nils-herrmann/clean_scirepeval huggingface dataset:
A Review on Deep Learning Techniques Applied to Semantic Segmentation

Image semantic segmentation is more and more being of interest for computer vision and machine learning researchers. Many applications on the rise need accurate and efficient segmentation mechanisms: autonomous driving, indoor navigation, and even virtual or augmented reality systems to name a few. This demand coincides with the rise of deep learning approaches in almost every field or application target related to computer vision, including semantic segmentation or scene understanding. This paper provides a review on deep learning methods for semantic segmentation applied to various application areas. Firstly, we describe the terminology of this field as well as mandatory background concepts. Next, the main datasets and challenges are exposed to help researchers decide which are the ones that best suit their needs and their targets. Then, existing methods are reviewed, highlighting their contributions and their significance in the field. Finally, quantitative results are given for the described methods and the datasets in which they were evaluated, following up with a discussion of the results. At last, we point out a set of promising future works and draw our own conclusions about the state of the art of semantic segmentation using deep learning techniques.
Summarizing sporting events using twitter

The status updates posted to social networks, such as Twitter and Facebook, contain a myriad of information about what people are doing and watching. During events, such as sports games, many updates are sent describing and expressing opinions about the event. In this paper, we describe an algorithm that generates a journalistic summary of an event using only status updates from Twitter as a source. Temporal cues, such as spikes in the volume of status updates, are used to identify the important moments within an event, and a sentence ranking method is used to extract relevant sentences from the corpus of status updates describing each important moment within an event. We evaluate our algorithm compared to human-generated summaries and the previous best summarization algorithm, and find that the results of our method are superior to the previous algorithm and approach the readability and grammaticality of the human-generated summaries.
Cross-Domain Ranking via Latent Space Learning

We study the problem of cross-domain ranking, which addresses learning to rank objects from multiple interrelated domains. In many applications, we may have multiple interrelated domains, some of them with a large amount of training data and others with very little. We often wish to utilize the training data from all these related domains to help improve ranking performance. In this paper, we present a unified model: BayCDR for cross-domain ranking. BayCDR uses a latent space to measure the correlation between different domains, and learns the ranking functions from the interrelated domains via the latent space by a Bayesian model, where each ranking function is based on a weighted average model. An efficient learning algorithm based on variational inference and a generalization bound has been developed. To scale up to handle real large data, we also present a learning algorithm under the Map-Reduce programming model. Finally, we demonstrate the effectiveness and efficiency of BayCDR on large datasets.
RAN: Radical analysis networks for zero-shot learning of Chinese characters

Chinese characters have a huge set of character categories, more than 20,000 and the number is still increasing as more and more novel characters continue being created. However, the enormous characters can be decomposed into a few fundamental structural radicals, only about 500. This paper introduces the Radical Analysis Networks (RAN) that recognize Chinese characters by identifying radicals and analyzing 2D spatial structures between them. The proposed RAN first extracts visual features from Chinese character input by employing convolutional neural networks as an encoder. Then a decoder based on recurrent neural networks is employed, who aims to generate a caption of Chinese character by detecting radicals and 2D structures through a spatial attention mechanism. The manner of treating Chinese character input as a composition of radicals rather than a single picture severely reduces the size of vocabulary and enables RAN to possess the ability of recognizing unseen Chinese character classes only if their radicals have been seen, called zero-shot learning. We test a simple implementation of RAN on experiments of recognizing printed Chinese characters with seen and unseen classes and RAN simultaneously obtains convincing performance on unseen classes and state-of-the-art performance on seen classes.
Major League Soccer Scheduling Problem

Major League Soccer is the highest level mens professional soccer league in United States and Canada, consisting of 20 teams, with 17 in United States and 3 in Canada. The League is divided by two Conferences: Western and Eastern with 10 teams each. The regular season of the League runs from early March to late October each year. It is important to look at MLS mainly for three reasons. Firstly, it is a fast-growing league, with only 10 teams competed in 1996 when the League started. Secondly, the popularity of the game has increased dramatically, with the average per-game attendance in MLS increased by 40% in the past decade, which exceeds that of NHL and NBA. Lastly, the big TV broadcasting contract signed with ESPN, Fox Sports, and Univision has demonstrated the huge commercial value of the League.
Assessing the Impact of Real-time Ridesharing on Urban Traffic using Mobile Phone Data

