nils-herrmann/clean_scirepeval huggingface dataset:
RASSH - Reinforced adaptive SSH honeypot

The wide spread of cyber-attacks made the need of gathering as much information as possible about them, a real demand in nowadays global context. The honeypot systems have become a powerful tool on the way to accomplish that. Researchers have already focused on the development of various honeypot systems but the fact that their administration is time consuming made clear the need of self-adaptive honeypot system capable of learning from their interaction with attackers. This paper presents a self-adaptive honeypot system we are developing that tries to overlap some of the disadvantaged that existing systems have. The proposed honeypot is a medium interaction system developed using Python and it emulates a SSH (Secure Shell) server. The system is capable of interacting with the attackers by means of reinforcement learning algorithms.
NNIME: The NTHU-NTUA Chinese interactive multimodal emotion corpus

The increasing availability of large-scale emotion corpus along with the advancement in emotion recognition algorithms have enabled the emergence of next-generation human-machine interfaces. This paper describes a newly-collected multimodal corpus, i.e., the NTHU-NTUA Chinese Interactive Emotion Corpus (NNIME). The database is a result of the collaborative work between engineers and drama experts. This database includes recordings of 44 subjects engaged in spontaneous dyadic spoken interactions. The multimodal data includes approximately 11-hour worth of audio, video, and electrocardiogram data recorded continuously and synchronously. The database is also completed with a rich set of emotion annotations on discrete and continuous-in-time annotation by a total of 49 annotators. Thees emotion annotations include a diverse perspectives: peer-report, director-report, self-report, and observer-report. This carefully-engineered data collection and annotation processes provide an additional valuable resource to quantify and investigate various aspects of affective phenomenon and human communication. To our best knowledge, the NNIME is one of the few large-scale Chinese affective dyadic interaction database that have been systematically collected, organized, and to be publicly-released to the research community.
SPAdes: A New Genome Assembly Algorithm and Its Applications to Single-Cell Sequencing

The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.
Significant Change Spotting for Periodic Human Motion Segmentation of Cleaning Tasks Using Wearable Sensors

The proportion of the aging population is rapidly increasing around the world, which will cause stress on society and healthcare systems. In recent years, advances in technology have created new opportunities for automatic activities of daily living (ADL) monitoring to improve the quality of life and provide adequate medical service for the elderly. Such automatic ADL monitoring requires reliable ADL information on a fine-grained level, especially for the status of interaction between body gestures and the environment in the real-world. In this work, we propose a significant change spotting mechanism for periodic human motion segmentation during cleaning task performance. A novel approach is proposed based on the search for a significant change of gestures, which can manage critical technical issues in activity recognition, such as continuous data segmentation, individual variance, and category ambiguity. Three typical machine learning classification algorithms are utilized for the identification of the significant change candidate, including a Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Naive Bayesian (NB) algorithm. Overall, the proposed approach achieves 96.41% in the F1-score by using the SVM classifier. The results show that the proposed approach can fulfill the requirement of fine-grained human motion segmentation for automatic ADL monitoring.
Motivation and Transactional , Charismatic , and Transformational Leadership : A Test of Antecedents

Relationships between leaders' motivation and their use of charismatic, transactional, and / or transformational leadership were examined in this study. One hundred eighty-six leaders and 759 direct reports from a variety of organizations were sampled. Leaders were administered the Motivation Sources Inventory (MSO while followers reported leaders' full range leadership behaviors using the Multi-factor Leadership Questionnaire (MLQ-rater version). Leaders were also administered the self-rating version of the Multi-factor Leadership Questionnaire (MLQ-rater version). The Motivation Sources Inventory subscales. subsequently signzjkantly correlated with leader self-reports of inspirational motivation, idealized influence (behavior) and individualized consideration (range, r = .10 to .29), as well as with raters' perceptions of inspirational motivation, idealized influence (behavior) and individualized consideration (range, r = .18 to .19). The Motivation Sources Inventory subscales significantly correlated with leaders' self-reports of charisma, transactional and laissez-faire leadership (range, r = .12 to .28), with rater-reports of the same variables (range, r = .16 to .29). Antecedents of transformational behavior have been examined sparsely since the concept was first articulated and researched (Burns, 1978; Bass, 1985). Those few studies that have examined the construct as a criterion variable have included Avolio's (1 994) examination of life events and experiences, Bass's (1985) exploration of early career challenges, Howard and Bray's (1988) study of personality variables, Atwater and Yammarino's (1993) study of personal attributes as precedents to transformational leader behaviors, and Barbuto, Fritz, and Marx's (2000) study of work motivation and transformational leadership. Results of these inquiries demonstrate that dispositional variables play some role in transformational leadership, but much research is necessary to ascertain which variables explain the greatest variance in data. This study tests the relationship between leaders' sources of motivation and their use of transactional, charismatic, and transformational leadership. Full Range Model of Leadership Transformational leadership theories grew from Bums's (1978) work in political leadership. Bums (1 978) described the transforming leader as one who is able to lift followers up from their petty preoccupations and rally around a common purpose to achieve things never thought possible. Bass (1 985) developed a typology of leadership behaviors fitting into the broad categories of transactional and transformational leadership. Bass (1 985) identified laissez-faire, management-by-exception, and contingent reward as the key types of transactional leadership. Most conceptualizations of transactional leadership, however, exclude laissez-faire because it represents the absence of leadership. Transformational leadership was operationalized at the time to include charisma, intellectual stimulation, and individualized consideration (see …
On some applications of finite-state automata theory to natural language processing

