nils-herrmann/clean_scirepeval huggingface dataset:
The Truth and Nothing But the Truth: Multimodal Analysis for Deception Detection

We propose a data-driven method for automatic deception detection in real-life trial data using visual and verbal cues. Using OpenFace with facial action unit recognition, we analyze the movement of facial features of the witness when posed with questions and the acoustic patterns using OpenSmile. We then perform a lexical analysis on the spoken words, emphasizing the use of pauses and utterance breaks, feeding that to a Support Vector Machine to test deceit or truth prediction. We then try out a method to incorporate utterance-based fusion of visual and lexical analysis, using string based matching.
Modeling Heterogeneous Network Interference Using Poisson Point Processes

Cellular systems are becoming more heterogeneous with the introduction of low power nodes including femtocells, relays, and distributed antennas. Unfortunately, the resulting interference environment is also becoming more complicated, making evaluation of different communication strategies challenging in both analysis and simulation. Leveraging recent applications of stochastic geometry to analyze cellular systems, this paper proposes to analyze downlink performance in a fixed-size cell, which is inscribed within a weighted Voronoi cell in a Poisson field of interferers. A nearest out-of-cell interferer, out-of-cell interferers outside a guard region, and cross-tier interferers are included in the interference calculations. Bounding the interference power as a function of distance from the cell center, the total interference is characterized through its Laplace transform. An equivalent marked process is proposed for the out-of-cell interference under additional assumptions. To facilitate simplified calculations, the interference distribution is approximated using the Gamma distribution with second order moment matching. The Gamma approximation simplifies calculation of the success probability and average rate, incorporates small-scale and large-scale fading, and works with co-tier and cross-tier interference. Simulations show that the proposed model provides a flexible way to characterize outage probability and rate as a function of the distance to the cell edge.
Feature Selection Methods in Persian Sentiment Analysis

With the enormous growth of digital content in internet, various types of online reviews such as product and movie reviews present a wealth of subjective information that can be very helpful for potential users. Sentiment analysis aims to use automated tools to detect subjective information from reviews. Up to now as there are few researches conducted on feature selection in sentiment analysis, there are very rare works for Persian sentiment analysis. This paper considers the problem of sentiment classification using different feature selection methods for online customer reviews in Persian language. Three of the challenges of Persian text are using of a wide variety of declensional suffixes, different word spacing and many informal or colloquial words. In this paper we study these challenges by proposing a model for sentiment classification of Persian review documents. The proposed model is based on stemming and feature selection and is employed Naive Bayes algorithm for classification. We evaluate the performance of the model on a collection of cellphone reviews, where the results show the effectiveness of the proposed approaches.
Multi-domain Neural Network Language Generation for Spoken Dialogue Systems

Moving from limited-domain natural language generation (NLG) to open domain is difficult because the number of semantic input combinations grows exponentially with the number of domains. Therefore, it is important to leverage existing resources and exploit similarities between domains to facilitate domain adaptation. In this paper, we propose a procedure to train multi-domain, Recurrent Neural Network-based (RNN) language generators via multiple adaptation steps. In this procedure, a model is first trained on counterfeited data synthesised from an out-of-domain dataset, and then fine tuned on a small set of in-domain utterances with a discriminative objective function. Corpus-based evaluation results show that the proposed procedure can achieve competitive performance in terms of BLEU score and slot error rate while significantly reducing the data needed to train generators in new, unseen domains. In subjective testing, human judges confirm that the procedure greatly improves generator performance when only a small amount of data is available in the domain.
Ultra-wide band CPW to substrate integrated waveguide (SIW) transition based on a U-shaped slot antenna

A mm-wave transition from coplanar waveguide (CPW) to substrate integrated waveguide (SIW) is presented based on a U-shaped coplanar slot antenna. Using 3D FEM simulations the input impedance exhibited by the structure is analyzed, and simple equations are extracted to design an ultra-wide band transition once the substrate material and the waveguide cut-off frequency are chosen. As an example, a W-band transition on a high resistivity silicon-filled waveguide is implemented: an input matching better than -14.5 dB from 79 to 110 GHz and insertion loss of 0.6 dB at 110 GHz are measured.
What drives mobile commerce?: An empirical evaluation of the revised technology acceptance model

