nils-herrmann/clean_scirepeval huggingface dataset:
CMOS Active Quasi-Circulator With Dual Transmission Gains Incorporating Feedforward Technique at $K$-Band

This paper presents an innovative architecture for the active quasi-circulator to break the structural limitation on the leakage suppression. A practical prototype is implemented at K -band and fabricated by using standard 0.18- μm 1P6M CMOS technology. The comparisons between simulations and on-wafer measurements are reported in detail to confirm the feasibility and capability of the proposed active quasi-circulator. At 24 GHz, the prototype has dual transmission gains of 22.4 and 12.3 dB in the transmitting and receiving paths, respectively. The measured isolation, which is defined by the ratio of the forward transmission to reverse transmission coefficient in the same path, is higher than 50.0 dB at 24.0 GHz. By comparing the leakage phenomena between the prototype with and without feedforward cancellations, the leakage suppression can be improved with a maximum value of 44.7 dB at 23.63 GHz.
Drivers of Finished-Goods Inventory in the U.S. Automobile Industry

Automobile manufacturers in the U.S. supply chain exhibit signicant di¤erences in their days-of-supply of nished vehicles (average inventory divided by average daily sales rate). For example, from 1995 to 2004, Toyota consistently carried approximately 30 fewer days-of-supply than General Motors. This suggests that Toyotas well documented advantage in manufacturing e¢ ciency, product design and upstream supply chain management extends to their nished-goods inventory in their downstream supply chain from their assembly plants to their dealerships. Our objective in this research is to measure for this industry the e¤ect of several factors on inventory holdings. We nd that two factors, the number of dealerships in a manufacturers distribution network and a manufacturers production exibility, explain essentially all of the di¤erence in nished goods inventory between Toyota and three other makes, Chrysler, Ford and General Motors.
GridMonitoring: Secured Sovereign Blockchain Based Monitoring on Smart Grid

Electricity is the commonest commodity for most businesses in our world today. The use of electricity has been a breakthrough for the discovery of new technologies and has become the main driving force behind several innovations. With the introduction of smart grid systems, there have been improvements in how utility companies interact with their customers with regards to electricity use. However, since the readings are done via the Internet, there is the tendency for the data to be compromised when it gets into the hands of the wrong people. Moreover, customers mostly do not know why they pay huge amounts and which appliances use more electricity, since they are not privy to the readings. The sovereign blockchain technology, which provides transparency and provenance, is utilized in this paper to mitigate these above mentioned problems. A smart contract, which executes laid down procedures to provide a trust-based system between participants on the network is also implemented. Our system proves very efficient as the user can monitor how the electricity is used, and it also provides a platform where there is no manipulation from either party.
HeteroMed: Heterogeneous Information Network for Medical Diagnosis

With the recent availability of Electronic Health Records (EHR) and great opportunities they offer for advancing medical informatics, there has been growing interest in mining EHR for improving quality of care. Disease diagnosis due to its sensitive nature, huge costs of error, and complexity has become an increasingly important focus of research in past years. Existing studies model EHR by capturing co-occurrence of clinical events to learn their latent embeddings. However, relations among clinical events carry various semantics and contribute differently to disease diagnosis which gives precedence to a more advanced modeling of heterogeneous data types and relations in EHR data than existing solutions. To address these issues, we represent how high-dimensional EHR data and its rich relationships can be suitably translated into HeteroMed, a heterogeneous information network for robust medical diagnosis. Our modeling approach allows for straightforward handling of missing values and heterogeneity of data. HeteroMed exploits metapaths to capture higher level and semantically important relations contributing to disease diagnosis. Furthermore, it employs a joint embedding framework to tailor clinical event representations to the disease diagnosis goal. To the best of our knowledge, this is the first study to use Heterogeneous Information Network for modeling clinical data and disease diagnosis. Experimental results of our study show superior performance of HeteroMed compared to prior methods in prediction of exact diagnosis codes and general disease cohorts. Moreover, HeteroMed outperforms baseline models in capturing similarities of clinical events which are examined qualitatively through case studies.
Deep Web Content Mining

