nils-herrmann/clean_scirepeval huggingface dataset:
Event Detection and Retrieval on Social Media

In the recent years, we have witnessed the rapid adoption of social media platforms, such as Twitter, Facebook and YouTube, and their use as part of the everyday life of billions of people worldwide. Given the habit of people to use these platforms to share thoughts, daily activities and experiences it is not surprising that the amount of user generated content has reached unprecedented levels, with a substantial part of that content being related to real-world events, i.e. actions or occurrences taking place at a certain time and location.  1 illustrates three main categories of events along with characteristic photos from Flickr for each of them: a) news-related events, e.g. demonstrations, riots, public speeches, natural disasters, terrorist attacks, b) entertainment events, e.g. sports, music, live shows, exhibitions, festivals, and c) personal events, e.g. wedding, birthday, graduation ceremonies, vacations, and going out. Depending on the event, different types of multimedia and social media platform are more popular. For instance, news-related events are extensively published in the form of text updates, images and videos on Twitter and YouTube, entertainment and social events are often captured in the form of images and videos and shared on Flickr and YouTube, while personal events are mostly represented by images that are shared on Facebook and Instagram. Given the key role of events in our life, the task of annotating and organizing social media content around them is of crucial importance for ensuring real-time and future access to multimedia content about an event of interest. However, the vast amount of noisy and non-informative social media posts, in conjunction with their large scale, makes that task very challenging. For instance, in the case of popular events that are covered live on Twitter, there are often millions of posts referring to a single event, as in the case of the World Cup Final 2014 between Brazil and Germany, which produced approximately 32.1 million tweets with a rate of 618,725 tweets per minute. Processing, aggregating and selecting the most informative, entertaining and representative tweets among such a large dataset is a very challenging multimedia retrieval problem.
Gain correction and phase compensation of a smart electrical energy meter

We present in this paper a calibration program that controls a calibration board integrated in a Smart Electrical Energy Meter (SEEM). The “SEEM” allows to measure the energy from a single phase line and transmits the value of this energy to a central through a wireless network. The “SEEM” needs to be calibrated in only one point of load to correct the gain and compensate the phase added by the system of measure. Since the calibration is performed for one point of load, this reduces the material used, therefore reduces the cost. Furthermore, the calibration of gain and phase is performed simultaneously which decrease the time of this operation.
Using News Articles to Predict Stock Price Movements

This paper shows that short-term stock price movements can be predicted using financial news articles. Given a stock price time series, for each time interval we classify price movement as "up," "down," or (approximately) "unchanged" relative to the volatility of the stock and the change in a relevant index. Each article in a training set of news articles is then labeled "up," "down," or "unchanged" according to the movement of the associated stock in a time interval surrounding publication of the article. A naïve Bayesian text classifier is trained to predict which movement class an article belongs to. Given a test article, the trained classifier potentially predicts the price movement of the associated stock. However, the efficient markets hypothesis asserts that this classifier cannot have predictive power. In careful experiments we find definite predictive power for the stock price movement in the interval starting 20 minutes before and ending 20 minutes after news articles become publicly available.
SFCM: Learn a Pooling Kernel for Weakly Supervised Object Localization

The weakly supervised object localization (WSOL) is to locate the objects in an image while only image-level labels are available during the training procedure. In this work, the Selective Feature Category Mapping (SFCM) method is proposed, which introduces the Feature Category Mapping (FCM) and the widely-used selective search method to solve the WSOL task. Our FCM replaces layers after the specific layer in the state-of-the-art CNNs with a set of kernels and learns the weighted pooling for previous feature maps. It is trained with only image-level labels and then map the feature maps to their corresponding categories in the test phase. Together with selective search method, the location of each object is finally obtained. Extensive experimental evaluation on ILSVRC2012 and PASCAL VOC2007 benchmarks shows that SFCM is simple but very effective, and it is able to achieve outstanding classification performance and outperform the state-of-the-art methods in the WSOL task.
Cartograph: Unlocking Spatial Visualization Through Semantic Enhancement

This paper introduces Cartograph, a visualization system that harnesses the vast amount of world knowledge encoded within Wikipedia to create thematic maps of almost any data. Cartograph extends previous systems that visualize non-spatial data using geographic approaches. While these systems required data with an existing semantic structure, Cartograph unlocks spatial visualization for a much larger variety of datasets by enhancing input datasets with semantic information extracted from Wikipedia. Cartograph's map embeddings use neural networks trained on Wikipedia article content and user navigation behavior. Using these embeddings, the system can reveal connections between points that are unrelated in the original data sets, but are related in meaning and therefore embedded close together on the map. We describe the design of the system and key challenges we encountered, and we present findings from an exploratory user study
Fast Cost-Volume Filtering for Visual Correspondence and Beyond

