nils-herrmann/clean_scirepeval huggingface dataset:
Universal Battery Charging System for Electric Vehicle Using Single Phase Variable PWM Control Buck Converter

The main goal of this project is to use the solar or AC power to charge all kind of regulated and unregulated battery like electric vehicle’s battery. Besides that, it will charge Lithium-ion (Li-ion) batteries of different voltage level. A standard pulse width modulation (PWM) which is controlled by duty cycle is used to build the solar or AC fed battery charger. A microcontroller unit and Buck/Boost converters are also used to build the charger. This charger changes the output voltages from variable input voltages with fixed amplitude in PWM. It gives regulated voltages for charging sensitive batteries. An unregulated output voltage can be obtained for electric vehicle’s battery. The battery charger is tested and the obtained result allowed to conclude the conditions of permanent control on the battery charger.
An Evaluation of Factors Affecting Brand Awareness in the Context of Social Media in Malaysia

Living in the middle of a global communication boom with vast usage of social media, the business environment has become more complicated. So, it is more difficult for marketers to create and increase brand awareness as they have to be able to coordinate messages and efforts across all the existing media to capture customers. Therefore, marketers have to consider these communication tools on branding process in the current competitive market-space. The purpose of this study is to evaluate the factors affecting on brand awareness through social media in Malaysia. Data for this study was obtained from 391 students of Universiti Putra Malaysia. The results indicate customer engagement, brand exposure, and electronic-word-of-mouth have positive correlation with brand awareness in the context of social media and the most effective factor is customer engagement. The study recommends that brands will be profited from social media in order to create and enhance brand awareness and the benefits will be mostly increased by using this media’s interactivity features to tie customers more closely to a brand.
Interior Design in Augmented Reality Environment

This article presents an application of Augmented Reality technology for interior design. Plus, an Educational Interior Design Project is reviewed. Along with the dramatic progress of digital technology, virtual information techniques are also required for architectural projects. Thus, the new technology of Augmented Reality offers many advantages for digital architectural design and construction fields. AR is also being considered as a new design approach for interior design. In an AR environment, the virtual furniture can be displayed and modified in real-time on the screen, allowing the user to have an interactive experience with the virtual furniture in a real-world environment. Here, AR environment is exploited as the new working environment for architects in architectural design works, and then they can do their work conveniently as such collaborative discussion through AR environment. Finally, this study proposes a new method for applying AR technology to interior design work, where a user can view virtual furniture and communicate with 3D virtual furniture data using a dynamic and flexible user interface. Plus, all the properties of the virtual furniture can be adjusted using occlusionbased interaction method for a Tangible Augmented Reality.
Implementation and Optimization of Differentiable Neural Computers

We implemented and optimized Differentiable Neural Computers (DNCs) as described in the Oct. 2016 DNC paper  on the bAbI dataset  and on copy tasks that were described in the Neural Turning Machine paper . This paper will give the reader a better understanding of this new and promising architecture through the documentation of the approach in our DNC implementation and our experience of the challenges of optimizing DNCs. Given how recently the DNC paper has come out, other than the original paper, there were no such explanation, implementation and experimentation of the same level of detail as this project has produced, which is why this project will be useful for others who want to experiment with DNCs since this project successfully trained a high performing DNC on the copy task while our DNC performance on the bAbI dataset was better than or equal to our LSTM baseline.
A Theoretical Analysis of Feature Pooling in Visual Recognition

Many modern visual recognition algorithms incorporate a step of spatial ‘pooling’, where the outputs of several nearby feature detectors are combined into a local or global ‘bag of features’, in a way that preserves task-related information while removing irrelevant details. Pooling is used to achieve invariance to image transformations, more compact representations, and better robustness to noise and clutter. Several papers have shown that the details of the pooling operation can greatly influence the performance, but studies have so far been purely empirical. In this paper, we show that the reasons underlying the performance of various pooling methods are obscured by several confounding factors, such as the link between the sample cardinality in a spatial pool and the resolution at which low-level features have been extracted. We provide a detailed theoretical analysis of max pooling and average pooling, and give extensive empirical comparisons for object recognition tasks.
A Customized Attention-Based Long Short-Term Memory Network for Distant Supervised Relation Extraction

