nils-herrmann/clean_scirepeval huggingface dataset:
Compressed K-Means for Large-Scale Clustering

Large-scale clustering has been widely used in many applications, and has received much attention. Most existing clustering methods suffer from both expensive computation and memory costs when applied to large-scale datasets. In this paper, we propose a novel clustering method, dubbed compressed k-means (CKM), for fast large-scale clustering. Specifically, high-dimensional data are compressed into short binary codes, which are well suited for fast clustering. CKM enjoys two key benefits: 1) storage can be significantly reduced by representing data points as binary codes; 2) distance computation is very efficient using Hamming metric between binary codes. We propose to jointly learn binary codes and clusters within one framework. Extensive experimental results on four large-scale datasets, including two million-scale datasets demonstrate that CKM outperforms the state-of-theart large-scale clustering methods in terms of both computation and memory cost, while achieving comparable clustering accuracy.
Words are not Equal: Graded Weighting Model for building Composite Document Vectors

Despite the success of distributional semantics, composing phrases from word vectors remains an important challenge. Several methods have been tried for benchmark tasks such as sentiment classification, including word vector averaging, matrix-vector approaches based on parsing, and on-the-fly learning of paragraph vectors. Most models usually omit stop words from the composition. Instead of such an yes-no decision, we consider several graded schemes where words are weighted according to their discriminatory relevance with respect to its use in the document (e.g., idf). Some of these methods (particularly tf-idf) are seen to result in a significant improvement in performance over prior state of the art. Further, combining such approaches into an ensemble based on alternate classifiers such as the RNN model, results in an 1.6% performance improvement on the standard IMDB movie review dataset, and a 7.01% improvement on Amazon product reviews. Since these are language free models and can be obtained in an unsupervised manner, they are of interest also for underresourced languages such as Hindi as well and many more languages. We demonstrate the language free aspects by showing a gain of 12% for two review datasets over earlier results, and also release a new larger dataset for future testing (Singh, 2015).
Mobile edge computing-enabled channel-aware video streaming for 4G LTE

Ever increasing numbers of users are surfing the net via an LTE access network. It has been predicted that three quarters (75%) of the world's mobile data traffic will be video-based by 2020. As the video space grows, media companies have been using adaptive bitrate technology for many years. The MPEG-DASH is the widely used HTTP-based adaptive streaming solution based on TCP. To address the bandwidth utilization issue, in this paper we investigate the ETSI Mobile Edge Computing (MEC) Intelligent Video Acceleration Service mechanism to improve video transmission over 4G LTE. Specifically, with the radio information provided by the MEC server, the video server can adaptively transmit just one copy of suitable bitrate data based on the throughput. We use OpenAirInterface (OAI), a 4G compliance emulator, as our testbed to implement and verify our proposed MEC-based adaptive video transmission mechanism. We design and implement a “filter” module in the Medium Access Control (MAC) layer over the OAI platform, which can reference Channel Quality Indicator (CQI) as a parameter to decide which bitrate type of data should be passed to the MAC Packet Scheduler for real resource allocation. By using constant-bitrate as our control groups, the testbed indicates that, consistent with our expectations, our mechanism can significantly benefit from lower latency with a reasonable throughput.
Object Recognition Using Vision and Touch

A system is described that integrates vision and tactile sensing in a robotics environment to perform object recognition tasks. It uses multiple sensor systems (active touch and passive stereo vision) to compute three dimensional primitives that can be matched against a model data base of complex curved surface objects containing holes and cavities. The low level sensing elements provide local surface and feature matches which arc constrained by relational criteria embedded in the models. Once a model has been invoked, a verification procedure establishes confidence measures for a correct recognition. The three dimen* sional nature of the sensed data makes the matching process more robust as does the system's ability to sense visually occluded areas with touch. The model is hierarchic in nature and allows matching at different levels to provide support or inhibition for recognition. 1.  Robotic systems are being designed and built to perform complex tasks such as object recognition, grasping, parts manipulation, inspection and measurement. In the case of object recognition, many systems have been designed that have tried to exploit a single sensing modality [1,2,3,4,5,6]. Single sensor systems are necessarily limited in their power. The approach described here to overcome the inherent limitations of a single sensing modality is to integrate multiple sensing modalities (passive stereo vision and active tactile sensing) for object recognition. The advantages of multiple sensory systems in a task like this are many. Multiple sensor systems supply redundant and complementary kinds of data that can be integrated to create a more coherent understanding of a scene. The inclusion of multiple sensing systems is becoming more apparent as research continues in distributed systems and parallel approaches to problem solving. The redundancy and support for a hypothesis that comes from more than one sensing subsystem is important in establishing confidence measures during a recognition process, just as the disagreement between two sensors will inhibit a hypothesis and point to possible sensing or reasoning error. The complementary nature of these sensors allows more powerful matching primitives to be used. The primitives that are the outcome of sensing with these complementary sensors are throe dimensional in nature, providing stronger invariants and a more natural way to recognize objects which are also three dimensional in nature [7].
Wireless Sensor Network Prototype for Solid Waste Bin Monitoring with Energy Efficient Sensing Algorithm