Many computer vision tasks can be formulated as labeling problems. The desired solution is often a spatially smooth labeling where label transitions are aligned with color edges of the input image. We show that such solutions can be efficiently achieved by smoothing the label costs with a very fast edge-preserving filter. In this paper, we propose a generic and simple framework comprising three steps: 1) constructing a cost volume, 2) fast cost volume filtering, and 3) Winner-Takes-All label selection. Our main contribution is to show that with such a simple framework state-of-the-art results can be achieved for several computer vision applications. In particular, we achieve 1) disparity maps in real time whose quality exceeds those of all other fast (local) approaches on the Middlebury stereo benchmark, and 2) optical flow fields which contain very fine structures as well as large displacements. To demonstrate robustness, the few parameters of our framework are set to nearly identical values for both applications. Also, competitive results for interactive image segmentation are presented. With this work, we hope to inspire other researchers to leverage this framework to other application areas.
A comparative study of curvature scale space and Fourier descriptors for shape-based image retrieval

Contour shape descriptors are among the important shape description methods. Fourier descriptors (FD) and curvature scale space descriptors (CSSD) are widely used as contour shape descriptors for image retrieval in the literature. In MPEG-7, CSSD has been proposed as one of the contour-based shape descriptors. However, no comprehensive comparison has been made between these two shape descriptors. In this paper we study and compare FD and CSSD using standard principles and standard database. The study targets image retrieval application. Our experimental results show that FD outperforms CSSD in terms of robustness, low computation, hierarchical representation, retrieval performance and suitability for efficient indexing.
Control of an LLC Resonant Converter Using Load Feedback Linearization

LLC resonant converter is a nonlinear system, limiting the use of typical linear control methods. This paper proposed a new nonlinear control strategy, using load feedback linearization for an LLC resonant converter. Compared with the conventional PI controllers, the proposed feedback linearized control strategy can achieve better performance with elimination of the nonlinear characteristics. The LLC resonant converter's dynamic model is built based on fundamental harmonic approximation using extended describing function. By assuming the dynamics of resonant network is much faster than the output voltage and controller, the LLC resonant converter's model is simplified from seven-order state equations to two-order ones. Then, the feedback linearized control strategy is presented. A double loop PI controller is designed to regulate the modulation voltage. The switching frequency can be calculated as a function of the load, input voltage, and modulation voltage. Finally, a 200 W laboratory prototype is built to verify the proposed control scheme. The settling time of the LLC resonant converter is reduced from 38.8 to 20.4 ms under the positive load step using the proposed controller. Experimental results prove the superiority of the proposed feedback linearized controller over the conventional PI controller.
Wide band open ended air gap RLSA antenna at 5.8GHz frequency band

A prototype of Air Gap RLSA has been designed and simulated using hybrid air gap and FR4 dielectric material. The 28% wide bandwidth has been recorded through this approach. A 12.35dBi directive gain also recorded from the simulation. The 13.3 degree beamwidth of the radiation pattern is sufficient for high directional application. Since the proposed application was for Point to Point Link, this study concluded the Air Gap RLSA is a new candidate for this application.
Control strategies in object manipulation tasks

The remarkable manipulative skill of the human hand is not the result of rapid sensorimotor processes, nor of fast or powerful effector mechanisms. Rather, the secret lies in the way manual tasks are organized and controlled by the nervous system. At the heart of this organization is prediction. Successful manipulation requires the ability both to predict the motor commands required to grasp, lift, and move objects and to predict the sensory events that arise as a consequence of these commands.
Evolution of a subsumption architecture that performs a wall following task for an autonomous mobile robot

The goal in automatic programming is to get a computer to perform a task by telling it what needs to be done, rather than by explicitly programming it. This paper considers the task of automatically generating a computer program to enable an autonomous mobile robot to perform the task of following the wall of an irregular shaped room. A human programmer has written such a program in the style of the subsumption architecture. The solution produced by genetic programming emerges as a result of Darwinian natural selection and genetic crossover (sexual recombination) in a population of computer programs. This evolutionary process is driven by a fitness measure which communicates the nature of the task to the computer.
Biochemical Modulation of NMDA Receptors: Role in Conditioned Taste Aversion