Recently, smart-phone based technology has enabled ridesharing services to match customers making similar trips in realtime for a reduced rate and minimal inconvenience. But what are the impacts of such services on city-wide congestion? The answer lies in whether or not ridesharing adds to vehicle traffic by diverting non-driving trips like walking, transit, or cycling, or reduces vehicle traffic by diverting trips otherwise made in private, single occupancy cars or taxis. This research explores the impact of rideshare adoption on congestion using mobile phone data. We extract average daily origin-destination (OD) trips from mobile phone records and estimate the proportions of these trips made by auto and other non-auto travelers. Next, we match spatially and temporally similar trips, and assume a range of adoption rates for auto and non-auto users, in order to distill rideshare vehicle trips. Finally, for several adoption scenarios, we evaluate the impacts of congestion network-wide.
Low Profile Fully Planar Folded Dipole Antenna on a High Impedance Surface

A fully planar antenna design incorporating a high impedance surface (HIS) is presented. The HIS is composed by a periodic array of subwavelength dogbone-shaped conductors printed on top of a thin dielectric substrate and backed by a metallic ground plane. First, the characteristics of a dipole over PEC or PMC layers, a dielectric slab, and the HIS are compared and studied in detail, highlighting the advantages provided by the use of the HIS. Then, the design of a low profile folded dipole antenna working at 5.5 GHz on top of the HIS is described. The surface provides close to 6% antenna impedance bandwidth and increased gain up to 7 dBi, while shielding the lower half space from radiation. The antenna structure comprises three metal layers without any vias between them, and its overall thickness is 0.059λ0. The dipole is fed by a balanced twin lead line through a balun transformer integrated in the same antenna layer. A prototype has been built and measurements confirming simulation results are provided.
A Comparison of Playlist Generation Strategies for Music Recommendation and a New Baseline Scheme

The digitalization of music and the instant availability of millions of tracks on the Internet require new approaches to support the user in the exploration of these huge music collections. One possible approach to address this problem, which can also be found on popular online music platforms, is the use of user-created or automatically generated playlists (mixes). The automated generation of such playlists represents a particular type of the music recommendation problem with two special characteristics. First, the tracks of the list are usually consumed immediately at recommendation time; secondly, songs are listened to mostly in consecutive order so that the sequence of the recommended tracks can be relevant. In the past years, a number of different approaches for playlist generation have been proposed in the literature. In this paper, we review the existing core approaches to playlist generation, discuss aspects of appropriate offline evaluation designs and report the results of a comparative evaluation based on different datasets. Based on the insights from these experiments, we propose a comparably simple and computationally tractable new baseline algorithm for future comparisons, which is based on track popularity and artist information and is competitive with more sophisticated techniques in our evaluation settings.
Is Sentiment Analysis Domain-Dependent

The purpose of this research carried out within applied linguistics is to consider the dependency of the sentiment lexicon and other sentiment analysis tools on the domain under study. For the experiment, we used the REGEX algorithm including the sentiment lexicon and formal grammar rules applied with the certain priorities. These rules and the corresponding syntactic models are similar to regular expressions which detect certain text elements, simplify each sentence and present the text as a formal model. The reviews in Russian from three domains (Bank Service Quality, Hotel Service Quality and Sightseeing) are analyzed; F1 measure is used as the efficiency criterion. The experiment does not reveal the domain-dependency of the algorithm applied. It is determined that the system generally detects positive reviews better than negative ones. When negative opinions are expressed, there is a tendency to use non-standard vocabulary and syntax.
Information extraction - a text mining approach

Text mining concerns looking for patterns in unstructured text. The related task of Information Extraction (IE) is about locating specific items in natural-language documents. This paper presents a framework for text mining, called DISCOTEX (Discovery from Text Extraction), using a learned information extraction system to transform text into more structured data which is then mined for interesting relationships. The initial version of DISCOTEX integrates an IE module acquired by an IE learning system, and a standard rule induction module. In addition, rules mined from a database extracted from a corpus of texts are used to predict additional information to extract from future documents, thereby improving the recall of the underlying extraction system. Encouraging results are presented on applying these techniques to a corpus of computer job announcement postings from an Internet newsgroup.
A Linearly Relaxed Approximate Linear Program for Markov Decision Processes

Approximate linear programming (ALP) and its variants have been widely applied to Markov decision processes (MDPs) with a large number of states. A serious limitation of ALP is that it has an intractable number of constraints, as a result of which constraint approximations are of interest. In this paper, we define a linearly relaxed approximation linear program (LRALP) that has a tractable number of constraints, obtained as positive linear combinations of the original constraints of the ALP. The main contribution is a novel performance bound for LRALP.
Optimized Operation of Current-Fed Dual Active Bridge DC–DC Converter for PV Applications

