nils-herrmann/clean_scirepeval huggingface dataset:
Generalised spatial modulation with multiple active transmit antennas

We propose a new generalised spatial modulation (GSM) technique, which can be considered as a generalisation of the recently proposed spatial modulation (SM) technique. SM can be seen as a special case of GSM with only one active transmit antenna. In contrast to SM, GSM uses the indices of multiple transmit antennas to map information bits, and is thus able to achieve substantially increased spectral efficiency. Furthermore, selecting multiple active transmit antennas enables GSM to harvest significant transmit diversity gains in comparison to SM, because all the active antennas transmit the same information. On the other hand, inter-channel interference (ICI) is completely avoided by transmitting the same symbols through these active antennas. We present theoretical analysis using order statistics for the symbol error rate (SER) performance of GSM. The analytical results are in close agreement with our simulation results. The bit error rate performance of GSM and SM is simulated and compared, which demonstrates the superiority of GSM. Moreover, GSM systems with configurations of different transmit and receive antennas are studied. Our results suggest that using a less number of transmit antennas with a higher modulation order will lead to better BER performance.
Recovery of underwater visibility and structure by polarization analysis

Underwater imaging is important for scientific research and technology as well as for popular activities, yet it is plagued by poor visibility conditions. In this paper, we present a computer vision approach that removes degradation effects in underwater vision. We analyze the physical effects of visibility degradation. It is shown that the main degradation effects can be associated with partial polarization of light. Then, an algorithm is presented, which inverts the image formation process for recovering good visibility in images of scenes. The algorithm is based on a couple of images taken through a polarizer at different orientations. As a by-product, a distance map of the scene is also derived. In addition, this paper analyzes the noise sensitivity of the recovery. We successfully demonstrated our approach in experiments conducted in the sea. Great improvements of scene contrast and color correction were obtained, nearly doubling the underwater visibility range.
Accelerometer's position free human activity recognition using a hierarchical recognition model

Monitoring of physical activities is a growing field with potential applications such as lifecare and healthcare. Accelerometry shows promise in providing an inexpensive but effective means of long-term activity monitoring of elderly patients. However, even for the same physical activity the output of any body-worn Triaxial Accelerometer (TA) varies at different positions of a subject's body, resulting in a high within-class variance. Thus almost all existing TA-based human activity recognition systems require firm attachment of TA to a specific body part, making them impractical for long-term activity monitoring during unsupervised free living. Therefore, we present a novel hierarchical recognition model that can recognize human activities independent of TA's position along a human body. The proposed model minimizes the high within-class variance significantly and allows subjects to carry TA freely in any pocket without attaching it firmly to a body-part. We validated our model using six daily physical activities: resting (sit/stand), walking, walk-upstairs, walk-downstairs, running, and cycling. Activity data is collected from four most probable body positions of TA: chest pocket, front trousers pocket, rear trousers pocket, and inner jacket pocket. The average accuracy of about 95% illustrates the effectiveness of the proposed method.
Vinyl Acetate Monomer ( VAM ) Plant Model : A New Benchmark Problem for Control and Operation Study

A rigorous dynamic plant model of a vinyl acetate monomer (VAM) production was developed. This plant model enables the users to experience realistic plant operation, since it reflects the real plant characteristics and practical problems on the basis of experienced practitioners’ opinions. More importantly, the plant model provides a new benchmark problem; the users can investigate start-up/shutdown operation, plant-wide process control, fault detection and diagnosis, and others. Multiple scenarios prepared in the developed model cannot be simulated in conventional benchmark problems. The plant model can be used also for chemical engineering education. This advantageous plant model is released from Omega Simulation Co., Ltd. with a free limited license of Visual Modeler, which is a commercial dynamic simulator and can be linked with MATLAB®. This article aims to introduce the VAM plant model, the steady-state balance, various disturbances and malfunctions, and operation scenarios.
The emergence of psychopathy: Implications for the neuropsychological approach to developmental disorders

In this paper, I am going to examine the disorder of psychopathy and consider how genetic anomalies could give rise to the relatively specific neuro-cognitive impairments seen in individuals with this disorder. I will argue that genetic anomalies in psychopathy reduce the salience of punishment information (perhaps as a function of noradrenergic disturbance). I will argue that the ability of the amygdala to form the stimulus-punishment associations necessary for successful socialization is disrupted and that because of this, individuals with psychopathy do not learn to avoid actions that will harm others. It is noted that this model follows the neuropsychological approach to the study of developmental disorders, an approach that has been recently criticized. I will argue that these criticisms are less applicable to psychopathy. Indeed, animal work on the development of the neural systems necessary for emotion, does not support a constructivist approach with respect to affect. Importantly, such work indicates that while environmental effects can alter the responsiveness of the basic neural architecture mediating emotion, environmental effects do not construct this architecture. However, caveats to the neuropsychological approach with reference to this disorder are noted.
The social role of social media: the case of Chennai rains-2015

