nils-herrmann/clean_scirepeval huggingface dataset:
E-commerce Product Recommendation by Personalized Promotion and Total Surplus Maximization

Existing recommendation algorithms treat recommendation problem as rating prediction and the recommendation quality is measured by RMSE or other similar metrics. However, we argued that when it comes to E-commerce product recommendation, recommendation is more than rating prediction by realizing the fact price plays a critical role in recommendation result. In this work, we propose to build E-commerce product recommender systems based on fundamental economic notions. We first proposed an incentive compatible method that can effectively elicit consumer's willingness-to-pay in a typical E-commerce setting and in a further step, we formalize the recommendation problem as maximizing total surplus. We validated the proposed WTP elicitation algorithm through crowd sourcing and the results demonstrated that the proposed approach can achieve higher seller profit by personalizing promotion. We also proposed a total surplus maximization (TSM) based recommendation framework. We specified TSM by three of the most representative settings - e-commerce where the product quantity can be viewed as infinity, P2P lending where the resource is bounded and freelancer marketing where the resource (job) can be assigned to one freelancer. The experimental results of the corresponding datasets shows that TSM exceeds existing approach in terms of total surplus.
A thermal imaging based wireless sensor network for automatic water leakage detection in distribution pipes

Water leakage is a significant problem in both developing and developed countries causing water loss in water-distribution systems. Leakage causes economic loss in the form of wastage of water, damage to pipe networks and foundations of roads and buildings, and also poses risk to public health due to water contamination. The lost or unaccounted amount of water is typically 20-30 percent of production. Some older systems may lose even up to 50 percent. The water pipe networks in houses as well as public places are generally concealed and hence detecting water leakage in the initial stages before an upcoming damage is difficult. The existing technologies for detecting leakage have various limitations such as efficiency being dependent on size, material and depth of pipes, need for manual intervention, dependency on weather and surface conditions and effect of water pressure. In this paper, we propose an automated water leakage detection system using a wireless sensor network created along the distribution pipes based on thermal (IR) imaging. Thermal imaging has the capability to work in low lighting or dark conditions and helps in capturing the contrast between hot and cold areas created due to water leakage. A network of low cost, low power thermal imaging sensors each having its own processing and Radiofrequency (RF) Transreceiver units and operating independent of pipe, weather or surface conditions is proposed in this paper. A central database is updated on a real-time basis, enabling very early leakage detection and initiating subsequent action to address the problem. An example system evaluation is performed and results highlighting the power and cost impact of the sensor network system are presented.
Comparing performances of logistic regression, classification and regression tree, and neural networks for predicting coronary artery disease

In this study, performances of classification techniques were compared in order to predict the presence of coronary artery disease (CAD). A retrospective analysis was performed in 1245 subjects (865 presence of CAD and 380 absence of CAD). We compared performances of logistic regression (LR), classification and regression tree (CART), multi-layer perceptron (MLP), radial basis function (RBF), and self-organizing feature maps (SOFM). Predictor variables were age, sex, family history of CAD, smoking status, diabetes mellitus, systemic hypertension, hypercholesterolemia, and body mass index (BMI). Performances of classification techniques were compared using ROC curve, Hierarchical Cluster Analysis (HCA), and Multidimensional Scaling (MDS). Areas under the ROC curves are 0.783, 0.753, 0.745, 0.721, and 0.675, respectively for MLP, LR, CART, RBF, and SOFM. MLP was found the best technique to predict presence of CAD in this data set, given its good classificatory performance. MLP, CART, LR, and RBF performed better than SOFM in predicting CAD in according to HCA and MDS.
Static Analysis of Android Programs

Our goal is to extend the Julia static analyzer, based on abstract interpretation, to perform formally correct analyses of Android programs. This article is an in depth description of such an extension,of the difficulties that we faced and of the results that we obtained. • We have extended the class analysis of the Julia analyzer, which lies at the heart of many other analyses, by considering some Android key specific features • Classcast, dead code, nullness and termination analysis are done. • Formally correct results in at most 7 min and on standard hardware. • As a language, Android is Java with an extended library for mobile and interactive applications, hence based on an eventdriven architecture.
Sentential Paraphrasing as Black-Box Machine Translation

We present a simple, prepackaged solution to generating paraphrases of English sentences. We use the Paraphrase Database (PPDB) for monolingual sentence rewriting and provide machine translation language packs: prepackaged, tuned models that can be downloaded and used to generate paraphrases on a standard Unix environment. The language packs can be treated as a black box or customized to specific tasks. In this demonstration, we will explain how to use the included interactive webbased tool to generate sentential paraphrases.
Self-heat modeling of multi-finger n-MOSFETs for RF-CMOS applications

