nils-herrmann/clean_scirepeval huggingface dataset:
Deep Multimodal Representation Learning from Temporal Data

In recent years, Deep Learning has been successfully applied to multimodal learning problems, with the aim of learning useful joint representations in data fusion applications. When the available modalities consist of time series data such as video, audio and sensor signals, it becomes imperative to consider their temporal structure during the fusion process. In this paper, we propose the Correlational Recurrent Neural Network (CorrRNN), a novel temporal fusion model for fusing multiple input modalities that are inherently temporal in nature. Key features of our proposed model include: (i) simultaneous learning of the joint representation and temporal dependencies between modalities, (ii) use of multiple loss terms in the objective function, including a maximum correlation loss term to enhance learning of cross-modal information, and (iii) the use of an attention model to dynamically adjust the contribution of different input modalities to the joint representation. We validate our model via experimentation on two different tasks: video-and sensor-based activity classification, and audio-visual speech recognition. We empirically analyze the contributions of different components of the proposed CorrRNN model, and demonstrate its robustness, effectiveness and state-of-the-art performance on multiple datasets.
Multilingual Training of Crosslingual Word Embeddings

Crosslingual word embeddings represent lexical items from different languages using the same vector space, enabling crosslingual transfer. Most prior work constructs embeddings for a pair of languages, with English on one side. We investigate methods for building high quality crosslingual word embeddings for many languages in a unified vector space. In this way, we can exploit and combine information from many languages. We report competitive performance on bilingual lexicon induction, monolingual similarity and crosslingual document classification
Smart and Digital City : A Systematic Literature Review

The concept of Smart City embraces several definitions depending on the meanings of the word “smart”: intelligent city, knowledge city, ubiquitous city, sustainable city, digital city, etc. Many definitions of Smart City exist, but no one has been universally acknowledged yet. From literature analysis it emerges that Smart City and Digital City are the most used terminologies in literature to indicate the smartness of a city. This Chapter explores the literature about Smart City and Digital City from 1993 to the end of 2012 in order to investigate how these two concepts were born, how they have developed, which are the shared features and differences between them. To accomplish with these goals, three steps were followed: (1) to set up a search strategy for systematic literature review to collect a representative subset of papers about Smart City and Digital City using Google Scholar; (2) to store the selected subset in an ad-doc database to synthesize the literature review; (3) to organize the literature review subset to extract quantitative and qualitative data and information about Smart City and Digital City evolution. The author proposes a literature review taxonomy through five specific analysis: (1) time analysis, to explore the causes of the trend of Smart City and Digital City literature in the latest twenty years; (2) terminology analysis, to examine how and where these two ideas were born and what have been the main events influenced their development; (3) definitions analysis, to select and compare the most cited and validated definitions of Smart City and Digital City trying to identify similarities, differences or overlaps between these two concepts; (4) typology analysis, to investigate if Smart City and Digital City are included into a specific urban strategy pursued by government or if they face specific urban problems without a comprehensive framework; (5) geographic analysis, to understand where are the largest concentrations of Smart Cities and Digital Cities in the world and which are their main characteristics and best practices. Smart and Digital City: A Systematic Literature
ASSESSING THE ECONOMY-WIDE EFFECTS OF QUANTITATIVE EASING

This article examines the macroeconomic impact of the first round of quantitative easing (QE) by the Bank of England. We attempt to quantify the effects of these purchases by focusing on the impact of lower long-term interest rates on the wider economy. We use three different models to estimate the impact of QE on output and inflation: a large Bayesian vector autoregression (VAR), a changepoint structural VAR and a time-varying parameter VAR. Our estimates suggest that QE may have had a peak effect on the level of real GDP of around 1 2 % and a peak effect on annual CPI inflation of about 1 4% points.
Probabilistic Bag-Of-Hyperlinks Model for Entity Linking

Many fundamental problems in natural language processing rely on determining what entities appear in a given text. Commonly referenced as entity linking, this step is a fundamental component of many NLP tasks such as text understanding, automatic summarization, semantic search or machine translation. Name ambiguity, word polysemy, context dependencies and a heavy-tailed distribution of entities contribute to the complexity of this problem. We here propose a probabilistic approach that makes use of an effective graphical model to perform collective entity disambiguation. Input mentions (i.e., linkable token spans) are disambiguated jointly across an entire document by combining a document-level prior of entity co-occurrences with local information captured from mentions and their surrounding context. The model is based on simple sufficient statistics extracted from data, thus relying on few parameters to be learned. Our method does not require extensive feature engineering, nor an expensive training procedure. We use loopy belief propagation to perform approximate inference. The low complexity of our model makes this step sufficiently fast for real-time usage. We demonstrate the accuracy of our approach on a wide range of benchmark datasets, showing that it matches, and in many cases outperforms, existing stateof-the-art methods.
Adversarial Generation of Natural Language

