nils-herrmann/clean_scirepeval huggingface dataset:
Sensorimotor Input as a Language Generalisation Tool: A Neurorobotics Model for Generation and Generalisation of Noun-Verb Combinations with Sensorimotor Inputs

The paper presents a neurorobotics cognitive model to explain the understanding and generalisation of nouns and verbs combinations when a vocal command consisting of a verb-noun sentence is provided to a humanoid robot. This generalisation process is done via the grounding process: different objects are being interacted, and associated, with different motor behaviours, following a learning approach inspired by developmental language acquisition in infants. This cognitive model is based on Multiple Time-scale Recurrent Neural Networks (MTRNN). With the data obtained from object manipulation tasks with a humanoid robot platform, the robotic agent implemented with this model can ground the primitive embodied structure of verbs through training with verb-noun combination samples. Moreover, we show that a functional hierarchical architecture, based on MTRNN, is able to generalise and produce novel combinations of noun-verb sentences. Further analyses of the learned network dynamics and representations also demonstrate how the generalisation is possible via the exploitation of this functional hierarchical recurrent network. J. Zhong Department of Intermedia Art and Science, Waseda University, 3-4-1 Ohkubo, Shinjuku, Tokyo, Japan, 169-8555 Centre for Robotics and Neural Systems, University of Plymouth, PL4 8AA, United Kingdom Tel.: +44 (0)175284908, E-mail: zhong@junpei.eu M. Peniak Cortexica Vision Systems, London, United Kingdom J. Tani Korea Advanced Institute of Science and Technology, Daejeon, South Korea T. Ogata Department of Intermedia Art and Science, Waseda University, Tokyo, Japan A. Cangelosi Centre for Robotics and Neural Systems, University of Plymouth, United Kingdom
Residual MeshNet: Learning to Deform Meshes for Single-View 3D Reconstruction

This work presents a novel architecture of deep neural networks to generate meshes approximating the surface of a 3D object from a single image. Compared to existing learning-based 3D reconstruction models, our architecture is characterized by (1) deep mesh deformation stacks with residual network design, where a simple mesh is transformed to approximate the target surface and undergoes multiple deformation steps to progressively refine the result and reduce the residuals, and (2) parallel paths per deformation step, which can exponentially enrich the generated meshes using deeper structure and more model parameters. We also propose novel regularization scheme that encourages the meshes to be both globally complementary to cover the target surface and locally consistent with each other. Empirical evaluation on benchmark datasets show advantage of the proposed architecture over existing methods.
Dynamic Pricing with Model Uncertainty : Learning & Earning ( tutorial proposal at ACM EC 2015 )

This tutorial focuses on dynamic pricing under model uncertainty: a class of problems whose first instance dates back at least 40 years, is relatively simple in structure, is widely considered fundamental, and has numerous manifestations across multiple application domains and academic disciplines. While significant progress has been made throughout the last several decades, including a flurry of recent work, many variants of this problem class remain essentially unsolved. Briefly stated, the problem can be described as sequential pricing when the underlying demand model (or demand curve) is unknown and the market response to any given price is confounded by statistical noise. It will be helpful to hold in mind the following simple problem instance. The decision maker (“seller”) faces demand for a product s/he is selling. At every successive time unit the seller fixes a price for the product, subsequent to which demand is realized. The demand realizations are “noisy” observations of an ambient demand curve which is unbeknownst to the seller. The seller’s objective is to maximize expected cumulative (either discounted or not) profits over the time horizon that governs the interactions with the buyers. This situation is a quintessential example of a trade-off between exploration of the environment (to learn demand characteristics) and exploitation of that knowledge (via pricing) to maximize expected rewards.
A Mathematical Framework for Superintelligent Machines

We describe a class calculus that is expressive enough to describe and improve its own learning process. It can design and debug programs that satisfy given input/output constraints, based on its ontology of previously learned programs. It can improve its own model of the world by checking the actual results of the actions of its robotic activators. For instance, it could check the black box of a car crash to determine if it was probably caused by electric failure, a stuck electronic gate, dark ice, or some other condition that it must add to its ontology in order to meet its sub-goal of preventing such crashes in the future. Class algebra basically defines the eval/eval Galois connection between the residuated Boolean algebras of 1. equivalence classes and super/sub classes of class algebra type expressions, and 2. a residual Boolean algebra of biclique relationships. It distinguishes which formulas are equivalent, !, ", or unrelated, based on a simplification algorithm that may be thought of as producing a unique pair of Karnaugh maps that describe the rough sets of maximal bicliques of relations. Such maps divide the n-dimensional space of up to 2 conjunctions of up to n propositions into clopen (i.e. a closed set of regions and their boundaries) causal sets. This class algebra is generalized to type-2 fuzzy class algebra by using relative frequencies as probabilities. It is also generalized to a class calculus involving assignments that change the states of programs. INDEX TERMS 4-valued Boolean Logic, Artificial Intelligence, causal sets, class algebra, consciousness, intelligent design, IS-A hierarchy, mathematical logic, meta-theory, pointless topological space, residuated lattices, rough sets, type-2 fuzzy sets
Doing interpretive research