The purpose of this research is to characterize and model the self-heating effect of multi-finger n-channel MOSFETs. Self-heating effect (SHE) does not need to be analyzed for single-finger bulk CMOS devices. However, it should be considered for multi-finger n-channel MOSFETs that are mainly used for RF-CMOS applications. The SHE mechanism was analyzed based on a two-dimensional device simulator. A compact model, which is a BSIM6 model with additional equations, was developed and implemented in a SPICE simulator with Verilog-A language. Using the proposed model and extracted parameters excellent agreements have been obtained between measurements and simulations in DC and S-parameter domain whereas the original BSIM6 shows inconsistency between static DC and small signal AC simulations due to the lack of SHE. Unlike the generally-used sub-circuits based SHE models including in BSIMSOI models, the proposed SHE model can converge in large scale circuits.
Brain segmentation and white matter lesion detection in MR images.

This paper presents a review of methods and techniques that have been proposed for the segmentation of magnetic resonance (MR) images of the brain, with a special emphasis on the segmentation of white matter lesions. First, artifacts affecting MR images (noise, partial volume effect, and shading artifact) are reviewed and methods that have been proposed to correct for these artifacts are discussed. Next, a taxonomy of generic segmentation algorithms is presented, categorized as region-based, edge-based, and classification algorithms. For each category, the applications proposed in the literature are subdivided into 2-D, 3-D, or multimodal approaches. In each case, tables listing authors, bibliographic references, and methods used have been compiled and are presented. This description of segmentation algorithms is followed by a section on techniques proposed specifically for the analysis of white matter lesions. Finally, a section is dedicated to a review and a comparison of validation methods proposed to assess the accuracy and the reliability of the results obtained with various segmentation algorithms.
A robust human activity recognition system using smartphone sensors and deep learning

In last few decades, human activity recognition grabbed considerable research attentions from a wide range of pattern recognition and human-computer interaction researchers due to its prominent applications such as smart home health care. For instance, activity recognition systems can be adopted in a smart home health care system to improve their rehabilitation processes of patients. There are various ways of using different sensors for human activity recognition in a smartly controlled environment. Among which, physical human activity recognition through wearable sensors provides valuable information about an individual’s degree of functional ability and lifestyle. In this paper, we present a smartphone inertial sensors-based approach for human activity recognition. Efficient features are first extracted from raw data. The features include mean, median, autoregressive coefficients, etc. The features are further processed by a kernel principal component analysis (KPCA) and linear discriminant analysis (LDA) to make them more robust. Finally, the features are trained with a Deep Belief Network (DBN) for successful activity recognition. The proposed approach was compared with traditional expression recognition approaches such as typical multiclass Support Vector Machine (SVM) and Artificial Neural Network (ANN) where it outperformed them.  Activity Recognition, Sensors, Smartphones, Deep Belief Network.
An investigation of multi-speaker training for wavenet vocoder

In this paper, we investigate the effectiveness of multi-speaker training for WaveNet vocoder. In our previous work, we have demonstrated that our proposed speaker-dependent (SD) WaveNet vocoder, which is trained with a single speaker's speech data, is capable of modeling temporal waveform structure, such as phase information, and makes it possible to generate more naturally sounding synthetic voices compared to conventional high-quality vocoder, STRAIGHT. However, it is still difficult to generate synthetic voices of various speakers using the SD-WaveNet due to its speaker-dependent property. Towards the development of speaker-independent WaveNet vocoder, we apply multi-speaker training techniques to the WaveNet vocoder and investigate its effectiveness. The experimental results demonstrate that 1) the multispeaker WaveNet vocoder still outperforms STRAIGHT in generating known speakers' voices but it is comparable to STRAIGHT in generating unknown speakers' voices, and 2) the multi-speaker training is effective for developing the WaveNet vocoder capable of speech modification.
Iterative ICI Cancellation in MIMO OFDM WiMAX System with LDPC Channel Coding

The employment of MIMO OFDM technique constitutes a cost effective approach to high throughput wireless communications. The system performance is sensitive to frequency offset which increases with the doppler spread and causes Intercarrier interference (ICI). ICI is a major concern in the design as it can potentially cause a severe deterioration of quality of service (QoS) which necessitates the need for a high speed data detection and decoding with ICI cancellation along with the intersymbol interference (ISI) cancellation in MIMO OFDM communication systems. Iterative parallel interference canceller (PIC) with joint detection and decoding is a promising approach which is used in this work. The receiver consists of a two stage interference canceller. The co channel interference cancellation is performed based on Zero Forcing (ZF) Detection method used to suppress the effect of ISI in the first stage. The latter stage consists of a simplified PIC scheme. High bit error rates of wireless communication system require employing forward error correction (FEC) methods on the data transferred in order to avoid burst errors that occur in physical channel. To achieve high capacity with minimum error rate Low Density Parity Check (LDPC) codes which have recently drawn much attention because of their error correction performance is used in this system. The system performance is analyzed for two different values of normalized doppler shift for varying speeds. The bit error rate (BER) is shown to improve in every iteration due to the ICI cancellation. The interference analysis with the use of ICI cancellation is examined for a range of normalized doppler shift which corresponds to mobile speeds varying from 5Km/hr to 250Km/hr.
BlueTorrent: Cooperative Content Sharing for Bluetooth Users

