nils-herrmann/clean_scirepeval huggingface dataset:
Design of Low Actuation Voltage RF MEMS Switch

Low-loss microwave microelectromechanical systems (MEMS) shunt switches are reported that utilize highly compliant serpentine spring folded suspensions together with large area capacitive actuators to achieve low actuation voltages while maintaining sufficient off-state isolation. The RF MEMS switches were fabricated via a surface micromachining process using PI2545 Polyimide 1 as the sacrificial layer. The switch structure was composed of electroplated nickel and the serpentine folded suspensions had varying number of meanders from 1 to 5. DC measurements indicate actuation voltages as low as 9 V with an on-to-off capacitance ratio of 48. Power handling measurement results showed no ”selfbiasing” or failure of the MEMS switches for power levels up to 6.6W. RF measurements demonstrate an isolation of -26 dB at 40 GHz.
Segment-aware energy-efficient management of heterogeneous memory system for ultra-low-power IoT devices

The emergence of IoT (Internet of Things) has brought various studies on low-power techniques back to embedded systems. In general, minimizing power consumed by program executions is the main consideration of system design. While running programs, executing data-dependent program code results in a large number of memory accesses which consume huge amount of power. Further, most embedded systems consist of multiple types of memory devices, i.e., heterogeneous memory system, to benefit the different characteristics of memory devices. In this paper, we conduct a research on low-power techniques to reduce the power consumption of heterogeneous memory to achieve ultra-low-power in the system level. This study proposes a segment-aware energy-efficient management to improve the power efficiency considering the characteristics and structures of the memory devices. In the proposed approach, the technique migrates program code from allocated memory device to another if the consumed power is considered to be less. We also analyze and evaluate the comprehensive effects on energy efficiency by applying the technique as well. Compared to the unmodified program code, our model reduces power consumption up to 12.98% by migrating functions.
Extensive Analysis and Prediction of Optimal Inventory levels in supply chain management based on Particle Swarm Optimization Algorithm

Efficient inventory management is a complex process which entails the management of the inventory in the whole supply chain. The dynamic nature of the excess stock level and shortage level from one period to another is a serious issue. In addition, consideration of multiple products and more supply chain members leads to very complex inventory management process. Moreover, the supply chain cost increases because of the influence of lead times for supplying the stocks as well as the raw materials. A better optimization methodology would consider all these factors in the prediction of the optimal stock levels to be maintained in order to minimize the total supply chain cost. Here, we are proposing an optimization methodology that utilizes the Particle Swarm Optimization algorithm, one of the best optimization algorithms, to overcome the impasse in maintaining the optimal stock levels at each member of the supply chain.
Generic Digital Monopulse Tracking Receiver for advanced communication satellites

Monopulse tracking receiver also referred as Monopulse processor is a key subsystem of monopulse tracking system. The monopulse tracking system is commonly used in two configurations i.e. Single-Channel & Two-Channel to correct the pointing error of antenna. In order to meet the requirement of both the configurations using same hardware, a Generic Digital Monopulse Tracking Receiver (GDMTR) design is proposed to serve the tracking requirement of upcoming advance communication satellites. It can be configured to function as single or two-channel tracking receiver by changing the firmware. It is small in size, having less weight & power consumption and more design flexibility as compared to its analog counterpart. The GDMTR has been developed using FPGA based hardware to provide the re-configurability and its algorithms are successfully tested in laboratory with the help of stimulus generators. These algorithms have also been verified with operational tracking chain of satellite earth stations.
Variability Bugs in Highly Configurable Systems: A Qualitative Analysis

Variability-sensitive verification pursues effective analysis of the exponentially many variants of a program family. Several variability-aware techniques have been proposed, but researchers still lack examples of concrete bugs induced by variability, occurring in real large-scale systems. A collection of real world bugs is needed to evaluate tool implementations of variability-sensitive analyses by testing them on real bugs. We present a qualitative study of 98 diverse variability bugs (i.e., bugs that occur in some variants and not in others) collected from bug-fixing commits in the Linux, Apache, BusyBox, and Marlin repositories. We analyze each of the bugs, and record the results in a database. For each bug, we create a self-contained simplified version and a simplified patch, in order to help researchers who are not experts on these subject studies to understand them, so that they can use these bugs for evaluation of their tools. In addition, we provide single-function versions of the bugs, which are useful for evaluating intra-procedural analyses. A web-based user interface for the database allows to conveniently browse and visualize the collection of bugs. Our study provides insights into the nature and occurrence of variability bugs in four highly-configurable systems implemented in C/C++, and shows in what ways variability hinders comprehension and the uncovering of software bugs.
Role of notochord cells and sclerotome-derived cells in vertebral column development in fugu, Takifugu rubripes: histological and gene expression analyses

