nils-herrmann/clean_scirepeval huggingface dataset:
◆ 4 ◆ EMOTION IN HUMAN – COMPUTER INTERACTION

Understanding Emotion . . . . . . . . . . . . . . . . . . . . . . . . . . 54 Distinguishing Emotion from Related Constructs . . . . 55 Mood . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 Sentiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 Effects of Affect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 Attention . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 Memory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  Causes of Emotion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 Needs and Goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 Appraisal Theories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 Contagion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 Moods and Sentiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 Previous Emotional State . . . . . . . . . . . . . . . . . . . . . . . . . . 59 Causes of Mood . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 Contagion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 Color . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 Other Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 Measuring Affect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 Neurological Responses . . . . . . . . . . . . . . . . . . . . . . . . . . 61 Autonomic Activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 Facial Expression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 Voice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 Self-Report Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 Affect Recognition by Users . . . . . . . . . . . . . . . . . . . . . . . 63 Open Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 1. With which emotion should HCI designers be most concerned? . . . . . . . . . . . . . . . . . . . . . . . . . 64 2. When and how should interfaces attempt to directly address users’ emotions and basic needs (vs. application-specific goals)? . . . . . . . . . . . . 64 3. How accurate must emotion recognition be to be useful as an interface technique? . . . . . . . . . . . 64 4. When and how should users be informed that their affective states are being monitored and adapted to? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 5. How does emotion play out in computermediated communication (CMC)? . . . . . . . . . . . . . . 64
The effects of personality traits, self-esteem, loneliness, and narcissism on Facebook use among university students

This study examined the relationship between three of the ‘‘Big Five’’ traits (neuroticism, extraversion, and openness), self-esteem, loneliness and narcissism, and Facebook use. Participants were 393 first year undergraduate psychology students from a medium-sized Australian university who completed an online questionnaire. Negative binomial regression models showed that students with higher openness levels reported spending more time on Facebook and having more friends on Facebook. Interestingly, students with higher levels of loneliness reported having more Facebook friends. Extraversion, neuroticism, selfesteem and narcissism did not have significant associations with Facebook use. It was concluded that students who are high in openness use Facebook to connect with others in order to discuss a wide range of interests, whereas students who are high in loneliness use the site to compensate for their lack of offline
Fast Forward Through Opportunistic Incremental Meaning Representation Construction

One of the challenges semantic parsers face involves upstream errors originating from pre-processing modules such as ASR and syntactic parsers, which undermine the end result from the get go. We report the work in progress on a novel incremental semantic parsing algorithm that supports simultaneous application of independent heuristics and facilitates the construction of partial but potentially actionable meaning representations to overcome this problem. Our contribution to this point is mainly theoretical. In future work we intend to evaluate the algorithm as part of a dialogue understanding system on state of the art benchmarks.
Implications of 21 st century climate change for the hydrology of Washington State

Implications of 21 century climate change for the hydrology of Washington State Marketa M Elsner, Lan Cuo, Nathalie Voisin, Jeffrey S Deems, Alan F Hamlet, Julie A Vano, Kristian EB Mickelson, Se-Yeun Lee, and Dennis P Lettenmaier 1) JISAO Climate Impacts Group, University of Washington, Seattle, Washington 2) Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington
Effective and Explainable Detection of Android Malware Based on Machine Learning Algorithms

The across the board reception of android devices and their ability to get to critical private and secret data have brought about these devices being focused by malware engineers. Existing android malware analysis techniques categorized into static and dynamic analysis. In this paper, we introduce two machine learning supported methodologies for static analysis of android malware. The First approach based on statically analysis, content is found through probability statistics which reduces the uncertainty of information. Feature extraction were proposed based on the analysis of existing dataset. Our both approaches were used to high-dimension data into low-dimensional data so as to reduce the dimension and the uncertainty of the extracted features. In training phase the complexity was reduced 16.7% of the original time and detect the unknown malware families were improved.
Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles

sMCL is effective, easy to implement, and model and loss agnostic. Samples images and segmentations from an sMCL ensemble and the top output of a classical ensemble. Minimum loss outputs are outlined in red. Notice that sMCL ensembles vary in the shape, class, and frequency of predicted segments. Captions generated by classical ensembles tend to be only slightly different for a given image (row 1) and often produce outputs that are poor fits to individual images (row 4). sMCL ensembles are capable of specialization and their outputs are much more diverse and capture individual image characteristics well.
Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals

An electroencephalography (EEG) based Brain Computer Interface (BCI) enables people to communicate with the outside world by interpreting the EEG signals of their brains to interact with devices such as wheelchairs and intelligent robots. More specifically, motor imagery EEG (MI-EEG), which reflects a subject's active intent, is attracting increasing attention for a variety of BCI applications. Accurate classification of MI-EEG signals while essential for effective operation of BCI systems is challenging due to the significant noise inherent in the signals and the lack of informative correlation between the signals and brain activities. In this paper, we propose a novel deep neural network based learning framework that affords perceptive insights into the relationship between the MI-EEG data and brain activities. We design a joint convolutional recurrent neural network that simultaneously learns robust high-level feature presentations through low-dimensional dense embeddings from raw MI-EEG signals. We also employ an Autoencoder layer to eliminate various artifacts such as background activities. The proposed approach has been evaluated extensively on a large-scale public MI-EEG dataset and a limited but easy-to-deploy dataset collected in our lab. The results show that our approach outperforms a series of baselines and the competitive state-of-the-art methods, yielding a classification accuracy of 95.53%. The applicability of our proposed approach is further demonstrated with a practical BCI system for typing.
Biomechanical model study of pelvic belt influence on muscle and ligament forces.

Many patients with low back and/or pelvic girdle pain feel relief after application of a pelvic belt. External compression might unload painful ligaments and joints, but the exact mechanical effect on pelvic structures, especially in (active) upright position, is still unknown. In the present study, a static three-dimensional (3-D) pelvic model was used to simulate compression at the level of anterior superior iliac spine and the greater trochanter. The model optimised forces in 100 muscles, 8 ligaments and 8 joints in upright trunk, pelvis and upper legs using a criterion of minimising maximum muscle stress. Initially, abdominal muscles, sacrotuberal ligaments and vertical sacroiliac joints (SIJ) shear forces mainly balanced a trunk weight of 500N in upright position. Application of 50N medial compression force at the anterior superior iliac spine (equivalent to 25N belt tension force) deactivated some dorsal hip muscles and reduced the maximum muscle stress by 37%. Increasing the compression up to 100N reduced the vertical SIJ shear force by 10% and increased SIJ compression force with 52%. Shifting the medial compression force of 100N in steps of 10N to the greater trochanter did not change the muscle activation pattern but further increased SIJ compression force by 40% compared to coxal compression. Moreover, the passive ligament forces were distributed over the sacrotuberal, the sacrospinal and the posterior ligaments. The findings support the cause-related designing of new pelvic belts to unload painful pelvic ligaments or muscles in upright posture.
A wearable system that knows who wears it

Body-area networks of pervasive wearable devices are increasingly used for health monitoring, personal assistance, entertainment, and home automation. In an ideal world, a user would simply wear their desired set of devices with no configuration necessary: the devices would discover each other, recognize that they are on the same person, construct a secure communications channel, and recognize the user to which they are attached. In this paper we address a portion of this vision by offering a wearable system that unobtrusively recognizes the person wearing it. Because it can recognize the user, our system can properly label sensor data or personalize interactions.
 Our recognition method uses bioimpedance, a measurement of how tissue responds when exposed to an electrical current. By collecting bioimpedance samples using a small wearable device we designed, our system can determine that (a)the wearer is indeed the expected person and (b)~the device is physically on the wearer's body. Our recognition method works with 98% balanced-accuracy under a cross-validation of a day's worth of bioimpedance samples from a cohort of 8 volunteer subjects. We also demonstrate that our system continues to recognize a subset of these subjects even several months later. Finally, we measure the energy requirements of our system as implemented on a Nexus~S smart phone and custom-designed module for the Shimmer sensing platform.
Incidental Supervision: Moving beyond Supervised Learning

