nils-herrmann/clean_scirepeval huggingface dataset:
Review of data features-based music emotion recognition methods

The ability of music to induce or convey emotions ensures the importance of its role in human life. Consequently, research on methods for identifying the high-level emotion states of a music segment from its low-level features has attracted attention. This paper offers new insights on music emotion recognition methods based on different combinations of data features that they use during the modeling phase from three aspects, music features only, ground-truth data only, and their combination, and provides a comprehensive review of them. Then, focusing on the relatively popular methods in which the two types of data, music features and ground-truth data, are combined, we further subdivide the methods in the literature according to the label- and numerical-type ground-truth data, and analyze the development of music emotion recognition with the cue of modeling methods and time sequence. Three current important research directions are then summarized. Although much has been achieved in the area of music emotion recognition, many issues remain. We review these issues and put forward some suggestions for future work.
Maximal causality reduction for TSO and PSO

Verifying concurrent programs is challenging due to the exponentially large thread interleaving space. The problem is exacerbated by relaxed memory models such as Total Store Order (TSO) and Partial Store Order (PSO) which further explode the interleaving space by reordering instructions. A recent advance, Maximal Causality Reduction (MCR), has shown great promise to improve verification effectiveness by maximally reducing redundant explorations. However, the original MCR only works for the Sequential Consistency (SC) memory model, but not for TSO and PSO. In this paper, we develop novel extensions to MCR by solving two key problems under TSO and PSO: 1) generating interleavings that can reach new states by encoding the operational semantics of TSO and PSO with first-order logical constraints and solving them with SMT solvers, and 2) enforcing TSO and PSO interleavings by developing novel replay algorithms that allow executions out of the program order. We show that our approach successfully enables MCR to effectively explore TSO and PSO interleavings. We have compared our approach with a recent Dynamic Partial Order Reduction (DPOR) algorithm for TSO and PSO and a SAT-based stateless model checking approach. Our results show that our approach is much more effective than the other approaches for both state-space exploration and bug finding – on average it explores 5-10X fewer executions and finds many bugs that the other tools cannot find.
A Proposed Curriculum in Cybersecurity Education Targeting Homeland Security Students

Homeland Security (HS) is a growing field of study in the U.S. today, generally covering risk management, terrorism studies, policy development, and other topics related to the broad field. Information security threats to both the public and private sectors are growing in intensity, frequency, and severity, and are a very real threat to the security of the nation. While there are many models for information security education at all levels of higher education, these programs are invariably offered as a technical course of study, these curricula are generally not well suited to HS students. As a result, information systems and cyber security principles are under represented in the typical HS program. The authors propose a course of study in cyber security designed to capitalize on the intellectual strengths of students in this discipline and that are consistent with the broad suite of professional needs in this discipline.
Augmenting human intellect: a conceptual framework

This is an initial summary report of a project taking a new and systematic approach to improving the intellectual effectiveness of the individual human being. A detailed conceptual framework explores the nature of the system composed of the individual and the tools, concepts, and methods that match his basic capabilities to his problems. One of the tools that shows the greatest immediate promise is the computer, when it can be harnessed for direct online assistance, integrated with new concepts and methods.
Energy trade-offs in the IBM Wristwatch computer

We recently demonstrated a high function wrist watch computer prototype that runs the Linux operating system and also X11 graphics libraries. In this paper we describe the unique energy related challenges and tradeoffs we encountered while building this watch. We show that the usage duty factor for the device heavily dictates which of the powers, active power or sleep power, needs to be minimized more aggressively in order to achieve the longest perceived battery life. We also describe the energy issues that percolate through several layers of software all the way from device usage scenarios, applications, user interfaces, system level software to device drivers and the need to systematically address all of them to achieve the battery life dictated by the hardware components and the capacity of the battery in the device.
A Ticket-Based Authentication Scheme for VANETs Preserving Privacy