Generative Adversarial Networks (GANs) have gathered a lot of attention from the computer vision community, yielding impressive results for image generation. Advances in the adversarial generation of natural language from noise however are not commensurate with the progress made in generating images, and still lag far behind likelihood based methods. In this paper, we take a step towards generating natural language with a GAN objective alone. We introduce a simple baseline that addresses the discrete output space problem without relying on gradient estimators and show that it is able to achieve state-of-the-art results on a Chinese poem generation dataset. We present quantitative results on generating sentences from context-free and probabilistic context-free grammars, and qualitative language modeling results. A conditional version is also described that can generate sequences conditioned on sentence characteristics.
The Blockchain and Kudos: A Distributed System for Educational Record, Reputation and Reward

The ‘blockchain’ is the core mechanism for the Bitcoin digital payment system. It embraces a set of inter-related technologies: the blockchain itself as a distributed record of digital events, the distributed consensus method to agree whether a new block is legitimate, automated smart contracts, and the data structure associated with each block. We propose a permanent distributed record of intellectual effort and associated reputational reward, based on the blockchain that instantiates and democratises educational reputation beyond the academic community. We are undertaking initial trials of a private blockchain or storing educational records, drawing also on our previous research into reputation management for educational systems.
Real Time Fabric Defect Detection System on an Embedded DSP Platform

In industrial fabric productions, automated real time systems are needed to find out the minor defects. It will save the cost by not transporting defected products and also would help in making compmay image of quality fabrics by sending out only undefected products. A real time fabric defect detection system (FDDS), implementd on an embedded DSP platform is presented here. Textural features of fabric image are extracted based on gray level co-occurrence matrix (GLCM). A sliding window technique is used for defect detection where window moves over the whole image computing a textural energy from the GLCM of the fabric image. The energy values are compared to a reference and the deviations beyond a threshold are reported as defects and also visually represented by a window. The implementation is carried out on a TI TMS320DM642 platform and programmed using code composer studio software. The real time output of this implementation was shown on a monitor. KeywordsFabric Defects, Texture, Grey Level Co-occurrence Matrix, DSP Kit, Energy Computation, Sliding Window,
Current and emerging techniques for antibiotic susceptibility tests

Infectious diseases caused by bacterial pathogens are a worldwide burden. Serious bacterial infection-related complications, such as sepsis, affect over a million people every year with mortality rates ranging from 30% to 50%. Crucial clinical microbiology laboratory responsibilities associated with patient management and treatment include isolating and identifying the causative bacterium and performing antibiotic susceptibility tests (ASTs), which are labor-intensive, complex, imprecise, and slow (taking days, depending on the growth rate of the pathogen). Considering the life-threatening condition of a septic patient and the increasing prevalence of antibiotic-resistant bacteria in hospitals, rapid and automated diagnostic tools are needed. This review summarizes the existing commercial AST methods and discusses some of the promising emerging AST tools that will empower humans to win the evolutionary war between microbial genes and human wits.
Feasibility of Fog Computing

As billions of devices get connected to the Internet, it will not be sustainable to use the cloud as a centralised server. The way forward is to decentralise computations away from the cloud towards the edge of the network closer to the user. This reduces the latency of communication between a user device and the cloud, and is the premise of ‘fog computing’ defined in this paper. The aim of this paper is to highlight the feasibility and the benefits in improving the Quality-of-Service and Experience by using fog computing. For an online game use-case, we found that the average response time for a user is improved by 20% when using the edge of the network in comparison to using a cloud-only model. It was also observed that the volume of traffic between the edge and the cloud server is reduced by over 90% for the use-case. The preliminary results highlight the potential of fog computing in achieving a sustainable computing model and highlights the benefits of integrating the edge of the network into the computing ecosystem.
Evaluating machine learning algorithms for fake news detection

This paper explores the application of natural language processing techniques for the detection of ‘fake news’, that is, misleading news stories that come from non-reputable sources. Using a dataset obtained from Signal Media and a list of sources from OpenSources.co, we apply term frequency-inverse document frequency (TF-IDF) of bi-grams and probabilistic context free grammar (PCFG) detection to a corpus of about 11,000 articles. We test our dataset on multiple classification algorithms — Support Vector Machines, Stochastic Gradient Descent, Gradient Boosting, Bounded Decision Trees, and Random Forests. We find that TF-IDF of bi-grams fed into a Stochastic Gradient Descent model identifies non-credible sources with an accuracy of 77.2%, with PCFGs having slight effects on recall.
Identifying Web Queries with Question Intent