The rapid expansion of the web is causing the constant growth of information, leading to several problems such as increased difficulty of extracting potentially useful knowledge. Web content mining confronts this problem gathering explicit information from different web sites for its access and knowledge discovery. Query interfaces of web databases share common building blocks. After extracting information with parsing approach, we use a new data mining algorithm to match a large number of schemas in databases at a time. Using this algorithm increases the speed of information matching. In addition, instead of simple 1:1 matching, they do complex (m:n) matching between query interfaces. In this paper we present a novel correlation mining algorithm that matches correlated attributes with smaller cost. This algorithm uses Jaccard measure to distinguish positive and negative correlated attributes. After that, system matches the user query with different query interfaces in special domain and finally chooses the nearest query interface with user query to answer to it. Content mining, complex matching, correlation mining, information extraction.
Video games as a multifaceted medium: a review of quantitative social science research on video games and a typology of video game research approaches

• Much quantitative social science research has explored video games’ social impact using widely varied methods and approaches. • As light is sometimes studied as a wave and sometimes as a particle, video game research has used many perspectives. • It is difficult to compare some game research because studies often examine one social dimension of games while ignoring others. • Researchers exploring different video game dimensions are sometimes like the Indian parable of the blind men and the elephant. • A typology of social science research approaches to video games will aid comparison, synthesis, and expansion of research. • This review of video game research approaches identifies four distinct perspectives used in much video game research. • The “video games as stimulus” perspective includes research focused on effects of game content and features on users. • The “video games as avocation” perspective includes research focused on users of video games and their commitment to the medium. • The “video games as skill” perspective includes research focused on video games as a tool for developing skills and abilities. • The “video games as social environment” perspective includes research focused on social interaction between game users.
A tablet-based paper exam grading system

We present the design and implementation of a system which allows a standard paper-based exam to be graded via tablet computers. The paper exam is given normally in a course, with a specialized footer that allows for automated recognition of each exam page. The exam pages are then scanned in via a high-speed scanner, graded by one or more people using tablet computers, and returned electronically to the students. The system provides many advantages over regular paper-based exam grading, and boasts a faster grading experience than traditional grading methods.
A 2.4-GHz CMOS Class-E Synchronous Rectifier

A class-E synchronous rectifier has been designed and implemented using 0.13-μm CMOS technology. A design methodology based on the theory of time-reversal duality has been used where a class-E amplifier circuit is transformed into a class-E rectifier circuit. The methodology is distinctly different from other CMOS RF rectifier designs which use voltage multiplier techniques. Power losses in the rectifier are analyzed including saturation resistance in the switch, inductor losses, and current/voltage overlap losses. The rectifier circuit includes a 50-Ω single-ended RF input port with on-chip matching. The circuit is self-biased and completely powered from the RF input signal. Experimental results for the rectifier show a peak RF-to-dc conversion efficiency of 30% measured at a frequency of 2.4 GHz.
Chatbot Evaluation and Database Expansion via Crowdsourcing

Chatbots use a database of responses often culled from a corpus of text generated for a different purpose, for example film scripts or interviews. One consequence of this approach is a mismatch between the data and the inputs generated by participants. We describe an approach that while starting from an existing corpus (of interviews) makes use of crowdsourced data to augment the response database, focusing on responses that people judge as inappropriate. The long term goal is to create a data set of more appropriate chat responses; the short term consequence appears to be the identification and replacement of particularly inappropriate responses. We found the version with the expanded database was rated significantly better in terms of the response level appropriateness and the overall ability to engage users. We also describe strategies we developed that target certain breakdowns discovered during data collection. Both the source code of the chatbot, TickTock, and the data collected are publicly available.
Graph-Based Methods for Multi-document Summarization: Exploring Relationship Maps, Complex Networks and Discourse Information

In this work we investigate the use of graphs for multi-document summarization. We adapt the traditional Relationship Map approach to the multidocument scenario and, in a hybrid approach, we consider adding CST (Crossdocument Structure Theory) relations to this adapted model. We also investigate some measures derived from graphs and complex networks for sentence selection. We show that the superficial graph-based methods are promising for the task. More importantly, some of them perform almost as good as a deep approach.
Augmented Reality Trends in Education: A Systematic Review of Research and Applications

In recent years, there has been an increasing interest in applying Augmented Reality (AR) to create unique educational settings. So far, however, there is a lack of review studies with focus on investigating factors such as: the uses, advantages, limitations, effectiveness, challenges and features of augmented reality in educational settings. Personalization for promoting an inclusive learning using AR is also a growing area of interest. This paper reports a systematic review of literature on augmented reality in educational settings considering the factors mentioned before. In total, 32 studies published between 2003 and 2013 in 6 indexed journals were analyzed. The main findings from this review provide the current state of the art on research in AR in education. Furthermore, the paper discusses trends and the vision towards the future and opportunities for further research in augmented reality for educational settings.
Secondary-structure matching (SSM), a new tool for fast protein structure alignment in three dimensions.

