nils-herrmann/clean_scirepeval huggingface dataset:
A-priori map information and path planning for automated valet-parking

Automated valet parking can help manage space in parking garages more efficiently and increase the comfort for the driver by delegating the parking task to the system. To successfully navigate the vehicle in a parking garage, the valet parking system requires a-priori information about the environment. This paper shows an approach to derive a-priori information from available blueprints of a parking lot. Based on the derived map data, paths for Ackermann-steered vehicles are planned to navigate vehicles through the parking area and park them into desired parking spots.
Learning Sparsifying Transforms

The sparsity of signals and images in a certain transform domain or dictionary has been exploited in many applications in signal and image processing. Analytical sparsifying transforms such as Wavelets and DCT have been widely used in compression standards. Recently, synthesis sparsifying dictionaries that are directly adapted to the data have become popular especially in applications such as image denoising, inpainting, and medical image reconstruction. While there has been extensive research on learning synthesis dictionaries and some recent work on learning analysis dictionaries, the idea of learning sparsifying transforms has received no attention. In this work, we propose novel problem formulations for learning sparsifying transforms from data. The proposed alternating minimization algorithms give rise to well-conditioned square transforms. We show the superiority of our approach over analytical sparsifying transforms such as the DCT for signal and image representation. We also show promising performance in signal denoising using the learnt sparsifying transforms. The proposed approach is much faster than previous approaches involving learnt synthesis, or analysis dictionaries.
Inventory, evolution and expression profiling diversity of the LEA (late embryogenesis abundant) protein gene family in Arabidopsis thaliana

We analyzed the Arabidopsis thaliana genome sequence to detect Late Embryogenesis Abundant (LEA) protein genes, using as reference sequences proteins related to LEAs previously described in cotton or which present similar characteristics. We selected 50 genes representing nine groups. Most of the encoded predicted proteins are small and contain repeated domains that are often specific to a unique LEA group. Comparison of these domains indicates that proteins with classical group 5 motifs are related to group 3 proteins and also gives information on the possible history of these repetitions. Chromosomal gene locations reveal that several LEA genes result from whole genome duplications (WGD) and that 14 are organized in direct tandem repeats. Expression of 45 of these genes was tested in different plant organs, as well as in response to ABA and in mutants (such as abi3, abi5, lec2 and fus3) altered in their response to ABA or in seed maturation. The results demonstrate that several so-called LEA genes are expressed in vegetative tissues in the absence of any abiotic stress, that LEA genes from the same group do not present identical expression profile and, finally, that regulation of LEA genes with apparently similar expression patterns does not systematically involve the same regulatory pathway.
The Extroverted Firm: How External Information Practices Affect Innovation and Productivity

We gather detailed data on organizational practices and IT use at 253 firms to examine the hypothesis that external focus – the ability of a f irm to detect and therefore respond to changes in its external operating environment – increases retu rns to information technology, especially when combined with decentralized decision-making. First, using survey-based measures, we find that external focus is correlated with both organization al decentralization and IT investment. Second, we find that a cluster of practices including exter nal focus, decentralization and IT is associated with improved product innovation capabilities. Thir d, we develop and test a 3-way complementarities model that indicates that the com bination of external focus, decentralization and IT is associated with significantly higher prod uctivity in our sample. We also introduce a new set of instrumental variables representing barriers to IT-related organizational change and find that our results are robust when we account for the pote ntial endogeneity of organizational investments. Our results may help explain why firm s that operate in information-rich environments such as high-technology clusters or ar eas with high worker mobility have experienced especially high returns to IT investmen t and suggest a set of practices that some managers may be able to use to increase their retur ns from IT investments.
An Explorative Study of the Mobile App Ecosystem from App Developers' Perspective

With the prevalence of smartphones, app markets such as Apple App Store and Google Play has become the center stage in the mobile app ecosystem, with millions of apps developed by tens of thousands of app developers in each major market. This paper presents a study of the mobile app ecosystem from the perspective of app developers. Based on over one million Android apps and 320,000 developers from Google Play, we analyzed the Android app ecosystem from different aspects. Our analysis shows that while over half of the developers have released only one app in the market, many of them have released hundreds of apps. We classified developers into different groups based on the number of apps they have released, and compared their characteristics. Specially, we have analyzed the group of aggressive developers who have released more than 50 apps, trying to understand how and why they create so many apps. We also investigated the privacy behaviors of app developers, showing that some developers have a habit of producing apps with low privacy ratings. Our study shows that understanding the behavior of mobile developers can be helpful to not only other app developers, but also to app markets and mobile users.
Stock Direction Forecasting Techniques: An Empirical Study Combining Machine Learning System with Market Indicators in the Indian Context