This study presents an extended technology acceptance model (TAM) that integrates innovation diffusion theory, perceived risk and cost into the TAM to investigate what determines user mobile commerce (MC) acceptance. The proposed model was empirically tested using data collected from a survey of MC consumers. The structural equation modeling technique was used to evaluate the causal model and confirmatory factor analysis was performed to examine the reliability and validity of the measurement model. Our findings indicated that all variables except perceived ease of use significantly affected users’ behavioral intent. Among them, the compatibility had the most significant influence. Furthermore, a striking, and somewhat puzzling finding was the positive influence of perceived risk on behavioral intention to use. The implication of this work to both researchers and practitioners is discussed.
A level set method for structural topology optimization

This paper presents a new approach to structural topology optimization. We represent the structural boundary by a level set model that is embedded in a scalar function of a higher dimension. Such level set models are flexible in handling complex topological changes and are concise in describing the boundary shape of the structure. Furthermore, a wellfounded mathematical procedure leads to a numerical algorithm that describes a structural optimization as a sequence of motions of the implicit boundaries converging to an optimum solution and satisfying specified constraints. The result is a 3D topology optimization technique that demonstrates outstanding flexibility of handling topological changes, fidelity of boundary representation and degree of automation. We have implemented the algorithm with the use of several robust and efficient numerical techniques of level set methods. The benefit and the advantages of the proposed method are illustrated with several 2D examples that are widely used in the recent literature of topology optimization, especially in the homogenization based methods.
Security Challenges and Approaches in Online Social Networks : A Survey

Social Networks (SN) Sites are becoming very popular and the number of users is increasing rapidly. However, with that increase there is also an increase in the security threats which affect the users’ privacy, identity and confidentiality. Different research groups highlighted the security threats in SN and attempted to offer some solutions to these issues. In this paper we survey several examples of this research and highlight the approaches. All the models we surveyed were focusing on protecting users’ information yet they failed to cover other important issues. For example, none of the mechanisms provided the users with control over what others can reveal about them; and encryption of images is still not achieved properly. Generally having higher security measures will affect the system’s performance in terms of speed and response time. However, this trade-off was not discussed or addressed in any of the models we surveyed.
A low energy crystal-less double-FSK transceiver for wireless body-area-network

An energy-efficient crystal-less double-FSK transceiver for wireless body-area-network (WBAN) is implemented in 0.18 μm CMOS technology with 1 V supply. The injection-locking digitally-controlled oscillator (IL-DCO) replaces the crystal oscillator (XO), which leads to reduce energy consumption and system cost. It can detect whether injection locking occurs or not, and calibrates the frequency drift of DCO within 100 kHz accuracy over 100 degrees C temperature variation. The scalable divider-based double-FSK transmitter eliminates the necessity of power-consuming voltage-controlled oscillator (VCO) and direct digital synthesizer (DDS). The frequency calibration with IL-DCO and transmitter consumes 1 mW and 2 mW, respectively, with a data rate of 10 Mb/s, corresponding to energy consumption of 0.2 nJ per transmitted bit.
UQAM-NTL: Named entity recognition in Twitter messages

This paper describes our system used in the 2 Workshop on Noisy User-generated Text (WNUT) shared task for Named Entity Recognition (NER) in Twitter, in conjunction with Coling 2016. Our system is based on supervised machine learning by applying Conditional Random Fields (CRF) to train two classifiers for two different evaluations. The first evaluation aims at predicting the 10 fine-grained types of named entities; while the second evaluation aims at predicting no type of named entities. The experimental results show that our method has significantly improved Twitter NER performance.
An IoT based scholar bus monitoring system

School transport is used by millions of children worldwide. However, not a substantial effort is done in order to improve the existing school transport systems. This paper presents the development of an IoT based scholar bus monitoring system. The development of new telematics technologies has enabled the development of various Intelligent Transport Systems. However, these are not presented as ITS services to end users. This paper presents the development of an IoT based scholar bus monitoring system that through localization and speed sensors will allow many stakeholders such as parents, the goverment, the school and many other authorities to keep realtime track of the scholar bus behavior, resulting in a better controlled scholar bus.
A Novel Scheme for an Energy Efficient Internet of Things Based on Wireless Sensor Networks

One of the emerging networking standards that gap between the physical world and the cyber one is the Internet of Things. In the Internet of Things, smart objects communicate with each other, data are gathered and certain requests of users are satisfied by different queried data. The development of energy efficient schemes for the IoT is a challenging issue as the IoT becomes more complex due to its large scale the current techniques of wireless sensor networks cannot be applied directly to the IoT. To achieve the green networked IoT, this paper addresses energy efficiency issues by proposing a novel deployment scheme. This scheme, introduces: (1) a hierarchical network design; (2) a model for the energy efficient IoT; (3) a minimum energy consumption transmission algorithm to implement the optimal model. The simulation results show that the new scheme is more energy efficient and flexible than traditional WSN schemes and consequently it can be implemented for efficient communication in the IoT.
Using Block Chain for Peer-to-Peer Proof-of-Location

