nils-herrmann/clean_scirepeval huggingface dataset:
Artificial Intelligence in XPRIZE DeepQ Tricorder

The DeepQ tricorder device developed by HTC from 2013 to 2016 was entered in the Qualcomm Tricorder XPRIZE competition and awarded the second prize in April 2017. This paper presents DeepQ»s three modules powered by artificial intelligence: symptom checker, optical sense, and vital sense. We depict both their initial design and ongoing enhancements.
Power Electronic Traction Transformer: Efficiency Improvements Under Light-Load Conditions

Power electronic transformer (PET), a converter technology that utilizes power semiconductors in combination with medium-frequency transformers, is considered a promising solution for certain applications requiring flexible galvanic isolation. Among are those where space occupied by bulky low-frequency transformers is of concern and/or where advanced power quality control features are needed. In this paper, the PET for a single-phase traction on-board application is discussed with emphasis on the efficiency improvements and reductions of energy consumption during the operation on the vehicle. Several control algorithms devised to improve efficiency under light-load conditions are tested on a low-voltage prototype of the PET, and experimental results are presented demonstrating the effectiveness of the proposed algorithms.
On Unifying Deep Generative Models

Deep generative models have achieved impressive success in recent years. Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), as powerful frameworks for deep generative model learning, have largely been considered as two distinct paradigms and received extensive independent studies respectively. This paper aims to establish formal connections between GANs and VAEs through a new formulation of them. We interpret sample generation in GANs as performing posterior inference, and show that GANs and VAEs involve minimizing KL divergences of respective posterior and inference distributions with opposite directions, extending the two learning phases of classic wake-sleep algorithm, respectively. The unified view provides a powerful tool to analyze a diverse set of existing model variants, and enables to transfer techniques across research lines in a principled way. For example, we apply the importance weighting method in VAE literatures for improved GAN learning, and enhance VAEs with an adversarial mechanism that leverages generated samples. Experiments show generality and effectiveness of the transfered techniques.
Survey on Software Defect Prediction Using Machine Learning Techniques

Software defect prediction plays an important role in improving software quality and it help to reducing time and cost for software testing. Machine learning focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data. The ability of a machine to improve its performance based on previous results. Machine learning improves efficiency of human learning, discover new things or structure that is unknown to humans and find important information in a document. For that purpose, different machine learning techniques are used to remove the unnecessary, erroneous data from the dataset. Software defect prediction is seen as a highly important ability when planning a software project and much greater effort is needed to solve this complex problem using a software metrics and defect dataset. Metrics are the relationship between the numerical value and it applied on the software therefore it is used for predicting defect. The primary goal of this survey paper is to understand the existing techniques for predicting software defect.
Annotation Artifacts in Natural Language Inference Data

Large-scale datasets for natural language inference are created by presenting crowd workers with a sentence (premise), and asking them to generate three new sentences (hypotheses) that it entails, contradicts, or is logically neutral with respect to. We show that, in a significant portion of such data, this protocol leaves clues that make it possible to identify the label by looking only at the hypothesis, without observing the premise. Specifically, we show that a simple text categorization model can correctly classify the hypothesis alone in about 67% of SNLI (Bowman et al., 2015) and 53% of MultiNLI (Williams et al., 2018). Our analysis reveals that specific linguistic phenomena such as negation and vagueness are highly correlated with certain inference classes. Our findings suggest that the success of natural language inference models to date has been overestimated, and that the task remains a hard open problem.
Incremental Author Name Disambiguation for Scientific Citation Data

Name disambiguation is a perennial challenge for any large and growing dataset but is particularly significant for scientific publication data where documents and ideas are linked through citations and depend on highly accurate authorship. Differentiating personal names in scientific publications is a substantial problem as many names are not sufficiently distinct due to the large number of researchers active in most academic disciplines today. As more and more documents and citations are published every year, any system built on this data must be continually retrained and reclassified to remain relevant and helpful. Recently, some incremental learning solutions have been proposed, but most of these have been limited to small-scale simulations and do not exhibit the full heterogeneity of the millions of authors and papers in real world data. In our work, we propose a probabilistic model that simultaneously uses a rich set of metadata and reduces the amount of pairwise comparisons needed for new articles. We suggest an approach to disambiguation that classifies in an incremental fashion to alleviate the need for retraining the model and re-clustering all papers and uses fewer parameters than other algorithms. Using a published dataset, we obtained the highest K-measure which is a geometric mean of cluster and author-class purity. Moreover, on a difficult author block from the Clarivate Analytics Web of Science, we obtain higher precision than other algorithms.
Rural Electrification in India : Economic and Institutional aspects of Renewables

