nils-herrmann/clean_scirepeval huggingface dataset:
Facilitating Document Reading by Linking Text and Tables

Document authors commonly use tables to support arguments presented in the text. But, because tables are usually separate from the main body text, readers must split their attention between different parts of the document. We present an interactive document reader that automatically links document text with corresponding table cells. Readers can select a sentence (or tables cells) and our reader highlights the relevant table cells (or sentences). We provide an automatic pipeline for extracting such references between sentence text and table cells for existing PDF documents that combines structural analysis of tables with natural language processing and rule-based matching. On a test corpus of 330 (sentence, table) pairs, our pipeline correctly extracts 48.8% of the references. An additional 30.5% contain only false negatives (FN) errors -- the reference is missing table cells. The remaining 20.7% contain false positives (FP) errors -- the reference includes extraneous table cells and could therefore mislead readers. A user study finds that despite such errors, our interactive document reader helps readers match sentences with corresponding table cells more accurately and quickly than a baseline document reader.
VisTA: Visual Terminology Alignment Tool for Factual Knowledge Aggregation

The alignment of terminologies can be considered as a kind of “translation” between two or more terminologies that aims to enhance the communication among people coming from different domains or expertise. In this paper we introduce the Visual Terminology Alignment tool (VisTA) that enables the exact alignment for RDF/SKOS-like terminologies, suitable for integrating knowledge bases, rather than information retrieval systems. The tool provides a simple and friendly web-based user interface for the alignment between two terminologies, while it visualizes the terminology hierarchies, enables the interactive alignment process, and presents the alignment result. The latter is a native RDF/SKOS graph that interconnects the two terminology graphs, supporting interoperability and extending the search capabilities over the integrated semantic graph, using the broader and exact match properties.
BAP: A Binary Analysis Platform

BAP is a publicly available infrastructure for performing program verification and analysis tasks on binary (i.e., executable) code. In this paper, we describe BAP as well as lessons learned from previous incarnations of binary analysis platforms. BAP explicitly represents all side effects of instructions in an intermediate language (IL), making syntaxdirected analysis possible. We have used BAP to routinely generate and solve verification conditions that are hundreds of megabytes in size and encompass 100,000’s of assembly instructions.
Topic Modeling using Variational Auto-Encoders with Gumbel-Softmax and Logistic-Normal Mixture Distributions

Probabilistic Topic Models are widely applied in many NLP-related tasks due to their effective use of unlabeled data to capture variable dependencies. Analytical solutions for Bayesian inference of such models, however, are usually intractable, hindering the proposition of highly expressive text models. In this scenario, Variational Auto-Encoders (VAEs), where an inference network (the encoder) is used to approximate the posterior distribution, became a promising alternative for inferring latent topic distributions of text documents. These models, however, also pose new challenges such as the requirement of continuous and reparameterizable distributions which may not fit so well the true latent topic distributions. Moreover, inference networks are prone to component collapsing, impairing the collection of coherent topics. To overcome these problems, we propose two new text topic models based on the categorical distribution Gumbel-Softmax (GSDTM) and on mixtures of Logistic-Normal distributions (LMDTM). We also provide a study on the impact of different modeling choices on the generated topics, observing a trade-off between topic coherence and document reconstruction. Through experiments using two reference datasets, we show that GSDTM largely outperforms previous state-of-the-art baselines when considering three different evaluation metrics.
Age Progression/Regression by Conditional Adversarial Autoencoder

If I provide you a face image of mine (without telling you the actual age when I took the picture) and a large amount of face images that I crawled (containing labeled faces of different ages but not necessarily paired), can you show me what I would look like when I am 80 or what I was like when I was 5? The answer is probably a No. Most existing face aging works attempt to learn the transformation between age groups and thus would require the paired samples as well as the labeled query image. In this paper, we look at the problem from a generative modeling perspective such that no paired samples is required. In addition, given an unlabeled image, the generative model can directly produce the image with desired age attribute. We propose a conditional adversarial autoencoder (CAAE) that learns a face manifold, traversing on which smooth age progression and regression can be realized simultaneously. In CAAE, the face is first mapped to a latent vector through a convolutional encoder, and then the vector is projected to the face manifold conditional on age through a deconvolutional generator. The latent vector preserves personalized face features (i.e., personality) and the age condition controls progression vs. regression. Two adversarial networks are imposed on the encoder and generator, respectively, forcing to generate more photo-realistic faces. Experimental results demonstrate the appealing performance and flexibility of the proposed framework by comparing with the state-of-the-art and ground truth.
Brain-computer interfaces: communication and restoration of movement in paralysis.

