nils-herrmann/clean_scirepeval huggingface dataset:
Meta-Path Guided Embedding for Similarity Search in Large-Scale Heterogeneous Information Networks

Most real-world data can be modeled as heterogeneous information networks (HINs) consisting of vertices of multiple types and their relationships. Search for similar vertices of the same type in large HINs, such as bibliographic networks and business-review networks, is a fundamental problem with broad applications. Although similarity search in HINs has been studied previously, most existing approaches neither explore rich semantic information embedded in the network structures nor take user’s preference as a guidance. In this paper, we re-examine similarity search in HINs and propose a novel embedding-based framework. It models vertices as low-dimensional vectors to explore network structureembedded similarity. To accommodate user preferences at defining similarity semantics, our proposed framework, ESim, accepts user-defined meta-paths as guidance to learn vertex vectors in a user-preferred embedding space. Moreover, an efficient and parallel sampling-based optimization algorithm has been developed to learn embeddings in large-scale HINs. Extensive experiments on real-world large-scale HINs demonstrate a significant improvement on the effectiveness of ESim over several state-of-the-art algorithms as well as its scalability.
Performance of Some Image Processing Algorithms in Tensorflow

Signal, image and Synthetic Aperture Radar imagery algorithms in recent time are used in a daily routine. Due to huge data and complexity, their processing is almost impossible in a real time. Often image processing algorithms are inherently parallel in nature, so they fit nicely into parallel architectures multicore Central Processing Unit (CPU) and Graphics Processing Unit GPUs. In this paper image processing algorithms were evaluated, which are capable to execute in parallel manner on several platforms CPU and GPU. All algorithms were tested in TensorFlow, which is a novel framework for deep learning, but also for image processing. Relative speedups compared to CPU were given for all algorithms. TensorFlow GPU implementation can outperform multi-core CPUs for tested algorithms, obtained speedups range from 3.6 to 15 times.
Adaptive synergies for a humanoid robot hand

One of the motivations behind the development of humanoid robots is the will to comply with, and fruitfully integrate in the human environment, a world forged by humans for humans, where the importance of the hand shape dominates prominently. This paper presents the novel hand under-actuation framework which goes under the name of synergies. In particular two incarnations of this concept are considered, soft synergies and adaptive synergies. They are presented and their substantial equivalence is demonstrated. After this, it presents the first implementation of THE UNIPI-hand, a prototype which conciliates the idea of adaptive synergies for actuation with an high degree of integration, in a humanoid shape. The hand is validated experimentally through some grasps and measurements. Results are reported also in the attached video.
Head, Eye, and Hand Patterns for Driver Activity Recognition

In this paper, a multiview, multimodal vision framework is proposed in order to characterize driver activity based on head, eye, and hand cues. Leveraging the three types of cues allows for a richer description of the driver's state and for improved activity detection performance. First, regions of interest are extracted from two videos, one observing the driver's hands and one the driver's head. Next, hand location hypotheses are generated and integrated with a head pose and facial landmark module in order to classify driver activity into three states: wheel region interaction with two hands on the wheel, gear region activity, or instrument cluster region activity. The method is evaluated on a video dataset captured in on-road settings.
Towards a theory of natural language interfaces to databases

The need for Natural Language Interfaces (NLIs) to databases has become increasingly acute as more nontechnical people access information through their web browsers, PDAs and cell phones. Yet NLIs are only usable if they map natural language questions to SQL queries correctly. We introduce the Precise NLI , which reduces the semantic interpretation challenge in NLIs to a graph matching problem. Precise uses the max-flow algorithm to efficiently solve this problem. Each max-flow solution corresponds to a possible semantic interpretation of the sentence. precise collects max-flow solutions, discards the solutions that do not obey syntactic constraints and retains the rest as the basis for generating SQL queries corresponding to the question q. The syntactic information is extracted from the parse tree corresponding to the given question which is computed by a statistical parser . For a broad, well-defined class of semantically tractable natural language questions, Precise is guaranteed to map each question to the corresponding SQL querySemantically tractable questions correspond to a natural, domain-independent subset of English that can be efficiently and accurately interpreted as nonrecursive Datalog clauses. Precise is transportable to arbitrary databases, such as the Restaurants,Jobs and Geography databases used in our implementation. Examples of semantically tractable questions include: "What Chinese restaurants with a 3.5 rating are in Seattle?", "What are the areas of US states with large populations?", "What jobs require 4 years of experience and desire a B.S.CS degree?".Given a question which is not semantically tractable, Precise recognizes it as such and informs the user that it cannot answer it.Given a semantically tractable question, Precise computes the set of non-equivalent SQL interpretations corresponding to the question. If a unique such SQL interpretation exists, Precise outputs it together with the corresponding result set obtained by querying the current database. If the set contains more than one SQL interpretation, the natural language question is ambiguous in the context of the current database. In this case, Precise asks for the user's help in determining which interpretation is the correct one.Our experiments have shown that Precise has high coverage and accuracy over common English questions. In future work, we plan to explore increasingly broad classes of questions and include Precise as a module in a full-fledged dialog system. An important direction for future work is helping users understand the types of questions Precise cannot handle via dialog, enabling them to build an accurate mental model of the system and its capabilities. Also, our own group's work on the EXACT natural language interface  builds on Precise and on the underlying theoretical framework. EXACT composes an extended version of Precise with a sound and complete planner to develop a powerful and provably reliable interface to household appliances
A Ground Control Station for a Multi-UAV Surveillance System - Design and Validation in Field Experiments

