nils-herrmann/clean_scirepeval huggingface dataset:
Action-Driven Object Detection with Top-Down Visual Attentions

A dominant paradigm for deep learning based object detection relies on a “bottom-up” approach using “passive” scoring of class agnostic proposals. These approaches are efficient but lack of holistic analysis of scene-level context. In this paper, we present an “action-driven” detection mechanism using our “top-down” visual attention model. We localize an object by taking sequential actions that the attention model provides. The attention model conditioned with an image region provides required actions to get closer toward a target object. An action at each time step is weak itself but an ensemble of the sequential actions makes a bounding-box accurately converge to a target object boundary. This attention model we call AttentionNet is composed of a convolutional neural network. During our whole detection procedure, we only utilize the actions from a single AttentionNet without any modules for object proposals nor post bounding-box regression. We evaluate our top-down detection mechanism over the PASCAL VOC series and ILSVRC CLS-LOC dataset, and achieve state-of-the-art performances compared to the major bottom-up detection methods. In particular, our detection mechanism shows a strong advantage in elaborate localization by outperforming Faster R-CNN with a margin of +7.1% over PASCAL VOC 2007 when we increase the IoU threshold for positive detection to 0.7.
Wireless sensor based healthcare monitoring system using cloud

Technology plays vital role in Media, Medical Healthcare Systems, Communications, Home Automation System, etc., Internet of Things is the latest trend used in healthcare monitoring system. IOT devices gather and share information directly with each other and the cloud making it possible to collect, record and analyze data streams faster and more accurately. Healthcare management is one of the most promising applications of Information Technology. In the existing system patient is monitored using bedside monitoring station with wired sensors, which makes the patients to be periodically monitored by doctors/nurses. It is impossible to collect data for wider range and coverage of wireless network is not available. This paper proposes a cooperative approach for an automated Healthcare Monitoring System which uses sensors to measure various parameters of the patient like temperature, pulse and body movement using wireless communications. The patient's status will be recorded and stored on the cloud. This proposed system process the sensor data and provides a real time monitoring information about physiological conditions of a patient to the doctor, so that the Healthcare professionals can monitor and access patient's data from anywhere of the world at any time. It enables personalization of treatment and management.
A Compact Dual-Polarized Printed Dipole Antenna With High Isolation for Wideband Base Station Applications

A compact dual-polarized printed dipole antenna for wideband base station applications is presented in this communication. The proposed dipole antenna is etched on three assembled substrates. Four horizontal triangular patches are introduced to form two dipoles in two orthogonal polarizations. Two integrated baluns connected with 50 Ω SMA launchers are used to excite the dipole antenna. The proposed dipole antenna achieves a more compact size than many reported wideband printed dipole and magneto-electric dipole antennas. Both simulated and measured results show that the proposed antenna has a port isolation higher than 35 dB over 52% impendence bandwidth (VSWR <; 1.5). Moreover, stable radiation pattern with a peak gain of 7 dBi - 8.6 dBi is obtained within the operating band. The proposed dipole antenna is suitable as an array element and can be used for wideband base station antennas in the next generation IMT-advanced communications.
Social-Aware Movie Recommendation via Multimodal Network Learning

With the rapid development of Internet movie industry social-aware movie recommendation systems (SMRs) have become a popular online web service that provide relevant movie recommendations to users. In this effort many existing movie recommendation approaches learn a user ranking model from user feedback with respect to the movie's content. Unfortunately this approach suffers from the sparsity problem inherent in SMR data. In the present work we address the sparsity problem by learning a multimodal network representation for ranking movie recommendations. We develop a heterogeneous SMR network for movie recommendation that exploits the textual description and movie-poster image of each movie as well as user ratings and social relationships. With this multimodal data we then present a heterogeneous information network learning framework called SMR-multimodal network representation learning (MNRL) for movie recommendation. To learn a ranking metric from the heterogeneous information network we also developed a multimodal neural network model. We evaluated this model on a large-scale dataset from a real world SMR Web site and we find that SMR-MNRL achieves better performance than other state-of-the-art solutions to the problem.
The use, benefits and challenges of using the Internet of Things (IoT) in retail businesses: A literature review

This paper presents the use, benefits and challenges of using of the Internet of Things (IoT) in retail businesses. The paper aims to inform Chief Information Officers (CIOs), digital strategists or Head of Information Technology in retail of the use, benefits and challenges of using IoT. IoT is a new paradigm that uses different technologies to achieve various tasks. The use of IoT is growing among business and individuals and businesses that are not currently using IoT are set to invest in its use in the coming years. The use of IoT is increasing in the retail market, giving to the vast benefits that IoT offers. For businesses to fully use IoT they will need to change their business processes to realise its benefits. However, businesses to fully realise the benefits of IoT they need to overcome the challenges that it presents, this includes but not limited to security, privacy and network challenges. The Technology Organisation Environment (TOE) Framework was used to study the use, benefit and challenges of using IoT in retail businesses.
One-Class Convolutional Neural Network

