nils-herrmann/clean_scirepeval huggingface dataset:
Sampling of Graph Signals With Successive Local Aggregations

A new scheme to sample signals defined on the nodes of a graph is proposed. The underlying assumption is that such signals admit a sparse representation in a frequency domain related to the structure of the graph, which is captured by the so-called graph-shift operator. Instead of using the value of the signal observed at a subset of nodes to recover the signal in the entire graph, the sampling scheme proposed here uses as input observations taken at a single node. The observations correspond to sequential applications of the graph-shift operator, which are linear combinations of the information gathered by the neighbors of the node. When the graph corresponds to a directed cycle (which is the support of time-varying signals), our method is equivalent to the classical sampling in the time domain. When the graph is more general, we show that the Vandermonde structure of the sampling matrix, critical when sampling time-varying signals, is preserved. Sampling and interpolation are analyzed first in the absence of noise, and then noise is considered. We then study the recovery of the sampled signal when the specific set of frequencies that is active is not known. Moreover, we present a more general sampling scheme, under which, either our aggregation approach or the alternative approach of sampling a graph signal by observing the value of the signal at a subset of nodes can be both viewed as particular cases. Numerical experiments illustrating the results in both synthetic and real-world graphs close the paper.
Energy reduction for STT-RAM using early write termination

The emerging Spin Torque Transfer memory (STT-RAM) is a promising candidate for future on-chip caches due to STT-RAM's high density, low leakage, long endurance and high access speed. However, one of the major challenges of STT-RAM is its high write current, which is disadvantageous when used as an on-chip cache since the dynamic power generated is too high.
 In this paper, we propose Early Write Termination (EWT), a novel technique to significantly reduce write energy with no performance penalty. EWT can be implemented with low complexity and low energy overhead. Our evaluation shows that up to 80% of write energy reduction can be achieved through EWT, resulting 33% less total energy consumption, and 34% reduction in ED2. These results indicate that EWT is an effective and practical scheme to improve the energy efficiency of a STT-RAM cache.
Enabling Green cellular networks: A survey and outlook

In last couple of decades, cellular networks have revolutionized the way users access communication networks but they required a huge effort to operators for the development of a wireless infrastructure which has been designed considering deployment costs with ubiquitous coverage and service quality targets. The traditional ”macro” Base Stations (BSs) that have been used so far turned out to be inefficient from the operational costs point of view mainly because of their high energy consumption. Today, green communication is one of the main design goals of future mobile networks and current research aims to enable sustainable growth of broadband wireless infrastructure. Different solutions have been proposed so far for improving the energy efficiency of wireless networks. Small cells based on low-cost low-power Access Points (APs) are a promising solution to limit emission power and improve the spectral efficiency. Dynamic radio resource management can avoid energy wastage by adapting network parameters to load variations while satisfying quality constraints. Flexible hardware platforms enables APs to adapt operational point to changing conditions. The contribution of this survey is threefold. We provide an analysis of the models proposed in literature to evaluate the energy efficiency of current wireless architecture. We present green metrics that have been used and theoretical trade-offs that have been investigated. And finally, following a proposed classification, we present and critically discuss energy efficiency enablers recently proposed by the wireless research community.
Sensitivity-Based Model of Low Voltage Distribution Systems with Distributed Energy Resources

A key issue in Low Voltage (LV) distribution systems is to identify strategies for the optimal management and control in the presence of Distributed Energy Resources (DERs). To reduce the number of variables to be monitored and controlled, virtual levels of aggregation, called Virtual Microgrids (VMs), are introduced and identified by using new models of the distribution system. To this aim, this paper, revisiting and improving the approach outlined in a conference paper, presents a sensitivity-based model of an LV distribution system, supplied by an Medium/Low Voltage (MV/LV) substation and composed by several feeders, which is suitable for the optimal management and control of the grid and for VM definition. The main features of the proposed method are: it evaluates the sensitivity coefficients in a closed form; it provides an overview of the sensitivity of the network to the variations of each DER connected to the grid; and it presents a limited computational burden. A comparison of the proposed method with both the exact load flow solutions and a perturb-and-observe method is discussed in a case study. Finally, the method is used to evaluate the impact of the DERs on the nodal voltages of the network.
Powered lower limb orthoses for gait rehabilitation.

