nils-herrmann/clean_scirepeval huggingface dataset:
Time and free will; an essay on the immediate data of consciousness
Year: 2013

WE necessarily express ourselves by means of words and we usually think in terms of space. That is to say, language requires us to establish between our ideas the same sharp and precise distinctions, the same discontinuity, as between material objects. This assimilation of thought to things is useful in practical life and necessary in most of the sciences. But it may be asked whether the insurmountable difficulties presented by certain philosophical problems do not arise from our placing side by side in space phenomena which do not occupy space, and whether, by merely getting rid of the clumsy symbols round which we are fighting, we might not bring the fight to an end. When an illegitimate translation of the unextended into the extended, of quality into quantity, has introduced contradiction into the very heart of, the question, contradiction must, of course, recur in the answer. The problem which I have chosen is one which is common to metaphysics and psychology, the problem of free will. What I attempt to prove is that all discussion between the determinists and their opponents implies a previous confusion (xxiv) of duration with extensity, of succession with simultaneity, of quality with quantity: this confusion once dispelled, we may perhaps witness the disappearance of the objections raised against free will, of the definitions given of it, and, in a certain sense, of the problem of free will itself. To prove this is the object of the third part of the present volume the first two chapters, which treat of the conceptions of intensity and duration, have been written as an introduction to the third. IT is usually admitted that states of consciousness, sensations, feelings, passions, efforts, are capable of growth and diminution; we are even told that a sensation can be said to be twice, thrice, four times as intense as another sensation of the same kind. This latter thesis, which is maintained by psychophysicists, we shall examine later but even the opponents of psychophysics do not see any harm in speaking of one sensation as being more intense than another, of one effort as being greater than another, and in thus setting up differences of quantity between purely internal states.
Scale Invariant Feature Learning using Deconvolutional Neural Networks for Weakly Supervised Semantic Segmentation
Venue: ArXiv
Year: 2016

A weakly supervised semantic segmentation framework using tied deconvolutional neural networks is proposed for scale invariant feature learning. Each deconvolution layer in the proposed framework consists of unpooling and deconvolution operations. 'Unpooling' upsamples the input feature map based on unpooling switches defined by corresponding convolution layer's pooling operation. 'Deconvolution' convolves the input unpooled features by using convolutional weights tied with the corresponding convolution layer's convolution operation. This unpooling deconvolution combination results in reduction of false positives, since output features of the deconvolution layer are reconstructed from the most discriminative unpooled features instead of the raw one. All the feature maps restored from the entire deconvolution layers can constitute a rich feature set according to different abstraction levels. Those features are selectively used for generating class specific activation maps. Under the weak supervision (image level labels) the proposed framework shows promising results on medical images (chest X rays) and achieves state of the art performance on the PASCAL VOC segmentation dataset in the same experimental condition.
Using Pulse Waves for Self Cognition
Venue: 2018 International Joint Conference on Information, Media and Engineering (ICIME)
Year: 2018

In this research, we obtain various indicators by analyzing pulse waves, which are then used to self cognize one's own psychological state and characteristics. Pulse wave measurement is simple and safe. What's more, we can get three indicators by analyzing the measured data: the largest Lyapunov exponent (LLE) autonomic nerve balance (ANB) and sample entropy (SampEn) Using these three indicators, we conducted research on a number of factors, including the discrimination of depression, the psychological state analysis of Alzheimer's disease, the effects of Parkinson's disease on indicators, and the change of mental state after long term study. However, in order to utilize this technology more scientifically, we need to assemble a more complete database and make more systematic measurements.
Exploring Service Quality among Online Sharing Economy Platforms from an Online Media Perspective
Venue: Sustainability
Year: 2019

The sharing economy has become a new socioeconomic activity that allows the co creation, production, distribution and consumption of goods and services between individuals, driven by Web 2.0 and e word of mouth. How these online platforms communicate their content is critical. This paper seeks to develop a scale for measuring the perceived quality of websites that offer information about sharing economy services focused on online peer to peer accommodation. The resulting model is validated by means of structural equations and is composed of four dimensions (efficiency; reliability, privacy/security; and communication) The influence that perceived quality exerts on satisfaction and loyalty is then explored from an online media perspective. A scale resulting from the combination of these two areas of knowledge (online media and e service quality) may take advantage of the more beneficial features they have to offer and provide us with as balanced and convergent a model as possible. The results of the study show that all of the initial hypotheses are confirmed except one. Efficiency, reliability and privacy/security positively influence the perceived quality of a collaborative accommodation portal, whereas communication does not.
The Advantages Of Cooperative Game Model In Enterprises Interaction In Vertical Marketing Channels By Varying Small Values Of Parameters Of The Advertising Cost Response Function
Year: 2020