Location-Based Services (LBSs) build upon geographic information to provide users with locationdependent functionalities. In such a context, it is particularly important that geographic locations claimed by users are the actual ones. Verification approaches proposed in the last few years are not satisfactory, as they depend on a centralized infrastructure that entails a high risk to the privacy of users. In this paper, we present a novel approach for producing proofs-of-location, i.e., digital certificates that attest someone’s presence at a certain geographic location, at some point in time, whereby LBSs can validate user locations. In particular, our approach relies on the block chain — mostly known for being Bitcoin’s core technology — to design a completely decentralized peerto-peer architecture that guarantees location trustworthiness and preserves user privacy, at the same time.
Forecasting user visits for online display advertising

Online display advertising is a multi-billion dollar industry where advertisers promote their products to users by having publishers display their advertisements on popular Web pages. An important problem in online advertising is how to forecast the number of user visits for a Web page during a particular period of time. Prior research addressed the problem by using traditional time-series forecasting techniques on historical data of user visits; (e.g., via a single regression model built for forecasting based on historical data for all Web pages) and did not fully explore the fact that different types of Web pages and different time stamps have different patterns of user visits. In this paper, we propose a series of probabilistic latent class models to automatically learn the underlying user visit patterns among multiple Web pages and multiple time stamps. The last (and the most effective) proposed model identifies latent groups/classes of (i) Web pages and (ii) time stamps with similar user visit patterns, and learns a specialized forecast model for each latent Web page and time stamp class. Compared with a single regression model as well as several other baselines, the proposed latent class model approach has the capability of differentiating the importance of different types of information across different classes of Web pages and time stamps, and therefore has much better modeling flexibility. An extensive set of experiments along with detailed analysis carried out on real-world data from Yahoo! demonstrates the advantage of the proposed latent class models in forecasting online user visits in online display advertising.
Document Representation and Query Expansion Models for Blog Recommendation

We explore several different document representation models and two query expansion models for the task of recommending blogs to a user in response to a query. Blog relevance ranking differs from traditional document ranking in ad-hoc information retrieval in several ways: (1) the unit of output (the blog) is composed of a collection of documents (the blog posts) rather than a single document, (2) the query represents an ongoing – and typically multifaceted – interest in the topic rather than a passing ad-hoc information need and (3) due to the propensity of spam, splogs, and tangential comments, the blogosphere is particularly challenging to use as a source for high-quality query expansion terms. We address these differences at the document representation level, by comparing retrieval models that view either the blog or its constituent posts as the atomic units of retrieval, and at the query expansion level, by making novel use of the links and anchor text in Wikipedia to expand a user’s initial query. We develop two complementary models of blog retrieval that perform at comparable levels of precision and recall. We also show consistent and significant improvement across all models using our Wikipedia expansion strategy.
Object Counts! Bringing Explicit Detections Back into Image Captioning

The use of explicit object detectors as an intermediate step to image captioning – which used to constitute an essential stage in early work – is often bypassed in the currently dominant end-to-end approaches, where the language model is conditioned directly on a midlevel image embedding. We argue that explicit detections provide rich semantic information, and can thus be used as an interpretable representation to better understand why end-to-end image captioning systems work well. We provide an in-depth analysis of end-to-end image captioning by exploring a variety of cues that can be derived from such object detections. Our study reveals that end-to-end image captioning systems rely on matching image representations to generate captions, and that encoding the frequency, size and position of objects are complementary and all play a role in forming a good image representation. It also reveals that different object categories contribute in different ways towards image captioning.
A matrix-free cone complementarity approach for solving large-scale , nonsmooth , rigid body dynamics Preprint ANL / MCS-P 1692-1109

This paper proposes an iterative method that can simulate mechanical systems featuring a large number of contacts and joints between rigid bodies. The numerical method behaves as a contractive mapping that converges to the solution of a cone complementarity problem by means of iterated fixed-point steps with separable projections onto convex manifolds. Since computational speed and robustness are important issues when dealing with a large number of frictional contacts, we have performed special algorithmic optimizations in order to translate the numerical scheme into a matrix-free algorithm with O(n) space complexity and easy implementation.
Adaptable Blockchain-Based Systems: A Case Study for Product Traceability