Bodyweight supported treadmill training has become a prominent gait rehabilitation method in leading rehabilitation centers. This type of locomotor training has many functional benefits but the labor costs are considerable. To reduce therapist effort, several groups have developed large robotic devices for assisting treadmill stepping. A complementary approach that has not been adequately explored is to use powered lower limb orthoses for locomotor training. Recent advances in robotic technology have made lightweight powered orthoses feasible and practical. An advantage to using powered orthoses as rehabilitation aids is they allow practice starting, turning, stopping, and avoiding obstacles during overground walking.
Predicting Multiple Attributes via Relative Multi-task Learning

Relative attributes learning aims to learn ranking functions describing the relative strength of attributes. Most of current learning approaches learn ranking functions for each attribute independently without considering possible intrinsic relatedness among the attributes. For a problem involving multiple attributes, it is reasonable to assume that utilizing such relatedness among the attributes would benefit learning, especially when the number of labeled training pairs are very limited. In this paper, we proposed a relative multi-attribute learning framework that integrates relative attributes into a multi-task learning scheme. The formulation allows us to exploit the advantages of the state-of-the-art regularization-based multi-task learning for improved attribute learning. In particular, using joint feature learning as the case studies, we evaluated our framework with both synthetic data and two real datasets. Experimental results suggest that the proposed framework has clear performance gain in ranking accuracy and zero-shot learning accuracy over existing methods of independent relative attributes learning and multi-task learning.
Towards an Optimized BlockChain for IoT

There has been increasing interest in adopting BlockChain (BC), that underpins the crypto-currency Bitcoin, in Internet of Things (IoT) for security and privacy. However, BCs are computationally expensive and involve high bandwidth overhead and delays, which are not suitable for most IoT devices. This paper proposes a lightweight BC-based architecture for IoT that virtually eliminates the overheads of classic BC, while maintaining most of its security and privacy benefits. IoT devices benefit from a private immutable ledger, that acts similar to BC but is managed centrally, to optimize energy consumption. High resource devices create an overlay network to implement a publicly accessible distributed BC that ensures end-to-end security and privacy. The proposed architecture uses distributed trust to reduce the block validation processing time. We explore our approach in a smart home setting as a representative case study for broader IoT applications. Qualitative evaluation of the architecture under common threat models highlights its effectiveness in providing security and privacy for IoT applications. Simulations demonstrate that our method decreases packet and processing overhead significantly compared to the BC implementation used in Bitcoin.
Proteus: agile ML elasticity through tiered reliability in dynamic resource markets

Many shared computing clusters allow users to utilize excess idle resources at lower cost or priority, with the proviso that some or all may be taken away at any time. But, exploiting such dynamic resource availability and the often fluctuating markets for them requires agile elasticity and effective acquisition strategies. Proteus aggressively exploits such transient revocable resources to do machine learning (ML) cheaper and/or faster. Its parameter server framework, AgileML, efficiently adapts to bulk additions and revocations of transient machines, through a novel 3-stage active-backup approach, with minimal use of more costly non-transient resources. Its BidBrain component adaptively allocates resources from multiple EC2 spot markets to minimize average cost per work as transient resource availability and cost change over time. Our evaluations show that Proteus reduces cost by 85% relative to non-transient pricing, and by 43% relative to previous approaches, while simultaneously reducing runtimes by up to 37%.
Associations of students' creativity, motivation, and self-regulation with learning and achievement in college computer science courses

The need for more post-secondary students to major and graduate in STEM fields is widely recognized. Students' motivation and strategic self-regulation have been identified as playing crucial roles in their success in STEM classes. But, how students' strategy use, self-regulation, knowledge building, and engagement impact different learning outcomes is not well understood. Our goal in this study was to investigate how motivation, strategic self-regulation, and creative competency were associated with course achievement and long-term learning of computational thinking knowledge and skills in introductory computer science courses. Student grades and long-term retention were positively associated with self-regulated strategy use and knowledge building, and negatively associated with lack of regulation. Grades were associated with higher study effort and knowledge retention was associated with higher study time. For motivation, higher learning- and task-approach goal orientations, endogenous instrumentality, and positive affect and lower learning-, task-, and performance-avoid goal orientations, exogenous instrumentality and negative affect were associated with higher grades and knowledge retention and also with strategic self-regulation and engagement. Implicit intelligence beliefs were associated with strategic self-regulation, but not grades or knowledge retention. Creative competency was associated with knowledge retention, but not grades, and with higher strategic self-regulation. Implications for STEM education are discussed.
Multiple Access for 5G New Radio: Categorization, Evaluation, and Challenges