This paper presents a novel prototype of solid waste bin monitoring system using wireless sensor network which can respond as soon as someone throw waste insight the bins. The system architecture uses ZigBee and GSM/GPRS communication technologies and a set of carefully chosen sensors to monitor the status of solid waste bins in real time. The system is composed of three tier structure such as lower, middle and upper tier. The lower tier contains bin with sensor node installed in it to measure and transmit bin status to the next tier, the middle tier contains the gateway that stores and transmits bin data to control station and control station resides in the upper tier that stores and analyze the data for further using. An energy efficient sensing algorithm is also used in the first tier to collect the bin parameters. In this way, the system can help to minimize the operation costs and emissions by feeding the collected data to a decision support system for route optimization.
Collusive Data Leak and More: Large-scale Threat Analysis of Inter-app Communications

Inter-Component Communication (ICC) provides a message passing mechanism for data exchange between Android applications. It has been long believed that inter-app ICCs can be abused by malware writers to launch collusion attacks using two or more apps. However, because of the complexity of performing pairwise program analysis on apps, the scale of existing analyses is too small (e.g., up to several hundred) to produce concrete security evidence. In this paper, we report our findings in the first large-scale detection of collusive and vulnerable apps, based on inter-app ICC data flows among 110,150 real-world apps. Our system design aims to balance the accuracy of static ICC resolution/data-flow analysis and run-time scalability. This large-scale analysis provides real-world evidence and deep insights on various types of inter-app ICC abuse. Besides the empirical findings, we make several technical contributions, including a new open-source ICC resolution tool with improved accuracy over the state-of-the-art, and a large database of inter-app ICCs and their attributes.
Solving the pallet loading problem

This paper presents new bounds, heuristics, and an exact algorithm for the Pallet Loading Problem (PLP). PLP maximizes the number of boxes placed on a rectangular pallet. All boxes have identical rectangular dimensions and, when placed, must be located completely within the pallet. Boxes may be rotated 90 so long as they are placed with edges parallel to the pallet’s edges. The set of all PLP instances with an area ratio (pallet area divided by box area) less than 101 boxes can be represented by 3,080,730 equivalent classes. Our G5-heuristic finds optimal solutions to 3,073,724 of these 3,080,730 classes and in the remaining 7006 classes only differs from the best known bound by one box. We develop three other heuristics that solve another 54 instances. Finally, we solve the 6952 remaining classes with our exact HVZ algorithm. Only a subset of these classes has been solved previously.
Optimal design and multifield coupling analysis of propelling motor used in a novel integrated motor propeller

A novel permanent-magnet (PM) motor used in an integrated motor propeller (IMP) is designed. The motor has no bearings and employs a Halbach array in its rotor structure. Optimization is conducted in such aspects as PM structure, magnetizing angle of PM, and the best ratio of PM length to poles. The designed motor has a thinner rotor and a larger air gap, and a 3-D coupled-field finite element method (FEM) is used to analyze the temperature distribution of this water-cooled thruster. Losses in windings and stator core are calculated in 3-D eddy current field, and the loss is used as the input to calculate the thermal field. Furthermore, a multicomponent fluid method is proposed to deal with the influence from rotating rotor upon the water convection in the air gap. An experiment of this IMP is done under water and temperatures in it are obtained. The temperature results of experiment and calculation are in good agreement, verifying the effectiveness of the analyzing method proposed in this paper.
Early completion of occluded objects

We show that early vision can use monocular cues to rapidly complete partially-occluded objects. Visual search for easily-detected fragments becomes difficult when the completed shape is similar to others in the display; conversely, search for fragments that are difficult to detect becomes easy when the completed shape is distinctive. Results indicate that completion occurs via the occlusion-triggered removal of occlusion edges and linking of associated regions. We fail to find evidence for a visible filling-in of contours or surfaces, but do find evidence for a 'functional' filling-in that prevents the constituent fragments from being rapidly accessed. As such, it is only the completed structures--and not the fragments themselves--that serve as the basis for rapid recognition.
NeuCube Neuromorphic Framework for Spatio-temporal Brain Data and Its Python Implementation