This paper presents the ground control station developed for a platform composed by multiple unmanned aerial vehicles in surveillance missions. The software application is fully based on open source libraries and it has been designed as a robust and decentralized system. It allows the operator to dynamically allocate different tasks to the UAVs and to show their operational information in a 3D realistic environment in real time. The ground control station has been designed to assist the operator in the challenging task of managing a system with multiple UAVs, trying to reduce his workload. The multi-UAV surveillance system has been demonstrated in field experiments with two quadrotors equipped with visual cameras.
The haptic hand: providing user interface feedback with the non-dominant hand in virtual environments

We present a user interface for virtual environments that utilizes the non-dominant hand to provide haptic feedback to the dominant hand while it interacts with widgets on a virtual control panel. We believe this technique improves on existing prop-based methods of providing haptic feedback. To gauge the interface’s effectiveness, we performed a usability study. We do not present a formal comparison with prior techniques here. The goal of this study was to determine the feasibility of using the non-dominant hand for haptic feedback, and to obtain subjective data about usability. The results demonstrated that the interface allowed users to perform precision tasks. These results have convinced us that this technique has potential and warrants further development.
Cross-Genre Author Profile Prediction Using Stylometry-Based Approach

Author profiling task aims to identify different traits of an author by analyzing his/her written text. This study presents a Stylometry-based approach for detection of author traits (gender and age) for cross-genre author profiles. In our proposed approach, we used different types of stylistic features including 7 lexical features, 16 syntactic features, 26 character-based features and 6 vocabulary richness (total 56 stylistic features). On the training corpus, the proposed approach obtained promising results with an accuracy of 0.787 for gender, 0.983 for age and 0.780 for both (jointly detecting age and gender). On the test corpus, proposed system gave an accuracy of 0.576 for gender, 0.371 for age and 0.256 for both.
Automatic logging of operating system effects to guide application-level architecture simulation

Modern architecture research relies heavily on application-level detailed pipeline simulation. A time consuming part of building a simulator is correctly emulating the operating system effects, which is required even if the goal is to simulate just the application code, in order to achieve functional correctness of the application's execution. Existing application-level simulators require manually hand coding the emulation of each and every possible system effect (e.g., system call, interrupt, DMA transfer) that can impact the application's execution. Developing such an emulator for a given operating system is a tedious exercise, and it can also be costly to maintain it to support newer versions of that operating system. Furthermore, porting the emulator to a completely different operating system might involve building it all together from scratch.In this paper, we describe a tool that can automatically log operating system effects to guide architecture simulation of application code. The benefits of our approach are: (a) we do not have to build or maintain any infrastructure for emulating the operating system effects, (b) we can support simulation of more complex applications on our application-level simulator, including those applications that use asynchronous interrupts, DMA transfers, etc., and (c) using the system effects logs collected by our tool, we can deterministically re-execute the application to guide architecture simulation that has reproducible results.
Hierarchical multi-label classification over ticket data using contextual loss

Maximal automation of routine IT maintenance procedures is an ultimate goal of IT service management. System monitoring, an effective and reliable means for IT problem detection, generates monitoring tickets to be processed by system administrators. IT problems are naturally organized in a hierarchy by specialization. The problem hierarchy is used to help triage tickets to the processing team for problem resolving. In this paper, a hierarchical multi-label classification method is proposed to classify the monitoring tickets by utilizing the problem hierarchy. In order to find the most effective classification, a novel contextual hierarchy (CH) loss is introduced in accordance with the problem hierarchy. Consequently, an arising optimization problem is solved by a new greedy algorithm. An extensive empirical study over ticket data was conducted to validate the effectiveness and efficiency of our method.
Recognizing subjectivity: a case study in manual tagging