Despite the common structure of vertebrates, the development of the vertebral column differs widely between teleosts and tetrapods in several respects, including the ossification of the centrum and the function of the notochord. In contrast to tetrapods, vertebral development in teleosts is not fully understood, particularly for large fish with highly ossified bones. We therefore examined the histology and gene expression profile of vertebral development in fugu, Takifugu rubripes, a model organism for genomic research. Ossification of the fugu centrum is carried out by outer osteoblasts expressing col1a1, col2a1, and sparc, and the growing centra completely divide the notochord into double cone-shaped segments that function as intercentral joints. In this process, the notochord basal cells produce a thick notochord sheath exhibiting Alcian-blue–reactive cartilaginous properties and composing the intercentral ligament in cooperation with the external ligament connective tissue. Synthesis of the matrix by the basal cells was ascertained by an in vitro test. Expression of twist2 indicates that this connective tissue is descended from the embryonic sclerotome. Notochord basal cells express sox9, ihhb, shh, and col2a1a, suggesting that the signaling system involved in chondrocyte proliferation and matrix production also functions in notochord cells for notochord sheath formation. We further found that the notochord expression of both ntla and shh is maintained in the fugu vertebral column, whereas it is turned off after embryogenesis in zebrafish. Thus, our results demonstrate that, in contrast to zebrafish, a dynamic morphogenesis and molecular network continues to function in fugu until the establishment of the adult vertebral column.
A ZVS Pulsewidth Modulation Full-Bridge Converter With a Low-RMS-Current Resonant Auxiliary Circuit

This paper presents the description and analysis of a phase-shift-modulated full-bridge converter with a novel robust passive low-rms-current resonant auxiliary circuit for zero-voltage switching (ZVS) operation of both the leading and lagging switch legs. Detailed time-domain analysis describes the steady-state behavior of the auxiliary circuit in different operating conditions. An in-depth comparative study between a fully specified baseline converter and the equivalent converter using the proposed resonant auxiliary circuit is presented. For a similar peak auxiliary current to ensure ZVS operation, a minimum of 20% reduction in rms current is obtained, which decreases the conduction losses. Key characteristics and design considerations are also fully discussed. Experimental results from a 750-W prototype confirm the predicted enhancements using the proposed resonant auxiliary circuit.
Social norms and self-presentation on social network sites: Profile work in action

Profile work,” that is strategic self-presentation in social network sites, is configured by both the technical affordances and related social norms. In this article, we address technical and social psychological aspects that underlie acts of sharing by analyzing the social in relation to the technical. Our analysis is based on two complementary sets of qualitative data gleaned from in situ experiences of Finnish youth and young adults within the sharing mechanisms of Facebook and Last.fm. In our analysis, we identified social norms that were formed around the prevailing sharing practices in the two sites and compared them in relation to the sharing mechanisms. The analysis revealed that automated and manual sharing were sanctioned differently. We conclude that although the social norms that guide content sharing differed between the two contexts, there was an identical sociocultural goal in profile work: presentation of authenticity.
Semi-supervised auto-encoder based on manifold learning

Auto-encoder is a popular representation learning technique which can capture the generative model of data via a encoding and decoding procedure typically driven by reconstruction errors in an unsupervised way. In this paper, we propose a semi-supervised manifold learning based auto-encoder (named semAE). semAE is based on a regularized auto-encoder framework which leverages semi-supervised manifold learning to impose regularization based on the encoded representation. Our proposed approach suits more practical scenarios in which a small number of labeled data are available in addition to a large number of unlabeled data. Experiments are conducted on several well-known benchmarking datasets to validate the efficacy of semAE from the aspects of both representation and classification. The comparisons to state-of-the-art representation learning methods on classification performance in semi-supervised settings demonstrate the superiority of our approach.
Archetypal Game Recommender Systems

Contemporary users (players, consumers) of digital games have thousands of products to choose from, which makes finding games that fit their interests challenging. Towards addressing this challenge, in this paper two different formulations of Archetypal Analysis for Top-L recommender tasks using implicit feedback are presented: factorand neighborhood-oriented models. These form the first application of recommender systems to digital games. Both models are tested on a dataset of 500,000 users of the game distribution platform Steam, covering game ownership and playtime data across more than 3000 games. Compared to four other recommender models (nearest neighbor, two popularity models, random baseline), the archetype based models provide the highest recall rates showing that Archetypal Analysis can be successfully applied for Top-L recommendation purposes.
Meta-cognitive extreme learning machine for regression problems