Glutamate neurotransmission plays a crucial role in a variety of functions in the central nervous system, including learning and memory. However, little is known about the mechanisms underlying this process in mammals because of the scarceness of experimental models that permit correlation of behavioral and biochemical changes occurring during the different stages of learning and the retrieval of the acquired information. One model that has been useful to study these mechanisms is conditioned taste aversion (CTA), a paradigm in which animals learn to avoid new tastes when they are associated with gastrointestinal malaise. Glutamate receptors of the N-methyl-D-aspartate (NMDA) type appear to be necessary in this process, because blockade of this receptor prevents CTA. Phosphorylation of the main subunits of the NMDA receptor is a well-established biochemical mechanism for the modulation of the receptor response. Such modulation seems to be involved in CTA, because inhibitors of protein kinase C (PKC) block CTA acquisition and because the exposure to an unfamiliar taste results in an increased phosphorylation of tyrosine and serine residues of the NR2B subunit of the receptor in the insular cortex, the cerebral region where gustatory and visceral information converge. In this work we review these mechanisms of NMDA receptor modulation in CTA.
Auctioning over Probabilistic Options for Temporal Logic-Based Multi-Robot Cooperation Under Uncertainty

Coordinating a team of robots to fulfill a common task is still a demanding problem. This is even more the case when considering uncertainty in the environment, as well as temporal dependencies within the task specification. A multi-robot cooperation from a single goal specification requires mechanisms for decomposing the goal as well as an efficient planning for the team. However, planning action sequences offline is insufficient in real world applications. Rather, due to uncertainties, the robots also need to closely coordinate during execution and adjust their policies when additional observations are made. The framework presented in this paper enables the robot team to cooperatively fulfill tasks given as temporal logic specifications while explicitly considering uncertainty and incorporating observations during execution. We present the effectiveness of our ROS implementation of this approach in a case study scenario.
Mitochondrial dysfunction and type 2 diabetes.

Insulin resistance plays a major role in the pathogenesis of the metabolic syndrome and type 2 diabetes, and yet the mechanisms responsible for it remain poorly understood. Magnetic resonance spectroscopy studies in humans suggest that a defect in insulin-stimulated glucose transport in skeletal muscle is the primary metabolic abnormality in insulin-resistant patients with type 2 diabetes. Fatty acids appear to cause this defect in glucose transport by inhibiting insulin-stimulated tyrosine phosphorylation of insulin receptor substrate-1 (IRS-1) and IRS-1-associated phosphatidylinositol 3-kinase activity. A number of different metabolic abnormalities may increase intramyocellular and intrahepatic fatty acid metabolites; these include increased fat delivery to muscle and liver as a consequence of either excess energy intake or defects in adipocyte fat metabolism, and acquired or inherited defects in mitochondrial fatty acid oxidation. Understanding the molecular and biochemical defects responsible for insulin resistance is beginning to unveil novel therapeutic targets for the treatment of the metabolic syndrome and type 2 diabetes.
Value-centric design of the internet-of-things solution for food supply chain: Value creation, sensor portfolio and information fusion

The revolution of Internet-of-Things (IoT) is reshaping the modern food supply chains with promising business prospects. To be successful in practice, the IoT solutions should create “income-centric” values beyond the conventional “traceability-centric” values. To accomplish what we promised to users, sensor portfolios and information fusion must correspond to the new requirements introduced by this income-centric value creation. In this paper, we propose a value-centric business-technology joint design framework. Based on it the income-centric added-values including shelf life prediction, sales premium, precision agriculture, and reduction of assurance cost are identified and assessed. Then corresponding sensor portfolios are developed and implemented. Three-tier information fusion architecture is proposed as well as examples about acceleration data processing, self-learning shelf life prediction and real-time supply chain re-planning. The feasibilities of the proposed design framework and solution have been confirmed by the field trials and an implemented prototype system.
To what extent does the longevity of fixed dental prostheses depend on the function of the cement? Working Group 4 materials: cementation.

The objective of this review was to define the impact of cementation mode on the longevity of different types of single tooth restorations and fixed dental prostheses (FDP).



Literature search by PubMed as the major database was used utilizing the terms namely, adhesive techniques, all-ceramic crowns, cast-metal, cement, cementation, ceramic inlays, gold inlays, metal-ceramic, non-bonded fixed-partial-dentures, porcelain veneers, resin-bonded fixed-partial-dentures, porcelain-fused-to-metal, and implant-supported-restorations together with manual search of non-indexed literature. Cementation of root canal posts and cores were excluded. Due to lack of randomized prospective clinical studies in some fields of cementation, recommendations had to be based on lower evidence level (Centre of Evidence Based Medicine, Oxford) for special applications of current cements.