Research of business interaction in marketing channels is often associated with the necessity to study the optimal business game scenarios, which provide the best implementation of enterprises business behavior strategies and maximize their profits. In such situations involvement of gametheoretic modeling apparatus in the process of determining key factors influencing profits formation of manufacturers and retailers, contributes to a better understanding of the cause and the effect relationships between the magnitude of profits, interpreted in the form of indicators of business strategies, and creates the prerequisites for finding ways of sharing joint profits in marketing channels. The purpose of study is to find optimal values in terms of channel members profits maximizing of the parameters of the advertising cost response function, in which the manufacturer and the retailer by agreement make decisions on the implementation of business game strategies, including pricing strategies and strategies for generating joint advertising costs. The paper presents a numerical experiment on the possible values of the parameters of the advertising cost response function, which takes into account the recommendations of the practice of enterprises functioning in distribution channels. Mathematical calculations have been made, which allow to build a model of cooperative game of the manufacturer and the retailer, based on the non dimensional form of retail price values as well as values of joint advertising costs. The optimal solution of cooperative game, which within the numerical experiment is presented as the possible maximum values of profits of the manufacturer, the retailer and the vertical marketing channel, is made. The comparative analysis of the solutions of cooperative game and non cooperative game, which involves the formation of Nash equilibrium, is given to determine the benefits of cooperative game model for both participants of the vertical marketing channel in terms of maximizing their profits and profit of the channel as a whole. The results obtained can be an information base for further calculations and comparisons of cooperative and non cooperative models as interactions between participants of supply chains. In addition, the results of numerical simulations form the initial base for comparisons with other variants of cooperative game model, which involve changing a range and a step change of output values of the model.
Design Considerations for Edge Neural Network Accelerators: An Industry Perspective
Venue: 2021 34th International Conference on VLSI Design and 2021 20th International Conference on Embedded Systems (VLSID)
Year: 2021

AI acceleration is one of the most actively researched fields in IP and system design. The introduction of specialized AI accelerators in the cloud and at the edge has made it possible to deploy large scaled AI solutions that automate tasks that were previously not possible without AI. The growth of big data and the compute horsepower needed to process this data to provide business intelligence is key for several companies in gaining a competitive edge. AI workloads are both data and compute intensive and improving the efficiency often requires an end to end solution. In this perspective paper, we identify key considerations for the design of AI accelerators. The focus of this paper is on Deep Neural Networks (DNN) and how to build efficient deep neural network accelerators through microarchitectural exploration, energy efficient memory hierarchies, flexible dataflow distribution, domain specific compute optimizations and finally hardware software co design techniques. The importance of interconnect topology and the impact of its scaling to the physical design of an AI accelerator is also a key consideration that is described in this paper. In the future, the energy efficiency of these accelerators may rely on approximation computing, compute in memory and runtime flexibility for significant improvement.
Multispectral and Hyperspectral Image Fusion by MS/HS Fusion Net
Venue: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2019

Hyperspectral imaging can help better understand the characteristics of different materials, compared with traditional image systems. However, only high resolution multispectral (HrMS) and low resolution hyperspectral (LrHS) images can generally be captured at video rate in practice. In this paper, we propose a model based deep learning approach for merging an HrMS and LrHS images to generate a high resolution hyperspectral (HrHS) image. In specific, we construct a novel MS/HS fusion model which takes the observation models of low resolution images and the low rankness knowledge along the spectral mode of HrHS image into consideration. Then we design an iterative algorithm to solve the model by exploiting the proximal gradient method. And then, by unfolding the designed algorithm, we construct a deep network, called MS/HS Fusion Net, with learning the proximal operators and model parameters by convolutional neural networks. Experimental results on simulated and real data substantiate the superiority of our method both visually and quantitatively as compared with state of the art methods along this line of research.
Essays on Two sided Platforms: Market Entry Strategy and Dynamic Pricing
Year: 2019

This dissertation consists of two chapters: In the first chapter, we build a theoretical framework to study the dynamic entry interactions between two platforms with homogeneous products into city based markets. This research is applicable for studying the entry strategies between, for example, Uber and Lyft; Groupon and Living Social, and other business models with the attributes of switching cost, network effect, and segregated markets. We address three questions in this paper: 1) What determines the expansion path of city based platforms? 2) What factors are affecting the market concentration structures; and 3) Under what conditions can a second mover become the market leader (with more than 50% of the market share) We find that a significant degree of the network effect and large switching cost will build a natural barrier for the late entrant; Transaction efficient markets with larger transaction volume are less likely to be concentrated than transaction inefficient markets. We take consideration of entry cost and initial fund in our dynamic settings, and find that the uncertainty in market return will make the platforms' expansion path and the final outcome less predictable. However, on average, the capability of capturing the largest market first is crucial for both players; if a platform loses the opportunity of being the first to capture the largest market, it may have to raise a considerable amount of money to overcome its disadvantages in the
Prevalence and Economic Losses Due to Bovine Tuberculosis in Cattle Slaughtered at Bodija Municipal Abattoir, Ibadan, Nigeria
Venue: Folia Veterinaria
Year: 2019