People wish to enjoy their everyday lives in various ways, among which entertainment plays a major role. In order to improve lifestyle with more ready access to entertainment content, we propose BlueTorrent, a P2P file sharing application based on ubiquitous Blue tooth-enabled devices such as PDAs, cellphones and smart phones. Using BlueTorrent, people can share audio/video contents as they move about shopping malls, airports, subway stations etc. BlueTorrent poses new challenges caused by limited bandwidth, short communications range, mobile users and variable population density. A key ingredient is efficient peer discovery. This paper approaches the problem by analyzing the Bluetooth periodic inquiry mode and by finding the optimum inquiry/connection time settings. At the application layer, the BlueTorrent index/block dissemination protocol is then designed and analyzed. The entire system is integrated and implemented both in simulation and in an experimental testbed. Simulation and measurement results are used to evaluate and validate the performance of BlueTorrent in content sharing scenarios
MuseUs: Case study of a pervasive cultural heritage serious game

This article describes a case study on MuseUs, a pervasive serious game for use in museums, running as a smartphone app. During the museum visit, players are invited to create their own exposition and are guided by the application in doing so. The aim is to provide a learning effect during a visit to a museum exhibition. Central to the MuseUs experience is that it does not necessitate a predefined path trough the museum and that it does not draw the attention away from the exposition itself. Also, the application stimulates the visitor to look at cultural heritage elements in a different way, permitting the construction of personal narratives while creating a personal exposition. Using a methodology derived from action research, we present recommendations for the design of similar applications and conclude by proposing a high-level architecture for pervasive serious games applied to cultural heritage.
Cooperative Game Theory for Distributed Spectrum Sharing

There is a need for new spectrum access protocols that are opportunistic, flexible and efficient, yet fair. Game theory provides a framework for analyzing spectrum access, a problem that involves complex distributed decisions by independent spectrum users. We develop a cooperative game theory model to analyze a scenario where nodes in a multi-hop wireless network need to agree on a fair allocation of spectrum. We show that in high interference environments, the utility space of the game is non-convex, which may make some optimal allocations unachievable with pure strategies. However, we show that as the number of channels available increases, the utility space becomes close to convex and thus optimal allocations become achievable with pure strategies. We propose the use of the Nash Bargaining Solution and show that it achieves a good compromise between fairness and efficiency, using a small number of channels. Finally, we propose a distributed algorithm for spectrum sharing and show that it achieves allocations reasonably close to the Nash Bargaining Solution.
How shall I trust the faceless and the intangible? A literature review on the antecedents of online trust

0747-5632/$ see front matter 2010 Elsevier Ltd. A doi:10.1016/j.chb.2010.03.013 * Corresponding author.  fax E-mail addresses: a.beldad@utwente.nl, adbeldad@ jong@utwente.nl (M. de Jong), m.f.steehouder@utwen 1 Tel.: +31 53 489 3313; fax: +31 53 489 4259. 2 Tel.: +31 53 489 3315; fax: +31 53 489 4259. Trust is generally assumed to be an important precondition for people’s adoption of electronic services. This paper provides an overview of the available research into the antecedents of trust in both commercial and non-commercial online transactions and services. A literature review was conducted covering empirical studies on people’s trust in and adoption of computer-mediated services. Results are described using a framework of three clusters of antecedents: customer/client-based, website-based, and company/ organization-based antecedents. Results show that there are many possible antecedents of trust in electronic services. The majority of the research has been conducted in the context of e-commerce; only few studies are available in the domains of e-government and e-health. For many antecedents, some empirical support can be found, but the results are far from univocal. The research calls for more, and particularly more systematic, research attention for the antecedents of trust in electronic services. The review presented in this paper offers practitioners an overview of possibly relevant variables that may affect people’s trust in electronic services. It also gives a state-of-the-art overview of the empirical support for the relevance of these variables.
Evolving Signal Processing for Brain–Computer Interfaces

