nils-herrmann/clean_scirepeval huggingface dataset:
Temperature-aware microarchitecture: Modeling and implementation

With cooling costs rising exponentially, designing cooling solutions for worst-case power dissipation is prohibitively expensive. Chips that can autonomously modify their execution and power-dissipation characteristics permit the use of lower-cost cooling solutions while still guaranteeing safe temperature regulation. Evaluating techniques for this dynamic thermal management (DTM), however, requires a thermal model that is practical for architectural studies.This paper describes HotSpot, an accurate yet fast and practical model based on an equivalent circuit of thermal resistances and capacitances that correspond to microarchitecture blocks and essential aspects of the thermal package. Validation was performed using finite-element simulation. The paper also introduces several effective methods for DTM: "temperature-tracking" frequency scaling, "migrating computation" to spare hardware units, and a "hybrid" policy that combines fetch gating with dynamic voltage scaling. The latter two achieve their performance advantage by exploiting instruction-level parallelism, showing the importance of microarchitecture research in helping control the growth of cooling costs.Modeling temperature at the microarchitecture level also shows that power metrics are poor predictors of temperature, that sensor imprecision has a substantial impact on the performance of DTM, and that the inclusion of lateral resistances for thermal diffusion is important for accuracy.
Autotagger: A Model for Predicting Social Tags from Acoustic Features on Large Music Databases

Social tags are user-generated keywords associated with some resource on the Web. In the case of music, social tags have become an important component of “Web 2.0” recommender systems, allowing users to generate playlists based on use-dependent terms such as chill or jogging that have been applied to particular songs. In this paper, we propose a method for predicting these social tags directly from MP3 files. Using a set of 360 classifiers trained using the online ensemble learning algorithm FilterBoost, we map audio features onto social tags collected from the Web. The resulting automatic tags (or autotags) furnish information about music that is otherwise untagged or poorly tagged, allowing for insertion of previously unheard music into a social recommender. This avoids the “cold-start problem” common in such systems. Autotags can also be used to smooth the tag space from which similarities and recommendations are made by providing a set of comparable baseline tags for all tracks in a recommender system. Because the words we learn are the same as those used by people who label their music collections, it is easy to integrate our predictions into existing similarity and prediction methods based on web data.
An Initial Investigation of Test Driven Development in Industry

Test Driven Development (TDD) is a software development practice in which unit test cases are incrementally written prior to code implementation. In our research, we ran a set of structured experiments with 24 professional pair programmers. One group developed code using TDD while the other a waterfall-like approach. Both groups developed a small Java program. We found that the TDD developers produced higher quality code, which passed 18% more functional black box test cases. However, TDD developer pairs took 16% more time for development. A moderate correlation between time spent and the resulting quality was established upon analysis. It is conjectured that the resulting high quality of code written using the TDD practice may be due to the granularity of TDD, which may encourage more frequent and tighter verification and validation. Lastly, the programmers which followed a waterfall-like process often did not write the required automated test cases after completing their code, which might be indicative of the tendency among practitioners toward inadequate testing. This observation supports that TDD has the potential of increasing the level of testing in the industry as testing as an integral part of code development.
Autonomous Agents and Ethical Decision-Making

Machine ethics, also known as artificial morality, is a newly emerging field concerned with ensuring appropriate behavior of machines toward humans and other machines. In this article, we discuss the importance of machine ethics and present a computational model of ethical decision-making for autonomous agents. The proposed model implements a mechanism for integrating the results of diverse assessments into a unique cue, and takes into account the agent’s preferences, good and bad past experiences, ethical rules, and current emotional state as the main factors involved in choosing the most appropriate option. The design of the model is based on theories and models developed in fields such as neuroscience, psychology, artificial intelligence, and cognitive informatics. In particular, the model attempts to emulate neural mechanisms of the human brain involved in ethical decision-making.
Radiation Performance Enhancement of E-Band Antenna in Package