In Vehicular Ad hoc NETworks (VANETs), vehicles exchange safety messages in order to enhance road safety. Because of their critical role, safety messages should be authenticated before being accepted while preserving the privacy of vehicles. However, misbehaving vehicles should be traced by legal authorities and evicted from the network. In this paper, we propose a new conditional privacy-preserving authentication scheme for VANETs. The proposed scheme is based on the use of temporary tickets to maintain the privacy of vehicles. An identity-based signature technique is employed for authentication. The trusted authority can trace misbehaving vehicles, given their tickets, and RSUs handle the task of evicting them from the network. An in-depth security analysis is performed to demonstrate the efficiency of our proposal.
Haggle: Seamless Networking for Mobile Applications

This paper presents Haggle, an architecture for mobile devices that enables seamless network connectivity and application functionality in dynamic mobile environments. Current applications must contain significant network binding and protocol logic, which makes them inflexible to the dynamic networking environments facing mobile devices. Haggle allows separating application logic from transport bindings so that applications can be communication agnostic. Internally, the Haggle framework provides a mechanism for late-binding interfaces, names, protocols, and resources for network communication. This separation allows applications to easily utilize multiple communication modes and methods across infrastructure and infrastructure-less environments. We provide a prototype implementation of the Haggle framework and evaluate it by demonstrating support for two existing legacy applications, email and web browsing. Haggle makes it possible for these applications to seamlessly utilize mobile networking opportunities both with and without infrastructure.
Accuracy of a Custom Physical Activity and Knee Angle Measurement Sensor System for Patients with Neuromuscular Disorders and Gait Abnormalities

Long-term assessment of ambulatory behavior and joint motion are valuable tools for the evaluation of therapy effectiveness in patients with neuromuscular disorders and gait abnormalities. Even though there are several tools available to quantify ambulatory behavior in a home environment, reliable measurement of joint motion is still limited to laboratory tests. The aim of this study was to develop and evaluate a novel inertial sensor system for ambulatory behavior and joint motion measurement in the everyday environment. An algorithm for behavior classification, step detection, and knee angle calculation was developed. The validation protocol consisted of simulated daily activities in a laboratory environment. The tests were performed with ten healthy subjects and eleven patients with multiple sclerosis. Activity classification showed comparable performance to commercially available activPAL sensors. Step detection with our sensor system was more accurate. The calculated flexion-extension angle of the knee joint showed a root mean square error of less than 5° compared with results obtained using an electro-mechanical goniometer. This new system combines ambulatory behavior assessment and knee angle measurement for long-term measurement periods in a home environment. The wearable sensor system demonstrated high validity for behavior classification and knee joint angle measurement in a laboratory setting.
Understanding structural-functional relationships in the human brain: a large-scale network perspective.

Relating the brain's structural connectivity (SC) to its functional connectivity (FC) is a fundamental goal in neuroscience because it is capable of aiding our understanding of how the relatively fixed SC architecture underlies human cognition and diverse behaviors. With the aid of current noninvasive imaging technologies (e.g., structural MRI, diffusion MRI, and functional MRI) and graph theory methods, researchers have modeled the human brain as a complex network of interacting neuronal elements and characterized the underlying structural and functional connectivity patterns that support diverse cognitive functions. Specifically, research has demonstrated a tight SC-FC coupling, not only in interregional connectivity strength but also in network topologic organizations, such as community, rich-club, and motifs. Moreover, this SC-FC coupling exhibits significant changes in normal development and neuropsychiatric disorders, such as schizophrenia and epilepsy. This review summarizes recent progress regarding the SC-FC relationship of the human brain and emphasizes the important role of large-scale brain networks in the understanding of structural-functional associations. Future research directions related to this topic are also proposed.
Religion and Health: Anxiety, Religiosity, Meaning of Life and Mental Health

We examined the association among anxiety, religiosity, meaning of life and mental health in a nonclinical sample from a Chinese society. Four hundred fifty-one Taiwanese adults (150 males and 300 females) ranging in age from 17 to 73 years (M = 28.9, SD = 11.53) completed measures of Beck Anxiety Inventory, Medical Outcomes Study Health Survey, Perceived Stress Scale, Social Support Scale, and Personal Religiosity Scale (measuring religiosity and meaning of life). Meaning of life has a significant negative correlation with anxiety and a significant positive correlation with mental health and religiosity; however, religiosity does not correlate significantly anxiety and mental health after controlling for demographic measures, social support and physical health. Anxiety explains unique variance in mental health above meaning of life. Meaning of life was found to partially mediate the relationship between anxiety and mental health. These findings suggest that benefits of meaning of life for mental health can be at least partially accounted for by the effects of underlying anxiety.
Infinitely Many-Armed Bandits with Budget Constraints