Social media has altered the way individuals communicate in present scenario. Individuals feel more connected on Facebook and Twitter with greater communication freedom to chat, share pictures, and videos. Hence, social media is widely employed by various companies to promote their product and services and establish better customer relationships. Owing to the increasing popularity of these social media platforms, their usage is also expanding significantly. Various studies have discussed the importance of social media in the corporate world for effective marketing communication, customer relationships, and firm performance, but no studies have focused on the social role of social media, i.e., in disaster resilience in India. Various academicians and practitioners have advocated the importance and use of social media in disaster resilience. This article focuses on the role that social media can play during the time of natural disasters, with the help of the recent case of Chennai floods in India. This study provides a better understanding about the role social media can play in natural disaster resilience in Indian context.
Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features

Viewpoint invariant pedestrian recognition is an important yet under-addressed problem in computer vision. This is likely due to the difficulty in matching two objects with unknown viewpoint and pose. This paper presents a method of performing viewpoint invariant pedestrian recognition using an efficiently and intelligently designed object representation, the ensemble of localized features (ELF). Instead of designing a specific feature by hand to solve the problem, we define a feature space using our intuition about the problem and let a machine learning algorithm find the best representation. We show how both an object class specific representation and a discriminative recognition model can be learned using the AdaBoost algorithm. This approach allows many different kinds of simple features to be combined into a single similarity function. The method is evaluated using a viewpoint invariant pedestrian recognition dataset and the results are shown to be superior to all previous benchmarks for both recognition and reacquisition of pedestrians.
Corroborating Answers from Multiple Web Sources

The Internet has changed the way people look for information. Users now expect the answers to their questions to be available through a simple web search. Web search engines are increasingly efficient at identifying the best sources for any given keyword query, and are often able to identify the answer within the sources. Unfortunately, many web sources are not trustworthy, because of erroneous, misleading, biased, or outdated information. In many cases, users are not satisfied with —or do not trust— the results from any single source and prefer checking several sources for corroborating evidence. In this paper, we propose methods to aggregate query results from different sources in order to save users the hassle of individually checking query-related web sites to corroborate answers. To return the best aggregated answers to the users, our techniques consider the number, importance, and similarity of the web sources reporting each answer, as well as the importance of the answer within the source. We present an experimental evaluation of our technique on real web queries, comparing the corroborated answers returned with real user clicks.
The Design and Implementation of the Wireless Remote Control Car

Due to the limitation of a system platform, in the process of the intelligent robots research, many graphics and image processing algorithms which designed for intelligent robots can only be emulated in the PC, but the practical effect of these algorithms can not be tested in a real robot platform. Build a wireless remote control car system as an experimental platform can solve this problem. We use an Atmega128 MCU to control the car’s movement, and adopt a NRF24L01 chip as a transceiver to communicate with computer. By loading a C3088 digital camera and a gesture recognition test, the usability of this platform was eventually verified. However, the test result shows that the system still has much room for optimization in real time, wireless transmission distance and functional complexity. KeywordsIntelligent Robot, Remote Controlling Car, Wireless Communication, Human-Computer Interaction, Gesture Recognition
The serval mesh: A platform for resilient communications in disaster & crisis

The challenges of many crisis communication needs can be summarised as: the need to allow civilians to safely communicate with one another, and with the outside world, without reliance on any domestic terrestrial infrastructure, or on the import of physical materials. Therefore, any solution should place high precedence on infrastructure-independent operation, and the re-use of existing hardware technology. Expanding on this concept, we present a prototype solution, the Serval Mesh, and briefly discuss the design decisions that were made, and summarise the trial and pilots conducted to date. Together, these show that the Serval Mesh is well placed to provide secure, resilient mobile communications services in a variety of situations, and in conjunction with the air-droppable UHF-packet-radio enabled Serval Mesh Extender concept, to provide such services over longer distances than is possible for Wi-Fi based mobile mesh networks. Thus we argue by example that it is possible to enable effective use of mobile phones during periods of infrastructure-deprivation.
Online Adaptive Passive-Aggressive Methods for Non-Negative Matrix Factorization and Its Applications