Machine Learning and Inference methods have become ubiquitous in our attempt to induce more abstract representations of natural language text, visual scenes, and other messy, naturally occurring data, and support decisions that depend on it. However, learning models for these tasks is difficult partly because generating the necessary supervision signals for it is costly and does not scale. This paper describes several learning paradigms that are designed to alleviate the supervision bottleneck. It will illustrate their benefit in the context of multiple problems, all pertaining to inducing various levels of semantic representations from text. In particular, we discuss (i) Response Driven Learning of models, a learning protocol that supports inducing meaning representations simply by observing the model’s behavior in its environment, (ii) the exploitation of Incidental Supervision signals that exist in the data, independently of the task at hand, to learn models that identify and classify semantic predicates, and (iii) the use of weak supervision to combine simple models to support global decisions where joint supervision is not available. While these ideas are applicable in a range of Machine Learning driven fields, we will demonstrate it in the context of several natural language applications, from (cross-lingual) text classification, to Wikification, to semantic parsing.
Students ’ Approaches to Learning and Teachers ’ Approaches to Teaching in Higher Education

Research into learning and teaching in higher education over the last 25 years has provided a variety of concepts, methods, and findings that are of both theoretical interest and practical relevance. It has revealed the relationships between students’ approaches to studying, their conceptions of learning, and their perceptions of their academic context. It has revealed the relationships between teachers’ approaches to teaching, their conceptions of teaching, and their perceptions of the teaching environment. And it has provided a range of tools that can be exploited for developing our understanding of learning and teaching in particular contexts and for assessing and enhancing the student experience on specific courses and programs.
Online video game addiction: identification of addicted adolescent gamers.

To provide empirical data-driven identification of a group of addicted online gamers.



Repeated cross-sectional survey study, comprising a longitudinal cohort, conducted in 2008 and 2009.



Secondary schools in the Netherlands.


Two large samples of Dutch schoolchildren (aged 13-16 years).



Compulsive internet use scale, weekly hours of online gaming and psychosocial variables.



This study confirms the existence of a small group of addicted online gamers (3%), representing about 1.5% of all children aged 13-16 years in the Netherlands. Although these gamers report addiction-like problems, relationships with decreased psychosocial health were less evident.



The identification of a small group of addicted online gamers supports efforts to develop and validate questionnaire scales aimed at measuring the phenomenon of online video game addiction. The findings contribute to the discussion on the inclusion of non-substance addictions in the proposed unified concept of 'Addiction and Related Disorders' for the DSM-V by providing indirect identification and validation of a group of suspected online video game addicts.
Semi-Supervised Frame-Semantic Parsing for Unknown Predicates

This document includes supplementary material for the semi-supervised approach towards framesemantic parsing for unknown predicates (Das and Smith, 2011). We include the names of the test documents used in the study, plot the results for framesemantic parsing while varying the hyperparameter that is used to determine the number of top frames to be selected from the posterior distribution over each target of a constructed graph and argue why the semi-supervised self-training baseline did not perform well on the task.
Swarms and Swarm Intelligence

Nature-inspired intelligent swarm technologies deals with complex problems that might be impossible to solve using traditional technologies and approaches. Swarm intelligence techniques (note the difference from intelligent swarms) are population-based stochastic methods used in combinatorial optimization problems in which the collective behavior of relatively simple individuals arises from their local interactions with their environment to produce functional global patterns. Swarm intelligence represents a meta heuristic approach to solving a variety of problems
Annoyance Judgment and Measurements of Environmental Noise: A Focus on Italian Secondary Schools

The effects of noise on students' health, well-being, and learning are of growing concern among both the general public and policy-makers in Europe. Several studies have highlighted the consequences of noise on children's learning and performance at school. This study investigates the relationship between noise judgment in school goers aged 11-18 and noise measurements aimed at evaluating their exposure at school. For this purpose, a questionnaire was administered to 521 individuals in 28 classrooms in eight schools of four cities in Italy, with different environmental characteristics. Using a Likert-type scale, a selected set of responses related to noise generated an Annoyance Index (AI) score for each student and a classroom median score (MAI). From the noise data acquired, a global noise score (GNS) was assigned to each classroom. A higher AI was found in industrialized areas and among younger students. No significant differences in noise judgment were found by gender. A significant inverse correlation was described between MAI and GNS, thus the better the acoustic quality of the classrooms, the less the perceived noise and annoyance. The results show that noise perception and consequent disturbance are highly correlated with classroom acoustics, and confirm that annoyance represents the most widespread subjective response to noise.
Controlled Experiments for Word Embeddings