29 November 2004  26 July 2005  4 November 2005  Interpretive research in information systems (IS) is now a well-established part of the field. However, there is a need for more material on how to carry out such work from inception to publication. I published a paper a decade ago (Walsham, 1995) which addressed the nature of interpretive IS case studies and methods for doing such research. The current paper extends this earlier contribution, with a widened scope of all interpretive research in IS, and through further material on carrying out fieldwork, using theory and analysing data. In addition, new topics are discussed on constructing and justifying a research contribution, and on ethical issues and tensions in the conduct of interpretive work. The primary target audience for the paper is lessexperienced IS researchers, but I hope that the paper will also stimulate reflection for the more-experienced IS researcher and be of relevance to interpretive researchers in other social science fields.  (2006) 15, 320–330. doi:10.1057/palgrave.ejis.3000589
Caffeine as a psychomotor stimulant: mechanism of action

The popularity of caffeine as a psychoactive drug is due to its stimulant properties, which depend on its ability to reduce adenosine transmission in the brain. Adenosine A1 and A2A receptors are expressed in the basal ganglia, a group of structures involved in various aspects of motor control. Caffeine acts as an antagonist to both types of receptors. Increasing evidence indicates that the psychomotor stimulant effect of caffeine is generated by affecting a particular group of projection neurons located in the striatum, the main receiving area of the basal ganglia. These cells express high levels of adenosine A2A receptors, which are involved in various intracellular processes, including the expression of immediate early genes and regulation of the dopamine- and cyclic AMP-regulated 32-kDa phosphoprotein DARPP-32. The present review focuses on the effects of caffeine on striatal signal transduction and on their involvement in caffeine-mediated motor stimulation.
Reconfigurable circularly polarized antenna for short-range communication systems

This paper describes the design rules of a compact microstrip patch antenna with polarization reconfigurable features (right-handed circular polarization (CP)/left-handed CP). The basic antenna is a circular coplanar-waveguide (CPW)-fed microstrip antenna excited by a diagonal slot and the CPW open end. This device is developed for short-range communications or contactless identification systems requiring polarization reconfigurability to optimize the link reliability. First, experimental and simulated results are presented for the passive version of the antenna excited by an asymmetric slot. A reconfigurable antenna using beam-lead p-i-n diodes to switch the polarization sense is then simulated with an electrical modeling of the diodes. Finally, the efficiency reduction resulting from the diode losses is discussed
Sleep function and synaptic homeostasis.

This paper reviews a novel hypothesis about the functions of slow wave sleep-the synaptic homeostasis hypothesis. According to the hypothesis, plastic processes occurring during wakefulness result in a net increase in synaptic strength in many brain circuits. The role of sleep is to downscale synaptic strength to a baseline level that is energetically sustainable, makes efficient use of gray matter space, and is beneficial for learning and memory. Thus, sleep is the price we have to pay for plasticity, and its goal is the homeostatic regulation of the total synaptic weight impinging on neurons. The hypothesis accounts for a large number of experimental facts, makes several specific predictions, and has implications for both sleep and mood disorders.
Efficient 3D reconstruction for face recognition

Face recognition with variant pose, illumination and expression (PIE) is a challenging problem. In this paper, we propose an analysis-by-synthesis framework for face recognition with variant PIE. First, an efficient 2D-to-3D integrated face reconstruction approach is introduced to reconstruct a personalized 3D face model from a single frontal face image with neutral expression and normal illumination; Then, realistic virtual faces with different PIE are synthesized based on the personalized 3D face to characterize the face subspace; Finally, face recognition is conducted based on these representative virtual faces. Compared with other related work, this framework has following advantages: 1) only one single frontal face is required for face recognition, which avoids the burdensome enrollment work; 2) the synthesized face samples provide the capability to conduct recognition under difficult conditions like complex PIE; and 3) compared with other 3D reconstruction approaches, our proposed 2D-to-3D integrated face reconstruction approach is fully automatic and more efficient. The extensive experimental results show that the synthesized virtual faces significantly improve the accuracy of face recognition with changing PIE.
Measurement Issues in Galvanic Intrabody Communication: Influence of Experimental Setup