Vertical selection is the task of predicting relevant verticals for a Web query so as to enrich the Web search results with complementary vertical results. We investigate a novel variant of this task, where the goal is to detect queries with a question intent. Specifically, we address queries for which the user would like an answer with a human touch. We call these CQA-intent queries, since answers to them are typically found in community question answering (CQA) sites. A typical approach in vertical selection is using a vertical’s specific language model of relevant queries and computing the query-likelihood for each vertical as a selective criterion. This works quite well for many domains like Shopping, Local and Travel. Yet, we claim that queries with CQA intent are harder to distinguish by modeling content alone, since they cover many different topics. We propose to also take the structure of queries into consideration, reasoning that queries with question intent have quite a different structure than other queries. We present a supervised classification scheme, random forest over word-clusters for variable length texts, which can model the query structure. Our experiments show that it substantially improves classification performance in the CQA-intent selection task compared to content-oriented based classification, especially as query length grows.
A Review on the Use of Blockchain for the Internet of Things

The paradigm of Internet of Things (IoT) is paving the way for a world, where many of our daily objects will be interconnected and will interact with their environment in order to collect information and automate certain tasks. Such a vision requires, among other things, seamless authentication, data privacy, security, robustness against attacks, easy deployment, and self-maintenance. Such features can be brought by blockchain, a technology born with a cryptocurrency called Bitcoin. In this paper, a thorough review on how to adapt blockchain to the specific needs of IoT in order to develop Blockchain-based IoT (BIoT) applications is presented. After describing the basics of blockchain, the most relevant BIoT applications are described with the objective of emphasizing how blockchain can impact traditional cloud-centered IoT applications. Then, the current challenges and possible optimizations are detailed regarding many aspects that affect the design, development, and deployment of a BIoT application. Finally, some recommendations are enumerated with the aim of guiding future BIoT researchers and developers on some of the issues that will have to be tackled before deploying the next generation of BIoT applications.
Fast Vehicle Detection in Aerial Imagery

In recent years, several real-time or near real-time object detectors have been developed. However these object detectors are typically designed for first-person view images where the subject is large in the image and do not directly apply well to detecting vehicles in aerial imagery. Though some detectors have been developed for aerial imagery, these are either slow or do not handle multi-scale imagery very well. Here the popular YOLOv2 detector is modified to vastly improve it’s performance on aerial data. The modified detector is compared to Faster RCNN on several aerial imagery datasets. The proposed detector gives near state of the art performance at more than 4x the speed.
Container Network Functions: Bringing NFV to the Network Edge

In order to cope with the increasing network utilization driven by new mobile clients, and to satisfy demand for new network services and performance guarantees, telecommunication service providers are exploiting virtualization over their network by implementing network services in virtual machines, decoupled from legacy hardware accelerated appliances. This effort, known as NFV, reduces OPEX and provides new business opportunities. At the same time, next generation mobile, enterprise, and IoT networks are introducing the concept of computing capabilities being pushed at the network edge, in close proximity of the users. However, the heavy footprint of today's NFV platforms prevents them from operating at the network edge. In this article, we identify the opportunities of virtualization at the network edge and present Glasgow Network Functions (GNF), a container-based NFV platform that runs and orchestrates lightweight container VNFs, saving core network utilization and providing lower latency. Finally, we demonstrate three useful examples of the platform: IoT DDoS remediation, on-demand troubleshooting for telco networks, and supporting roaming of network functions.
Prediction error in reinforcement learning: A meta-analysis of neuroimaging studies

Activation likelihood estimation (ALE) meta-analyses were used to examine the neural correlates of prediction error in reinforcement learning. The findings are interpreted in the light of current computational models of learning and action selection. In this context, particular consideration is given to the comparison of activation patterns from studies using instrumental and Pavlovian conditioning, and where reinforcement involved rewarding or punishing feedback. The striatum was the key brain area encoding for prediction error, with activity encompassing dorsal and ventral regions for instrumental and Pavlovian reinforcement alike, a finding which challenges the functional separation of the striatum into a dorsal 'actor' and a ventral 'critic'. Prediction error activity was further observed in diverse areas of predominantly anterior cerebral cortex including medial prefrontal cortex and anterior cingulate cortex. Distinct patterns of prediction error activity were found for studies using rewarding and aversive reinforcers; reward prediction errors were observed primarily in the striatum while aversive prediction errors were found more widely including insula and habenula.
CANONICAL CORRELATION ANALYSIS