We present a novel convolutional neural network (CNN) based approach for one-class classification. The idea is to use a zero centered Gaussian noise in the latent space as the pseudo-negative class and train the network using the cross-entropy loss to learn a good representation as well as the decision boundary for the given class. A key feature of the proposed approach is that any pre-trained CNN can be used as the base network for one-class classification. The proposed one-class CNN is evaluated on the UMDAA-02 Face, Abnormality-1001, and FounderType-200 datasets. These datasets are related to a variety of one-class application problems such as user authentication, abnormality detection, and novelty detection. Extensive experiments demonstrate that the proposed method achieves significant improvements over the recent state-of-the-art methods. The source code is available at: github.com/otkupjnoz/oc-cnn.
Discriminative Orderlet Mining for Real-Time Recognition of Human-Object Interaction

This paper presents a novel visual representation, called orderlets, for real-time human action recognition with depth sensors. An orderlet is a middle level feature that captures the ordinal pattern among a group of low level features. For skeletons, an orderlet captures specific spatial relationship among a group of joints. For a depth map, an orderlet characterizes a comparative relationship of the shape information among a group of subregions. The orderlet representation has two nice properties. First, it is insensitive to small noise since an orderlet only depends on the comparative relationship among individual features. Second, it is a frame-level representation thus suitable for real-time online action recognition. Experimental results demonstrate its superior performance on online action recognition and cross-environment action recognition.
A SiC JFET driver for a 5 kW, 150 kHz three-phase PWM converter

Silicon carbide (SiC) power semiconductor devices are capable of being operated at higher voltages, frequencies and temperatures than silicon power devices. These SiC device capabilities will provide the power electronics designer with new possibilities to produce compact designs. Presently the JFET is the only controlled turn off/on SiC device that is close to commercialization and available as restricted samples. However the JFET is a normally-on device that requires a negative gate voltage to turn off. In order to correctly design a gate driver one must understand the characteristics of the JFET. This paper presents a description of the JFET semiconductor structure, and the SiC JFET's static and dynamic characteristics from room temperature to 200 /spl deg/C. A SiC JFET gate driver circuit is presented and its performance described. The proposed gate driver improves the switching performance of the JFET by operating the gate in avalanche during the off time.
Role of mask pattern in intelligibility of ideal binary-masked noisy speech.

Intelligibility of ideal binary masked noisy speech was measured on a group of normal hearing individuals across mixture signal to noise ratio (SNR) levels, masker types, and local criteria for forming the binary mask. The binary mask is computed from time-frequency decompositions of target and masker signals using two different schemes: an ideal binary mask computed by thresholding the local SNR within time-frequency units and a target binary mask computed by comparing the local target energy against the long-term average speech spectrum. By depicting intelligibility scores as a function of the difference between mixture SNR and local SNR threshold, alignment of the performance curves is obtained for a large range of mixture SNR levels. Large intelligibility benefits are obtained for both sparse and dense binary masks. When an ideal mask is dense with many ones, the effect of changing mixture SNR level while fixing the mask is significant, whereas for more sparse masks the effect is small or insignificant.
Cross-View People Tracking by Scene-Centered Spatio-Temporal Parsing

In this paper, we propose a Spatio-temporal Attributed Parse Graph (ST-APG) to integrate semantic attributes with trajectories for cross-view people tracking. Given videos from multiple cameras with overlapping field of view (FOV), our goal is to parse the videos and organize the trajectories of all targets into a scene-centered representation. We leverage rich semantic attributes of human, e.g., facing directions, postures and actions, to enhance cross-view tracklet associations, besides frequently used appearance and geometry features in the literature. In particular, the facing direction of a human in 3D, once detected, often coincides with his/her moving direction or trajectory. Similarly, the actions of humans, once recognized, provide strong cues for distinguishing one subject from the others. The inference is solved by iteratively grouping tracklets with cluster sampling and estimating people semantic attributes by dynamic programming. In experiments, we validate our method on one public dataset and create another new dataset that records people’s daily life in public, e.g., food court, office reception and plaza, each of which includes 3-4 cameras. We evaluate the proposed method on these challenging videos and achieve promising multi-view tracking results.
Fusion of laser and monocular camera data in object grid maps for vehicle environment perception