Classification and knowledge extraction from complex spatiotemporal brain data such as EEG or fMRI is a complex challenge. A novel architecture named the NeuCube has been established in prior literature to address this. A number of key points in the implementation of this framework, including modular design, extensibility, scalability, the source of the biologically inspired spatial structure, encoding, classification, and visualisation tools must be considered. A Python version of this framework that conforms to these guidelines has been implemented.
A CMOS VGA With DC Offset Cancellation for Direct-Conversion Receivers

A CMOS dB-linear variable gain amplifier (VGA) with a novel I/Q tuning loop for dc-offset cancellation is presented. The CMOS dB-linear VGA provides a variable gain of 60 dB while maintaining its 3-dB bandwidth greater than 2.5 MHz. A novel exponential circuit is proposed to obtain the dB-linear gain control characteristics. Nonideal effects on dB linearity are analyzed and the methods for improvement are suggested. A varying-bandwidth LPF is employed to achieve fast settling. The chip is fabricated in a 0.35m CMOS technology and the measurement results demonstrate the good dB linearity of the proposed VGA and show that the tuning loop can effectively remove dc offset and suppress I/Q mismatch effects simultaneously.
Tensor decomposition of EEG signals: A brief review

Electroencephalography (EEG) is one fundamental tool for functional brain imaging. EEG signals tend to be represented by a vector or a matrix to facilitate data processing and analysis with generally understood methodologies like time-series analysis, spectral analysis and matrix decomposition. Indeed, EEG signals are often naturally born with more than two modes of time and space, and they can be denoted by a multi-way array called as tensor. This review summarizes the current progress of tensor decomposition of EEG signals with three aspects. The first is about the existing modes and tensors of EEG signals. Second, two fundamental tensor decomposition models, canonical polyadic decomposition (CPD, it is also called parallel factor analysis-PARAFAC) and Tucker decomposition, are introduced and compared. Moreover, the applications of the two models for EEG signals are addressed. Particularly, the determination of the number of components for each mode is discussed. Finally, the N-way partial least square and higher-order partial least square are described for a potential trend to process and analyze brain signals of two modalities simultaneously.
How to adapt applications for the Cloud environment

The migration of existing applications to the Cloud requires adapting them to a new computing paradigm. Existing works have focused on migrating the whole application stack by means of virtualization and deployment on the Cloud, delegating the required adaptation effort to the level of resource management. With the proliferation of Cloud services allowing for more flexibility and better control over the application migration, the migration of individual application layers, or even individual architectural components to the Cloud, becomes possible. Towards this goal, in this work we focus on the challenges and solutions for each layer when migrating different parts of the application to the Cloud. We categorize different migration types and identify the potential impact and adaptation needs for each of these types on the application layers based on an exhaustive survey of the State of the Art. We also investigate various cross-cutting concerns that need to be considered for the migration of the application, and position them with respect to the identified migration types. Finally, we present some of the open research issues in the field and position our future work targeting these research questions.
Aviation Cyber–Physical Systems: Foundations for Future Aircraft and Air Transport

A century of revolutionary growth in aviation has made global travel a reality of daily life. Aircraft and air transport overcame a number of formidable challenges and hostilities in the physical world. Success in this arduous pursuit was not without leveraging advances of the “cyber” layer, i.e., digital computing, data storage and networking, and software, in hardware, infrastructures, humans, and processes, within the airframe, in space, and on the ground. The physical world, however, is evolving continuously in the 21st century, contributing traffic growth and diversity, fossil fuel and ozone layer depletion, demographics and economy dynamics, as some major factors in aviation performance equations. In the next 100 years, apart from breakthrough physical advances, such as aircraft structural and electrical designs, we envision aviation's progress will depend on conquering cyberspace challenges and adversities, while safely and securely transitioning cyber benefits to the physical world. A tight integration of cyberspace with the physical world streamlines this vision. This paper proposes a novel cyber-physical system (CPS) framework to understand the cyber layer and cyber-physical interactions in aviation, study their impacts, and identify valuable research directions. This paper presents CPS challenges and solutions for aircraft, aviation users, airports, and air traffic management.
Continuing evolution of Mars sample return