Traceability allows tracking products through all stages of a supply chain, which is crucial for product quality control. To provide accountability and forensic information, traceability information must be secured. This is challenging because traceability systems often must adapt to changes in regulations and to customized traceability inspection processes. OriginChain is a real-world traceability system using a blockchain. Blockchains are an emerging data storage technology that enables new forms of decentralized architectures. Components can agree on their shared states without trusting a central integration point. OriginChain’s architecture provides transparent tamper-proof traceability information, automates regulatory compliance checking, and enables system adaptability.
Efficient Testing of Recovery Code Using Fault Injection

A critical part of developing a reliable software system is testing its recovery code. This code is traditionally difficult to test in the lab, and, in the field, it rarely gets to run; yet, when it does run, it must execute flawlessly in order to recover the system from failure. In this article, we present a library-level fault injection engine that enables the productive use of fault injection for software testing. We describe automated techniques for reliably identifying errors that applications may encounter when interacting with their environment, for automatically identifying high-value injection targets in program binaries, and for producing efficient injection test scenarios. We present a framework for writing precise triggers that inject desired faults, in the form of error return codes and corresponding side effects, at the boundary between applications and libraries. These techniques are embodied in LFI, a new fault injection engine we are distributing http://lfi.epfl.ch. This article includes a report of our initial experience using LFI. Most notably, LFI found 12 serious, previously unreported bugs in the MySQL database server, Git version control system, BIND name server, Pidgin IM client, and PBFT replication system with no developer assistance and no access to source code. LFI also increased recovery-code coverage from virtually zero up to 60% entirely automatically without requiring new tests or human involvement.
Unsupervised Visual Hashing with Semantic Assistant for Content-Based Image Retrieval

As an emerging technology to support scalable content-based image retrieval (CBIR), hashing has recently received great attention and became a very active research domain. In this study, we propose a novel unsupervised visual hashing approach called semantic-assisted visual hashing (SAVH). Distinguished from semi-supervised and supervised visual hashing, its core idea is to effectively extract the rich semantics latently embedded in auxiliary texts of images to boost the effectiveness of visual hashing without any explicit semantic labels. To achieve the target, a unified unsupervised framework is developed to learn hash codes by simultaneously preserving visual similarities of images, integrating the semantic assistance from auxiliary texts on modeling high-order relationships of inter-images, and characterizing the correlations between images and shared topics. Our performance study on three publicly available image collections: Wiki, MIR Flickr, and NUS-WIDE indicates that SAVH can achieve superior performance over several state-of-the-art techniques.
1 THE ARCHITECTURE OF INTELLIGENT CITIES Integrating human , collective , and artificial intelligence to enhance knowledge and innovation

Intelligent communities and cities belong to an emerging movement targeting the creation of environments that improve cognitive skills and abilities to learn and innovate. They represent environments that enable superior cognitive capabilities and creativity to be collectively constructed from combinations of individual cognitive skills and information systems that operate in the physical, institutional, and digital spaces of cities. Two academic traditions have been feeding the discussion concerning intelligent communities and cities: the literature on innovative environments and the planning of digital cities. Following an introduction on the meaning of ICs, we discuss the structuring of innovative environments such as clusters, technology districts and territorial systems of innovation, which rely on different architectures of knowledge networks enhancing product, process, and organizational innovation. Then we turn to digital cities and examine their concept, architecture, and constituent elements. In the final section of the paper we describe intelligent cities as overlapping of innovative clusters and digital cities. Intelligent cities integrate knowledge-intensive activities and clusters; embedded routines of social cooperation enabling knowledge sharing and innovation; advanced communication infrastructure and digital spaces for knowledge and innovation management; and proven ability to innovate and resolve problems that appear for the first time, since the capacity to innovate and manage uncertainty are critical factors in characterizing intelligence.
PAMAS - power aware multi-access protocol with signalling for ad hoc networks

In this paper we develop a new multiaccess protocol for ad hoc radio networks. The protocol is based on the original MACA protocol with the adition of a separate signalling channel. The unique feature of our protocol is that it conserves battery power at nodes by intelligently powering off nodes that are not actively transmitting or receiving packets. The manner in which nodes power themselves off does not influence the delay or throughput characteristics of our protocol. We illustrate the power conserving behavior of PAMAS via extensive simulations performed over ad hoc networks containing 10-20 nodes. Our results indicate that power savings of between 10% and 70% are attainable in most systems. Finally, we discuss how the idea of power awareness can be built into other multiaccess protocols as well.