This paper presents an antenna-in-package (AiP) solution with embedded wafer Level ball grid array (eWLB) packaging technology. The primary antenna is realized in the redistribution layer of the eWLB package. It is composed of two patches driven by differential signals and supports seamless connection with differential output monolithic microwave integrated circuits in a compact package. A dielectric rod lens was designed to optimize the radiation performance of the whole package. The final lens has the dimensions 10 mm × 10 mm × 10.5 mm. Our measurements show that the whole package (chip+AiP) including the lens reaches 16-dBm effective isotropic radiated power for the frequency range from 71.4 to 81.7 GHz. The radiation beam is relatively symmetric for both the E- and H-plane. The proposed AiP solution has great potential for millimeter-wave applications.
A General Coefficient of Similarity and Some of Its Properties

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Multiple-output ZCS resonant inverter for multi-coil induction heating appliances

This paper presents a multiple-output resonant inverter for multi-coil systems featuring high efficiency and flexible output power control for modern induction heating appliances. By adopting a matrix structure, the number of controlled devices can be reduced to the square root of the number of controlled induction heating loads. Therefore, an elevated component count reduction can be achieved compared to classical multi-outputs solutions. The proposed converter is analyzed and verified experimentally, proving the feasibility of this proposal and benefits compared with previous alternatives.
Get To The Point: Summarization with Pointer-Generator Networks

Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text). However, these models have two shortcomings: they are liable to reproduce factual details inaccurately, and they tend to repeat themselves. In this work we propose a novel architecture that augments the standard sequence-to-sequence attentional model in two orthogonal ways. First, we use a hybrid pointer-generator network that can copy words from the source text via pointing, which aids accurate reproduction of information, while retaining the ability to produce novel words through the generator. Second, we use coverage to keep track of what has been summarized, which discourages repetition. We apply our model to the CNN / Daily Mail summarization task, outperforming the current abstractive state-of-the-art by at least 2 ROUGE points.
An Orbital Angular Momentum (OAM) Mode Reconfigurable Antenna for Channel Capacity Improvement and Digital Data Encoding

For purpose of utilizing orbital angular momentum (OAM) mode diversity, multiple OAM beams should be generated preferably by a single antenna. In this paper, an OAM mode reconfigurable antenna is proposed. Different from the existed OAM antennas with multiple ports for multiple OAM modes transmitting, the proposed antenna with only a single port, but it can be used to transmit mode 1 or mode −1 OAM beams arbitrary by controlling the PIN diodes on the feeding network through a programmable microcontroller which control by a remote controller. Simulation and measurement results such as return loss, near-field and far-field radiation patterns of two operating states for mode 1 and mode −1, and OAM mode orthogonality are given. The proposed antenna can serve as a candidate for utilizing OAM diversity, namely phase diversity to increase channel capacity at 2.4 GHz. Moreover, an OAM-mode based encoding method is experimentally carried out by the proposed OAM mode reconfigurable antenna, the digital data are encoded and decoded by different OAM modes. At the transmitter, the proposed OAM mode reconfigurable antenna is used to encode the digital data, data symbol 0 and 1 are mapped to OAM mode 1 and mode −1, respectively. At the receiver, the data symbols are decoded by phase gradient method.
Childhood sexual abuse and psychiatric disorder in young adulthood: II. Psychiatric outcomes of childhood sexual abuse.

This is the second in a series of articles that describe the prevalence, correlates, and consequences of childhood sexual abuse (CSA) in a birth cohort of more than 1,000 New Zealand children studied to the age of 18 years. This article examines the associations between reports of CSA at age 18 and DSM-IV diagnostic classifications at age 18.



A birth cohort of New Zealand children was studied at annual intervals from birth to age 16 years. At age 18 years retrospective reports of CSA prior to age 16 and concurrently measured psychiatric symptoms were obtained.



Those reporting CSA had higher rates of major depression, anxiety disorder, conduct disorder, substance use disorder, and suicidal behaviors than those not reporting CSA (p < .002). There were consistent relationships between the extent of CSA and risk of disorder, with those reporting CSA involving intercourse having the highest risk of disorder. These results persisted when findings were adjusted for prospectively measured childhood family and related factors. Similar but less marked relationships between CSA and nonconcurrently measured disorders were found.



The findings suggest that CSA, and particularly severe CSA, was associated with increased risk of psychiatric disorder in young adults even when due allowance was made for prospectively measured confounding factors.
Impact of psychological factors in the experience of pain.

