nils-herrmann/clean_scirepeval huggingface dataset:
Exploring Human-Like Attention Supervision in Visual Question Answering

Attention mechanisms have been widely applied in the Visual Question Answering (VQA) task, as they help to focus on the area-of-interest of both visual and textual information. To answer the questions correctly, the model needs to selectively target different areas of an image, which suggests that an attention-based model may benefit from an explicit attention supervision. In this work, we aim to address the problem of adding attention supervision to VQA models. Since there is a lack of human attention data, we first propose a Human Attention Network (HAN) to generate human-like attention maps, training on a recently released dataset called Human ATtention Dataset (VQA-HAT). Then, we apply the pre-trained HAN on the VQA v2.0 dataset to automatically produce the human-like attention maps for all image-question pairs. The generated human-like attention map dataset for the VQA v2.0 dataset is named as Human-Like ATtention (HLAT) dataset. Finally, we apply human-like attention supervision to an attention-based VQA model. The experiments show that adding human-like supervision yields a more accurate attention together with a better performance, showing a promising future for human-like attention supervision in VQA.
The BigDAWG polystore system and architecture

Organizations are often faced with the challenge of providing data management solutions for large, heterogenous datasets that may have different underlying data and programming models. For example, a medical dataset may have unstructured text, relational data, time series waveforms and imagery. Trying to fit such datasets in a single data management system can have adverse performance and efficiency effects. As a part of the Intel Science and Technology Center on Big Data, we are developing a polystore system designed for such problems. BigDAWG (short for the Big Data Analytics Working Group) is a polystore system designed to work on complex problems that naturally span across different processing or storage engines. BigDAWG provides an architecture that supports diverse database systems working with different data models, support for the competing notions of location transparency and semantic completeness via islands and a middleware that provides a uniform multi-island interface. Initial results from a prototype of the BigDAWG system applied to a medical dataset validate polystore concepts. In this article, we will describe polystore databases, the current BigDAWG architecture and its application on the MIMIC II medical dataset, initial performance results and our future development plans.
Fire Detection using YCbCr Color Model

The proposed method adopts rule based color model which are defined based on luminance and chrominance content present in an image. YCbCr color space effectively isolates luminance from chrominance compared to other color spaces like RGB and normalized RGB (rgb). The proposed method not only separates fire flame pixels but also isolates high temperature fire centre pixels by taking into account statistical parameters of fire image in YCbCr color space like mean and standard deviation. In this method four rules are defined to separate the true fire region. Two rules are defined for segmenting the fire region and other two rules are defined for segmenting the high temperature fire centre region. The results are obtained and tested for a 200 images and achieves 88% of higher true fire detection rate and less false detection rate. The proposed method can be used for real time forest fire detection with moving camera.
Hybrid crossbar architecture for a memristor based memory

This paper describes a new memristor crossbar architecture that is proposed for use in a high density cache design. This design has less than 10% of the write energy consumption than a simple memristor crossbar. Also, it has up to 4 times the bit density of an STT-MRAM system and up to 11 times the bit density of an SRAM architecture. The proposed architecture is analyzed using a detailed SPICE analysis that accounts for the resistance of the wires in the memristor structure. Additionally, the memristor model used in this work has been matched to specific device characterization data to provide accurate results in terms of energy, area, and timing.
Containers and Clusters for Edge Cloud Architectures -- A Technology Review

Cloud technology is moving towards more distribution across multi-clouds and the inclusion of various devices, as evident through IoT and network integration in the context of edge cloud and fog computing. Generally, lightweight virtualisation solutions are beneficial for this architectural setting with smaller, but still virtualised devices to host application and platform services, and the logistics required to manage this. Containerisation is currently discussed as a lightweight virtualisation solution. In addition to having benefits over traditional virtual machines in the cloud in terms of size and flexibility, containers are specifically relevant for platform concerns typically dealt with Platform-as-a-Service (PaaS) clouds such as application packaging and orchestration. For the edge cloud environment, application and service orchestration can help to manage and orchestrate applications through containers as an application packaging mechanism. We review edge cloud requirements and discuss the suitability container and cluster technology of that arise from having to facilitate applications through distributed multi-cloud platforms build from a range of networked nodes ranging from data centres to small devices, which we refer to here as edge cloud.
LabVIEW based real time bio-telemetry system for healthcare

