nils-herrmann/clean_scirepeval huggingface dataset:
Beidou combined-FFT acquisition algorithm for resource-constrained embedded platform

The length of Beidou CB2I code is twice that of the Global Position System (GPS) C/A code, and the resource consumption will be doubled on embedded hardware with a finite resource if it is processed with the traditional algorithm. Hence, this paper proposes an acquisition algorithm based on combined Fast Fourier Transform (FFT), which separates a signal into odd and even sequences and processes them in serial. By doing so, FFT length is halved and memory usage is decreased. The paper then goes on to analyze the space complexity and time complexity of the proposed algorithm and the traditional algorithm on the embedded platform. The results show that the memory usage of the proposed algorithm is reduced by 23.3% and the amount of multiplication by 12% while the acquisition accuracy remains stable as the PTP values of these two algorithms are close. In other words, the proposed algorithm consumes less resource with no acquisition accuracy losses.
3D Face Recognition Using Shapes of Facial Curves

Recognition of human beings using shapes of their full facial surfaces is a difficult problem. Our approach is to approximate a facial surface using a collection of (closed) facial curves, and to compare surfaces by comparing their corresponding curves. The differences between shapes of curves are quantified using lengths of geodesic paths between them on a pre-defined curve shape space. The metric for comparing facial surfaces is a composition of the metric involving individual facial curves. These ideas are demonstrated in the context of face recognition using the nearest-neighbor classifier
ZoomNet: Deep Aggregation Learning for High-Performance Small Pedestrian Detection

It remains very challenging for a single deep model to detect pedestrians of different sizes appears in an image. One typical remedy for the small pedestrian detection is to upsample the input and pass it to the network multiple times. Unfortunately this strategy not only exponentially increases the computational cost but also probably impairs the model effectiveness. In this work, we present a deep architecture, refereed to as ZoomNet, which performs small pedestrian detection by deep aggregation learning without up-sampling the input. ZoomNet learns and aggregates deep feature representations at multiple levels and retains the spatial information of the pedestrian from different scales. ZoomNet also learns to cultivate the feature representations from the classification task to the detection task and obtains further performance improvements. Extensive experimental results demonstrate the state-of-the-art performance of ZoomNet. The source code of this work will be made public available to facilitate further studies on this problem.
Active Learning for Example-Based Dialog Systems

While example-based dialog is a popular option for the construction of dialog systems, creating example bases for a specific task or domain requires significant human effort. To reduce this human effort, in this paper, we propose an active learning framework to construct example-based dialog systems efficiently. Specifically, we propose two uncertainty sampling strategies for selecting inputs to present to human annotators who create system responses for the selected inputs. We compare performance of these proposed strategies with a random selection strategy in simulation-based evaluation on 6 different domains. Evaluation results show that the proposed strategies are good alternatives to random selection in domains where the complexity of system utterances is low.
Comparative Survey of Indoor Positioning Technologies, Techniques, and Algorithms

The user location information represents a core dimension as understanding user context is a prerequisite for providing human-centered services that generally improve quality of life. In comparison with outdoor environments, sensing location information in indoor environments requires a higher precision and is a more challenging task due in part to the expected various objects (such as walls and people) that reflect and disperse signals. In this paper, we survey the related work in the field of indoor positioning by providing a comparative analysis of the state-of-the-art technologies, techniques, and algorithms. Unlike previous studies and surveys, our survey present new taxonomies, review some major recent advances, and argue on the area open problems and future potential. We believe this paper would spur further exploration by the research community of this challenging problem space.
Fast Robust Logistic Regression for Large Sparse Datasets with Binary Outputs

Although popular and extremely well established in mainstream statistical data analysis, logistic regression is strangely absent in the field of data mining. There are two possible explanations of this phenomenon. First, there might be an assumption that any tool which can only produce linear classification boundaries is likely to be trumped by more modern nonlinear tools. Second, there is a legitimate fear that logistic regression cannot practically scale up to the massive dataset sizes to which modern data mining tools are This paper consists of an empirical examination of the first assumption, and surveys, implements and compares techniques by which logistic regression can be scaled to data with millions of attributes and records. Our results, on a large life sciences dataset, indicate that logistic regression can perform surprisingly well, both statistically and computationally, when compared with an array of more recent classification algorithms.
Automatic mood detection of indian music using mfccs and k-means algorithm