Occupancy grid maps provide a reliable vehicle environmental model and usually process data from range finding sensors. Object grid maps additionally contain information about the classes of objects, which is crucial for applications like autonomous driving. Unfortunately, they lack the precision of occupancy grid maps, since they mostly process classification results from camera data by projecting the corresponding images onto the ground plane. This paper proposes a modular framework to create precise object grid maps. The presented algorithm creates classical occupancy grid maps and object grid maps. In a combination step, it transforms both maps into the same frame of discernment based on the Dempster-Shafer theory of evidence. This allows fusing the maps to one object grid map, which contains valuable object information and at the same time benefits from the precision of the occupancy grid map.
An Algorithm For Open Text Semantic Parsing

This paper describes an algorithm for open text shallow semantic parsing. The algorithm relies on a frame dataset (FrameNet) and a semantic network (WordNet), to identify semantic relations between words in open text, as well as shallow semantic features associated with concepts in the text. Parsing semantic structures allows semantic units and constituents to be accessed and processed in a more meaningful way than syntactic parsing, moving the automation of understanding natural language text to a higher level.
Serving Deep Learning Models in a Serverless Platform

Serverless computing has emerged as a compelling paradigm for the development and deployment of a wide range of event based cloud applications. At the same time, cloud providers and enterprise companies are heavily adopting machine learning and Artificial Intelligence to either differentiate themselves, or provide their customers with value added services. In this work we evaluate the suitability of a serverless computing environment for the inferencing of large neural network models. Our experimental evaluations are executed on the AWS Lambda environment using the MxNet deep learning framework. Our experimental results show that while the inferencing latency can be within an acceptable range, longer delays due to cold starts can skew the latency distribution and hence risk violating more stringent SLAs.
Power-Dependence Relations

A simple theory of power relations is developed in an effort to resolve some of the ambiguities surrounding "power," "authority," "legitimacy," and power "structures," through bringing them together in a coherent scheme. After defining a reciprocal power-dependence relation, attention is focused upon properties of balance and "balancing operations" in such relations. The theory dictates exactly four generic types of balancing process, and discussion of these leads directly into processes of group formation, including the emergence of group norms, role structure and status hierarchy, all presented as the outcome of balancing tendencies in power relations. Within the framework of this theory, authority appears quite naturally to be legitimized power, vested in roles, and "legitimation" is seen as a special case of the coalition process through which norms and role-prescriptions are formed. Finally, through treating both persons and groups as actors in a power-network (two or more connected powerdependence relations) the door is opened for meaningful analysis of complex power structures. Brief reference is made to findings from two experiments pertaining to hypotheses advanced in this theory.
A camera-phone based study reveals erratic eating pattern and disrupted daily eating-fasting cycle among adults in India

The daily rhythm of feeding-fasting and meal-timing are emerging as important determinants of health. Circadian rhythm research in animal models and retrospective analyses of human nutrition data have shown that reduced length of overnight fasting or increased late night eating increases risk for metabolic diseases including obesity and diabetes. However, the daily rhythm in eating pattern in humans is rarely measured. Traditional methods to collect nutrition information through food diary and food log pay little attention to the timing of eating which may also change from day to day. We adopted a novel cell-phone based approach to longitudinally record all events of food and beverage intake in adults. In a feasibility study daily food-eating patterns of 93 healthy individuals were recorded for 21 days using camera phones. Analysis of the daily eating patterns of these individuals indicates deviation from conventional assumption that people eat three meals-a-day within a 12 h interval. We found that eating events are widespread throughout the day, with <30% of calories consumed before noon and >30% consumed in evening and late night hours. There was little difference in eating pattern between weekdays and weekends. In this cohort more than 50% of people spread their caloric intake events over 15 h or longer. One decile of the cohort who were spouses of shift-workers or had flexible work schedule spread their caloric intake over 20 h. Although the nutrition quality and diversity of food consumed is different between South-East Asian and Western countries, such overall disruption of daily eating-fasting rhythm is similar. Therefore, in view of hypothesis that disrupted daily eating pattern may contribute to the global increase in metabolic diseases and modification of daily eating pattern is a potential modifiable behavior to contain these diseases, monitoring eating pattern is an important aspect of lifestyle.
Multimodal recognition of personality traits in social interactions

This paper targets the automatic detection of personality traits in a meeting environment by means of audio and visual features; information about the relational context is captured by means of acoustic features designed to that purpose. Two personality traits are considered: Extraversion (from the Big Five) and the Locus of Control. The classification task is applied to thin slices of behaviour, in the form of 1-minute sequences. SVM were used to test the performances of several training and testing instance setups, including a restricted set of audio features obtained through feature selection. The outcomes improve considerably over existing results, provide evidence about the feasibility of the multimodal analysis of personality, the role of social context, and pave the way to further studies addressing different features setups and/or targeting different personality traits.
Universal Dependency Parsing for Hindi-English Code-Switching