The need for increasingly energy-efficient and miniaturized bio-devices for ubiquitous health monitoring has paved the way for considerable advances in the investigation of techniques such as intrabody communication (IBC), which uses human tissues as a transmission medium. However, IBC still poses technical challenges regarding the measurement of the actual gain through the human body. The heterogeneity of experimental setups and conditions used together with the inherent uncertainty caused by the human body make the measurement process even more difficult.  The objective of this study, focused on galvanic coupling IBC, is to study the influence of different measurement equipments and conditions on the IBC channel.  Different experimental setups have been proposed in order to analyze key issues such as grounding, load resistance, type of measurement device and effect of cables. In order to avoid the uncertainty caused by the human body, an IBC electric circuit phantom mimicking both human bioimpedance and gain has been designed. Given the low-frequency operation of galvanic coupling, a frequency range between 10 kHz and 1 MHz has been selected.  The correspondence between simulated and experimental results obtained with the electric phantom have allowed us to discriminate the effects caused by the measurement equipment.  This study has helped us obtain useful considerations about optimal setups for galvanic-type IBC as well as to identify some of the main causes of discrepancy in the literature.
Childhood obesity: public-health crisis, common sense cure

During the past two decades, the prevalence of obesity in children has risen greatly worldwide. Obesity in childhood causes a wide range of serious complications, and increases the risk of premature illness and death later in life, raising public-health concerns. Results of research have provided new insights into the physiological basis of bodyweight regulation. However, treatment for childhood obesity remains largely ineffective. In view of its rapid development in genetically stable populations, the childhood obesity epidemic can be primarily attributed to adverse environmental factors for which straightforward, if politically difficult, solutions exist.
Epidemiological characteristics and causes of deaths in hospitalized patients under intensive care.

to describe the epidemiological and sociodemographic characteristics of patients hospitalized in an ICU.  an epidemiological, descriptive and retrospective study.  695 patients admitted from January to December 2011. The data collected were statistically analyzed with both absolute and relative frequency distribution.  61.6% of the patients are male, aged 40 to 69 years, and most of them came from the surgery rooms. The most frequent reason for admission was diseases of the circulatory system (23.3%). At discharge from the ICU, 72.4% of the patients were sent to other units of the same institution, 31.1% to the intermediate care unit, and 20.4% died, of which 24.6% from diseases of the circulatory system. The afternoon shift had 45.8% of the admissions and 53.3% of the discharges.  the description of the sociodemographic and epidemiological features guides the planning of nursing actions, providing a better quality service.
Multi-threaded game engine design

Game engines are specialized middleware which facilitate rapid game development. Until now they have been highly optimized to extract maximum performance from single processor hardware. In the last couple of years improvements in single processor hardware have approached physical limits and performance gains have slowed to become incremental. As a consequence, improvements in game engine performance have also become incremental. Currently, hardware manufacturers are shifting to dual and multi-core processor architectures, and the latest game consoles also feature multiple processors. This presents a challenge to game engine developers because of the unfamiliarity and complexity of concurrent programming. The next generation of game engines must address the issues of concurrency if they are to take advantage of the new hardware. This paper discusses the issues, approaches, and tradeoffs that need to be considered in the design of a multi-threaded game engine.
Silhouettes : a graphical aid to the interpretation and validation of cluster analysis

A new graphical display is proposed for partitioning techniques. Each cluster is represented by a so-called silhouette, which is based on the comparison of its tightness and separation. This silhouette shows which objects he well within their cluster, and which ones are merely somewhere in between clusters. The entire clustering is displayed by combining the silhouettes into a single plot, allowing an appreciation of the relative quality of the clusters and an overview of the data configuration. The average silhouette width provides an evaluation of clustering validity, and might be used to select an ‘appropriate’ number of clusters.
Synaptic electronics: materials, devices and applications.

In this paper, the recent progress of synaptic electronics is reviewed. The basics of biological synaptic plasticity and learning are described. The material properties and electrical switching characteristics of a variety of synaptic devices are discussed, with a focus on the use of synaptic devices for neuromorphic or brain-inspired computing. Performance metrics desirable for large-scale implementations of synaptic devices are illustrated. A review of recent work on targeted computing applications with synaptic devices is presented.
[Hippotherapy--a supplementary treatment for motion disturbance caused by cerebral palsy (author's transl)].