This paper proposes a method of identifying the mood underlying a piece of music by extracting suitable and robust features from music clip. To recognize the mood, K-means clustering and global thresholding was used. Three features were amalgamated to decide the mood tag of the musical piece. Mel frequency cepstral coefficients, frame energy and peak difference are the features of interest. These features were used for clustering and further achieving silhouette plot which formed the basis of deciding the limits of threshold for classification. Experiments were performed on a database of audio clips of various categories. The accuracy of the mood extracted is around 90% indicating that the proposed technique provides encouraging results.
What Constitutes Normal Sleeping Patterns ? Childhood to Adolescence 2 . 1 Developmental Patterns in Sleep Duration : Birth to Adolescence

Healthy, adequate sleep is integral to the process of growth and development during adolescence. At puberty, maturational changes in th e underlying homeostatic and circadian sleep regulatory mechanisms influence the sleep-wak e p tterns of adolescents. These changes interact with psychosocial factors, such as increas ing academic demands, hours spent in paid employment, electronic media use, and social opport unities, and constrict the time available for adolescents to sleep. Survey studies reveal tha t adolescents’ habitual sleep schedules are associated with cumulative sleep loss. As a consequ ence, there is growing concern about the effects of insufficient sleep on adolescents’ wakin g function. This review identifies and examines the characteristics of sleep and sleep los s in adolescents. It highlights the need for more research into the effects of chronic partial s leep deprivation in adolescents, and the process of extending sleep on weekends to recover t he effects of sleep debt. An understanding of chronic sleep deprivation and recovery sleep in adolescents will facilitate the development of evidence-based sleep guidelines and recommendati o s for recovery sleep opportunities when habitual sleep times are insufficient.
Knowledge Technologies

Several technologies are emerging that provide new ways to capture, store, present and use knowledge. This book is the first to provide a comprehensive introduction to five of the most important of these technologies: Knowledge Engineering, Knowledge Based Engineering, Knowledge Webs, Ontologies and Semantic Webs. For each of these, answers are given to a number of key questions (What is it? How does it operate? How is a system developed? What can it be used for? What tools are available? What are the main issues?). The book is aimed at students, researchers and practitioners interested in Knowledge Management, Artificial Intelligence,  Nick worked at the University of Nottingham on the application of AI techniques to knowledge management and on various knowledge acquisition projects to develop expert systems for military applications. In 1999, he joined Epistemics where he worked on numerous knowledge projects and helped establish knowledge management programmes at large organisations in the engineering, technology and legal sectors. He is author of the book"Knowledge Acquisition in Practice", which describes a step-by-step procedure for acquiring and implementing expertise. He maintains strong links with leading research organisations working on knowledge technologies, such as knowledge-based engineering, ontologies and semantic technologies.
An adaptive coding model of neural function in prefrontal cortex

The prefrontal cortex has a vital role in effective, organized behaviour. Both functional neuroimaging in humans and electrophysiology in awake monkeys indicate that a fundamental principle of prefrontal function might be adaptive neural coding — in large regions of the prefrontal cortex, neurons adapt their properties to carry specifically information that is relevant to current concerns, producing a dense, distributed representation of related inputs, actions, rewards and other information. A model based on such adaptive coding integrates the role of the prefrontal cortex in working memory, attention and control. Adaptive coding points to new perspectives on several basic questions, including mapping of cognitive to neurophysiological functions, the influences of task content and difficulty, and the nature of frontal lobe specializations.
Critical success factors in enterprise wide information management systems projects

In the past several years many organizations have initiated enterprise-wide information management systems projects, using such packages as SAP, Peoplesoft, and Oracle. These projects often represent the single largest investment in an information systems project in the history of these companies, and in many cases the largest single investment in any corporate-wide project. These enterprise-wide information management systems projects bring about a host of new questions. Some of these questions and issues are: • What is the purpose and scope of the enterprise-wide information management systems project? • What are the project objectives and outcomes to date? • How was the investment in the integrated system justified? What were the tangible and intangible business benefits that were considered? • What was the role and importance of top management support? • How were business processes affected by the software? • What investments in training, support, and maintenance were needed to assure project success? • Was external vendor expertise used to accomplish certain aspects of the project? • What was the role of end-users in project management and systems development?
A highly accurate constant voltage (CV) and constant current (CC) primary side controller for offline applications

This paper presents an innovative primary-sensing technique for constant voltage (CV) and constant current (CC) for flyback converter, eliminating the need for the opto-coupler, the secondary voltage reference, as well as the current sensor. The voltage regulation can achieve +/-2.5%, and current +/-5%. Furthermore, the control IC is integrated with high voltage (800V) MOSFET, which makes it suitable for low power battery chargers, LED applications, and auxiliary power supplies.
A new force-feedback arm exoskeleton for haptic interaction in virtual environments

