nils-herrmann/clean_scirepeval huggingface dataset:
Practical Visual Localization for Autonomous Driving: Why Not Filter?

A major focus of current research on place recognition is visual localization for autonomous driving. However, while many visual localization algorithms for autonomous driving have achieved impressive results, it seems not all previous works have been set in a realistic setting for the problem, namely using training and testing videos that were collected in a distributed manner from multiple vehicles, all traversing through a road network in an urban area under different environmental conditions (weather, lighting, etc.). More importantly, in this setting, we show that exploiting temporal continuity in the testing sequence significantly improves visual localization qualitatively and quantitatively. Although intuitive, this idea has not been fully explored in recent works. Our main contribution is a novel particle filtering technique that works in conjunction with a visual localization method to achieve accurate city-scale localization that is robust against environmental variations. We provide convincing results on synthetic and real datasets.
Do Tangible User Interfaces Impact Spatial Cognition in Collaborative Design?

Developments in digital design workbenches that combine Augmented Reality (AR) systems and tangible user interfaces (TUIs) on a horizontal display surface provide a new kind of physical and digital environment for collaborative design. The combination of tangible interaction with AR display techniques change the dynamics of the collaboration and have an impact on the designers’ perception of 3D models. We are studying the effects of TUIs on designers’ spatial cognition and design communication in order to identify how such tangible systems can be used to provide better support for collaborative design. Specifically, we compared tangible user interfaces (TUIs) with graphical user interfaces (GUIs) in a collaborative design task with a focus on characterising the impact these user interfaces have on spatial cognition.
Hierarchical data-driven vehicle dispatch and ride-sharing

Modern transportation system suffers from increasing passenger demand, limited vehicle supply and inefficient mobility service. Towards building an intelligent transportation system to address these issues, we propose a hierarchical framework to implement strategies that is capable of allocating vehicles to serve passengers in different locations based on limited supply. In the higher hierarchy, we optimize idle mileage induced by rebalancing vehicles across regions using receding horizon control towards current and predicted future requests. In addition, we design a dispatch strategy that is robust against passenger demand and vehicle mobility pattern uncertainties. In the lower hierarchy, within each region, pick-up and drop-off schedules for real-time requests are obtained for each vehicle by solving mixed-integer linear programs (MILP). The objective of the MILP is to minimize total mileage delay due to ride-sharing while serving as many requests as possible. We illustrate the validity of our framework via numerical simulations on taxi trip data from New York City.
Thinking Aloud : Reconciling Theory and Practice —

—M. TED BOREN AND JUDITH RAMEY, ASSOCIATE MEMBER, Thinking-aloud protocols may be the most widely used method in usability testing, but the descriptions of this practice in the usability literature and the work habits of practitioners do not conform to the theoretical basis most often cited for it: Ericsson and Simon’s seminal work PROTOCOL ANALYSIS: VERBAL REPORTS AS DATA . After reviewing Ericsson and Simon’s theoretical basis for thinking aloud, we review the ways in which actual usability practice diverges from this model. We then explore the concept of SPEECH GENRE as an alternative theoretical framework. We first consider uses of this new framework that are consistent with Simon and Ericsson’s goal of eliciting a verbal report that is as undirected, undisturbed, and constant as possible. We then go on to consider how the proposed new approach might handle problems that arise in usability testing that appear to require interventions not supported in the older model.
Assessment of machine learning algorithms in cloud computing frameworks

In the past decade, digitization of information has led to a data explosion in both volume and complexity. While traditional computing frameworks have failed to provide adequate computing power for the now common data-intensive computing tasks, cloud computing provides an effective alternative to enhance computing power. Machine learning algorithms are powerful analytical methods that allow machines to recognize patterns and facilitate human learning. However, the performance of individual machine learning algorithms within each cloud computing framework remains largely unknown. Furthermore, the lack of a robust selection methodology matching input data with effective machine learning algorithms limits the ability of practitioners to make effective use of cloud computing. This research compares various machine learning algorithms on the widely adopted Apache Mahout framework and the recently introduced GraphLab framework. Whereas previous work has examined the computational architectures of various cloud computing frameworks, this work focuses on a problem-based approach to architecture selection. The experimental results demonstrate that GraphLab generally outperforms Mahout with respect to runtime, scalability, and usability. However, Mahout outperforms GraphLab when the experiment focus shifts to error measurement.
Functional neuroanatomy of human rapid-eye-movement sleep and dreaming