The paper assesses the demand for rural electricity services and contrasts it with the technology options available for rural electrification. Decentralised Distributed Generation can be economically viable as reflected by case studies reported in literature and analysed in our field study. Project success is driven by economically viable technology choice; however it is largely contingent on organisational leadership and appropriate institutional structures. While individual leadership can compensate for deployment barriers, we argue that a large scale roll out of rural electrification requires an alignment of economic incentives and institutional structures to implement, operate and maintain the scheme. This is demonstrated with the help of seven case studies of projects across north India. 1  We explore the contribution that decentralised and renewable energy technologies can make to rural electricity supply in India. We take a case study approach, looking at seven sites across northern India where renewable energy technologies have been established to provide electrification for rural communities. We supplement our case studies with stakeholder interviews and household surveys, estimating levels of demand for electricity services from willingness and ability to pay. We also assess the overall viability of Distributed Decentralised Generation (DDG) projects by investigating the costs of implementation as well as institutional and organisational barriers to their operation and replication. Renewable energy technologies represent some of the most promising options available for distributed and decentralised electrification. Demand for reliable electricity services is significant. It represents a key driver behind economic development and raising basic standards of living. This is especially applicable to rural India home to 70% of the nation's population and over 25% of the world's poor. Access to reliable and affordable electricity can help support income-generating activity and allow utilisation of modern appliances and agricultural equipment whilst replacing inefficient and polluting kerosene lighting. Presently only around 55% of households are electrified (MOSPI 2006) leaving over 20 million households without power. The supply of electricity across India currently lacks both quality and quantity with an extensive shortfall in supply, a poor record for outages, high levels of transmission and distribution (T&D) losses and an overall need for extended and improved infrastructure (GoI 2006). The Indian Government recently outlined an ambitious plan for 100% village level electrification by the end of 2007 and total household electrification by 2012. To achieve this, a major programme of grid extension and strengthening of the rural electricity infrastructure has been initiated under …
Integrating Innovation Diffusion Theory and the Technology Acceptance Model : The adoption of blockchain technology from business managers ’ perspective

Financial technology (FinTech) is the new business model and technology which aims to compete with traditional financial services and blockchain is one of most famous technology use of FinTech. Blockchain is a type of distributed, electronic database (ledger) which can hold any information (e.g. records, events, transactions) and can set rules on how this information is updated. The most well-known application of blockchain is bitcoin, which is a kind of cryptocurrencies. But it can also be used in many other financial and commercial applications. A prominent example is smart contracts, for instance as offered in Ethereum. A contract can execute a transfer when certain events happen, such as payment of a security deposit, while the correct execution is enforced by the consensus protocol. The purpose of this paper is to explore the research and application landscape of blockchain technology acceptance by following a more comprehensive approach to address blockchain technology adoption. This research is to propose a unified model integrating Innovation Diffusion Theory (IDT) model and Technology Acceptance Model (TAM) to investigate continuance intention to adopt blockchain technology.
Adaptive Noise Smoothing Filter for Images with Signal-Dependent Noise

In this paper, we consider the restoration of images with signal-dependent noise. The filter is noise smoothing and adapts to local changes in image statistics based on a nonstationary mean, nonstationary variance (NMNV) image model. For images degraded by a class of uncorrelated, signal-dependent noise without blur, the adaptive noise smoothing filter becomes a point processor and is similar to Lee's local statistics algorithm . The filter is able to adapt itself to the nonstationary local image statistics in the presence of different types of signal-dependent noise. For multiplicative noise, the adaptive noise smoothing filter is a systematic derivation of Lee's algorithm with some extensions that allow different estimators for the local image variance. The advantage of the derivation is its easy extension to deal with various types of signal-dependent noise. Film-grain and Poisson signal-dependent restoration problems are also considered as examples. All the nonstationary image statistical parameters needed for the filter can be estimated from the noisy image and no a priori information about the original image is required.
The effect of grammatical gender on object categorization.

