augmentation, Modification and Redefinition) framework of technology integration in education to access and evaluate technology use in the classroom. The respondents were a group of 62 university students from a Kenyan University whose lecturer had created an Edmodo account and introduced the students to participate in their course work during the September to December 2015 semester. More than 82% of the students found that they had a personal stake in the quality of work presented through the platforms and that they were able to take on different subtopics and collaborate to create one final product. This underscores the importance of Edmodo as an environment with skills already in the hands of the students that we can use to integrate technology in the classroom.
SEMI-PARAMETRIC TOPOLOGICAL MEMORY

We introduce a new memory architecture for navigation in previously unseen environments, inspired by landmark-based navigation in animals. The proposed semiparametric topological memory (SPTM) consists of a (non-parametric) graph with nodes corresponding to locations in the environment and a (parametric) deep network capable of retrieving nodes from the graph based on observations. The graph stores no metric information, only connectivity of locations corresponding to the nodes. We use SPTM as a planning module in a navigation system. Given only 5 minutes of footage of a previously unseen maze, an SPTM-based navigation agent can build a topological map of the environment and use it to confidently navigate towards goals. The average success rate of the SPTM agent in goal-directed navigation across test environments is higher than the best-performing baseline by a factor of three.
Inception-v3 for flower classification

The study of flower classification system is a very important subject in the field of Botany. A classifier of flowers with high accuracy will also bring a lot of fun to people's lives. However, because of the complex background of flowers, the similarity between the different species of flowers, and the differences among the same species of flowers, there are still some challenges in the recognition of flower images. The traditional flower classification is mainly based on the three features: color, shape and texture, this classification requires people to select features for classification, and the accuracy is not very high. In this paper, based on Inception-v3 model of TensorFlow platform, we use the transfer learning technology to retrain the flower category datasets, which can greatly improve the accuracy of flower classification.
A Planarity Test via Construction Sequences

Linear-time algorithms for testing the planarity of a graph are well known for over 35 years. However, these algorithms are quite involved and recent publications still try to give simpler linear-time tests. We give a conceptually simple reduction from planarity testing to the problem of computing a certain construction of a 3-connected graph. This implies a linear-time planarity test. Our approach is radically different from all previous linear-time planarity tests; as key concept, we maintain a planar embedding that is 3-connected at each point in time. The algorithm computes a planar embedding if the input graph is planar and a Kuratowski-subdivision otherwise.
Introduction to Text Mining in Big Data Analytics Minitrack

As a contribution to the HICSS 50 Anniversary Conference, we proposed a new mini-track on Text Mining in Big Data Analytics. This mini-track builds on the successful HICSS Workshop on Text Mining and recognizes the growing importance of unstructured text as a data source for descriptive and predictive analytics in research on collaboration systems and technologies. In this initial iteration of the mini-track, we have accepted three papers that cover conceptual issues, methodological approaches to social media, and the development of categorization models and dictionaries useful in a corporate context. The minitrack highlights the potential of an interdisciplinary research community within the HICSS collaboration systems and technologies track.
Associative Adversarial Networks

We propose a higher-level associative memory for learning adversarial networks. Generative adversarial network (GAN) framework has a discriminator and a generator network. The generator (G) maps white noise (z) to data samples while the discriminator (D) maps data samples to a single scalar. To do so, G learns how to map from high-level representation space to data space, and D learns to do the opposite. We argue that higher-level representation spaces need not necessarily follow a uniform probability distribution. In this work, we use Restricted Boltzmann Machines (RBMs) as a higher-level associative memory and learn the probability distribution for the high-level features generated by D. The associative memory samples its underlying probability distribution and G learns how to map these samples to data space. The proposed associative adversarial networks (AANs) are generative models in the higher-levels of the learning, and use adversarial nonstochastic models D and G for learning the mapping between data and higher-level representation spaces. Experiments show the potential of the proposed networks.
Micro Power Generator for Harvesting Low-Frequency and Nonperiodic Vibrations

This paper presents a new inertial power generator for scavenging low-frequency nonperiodic vibrations called the Parametric Frequency-Increased Generator (PFIG). The PFIG utilizes three magnetically coupled mechanical structures to initiate high-frequency mechanical oscillations in an electromechanical transducer. The fixed internal displacement and dynamics of the PFIG allow it to operate more effectively than resonant generators when the ambient vibration amplitude is higher than the internal displacement limit of the device. The design, fabrication, and testing of an electromagnetic PFIG are discussed. The developed PFIG can generate a peak power of 163 μW and an average power of 13.6 μW from an input acceleration of 9.8 m/s2 at 10 Hz, and it can operate at frequencies up to 65 Hz, giving it an unprecedented operating bandwidth and versatility. The internal volume of the generator is 2.12 cm3 (3.75 cm3 including the casing). The harvester has a volume figure of merit of 0.068% and a bandwidth figure of merit of 0.375%. These values, although seemingly low, are the highest reported in the literature for a device of this size and operating in the difficult frequency range of ≤ 20 Hz.
The errors, insights and lessons of famous AI predictions - and what they mean for the future

Predicting the development of artificial intelligence (AI) is a difficult project – but a vital one, according to some analysts. AI predictions already abound: but are they reliable? This paper will start by proposing a decomposition schema for classifying them. Then it constructs a variety of theoretical tools for analysing, judging and improving them. These tools are demonstrated by careful analysis of five famous AI predictions: the initial Dartmouth conference, Dreyfus’s criticism of AI, Searle’s Chinese Room paper, Kurzweil’s predictions in the ‘Age of Spiritual Machines’, and Omohundro’s ‘AI Drives’ paper. These case studies illustrate several important principles, such as the general overconfidence of experts, the superiority of models over expert judgement, and the need for greater uncertainty in all types of predictions. The general reliability of expert judgement in AI timeline predictions is shown to be poor, a result that fits in with previous studies of expert