This paper aims to investigate efficient and scalable machine learning algorithms for resolving Non-negative Matrix Factorization (NMF), which is important for many real-world applications, particularly for collaborative filtering and recommender systems. Unlike traditional batch learning methods, a recently proposed online learning technique named "NN-PA" tackles NMF by applying the popular Passive-Aggressive (PA) online learning, and found promising results. Despite its simplicity and high efficiency, NN-PA falls short in at least two critical limitations: (i) it only exploits the first-order information and thus may converge slowly especially at the beginning of online learning tasks; (ii) it is sensitive to some key parameters which are often difficult to be tuned manually, particularly in a practical online learning system. In this work, we present a novel family of online Adaptive Passive-Aggressive (APA) learning algorithms for NMF, named "NN-APA", which overcomes two critical limitations of NN-PA by (i) exploiting second-order information to enhance PA in making more informative updates at each iteration; and (ii) achieving the parameter auto-selection by exploring the idea of online learning with expert advice in deciding the optimal combination of the key parameters in NMF. We theoretically analyze the regret bounds of the proposed method and show its advantage over the state-of-the-art NN-PA method, and further validate the efficacy and scalability of the proposed technique through an extensive set of experiments on a variety of large-scale real recommender systems datasets.
Build, Compute, Critique, Repeat: Data Analysis with Latent Variable Models

We survey latent variable models for solving data-analysis problems. A latent variable model is a probabilistic model that encodes hidden patterns in the data. We uncover these patterns from their conditional distribution and use them to summarize data and form predictions. Latent variable models are important in many fields, including computational biology, natural language processing, and social network analysis. Our perspective is that models are developed iteratively: We build a model, use it to analyze data, assess how it succeeds and fails, revise it, and repeat. We describe how new research has transformed these essential activities. First, we describe probabilistic graphical models, a language for formulating latent variable models. Second, we describe mean field variational inference, a generic algorithm for approximating conditional distributions. Third, we describe how to use our analyses to solve problems: exploring the data, forming predictions, and pointing us in the direction of improved models. 203 A nn ua l R ev ie w o f St at is tic s an d It s A pp lic at io n 20 14 .1 :2 03 -2 32 . D ow nl oa de d fr om w w w .a nn ua lr ev ie w s. or g by P ri nc et on U ni ve rs ity L ib ra ry o n 01 /0 9/ 14 . F or p er so na l u se o nl y. ST01CH10-Blei ARI 4 December 2013 17:0
Factors Influencing eWOM Effects : Using Experience , Credibility , and Susceptibility

Electronic Word-of-mouth(eWOM) is important information affecting consumers’ online buying. This study examined factors influencing eWOM effects in China. We developed a structural model of eWOM effects including customer susceptibility to interpersonal influence (CSII), using experience of eWOM , perceived eWOM credibility, and eWOM effects. As results of 209 survey data analysis, CSII influences using experience of eWOM , perceived eWOM credibility, and the eWOM effects, and using experience of eWOM influences perceived eWOM credibility and eWOM effects, and perceived eWOM credibility influences eWOM effects. Implication of the study and further research are suggested Index Terms eWOM effect; Using experience of eWOM; Perceived eWOM credibility
A ZVZCS PWM FB DC/DC converter using a modified energy-recovery snubber

The conventional high-frequency phase-shifted zerovoltage-switching (ZVS) full-bridge dc/dc converter has a disadvantage, in that a circulating current flows through transformer and switching devices during the freewheeling interval. Due to this circulating current, rms current stress, conduction losses of the transformer and switching devices are increased. To alleviate this problem, this paper proposes an improved zero-voltage zero-current switching (ZVZCS) phase-shifted full-bridge (FB) dc/dc converter with a modified energy-recovery snubber (ERS) attached at the secondary side of transformer. Also, the small signal model of the proposed ZVZCS FB dc/dc converter is derived by incorporating the effects introduced by a transformer leakage inductance and an ERS to achieve ZVZCS. Both analysis and experiment are performed to verify the proposed topology by implementing a 7-kW (120 Vdc, 58 A) 30-kHz insulated-gate-bipolartransistor-based experimental circuit
Smart home energy management system including renewable energy based on ZigBee and PLC

As home energy use is increasing and renewable energy systems are deployed, home energy management system (HEMS) needs to consider both energy consumption and generation simultaneously to minimize the energy cost. This paper proposes a smart HEMS architecture that considers both energy consumption and generation simultaneously. ZigBee-based energy measurement modules are used to monitor the energy consumption of home appliances and lights. A PLC-based renewable energy gateway is used to monitor the energy generation of renewable energies. The home server gathers the energy consumption and generation data, analyzes them for energy estimation, and controls the home energy use schedule to minimize the energy cost. The remote energy management server aggregates the energy data from numerous home servers, compares them, and creates useful statistical analysis information. By considering both energy consumption and generation, the proposed HEMS architecture is expected to optimize home energy use and result in home energy cost saving.
The WHO public health approach to HIV treatment and care: looking back and looking ahead.