Stock price movement prediction has been one of the most challenging issues in finance since the time immemorial. Many researchers in past have carried out extensive studies with the intention of investigating the approaches that uncover the hidden information in stock market data. As a result of which, Artificial Intelligence and data mining techniques have come to the forefront because of their ability to map nonlinear data. The study encapsulates market indicators with AI techniques to generate useful extracts to improve decisions under conditions of uncertainty. Three approaches (fundamental model, technical indicators model and hybrid model) have been tested using the standalone and integrated machine learning algorithms viz. SVM, ANN, GA-SVM, and GA-ANN and the results of all the three approaches have been compared in the four above mentioned methods. The core objective of this paper is to identify an approach from the above mentioned algorithms that best predicts the Indian stocks price movement. It is observed from the results that the use of GA significantly increases the accuracy of ANN and that the use of technical analysis with SVM and ANN is well suited for Indian stocks and can help investors and traders maximize their quarterly profits.
Democrats, republicans and starbucks afficionados: user classification in twitter

More and more technologies are taking advantage of the explosion of social media (Web search, content recommendation services, marketing, ad targeting, etc.). This paper focuses on the problem of automatically constructing user profiles, which can significantly benefit such technologies. We describe a general and robust machine learning framework for large-scale classification of social media users according to dimensions of interest. We report encouraging experimental results on 3 tasks with different characteristics: political affiliation detection, ethnicity identification and detecting affinity for a particular business.
Earliest Deadline Scheduling for Real-Time Database Systems

Earlier studies have observed that in moderately-loaded real-time database systems, using an Earliest Deadline policy to schedule tasks results in the fewest missed deadlines. When the real-time system is overloaded, however, an Earliest Deadline schedule performs worse than most other policies. This is due to Earliest Deadline giving the highest priority to transactions that are close to missing their deadlines. In this paper, we present a new priority assignment algorithm called Adaptive Earliest Deadline (AED), which features a feedback control mechanism that detects overload conditions and modifies transaction priority assignments accordingly. Using a detailed simulation model, we compare the performance of AED with respect to Earliest Deadline and other fixed priority schemes. We also present and evaluate an extension of the AED algorithm called Hierarchical Earliest Deadline (HED), which is designed to handle applications that assign different values to transactions and where the goal is to maximize the total value of the in-time transactions.
A Cascaded High Step-Up DC–DC Converter With Single Switch for Microsource Applications

This paper proposes a new high step-up dc-dc converter designed especially for regulating the dc interface between various microsources and a dc-ac inverter to electricity grid. The figuration of the proposed converter is a quadratic boost converter with the coupled inductor in the second boost converter. The converter achieves high step-up voltage gain with appropriate duty ratio and low voltage stress on the power switch. Additionally, the energy stored in the leakage inductor of the coupled inductor can be recycled to the output capacitor. The operating principles and steady-state analyses of continuous-conduction mode and boundary-conduction mode are discussed in detail. To verify the performance of the proposed converter, a 280-W prototype sample is implemented with an input voltage range of 20-40 V and an output voltage of up to 400 V. The upmost efficiency of 93.3% is reached with high-line input; on the other hand, the full-load efficiency remains at 89.3% during low-line input.
Optimal control of execution costs for portfolios

We derive dynamic optimal trading strategies that minimize the expected cost of trading blocks of securities over a xed time horizon. Given xed blocks si of shares of stock i to be traded within a nite number of periods T , i = 1; : : : ; n, and given price-impact functions that yield the execution price of an individual trade as a function of the shares of stock i traded and current market conditions, we obtain the optimal sequence of trades as a function of market conditions|closed-form expressions in some cases|that minimizes the expected cost of executing si within T periods. We also propose an approximation algorithm for incorporating constraints, a particularly important extension in practice. To illustrate the practical relevance of our methods, we apply them to a hypothetical portfolio of 25 stocks by estimating their price-impact functions using historical trade data from 1996 and deriving the optimal execution strategies for the portfolio, and by performing several Monte Carlo simulation experiments. This research is part of the MIT Laboratory for Financial Engineering's Transactions Costs Project. We are grateful to Investment Technology Group, the National Science Foundation (Grant Nos. DMI{9610486 and SBR{9709976), and the sponsors of the LFE for nancial support. We thank Dave Cushing, Chris Darnell, Robert Ferstenberg, Rohit D'Souza, John Heaton, Leonid Kogan, Bruce Lehmann, Greg Peterson, Jiang Wang, and seminar participants at Columbia, Harvard, London Business School, MIT, Northwestern, the NYSE Conference on Best Execution, the Society of Quantitative Analysts, and Yale for helpful comments and discussion. Boeing Professor of Operations Research, MIT Sloan School of Management and Operations Research Center, Massachusetts Institute of Technology, E53-359, Cambridge, MA 02142{1347. Portfolio Strategist, Long Term Capital Management, One East Weaver St., Greenwich, CT 06831. Harris & Harris Group Professor, MIT Sloan School of Management and Operations Research Center, Cambridge, MA 02142{1347.
Resolution Theorem Proving