The present paper describes the SSM algorithm of protein structure comparison in three dimensions, which includes an original procedure of matching graphs built on the protein's secondary-structure elements, followed by an iterative three-dimensional alignment of protein backbone Calpha atoms. The SSM results are compared with those obtained from other protein comparison servers, and the advantages and disadvantages of different scores that are used for structure recognition are discussed. A new score, balancing the r.m.s.d. and alignment length Nalign, is proposed. It is found that different servers agree reasonably well on the new score, while showing considerable differences in r.m.s.d. and Nalign.
Stock chatter: Using stock sentiment to predict price direction

This paper examines a popular stock message board and finds slight daily predictability using supervised learning algorithms when combining daily sentiment with historical price information. Additionally, with the profit potential in trading stocks, it is of no surprise that a number of popular financial websites are attempting to capture investor sentiment by providing an aggregate of this negative and positive online emotion. We question if the existence of dishonest posters are capitalizing on the popularity of the boards by writing sentiment in line with their trading goals as a means of influencing others, and therefore undermining the purpose of the boards. We exclude these posters to determine if predictability increases, but find no discernible difference.
Learning object identification rules for information integration

When integrating information from multiple websites, the same data objects can exist in inconsistent text formats across sites, making it difficult to identify matching objects using exact text match. We have developed an object identification system called Active Atlas, which compares the objects’ shared attributes in order to identify matching objects. Certain attributes are more important for deciding if a mapping should exist between two objects. Previous methods of object identification have required manual construction of object identification rules or mapping rules for determining the mappings between objects. This manual process is time consuming and error-prone. In our approach, Active Atlas learns to tailor mapping rules, through limited user input, to a specific application domain. The experimental results demonstrate that we achieve higher accuracy and require less user involvement than previous methods across various application domains.
Goal theory, motivation, and school achievement: an integrative review.

The purpose of this review is to document the directions and recent progress in our understanding of the motivational dynamics of school achievement. Based on the accumulating research it is concluded that the quality of student learning as well as the will to continue learning depends closely on an interaction between the kinds of social and academic goals students bring to the classroom, the motivating properties of these goals and prevailing classroom reward structures. Implications for school reform that follow uniquely from a motivational and goal-theory perspective are also explored.
Visual Thinking In Action: Visualizations As Used On Whiteboards

While it is still most common for information visualization researchers to develop new visualizations from a data-or taskdriven perspective, there is growing interest in understanding the types of visualizations people create by themselves for personal use. As part of this recent direction, we have studied a large collection of whiteboards in a research institution, where people make active use of combinations of words, diagrams and various types of visuals to help them further their thought processes. Our goal is to arrive at a better understanding of the nature of visuals that are created spontaneously during brainstorming, thinking, communicating, and general problem solving on whiteboards. We use the qualitative approaches of open coding, interviewing, and affinity diagramming to explore the use of recognizable and novel visuals, and the interplay between visualization and diagrammatic elements with words, numbers and labels. We discuss the potential implications of our findings on information visualization design.
Ray Marching Distance Fields in Real-time on WebGL

Ray marching, also known as sphere tracing, is an efficient empirical method for rendering implicit surfaces using distance fields. The method marches along the ray with step lengths, provided by the distance field, that are guaranteed not to penetrate the scene. As a result, it provides an efficient method of rendering implicit surfaces, such as constructive solid geometry, recursive shapes, and fractals, as well as producing cheap empirical visual effects, such as ambient occlusion, subsurface scattering, and soft shadows. The goal of this project is to bring interactive ray marching to the web platform. The project will focus on the robustness of the render itself. It should run with reasonable performance in real-time and provide an interface where the user can interactively change the viewing angle and modify rendering options. It is also expected to run on the latest WebGL supported browser, on any machine. CR Categories: I.3.3 [Computer Graphics]: Three-Dimensional Graphics and Realism—Display Algorithms
Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples

Sometimes it is not enough for a DNN to produce an outcome. For example, in applications such as healthcare, users need to understand the rationale of the decisions. Therefore, it is imperative to develop algorithms to learn models with good interpretability (Doshi-Velez 2017). An important factor that leads to the lack of interpretability of DNNs is the ambiguity of neurons, where a neuron may fire for various unrelated concepts. This work aims to increase the interpretability of DNNs on the whole image space by reducing the ambiguity of neurons. In this paper, we make the following contributions:
Communication network architecture and design principles for smart grids