The current-fed dual active bridge (CF-DAB) dc-dc converter gains growing applications in photovoltaic (PV) and energy storage systems due to its advantages, e.g., a wide input voltage range, a high step-up ratio, a low input current ripple, and a multiport interface capability. In addition, the direct input current controllability and extra control freedom of the CF-DAB converter make it possible to buffer the double-line-frequency energy in grid-interactive PV systems without using electrolytic capacitors in the dc link. Therefore, a PV system achieves high reliability and highly efficient maximum power point tracking. This paper studies the optimized operation of a CF-DAB converter for a PV application in order to improve the system efficiency. The operating principle and soft-switching conditions over the wide operating range are thoroughly analyzed with phase-shift control and duty-cycle control, and an optimized operating mode is proposed to achieve the minimum root-mean-square transformer current. The proposed operating mode can extend the soft-switching region and reduce the power loss, particularly under a heavy load and a high input voltage. Moreover, the efficiency can be further improved with a higher dc-link voltage. A 5-kW hardware prototype was built in the laboratory, and experimental results are provided for verification. This paper provides a design guideline for the CF-DAB converter applied to PV systems, as well as other applications with a wide input voltage variation.
Computational Capabilities of Graph Neural Networks

In this paper, we will consider the approximation properties of a recently introduced neural network model called graph neural network (GNN), which can be used to process-structured data inputs, e.g., acyclic graphs, cyclic graphs, and directed or undirected graphs. This class of neural networks implements a function tau(G, n) isin R m that maps a graph G and one of its nodes n onto an m-dimensional Euclidean space. We characterize the functions that can be approximated by GNNs, in probability, up to any prescribed degree of precision. This set contains the maps that satisfy a property called preservation of the unfolding equivalence, and includes most of the practically useful functions on graphs; the only known exception is when the input graph contains particular patterns of symmetries when unfolding equivalence may not be preserved. The result can be considered an extension of the universal approximation property established for the classic feedforward neural networks (FNNs). Some experimental examples are used to show the computational capabilities of the proposed model.
Exploration and Exploitation in Organizational Learning

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Learning a MultiView Stereo Machine

We present a learnt system for multi-view stereopsis. In contrast to recent learning based methods for 3D reconstruction, we leverage the underlying 3D geometry of the problem through feature projection and unprojection along viewing rays. By formulating these operations in a differentiable manner, we are able to learn the system end-to-end for the task of metric 3D reconstruction. End-to-end learning allows us to jointly reason about shape priors while conforming geometric constraints, enabling reconstruction from much fewer images (even a single image) than required by classical approaches as well as completion of unseen surfaces. We thoroughly evaluate our approach on the ShapeNet dataset and demonstrate the benefits over classical approaches as well as recent learning based methods.
Big Data Research in Information Systems: Toward an Inclusive Research Agenda

Big data has received considerable attention from the information systems (IS) discipline over the past few years, with several recent commentaries, editorials, and special issue introductions on the topic appearing in leading IS outlets. These papers present varying perspectives on promising big data research topics and highlight some of the challenges that big data poses. In this editorial, we synthesize and contribute further to this discourse. We offer a first step toward an inclusive big data research agenda for IS by focusing on the interplay between big data’s characteristics, the information value chain encompassing people-process-technology, and the three dominant IS research traditions (behavioral, design, and economics of IS). We view big data as a disruption to the value chain that has widespread impacts, which include but are not limited to changing the way academics conduct scholarly work. Importantly, we critically discuss the opportunities and challenges for behavioral, design science, and economics of IS research and the emerging implications for theory and methodology arising due to big data’s disruptive effects.
Unification of Cas protein families and a simple scenario for the origin and evolution of CRISPR-Cas systems