Next generation wireless networks require massive uplink connections as well as high spectral efficiency. It is well known that, theoretically, it is not possible to achieve the sum capacity of multi-user communications with orthogonal multiple access. To meet the challenging requirements of next generation networks, researchers have explored non-orthogonal and overloaded transmission technologies–known as new radio multiple access (NR-MA) schemes–for fifth generation (5G) networks. In this article, we discuss the key features of the promising NRMA schemes for the massive uplink connections. The candidate schemes of NR-MA can be characterized by multiple access signatures (MA-signatures), such as codebook, sequence, and interleaver/scrambler. At the receiver side, advanced multiuser detection (MUD) schemes are employed to extract each user’s data from non-orthogonally superposed data according to MA-signatures. Through link-level simulations, we compare the performances of NR-MA candidates under the same conditions. We further evaluate the sum rate performances of the NRMA schemes using a 3-dimensional (3D) ray tracing tool based system-level simulator by reflecting realistic environments. Lastly, we discuss the tips for system operations as well as call attention to the remaining technical challenges.
A Multimodal In-Car Dialogue System That Tracks The Driver's Attention

When a passenger speaks to a driver, he or she is co-located with the driver, is generally aware of the situation, and can stop speaking to allow the driver to focus on the driving task. In-car dialogue systems ignore these important aspects, making them more distracting than even cell-phone conversations. We developed and tested a "situationally-aware" dialogue system that can interrupt its speech when a situation which requires more attention from the driver is detected, and can resume when driving conditions return to normal. Furthermore, our system allows driver-controlled resumption of interrupted speech via verbal or visual cues (head nods). Over two experiments, we found that the situationally-aware spoken dialogue system improves driving performance and attention to the speech content, while driver-controlled speech resumption does not hinder performance in either of these two tasks
QRS Detection Based on Multiscale Mathematical Morphology for Wearable ECG Devices in Body Area Networks

A novel wearable electrocardiograph (ECG) QRS detection algorithm for wearable ECG devices in body area networks is presented in this paper, which utilizes the multistage multiscale mathematical morphology filtering to suppress the impulsive noise and uses the multiframe differential modulus accumulation to remove the baseline drift and enhance the signal. The proposed algorithm, verified with data from the MIT/BIH Arrhythmia Database and wearable ECG devices, achieves an average QRS detection rate of 99.61%, a sensitivity of 99.81%, and a positive prediction of 99.80%. It compares favorably to the published methods.
Parallel algorithms for distance-based and density-based outliers

An outlier is an observation that deviates so much from other observations as to arouse suspicion that it was generated by a different mechanism. Outlier detection has many applications, such as data cleaning, fraud detection and network intrusion. The existence of outliers can indicate individuals or groups that exhibit a behavior that is very different from most of the individuals of the dataset. In this paper we design two parallel algorithms, the first one is for finding out distance-based outliers based on nested loops along with randomization and the use of a pruning rule. The second parallel algorithm is for detecting density-based local outliers. In both cases data parallelism is used. We show that both algorithms reach near linear speedup. Our algorithms are tested on four real-world datasets coming from the Machine Learning Database Repository at the UCI.
Morphological Analysis for Unsegmented Languages using Recurrent Neural Network Language Model

We present a new morphological analysis model that considers semantic plausibility of word sequences by using a recurrent neural network language model (RNNLM). In unsegmented languages, since language models are learned from automatically segmented texts and inevitably contain errors, it is not apparent that conventional language models contribute to morphological analysis. To solve this problem, we do not use language models based on raw word sequences but use a semantically generalized language model, RNNLM, in morphological analysis. In our experiments on two Japanese corpora, our proposed model significantly outperformed baseline models. This result indicates the effectiveness of RNNLM in morphological analysis.
Observations : SAM : The Self-Assessment Manikin An Efficient Cross-Cultural Measurement Of Emotional Response

Although consumer research began focusing on emotional response to advertising during the 1980s (Goodstein, Edell, and Chapman Moore. 1990; Burke and Edell, 1989; Aaker, Stayman, and Vezina, 1988; Holbrook and Batra, 1988), perhaps one of the most practical measures of affective response has only recently emerged. Part of the difficulty in developing measures of emotional response stems from the complexity of emotion itself (Plummer and Leckenby, 1985). Researchers have explored several different measurement formats including: verbal self-reports (adjective checklists), physiological techniques, photodecks, and dial-turning instruments.
International relations : One world , many theories

Why should policymakers and practitioners care about the scholarly study of international affairs? Those who conduct foreign policy often dismiss academic theorists (frequently, one must admit, with good reason), but there is an inescapable link between the abstract world of theory and the real world of policy. We need theories to make sense of the blizzard of information that bombards us daily. Even policymakers who are contemptuous of "theory" must rely on their own (often unstated) ideas about how the world works in order to decide what to do. It is hard to make good policy if one's basic organizing principles are flawed, just as it is hard to construct good theories without knowing a lot about the real world. Everyone uses theories-whether he or she knows it or not-and disagreements about policy usually rest on more fundamental disagreements about the basic forces that shape international outcomes.
The Streptococcus thermophilus CRISPR/Cas system provides immunity in Escherichia coli

