nils-herrmann/clean_scirepeval huggingface dataset:
Factors Affecting Online Impulse Buying : Evidence from Chinese Social Commerce Environment

First, the purpose of this study is to examine the impact of situational variables, scarcity and serendipity, on online impulse buying (OIB) in Chinese social commerce (SC) environment. Second, the study further assesses the moderating role of five dimensions of hedonic shopping value. Data were gathered from 671 online shoppers who come from two metropolitan cities of China, Beijing, and Shanghai. Structure equation modeling utilized was generated by AMOS 23 version to test the study hypotheses. The results confirm that situational factors positively influence the online impulse buying among Chinese online shoppers in SC environment. Four dimensions of hedonic shopping value (social shopping, relaxation shopping, adventure shopping and idea shopping) positively moderate the relationship between serendipity and OIB; value shopping is insignificant with moderation effect. The finding is helpful to the online retailers and SC web developers by recommending them to take the scarcity and serendipity in their consideration. These factors have the potential to motivate the consumers to initiate the hedonic shopping aptitude to urge to buy impulsively. Unlike the previous work which remained unsuccessful in incorporating all factors into one study, this study has incorporated irrational and unplanned consumption along with rational and planned one in the same research.
Detection of Occluding Contours and Occlusion by Active Binocular Stereo

|We propose a reliable method to detect occluding contours and occlusion by active binocular stereo with an occluding contour model that describes the correspondence between the contour of curved objects and its projected image. Applying the image ow generated by moving one camera to the model, we can restrict possible matched points seen by the other camera. We detect occluding contours by tting these matched points to the model. This method can nd occluding contours and occlusion more reliably than conventional ones because stereo matching is performed by using the geometric constraint based on the occluding contour model instead of heuristic constraints. Experimental results of the proposed method applied to the actual scene are presented.
AAAI: An Argument Against Artificial Intelligence

The ethical concerns regarding the successful development of an Artificial Intelligence have received a lot of attention lately. The idea is that even if we have good reason to believe that it is very unlikely, the mere possibility of an AI causing extreme human suffering is important enough to warrant serious consideration. Others look at this problem from the opposite perspective, namely that of the AI itself. Here the idea is that even if we have good reason to believe that it is very unlikely, the mere possibility of humanity causing extreme suffering to an AI is important enough to warrant serious consideration. This paper starts from the observation that both concerns rely on problematic philosophical assumptions. Rather than tackling these assumptions directly, it proceeds to present an argument that if one takes these assumptions seriously, then one has a moral obligation to advocate for a ban on the development of a conscious AI.
How Much Faster is Fast Enough?: User Perception of Latency & Latency Improvements in Direct and Indirect Touch

This paper reports on two experiments designed to further our understanding of users' perception of latency in touch- based systems. The first experiment extends previous efforts to measure latency perception by reporting on a unified study in which direct and indirect form-factors are compared for both tapping and dragging tasks. Our results show significant effects from both form-factor and task, and inform system designers as to what input latencies they should aim to achieve in a variety of system types. A follow-up experiment investigates peoples' ability to perceive small improvements to latency in direct and indirect form-factors for tapping and dragging tasks. Our results provide guidance to system designers of the relative value of making improvements in latency that reduce but do not fully eliminate lag from their systems.
A High Power Factor, Electrolytic Capacitor-Less AC-Input LED Driver Topology With High Frequency Pulsating Output Current

Light emitting diode (LED) lamps with ac-input (50 or 60 Hz) usually require an electrolytic capacitor as the dc-link capacitor in the driver circuit to: 1) balance the energy between the input and output power, and 2) to minimize the low-frequency component of the output ripple across the LEDs. The lifetime of this capacitor, however, is much shorter than that of a LED. To maximize the potential lifetime of the LED lighting system, a new pulsating current driving LED driver that does not require any electrolytic capacitors or complicated control circuits to minimize the low-frequency (i.e., 100 or 120 Hz) output ripple is proposed in this paper. The proposed circuit is simple and a single-switch topology is designed to simplify the controller design. The proposed circuit is able to reduce the energy storage capacitance to a few microfarads range, so that film capacitor can be used to replace the unreliable electrolytic capacitor. The circuit operating principles and its theoretical analysis are provided in this paper. Simulation and experimental results are given on a 9-W LED lamp to highlight the merits of the proposed circuit.
Towards a Multimedia Knowledge-Based Agent with Social Competence and Human Interaction Capabilities