An integrated high performance, highly reliable, scalable, and secure communications network is critical for the successful deployment and operation of next-generation electricity generation, transmission, and distribution systems — known as “smart grids.” Much of the work done to date to define a smart grid communications architecture has focused on high-level service requirements with little attention to implementation challenges. This paper investigates in detail a smart grid communication network architecture that supports today's grid applications (such as supervisory control and data acquisition [SCADA], mobile workforce communication, and other voice and data communication) and new applications necessitated by the introduction of smart metering and home area networking, support of demand response applications, and incorporation of renewable energy sources in the grid. We present design principles for satisfying the diverse quality of service (QoS) and reliability requirements of smart grids.
Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications

In this paper, the problem of joint power and resource allocation for ultra reliable low latency communication (URLLC) in vehicular networks is studied. The key goal is to minimize the networkwide power consumption of vehicular users (VUEs) subject to high reliability in terms of probabilistic queuing delays. In particular, using extreme value theory (EVT), a new reliability measure is defined to characterize extreme events pertaining to vehicles’ queue lengths exceeding a predefined threshold with non-negligible probability. In order to learn these extreme events in a dynamic vehicular network, a novel distributed approach based on federated learning (FL) is proposed to estimate the tail distribution of the queues. Taking into account the communication delays incurred by FL over wireless links, Lyapunov optimization is used to derive the joint transmit power and resource allocation policies enabling URLLC for each VUE in a distributed manner. The proposed solution is then validated via extensive simulations using a Manhattan mobility model. Simulation results show that FL enables the proposed distributed method to estimate the tail distribution of queues with an accuracy that is very close to a centralized solution with up to 79% reductions in the amount of data that need to be exchanged. Furthermore, the proposed method yields up to 60% reductions of VUEs with large queue lengths, while reducing the average power consumption by two folds, compared to an average queue-based baseline. For the VUEs with large queue lengths, the proposed method reduces their average queue lengths and fluctuations therein by about 30% compared to the aforementioned baseline. ar X iv :1 80 7. 08 12 7v 1 [ cs .I T ] 2 1 Ju l 2 01 8
Design of an arm exoskeleton with scapula motion for shoulder rehabilitation

The evolution of an arm exoskeleton design for treating shoulder pathology is examined. Tradeoffs between various kinematics configurations are explored, and a device with five active degrees of freedom is proposed. Two rapid-prototype designs were built and fitted to several subjects to verify the kinematic design and determine passive link adjustments. Control modes are developed for exercise therapy and functional rehabilitation, and a distributed software architecture that incorporates computer safety monitoring is described. Although intended primarily for therapy, the exoskeleton is also used to monitor progress in strength, range of motion, and functional task performance
Multi Bit Plane Image Steganography

This paper addresses a novel steganography method for images. Most statistical steganalysis algorithms are strong to defeat previous steganography algorithms. RS steganalysis and pixel difference histogram analysis are two well-known statistical steganalysis algorithms which detect non-random changes caused by embedding a secret message into cover image. In this paper, we first explain how two steganalysis algorithms exploit the effect of the non-random changes and then propose a new steganography method that avoids the non-random changes to evade statistical analysis methods. For this purpose, we adjust the embedding process to be more adaptive to cover image by considering embedding in Gray code bit planes, not natural binary bit planes, of cover images, and two parameters: (1) similarity threshold for selecting non-flat area in lower bit planes, and (2) size of flat blocks n × n in embedding bit planes. Experimental results show that the secret messages embedded by our method are undetectable under RS steganalysis and pixel difference histogram analysis.
Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems

This paper studies gradient-based schemes for image denoising and deblurring problems based on the discretized total variation (TV) minimization model with constraints. We derive a fast algorithm for the constrained TV-based image deburring problem. To achieve this task, we combine an acceleration of the well known dual approach to the denoising problem with a novel monotone version of a fast iterative shrinkage/thresholding algorithm (FISTA) we have recently introduced. The resulting gradient-based algorithm shares a remarkable simplicity together with a proven global rate of convergence which is significantly better than currently known gradient projections-based methods. Our results are applicable to both the anisotropic and isotropic discretized TV functionals. Initial numerical results demonstrate the viability and efficiency of the proposed algorithms on image deblurring problems with box constraints.