Bovine tuberculosis (BTB) is a zoonotic disease responsible for considerable economic losses; with consequent negative impact on both public health and the livestock industry. However, the burden of this disease and associated economic losses remain un investigated among slaughtered cattle in Ibadan, the hub of livestock activities in south western Nigeria. A cross sectional study was conducted over a three month period to determine the prevalence and economic losses due to BTB among slaughtered cattle at the Bodija Municipal Abattoir, Ibadan, in south western Nigeria. Individual slaughtered cattle were purposively inspected for the presence of tuberculous lesions, and representative sample of affected organs and associated lymph nodes from the BTB suspected animals were taken. The suspected lesions were processed based on Becton Dickinson digestion and decontamination procedure and the losses due to BTB were estimated mathematically, using a formula. The BTB prevalence of 9.3 (38/408) was obtained with a significant statistical association (P 0.05) of the disease with the breeds of cattle slaughtered. Out of the 46 visceral organs condemned: 29 (63 were lungs, 12 (26.1 livers, 4 (8.7 hearts and 1 (2.2 kidney. The total estimated annual economic loss (direct and indirect) due to BTB was estimated as Nigerian naria NGN 703,980,070 (EUR 1,725,441.4) This study revealed BTB as endemic and a major cause of concern considering its ill health and economic effects on both humans and the livestock industry. Efforts are therefore required: to control this disease along the beef value chain in order to safeguard human and livestock health; as well as to limit the economic losses associated with its prevalence.
Influential Analysis of Higher Order Thinking Skills (HOTS) Teaching and Learning Techniques and Teachers' Competence
Year: 2020

The High Order Thinking Skills (HOTS) in teaching and learning had been introduced in the Malaysian education system, but due to the absence of HOTS experts it was only used at a moderate level. This is because the instructor is not properly qualified and the student's ability to use HOTS in learning remains low. Nevertheless, the dedication of all teachers to quality education must address these problems. The aim of this study was to assess the perception of the competence and application of HOTS in teaching and learning in the classroom by high school teachers in rural areas. The survey employed 187 high school teachers in the district of Jeli with this quantitative approach. Statistical methods used in this study were descriptive statistics, mean, and inference statistic was using Smart PLS. Findings shows, that there was significant influence between HOTS Assessment to Teaching competencies (b=0.275, t 3.051, p 0.05) All of the constructs were significant except HOTS Student. In conclusion, this study will enable teachers, schools and stakeholders to improve the effective implementation of HOTS training and learning in the education system. Continued support and professional development on both sides are important. Teachers need to develop and incorporate HOTS skills into the teaching and learning process.
Studying Product Competition Using Representation Learning
Venue: SIGIR
Year: 2020

Studying competition and market structure at the product level instead of brand level can provide firms with insights on cannibalization and product line optimization. However, it is computationally challenging to analyze product level competition for the millions of products available on e commerce platforms. We introduce Product2Vec, a method based on the representation learning algorithm Word2Vec, to study product level competition, when the number of products is large. The proposed model takes shopping baskets as inputs and, for every product, generates a low dimensional embedding that preserves important product information. In order for the product embeddings to be useful for firm strategic decision making, we leverage economic theories and causal inference to propose two modifications to Word2Vec. First of all, we create two measures, complementarity and exchangeability, that allow us to determine whether product pairs are complements or substitutes. Second, we combine these vectors with random utility based choice models to forecast demand. To accurately estimate price elasticities, i.e. how demand responds to changes in price, we modify Word2Vec by removing the influence of price from the product vectors. We show that, compared with state of the art models, our approach is faster, and can produce more accurate demand forecasts and price elasticities.
InAs/InAsSb type 2 superlattices band parameters determination via magnetoabsorption and k.p modeling
Year: 2020