Distant supervision, a widely applied approach in the field of relation extraction can automatically generate large amounts of labeled training corpus with minimal manual effort. However, the labeled training corpus may have many false-positive data, which would hurt the performance of relation extraction. Moreover, in traditional feature-based distant supervised approaches, extraction models adopt human design features with natural language processing. It may also cause poor performance. To address these two shortcomings, we propose a customized attention-based long short-term memory network. Our approach adopts word-level attention to achieve better data representation for relation extraction without manually designed features to perform distant supervision instead of fully supervised relation extraction, and it utilizes instance-level attention to tackle the problem of false-positive data. Experimental results demonstrate that our proposed approach is effective and achieves better performance than traditional methods.
Average Current-Mode Control Scheme for a Quadratic Buck Converter With a Single Switch

A controller for a quadratic buck converter is given using average current-mode control. The converter has two filters; thus, it will exhibit fourth-order characteristic dynamics. The proposed scheme employs an inner loop that uses the current of the first inductor. This current can also be used for overload protection; therefore, the full benefits of current-mode control are maintained. For the outer loop, a conventional controller which provides good regulation characteristics is used. The design-oriented analytic results allow the designer to easily pinpoint the control circuit parameters that optimize the converter's performance. Experimental results are given for a 28 W switching regulator where current-mode control and voltage-mode control are compared.
Study of Semantic Models in Identifying Aspects in Customer Reviews

With the outburst of ecommerce sentiment-rich resources such as online review sites and blogs, people actively use this information to understand what others think about a particular subject. This area of study helps to derive the opinion, sentiment or the outlook of a speaker mainly used when conducting market research. This paper does an evaluation of the systems such as LSA and PMI that set up semantic association between aspects and opinions found in customer reviews. PMI-IR is predicted to give better results as observed in a user study.
Efficient exploration with Double Uncertain Value Networks

This paper studies directed exploration for reinforcement learning agents by tracking uncertainty about the value of each available action. We identify two sources of uncertainty that are relevant for exploration. The first originates from limited data (parametric uncertainty), while the second originates from the distribution of the returns (return uncertainty). We identify methods to learn these distributions with deep neural networks, where we estimate parametric uncertainty with Bayesian drop-out, while return uncertainty is propagated through the Bellman equation as a Gaussian distribution. Then, we identify that both can be jointly estimated in one network, which we call the Double Uncertain Value Network. The policy is directly derived from the learned distributions based on Thompson sampling. Experimental results show that both types of uncertainty may vastly improve learning in domains with a strong exploration challenge.
Computational thinking tools

Computational Thinking is an essential skill for all students in the 21st Century. A fundamental question is how can we create computer affordances to empower novice teachers and students, in a variety of STEM and art disciplines, to think computationally while avoiding difficult overhead emerging from traditional coding? Over the last 20 years we have iteratively developed tools that aim to support computational thinking. As these tools evolved a philosophy emerged to support Computational Thinking by joining human abilities with computer affordances. Chief among these findings is that supporting Computational Thinking is much more than making coding accessible. Computational Thinking Tools aim to minimize coding overhead by supporting users through three fundamental stages of the Computational Thinking development cycle: problem formulation, solution expression, and solution execution/evaluation.
Reassembleable Disassembling

Reverse engineering has many important applications in computer security, one of which is retrofitting software for safety and security hardening when source code is not available. By surveying available commercial and academic reverse engineering tools, we surprisingly found that no existing tool is able to disassemble executable binaries into assembly code that can be correctly assembled back in a fully automated manner, even for simple programs. Actually in many cases, the resulted disassembled code is far from a state that an assembler accepts, which is hard to fix even by manual effort. This has become a severe obstacle. People have tried to overcome it by patching or duplicating new code sections for retrofitting of executables, which is not only inefficient but also cumbersome and restrictive on what retrofitting techniques can be applied to. In this paper, we present UROBOROS, a tool that can disassemble executables to the extent that the generated code can be assembled back to working binaries without manual effort. By empirically studying 244 binaries, we summarize a set of rules that can make the disassembled code relocatable, which is the key to reassembleable disassembling. With UROBOROS, the disassembly-reassembly process can be repeated thousands of times. We have implemented a prototype of UROBOROS and tested over the whole set of GNU Coreutils, SPEC2006, and a set of other real-world application and server programs. The experiment results show that our tool is effective with a very modest cost.
Artificial intelligence in medicine.

Artificial intelligence is a branch of computer science capable of analysing complex medical data. Their potential to exploit meaningful relationship with in a data set can be used in the diagnosis, treatment and predicting outcome in many clinical scenarios.