The CRISPR-Cas adaptive immunity systems that are present in most Archaea and many Bacteria function by incorporating fragments of alien genomes into specific genomic loci, transcribing the inserts and using the transcripts as guide RNAs to destroy the genome of the cognate virus or plasmid. This RNA interference-like immune response is mediated by numerous, diverse and rapidly evolving Cas (CRISPR-associated) proteins, several of which form the Cascade complex involved in the processing of CRISPR transcripts and cleavage of the target DNA. Comparative analysis of the Cas protein sequences and structures led to the classification of the CRISPR-Cas systems into three Types (I, II and III). A detailed comparison of the available sequences and structures of Cas proteins revealed several unnoticed homologous relationships. The Repeat-Associated Mysterious Proteins (RAMPs) containing a distinct form of the RNA Recognition Motif (RRM) domain, which are major components of the CRISPR-Cas systems, were classified into three large groups, Cas5, Cas6 and Cas7. Each of these groups includes many previously uncharacterized proteins now shown to adopt the RAMP structure. Evidence is presented that large subunits contained in most of the CRISPR-Cas systems could be homologous to Cas10 proteins which contain a polymerase-like Palm domain and are predicted to be enzymatically active in Type III CRISPR-Cas systems but inactivated in Type I systems. These findings, the fact that the CRISPR polymerases, RAMPs and Cas2 all contain core RRM domains, and distinct gene arrangements in the three types of CRISPR-Cas systems together provide for a simple scenario for origin and evolution of the CRISPR-Cas machinery. Under this scenario, the CRISPR-Cas system originated in thermophilic Archaea and subsequently spread horizontally among prokaryotes. Because of the extreme diversity of CRISPR-Cas systems, in-depth sequence and structure comparison continue to reveal unexpected homologous relationship among Cas proteins. Unification of Cas protein families previously considered unrelated provides for improvement in the classification of CRISPR-Cas systems and a reconstruction of their evolution. This article was reviewed by Malcolm White (nominated by Purficacion Lopez-Garcia), Frank Eisenhaber and Igor Zhulin. For the full reviews, see the Reviewers' Comments section.
An automated system to detect and recognize vehicle license plates of Bangladesh

Bangladesh is a country in South Asia that uses Retro Reflective license plates. The plate has two lines with words, letters, and digits. An automated system to detect and recognize these plates is presented in this paper. The system is divided into four parts: plate detection, extraction, character segmentation and recognition. At first, the input image is enhanced using CLAHE and a matched filter specially designed for license plates with two lines is applied. Then tilt correction using Radon transformation, binarization and cleaning are performed. For character segmentation, mean intensity based horizontal and vertical projection is used. In recognition, we have used two different Convolutional Neural Network (CNN) to classify digits and letters. Tesseract OCR is used for district names. We have developed a dataset of over 400 images of different vehicles (e.g., private car, bus, truck etc.) taken at different times of day (including nights). The plates in this dataset are in different angles, including blurry, worn-out and muddy ones. On this dataset, the proposed system achieved a success rate of 96.8% in detection, 89.5% in extraction, 98.6% in segmentation and 98.0% in character recognition.
Narrative Schema Stability in News Text

We investigate the stability of narrative schemas (Chambers & Jurafsky, 2009) automatically induced from a news corpus, representing recurring narratives in a corpus. If such techniques produce meaningful results, we should expect that small changes to the corpus will result in only small changes to the induced schemas. We describe experiments involving successive ablation of a corpus and cross-validation at each stage of ablation, on schemas generated by three different techniques over a general news corpus and topically-specific subcorpora. We also develop a method for evaluating the similarity between sets of narrative schemas, and thus the stability of the schema induction algorithms. This stability analysis affirms the heterogeneous/homogeneous document category hypothesis first presented in Simonson & Davis (2016), whose technique is problematically limited. Additionally, increased ablation leads to increasing stability, so the smaller the remaining corpus, the more stable schema generation appears to be. We surmise that as a corpus grows larger, novel and more varied narratives continue to appear and stability declines, though at some point this decline levels off as new additions to the corpus consist essentially of “more of the same.”
Design of a novel Simulink model for surface electromyographic (SEMG) sensor design for prosthesis control

This paper presents design, simulation and fabrication of a surface electromyographic (SEMG) sensor for control of prosthetic devices. EMG activity is mainly the generation of a bio-potential signal (electrical signals) due to muscle action. These signals picked from motor points of muscles are contaminated with various intrinsic/extrinsic noises, which must be removed through different filtering techniques in order to develop a sensor that has a high signal-to-noise ratio. Power spectral density (PSD) of any EMG signal plays a vital role to determine the signal strength. A novel Simulink model has been developed which mimics various elements of an active SEMG sensor. By using this model, low/high/notch filters are designed and optimized. The model is also used to simulate the effects of these filters on power spectral density (PSD) of the EMG signal. Simulation of double rectification and smoothing (envelopment) is also carried out. EMG signals recorded from the Tibialis anterior, monopolar needle, and fine wire isometric contraction were used in this simulation. Finally, on the basis of simulation results, instrumentation of surface EMG sensor is designed and fabricated. Performance/results of developed SEMG sensor are in accordance with the simulation results of the developed Simulink model.