Because of the increasing portability and wearability of noninvasive electrophysiological systems that record and process electrical signals from the human brain, automated systems for assessing changes in user cognitive state, intent, and response to events are of increasing interest. Brain-computer interface (BCI) systems can make use of such knowledge to deliver relevant feedback to the user or to an observer, or within a human-machine system to increase safety and enhance overall performance. Building robust and useful BCI models from accumulated biological knowledge and available data is a major challenge, as are technical problems associated with incorporating multimodal physiological, behavioral, and contextual data that may in the future be increasingly ubiquitous. While performance of current BCI modeling methods is slowly increasing, current performance levels do not yet support widespread uses. Here we discuss the current neuroscientific questions and data processing challenges facing BCI designers and outline some promising current and future directions to address them.
Electric field and potential determination for electrowinning cells with bipolar electrodes by finite difference models

This paper is a contribution to the electric modeling of electrochemical cells. Specifically, cells for a new copper electrowinning process, which uses bipolar electrodes, are studied. Electrowinning is used together with solvent extraction and has gained great importance, due to its significant cost and environmental advantages, as compared to other copper reduction methods. Current electrowinning cells use unipolar electrodes connected electrically in parallel. Instead, bipolar electrodes, are connected in series. They are also called floating, because they are not wire-connected, but just immersed in the electrolyte. The main advantage of this technology is that, for the same copper production, a cell requires a much lower DC current, as compared with the unipolar case. This allows the cell to be supplied from a modular and compact PWM rectifier instead of a bulk high current thyristor rectifier, having a significant economic impact. In order to study the quality of the copper, finite difference algorithms in two dimensions are derived to obtain the distribution of the potential and the electric field inside the cell. Different geometrical configurations of cell and floating electrodes are analyzed. The proposed method is a useful tool for analysis and design of electrowinning cells, reducing the time-consuming laboratory implementations.
Progress and Prospects of Long Noncoding RNAs (lncRNAs) in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is one of the most frequently occurring cancers with poor prognosis, and novel diagnostic or prognostic biomarkers and therapeutic targets for HCC are urgently required. With the advance of high-resolution microarrays and massively parallel sequencing technology, lncRNAs are suggested to play critical roles in the tumorigenesis and development of human HCC. To date, dysregulation of many HCC-related lncRNAs such as HULC, HOTAIR, MALAT1, and H19 have been identified. From transcriptional "noise" to indispensable elements, lncRNAs may re-write the central dogma. Also, lncRNAs found in body fluids have demonstrated their utility as fluid-based noninvasive markers for clinical use and as therapeutic targets for HCC. Even though several lncRNAs have been characterized, the underlying mechanisms of their contribution to HCC remain unknown, and many important questions about lncRNAs need resolving. A better understanding of the molecular mechanism in HCC-related lncRNAs will provide a rationale for novel effective lncRNA-based targeted therapies. In this review, we highlight the emerging roles of lncRNAs in HCC, and discuss their potential clinical applications as biomarkers for the diagnosis, prognosis, monitoring and treatment of HCC.
LHD: Improving Cache Hit Rate by Maximizing Hit Density

Cloud application performance is heavily reliant on the hit rate of datacenter key-value caches.  caches typically use least recently used (LRU) as their eviction policy, but LRU’s hit rate is far from optimal under real workloads. Prior research has proposed many eviction policies that improve on LRU, but these policies make restrictive assumptions that hurt their hit rate, and they can be difficult to implement efficiently. We introduce least hit density (LHD), a novel eviction policy for key-value caches. LHD predicts each object’s expected hits-per-space-consumed (hit density), filtering objects that contribute little to the cache’s hit rate. Unlike prior eviction policies, LHD does not rely on heuristics, but rather rigorously models objects’ behavior using conditional probability to adapt its behavior in real time. To make LHD practical, we design and implement RankCache, an efficient key-value cache based on memcached. We evaluate RankCache and LHD on commercial memcached and enterprise storage traces, where LHD consistently achieves better hit rates than prior policies. LHD requires much less space than prior policies to match their hit rate, on average 8× less than LRU and 2–3× less than recently proposed policies. Moreover, RankCache requires no synchronization in the common case, improving request throughput at 16 threads by 8× over LRU and by 2× over CLOCK.
Requirements and Design Considerations for a Fully Immersive Robotic Telepresence System

In an ideal robotic telepresence system the visitor would perceive and be perceived as being fully present at a remote location. This can happen if the visitor has a first person experience of the destination location including full sensory stimulation, and if the people at the remote location would be able to naturally perceive and interact with the visitor as a regular human being. In this paper, we report on the requirements and design considerations for a fully immersive robotic telepresence system that were gathered for an FP7 EU telepresence project aiming to address these issues. Based on these requirements we list a set of user tasks that such a system should support and discuss some possible design tradeoffs.