Medline and internet searches were carried out using the keywords 'artificial intelligence' and 'neural networks (computer)'. Further references were obtained by cross-referencing from key articles. An overview of different artificial intelligent techniques is presented in this paper along with the review of important clinical applications.



The proficiency of artificial intelligent techniques has been explored in almost every field of medicine. Artificial neural network was the most commonly used analytical tool whilst other artificial intelligent techniques such as fuzzy expert systems, evolutionary computation and hybrid intelligent systems have all been used in different clinical settings.



Artificial intelligence techniques have the potential to be applied in almost every field of medicine. There is need for further clinical trials which are appropriately designed before these emergent techniques find application in the real clinical setting.
Hilbert modular forms and the Gross – Stark conjecture

Let F be a totally real field and χ an abelian totally odd character of F . In 1988, Gross stated a p-adic analogue of Stark’s conjecture that relates the value of the derivative of the p-adic L-function associated to χ and the p-adic logarithm of a p-unit in the extension of F cut out by χ. In this paper we prove Gross’s conjecture when F is a real quadratic field and χ is a narrow ring class character. The main result also applies to general totally real fields for which Leopoldt’s conjecture holds, assuming that either there are at least two primes above p in F , or that a certain condition relating the L invariants of χ and χ−1 holds. This condition on L -invariants is always satisfied when χ is quadratic.
Smart homes - current features and future perspectives.

In an ageing world, maintaining good health and independence for as long as possible is essential. Instead of hospitalization or institutionalization, the elderly and disabled can be assisted in their own environment 24h a day with numerous 'smart' devices. The concept of the smart home is a promising and cost-effective way of improving home care for the elderly and the disabled in a non-obtrusive way, allowing greater independence, maintaining good health and preventing social isolation. Smart homes are equipped with sensors, actuators, and/or biomedical monitors. The devices operate in a network connected to a remote centre for data collection and processing. The remote centre diagnoses the ongoing situation and initiates assistance procedures as required. The technology can be extended to wearable and in vivo implantable devices to monitor people 24h a day both inside and outside the house. This review describes a selection of projects in developed countries on smart homes examining the various technologies available. Advantages and disadvantages, as well as the impact on modern society, are discussed. Finally, future perspectives on smart homes as part of a home-based health care network are presented.
DRIVING CONSUMER ACCEPTANCE OF MOBILE MARKETING : A THEORETICAL FRAMEWORK AND EMPIRICAL STUDY

With the emergence of high speed wireless network technologies and the increasing market penetration of mobile phones the global advertising industry’s interest in using this medium as a means of marketing communication is rising. However, in spite of the increasing number of companies investing in mobile marketing campaigns, there is, as yet, little academic research on this topic and the nature and implications of this channel are not yet understood fully. This research attempts to help in bridging this gap; it investigates the factors that induce consumers to accept the mobile phone as a means of communicating promotional content. Underlying the research are a set of hypotheses that have been formulated to include determinants referring to consumer personality as well as determinants referring to the form of advertising. These hypotheses form the basis for this research and are empirically tested by means of a linear structural equation model. The empirical results (n = 1,028) identify entertainment value as well as information value as the strongest drivers of the acceptance of the mobile phone as an innovative medium for advertising content communication.
DSKG: A Deep Sequential Model for Knowledge Graph Completion

Knowledge graph (KG) completion aims to fill the missing facts in a KG, where a fact is represented as a triple in the form of (subject, relation, object). Current KG completion models compel twothirds of a triple provided (e.g., subject and relation) to predict the remaining one. In this paper, we propose a new model, which uses a KGspecific multi-layer recurrent neutral network (RNN) to model triples in a KG as sequences. It outperformed several state-of-the-art KG completion models on the conventional entity prediction task for many evaluation metrics, based on two benchmark datasets and a more difficult dataset. Furthermore, our model is enabled by the sequential characteristic and thus capable of predicting the whole triples only given one entity. Our experiments demonstrated that our model achieved promising performance on this new triple prediction task.
Complications of injectable fillers, part 2: vascular complications.