In this paper, we present an efficient fast learning algorithm for regression problems using meta-cognitive extreme learning machine(McELM). The proposed algorithm has two components, namely the cognitive component and meta-cognitive component. The cognitive component is an extreme learning machine (ELM) while the meta-cognitive component which controls the cognitive component employs a self-regulating learning mechanism to decide what to learn, when to learn and how to learn. The meta-cognitive component chooses suitable learning method based on the samples presented namely, delete sample, reserve sample and network update. The use of ELM improves the network speed and reduces computational cost. Unlike traditional ELM, the number of hidden layers is not fixed priori in McELM, instead, the network is built during the learning phase. This algorithm is evaluated on a set of benchmark regression and approximation problems and also on a real-world wind force and moment coefficient prediction problem. Performance results in this study highlight that McELM can achieve better results compared with conventional ELM, support vector regression (SVR).
Game theoretic approach on Real-time decision making for IoT-based traffic light control

1Department of Computer Engineering, Chung-Ang University, Seoul, Korea 2Department of Computer Science, Universidad Autônoma de Madrid, Spain Correspondence Jai E. Jung, Department of Computer Engineering, Chung-Ang University, Korea.  j3ung@cau.ac.kr David Camacho, Department of Computer Science, Universidad Autonoma de Madrid, Spain.  david.camacho@uam.es Funding information Chung-Ang University Summary Smart traffic light control at intersections is 1 of the major issues in Intelligent Transportation System. In this paper, on the basis of the new emerging technologies of Internet of Things, we introduce a new approach for smart traffic light control at intersection. In particular, we firstly propose a connected intersection system where every objects such as vehicles, sensors, and traffic lights will be connected and sharing information to one another. By this way, the controller is able to collect effectively and mobility traffic flow at intersection in real-time. Secondly, we propose the optimization algorithms for traffic lights by applying algorithmic game theory. Specially, 2 game models (which are Cournot Model and Stackelberg Model) are proposed to deal with difference scenarios of traffic flow. In this regard, based on the density of vehicles, controller will make real-time decisions for the time durations of traffic lights to optimize traffic flow. To evaluate our approach, we have used Netlogo simulator, an agent-based modeling environment for designing and implementing a simple working traffic. The simulation results shows that our approach achieves potential performance with various situations of traffic flow.
SecGDB: Graph Encryption for Exact Shortest Distance Queries with Efficient Updates

In the era of big data, graph databases have become increasingly important for NoSQL technologies, and many systems (e.g., online social networks, world-wide web and electrical grids, etc.) can be modeled as graphs for semantic queries. Meanwhile, with the advent of cloud computing, data owners are highly motivated to outsource and store their massive potentially-sensitive graph data on remote untrusted servers in an encrypted form, expecting to retain the ability to query over the encrypted graphs. To allow effective and private queries over encrypted data, the most wellstudied class of structured encryption schemes are searchable symmetric encryption (SSE) designs, which encrypt search structures (e.g., inverted indexes based on keyword-file pairs) for retrieving data files of interest from remote servers. So far, however, the problem of graph data encryption that supports customized queries has received limited attention in the literature. In this paper, we tackle the challenge of designing a Secure Graph DataBase encryption scheme (SecGDB) to encrypt graph structures and enforce private graph queries over the encrypted graph database. Specifically, our construction strategically makes use of efficient additively homomorphic encryption and garbled circuits to support the shortest distance queries with optimal time and storage complexities. To achieve better amortized time complexity over multiple queries, we further propose an auxiliary data structure called query history and store it on the remote server to act as a “caching” resource. Compared with the state-of-the-art solutions, our design returns exact shortest distance query results instead of approximate ones and allows efficient graph update queries over large-scale encrypted graphs. We prove that our construction is adaptively semantically-secure in the random oracle model and finally implement and evaluate it on various representative real-world datasets, showing that our approach is practically efficient in terms of both storage and computation.
Consumer Brand Engagement in Social Media : Conceptualization , Scale Development & Validation

Consumer Brand Engagement in Social Media: Conceptualization, Scale Development & Validation Abstract In the last three decades an influential research stream has emerged, which highlights the dynamics of focal consumer/brand relationships. Specifically, more recently the ‘consumer brand engagement’ (CBE) concept has been postulated to more comprehensively reflect the nature of consumers’ particular interactive brand relationships, relative to traditional concepts, including ‘involvement.’ However, despite the growing scholarly interest regarding the undertaking of marketing research addressing ‘engagement,’ studies have been predominantly exploratory in nature; thus generating a lack of empirical research in this area to date. By developing and validating a CBE scale in specific social media settings, we address this identified literature gap. Specifically, we conceptualize CBE as a consumer’s positively valenced brand-related cognitive, emotional and behavioral activity during or related to focal consumer/brand interactions. We derive three CBE dimensions, including cognitive processing, affection, and activation. Within three different social media contexts, we employ exploratory and confirmatory factor analyses to develop a reliable, 10-item CBE scale, which we proceed to validate within a nomological net of conceptual relationships and a rival model. The findings suggest that while consumer brand ‘involvement’ acts as a CBE antecedent, consumer ‘self-brand connection’ and ‘brand usage intent’ represent key CBE consequences; thus providing a platform for further research in this emerging area. We conclude with an overview of key managerial and scholarly implications arising from this research.
Comparing the biological and cultural inheritance of personality and social attitudes in the Virginia 30,000 study of twins and their relatives.