We present work in progress on an intelligent embodied conversation agent in the basic care and healthcare domain. In contrast to most of the existing agents, the presented agent is aimed to have linguistic cultural, social and emotional competence needed to interact with elderly and migrants. It is composed of an ontology-based and reasoning-driven dialogue manager, multimodal communication analysis and generation modules and a search engine for the retrieval of multimedia background content from the web needed for conducting a conversation on a given topic.
From cyber-physical systems to Industry 4.0: make future manufacturing become possible

Recently, ‘Industry 4.0’ has become the supporting force in the manufacturing field. From CPS to Industry 4.0, they are leading a new round of industrial revolution. To meet the fourth industrial revolution, USA, European countries, Japan, South Korea and China have presented different versions of the so-called ‘Industry 4.0’ strategies respectively, which are different from each other in the content, but their common goal is to achieve manufacturing structural adjustment and restructuring and upgrading, to seize a place for future manufacturing, to provide users with better service. What is more, we summarised the five major trends of the future manufacturing. Then, we introduce some enabling technologies about Industry 4.0, among them, CPS plays a key role in the Industry 4.0 factory. Based on the customer to business (C2B) mode, we regard everything as a service, and propose a structure of Industry 4.0. Finally, the automotive industry as an example, we make a brief introduction about an Industry 4.0 case.  May 2016]
Computer-aided design of analog and mixed-signal integrated circuits

This survey presents an overview of recent advances in the state of the art for computer-aided design (CAD) tools for analog and mixed-signal integrated circuits (ICs). Analog blocks typically constitute only a small fraction of the components on mixed-signal ICs and emerging systems-on-a-chip (SoC) designs. But due to the increasing levels of integration available in silicon technology and the growing requirement for digital systems to communicate with the continuous-valued external world, there is a growing need for CAD tools that increase the design productivity and improve the quality of analog integrated circuits. This paper describes the motivation and evolution of these tools and outlines progress on the various design problems involved: simulation and modeling, symbolic analysis, synthesis and optimization, layout generation, yield analysis and design centering, and test. This paper summarizes the problems for which viable solutions are emerging and those which are still unsolved.
Modeling and Control Design of the Interleaved Double Dual Boost Converter

The interleaved double dual boost (IDDB) is a non-isolated step-up dc-dc converter capable of high voltage gain and suitable to high-power applications. In this paper, the modeling and control design of this converter, valid for an arbitrary number of phases, is presented. The developed approach is then applied to a six-phase IDDB, and experimental results are obtained with a prototype operating with an input voltage of 60 V, an output voltage of 360 V, and with a nominal output power of 2.2 kW. The applications of this converter include electrical vehicles and renewable energy conversion.
Design of a compact high power phased array for 5G FD-MIMO system at 29 GHz

This paper presents a new design concept of a beam steerable high gain phased array antenna based on WR28 waveguide at 29 GHz frequency for fifth generation (5G) full dimension multiple input multiple output (FD-MIMO) system. The 8×8 planar phased array is fed by a three dimensional beamformer to obtain volumetric beam scanning ranging from −60 to +60 degrees both in azimuth and elevation direction. Beamforming network (BFN) is designed using 16 set of 8×8 Butler matrix beamformer to get 64 beam states, which control the horizontal and vertical angle. This is a new concept to design waveguide based high power three-dimensional beamformer for volumetric multibeam in Ka band for 5G application. The maximum gain of phased array is 28.5 dBi that covers 28.9 GHz to 29.4 GHz frequency band.
Collaborative games: lessons learned from board games

Collaborative mechanisms are starting to become prominent in computer games, like massively multiplayer online games (MMOGs); however, by their nature, these games are difficult to investigate. Game play is often complex and the underlying mechanisms are frequently opaque. In contrast, board games are simple. Their game play is fairly constrained and their core mechanisms are transparent enough to analyze. In this article, the authors seek to understand collaborative games. Because of their simplicity, they focus on board games. The authors present an analysis of collaborative games. In particular, they focus on Reiner Knizia’s LORD OF THE RINGS, considered by many to be the quintessential collaborative board game. Our analysis yields seven observations, four lessons, and three pitfalls, that game designers might consider useful for designing collaborative games. They reflect on the particular opportunities that computers have for the design of collaborative games as well as how some of the issues discussed apply to the case of computer games.
IMAGE QUALITY ASSESSMENT TECHNIQUES IMPROVE TRAINING

We propose a new, multi-component energy function for energy-based Generative Adversarial Networks (GANs) based on methods from the image quality assessment literature. Our approach expands on the Boundary Equilibrium Generative Adversarial Network (BEGAN) by outlining some of the short-comings of the original energy and loss functions. We address these short-comings by incorporating an l1 score, the Gradient Magnitude Similarity score, and a chrominance score into the new energy function. We then provide a set of systematic experiments that explore its hyper-parameters. We show that each of the energy function’s components is able to represent a slightly different set of features, which require their own evaluation criteria to assess whether they have been adequately learned. We show that models using the new energy function are able to produce better image representations than the BEGAN model in predicted ways.
Human-Robot Dialogue and Collaboration in Search and Navigation