The paper presents the mechanical design of the L-EXOS, a new exoskeleton for the human arm. The exoskeleton is a tendon driven wearable haptic interface with 5 dof 4 actuated ones, and is characterized by a workspace very close to the one of the human arm. The design has been optimized to obtain a solution with reduced mass and high stiffness, by employing special mechanical components and carbon fiber structural parts. The devised exoskeleton is very effective for simulating the touch by hand of large objects or the manipulation within the whole workspace of the arm. The main features of the first prototype that has been developed at PERCRO are presented, together with an indication of the achieved and tested performance.
RF Localization in Indoor Environment

In this paper indoor localization system based on the RF power measurements of the Received Signal Strength (RSS) in WLAN environment is presented. Today, the most viable solution for localization is the RSS fingerprinting based approach, where in order to establish a relationship between RSS values and location, different machine learning approaches are used. The advantage of this approach based on WLAN technology is that it does not need new infrastructure (it reuses already and widely deployed equipment), and the RSS measurement is part of the normal operating mode of wireless equipment. We derive the Cramér-Rao Lower Bound (CRLB) of localization accuracy for RSS measurements. In analysis of the bound we give insight in localization performance and deployment issues of a localization system, which could help designing an efficient localization system. To compare different machine learning approaches we developed a localization system based on an artificial neural network, k-nearest neighbors, probabilistic method based on the Gaussian kernel and the histogram method. We tested the developed system in real world WLAN indoor environment, where realistic RSS measurements were collected. Experimental comparison of the results has been investigated and average location estimation error of around 2 meters was obtained.
When D2D meets cloud: Hybrid mobile task offloadings in fog computing

In this paper we propose HyFog, a novel hybrid task offloading framework in fog computing, where device users have the flexibility of choosing among multiple options for task executions, including local mobile execution, Device-to-Device (D2D) offloaded execution, and Cloud offloaded execution. We further develop a novel three-layer graph matching algorithm for efficient hybrid task offloading among the devices. Specifically, we first construct a three-layer graph to capture the choice space enabled by these three execution approaches, and then the problem of minimizing the total task execution cost is recast as a minimum weight matching problem over the constructed three-layer graph, which can be efficiently solved using the Edmonds's Blossom algorithm. Numerical results demonstrate that the proposed three-layer graph matching solution can achieve superior performance, with more than 50% cost reduction over the case of local task executions by all the devices.
Animal monitoring with unmanned aerial vehicle-aided wireless sensor networks

In this paper, we focus on an application of wireless sensor networks (WSNs) with unmanned aerial vehicle (UAV). The aim of the application is to detect the locations of endangered species in large-scale wildlife areas or monitor movement of animals without any attachment devices. We first define the mathematical model of the animal monitoring problem in terms of the value of information (VoI) and rewards. We design a network model including clusters of sensor nodes and a single UAV that acts as a mobile sink and visits the clusters. We propose a path planning approach based on a Markov decision process (MDP) model that maximizes the VoI while reducing message delays. We used real-world movement dataset of zebras. Simulation results show that our approach outperforms greedy and random heuristics as well as the path planning based on the solution of the traveling salesman problem.
Penetration testing in a box

Network and application vulnerability assessments have a tendency to be difficult and costly; however, failing to have an assessment done and fixing security loopholes may result in a security breach by malicious attackers. A security breach can cost an organization time and money remediating the damage, such as lost confidential business information, which far exceeds the cost of a security assessment. Our solution for this problem is a semi-automated system that can help a penetration tester, security professional, or systems administrator, scan and report on the vulnerabilities of a network and services running on the network. It is also able to perform some simulated attacks. This system relies on a miniaturized computer to host the necessary components to conduct a security assessment. This system has been done in an open source manner to allow others to contribute and benefit from a community effort in the name of security.
A Comprehensive Survey of Technologies for Building a Hybrid High Performance Intrusion Detection System

Intrusion detection plays a vital role in maintaining the stability of any network. The major requirements for any intrusion detection system are speed, accuracy and less memory. Though various intrusion detection methods are available, they excel at some points while lack in the others. This paper presents a comprehensive survey of the technologies that are used for detecting intrusions. It analyzes the pros and cons of each technology and the literature works that utilizes these technologies. Challenges faced by the current IDS and the requirements for IDS in the current network scenario are discussed in detail. A detailed study on the datasets that can be used for effective building of an IDS is discussed. The research framework is proposed and a discussion of the various technologies that can be used for improving the efficiency of the IDS is provided.
Mobile marketing : A literature review on its value for consumers and retailers