We study the infinitely many-armed bandit problem with budget constraints, where the number of arms can be infinite and much larger than the number of possible experiments. The player aims at maximizing his/her total expected reward under a budget constraint B for the cost of pulling arms. We introduce a weak stochastic assumption on the ratio of expected-reward to expected-cost of a newly pulled arm which characterizes its probability of being a near-optimal arm. We propose an algorithm named RCB-I to this new problem, in which the player first randomly picks K arms, whose order is sub-linear in terms of B, and then runs the algorithm for the finite-arm setting on the selected arms. Theoretical analysis shows that this simple algorithm enjoys a sub-linear regret in term of the budget B. We also provide a lower bound of any algorithm under Bernoulli setting. The regret bound of RCB-I matches the lower bound up to a logarithmic factor. We further extend this algorithm to the any-budget setting (i.e., the budget is unknown in advance) and conduct corresponding theoretical analysis.
Understanding the Odd Science of Aging

Evolutionary considerations suggest aging is caused not by active gene programming but by evolved limitations in somatic maintenance, resulting in a build-up of damage. Ecological factors such as hazard rates and food availability influence the trade-offs between investing in growth, reproduction, and somatic survival, explaining why species evolved different life spans and why aging rate can sometimes be altered, for example, by dietary restriction. To understand the cell and molecular basis of aging is to unravel the multiplicity of mechanisms causing damage to accumulate and the complex array of systems working to keep damage at bay.
Dance movement therapy in the context of geriatric psychiatry

.......................................................................................................... 3 Introduction ...................................................................................................... 4 Methods........................................................................................................... 7 Procedure ..................................................................................................... 7 Inclusion and exclusion criteria ..................................................................... 8 Data extraction and quality assessment ....................................................... 8 Results ............................................................................................................ 9 Included studies ........................................................................................... 9 Quality of included articles .......................................................................... 13 Excluded studies ........................................................................................ 15 Fig. 1 CONSORT 2010 Flow Diagram ....................................................... 16 Table 1: Primary studies ............................................................................. 17 Table2: Secondary studies ......................................................................... 18
Adaptive Digital Predistortion of Wireless Power Amplifiers/Transmitters Using Dynamic Real-Valued Focused Time-Delay Line Neural Networks

Neural networks (NNs) are becoming an increasingly attractive solution for power amplifier (PA) behavioral modeling, due to their excellent approximation capability. Recently, different topologies have been proposed for linearizing PAs using neural based digital predistortion, but most of the previously reported results have been simulation based and addressed the issue of linearizing static or mildly nonlinear PA models. For the first time, a realistic and experimentally validated approach towards adaptive predistortion technique, which takes advantage of the superior dynamic modeling capability of a real-valued focused time-delay neural network (RVFTDNN) for the linearization of third-generation PAs, is proposed in this paper. A comparative study of RVFTDNN and a real-valued recurrent NN has been carried out to establish RVFTDNN as an effective, robust, and easy-to-implement baseband model, which is suitable for inverse modeling of RF PAs and wireless transmitters, to be used as an effective digital predistorter. Efforts have also been made on the selection of the most efficient training algorithm during the reverse modeling of PA, based on the selected NN. The proposed model has been validated for linearizing a mildly nonlinear class AB amplifier and a strongly nonlinear Doherty PA with wideband code-division multiple access (WCDMA) signals for single- and multiple-carrier applications. The effects of memory consideration on linearization are clearly shown in the measurement results. An adjacent channel leakage ratio correction of up to 20 dB is reported due to linearization where approximately 5-dB correction is observed due to memory effect nullification for wideband multicarrier WCDMA signals.
Motivational Measure of the Instruction Compared: Instruction Based on the ARCS Motivation Theory versus Traditional Instruction in Blended Courses