We describe new applications of the theory of automata to natural language processing: the representation of very large scale dictionaries and the indexation of natural language texts. They are based on new algorithms that we introduce and describe in detail. In particular, we give pseudocodes for the de-terminization of string to string transducers, the deterministic union of p-subsequential string to string transducers, and the indexation by automata. We report several experiments illustrating the applications.
Perceptions of Men on Puxa-Puxa, or Labia Minora Elongation, in Tete, Mozambique.

Labia minora elongation has been documented as being practiced by 98.6% of the women in Tete province, Mozambique. Women engage in this procedure before the first menstruation. In this article we report on the findings from the male respondents from Tete province that participated in the qualitative component of the World Health Organization-supported multicountry Gender, Sexuality, and Vaginal Practices study. The men reported that the main motivation behind labia minora elongation is to enhance the sexual pleasure for both female and male partners. Researching what men know and perceive of labia minora elongation is crucial to understanding the contribution of this practice for sexual health. This knowledge is helpful to inform the development of new human immunodeficiency virus (HIV)/sexually transmitted infection (STI) prevention technologies and culturally appropriate information, education, and communication interventions.
RHex: A Biologically Inspired Hexapod Runner

RHex is an untethered, compliant leg hexapod robot that travels at better than one body length per second over terrain few other robots can negotiate at all. Inspired by biomechanics insights into arthropod locomotion, RHex uses a clock excited alternating tripod gait to walk and run in a highly maneuverable and robust manner. We present empirical data establishing that RHex exhibits a dynamical (“bouncing”) gait—its mass center moves in a manner ∗This work was supported in part by DARPA/SPAWAR under contract N66001-00-C-8026. Portions of the material reported here were first presented in a conference paper appearing in the collection (Altendorfer et al., 2000). 208 Altendorfer et al. well approximated by trajectories from a Spring Loaded Inverted Pendulum (SLIP)—characteristic of a large and diverse group of running animals, when its central clock, body mass, and leg stiffnesses are appropriately tuned. The SLIP template can function as a useful control guide in developing more complex autonomous locomotion behaviors such as registration via visual servoing, local exploration via visual odometry, obstacle avoidance, and, eventually, global mapping and localization.
Factors Affecting the Z-Width of a Haptic Display

This paper addresses the performance of force-reflecting interfaces (“haptic displays”). We suggest that an important measure of performance is the dynamic range of achievable impedances — “Z-Width” — and that an impedance is achievable if it satisfies a robustness property such as passivity. Several factors affecting ZWidth — sample-and-hold, inherent interface dynamics, displacement sensor quantization, and velocity filtering — are discussed. A set of experiments designed to evaluate these factors is described, and experimental results are presented. A striking result is that inherent interface damping exerts an overwhelming influence on Z-Width.
Digital signature based on PlayGamal algorithm

To keep the security and integrity of digital messages, can be applied cryptography method. Digital Signature is one of the vital digital data, so the secrecy must be maintained at the time of distribution and storage of data. This study aims to combine modern cryptographic methods with ElGamal traditional algorithm method is called PlayGamal Cipher Algorithm. Moreover. the method will be implement as application for digital signature. The proposed method will be compare with the existing El-Gamal to know the performance during encryption and decryption process.
Emotional and physiological responses to touch massage Lenita Lindgren