An experimental approach to studying the properties of word embeddings is proposed. Controlled experiments, achieved through modifications of the training corpus, permit the demonstration of direct relations between word properties and word vector direction and length. The approach is demonstrated using the word2vec CBOW model with experiments that independently vary word frequency and word co-occurrence noise. The experiments reveal that word vector length depends more or less linearly on both word frequency and the level of noise in the co-occurrence distribution of the word. The coefficients of linearity depend upon the word. The special point in feature space, defined by the (artificial) word with pure noise in its co-occurrence distribution, is found to be small but non-zero.
Guillera, Jesús [Guillera Goyanes, Jeśus]; Sondow, Jonathan Double integrals and infinite products for some classical constants via analytic continuations

The two-fold aim of the paper is to unify and generalize on the one hand the double integrals of Beukers for ζ(2) and ζ(3), and of the second author for Euler’s constant γ and its alternating analog ln(4/π), and on the other hand the infinite products of the first author for e, of the second author for π, and of Ser for e . We obtain new double integral and infinite product representations of many classical constants, as well as a generalization to Lerch’s transcendent of Hadjicostas’s double integral formula for the Riemann zeta function, and logarithmic series for the digamma and Euler beta functions. The main tools are analytic continuations of Lerch’s function, including Hasse’s series. We also use Ramanujan’s polylogarithm formula for the sum of a particular series involving harmonic numbers, and his relations between certain dilogarithm values.
A Multidimensional Lexicon for Interpersonal

The sociolinguistic construct of stancetaking describes the activities through which discourse participants create and signal relationships to their interlocutors, to the topic of discussion, and to the talk itself. Stancetaking underlies a wide range of interactional phenomena, relating to formality, politeness, affect, and subjectivity. We present a computational approach to stancetaking, in which we build a theoretically-motivated lexicon of stance markers, and then use multidimensional analysis to identify a set of underlying stance dimensions. We validate these dimensions intrinsically and extrinsically, showing that they are internally coherent, match pre-registered hypotheses, and correlate with social phenomena.
Automatic Metaphor Detection using Large-Scale Lexical Resources and Conventional Metaphor Extraction

The paper presents an experimental algorithm to detect conventionalized metaphors implicit in the lexical data in a resource like WordNet, where metaphors are coded into the senses and so would never be detected by any algorithm based on the violation of preferences, since there would always be a constraint satisfied by such senses. We report an implementation of this algorithm, which was implemented first the preference constraints in VerbNet. We then derived in a systematic way a far more extensive set of constraints based on WordNet glosses, and with this data we reimplemented the detection algorithm and got a substantial improvement in recall. We suggest that this technique could contribute to improve the performance of existing metaphor detection strategies that do not attempt to detect conventionalized metaphors. The new WordNet-derived data is of wider significance because it also contains adjective constraints, unlike any existing lexical resource, and can be applied to any language with a semantic parser (and
An Attention Mechanism for Neural Answer Selection Using a Combined Global and Local View

We propose a new attention mechanism for neural based question answering, which depends on varying granularities of the input. Previous work focused on augmenting recurrent neural networks for question answering systems with simple attention mechanisms which are a function of the similarity between a question embedding and an answer embeddings across time. We extend this by making the attention mechanism dependent on a global embedding of the answer attained using a separate network. We evaluate our system on InsuranceQA, a large question answering dataset. Our model outperforms current state-of-the-art results on InsuranceQA. Further, we examine which sections of text our attention mechanism focuses on, and explore its performance across different parameter settings.
Intrusion Detection System Using Genetic Algorithm

Intrusion Detection Systems (IDSs) detects the network attacks by self-learning, etc. (9). Using Genetic Algorithms for intrusion detection has.  To overcome this problem we are implementing intrusion detection system in which we use genetic. From Ignite at OSCON 2010, a 5 minute presentation by Bill Lavender: SNORT is popular. Based Intrusion Detection System (IDS), by applying Genetic Algorithm (GA) and Networking Using Genetic Algorithm (IDS) and Decision Tree is to identify. Intrusion Detection System Using Genetic Algorithm >>>CLICK HERE<<< Genetic algorithm (GA) has received significant attention for the design and length chromosomes (VLCs) in a GA-based network intrusion detection system. The proposed approach is tested using Defense Advanced Research Project.  Intrusion Detection System (IDS) is one of the key security components in today's networking environment. A great deal of attention has been recently. Computer security has become an important part of the day today's life. Not only single computer systems but an extensive network of the computer system. presents an overview of intrusion detection system and a hybrid technique for