Measures of four dimensions of personality (Psychoticism, Extraversion, Neuroticism, and Lie scores) and six aspects of social attitudes (to sex, taxation, militarism, politics, religion and a general conservatism scale) were obtained by mailed questionnaire from 29,691 US subjects including adult twins (n = 14,761) their parents (n = 2360), their spouses (n = 4391), siblings (n = 3184) and adult children (n = 4800). After correction for the average effects of age, sex and source of sample, familial correlations were computed for 80 distinct biological and social relationships. The data allow for the estimation of the additive and non-additive effects of genes, assortative mating, vertical cultural inheritance and other non-parental effects of the shared environment on differences in personality and social attitudes. The interaction of genetic and environmental effects with sex may also be analyzed. Model-fitting analyses show that personality and social attitude measures differ markedly in major features of family resemblance. Additive and dominant genetic effects contribute to differences in both personality and attitudes, but the effects of the family environment, including vertical cultural transmission from parent to child, are much more marked for social attitudes than for personality. There is substantial assortative mating for social attitudes and almost none for personality. The causes of family resemblance depend significantly on sex for almost every variable studied. These findings clarify and extend the more tentative findings derived from previous twin, family and adoption studies.
Very greedy crossover in a genetic algorithm for the traveling salesman problem

In the traveling salesman problem, we are given a set of cities and the distances between them, and we seek a shortest tour that visits each city exactly once and returns to the starting city. Many researchers have described genetic algorithms for this problem, and they have often focused on the crossover operator, which builds offspring tours by combining two parental tours. Very greedy crossover extends several of these operators; as it builds a tour, it always appends the shortest parental edge to a city not yet visited, if there is such an edge. A steady-state genetic algorithm using this operator, mutation by inversion, and rank-based probabilities for both selection and deletion shows good results on a suite of flve test problems.
Automated Generation of Questions from Factual , Natural Language Sentences

This paper describes methods to examine factual, natural language sentences in order to generate factual, natural language questions whose answers are found in the sentences. These methods rely on rules that look for patterns in the syntactic structure, named entities, and grammatical relations of a sentence. The extraction of these pieces of information from a sentence is not the focus of this paper. Rather, this paper examines how these pieces of information can be used for the purpose of factual, natural language question generation. This paper also presents an implementation of these methods in the form of a computer program called Specimus. This paper describes the implementation of the system and presents an evaluation of the output of the system.
Job Resources Boost Work Engagement , Particularly When Job Demands Are High

This study of 805 Finnish teachers working in elementary, secondary, and vocational schools tested 2 interaction hypotheses. On the basis of the job demands–resources model, the authors predicted that job resources act as buffers and diminish the negative relationship between pupil misbehavior and work engagement. In addition, using conservation of resources theory, the authors hypothesized that job resources particularly influence work engagement when teachers are confronted with high levels of pupil misconduct. In line with these hypotheses, moderated structural equation modeling analyses resulted in 14 out of 18 possible 2-way interaction effects. In particular, supervisor support, innovativeness, appreciation, and organizational climate were important job resources that helped teachers cope with demanding interactions with students.
A novel ripple-based constant on-time control with virtual inductance and offset cancellation for DC power converters

In recent years, there has been a growing trend of mandating high power conversion efficiency, for both the heavy-load and the light-load conditions. To achieve this purpose, a ripple-based constant on-time (RBCOT) control for DC-DC converters has received wide attentions because of its natural characteristic of switching frequency reduction under light-load condition. However, a RBCOT converter suffers from output-voltage offset problem and sub-harmonic instability problem. In this paper, a modified RBCOT buck converter circuit will be proposed to solve both problems using the concept of virtual inductor current to stabilize the feedback and an offset cancellation circuit to cancel out the output DC offset. A control model based on describing function is also developed for the modified converter. From the model, it's found out that it's much easier to accomplish adaptive-voltage-positioning (AVP) using the proposed modified RBCOT scheme compared to a conventional constant-frequency controller. Simulation and experimental results are also given to verify the proposed scheme.