RAPID-EYE-MOVEMENT (REM) sleep is associated with intense neuronal activity, ocular saccades, muscular atonia and dreaming1,2. The function of REM sleep remains elusive and its neural correlates have not been characterized precisely in man. Here we use positron emission tomography and statistical parametric mapping to study the brain state associated with REM sleep in humans. We report a group study of seven subjects who maintained steady REM sleep during brain scanning and recalled dreams upon awakening. The results show that regional cerebral blood flow is positively correlated with REM sleep in pontine tegmentum, left thalamus, both amygdaloid complexes, anterior cingulate cortex and right parietal operculum. Negative correlations between regional cerebral blood flow and REM sleep are observed bilaterally, in a vast area of dorsolateral prefrontal cortex, in parietal cortex (supramarginal gyrus) as well as in posterior cingulate cortex and precuneus. Given the role of the amygdaloid complexes in the acquisition of emotionally influenced memories, the pattern of activation in the amygdala and the cortical areas provides a biological basis for the processing of some types of memory during REM sleep.
Concept Mining via Embedding

In this work, we study the problem of concept mining, which serves as the first step in transforming unstructured text into structured information, and supports downstream analytical tasks such as information extraction, organization, recommendation and search. Previous work mainly relies on statistical signals, existing knowledge bases, or predefined linguistic patterns. In this work, we propose a novel approach that mines concepts based on their occurrence contexts, by learning embedding vector representations that summarize the context information for each possible candidates, and use these embeddings to evaluate the concept's global quality and their fitness to each local context. Experiments over several real-world corpora demonstrate the superior performance of our method. A publicly available implementation is provided at https://github.com/kleeeeea/ECON.
Kernel estimation for motion blur removal using deep convolutional neural network

Blind deblurring can restore the sharp image from the blur version when the blur kernel is unknown, which is a challenging task. Kernel estimation is crucial for blind deblurring. In this paper, a novel blur kernel estimation method based on regression model is proposed for motion blur. The motion blur features are firstly mined through convolutional neural network (CNN), and then mapped to motion length and orientation by support vector regression (SVR). Experiments show that the proposed model, namely CNNSVR, can give more accurate kernel estimation and generate better deblurring result compared with other state-of-the-art algorithms.
Skipping Refinement

We introduce skipping refinement, a new notion of correctness for reasoning about optimized reactive systems. Reasoning about reactive systems using refinement involves defining an abstract, highlevel specification system and a concrete, low-level implementation system. One then shows that every behavior allowed by the implementation is also allowed by the specification. Due to the difference in abstraction levels, it is often the case that the implementation requires many steps to match one step of the specification, hence, it is quite useful for refinement to directly account for stuttering. Some optimized implementations, however, can actually take multiple specification steps at once. For example, a memory controller can buffer the commands to the memory and at a later time simultaneously update multiple memory locations, thereby skipping several observable states of the abstract specification, which only updates one memory location at a time. We introduce skipping simulation refinement and provide a sound and complete characterization consisting of “local” proof rules that are amenable to mechanization and automated verification. We present case studies that highlight the applicability of skipping refinement: a JVM-inspired stack machine, a simple memory controller and a scalar to vector compiler transformation. Our experimental results demonstrate that current model-checking and automated theorem proving tools have difficultly automatically analyzing these systems using existing notions of correctness, but they can analyze the systems if we use skipping refinement.
Source Code Metrics for Programmable Logic Controller (PLC) Ladder Diagram (LD) Visual Programming Language

The IEC 611131-3, an open international standard for Programmable Logic Controllers (PLC) defines several domain specific languages for industrial and process automation. Domain specific languages have several features, programming constructs and notations which are different than general purpose languages and hence the traditional source code metrics for general purpose programming languages cannot be directly applied to domain specific languages for writing control programs in the area of industrial automation. We propose source code level metrics to measure size, vocabulary, cognitive complexity and testing complexity of a visual Programmable Logic Controller (PLC) programming language. We present metrics for Ladder Diagram (LD) programming language which is one of the five languages defined in the IEC 61131-3 standard. The proposed metric is based on factors like number of distinct operators and operands, total number of operators and operands, number of rungs, weights of different basic control structures, structure of the program and control flow. We apply Weyukur's property to validate the metrics and evaluate the number of properties satisfied by the proposed metric.
Negation and the Temporal Structure of Narrative Discourse