Collaboration with a remotely located robot in tasks such as disaster relief and search and rescue can be facilitated by grounding natural language task instructions into actions executable by the robot in its current physical context. The corpus we describe here provides insight into the translation and interpretation a natural language instruction undergoes starting from verbal human intent, to understanding and processing, and ultimately, to robot execution. We use a ‘Wizard-of-Oz’ methodology to elicit the corpus data in which a participant speaks freely to instruct a robot on what to do and where to move through a remote environment to accomplish collaborative search and navigation tasks. This data offers the potential for exploring and evaluating action models by connecting natural language instructions to execution by a physical robot (controlled by a human ‘wizard’). In this paper, a description of the corpus (soon to be openly available) and examples of actions in the dialogue are provided.
Phase-Shifted PWM Converter With a Wide ZVS Range and Reduced Circulating Current

A phase-shifted (PS) pulse-width-modulation (PWM) converter that can solve the drawbacks of existing PS full-bridge converters, such as narrow zero-voltage-switching (ZVS) range, large duty-cycle loss, large circulating current, and serious secondary-voltage overshoot and oscillation, is proposed in this paper. The proposed converter is composed of two symmetric half-bridge converters (TSHBCs) that are placed in parallel on the primary side and are driven in a phase-shifting manner. Their transformers are connected in series on the secondary side. At the output, a simple energy-recovery circuit (ERC) is employed. Due to this structure, the proposed converter has the advantages of wide ZVS range, less duty-cycle loss, reduced circulating current, and no secondary-voltage overshoot and oscillation, which results in the reduction of rectifier diodes' voltage stress and EMI. Therefore, the conversion efficiency is higher and the cost of the secondary rectifier is reduced compared to its PS PWM counterparts. In this paper, the circuit configuration, operation principle, and relevant analysis results of the proposed converters are presented. Experimental results on a prototype converter realized with an 80-in plasma display panel sustain power module (320-385 Vdc input, 205 Vdc/5 A output) validate the theoretical analysis.
The pragmatics of swearing

The main purpose of swearing is to express emotions, especially anger and frustration. Swear words are well suited to express emotion as their primary meanings are connotative. The emotional impact of swearing depends on one’s experience with a culture and its language conventions. A cognitive psychological framework is used to account for swearing in a variety of contexts and provide a link to impoliteness research. In support of this framework, native and non-native English-speaking college students rated the offensiveness and likelihood of hypothetical scenarios involving taboo words. The ratings demonstrated that appropriateness of swearing is highly contextually variable, dependent on speaker-listener relationship, socialphysical context, and particular word used. Additionally, offensiveness ratings were shown to depend on gender (for native speakers) and English experience (for non-native speakers). Collectively these data support the idea that it takes time for speakers to learn where, when, and with whom swearing is appropriate.
Ultrasonography for clinical decision-making and intervention in airway management: from the mouth to the lungs and pleurae

To create a state-of-the-art overview of the new and expanding role of ultrasonography in clinical decision-making, intervention and management of the upper and lower airways, that is clinically relevant, up-to-date and practically useful for clinicians.



This is a narrative review combined with a structured Medline literature search.



Ultrasonography can be utilised to predict airway difficulty during induction of anaesthesia, evaluate if the stomach is empty or possesses gastric content that poses an aspiration risk, localise the essential cricothyroid membrane prior to difficult airway management, perform nerve blocks for awake intubation, confirm tracheal or oesophageal intubation and facilitate localisation of tracheal rings for tracheostomy. Ultrasonography is an excellent diagnostic tool in intraoperative and emergency diagnosis of pneumothorax. It also enables diagnosis and treatment of interstitial syndrome, lung consolidation, atelectasis, pleural effusion and differentiates causes of acute breathlessness during pregnancy. Patient safety can be enhanced by performing procedures under ultrasound guidance, e.g. thoracocentesis, vascular line access and help guide timing of removal of chest tubes by quantification of residual pneumothorax size.



Ultrasonography used in conjunction with hands-on management of the upper and lower airways has multiple advantages. There is a rapidly growing body of evidence showing its benefits.