The ARCS Motivation Theory was proposed to guide instructional designers and teachers who develop their own instruction to integrate motivational design strategies into the instruction. There is a lack of literature supporting the idea that instruction for blended courses if designed based on the ARCS Motivation Theory provides different experiences for learners in terms of motivation than instruction developed following the standard instructional design procedure for blended courses. This study was conducted to compare the students‘ motivational evaluation of blended course modules developed based on the ARCS Motivation Theory and students‘ motivational evaluation of blended course modules developed following the standard instructional design procedure. Randomly assigned fifty junior undergraduate students studying at the department of Turkish Language and Literature participated in the study.  Measure for the Blended Course Instruction (MMBCI) instrument was used to collect data for the study after the Confirmatory Factor Analysis (CFA). Results of the study indicated that designing instruction in blended courses based on the ARCS Motivation Theory provides more motivational benefits for students and consequently contributes student learning.
FRACTAL ANTENNAS : DESIGN , CHARACTERISTICS AND APPLICATION

1 University of Banjaluka, Faculty of Electrical Engineering, Patre 5, 78000 Banjaluka, Bosnia and Herzegovina, E-mail: nemanjapoprzen@gmail 2 University of Banjaluka, Faculty of Electrical Engineering, Patre 5, 78000 Banjaluka, Bosnia and Herzegovina, E-mail: bilchy@blic.net  This report will describe the theories and techniques for shrinking the size of an antenna through the use of fractals. Fractal antennas can obtain radiation pattern and input impedance similar to a longer antenna, yet take less area due to the many contours of the shape. Fractal antennas are a fairly new research area and are likely to have a promising future in many applications.
Utilizing Purchase Intervals in Latent Clusters for Product Recommendation

Collaborative filtering have become increasingly important with the development of Web 2.0. Online shopping service providers aim to provide users with quality list of recommended items that will enhance user satisfaction and loyalty. Matrix factorization approaches have become the dominant method as they can reduce the dimension of the data set and alleviate the sparsity problem. However, matrix factorization approaches are limited because they depict each user as one preference vector. In practice, we observe that users may have different preferences when purchasing different subsets of items, and the periods between purchases also vary from one user to another. In this work, we propose a probabilistic approach to learn latent clusters in the large user-item matrix, and incorporate temporal information into the recommendation process. Experimental results on a real world dataset demonstrate that our approach significantly improves the conversion rate, precision and recall of state-of-the-art methods.
The SMART automatic document retrieval systems - an illustration

A fully automatic document retrieval system operating on the IBM 7094 is described. The system is characterized by the fact that several hundred different methods are available to analyze documents and search requests. This feature is used in the retrieval process by leaving the exact sequence of operations initially unspecified, and adapting the search strategy to the needs of individual users. The system is used not only to simulate an actual operating environment, but also to test the effectiveness of the various available processing methods. Results obtained so far seem to indicate that some combination of analysis procedures can in general be relied upon to retrieve the wanted information. A typical search request is used as an example in the present report to illustrate systems operations and evaluation procedures .
Vertical Microstrip-Microstrip Transition for Ultra Wideband Applications

The design of an ultra wideband aperture-coupled vertical microstrip-microstrip transition is presented. The proposed transition exploits broadside coupling between exponentially tapered microstrip patches at the top and bottom layers via an exponentially tapered slot at the mid layer. The theoretical analysis indicates that the best performance concerning the insertion loss and the return loss over the maximum possible bandwidth can be achieved when the coupling factor is equal to 0.75 (or 2.5 dB). The calculated and simulated results show that the proposed transition has a linear phase performance, an important factor for distortionless pulse operation, with less than 0.4 dB insertion loss and more than 17 dB return loss across the frequency band 3.1 GHz to 10.6 GHz.
Research Paper on Basic of Artificial Neural Network

An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurons. This is true of ANNs as well. This paper gives overview of Artificial Neural Network, working & training of ANN. It also explain the application and advantages of ANN.