Infrared cameras are used in a wide variety of applications, from military to civilian. Cryogenically cooled IR cameras based on photon detector arrays are many times more sensitive and faster than uncooled microbolometer thermal imagers.Ga free InAs/InAsSb type 2 superlattice (T2SL) based devices have been recently fabricated and they showed a first realization of MWIR (Mid Wave InfraRed) broadband detection up to 5mm. However the band parameters (band offsets, effect of strain, effective mass) of this material system have not been determined accurately, thus limiting the understanding and the prediction of the electronic properties of the devices. In this work we determined the relevant parameters via magnetoabsorption measurements performed on dedicated T2SL samples. Interband magneto optical transitions lead to an accurate mapping of the Landau levels. The Landau level energies have been calculated using an 8 bands k.p model and the comparison with the experimental data provided a clear description of the T2SL band structure at low temperatures.
Speech Act of Teacher Student Interaction in Junior Middle School PE Class Based on Flanders' Interaction Analysis System
Year: 2014

The study used the method of Flanders' interaction analysis system to explore the speech act of teacher student interaction in a junior middle school PE class.The findings show that the speech act is still characteristic of " cramming style";the students are basically in a state of "suppression" in the process of learning.The PE teacher's speech act in class organization is mainly presented in commanding words,lacking in emotional exchange.The pattern for the teacher student interaction is single.Besides,a low quality of teacher's question is also found.Hence the suggestions are put forward in the study.
How Does Mythical Folklore Create a Framework for Mental Health
Year: 2017

Author(s) Puglisi, Maria; Reed, Asia; Campus, Christian; Turner,  Our research found that the perception of cannibalism varied across cultures; it has been shown that in some cultures cannibalism can be interpreted as a mental health disorder. We reviewed a total of eight articles that explored cannibalism, antisocial personality disorder, family dysfunction, anger, population density, and cult members. The studies examined the association between mental health disorder and behavioral problems. For the purposes of this review, only one study on Christi Cleek fit the criteria, which were (a) cannibalism amongst individuals, (b) dynamics among cult members, and (c) possible mental health disorders such as antisocial personality disorder. The study was then synthesized with the Sawney Bean Clan case study, a semi mythical folklore story that was based off an earlier tale on Christi Cleek. The results showed that anger in community members, family dysfunction, and cult behavior could have a possible correlation that is associated with a mental health disorder such as antisocial personality disorder
Participation and Engagement of Public Health Stakeholders in Climate and Health Adaptation
Year: 2020

Stakeholder participation at the intersection of climate and health is essential to assess and plan for the human health impacts of current and projected climate sensitive hazards. Using the Maricopa County Department of Public Health (MCDPH) Coalition on Climate Change and Public Health workgroup and the Climate Assessment for the Southwest (CLIMAS) program as examples, this paper describes the important role of scientist public health stakeholder collaboratives in addressing the public health impacts of climate sensitive hazards. Using the MCDPH and CLIMAS stakeholder groups, stakeholder connections were mapped to show relationships between the organization types and connections between scientists and public health stakeholders. Stakeholders, defined as meeting attendees, were primarily individuals from academic institutions (n 175) government agencies (n 114) non profits (n 90) and health departments (n 85) Engaging public health stakeholders in transdisciplinary regional climate initiatives and addressing gaps in their networks helped these programs to develop more collaborative projects over time.
Traffic Sign Recognition Using Deep Learning
Venue: Geometry and Vision
Year: 2021

Traffic sign recognition (TSR) has been an essential part of driver assistance systems, which is able to assist drivers in avoiding a vast number of potential hazards and improve the experience of driving. However, the TSR is a realistic task that is full of constraints, such as visual environment, physical damages, and partial occasions, etc. In order to deal with the constrains, convolutional neural networks (CNN) are accommodated to extract visual features of traffic signs and classify them into corresponding classes. In this project, we initially created a benchmark (NZ Traffic Sign 3K) for the traffic sign recognition in New Zealand. In order to determine which deep learning models are the most suitable one for the TSR, we choose two kinds of models to conduct deep learning computations: Faster R CNN and YOLOv5. According to the scores of various metrics, we summarized the pros and cons of the picked models for the TSR task.
HELPER: A home assisted and cost effective living system for people with disabilities and homebound elderly
Venue: 2015 IEEE 26th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)
Year: 2015

Although there has been significant research and development on automation devices for assisted living, there has always been trade offs in terms of the cost, complexity, design and efficiency. In this paper, a state of the art simple and efficient yet cost effective reconfigurable assisted living system is proposed and implemented which will cater for the needs of bed ridden patients, people with disability and senior citizens. The distinct feature which makes this implementation unique is its low cost, low power consuming hard ware and user friendly control, hence the user can operate the system without any external assistance. Additionally, the proposed work is a good head start towards Internet of Things (IoT) concept and provides multitude of input options to the user by integrating cellular communication and Zigbee protocol based wireless devices with Internet.