The objective of this paper is to present a real time bio-telemetry system using LabVIEW, which enables continuous monitoring of a patient, which in turn helps the doctor to make a better diagnosis, thus ensuring mobility of both the patient and the doctor. The system acquires, analyses and processes physio-logical parameters such as Heart Rate and Electrocardiogram. These are the parameters that determine whether the person is in normal or abnormal condition. In this proposed system we have developed an algorithm to acquire, process and analyse Electrocardiogram and Heart Rate using Virtual Instrumentation platform. The raw ECG signal is acquired by using EKG sensor which is pre-processed and signal conditioning takes place at the NI ELVIS platform which is further fed to the LabVIEW for feature extraction and analysis. We have also developed a database to store the corresponding results. The acquired ECG signal can be monitored via a remote location in real time using the Web Publishing Tool.
Context-Dependent Sentiment Analysis in User-Generated Videos

Multimodal sentiment analysis is a developing area of research, which involves the identification of sentiments in videos. Current research considers utterances as independent entities, i.e., ignores the interdependencies and relations among the utterances of a video. In this paper, we propose a LSTM-based model that enables utterances to capture contextual information from their surroundings in the same video, thus aiding the classification process. Our method shows 5-10% performance improvement over the state of the art and high robustness to generalizability.
Automating the Design of Graphical Presentations of Relational Information

The goal of the research described in this paper is to develop an application-independent presentation tool that automatically designs effective graphical presentations (such as bar charts, scatter plots, and connected graphs) of relational information. Two problems are raised by this goal: The codification of graphic design criteria in a form that can be used by the presentation tool, and the generation of a wide variety of designs so that the presentation tool can accommodate a wide variety of information. The approach described in this paper is based on the view that graphical presentations are sentences of graphical languages. The graphic design issues are codified as expressiveness and effectiveness criteria for graphical languages. Expressiveness criteria determine whether a graphical language can express the desired information. Effectiveness criteria determine whether a graphical language exploits the capabilities of the output medium and the human visual system. A wide variety of designs can be systematically generated by using a composition algebra that composes a small set of primitive graphical languages. Artificial intelligence techniques are used to implement a prototype presentation tool called APT (A Presentation Tool), which is based on the composition algebra and the graphic design criteria.
A 14b 3/6GHz current-steering RF DAC in 0.18μm CMOS with 66dB ACLR at 2.9GHz

This paper describes a 14b 3.0/6.0GS/S DAC implemented in 0.18μm CMOS capable of directly synthesizing multi-carrier standards directly at RF frequencies, as well as the 1GHz broadband cable spectrum. Modifications are made to the quad switch structure to support higher-speed operation and improve dynamic performance.
FDDB: A benchmark for face detection in unconstrained settings

Despite the maturity of face detection research, it remains difficult to compare different algorithms for face detection. This is partly due to the lack of common evaluation schemes. Also, existing data sets for evaluating face detection algorithms do not capture some aspects of face appearances that are manifested in real-world scenarios. In this work, we address both of these issues. We present a new data set of face images with more faces and more accurate annotations for face regions than in previous data sets. We also propose two rigorous and precise methods for evaluating the performance of face detection algorithms. We report results of several standard algorithms on the new benchmark.
Fake News Headline Classification using Neural Networks with Attention

The Fake News Challenge is a competition to classify articles as agreeing, disagreeing, discussing, or having no relation to their headlines. The competition provides an annotated dataset (FNC-1) for training supervised models as well as a baseline model to compare against. We cast our fake news problem as a textual entailment task by using multiple Bidirectional LSTMs and an attention mechanism to predict the entailment of the articles to their paired headlines. To tune our model, we adjust a multilayer perceptron predictor layer’s width as well as the amount of dropout and regularization to control overfitting. We evaluate the effectiveness of our model against the baseline and compare to implementations of solutions to the same FNC-1 stance classification task.
Distributed QoS Evaluation for Real-World Web Services

Quality-of-Service (QoS) is widely employed for describing non-functional characteristics of Web services. Although QoS of Web services has been investigated in a lot of previous works, there is a lack of real-world Web service QoS datasets for validating new QoS based techniques and models of Web services. To study the performance of real-world Web services as well as provide reusable research datasets for promoting the research of QoS-driven Web services, we conduct several large-scale evaluations on real-world Web services. Firstly, addresses of 21,358 Web services are obtained from the Internet. Then, invocation failure probability performance of 150 Web services is assessed by 100 distributed service users. After that, response time and throughput performance of 5,825 Web services are evaluated by 339 distributed service users. Detailed experimental results are presented in this paper and comprehensive Web service QoS datasets are publicly released for future research.
Fundamental analysis for visible-light communication system using LED lights