The article describes the existing knowledge of how mobile marketing can increase the value for consumers and retailers. Mobile device shopping, and consumers0 use of mobile devices while shopping is shown to be both an extension of consumers0 shopping behaviours developed on Internet-connected desktop and laptop computers (PC), and potentially new behaviours based on a mobile devices0 uniquely integrated features such as camera, scanners and GPS. The article focuses on how mobile marketing creates value for consumers and retailers, enabling more precise research and development of managerial concepts and tools while providing both managers and academics with increased understanding of mobile marketing and its value outcomes for retailers. & 2014 Elsevier Ltd. All rights reserved.
Extracting deep bottleneck features using stacked auto-encoders

In this work, a novel training scheme for generating bottleneck features from deep neural networks is proposed. A stack of denoising auto-encoders is first trained in a layer-wise, unsupervised manner. Afterwards, the bottleneck layer and an additional layer are added and the whole network is fine-tuned to predict target phoneme states. We perform experiments on a Cantonese conversational telephone speech corpus and find that increasing the number of auto-encoders in the network produces more useful features, but requires pre-training, especially when little training data is available. Using more unlabeled data for pre-training only yields additional gains. Evaluations on larger datasets and on different system setups demonstrate the general applicability of our approach. In terms of word error rate, relative improvements of 9.2% (Cantonese, ML training), 9.3% (Tagalog, BMMI-SAT training), 12% (Tagalog, confusion network combinations with MFCCs), and 8.7% (Switchboard) are achieved.
A general theory for inertial navigator error modeling

This paper presents a general theory for the development of error equations for an inertial navigator. A large space of possibilities is presented, based on how one defines the error parameters. For attitude, rotation vector parameterizations of the errors are introduced that are based on the difference between true can computed versions of each coordinate frame. Since these definitions are valid for large error angles, one can obtain general nonlinear equations for the psi- and phi-angle differential equations. These definitions are applicable to any mechanization and to either strapdown or platform navigators. This allows the development of general nonlinear formulas for the error dynamics, as well as new linear formulas using a different parameterization of the errors. There are two commonly used choices for the errors in position and velocity ; the more commonly used choice is to form the error as that of the true variable in the true frame less the computed variable in the computed frame. The alternate approach is one in which the error is the true variable less the computed variable, with both errors coordinatized in the computed frame. This latter approach gives new error equations not previously considered in the literature. Transformations between the new error parameterizations and the traditional ones are presented.
Active Learning with Feedback on Both Features and Instances

We extend the traditional active learning framework to include feedback on features in addition to labeling instances, and we execute a careful study of the effects of feature selection and human feedback on features in the setting of text categorization. Our experiments on a variety of categorization tasks indicate that there is significant potential in improving classifier performance by feature re-weighting, beyond that achieved via membership queries alone (traditional active learning) if we have access to an oracle that can point to the important (most predictive) features. Our experiments on human subjects indicate that human feedback on feature relevance can identify a sufficient proportion of the most relevant features (over 50% in our experiments). We find that on average, labeling a feature takes much less time than labeling a document. We devise an algorithm that interleaves labeling features and documents which significantly accelerates standard active learning in our simulation experiments. Feature feedback can complement traditional active learning in applications such as news filtering, e-mail classification, and personalization, where the human teacher can have significant knowledge on the relevance of features.
Quality driven web services composition

The process-driven composition of Web services is emerging as a promising approach to integrate business applications within and across organizational boundaries. In this approach, individual Web services are federated into composite Web services whose business logic is expressed as a process model. The tasks of this process model are essentially invocations to functionalities offered by the underlying component services. Usually, several component services are able to execute a given task, although with different levels of pricing and quality. In this paper, we advocate that the selection of component services should be carried out during the execution of a composite service, rather than at design-time. In addition, this selection should consider multiple criteria (e.g., price, duration, reliability), and it should take into account global constraints and preferences set by the user (e.g., budget constraints). Accordingly, the paper proposes a global planning approach to optimally select component services during the execution of a composite service. Service selection is formulated as an optimization problem which can be solved using efficient linear programming methods. Experimental results show that this global planning approach outperforms approaches in which the component services are selected individually for each task in a composite service.