Everything that is new or uncommon raises a pleasure in the imagination, because it fills the soul with an agreeable surprise, gratifies its curiosity, and gives it an idea of which it was not before possessed.  Clinical findings indicate that touch massage has the ability to induce positive emotions and influence stress responses. However, little is known about mechanisms that can explain observed responses.  To understand mechanisms behind observed emotional and physiological responses during and after touch massage.  This thesis is based upon healthy volunteers in Studies I, II, IV and patients undergone aortic surgery in Study III. Study I had a crossover design, participants served as their own controls. After randomization they received TM on one occasion and the other occasion served as control. Heart rate variability (HRV), heart rate (HR) saliva cortisol concentration, glucose, insulin in serum and extracellular (ECV) levels of glucose, lactate, glycerol and pyruvat were measured before, during and after TM/control. In study II, functional magnetic resonance imaging (fMRI) was used in order to measure brain activity during TM mvement. The study design included four different touch stimulations, human touch with movement (TM movement) human stationary touch and rubber glove with or without movement. Force (2.5 N) and velocity (1.5 cm/s) were held constant across conditions. The pleasantness of the four different touch stimulations was rated on a visual analog scale (VAS-scale). Study III had a randomized controlled design. The intervention group received TM and the control group rested. HRV, cortisol, glucose, insulin in serum, blood pressure, oxygen saturation, respiratory frequency and anxiety levels were measured before, during and after TM/control. In study IV participants were interviewed about experiences after TM and the text was analyzed by qualitative content analyze.  Study I. TM reduced the stress response as indicated by decreased heart rate and decreased activity in the sympathetic nervous system, followed by a compensatory decrease in parasympathetic nervous activity in order to maintain balance. Cortisol and insulin levels decreased significantly after intervention, while serum glucose levels remained stable. A similar, though less prominent, pattern was seen during the control session. There were no significant differences in ECV concentrations of analyzed substances. Study II. Human moving touch (TM movement) was significantly rated as the most pleasant touch stimulation. The fMRI results revealed that human moving touch (TM movement) most strongly activated the pregenual anterior cingulate cortex (pgACC). Studys …
ObliviStore: High Performance Oblivious Cloud Storage

We design and build ObliviStore, a high performance, distributed ORAM-based cloud data store secure in the malicious model. To the best of our knowledge, ObliviStore is the fastest ORAM implementation known to date, and is faster by 10X or more in comparison with the best known ORAM implementation. ObliviStore achieves high throughput by making I/O operations asynchronous. Asynchrony introduces security challenges, i.e., we must prevent information leakage not only through access patterns, but also through timing of I/O events. We propose various practical optimizations which are key to achieving high performance, as well as techniques for a data center to dynamically scale up a distributed ORAM. We show that with 11 trusted machines (each with a modern CPU), and 20 Solid State Drives, ObliviStore achieves a throughput of 31.5MB/s with a block size of 4KB.
An Attack Graph-Based Probabilistic Security Metric

To protect critical resources in today’s networked environments, it is desirable to quantify the likelihood of potential multi-step attacks that combine multiple vulnerabilities. This now becomes feasible due to a model of causal relationships between vulnerabilities, namely, attack graph. This paper proposes an attack graph-based probabilistic metric for network security and studies its efficient computation. We first define the basic metric and provide an intuitive and meaningful interpretation to the metric. We then study the definition in more complex attack graphs with cycles and extend the definition accordingly. We show that computing the metric directly from its definition is not efficient in many cases and propose heuristics to improve the efficiency of such computation.
Global context-dependent recurrent neural network language model with sparse feature learning

Recurrent neural network language models (RNNLMs) are an important type of language model. In recent years, context-dependent RNNLMs are the most widely used ones as they apply additional information summarized from other sequences to access the larger context. However, when the sequences are mutually independent or randomly shuffled, these models cannot learn useful additional information, resulting in no larger context taken into account. In order to ensure that the model can obtain more contextual information in any case, a new language model is proposed in this paper. It can capture the global context just by the words within the current sequences, incorporating all the preceding and following words of target, without resorting to additional information summarized from other sequences. This model includes two main modules: a recurrent global context module used for extracting the global contextual information of the target and a sparse feature learning module that learns the sparse features of all the possible output words to distinguish the target word from others at the output layer. The proposed model was tested on three language modeling tasks. Experimental results show that it improves the perplexity of the model, speeds up the convergence of the network and learns better word embeddings compared with other language models.
Building Poker Agent Using Reinforcement Learning with Neural Networks