White LED offers advantageous properties such as high brightness, reliability, lower power consumption and long lifetime. White LEDs are expected to serve in the next generation of lamps. An indoor visible-light communication system utilizing white LED lights has been proposed from our laboratory. In the proposed system, these devices are used not only for illuminating rooms but also for an optical wireless communication system. Generally, plural lights are installed in our room. So, their optical path difference must be considered. In this paper, we discuss about the influence of interference and reflection. Based on numerical analyses, we show that the system is expected to be the indoor communication of the next generation.
A Low-Power RF Direct-Conversion Receiver/Transmitter for 2.4-GHz-Band IEEE 802.15.4 Standard in 0.18- $\mu{\hbox {m}}$ CMOS Technology

This paper presents a low-power RF receiver/transmitter front-end for 2.4-GHz-band IEEE 802.15.4 standard in 0.18-mum CMOS technology. An RF receiver comprises a single-ended low-noise amplifier, a quadrature passive mixer, and a transimpedance amplifier. A current-mode passive mixer showing a very good 1/f noise performance is adopted to convert an RF signal directly to a baseband signal. Moreover, this type of passive mixer shows high-linearity performance, leading to overall RF receiver linearity improvement. A low-power, high-linearity transmitter front-end is implemented by using a passive mixer and two-stage driver amplifier in which the first stage is a conventional cascode amplifier and the second stage uses a folded cascode one. The receiver front-end achieves 30-dB voltage conversion gain, 7.3-dB noise figure with 1/f noise corner frequency of 70 kHz, -8-dBm input third-order intercept point, and +40-dBm input second-order intercept point. The transmitter front-end shows 12-dB power conversion gain, 0-dBm output power with 10-dBm output third-order intercept point, and -30-dB local-oscillator suppression. The receiver and transmitter front-end dissipate 3.5 and 3 mA from a 1.8-V supply, respectively
Review on Web Content Mining Techniques

Web data processing is the method of handling high volume of data. Previous research explains that handling/processing such data is not easy. Therefore, researchers utilize web mining, deals with identifying patterns, which user require. The second phase of web mining is known as web content mining, which dealt mining of pictures, text and graphs etc. The primary purpose of web content mining is to identify the relevance of content according to the queries. The focus of this paper is to present a detailed and comprehensive review of various methods applied for web mining/web content mining. The paper is divided into three parts to discuss, web content mining, web structure mining and web usage mining. Later, we presented application of these approaches for structured, unstructured, semi-structured and multimedia data mining techniques. The underlined motivation is to explore new possibilities in improving the existing techniques and identifying new ways/methods.
THE NECESSITY OF OTHERS IS THE MOTHER OF INVENTION : INTRINSIC AND PROSOCIAL MOTIVATIONS , PERSPECTIVE TAKING , AND CREATIVITY

Although many scholars believe that intrinsic motivation fuels creativity, research has returned equivocal results. Drawing on motivated information processing theory, we propose that the relationship between intrinsic motivation and creativity is enhanced by other-focused psychological processes. Perspective taking, as generated by prosocial motivation, encourages employees to develop ideas that are useful as well as novel. In three studies, using both field and lab data, we found that prosocial motivation strengthened the association between intrinsic motivation and independent creativity ratings. In our second and third studies, perspective taking mediated this moderating effect. We discuss theoretical implications for creativity and motivation.
Understanding fraudulent activities in online ad exchanges

Online advertisements (ads) provide a powerful mechanism for advertisers to effectively target Web users. Ads can be customized based on a user's browsing behavior, geographic location, and personal interests. There is currently a multi-billion dollar market for online advertising, which generates the primary revenue for some of the most popular websites on the Internet. In order to meet the immense market demand, and to manage the complex relationships between advertisers and publishers (i.e., the websites hosting the ads), marketplaces known as "ad exchanges" are employed. These exchanges allow publishers (sellers of ad space) and advertisers(buyers of this ad space) to dynamically broker traffic through ad networks to efficiently maximize profits for all parties. Unfortunately, the complexities of these systems invite a considerable amount of abuse from cybercriminals, who profit at the expense of the advertisers.
 In this paper, we present a detailed view of how one of the largest ad exchanges operates and the associated security issues from the vantage point of a member ad network. More specifically, we analyzed a dataset containing transactions for ingress and egress ad traffic from this ad network. In addition, we examined information collected from a command-and-control server used to operate a botnet that is leveraged to perpetrate ad fraud against the same ad exchange.
An Incremental Algorithm for a Generalization of the Shortest-Path Problem

hhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhh The grammar problem, a generalization of the single-source shortest-path problem introduced by Knuth, is to compute the minimum-cost derivation of a terminal string from each non-terminal of a given context-free grammar, with the cost of a derivation being suitably defined. This problem also subsumes the problem of finding optimal hyperpaths in directed hypergraphs (under varying optimization criteria) that has received attention recently. In this paper we present an incremental algorithm for a version of the grammar problem. As a special case of this algorithm we obtain an efficient incremental algorithm for the single-source shortest-path problem with positive edge lengths. The aspect of our work that distinguishes it from other work on the dynamic shortest-path problem is its ability to handle “multiple heterogeneous modifications”: between updates, the input graph is allowed to be restructured by an arbitrary mixture of edge insertions, edge deletions, and edge-length changes.
Graduated Consistency-Regularized Optimization for Multi-graph Matching

Graph matching has a wide spectrum of computer vision applications such as finding feature point correspondences across images. The problem of graph matching is generally NP-hard, so most existing work pursues suboptimal solutions between two graphs. This paper investigates a more general problem of matching N attributed graphs to each other, i.e. labeling their common node correspondences such that a certain compatibility/affinity objective is optimized. This multigraph matching problem involves two key ingredients affecting the overall accuracy: a) the pairwise affinity matching score between two local graphs, and b) global matching consistency that measures the uniqueness and consistency of the pairwise matching results by different sequential matching orders. Previous work typically either enforces the matching consistency constraints in the beginning of iterative optimization, which may propagate matching error both over iterations and across different graph pairs; or separates score optimizing and consistency synchronization in two steps. This paper is motivated by the observation that affinity score and consistency are mutually affected and shall be tackled jointly to capture their correlation behavior. As such, we propose a novel multigraph matching algorithm to incorporate the two aspects by iteratively approximating the global-optimal affinity score, meanwhile gradually infusing the consistency as a regularizer, which improves the performance of the initial solutions obtained by existing pairwise graph matching solvers. The proposed algorithm with a theoretically proven convergence shows notable efficacy on both synthetic and public image datasets.
Multiplexed electrical detection of cancer markers with nanowire sensor arrays

We describe highly sensitive, label-free, multiplexed electrical detection of cancer markers using silicon-nanowire field-effect devices in which distinct nanowires and surface receptors are incorporated into arrays. Protein markers were routinely detected at femtomolar concentrations with high selectivity, and simultaneous incorporation of control nanowires enabled discrimination against false positives. Nanowire arrays allowed highly selective and sensitive multiplexed detection of prostate specific antigen (PSA), PSA-α1-antichymotrypsin, carcinoembryonic antigen and mucin-1, including detection to at least 0.9 pg/ml in undiluted serum samples. In addition, nucleic acid receptors enabled real-time assays of the binding, activity and small-molecule inhibition of telomerase using unamplified extracts from as few as ten tumor cells. The capability for multiplexed real-time monitoring of protein markers and telomerase activity with high sensitivity and selectivity in clinically relevant samples opens up substantial possibilities for diagnosis and treatment of cancer and other complex diseases.
Can students really multitask? An experimental study of instant messaging while reading

Students often ‘‘multitask” with electronic media while doing schoolwork. We examined the effects of one form of media often used in such multitasking, instant messaging (IM). We predicted that students who engaged in IMing while reading a typical academic psychology passage online would take longer to read the passage and would perform more poorly on a test of comprehension of the passage. Participants were randomly assigned to one of three conditions (IM before reading, IM during reading, or no IM). We found that students took significantly longer to read the passage when they IMed during reading (not including time taken to IM) than in other conditions. However, test performance did not differ by condition. Students who are managing busy lives may think they are accomplishing more by multitasking, but our findings suggest they will actually need more time to achieve the same level of performance on an
EEG alpha activity reflects attentional demands, and beta activity reflects emotional and cognitive processes.

Two experiments were designed to examine the effects of attentional demands on the electroencephalogram during cognitive and emotional tasks. We found an interaction of task with hemisphere as well as more overall parietal alpha for tasks not requiring attention to the environment, such as mental arithmetic, than for those requiring such attention. Differential hemispheric activation for beta was found most strongly in the temporal areas for emotionally positive or negative tasks and in the parietal areas for cognitive tasks.