In 2006, WHO set forth its vision for a public health approach to delivering antiretroviral therapy. This approach has been broadly adopted in resource-poor settings and has provided the foundation for scaling up treatment to over 19·5 million people. There is a global commitment to end the AIDS epidemic as a public health threat by 2030 and, to support this goal, there are opportunities to adapt the public health approach to meet the ensuing challenges. These challenges include the need to improve identification of people with HIV infection through expanded approaches to testing; further simplify and improve treatment and laboratory monitoring; adapt the public health approach to concentrated epidemics; and link HIV testing, treatment, and care to HIV prevention. Implementation of these key public health principles will bring countries closer to the goals of controlling the HIV epidemic and providing universal health coverage.
Secondary Peak Detection of PPG Signal for Continuous Cuffless Arterial Blood Pressure Measurement

The arterial blood pressure (ABP) is one of the most important physiological parameters for health monitoring. Most of the blood measurement devices in the market determine the ABP through the inflation and the deflation of a cuff controlled by a bladder. This method is very uncomfortable for most of the users and may even cause anxiety, which in turn can affect the blood pressure (BP) (white coat syndrome). This paper investigates a cuffless nonintrusive approach to estimate the BP. The main idea is to measure the pulse transit time (PTT), i.e., the delay between the R-peak of the electrocardiogram (ECG) signal and the following peak of the finger photoplethysmograph (PPG) signal. The main problem of this approach is that when the dicrotic notch of the PPG signal is unobservable, the position and the amplitude of the main peak of the PPG signal will be changed. As a result, the correlation between the BP and the PTT can be affected. To overcome this problem, three types of secondary peak detection methods are designed to reveal the secondary peak from the original PPG signal. Actual ECG, PPG, and the BP measurements extracted from the Multiparameter Intelligent Monitoring in Intensive Care II database that contains clinical signal data reflecting real measurements are used. The results verify that the proposed detection methods improve the correlation relationship between the BP and the PTT, and demonstrate that the adjusted PTT can be used as an indicator of the ABP by removing the dicrotic notch impact on the PPG signal.
Robust model predictive control for an uncertain smart thermal grid

The focus of this paper is on modeling and control of Smart Thermal Grids (STGs) in which the uncertainties in the demand and/or supply are included. We solve the corresponding robust model predictive control (MPC) optimization problem using mixed-integer-linear programming techniques to provide a day-ahead prediction for the heat production in the grid. In an example, we compare the robust MPC approach with the robust optimal control approach, in which the day-ahead production plan is obtained by optimizing the objective function for entire day at once. There, we show that the robust MPC approach successfully keeps the supply-demand balance in the STG while satisfying the constraints of the production units in the presence of uncertainties in the heat demand. Moreover, we see that despite the longer computation time, the performance of the robust MPC controller is considerably better than the one of the robust optimal controller.
Fire risk evaluation using multicriteria analysis--a case study.

Forest fires are one of the major causes of ecological disturbance and environmental concerns in tropical deciduous forests of south India. In this study, we use fuzzy set theory integrated with decision-making algorithm in a Geographic Information Systems (GIS) framework to map forest fire risk. Fuzzy set theory implements classes or groupings of data with boundaries that are not sharply defined (i.e., fuzzy) and consists of a rule base, membership functions, and an inference procedure. We used satellite remote sensing datasets in conjunction with topographic, vegetation, climate, and socioeconomic datasets to infer the causative factors of fires. Spatial-level data on these biophysical and socioeconomic parameters have been aggregated at the district level and have been organized in a GIS framework. A participatory multicriteria decision-making approach involving Analytical Hierarchy Process has been designed to arrive at a decision matrix that identified the important causative factors of fires. These expert judgments were then integrated using spatial fuzzy decision-making algorithm to map the forest fire risk. Results from this study were quite useful in identifying potential "hotspots" of fire risk, where forest fire protection measures can be taken in advance. Further, this study also demonstrates the potential of multicriteria analysis integrated with GIS as an effective tool in assessing "where and when" forest fires will most likely occur.
A Dolev-Yao Model for Zero Knowledge

In cryptographic protocols, zero knowledge proofs are employed for a principal A to communicate some non-trivial information t to B while at the same time ensuring that B cannot derive any information “stronger” than t . Oen this implies that B can verify that some property holds without being able to produce a proof of this. While a rich theory of zero knowledge proofs exists, there are few formal models addressing verification questions.We propose an extension of the standard Dolev-Yao model of cryptographic protocols which involves not only constructibility of terms but also a form of verifiability. We present a proof system for term derivability, which is employed to yield a decision procedure for checking whether a given protocol meets its zero knowledge specification.