In 2001, JPL commissioned four industry teams to make a fresh examination of Mars Sample Return (MSR) mission architectures. As new fiscal realities of a cost-capped Mars Exploration Program unfolded, it was evident that the converged-upon MSR concept did not fit reasonably within a balanced program. Therefore, along with a new MSR Science Steering Group, JPL asked the industry teams plus JPL's Team-X to explore ways to reduce the cost. A paper presented at last year's conference described the emergence of a new, affordable "Groundbreaking-MSR" concept (Mattingly et al., 2003). This work addresses the continued evolution of the Groundbreaking MSR concept over the last year. One of the tenets of the low-cost approach is to use substantial heritage from an earlier mission, Mars Science Laboratory (MSL). Recently, the MSL project developed and switched its baseline to a revolutionary landing approach, coined "skycrane" where the MSL, which is a rover, would be lowered gently to the Martian surface from a hovering vehicle. MSR has adopted this approach in its mission studies, again continuing to capitalize on the heritage for a significant portion of the new lander. In parallel, a MSR Technology Board was formed to reexamine MSR technology needs and participate in a continuing refinement of architectural trades. While the focused technology program continues to be definitized through the remainder of this year, the current assessment of what technology development is required, is discussed in this paper. In addition, the results of new trade studies and considerations will be discussed. Adopting these changes, the Groundbreaking MSR concept has shifted to that presented in this paper. It remains a project that is affordable and meets the basic science needs defined by the MSR Science Steering Group in 2002.
A black-box testing tool for detecting SQL injection vulnerabilities

Web applications vulnerabilities allow attackers to perform malicious actions that range from gaining unauthorized account access to obtaining sensitive data. The number of web application vulnerabilities in last decade is growing constantly. Improper input validation and sanitization are reasons for most of them. The most important of these vulnerabilities based on improper input validation and sanitization is SQL injection (SQLI) vulnerability. The primary focus of our research was to develop a reliable black-box vulnerability scanner for detecting SQLI vulnerability - SQLIVDT (SQL Injection Vulnerability Detection Tool). The black-box approach is based on simulation of SQLI attacks against web applications. Thus, the scope of analysis is limited to HTTP responses and HTML pages received from the application server. In order to achieve efficient SQLI vulnerability detection, an efficient algorithm for HTML page similarity detection is used. The proposed tool showed promising results as compared to six well-known web application scanners.
Aspect based Sentiment Analysis in Hindi: Resource Creation and Evaluation

Due to the phenomenal growth of online product reviews, sentiment analysis (SA) has gained huge attention, for example, by online service providers. A number of benchmark datasets for a wide range of domains have been made available for sentiment analysis, especially in resource-rich languages. In this paper we assess the challenges of SA in Hindi by providing a benchmark setup, where we create an annotated dataset of high quality, build machine learning models for sentiment analysis in order to show the effective usage of the dataset, and finally make the resource available to the community for further advancement of research. The dataset comprises of Hindi product reviews crawled from various online sources. Each sentence of the review is annotated with aspect term and its associated sentiment. As classification algorithms we use Conditional Random Filed (CRF) and Support Vector Machine (SVM) for aspect term extraction and sentiment analysis, respectively. Evaluation results show the average F-measure of 41.07% for aspect term extraction and accuracy of 54.05% for sentiment classification.
Proto-value Functions: A Laplacian Framework for Learning Representation and Control in Markov Decision Processes

This paper introduces a novel spectral framework for solving Markov decision processes (MDPs) by jointly learning representations and optimal policies. The major components of the framework described in this paper include: (i) A general scheme for constructing representations or basis functions by diagonalizing symmetric diffusion operators (ii) A specific instantiation of this approach where global basis functions called proto-value functions (PVFs) are formed using the eigenvectors of the graph Laplacian on an undirected graph formed from state transitions induced by the MDP (iii) A three-phased procedure called representation policy iteration comprising of a sample collection phase, a representation learning phase that constructs basis functions from samples, and a final parameter estimation phase that determines an (approximately) optimal policy within the (linear) subspace spanned by the (current) basis functions. (iv) A specific instantiation of the RPI framework using least-squares policy iteration (LSPI) as the parameter estimation method (v) Several strategies for scaling the proposed approach to large discrete and continuous state spaces, including the Nyström extension for out-of-sample interpolation of eigenfunctions, and the use of Kronecker sum factorization to construct compact eigenfunctions in product spaces such as factored MDPs (vi) Finally, a series of illustrative discrete and continuous control tasks, which both illustrate the concepts and provide a benchmark for evaluating the proposed approach. Many challenges remain to be addressed in scaling the proposed framework to large MDPs, and several elaboration of the proposed framework are briefly summarized at the end.