The review describes the status of brain-computer or brain-machine interface research. We focus on non-invasive brain-computer interfaces (BCIs) and their clinical utility for direct brain communication in paralysis and motor restoration in stroke. A large gap between the promises of invasive animal and human BCI preparations and the clinical reality characterizes the literature: while intact monkeys learn to execute more or less complex upper limb movements with spike patterns from motor brain regions alone without concomitant peripheral motor activity usually after extensive training, clinical applications in human diseases such as amyotrophic lateral sclerosis and paralysis from stroke or spinal cord lesions show only limited success, with the exception of verbal communication in paralysed and locked-in patients. BCIs based on electroencephalographic potentials or oscillations are ready to undergo large clinical studies and commercial production as an adjunct or a major assisted communication device for paralysed and locked-in patients. However, attempts to train completely locked-in patients with BCI communication after entering the complete locked-in state with no remaining eye movement failed. We propose that a lack of contingencies between goal directed thoughts and intentions may be at the heart of this problem. Experiments with chronically curarized rats support our hypothesis; operant conditioning and voluntary control of autonomic physiological functions turned out to be impossible in this preparation. In addition to assisted communication, BCIs consisting of operant learning of EEG slow cortical potentials and sensorimotor rhythm were demonstrated to be successful in drug resistant focal epilepsy and attention deficit disorder. First studies of non-invasive BCIs using sensorimotor rhythm of the EEG and MEG in restoration of paralysed hand movements in chronic stroke and single cases of high spinal cord lesions show some promise, but need extensive evaluation in well-controlled experiments. Invasive BMIs based on neuronal spike patterns, local field potentials or electrocorticogram may constitute the strategy of choice in severe cases of stroke and spinal cord paralysis. Future directions of BCI research should include the regulation of brain metabolism and blood flow and electrical and magnetic stimulation of the human brain (invasive and non-invasive). A series of studies using BOLD response regulation with functional magnetic resonance imaging (fMRI) and near infrared spectroscopy demonstrated a tight correlation between voluntary changes in brain metabolism and behaviour.
Droop control in LV-grids

Remote electrification with island supply systems, the increasing acceptance of the microgrids concept and the penetration of the interconnected grid with DER and RES require the application of inverters and the development of new control algorithms. One promising approach is the implementation of conventional f/U-droops into the respective inverters, thus down scaling the conventional grid control concept to the low voltage grid. Despite contradicting line parameters, the applicability of this proceeding is outlined and the boundary conditions are derived
A Single-Stage Single-Switch Soft-Switching Power-Factor-Correction LED Driver

This paper proposes a new isolated single-stage single-switch soft-switching power-factor-correction (S6-PFC) driver without electrolytic capacitors for supplying light-emitting diodes (LEDs). In the proposed LED driver, all the semiconductor devices are soft-switched without employing any extra active switch. The leakage inductance of transformer acts as a resonant component so that the leakage energy is recycled. Soft-switching operation of the semiconductor devices together with absorption of the leakage energy improve the proposed driver efficiency. Furthermore, input current of the driver has very low harmonics resulting in high power-factor. Operating principles of the proposed S6-PFC LED driver as well as its design guidelines are presented. Also, experimental results of a laboratory prototype for supplying a 21 W/30 V LED module from 220 V/50 Hz ac mains are provided to verify the theoretical analysis. The presented results show that the implemented prototype has an efficiency of 92% under full-load condition and its input current total harmonic distortion is as low as 2.6%.
ANN Based Currency Recognition System using Compressed Gray Scale and Application for Sri Lankan Currency Notes - SLCRec

Automatic currency note recognition invariably depends on the currency note characteristics of a particular country and the extraction of features directly affects the recognition ability. Sri Lanka has not been involved in any kind of research or implementation of this kind. The proposed system “SLCRec” comes up with a solution focusing on minimizing false rejection of notes. Sri Lankan currency notes undergo severe changes in image quality in usage. Hence a special linear transformation function is adapted to wipe out noise patterns from backgrounds without affecting the notes’ characteristic images and re-appear images of interest. The transformation maps the original gray scale range into a smaller range of 0 to 125. Applying Edge detection after the transformation provided better robustness for noise and fair representation of edges for new and old damaged notes. A three layer back propagation neural network is presented with the number of edges detected in row order of the notes and classification is accepted in four classes of interest which are 100, 500, 1000 and 2000 rupee notes. The experiments showed good classification results and proved that the proposed methodology has the capability of separating classes properly in varying image conditions. Artificial intelligence, linear transformation and pattern recognition.
THREE LAYERS APPROACH FOR NETWORK SCANNING DETECTION