We discuss algorithms for performing canonical correlation analysis. In canonical correlation analysis we try to find correlations between two data sets. The canonical correlation coefficients can be calculated directly from the two data sets or from (reduced) representations such as the covariance matrices. The algorithms for both representations are based on singular value decomposition. The methods described here have been implemented in the speech analysis program PRAAT (Boersma & Weenink, 1996), and some examples will be demonstated for formant frequency and formant level data from 50 male Dutch speakers as were reported by Pols et al. (1973).
Preliminary Comparison of Sentinel-2 and Landsat 8 Imagery for a Combined Use

The availability of new generation multispectral sensors of the Landsat 8 and Sentinel-2 satellite platforms offers unprecedented opportunities for long-term high-frequency monitoring applications. The present letter aims at highlighting some potentials and challenges deriving from the spectral and spatial characteristics of the two instruments. Some comparisons between corresponding bands and band combinations were performed on the basis of different datasets: the first consists of a set of simulated images derived from a hyperspectral Hyperion image, the other five consist instead of pairs of real images (Landsat 8 and Sentinel-2A) acquired on the same date, over five areas. Results point out that in most cases the two sensors can be well combined; however, some issues arise regarding near-infrared bands when Sentinel-2 data are combined with both Landsat 8 and older Landsat images.
Uses and Misuses of Bronfenbrenner’s Bioecological Theory of Human Development

This paper evaluates the application of Bronfenbrenner’s bioecological theory as it is represented in empirical work on families and their relationships. We describe the ‘‘mature’’ form of bioecological theory of the mid-1990s and beyond, with its focus on proximal processes at the center of the Process-Person-Context-Time model. We then examine 25 papers published since 2001, all explicitly described as being based on Bronfenbrenner’s theory, and show that all but 4 rely on outmoded versions of the theory, resulting in conceptual confusion and inadequate testing of the theory.
on definitions of joint coordinate system of various joints for the reporting of human joint motion — part I : ankle , hip , and spine

The Standardization and Terminology Committee (STC) of the International Society of Biomechanics (ISB) proposes a general reporting standard for joint kinematics based on the Joint Coordinate System (JCS), first proposed by Grood and Suntay for the knee joint in 1983 ( (1983) 136). There is currently a lack of standard for reporting joint motion in the field of biomechanics for human movement, and the JCS as proposed by Grood and Suntay has the advantage of reporting joint motions in clinically relevant terms. In this communication, the STC proposes definitions of JCS for the ankle, hip, and spine. Definitions for other joints (such as shoulder, elbow, hand and wrist, temporomandibular joint (TMJ), and whole body) will be reported in later parts of the series. The STC is publishing these recommendations so as to encourage their use, to stimulate feedback and discussion, and to facilitate further revisions. For each joint, a standard for the local axis system in each articulating bone is generated. These axes then standardize the JCS. Adopting these standards will lead to better communication among researchers and clinicians. r 2002 Elsevier
Rovables: Miniature On-Body Robots as Mobile Wearables

We introduce Rovables, a miniature robot that can move freely on unmodified clothing. The robots are held in place by magnetic wheels, and can climb vertically. The robots are untethered and have an onboard battery, microcontroller, and wireless communications. They also contain a low-power localization system that uses wheel encoders and IMU, allowing Rovables to perform limited autonomous navigation on the body. In the technical evaluations, we found that Rovables can operate continuously for 45 minutes and can carry up to 1.5N. We propose an interaction space for mobile on-body devices spanning sensing, actuation, and interfaces, and develop application scenarios in that space. Our applications include on-body sensing, modular displays, tactile feedback and interactive clothing and jewelry.
Image coding using wavelet transform

A scheme for image compression that takes into account psychovisual features both in the space and frequency domains is proposed. This method involves two steps. First, a wavelet transform used in order to obtain a set of biorthogonal subclasses of images: the original image is decomposed at different scales using a pyramidal algorithm architecture. The decomposition is along the vertical and horizontal directions and maintains constant the number of pixels required to describe the image. Second, according to Shannon's rate distortion theory, the wavelet coefficients are vector quantized using a multiresolution codebook. To encode the wavelet coefficients, a noise shaping bit allocation procedure which assumes that details at high resolution are less visible to the human eye is proposed. In order to allow the receiver to recognize a picture as quickly as possible at minimum cost, a progressive transmission scheme is presented. It is shown that the wavelet transform is particularly well adapted to progressive transmission.