This paper develops an analysis of the temporal role of negative sentences in narrative discourse in Etigfati and French. The analysis focuses on differences in the aspectual systems of English and French, and their consequences for the interpretation of negation and quantification. A recursive rule for the introduction of discourse referents characterizes both quantificational and negated sequences as complex states. The notion of coercion explains why states (including complex states) can behave as events at the level of narrative discourse. The analysis is implemented in the framework of Discourse Representation Theory (DKI) developed by Kamp & Reyle (1993). 1 NEGATION IN SENTENCE AND DISCOURSE In temporal semantics, affirmative sentences are taken to introduce new eventualities into the discourse representation.' Eventualities are located in time with respect to the speech time and other events that are part of the narrative structure. We extend this model of interpretation to negative sentences, and claim that they refer to negative states of affairs. This position requires some motivation, for the predominant view seems to be that negative sentences do not describe eventualities (Kamp & Reyle 1993; Asher 1993). Given that we implement our analysis in the framework of Discourse Representation Theory (DRT) developed by Kamp & Reyle (1993), it is worthwhile running through their argumentation. Kamp & Reyle develop a theory of tense and aspect which assumes the general grammatical structure of tensed clauses in (1): (1) [Tense [Aspect* [eventuality description]]] The predicate-argument structure of the sentence provides the description of some eventuality. Aspectual operators are modifiers whose input and output is a set of eventualities. The tense operator locates the eventuality in ' Bach (1986) introduces the term 'eventuality' to generalize over states, processes, and events. 2 Negation and the Temporal Structure of Narrative Discourse time. In English, aspectual operators are optional, and they allow recursion, which is why we use the Kleene star to indicate zero, one, or more operations. According to Kamp & Reyle (1993: 546-55), negation is not an aspectual operator, and we do not need to introduce a discourse referent for negative sentences as a whole in order to obtain the correct interpretation for sentences containing frame adverbials such as on Sunday, compare: (2) a. Mary wrote a letter on Sunday. b. Mary didn't write a letter on Sunday. In the Discourse Representation Theory (DRT) of Kamp & Reyle, these sentences are interpreted in Figures 1 and 2. These Discourse Representation Structures (DRSs) contain discourse referents, and conditions which assign properties to the discourse referents:
Efficient Keyword-Aware Representative Travel Route Recommendation

With the popularity of social media (e.g., Facebook and Flicker), users can easily share their check-in records and photos during their trips. In view of the huge number of user historical mobility records in social media, we aim to discover travel experiences to facilitate trip planning. When planning a trip, users always have specific preferences regarding their trips. Instead of restricting users to limited query options such as locations, activities, or time periods, we consider arbitrary text descriptions as keywords about personalized requirements. Moreover, a diverse and representative set of recommended travel routes is needed. Prior works have elaborated on mining and ranking existing routes from check-in data. To meet the need for automatic trip organization, we claim that more features of Places of Interest (POIs) should be extracted. Therefore, in this paper, we propose an efficient Keyword-aware Representative Travel Route framework that uses knowledge extraction from users’ historical mobility records and social interactions. Explicitly, we have designed a keyword extraction module to classify the POI-related tags, for effective matching with query keywords. We have further designed a route reconstruction algorithm to construct route candidates that fulfill the requirements. To provide befitting query results, we explore Representative Skyline concepts, that is, the Skyline routes which best describe the trade-offs among different POI features. To evaluate the effectiveness and efficiency of the proposed algorithms, we have conducted extensive experiments on real location-based social network datasets, and the experiment results show that our methods do indeed demonstrate good performance compared to state-of-the-art works.
Semi-Supervised Clustering with Neural Networks

Clustering using neural networks has recently demonstrated promising performance in machine learning and computer vision applications. However, the performance of current approaches is limited either by unsupervised learning or their dependence on large set of labeled data samples. In this paper, we propose ClusterNet that uses pairwise semantic constraints from very few labeled data samples (< 5% of total data) and exploits the abundant unlabeled data to drive the clustering approach. We define a new loss function that uses pairwise semantic similarity between objects combined with constrained k-means clustering to efficiently utilize both labeled and unlabeled data in the same framework. The proposed network uses convolution autoencoder to learn a latent representation that groups data into k specified clusters, while also learning the cluster centers simultaneously. We evaluate and compare the performance of ClusterNet on several datasets and state of the art deep clustering approaches.
A capacitive-loaded level shift circuit for improving the noise immunity of high voltage gate drive IC