Neurodevelopmental treatment according to Dr. K. and B. Bobath can be supplemented by hippotherapy. At proper control and guidance, an improvement in posture tone, inhibition of pathological movement patterns, facilitation of normal automatical reactions and the promotion of sensorimotor perceptions is achieved. By adjustment to the swaying movements of the horse, the child feels how to retain straightening alignment, symmetry and balance. By pleasure in this therapy, the child can be motivated to satisfactory cooperation and accepts the therapy horse as its friend. The results of hippotherapy for 27 children afflicted with cerebral palsy permit a conclusion as to the value of this treatment for movement and behaviour disturbance to the drawn.
Mood Based Music Categorization System for Bollywood Music

Music shares a very special relation with human emotions. We often choose to listen to a song or music which best fits our mood at that instant. In spite of this strong correlation, most of the music applications today are devoid of providing the facility of mood-aware playlist generation. We wish to contribute to automatic identification of mood in audio songs by utilizing their spectral and temporal audio features. Our current work involves analysis of various features in order to learn, train and test the model representing the moods of the audio songs. Focus of our work is on the Indian popular music pieces and our work continues to analyse, develop and improve the algorithms to produce a system to recognize the mood category of the audio files automatically.
Impacts of Organizational Capabilities In Information Security

This research aims to examine the relationship between information security strategy and organization performance, with organizational capabilities as important factors influencing successful implementation of information security strategy and organization performance.  Based on existing literature in strategic management and information security, a theoretical model was proposed and validated. A self-administered survey instrument was developed to collect empirical data. Structural equation modeling was used to test hypotheses and to fit the theoretical model.  Evidence suggests that organizational capabilities, encompassing the ability to develop high-quality situational awareness of the current and future threat environment, the ability to possess appropriate means, and the ability to orchestrate the means to respond to information security threats, are positively associated with effective implementation of information security strategy, which in turn positively affects organization performance. However, there is no significant relationship between decision making and information security strategy implementation success.  The study provides a starting point for further research on the role of decision-making in information security.  Findings are expected to yield practical value for business leaders in understanding the viable predisposition of organizational capabilities in the context of information security, thus enabling firms to focus on acquiring the ones indispensable for improving organization performance.  This study provides the body of knowledge with an empirical analysis of organization’s information security capabilities as an aggregation of sense making, decision-making, asset availability, and operations management constructs.
Sleep deprivation and false memories.

Many studies have investigated factors that affect susceptibility to false memories. However, few have investigated the role of sleep deprivation in the formation of false memories, despite overwhelming evidence that sleep deprivation impairs cognitive function. We examined the relationship between self-reported sleep duration and false memories and the effect of 24 hr of total sleep deprivation on susceptibility to false memories. We found that under certain conditions, sleep deprivation can increase the risk of developing false memories. Specifically, sleep deprivation increased false memories in a misinformation task when participants were sleep deprived during event encoding, but did not have a significant effect when the deprivation occurred after event encoding. These experiments are the first to investigate the effect of sleep deprivation on susceptibility to false memories, which can have dire consequences.
A Study of Barriers to E-Commerce Adoption among SMEs in Malaysia *

The purpose of this study is to examine the barriers that SMEs are experiencing when confronted with the need to implement e-commerce to sustain their competitiveness. E-commerce is the medium that leads to economic growth of a country. Small and Medium Enterprises (SMEs) play an important role in contributing to the Gross Domestic Product and reducing the unemployment. However, there are some specific factors that inhibit the implementation of e-commerce among SMEs.  A questionnaire approach was employed in this study and 160 questionnaires have been distributed but only 91usable questionnaires have been collected from SMEs. Literature found that main barriers to e-commerce adoption among SMEs are organizational barriers, financial barriers, technical barriers, legal and regulatory barriers, and behavioral barriers.  Of this study showed that all these barriers carried an average influence on ecommerce adoption. The most important factor barriers of e-commerce adoption are legal and regulatory barriers followed by technical barriers, whereas lack of internet security is the highest barrier factor that inhibits the implementation of e-commerce in SMEs followed by the requirement to undertake additional training and skill development.  This paper is useful for the management of SMEs in understanding and gaining insights into the real and potential barriers to e-commerce adoption. This can help the organization to design strategy in taking up barriers tactfully to its advantage.
Traffic Accident Analysis Using Decision Trees and Neural Networks

This work employed Artificial Neural Networks and Decision Trees data analysis techniques to discover new knowledge from historical data about accidents in one of Nigeria’s busiest roads in order to reduce carnage on our highways. Data of accidents records on the first 40 kilometres from Ibadan to Lagos were collected from Nigeria Road Safety Corps. The data were organized into continuous and categorical data. The continuous data were analysed using Artificial Neural Networks technique and the categorical data were also analysed using Decision Trees technique .Sensitivity analysis was performed and irrelevant inputs were eliminated. The performance measures used to determine the performance of the techniques include Mean Absolute Error (MAE), Confusion Matrix, Accuracy Rate, True Positive, False Positive and Percentage correctly classified instances. Experimental results reveal that, between the machines learning paradigms considered, Decision Tree approach outperformed the Artificial Neural Network with a lower error rate and higher accuracy rate. Our research analysis also shows that, the three most important causes of accident are Tyre burst, loss of control and over speeding.