Accidental intra-arterial filler injection may cause significant tissue injury and necrosis. Hyaluronic acid (HA) fillers, currently the most popular, are the focus of this article, which highlights complications and their symptoms, risk factors, and possible treatment strategies. Although ischemic events do happen and are therefore important to discuss, they seem to be exceptionally rare and represent a small percentage of complications in individual clinical practices. However, the true incidence of this complication is unknown because of underreporting by clinicians. Typical clinical findings include skin blanching, livedo reticularis, slow capillary refill, and dusky blue-red discoloration, followed a few days later by blister formation and finally tissue slough. Mainstays of treatment (apart from avoidance by meticulous technique) are prompt recognition, immediate treatment with hyaluronidase, topical nitropaste under occlusion, oral acetylsalicylic acid (aspirin), warm compresses, and vigorous massage. Secondary lines of treatment may involve intra-arterial hyaluronidase, hyperbaric oxygen therapy, and ancillary vasodilating agents such as prostaglandin E1. Emergency preparedness (a "filler crash cart") is emphasized, since early intervention is likely to significantly reduce morbidity. A clinical summary chart is provided, organized by complication presentation.
Friction stir welding and processing

Friction stir welding (FSW) is a relatively new solid-state joining process. This joining technique is energy efficient, environment friendly, and versatile. In particular, it can be used to join high-strength aerospace aluminum alloys and other metallic alloys that are hard to weld by conventional fusion welding. FSW is considered to be the most significant development in metal joining in a decade. Recently, friction stir processing (FSP) was developed for microstructural modification of metallic materials. In this review article, the current state of understanding and development of the FSW and FSP are addressed. Particular emphasis has been given to: (a) mechanisms responsible for the formation of welds and microstructural refinement, and (b) effects of FSW/FSP parameters on resultant microstructure and final mechanical properties. While the bulk of the information is related to aluminum alloys, important results are now available for other metals and alloys. At this stage, the technology diffusion has significantly outpaced the fundamental understanding of microstructural evolution and microstructure–property relationships.
IDS : Intrusion Detection System the Survey of Information Security

With the increased use of computerized / online transactions it is very much of the importance to secure the information from intruders. Intrusion detection is the process of monitoring the activities or events occurring in the computer system or network and analyzing them to find out suspicious events intruding the system or network. Such events will be reported to the administrator of Intrusion Detection System(IDS) who will decide the further action. This Paper surveys different types of IDS and lists preventive methods.An intrusion detection system (IDS) is software that automates the intrusion detection process. An intrusion prevention system (IPS) is software that has all the capabilities of an intrusion detection system and can also attempt to stop possible incidents. Intruder, Intrusion, anomaly, IDS, NIDS,
Web-scale k-means clustering

We present two modifications to the popular k-means clustering algorithm to address the extreme requirements for latency, scalability, and sparsity encountered in user-facing web applications. First, we propose the use of mini-batch optimization for k-means clustering. This reduces computation cost by orders of magnitude compared to the classic batch algorithm while yielding significantly better solutions than online stochastic gradient descent. Second, we achieve sparsity with projected gradient descent, and give a fast ε-accurate projection onto the L1-ball. Source code is freely available: http://code.google.com/p/sofia-ml
Open-World Planning for Story Generation

The ability to generate narrative is of importance to computer systems that wish to use story effectively for entertainment, training, or education. One way to generate narrative is to use planning. However, story planners are limited by the fact that they can only operate on the story world provided, which impacts the ability of the planner to find a solution story plan and the quality and structure of the story plan if one is found. We present a planning algorithm for story generation that can nondeterministically make decisions about the description of the initial story world state in a leastcommitment fashion.
AMPNet: Asynchronous Model-Parallel Training for Dynamic Neural Networks

New types of machine learning hardware in development and entering the market hold the promise of revolutionizing deep learning in a manner as profound as GPUs. However, existing software frameworks and training algorithms for deep learning have yet to evolve to fully leverage the capability of the new wave of silicon. We already see the limitations of existing algorithms for models that exploit structured input via complex and instancedependent control flow, which prohibits minibatching. We present an asynchronous model-parallel (AMP) training algorithm that is specifically motivated by training on networks of interconnected devices. Through an implementation on multi-core CPUs, we show that AMP training converges to the same accuracy as conventional synchronous training algorithms in a similar number of epochs, but utilizes the available hardware more efficiently even for small minibatch sizes, resulting in significantly shorter overall training times. Our framework opens the door for scaling up a new class of deep learning models that cannot be efficiently trained today.
A relational database for bibliometric analysis

In this article a relational database schema for a bibliometric database is developed. After the introduction explaining the motivation to use relational databases in bibliometrics, an overview of the related literature is given. A review of typical bibliometric questions serves as an informal requirement analysis. The database schema is developed as an entity-relationship diagram using the structural information typically found in scientific articles. Several SQL queries for the tasks presented in the requirement analysis show the usefulness of the developed database schema.