In this paper, we describe a case study of a sentence-level categorization in which tagging instructions are developed and used by four judges to classify clauses from the Wall Street Journal as either subjective or objective. Agreement among the four judges is analyzed, and, based on that analysis, each clause is given a nal classiication. To provide empirical support for the classiications, correlations are assessed in the data between the subjective category and a basic semantic class posited by Quirk et al. (1985).
Thoughts on Vehicular Ad Hoc Networks ( VANETs ) in the Real World Traffic Scenarios

The real world scenario has changed from the wired network to wireless network. Latest enhancements in wireless technologies are enabling the design and implementation of different types of wireless networks that are being deployed in various environments. The Vehicular Ad hoc Network (VANET) is an emerging new wireless networking concept. It is a promising approach for the Intelligent Transportation System (ITS). VANETs play an important role in providing a high level of safety and convenience to drivers on the road. The rapid growth of vehicles on the roads calls for effective means to improve road safety, efficient transportation, and passengers’ comfort. In the real world, the research community, the vehicles industries and the governments all over the world are investing much of their efforts and money in the development of VANETs. In this paper, we present a significant role of VANET in real world traffic scenarios to secure communication platforms, reasonable coverage of wireless network among vehicles as well as cooperation from drivers themselves.
Radar micro-Doppler feature extraction using the Singular Value Decomposition

The micro-Doppler spectrogram depends on parts of a target moving and rotating in addition to the main body motion (e.g., spinning rotor blades) and is thus characteristic for the type of target. In this study, the micro-Doppler spectrogram is exploited to distinguish between birds and small unmanned aerial vehicles (UAVs). The focus hereby is on micro-Doppler features enabling fast classification of birds and mini-UAVs. In a second classification step, it is desired to exploit micro-Doppler features to further characterize the type of UAV, e.g., fixed-wing vs. rotary-wing. In this paper, potentially robust features are discussed supporting the first classification step, i.e., separation of birds and UAVs. The Singular Value Decomposition seems a powerful tool to extract such features, since the information content of the micro-Doppler spectrogram is preserved in the singular vectors. In the paper, some examples of micro-Doppler feature extraction via Singular Value Decomposition are given.
Key2Vec: Automatic Ranked Keyphrase Extraction from Scientific Articles using Phrase Embeddings

Keyphrase extraction is a fundamental task in natural language processing that facilitates mapping of documents to a set of representative phrases. In this paper, we present an unsupervised technique (Key2Vec) that leverages phrase embeddings for ranking keyphrases extracted from scientific articles. Specifically, we propose an effective way of processing text documents for training multi-word phrase embeddings that are used for thematic representation of scientific articles and ranking of keyphrases extracted from them using theme-weighted PageRank. Evaluations are performed on benchmark datasets producing state-of-the-art results.
Design and experimental evaluation of the hydraulically actuated prototype leg of the HyQ robot

This paper focuses on the design and experimental evaluation of a hydraulically actuated robot leg. The evaluation of the leg prototype is an important milestone in the development of HyQ, a Hydraulically actuated Quadruped robot. The prototype features two rotary joints actuated by hydraulic cylinders and has a mass of 4.5kg. We performed several experiments with the leg prototype attached to a vertical slider to tests the robustness of the mechanical design and the hydraulic actuation system. Besides the experimental evaluation of the hydraulic components, we also extensively studied the sensor data of the leg during periodic hopping. The results show that hydraulic actuation is suitable for legged robots because of its high power-to-weight ratio, fast response and ability to cope with high impact force peaks. Furthermore, we compare the cylinder force data obtained by the load cell with the calculated value based on the cylinder pressures to analyze if it is possible to eliminate this sensory system redundancy in the future. Through these studies, weaknesses of the design were identified and suggestions on how to improve them are presented.
Evolution and Organizational Information Systems: An Assessment of Nolan's Stage Model

Richard Nolan's stage model is the best known and most widely cited model of computing evolution in organizations. The model's development over a decade demonstrates its own evolution from a simple theory, based on the factoring of change states indicated by changes in computing budgets, to an elaborate account of the characteristics of six stages of computing growth. An analysis of the model's logical and empirical structure reveals a number of problems in its formulation that help to account for the fact that its principal tenets have not been independently validated. The model is shown to be an “evolutionistic” theory within the theories of evolution in the social sciences, focusing on assumed directions of growth and an implied end state toward which growth proceeds, and suffering from problems inherent in such theories. Further research based on an “evolutionary” view of computing growth is suggested as a means of improving theories of computing in organizations.
Advanced analytics: opportunities and challenges