Lifting is one of the most important general techniques for accelerating dii-cult computations. Lifting was used by Alan Robinson in the early 1960's to accelerate rst order inference Robinson, 1965]. A complete inference procedure had been done by Martin Davis and Hillary Putnam a few years prior to Robinson's work Davis and Putnam, 1960]. Unfortunately, it was a ground procedure and suuered from a large search space generated by unguided selection of gound instances of quantiied formulas. The general method of lifting any ground procedure was not known at the time. Without an understanding of the general technique, it is diicult to lift the Davis-Putnam procedure. Rather than lift the Davis-Putnam procedure, Robinson designed a ground procedure for which lifting was particularly simple. The lifted inference process became known as resolution. Because of the tremendous advantage of a lifted procedure over a ground procedure, resolution immediately became the central paradigm of automated rst order inference. Over the years resolution has been greatly reened. The basic procedure is deened below and several reenements are described. Although lifting is a good technique for accelerating diicult computations, lifting alone has not brought computers near human-level competence in mathematical reasoning. It is still instructive , however, to study the classical resolution theorem proving techniques. In order to using lifting one must rst construct a ground procedure. The rst step in constructing the ground procedure is to convert inference problems to a simple normal form.
A Tutorial on Interactive Sensing in Social Networks

This paper considers models and algorithms for interactive sensing in social networks in which individuals act as sensors and the information exchange between individuals is exploited to optimize sensing. Social learning is used to model the interaction between individuals that aim to estimate an underlying state of nature. In this context, the following questions are addressed: how can self-interested agents that interact via social learning achieve a tradeoff between individual privacy and reputation of the social group? How can protocols be designed to prevent data incest in online reputation blogs where individuals make recommendations? How can sensing by individuals that interact with each other be used by a global decision maker to detect changes in the underlying state of nature? When individual agents possess limited sensing, computation, and communication capabilities, can a network of agents achieve sophisticated global behavior? Social and game-theoretic learning are natural settings for addressing these questions. This article presents an overview, insights, and discussion of social learning models in the context of data incest propagation, change detection, and coordination of decision-making.
Comparative Analysis of Machine Learning Algorithms on Different Datasets

Machine learning calculations can make sense of how to perform imperative errands by summing up from illustrations. This research aims at comparing different algorithms used in machine learning. Machine Learning can be both experience and explanation-based learning. In this study most popular algorithms were used like Neural Network (NN), k-Nearest Neighbour (KNN) and Support Vector Machine SVM and two datasets were used to check the efficiency of algorithms. Comparative analysis of the classifiers shows that SVM outperforms the other methods with a high accuracy.
Degree of nonlinearity (DoN) measure for target tracking in videos

Performance evaluation of tracking methods includes methods of relative and absolute performance. Absolute tracking performance is the robust end result presented to a user which determines the product solution for real world analysis. However, to achieve robust performance, the tracking method is subject to the sensor data, filtering performance, and associated models, which requires relative performance evaluation. In this paper, we highlight the efficacy of using DoN measure in evaluating video tracking capabilities. Three developments are presented as to the real-world issues associated with the nonlinear video-based tracking: (1) challenges of performance evaluation with real data, (2) approaches to utilize DoN as improvements for relative track evaluation, and (3) operational implementations lessons associated from user-defined operating picture (UDOP) plugins. Results are presented using relevant data with a highlighted tracker.
Real-Time Cutting Tool Condition Monitoring in Milling