The CRISPR/Cas adaptive immune system provides resistance against phages and plasmids in Archaea and Bacteria. CRISPR loci integrate short DNA sequences from invading genetic elements that provide small RNA-mediated interference in subsequent exposure to matching nucleic acids. In Streptococcus thermophilus, it was previously shown that the CRISPR1/Cas system can provide adaptive immunity against phages and plasmids by integrating novel spacers following exposure to these foreign genetic elements that subsequently direct the specific cleavage of invasive homologous DNA sequences. Here, we show that the S. thermophilus CRISPR3/Cas system can be transferred into Escherichia coli and provide heterologous protection against plasmid transformation and phage infection. We show that interference is sequence-specific, and that mutations in the vicinity or within the proto-spacer adjacent motif (PAM) allow plasmids to escape CRISPR-encoded immunity. We also establish that cas9 is the sole cas gene necessary for CRISPR-encoded interference. Furthermore, mutation analysis revealed that interference relies on the Cas9 McrA/HNH- and RuvC/RNaseH-motifs. Altogether, our results show that active CRISPR/Cas systems can be transferred across distant genera and provide heterologous interference against invasive nucleic acids. This can be leveraged to develop strains more robust against phage attack, and safer organisms less likely to uptake and disseminate plasmid-encoded undesirable genetic elements.
Isolation Enhancement in Patch Antenna Array With Fractal UC-EBG Structure and Cross Slot

A compact patch antenna array with high isolation by using two decoupling structures including a row of fractal uniplanar compact electromagnetic bandgap (UC-EBG) structure and three cross slots is proposed. Simulated results show that significant improvement in interelement isolation of 13 dB is obtained by placing the proposed fractal UC-EBG structure between the two radiating patches. Moreover, three cross slots etched on the ground plane are introduced to further suppress the mutual coupling. The design is easy to be manufactured without the implementation of metal vias, and a more compact array with the edge-to-edge distance of 0.22 λ0 can be facilitated by a row of fractal UC-EBG, which can be well applied in the patch antenna array.
A SP9T cellular antenna switch in 2.5 V CMOS thin-film SOI

This paper presents the results of a compact wire-bond SP9T antenna switch that was designed as technology demonstrator for a 2.5 V CMOS, 250 fs Ron-Coff thin-film SOI process. Through “layout-driven” circuit design, a small die size of 1.52 mm2 was achieved for a fully integrated switch die containing RF-section, I/O pads, ESD, decoder, level shifters and dual frequency charge pump to generate negative vss. The dual frequency charge pump was a requirement to achieve a fast start-up time of 10 μs and switch rise times of 3 μs. A low insertion loss of 0.42 dB for cellular low-band at 915 MHz and 0.55 dB for cellular high-band at 1910 MHz, harmonic powers better -76 dBc over battery and Band I/V IMD3 of -110 dBm were achieved. All ports show a high ESD tolerance of 2 kV HBM.
Software Engineering for Self-Adaptive Systems: A Second Research Roadmap

The goal of this roadmap paper is to summarize the stateof-the-art and identify research challenges when developing, deploying and managing self-adaptive software systems. Instead of dealing with a wide range of topics associated with the field, we focus on four essential topics of self-adaptation: design space for adaptive solutions, processes, from centralized to decentralized control, and practical run-time verification and validation. For each topic, we present an overview, suggest future directions, and focus on selected challenges. This paper complements and extends a previous roadmap on software engineering for self-adaptive systems published in 2009 covering a different set of topics, and reflecting in part on the previous paper. This roadmap is one of the many results of the Dagstuhl Seminar 10431 on Software Engineering for Self-Adaptive Systems, which took place in October 2010.
Feminist HCI: taking stock and outlining an agenda for design

Feminism is a natural ally to interaction design, due to its central commitments to issues such as agency, fulfillment, identity, equity, empowerment, and social justice. In this paper, I summarize the state of the art of feminism in HCI and propose ways to build on existing successes to more robustly integrate feminism into interaction design research and practice. I explore the productive role of feminism in analogous fields, such as industrial design, architecture, and game design. I introduce examples of feminist interaction design already in the field. Finally, I propose a set of femi-nist interaction design qualities intended to support design and evaluation processes directly as they unfold.
Indoor Positioning Using Visible LED Lights: A Survey

Visible light LEDs, due to their numerous advantages, are expected to become the dominant indoor lighting technology. These lights can also be switched ON/OFF at high frequency, enabling their additional use for wireless communication and indoor positioning. In this article, visible LED light--based indoor positioning systems are surveyed and classified into two broad categories based on the receiver structure. The basic principle and architecture of each design category, along with various position computation algorithms, are discussed and compared. Finally, several new research, implementation, commercialization, and standardization challenges are identified and highlighted for this relatively novel and interesting indoor localization technology.