Poker is a game with incomplete and imperfect information. The ability to estimate opponent and interpret its actions makes a player as a world class player. Finding optimal game strategy is not enough to win poker game. As in real life as in online poker game, the most time of it consists of opponent analysis. This paper illustrates a development of poker agent using reinforcement learning with neural networks. 1 STAGE OF THE RESEARCH Poker is a game with incomplete and imperfect information. The ability to estimate opponent and interpret its actions makes a player as a world class player. Finding optimal game strategy is not enough to win poker game. As in real life as in online poker game, the most time of it consists of opponent analysis. Author is working on development of poker agent that would find optimal game strategy using reinforcement learning (RL) in combination with artificial neural network (ANN) for value function approximation. 2 OUTLINE OF OBJECTIVES This paper illustrates a development of poker agent using reinforcement learning with neural networks. Complete poker agent should have an ability to create optimal game strategy that makes decisions based on information:  Hand strength/potential estimation;  Table card estimation;  Opponent hand strength prediction;  Opponent classification (tight/loose passive/ aggressive);  Current state evaluation using neural network. AI Poker agent should be able to find an optimal strategy by itself (unsupervised learning) using information given above. It also should be able to adapt opponent play style and change its strategy during the game.
Deep visual foresight for planning robot motion

A key challenge in scaling up robot learning to many skills and environments is removing the need for human supervision, so that robots can collect their own data and improve their own performance without being limited by the cost of requesting human feedback. Model-based reinforcement learning holds the promise of enabling an agent to learn to predict the effects of its actions, which could provide flexible predictive models for a wide range of tasks and environments, without detailed human supervision. We develop a method for combining deep action-conditioned video prediction models with model-predictive control that uses entirely unlabeled training data. Our approach does not require a calibrated camera, an instrumented training set-up, nor precise sensing and actuation. Our results show that our method enables a real robot to perform nonprehensile manipulation — pushing objects — and can handle novel objects not seen during training.
A piezoelectric vibration based generator for wireless electronics

Enabling technologies for wireless sensor networks have gained considerable attention in research communities over the past few years. It is highly desirable, even necessary in certain situations, for wireless sensor nodes to be self-powered. With this goal in mind, a vibration based piezoelectric generator has been developed as an enabling technology for wireless sensor networks. The focus of this paper is to discuss the modeling, design, and optimization of a piezoelectric generator based on a two-layer bending element. An analytical model of the generator has been developed and validated. In addition to providing intuitive design insight, the model has been used as the basis for design optimization. Designs of 1 cm3 in size generated using the model have demonstrated a power output of 375 μW from a vibration source of 2.5 m s−2 at 120 Hz. Furthermore, a 1 cm3 generator has been used to power a custom designed 1.9 GHz radio transmitter from the same vibration source. (Some figures in this article are in colour only in the electronic version)
The Benefits of a Model of Annotation

Standard agreement measures for interannotator reliability are neither necessary nor sufficient to ensure a high quality corpus. In a case study of word sense annotation, conventional methods for evaluating labels from trained annotators are contrasted with a probabilistic annotation model applied to crowdsourced data. The annotation model provides far more information, including a certainty measure for each gold standard label; the crowdsourced data was collected at less than half the cost of the conventional approach.
Anatomical and biomechanical mechanisms of subacromial impingement syndrome.

Subacromial impingement syndrome is the most common disorder of the shoulder, resulting in functional loss and disability in the patients that it affects. This musculoskeletal disorder affects the structures of the subacromial space, which are the tendons of the rotator cuff and the subacromial bursa. Subacromial impingement syndrome appears to result from a variety of factors. Evidence exists to support the presence of the anatomical factors of inflammation of the tendons and bursa, degeneration of the tendons, weak or dysfunctional rotator cuff musculature, weak or dysfunctional scapular musculature, posterior glenohumeral capsule tightness, postural dysfunctions of the spinal column and scapula and bony or soft tissue abnormalities of the borders of the subacromial outlet. These entities may lead to or cause dysfunctional glenohumeral and scapulothoracic movement patterns. These various mechanisms, singularly or in combination may cause subacromial impingement syndrome.