In 3 experiments, we investigated the effect of grammatical gender on object categorization. Participants were asked to judge whether 2 objects, whose names did or did not share grammatical gender, belonged to the same semantic category by pressing a key. Monolingual speakers of English (Experiment 1), Italian (Experiments 1 and 2), and Spanish (Experiments 2 and 3) were tested in their native language. Italian and Spanish participants responded faster to pairs of stimuli sharing the same gender, whereas no difference was observed for English participants. In Experiment 2, the pictures were chosen in such a way that the grammatical gender of the names was opposite in Italian and Spanish. Therefore, the same pair of stimuli gave rise to different patterns depending on the gender congruency of the names in the languages. In Experiment 3, Spanish speakers performed the same task under an articulatory suppression condition, showing no grammatical gender effect. The locus where meaning and gender interact can be located at the level of the lexical representation that specifies syntactic information: Nouns sharing the same grammatical gender activate each other, thus facilitating their processing and speeding up responses, either to semantically related pairs or to semantically unrelated pairs.
Platform Thinking in Incumbent Firms: From Concept to Capability

Incumbent firms struggle with new forms of competition in today’s increasingly digital environments. To leverage the benefits of innovation ecosystems they often shift focus from products to platforms. However, existing research provides limited insight into how firms actually implement this shift. Addressing this void, we have conducted a comparative case study where we adopt the concept of platform thinking to comprehend what capabilities incumbents need when engaging in innovation ecosystems and how those capabilities are developed.
Mobile Phone Sensing Systems: A Survey

Mobile phone sensing is an emerging area of interest for researchers as smart phones are becoming the core communication device in people's everyday lives. Sensor enabled mobile phones or smart phones are hovering to be at the center of a next revolution in social networks, green applications, global environmental monitoring, personal and community healthcare, sensor augmented gaming, virtual reality and smart transportation systems. More and more organizations and people are discovering how mobile phones can be used for social impact, including how to use mobile technology for environmental protection, sensing, and to leverage just-in-time information to make our movements and actions more environmentally friendly. In this paper we have described comprehensively all those systems which are using smart phones and mobile phone sensors for humans good will and better human phone interaction.
An Information Framework for Creating a Smart City Through Internet of Things

Increasing population density in urban centers demands adequate provision of services and infrastructure to meet the needs of city inhabitants, encompassing residents, workers, and visitors. The utilization of information and communications technologies to achieve this objective presents an opportunity for the development of smart cities, where city management and citizens are given access to a wealth of real-time information about the urban environment upon which to base decisions, actions, and future planning. This paper presents a framework for the realization of smart cities through the Internet of Things (IoT). The framework encompasses the complete urban information system, from the sensory level and networking support structure through to data management and Cloud-based integration of respective systems and services, and forms a transformational part of the existing cyber-physical system. This IoT vision for a smart city is applied to a noise mapping case study to illustrate a new method for existing operations that can be adapted for the enhancement and delivery of important city services.
Machine Learning at the Wireless Edge: Distributed Stochastic Gradient Descent Over-the-Air

We study collaborative machine learning (ML) at the wireless edge, where power and bandwidth-limited wireless devices with local datasets carry out distributed stochastic gradient descent (DSGD) with the help of a remote parameter server (PS). Standard approaches assume separate computation and communication, where local gradient estimates are compressed and communicated to the PS over orthogonal links. Following this digital approach, we introduce D-DSGD, in which the wireless terminals, referred to as the workers, employ gradient quantization and error accumulation, and transmit their gradient estimates to the PS over the underlying wireless multiple access channel (MAC). We then introduce an analog scheme, called A-DSGD, which exploits the additive nature of the wireless MAC for over-the-air gradient computation. In A-DSGD, the workers first sparsify their gradient estimates, and then project them to a lower dimensional space imposed by the available channel bandwidth. These projections are transmitted directly over the MAC without employing any digital code. Numerical results show that ADSGD converges much faster than D-DSGD thanks to its more efficient use of the limited bandwidth and the natural alignment of the gradient estimates over the channel. The improvement is particularly compelling at low power and low bandwidth regimes. We also observe that the performance of A-DSGD improves with the number of workers (keeping the total size of the dataset constant), while D-DSGD deteriorates, limiting the ability of the latter in harnessing the computation power of edge devices. The lack of quantization and channel encoding/decoding in A-DSGD further speeds up communication, making it very attractive for low-latency ML applications at the wireless network edge.
Towards End-to-End License Plate Detection and Recognition: A Large Dataset and Baseline