One-hundred-and-twenty-five articles were selected for the review. The primary function of the cementation is to establish reliable retention, a durable seal of the space between the tooth and the restoration, and to provide adequate optical properties. The various types of cements used in dentistry could be mainly divided into two groups: Water-based cements and polymerizing cements. Water-based cements exhibited satisfying long-term clinical performance associated with cast metal (inlays, onlays, partial crowns) as well as single unit metal-ceramic FDPs and multiple unit FDPs with macroretentive preparation designs and adequate marginal fit. Early short-term clinical results with high-strength all-ceramic restorations luted with water-based cements are also promising. Current polymerizing cements cover almost all fields of water-based cements and in addition to that they are mainly indicated for non-retentive restorations. They are able to seal the tooth completely creating hybrid layer formation. Furthermore, adhesive capabilities of polymerizing cements allowed for bonded restorations, promoting at the same time the preservation of dental tissues.
Mobile Government Applications: Challenges and Needs for a Comprehensive Design Approach

The ubiquity of mobile devices has drawn new attention to the field of electronic government. Literature studies report on the significance of m-government, including its motivation, success, and failure in developed and developing countries. However, research on the design of m-government applications is still scarce. Design approaches in the literature lack a comprehensive way of addressing m-government challenges. This paper aims to (1) identify challenges of m-government in developed and developing countries and (2) investigate approaches used for designing m-government applications. The challenges are categorised based on the factors of PESTELMO and are further examined to identify requirements for suitable m-government design. Design approaches are analysed by the Content, Context and Process (CCP) framework and are examined to identify requirements, methods and guidelines addressed. The paper finally outlines research needs for a comprehensive design framework for m-government solutions and presents initial requirements for the framework.
Design of double L-slot microstrip patch antenna array for WiMAX and WLAN application using ceramic substrate

In this paper, a double L-slot microstrip patch antenna array using Coplanar waveguide feed for Wireless Local Area Network (WLAN) and Worldwide Interoperability for Microwave Access (WiMAX) frequency bands are presented. The proposed antenna is fabricated on Aluminum Nitride Ceramic substrate with dielectric constant 8.8 and thickness of 1.5mm. The key feature of this substrate is that it can withstand in high temperature. The return loss is about -31dB at the operating frequency of 3.6GHz with 50Ω input impedance. The basic parameters of the proposed antenna such as return loss, VSWR, and radiation pattern are simulated using Ansoft HFSS. Simulation results of antenna parameters of single patch and double patch antenna array are analyzed and presented.
Mammogram of Breast Cancer detection Based using Image Enhancement Algorithm

Principle objective of Image enhancement is to process an image so that result is more suitable than original image for specific application. Digital image enhancement techniques provide a multitude of choices for improving the visual quality of images. A frequency domain smoothingsharpening technique is proposed and its impact is assessed to beneficially enhance mammogram images. This technique aims to gain the advantages of enhance and sharpening process that aims to highlight sudden changes in the image intensity, it is usually applied to remove random noise from digital images. The already developed technique also eliminates the drawbacks of each of the two sharpening and smoothing techniques resulting from their individual application in image processing field. The selection of parameters is almost invariant of the type of background tissues and severity of the abnormality, giving significantly improved results even for denser mammographic images. The proposed technique is tested breast X-ray mammograms. The simulated results show that the high potential to advantageously enhance the image contrast hence giving extra aid to radiologists to detect and classify mammograms of breast cancer.  Fourier transform, Gabor filter, Image, enhancement, Mammograms,
SINGLE PRECISION FLOATING POINT

Binary Division is one of the most crucial and silicon-intensive and of immense importance in the field of hardware implementation. A Divider is one of the key hardware blocks in most of applications such as digital signal processing, encryption and decryption algorithms in cryptography and in other logical computations. Being sequential type of operation, it is more prominent in terms of computational complexity and latency. This paper deals with the novel division algorithm for single precision floating point division Verilog Code is written and implemented on Virtex-5 FPGA series. Power dissipation has been reduced. Moreover, significant improvement has been observed in terms of area-utilisation and latency bounds. KeywordsSingle precision, Binary Division, Long Division, Vedic, Virtex, FPGA, IEEE-754.
A Survey on Multiple-Antenna Techniques for Physical Layer Security

As a complement to high-layer encryption techniques, physical layer security has been widely recognized as a promising way to enhance wireless security by exploiting the characteristics of wireless channels, including fading, noise, and interference. In order to enhance the received signal power at legitimate receivers and impair the received signal quality at eavesdroppers simultaneously, multiple-antenna techniques have been proposed for physical layer security to improve secrecy performance via exploiting spatial degrees of freedom. This paper provides a comprehensive survey on various multiple-antenna techniques in physical layer security, with an emphasis on transmit beamforming designs for multiple-antenna nodes. Specifically, we provide a detailed investigation on multiple-antenna techniques for guaranteeing secure communications in point-to-point systems, dual-hop relaying systems, multiuser systems, and heterogeneous networks. Finally, future research directions and challenges are identified.