Code-switching is a phenomenon of mixing grammatical structures of two or more languages under varied social constraints. The code-switching data differ so radically from the benchmark corpora used in NLP community that the application of standard technologies to these data degrades their performance sharply. Unlike standard corpora, these data often need to go through additional processes such as language identification, normalization and/or back-transliteration for their efficient processing. In this thesis, we investigate these indispensable processes and other problems associated with syntactic parsing of code-switching data and propose methods to mitigate their effects. In particular, we study dependency parsing of code-switching data of Hindi and English multilingual speakers from Twitter. We present a treebank of Hindi-English code-switching tweets under Universal Dependencies scheme and propose domain adaptation techniques to efficiently leverage monolingual syntactic annotations and the annotations from the Hindi-English code-switching treebank. Firstly, we propose modifications to the parsing models which are trained only on the Hindi and English monolingual treebanks. We have shown that code-switching texts can be efficiently parsed by the monolingual parsing models if they are intelligently manipulated. Against an informed monolingual baseline, our parsing strategies are at-least 10 LAS points better. Secondly, we propose a neural stacking model for parsing that efficiently leverages part-of-speech tag and syntactic tree annotations in the code-switching treebank and the monolingual Hindi and English treebanks. We also present normalization and back-transliteration models with a decoding process tailored for code-switching data. Our neural stacking models achieve an accuracy of 90.53% for POS tagging and 80.23% UAS and 71.03% LAS for dependency parsing. Results show that our neural stacking parser is 1.5% LAS points better than the augmented parsing model and our decoding process improves results by 3.8% LAS points over the first-best normalization and/or back-transliteration.
A Survey on NoSQL Stores

Recent demands for storing and querying big data have revealed various shortcomings of traditional relational database systems. This, in turn, has led to the emergence of a new kind of complementary nonrelational data store, named as NoSQL. This survey mainly aims at elucidating the design decisions of NoSQL stores with regard to the four nonorthogonal design principles of distributed database systems: data model, consistency model, data partitioning, and the CAP theorem. For each principle, its available strategies and corresponding features, strengths, and drawbacks are explained. Furthermore, various implementations of each strategy are exemplified and crystallized through a collection of representative academic and industrial NoSQL technologies. Finally, we disclose some existing challenges in developing effective NoSQL stores, which need attention of the research community, application designers, and architects.
Compressive Sensing Based Positioning Using RSS of WLAN Access Points

The sparse nature of location finding problem makes the theory of compressive sensing desirable for indoor positioning in Wireless Local Area Networks (WLANs). In this paper, we address the received signal strength (RSS)-based localization problem in WLANs using the theory of compressive sensing (CS), which offers accurate recovery of sparse signals from a small number of measurements by solving an $\ell_1$-minimization problem. A pre-processing procedure of orthogonalization is used to induce incoherence needed in the CS theory. In order to mitigate the effects of RSS variations due to channel impediments, the proposed positioning system consists of two steps: coarse localization by exploiting affinity propagation, and fine localization by the CS theory. In the fine localization stage, access point selection problem is studied to further increase the accuracy. We implement the positioning system on a WiFi-integrated mobile device (HP iPAQ hx4700 with Windows Mobile 2003 Pocket PC) to evaluate the performance. Experimental results indicate that the proposed system leads to substantial improvements on localization accuracy and complexity over the widely used traditional fingerprinting methods.
Aspects of Graphical Models Connected with Causality

This paper demonstrates the use of graphs as a mathematical tool for expressing independenices, and as a formal language for communicating and processing causal information in statistical analysis. We show how complex information about external interventions can be organized and represented graphically and, conversely, how the graphical representation can be used to facilitate quantitative predictions of the eeects of interventions. We rst review the Markovian account of causation and show that directed acyclic graphs (DAGs) ooer an economical scheme for representing conditional independence assumptions and for deducing and displaying all the logical consequences of such assumptions. We then introduce the manipulative account of causation and show that any DAG deenes a simple transformation which tells us how the probability distribution will change as a result of external interventions in the system. Using this transformation it is possible to quantify, from non-experimental data, the eeects of external interventions and to specify conditions under which randomized experiments are not necessary. Finally, the paper ooers a graphical interpretation for Rubin's model of causal eeects, and demonstrates its equivalence to the manipulative account of causation. We exemplify the tradeoos between the two approaches by deriving nonparametric bounds on treatment eeects under conditions of imperfect compliance.