A high voltage gate drive IC achieving the high dVS/dt noise immunity up to 85V/ns and the allowable negative VS swing to -12V at 15V supply voltage is proposed for the first time. The robust features are due to the presented capacitive loaded level shift circuit used in the gate driver. Measured and simulated results are performed to verify the electrical characteristics of the designed gate driver which is implemented in a 0.5um 600V Bipolar-CMOS-DMOS (BCD) technology.
Origami structure toward floating aerial robot

In this paper, we introduce the application of Japanese origami art to the floating system of unmanned helicopter model for the case of emergency landing. The “origami floating system” is integrated compactly in the helicopter and expands by an electronic controlled system when the helicopter lands on water. We tested our proposed floating system using three types helicopters, with the origami made from waterproof paper. The results indicate that the proposed system is feasible for helicopter and other aerial vehicles. In addition, several origami balls were constricted as floating devices for the helicopter. The correlation between the diameter of inflated ball and the size of origami paper sheet, along with the effects of the weight of helicopter on the water, are also investigated in detail.
Making sense of social media streams through semantics: A survey

Using semantic technologies for mining and intelligent information access to social media is a challenging, emerging research area. Traditional search methods are no longer able to address the more complex information seeking behaviour in media streams, which has evolved towards sense making, learning, investigation, and social search. Unlike carefully authored news text and longer web context, social media streams pose a number of new challenges, due to their large-scale, short, noisy, contextdependent, and dynamic nature. This paper defines five key research questions in this new application area, examined through a survey of state-of-the-art approaches for mining semantics from social media streams; user, network, and behaviour modelling; and intelligent, semanticbased information access. The survey includes key methods not just from the Semantic Web research field, but also from the related areas of natural language processing and user modelling. In conclusion, key outstanding challenges are discussed and new directions for research are proposed.
Sindhi Language Processing: A survey

In this era of information technology, natural language processing (NLP) has become volatile field because of digital reliance of today's communities. The growth of Internet usage bringing the communities, cultures and languages online. In this regard much of the work has been done of the European and east Asian languages, in the result these languages have reached mature level in terms of computational processing. Despite the great importance of NLP science, still most of the South Asian languages are under developing phase. Sindhi language is one of them, which stands among the most ancient languages in the world. The Sindhi language has a great influence on the large community in Sindh province of Pakistan and some states of India and other countries. But unfortunately, it is at infant level in terms of computational processing, because it has not received such attention of language engineering community, due to its complex morphological structure and scarcity of language resources. Therefore, this study has been carried out in order to summarize the existing work on Sindhi Language Processing (SLP) and to explore future research opportunities, also some potential research problems. This paper will be helpful for the researchers in order to find all the information regarding SLP at one place in a unique way.
Credit card fraud detection using machine learning techniques: A comparative analysis

Financial fraud is an ever growing menace with far consequences in the financial industry. Data mining had played an imperative role in the detection of credit card fraud in online transactions. Credit card fraud detection, which is a data mining problem, becomes challenging due to two major reasons — first, the profiles of normal and fraudulent behaviours change constantly and secondly, credit card fraud data sets are highly skewed. The performance of fraud detection in credit card transactions is greatly affected by the sampling approach on dataset, selection of variables and detection technique(s) used. This paper investigates the performance of naïve bayes, k-nearest neighbor and logistic regression on highly skewed credit card fraud data. Dataset of credit card transactions is sourced from European cardholders containing 284,807 transactions. A hybrid technique of under-sampling and oversampling is carried out on the skewed data. The three techniques are applied on the raw and preprocessed data. The work is implemented in Python. The performance of the techniques is evaluated based on accuracy, sensitivity, specificity, precision, Matthews correlation coefficient and balanced classification rate. The results shows of optimal accuracy for naïve bayes, k-nearest neighbor and logistic regression classifiers are 97.92%, 97.69% and 54.86% respectively. The comparative results show that k-nearest neighbour performs better than naïve bayes and logistic regression techniques.
FastQRE: Fast Query Reverse Engineering

We study the problem of Query Reverse Engineering (QRE), where given a database and an output table, the task is to find a simple project-join SQL query that generates that table when applied on the database. This problem is known for its efficiency challenge due to mainly two reasons. First, the problem has a very large search space and its various variants are known to be NP-hard. Second, executing even a single candidate SQL query can be very computationally expensive. In this work we propose a novel approach for solving the QRE problem efficiently. Our solution outperforms the existing state of the art by 2-3 orders of magnitude for complex queries, resolving those queries in seconds rather than days, thus making our approach more practical in real-life settings.