Advanced analytics-driven data analyses allow enterprises to have a complete or “360 degrees” view of their operations and customers. The insight that they gain from such analyses is then used to direct, optimize, and automate their decision making to successfully achieve their organizational goals. Data, text, and web mining technologies are some of the key contributors to making advanced analytics possible. This paper aims to investigate these three mining technologies in terms of how they are used and the issues that are related to their effective implementation and management within the broader context of predictive or advanced analytics.  A range of recently published research literature on business intelligence (BI); predictive analytics; and data, text and web mining is reviewed to explore their current state, issues and challenges learned from their practice.  The findings are reported in two parts. The first part discusses a framework for BI using the data, text, and web mining technologies for advanced analytics; and the second part identifies and discusses the opportunities and challenges the business managers dealing with these technologies face for gaining competitive advantages for their businesses.  The study findings are intended to assist business managers to effectively understand the issues and emerging technologies behind advanced analytics implementation.
Implicit slicing for functionally tailored additive manufacturing

One crucial component of the additivemanufacturing software toolchain is a class of geometric algorithms known as ‘‘slicers.’’ The purpose of the slicer is to compute a parametric toolpath and associated commands, which direct an additive manufacturing system to produce a physical realization of a threedimensional inputmodel. Existing slicing algorithms operate by application of geometric transformations upon the input geometry in order to produce the toolpath. In this paper we introduce a new implicit slicing algorithmbased on the computation of toolpaths derived from the level sets of arbitrary heuristicsbased or physics-based fields defined over the input geometry. This enables computationally efficient slicing of arbitrarily complex geometries in a straight forward fashion. Additionally, the calculation of component ‘‘infill’’ (as a process control parameter) is explored due to its crucial effect on functional performance fields of interest such as strain and stress distributions. Several examples of the application of the proposed implicit slicer are presented. Finally, an example demonstrating improved structural performance during physical testing is presented. We conclude with remarks regarding the strengths of the implicit approach relative to existing explicit approaches, and discuss future work required in order to extend the methodology.
Extended Bandwidth Instantaneous Current Sharing Scheme for Parallel UPS Systems

This paper investigates the suitability of instantaneous average current sharing scheme (IACS) for the parallel operation of three-phase uninterruptible power supply systems. A discrete-time model is developed for the analysis and design of the control loops. Some key issues are discussed based on the model and it is found that there is a compromise between system stability and current sharing performance with the conventional IACS scheme when the lengths of interconnecting cables are not negligible. Subsequently, an improved IACS scheme is proposed that ensures proper current sharing by extending the close-loop bandwidth. Its performance is analytically predicted and subsequently validated by experimental results on a 15-kW laboratory prototype.
Factors affecting attitudes and intentions towards knowledge sharing in the Dubai Police Force

This study contributes to the limited research base on knowledge sharing in public sector organisations, specifically police forces, and organisations in the Middle East through a case study investigation into the factors that affect knowledge sharing in the Dubai Police Force. A questionnaire-based survey was conducted with staff in key departments in the Dubai Police Force. Informed by the literature and by interviews conducted in a previous phase, the core of the questionnaire was a bank of Likert-style questions covering the dependent variables intention to knowledge share, and attitude towards knowledge sharing, and the independent variables, trust, organisational structure, leadership, reward, time, and information technology. Data was analysed using structured equation modelling, in order to test the measurement model using confirmatory factor analysis, and to test the structural model. The structural model Purchase Export
A stochastic control strategy for hybrid electric vehicles

The supervisory control strategy of a hybrid vehicle coordinates the operation of vehicle sub-systems to achieve performance targets such as maximizing fuel economy and reducing exhaust emissions. This high-level control problem is commonly referred as the power management problem. In the past, many supervisory control strategies were developed on the basis of a few pre-defined driving cycles, using intuition and heuristics. The resulting control strategy was often inherently cycle-beating and lacked a guaranteed level of optimality. In this study, the power management problem is tackled from a stochastic viewpoint. An infinite-horizon stochastic dynamic optimization problem is formulated. The power demand from the driver is modeled as a random Markov process. The optimal control strategy is then obtained by using stochastic dynamic programming (SDP). The obtained control law is in the form of a stationary full-state feedback and can be directly implemented. Simulation results over standard driving cycles and random driving cycles are presented to demonstrate the effectiveness of the proposed stochastic approach. It was found that the obtained SDP control algorithm outperforms a sub-optimal rule-based control strategy trained from deterministic DP results.