Computer networks became one of the most important dimensions in any organization. This importance is due to the connectivity benefits that can be given by networks, such as computing power, data sharing and enhanced performance. However using networks comes with a cost, there are some threats and issues that need to be addressed, such as providing sufficient level of security. One of the most challenging issues in network security is network scanning. Network scanning is considered to be the initial step in any attacking process. Therefore, detecting networks scanning helps to protect networks resources, services, and data before the real attack happens. This paper proposes an approach that consists of three layers to detect Sequential and Random network scanning for both TCP and UDP protocols. The proposed Three Layers Approach aims to increase network scanning detection accuracy. The Three Layers Approach defines some packets to be used as signs of network scanning existence. Before applying the approach in a network, there is a Thresholds Generation Stage to that aims to determine descriptive set of thresholds. After that, the first layer of the approach aggregates sign packets in separated tables. Then the second layer of the approach analyzes these tables in new tables by counting packets generated by each IP. Finally, the last layer makes a decision of whether or not a network is being scanned.
Parking lot guidance software based on MQTT Protocol

To reduce the amount of CO2 produced from the personal cars in the world, the parking lot guidance systems are considered to be the solution in shopping malls or department stores in many countries. However, most of the current parking lot systems are located in the parking area of each shopping mall and they cannot show the parking lots information for the driver that are driving on the road. So, The drivers can see the parking lot information if and only if they arrive at the shopping mall area. This is the fact that the CO2 are still produced to the world although we use the parking lot guidance system. From the reason as mentioned, we propose the parking lots guidance software to share a parking lot information to a large number of clients in real-time based on MQTT Protocol. The proposed software can share the real-time parking lot information to the mobile devices of the drivers in any location or when they are driving on the road. The results show that the proposed software can share the parking lot information in real-time for at least 1,000 sessions simultaneously with the high average score of usability, design and the benefits.
SlangSD: Building and Using a Sentiment Dictionary of Slang Words for Short-Text Sentiment Classification

Sentiment in social media is increasingly considered as an important resource for customer segmentation, market understanding, and tackling other socio-economic issues. However, sentiment in social media is difficult to measure since user-generated content is usually short and informal. Although many traditional sentiment analysis methods have been proposed, identifying slang sentiment words remains untackled. One of the reasons is that slang sentiment words are not available in existing dictionaries or sentiment lexicons. To this end, we propose to build the first sentiment dictionary of slang words to aid sentiment analysis of social media content. It is laborious and time-consuming to collect and label the sentiment polarity of a comprehensive list of slang words. We present an approach to leverage web resources to construct an extensive Slang Sentiment word Dictionary (SlangSD) that is easy to maintain and extend. SlangSD is publicly available1 for research purposes. We empirically show the advantages of using SlangSD, the newly-built slang sentiment word dictionary for sentiment classification, and provide examples demonstrating its ease of use with an existing sentiment system.
A Survey of Machine Learning Applications to Cloud Computing

Due to the explosion of data made available due to cloud computing, we are faced with issues of resource management, energy efficiency, and security. This paper explores recent literature on all the aforementioned topics as they relate to cloud computing and examines a number of methods which propose to make use of machine learning to either allow for more dynamic resource management, better energy efficiency, or higher security. Additionally, the proposed methods are compared to one another to demonstrate their particular strengths and weaknesses, in order to allow further work to build upon the conclusions reached and to propose continually improving methods.
Designing and Prototyping a Novel Five-Phase Pancake-Shaped Axial-Flux SRM for Electric Vehicle Application Through Dynamic FEA Incorporating Flux-Tube Modeling

Switched reluctance motors (SRMs) show crucial attributes to applications where light weight, high-temperature adaptability, fault-tolerance capability, ruggedness, and simplicity are strongly required. The axial-flux configuration of SRM has additional features over the radial-flux configuration. This paper presents the design and analysis of a novel axial-flux SRM. Detailed procedures of deriving the output power equation as a function of the motor dimensions and parameters are provided. A modified phase winding design approach is thoroughly explained, a flowchart describing the design algorithm is presented, and the inductance determination by different methods is verified experimentally. The 3-D finite-element analysis (FEA) unveils the excessive end core and radial-flux fringing effects in the axial-flux configuration. An exclusive pole-shape design is also proposed. The operation of the motion model using 3-D dynamic FEA is analyzed, and its prototype development process and static testing are demonstrated.
Skeletal Quads: Human Action Recognition Using Joint Quadruples