Reliable tool wear monitoring system is one of the important aspects for achieving a self-adjusting manufacturing system. The original contribution of the research is the developed monitoring system that can detect tool breakage in real time by using a combination of neural decision system and ANFIS tool wear estimator. The principal presumption was that force signals contain the most useful information for determining the tool condition. Therefore, the ANFIS method is used to extract the features of tool states from cutting force signals. ANFIS method seeks to provide a linguistic model for the estimation of tool wear from the knowledge embedded in the artificial neural network. The ANFIS method uses the relationship between flank wear and the resultant cutting force to estimate tool wear. A series of experiments were conducted to determine the relationship between flank wear and cutting force as well as cutting parameters. Speed, feed, depth of cutting, time and cutting forces were used as input parameters and flank wear width and tool state were output parameters. The forces were measured using a piezoelectric dynamometer and data acquisition system. Simultaneously flank wear at the cutting edge was monitored by using a tool maker’s microscope. The experimental force and wear data were utilized to train the developed simulation environment based on ANFIS modelling. The artificial neural network, was also used to discriminate different malfunction states from measured signals. By developed tool monitoring system (TCM) the machining process can be on-line monitored and stopped for tool change based on a pre-set tool-wear limit. The fundamental limitation of research was to develop a singlesensor monitoring system, reliable as commercially available system, but 80% cheaper than multisensor approach.
Optimising aesthetics and facilitating clinical application of free-hand bonding using the 'natural layering concept'

The use of composites in the smile frame has evolved and gained maturity. However, ceramics remain the preferred aesthetic option for many clinicians because bonding techniques are still considered intricate, sensitive and, to a certain extent, unpredictable. These drawbacks, essentially related to the complexity of many composite systems, can be overcome today by the application of the 'natural layering concept', which makes use of only two basics masses, dentine and enamel, perfectly mimicking natural tooth structure. There are plentiful indications for the use of such a simplified but aesthetically uncompromised treatment modality. Actually, this approach is highly suitable for young and post-orthodontic patients where conservation of tissue is a must and in general for all patients with rather healthy dentitions.
THRIVE: Threshold Homomorphic encryption based secure and privacy preserving bIometric VErification system

In this paper, we introduce a new biometric verification and template protection system which we call THRIVE. The system includes novel enrollment and authentication protocols based on threshold homomorphic encryption where a private key is shared between a user and a verifier. In the THRIVE system, only encrypted binary biometric templates are stored in a database and verification is performed via homomorphically randomized templates, thus, original templates are never revealed during authentication. Due to the underlying threshold homomorphic encryption scheme, a malicious database owner cannot perform full decryption on encrypted templates of the users in the database. In addition, security of the THRIVE system is enhanced using a two-factor authentication scheme involving user’s private key and biometric data. Using simulation-based techniques, the proposed system is proven secure in the malicious model. The proposed system is suitable for applications where the user does not want to reveal her biometrics to the verifier in plain form, but needs to prove her identity by using biometrics. The system can be used with any biometric modality where a feature extraction method yields a fixed size binary template and a query template is verified when its Hamming distance to the database template is less than a threshold. The overall connection time for the proposed THRIVE system is estimated to be 336 ms on average for 256-bit biometric templates on a desktop PC running with quad core 3.2 GHz CPUs at 10 Mbit/s up/down link connection speed. Consequently, the proposed system can be efficiently used in real-life applications.
mDARAL: A Multi-Radio Version for the DARAL Routing Algorithm

Smart Cities are called to change the daily life of human beings. This concept permits improving the efficiency of our cities in several areas such as the use of water, energy consumption, waste treatment, and mobility both for people as well as vehicles throughout the city. This represents an interconnected scenario in which thousands of embedded devices need to work in a collaborative way both for sensing and modifying the environment properly. Under this scenario, the majority of devices will use wireless protocols for communicating among them, representing a challenge for optimizing the use of the electromagnetic spectrum. When the density of deployed nodes increases, the competition for using the physical medium becomes harder and, in consequence, traffic collisions will be higher, affecting data-rates in the communication process. This work presents mDARAL, a multi-radio routing algorithm based on the Dynamic and Adaptive Radio Algorithm (DARAL), which has the capability of isolating groups of nodes into sub-networks. The nodes of each sub-network will communicate among them using a dedicated radio frequency, thus isolating the use of the radio channel to a reduced number of nodes. Each sub-network will have a master node with two physical radios, one for communicating with its neighbours and the other for being the contact point among its group and other sub-networks. The communication among sub-networks is done through master nodes in a dedicated radio frequency. The algorithm works to maximize the overall performance of the network through the distribution of the traffic messages into unoccupied frequencies. The obtained results show that mDARAL achieves great improvement in terms of the number of control messages necessary to connect a node to the network, convergence time and energy consumption during the connection phase compared to DARAL.
Improving web search ranking by incorporating user behavior information

We show that incorporating user behavior data can significantly improve ordering of top results in real web search setting. We examine alternatives for incorporating feedback into the ranking process and explore the contributions of user feedback compared to other common web search features. We report results of a large scale evaluation over 3,000 queries and 12 million user interactions with a popular web search engine. We show that incorporating implicit feedback can augment other features, improving the accuracy of a competitive web search ranking algorithms by as much as 31% relative to the original performance.