Most current license plate (LP) detection and recognition approaches are evaluated on a small and usually unrepresentative dataset since there are no publicly available large diverse datasets. In this paper, we introduce CCPD, a large and comprehensive LP dataset. All images are taken manually by workers of a roadside parking management company and are annotated carefully. To our best knowledge, CCPD is the largest publicly available LP dataset to date with over 250k unique car images, and the only one provides vertices location annotations. With CCPD, we present a novel network model which can predict the bounding box and recognize the corresponding LP number simultaneously with high speed and accuracy. Through comparative experiments, we demonstrate our model outperforms current object detection and recognition approaches in both accuracy and speed. In real-world applications, our model recognizes LP numbers directly from relatively high-resolution images at over 61 fps and 98.5% accuracy.
MLPnP - A Real-Time Maximum Likelihood Solution to the Perspective-n-Point Problem

In this paper, a statistically optimal solution to the Perspective-n-Point (PnP) problem is presented. Many solutions to the PnP problem are geometrically optimal, but do not consider the uncertainties of the observations. In addition, it would be desirable to have an internal estimation of the accuracy of the estimated rotation and translation parameters of the camera pose. Thus, we propose a novel maximum likelihood solution to the PnP problem, that incorporates image observation uncertainties and remains real-time capable at the same time. Further, the presented method is general, as is works with 3D direction vectors instead of 2D image points and is thus able to cope with arbitrary central camera models. This is achieved by projecting (and thus reducing) the covariance matrices of the observations to the corresponding vector tangent space.
53 5 Activity Theory : Implications for Human-Computer Interaction

Recently interest has grown in applying activity theory, the leading theoretical approach in Russian psychology, to issues of human-computer interaction. This chapter analyzes why experts in the field are looking for an alternative to the currently dominant cognitive approach. The basic principles of activity theory are presented and their implications for human-computer interaction are discussed. The chapter concludes with an outline of the potential impact of activity theory on studies and design of computer use in real-life settings.
Dynamic Word Embeddings via Skip-Gram Filtering

We present a probabilistic language model for time-stamped text data which tracks the semantic evolution of individual words over time. The model represents words and contexts by latent trajectories in an embedding space. At each moment in time, the embedding vectors are inferred from a probabilistic version of word2vec (Mikolov et al., 2013b). These embedding vectors are connected in time through a latent diffusion process. We describe two scalable variational inference algorithms—skipgram smoothing and skip-gram filtering—that allow us to train the model jointly over all times; thus learning on all data while simultaneously allowing word and context vectors to drift. Experimental results on three different corpora demonstrate that our dynamic model infers word embedding trajectories that are more interpretable and lead to higher predictive likelihoods than competing methods that are based on static models trained separately on time slices.
The Role of Different Types of Information Systems In Business Organizations : A Review

For the last twenty years, different kinds of information systems are developed for different purposes, depending on the need of the business . In today’s business world, there are varieties of information systems such as transaction processing systems (TPS), office automation systems (OAS), management information systems (MIS), decision support system (DSS), and executive information systems (EIS), Expert System (ES) etc . Each plays a different role in organizational hierarchy and management operations. This study attempts to explain the role of each type of information systems in business organizations.
A LabVIEW-Based GPS Receiver Development and Testing Platform with DSP Peripherals : Case study with C 6713 DSK

The modernization of Global Positioning Systems (GPS) and the availability of more complex signals and modulation schemes boost the development of civil and military applications while the accuracy and coverage of receivers continually improve. Recently, software defined receiver solutions gained attention for flexible multimode operations. For them, developers address algorithmic and hardware accelerators or their hybrids for fast prototyping and testing high performance receivers for various conditions. This paper presents a new fast prototyping concept exploiting digital signal processor (DSP) peripherals and the benefits of the host environment using the National Instruments (NI) LabVIEW platform. With a reasonable distribution of tasks between the host hardware and reconfigurable peripherals, a higher performance is achieved. As a case study, in this paper the Texas Instruments (TI) TMS320C6713 DSP is used along with a Real Time Data Exchange (RTDX) communication link to compare with similar Simulink-based solutions. The proposed testbed GPS signal is created using the NI PXI signal generator and the NI GPS Simulation Toolkit.
A Meta-learning Method Based on Temporal Difference Error

In general, meta-parameters in a reinforcement learning system, such as a learning rate and a discount rate, are empirically determined and fixed during learning. When an external environment is therefore changed, the sytem cannot adapt itself to the variation. Meanwhile, it is suggested that the biological brain might conduct reinforcement learning and adapt itself to the external environment by controlling neuromodulators corresponding to the meta-parameters. In the present paper, based on the above suggestion, a method to adjust metaparameters using a temporal difference (TD) error is proposed. Through various computer simulations using a maze search problem and an inverted pendulum control problem, it is verified that the proposed method could appropriately adjust meta-parameters according to the variation of the external environment.