Recent advances on human motion analysis have made the extraction of human skeleton structure feasible, even from single depth images. This structure has been proven quite informative for discriminating actions in a recognition scenario. In this context, we propose a local skeleton descriptor that encodes the relative position of joint quadruples. Such a coding implies a similarity normalisation transform that leads to a compact (6D) view-invariant skeletal feature, referred to as skeletal quad. Further, the use of a Fisher kernel representation is suggested to describe the skeletal quads contained in a (sub)action. A Gaussian mixture model is learnt from training data, so that the generation of any set of quads is encoded by its Fisher vector. Finally, a multi-level representation of Fisher vectors leads to an action description that roughly carries the order of sub-action within each action sequence. Efficient classification is here achieved by linear SVMs. The proposed action representation is tested on widely used datasets, MSRAction3D and HDM05. The experimental evaluation shows that the proposed method outperforms state-of-the-art algorithms that rely only on joints, while it competes with methods that combine joints with extra cues.
Semantic Segmentation with Second-Order Pooling

Feature extraction, coding and pooling, are important components on many contemporary object recognition paradigms. In this paper we explore novel pooling techniques that encode the second-order statistics of local descriptors inside a region. To achieve this effect, we introduce multiplicative second-order analogues of average and maxpooling that together with appropriate non-linearities lead to state-ofthe-art performance on free-form region recognition, without any type of feature coding. Instead of coding, we found that enriching local descriptors with additional image information leads to large performance gains, especially in conjunction with the proposed pooling methodology. We show that second-order pooling over free-form regions produces results superior to those of the winning systems in the Pascal VOC 2011 semantic segmentation challenge, with models that are 20,000 times faster.
Physical and mental health effects of intimate partner violence for men and women.

Few population-based studies have assessed the physical and mental health consequences of both psychological and physical intimate partner violence (IPV) among women or men victims. This study estimated IPV prevalence by type (physical, sexual, and psychological) and associated physical and mental health consequences among women and men.



The study analyzed data from the National Violence Against Women Survey (NVAWS) of women and men aged 18 to 65. This random-digit-dial telephone survey included questions about violent victimization and health status indicators.



A total of 28.9% of 6790 women and 22.9% of 7122 men had experienced physical, sexual, or psychological IPV during their lifetime. Women were significantly more likely than men to experience physical or sexual IPV (relative risk [RR]=2.2, 95% confidence interval [CI]=2.1, 2.4) and abuse of power and control (RR=1.1, 95% CI=1.0, 1.2), but less likely than men to report verbal abuse alone (RR=0.8, 95% CI=0.7, 0.9). For both men and women, physical IPV victimization was associated with increased risk of current poor health; depressive symptoms; substance use; and developing a chronic disease, chronic mental illness, and injury. In general, abuse of power and control was more strongly associated with these health outcomes than was verbal abuse. When physical and psychological IPV scores were both included in logistic regression models, higher psychological IPV scores were more strongly associated with these health outcomes than were physical IPV scores.



Both physical and psychological IPV are associated with significant physical and mental health consequences for both male and female victims.
Directional antennas in FANETs: A performance analysis of routing protocols

Flying Ad-hoc Networks (FANETs)  are mobile ad-hoc networks formed by small and medium-sized UAVs. Nowadays, most UAVs are equipped with omnidirectional antennas. In addition, most of the existing routing protocols were designed assuming the use of omnidirectional antennas. Directional antennas have the potential to increase spatial reuse, save the battery's energy, and substantially increase the transmission range. However, these benefits come with a few challenges. Existing directional MAC protocols deal with these challenges, mostly in static ad-hoc networks. We define DA-FANETs as FANETs where directional antennas are used. In this paper, we investigate the performance of existing routing protocols in DA-FANETs. First we implement an 802.11b-based directional MAC protocol that embodies common directional MAC protocol features. Then we evaluate the following routing protocols in DA-FANET scenarios: AODV, OLSR, RGR, and GRP. The results show that each antenna beamwidth has an optimal network size that gives the highest amount of routing. In addition, RGR is the best option for DA-FANETs while GRP is the worst. Although RGR shows the best performance of all, it still leaves a lot of room for improvement with a PDR sitting at just 85% and a relatively high amount of overhead in terms of the number of transmissions performed.
Transient Liquid Phase Sintering Using Copper-Solder-Resin Composite for High-Temperature Power Modules

Transient liquid phase sintering (TLPS) using copper (Cu)-solder-resin composite was investigated for dieattach applications of high-temperature power modules. The copper particles were connected with Cu-Sn IMCs through transient liquid phase reaction at about 220 °C. The Cu-IMC skeleton structure was partially filled with polyimide resin. The joint strengths of die-attached specimens were tested. The die-attached specimens showed superior reliability through thermal cycling tests. These results indicate the potential of TLPS using Cu-solder-resin composite for power module dieattach applications.