• Ultrasonography is becoming essential in management of the upper and lower airways. • The tracheal structures can be identified by ultrasonography, even when unidentifiable by palpation. • Ultrasonography is the primary diagnostic approach in suspicion of intraoperative pneumothorax. • Point-of-care ultrasonography of the airways has a steep learning curve. • Lung ultrasonography allows treatment of interstitial syndrome, consolidation, atelectasis and effusion.
Decoding of EEG Signals Using Deep Long Short-Term Memory Network in Face Recognition Task

The paper proposes a novel approach to classify the human memory response involved in the face recognition task by the utilization of event related potentials. Electroencephalographic signals are acquired when a subject engages himself/herself in familiar or unfamiliar face recognition tasks. The signals are analyzed through source Iocalization using eLORETA and artifact removal by ICA from a set of channels corresponding to those selected sources, with an ultimate aim to classify the EEG responses of familiar and unfamiliar faces. The EEG responses of the two different classes (familiar and unfamiliar face recognition)are distinguished by analyzing the Event Related Potential signals that reveal the existence of large N250 and P600 signals during familiar face recognition.The paper introduces a novel LSTM classifier network which is designed to classify the ERP signals to fulfill the prime objective of this work. The first layer of the novel LSTM network evaluates the spatial and local temporal correlations between the obtained samples of local EEG time-windows. The second layer of this network models the temporal correlations between the time-windows. An attention mechanism has been introduced in each layer of the proposed model to compute the contribution of each EEG time-window in face recognition task. Performance analysis reveals that the proposed LSTM classifier with attention mechanism outperforms the efficiency of the conventional LSTM and other classifiers with a significantly large margin. Moreover, source Iocalization using eLORETA shows the involvement of inferior temporal and frontal lobes during familiar face recognition and pre-frontal lobe during unfamiliar face recognition. Thus, the present research outcome can be used in criminal investigation, where meticulous differentiation of familiar and unfamiliar face detection by criminals can be performed from their acquired brain responses.
Role of Software Agents in E-Commerce

This paper states how software agents will influence the present marketing prototype in interactive media. It focuses on the importance of these in E-Commerce. E-Commerce is a platform for the online activities of commerce, increase in the activities of trade in cyberspace increases the business opportunities over internet. Software agents are the useful tools to help individuals to undertake their activities on E-Commerce surroundings. Software agents create different paths for communication. Software agents increase the effectiveness and efficiency in several levels of market processes. The agent technology builds the bridge between the previously existing physical market and consequences of virtual markets. Software agents will have dramatic competition effects by quick transfer of information through new technology. As the name indicates software agents acts as mediators for the processes of choosing products and merchants. Overall software agents act as a vehicle between E-Commerce and business.
Efficient Event-Driven Simulation of Large Networks of Spiking Neurons and Dynamical Synapses

A simulation procedure is described for making feasible large-scale simulations of recurrent neural networks of spiking neurons and plastic synapses. The procedure is applicable if the dynamic variables of both neurons and synapses evolve deterministically between any two successive spikes. Spikes introduce jumps in these variables, and since spike trains are typically noisy, spikes introduce stochasticity into both dynamics. Since all events in the simulation are guided by the arrival of spikes, at neurons or synapses, we name this procedure event-driven. The procedure is described in detail, and its logic and performance are compared with conventional (synchronous) simulations. The main impact of the new approach is a drastic reduction of the computational load incurred upon introduction of dynamic synaptic efficacies, which vary organically as a function of the activities of the pre- and postsynaptic neurons. In fact, the computational load per neuron in the presence of the synaptic dynamics grows linearly with the number of neurons and is only about 6 more than the load with fixed synapses. Even the latter is handled quite efficiently by the algorithm. We illustrate the operation of the algorithm in a specific case with integrate-and-fire neurons and specific spike-driven synaptic dynamics. Both dynamical elements have been found to be naturally implementable in VLSI. This network is simulated to show the effects on the synaptic structure of the presentation of stimuli, as well as the stability of the generated matrix to the neural activity it induces.
Distributed Learning in Multiuser OFDMA Femtocell Networks

This paper elaborates on self-organized and distributed interference management for femtocells that share the available radio resources with macrocells. A multi-agent learning approach is examined, based on distributed Q-learning, where femtocell base stations control their transmit power, such that the femtocell capacity is maximized, while the aggregated downlink interference generated at macro users' receivers is maintained within acceptable limits. The distributed Q-learning algorithm is carried out at the femto nodes, in the way that the interference is controlled at each resource block. The contribution of this work is to integrate multi-user scheduling in the operation of the macrocell network, so that instantaneous changes, with 1 ms granularity, are encountered in the perception that the femtocell agents get of the environment under observation. We demonstrate that, by relying on 3GPP Long Term Evolution (LTE) compliant signalling from the macro network on the intended macrocell scheduling policies, the proposed learning approach allows each femto node to react on these instantaneous changes in the environment, such that the femto-to-macro interference is appropriately controlled.