This article reviews the role of psychological factors in the development of persistent pain and disability, with a focus on how basic psychological processes have been incorporated into theoretical models that have implications for physical therapy. To this end, the key psychological factors associated with the experience of pain are summarized, and an overview of how they have been integrated into the major models of pain and disability in the scientific literature is presented. Pain has clear emotional and behavioral consequences that influence the development of persistent problems and the outcome of treatment. Yet, these psychological factors are not routinely assessed in physical therapy clinics, nor are they sufficiently utilized to enhance treatment. Based on a review of the scientific evidence, a set of 10 principles that have likely implications for clinical practice is offered. Because psychological processes have an influence on both the experience of pain and the treatment outcome, the integration of psychological principles into physical therapy treatment would seem to have potential to enhance outcomes.
Genetically Improved PSO Algorithm for Efficient Data Clustering

Clustering is an important research topic in data mining that appears in a wide range of unsupervised classification applications. Partitional clustering algorithms such as the k-means algorithm are the most popular for clustering large datasets. The major problem with the k-means algorithm is that it is sensitive to the selection of the initial partitions and it may converge to local optima. In this paper, we present a hybrid two-phase GAI-PSO+k-means data clustering algorithm that performs fast data clustering and can avoid premature convergence to local optima. In the first phase we utilize the new genetically improved particle swarm optimization algorithm (GAI-PSO) which is a population-based heuristic search technique modeled on the hybrid of cultural and social rules derived from the analysis of the swarm intelligence (PSO) and the concepts of natural selection and evolution (GA). The GAI-PSO combines the standard velocity and position update rules of PSOs with the ideas of selection, mutation and crossover from GAs. The GAI-PSO algorithm searches the solution space to find the optimal initial cluster centroids for the next phase. The second phase is a local refining stage utilizing the k-means algorithm which can efficiently converge to the optimal solution. The proposed algorithm combines the ability of the globalized searching of the evolutionary algorithms and the fast convergence of the k-means algorithm and can avoid the drawback of both. The performance of the proposed algorithm is evaluated through several benchmark datasets. The experimental results show that the proposed algorithm is highly forceful and outperforms the previous approaches such as SA, ACO, PSO and k-means for the partitional clustering problem.
High efficiency SRM drive using a Quasi-Current Source Inverter

This paper proposes a high efficiency driving method for a Switched Reluctance Motor (SRM). Recently, most of SRMs are driven by using a Voltage Source Inverter (VSI) and the torque is controlled by a current hysteresis control. However, when SRM is driven in high speed, the load current is decreased because the number of current sampling is not enough, the current hysteresis control cannot be achieved. This study proposes a Quasi-Current Source Inverter (QCSI) for SRM drive and the effects of the proposed method are verified by simulations and experiments. Consequently, the load current is smoothly changed in the variable speed conditions. Therefore, a high-efficiency driving can be realized in the variable speed.
Computational intelligence and tower defence games

The aim of this paper is to introduce the use of Tower Defence (TD) games in Computational Intelligence (CI) research. We show how TD games can provide an important test-bed for the often under-represented casual games research area. Additionally, the use of CI in the TD games has the potential to create a more interesting, interactive and ongoing game experience for casual gamers. We present a definition of the current state and development of TD games, and include a classification of TD game components. We then describe some potential ways CI can be used to augment the TD experience. Finally, a prototype TD game based on experience-driven procedural content generation is presented.
Algorithms for asymptotic extrapolation ∗

Consider a power series f ∈ R[[z]], which is obtained by a precise mathematical construction. For instance, f might be the solution to some differential or functional initial value problem or the diagonal of the solution to a partial differential equation. In cases when no suitable method is beforehand for determining the asymptotics of the coefficients fn, but when many such coefficients can be computed with high accuracy, it would be useful if a plausible asymptotic expansion for fn could be guessed automatically. In this paper, we will present a general scheme for the design of such “asymptotic extrapolation algorithms”. Roughly speaking, using discrete differentiation and techniques from automatic asymptotics, we strip off the terms of the asymptotic expansion one by one. The knowledge of more terms of the asymptotic expansion will then allow us to approximate the coefficients in the expansion with high accuracy.
Social Exchange Theory . does it matter for employee performance ?

Employee engagement is critical to achieve the competitive advantage which, ultimately, affects the employee performance in the contemporary knowledge-based economy. The purpose of this study is to propose a model assists to understand the impact of employee engagement on various employee performance measures such as innovative work behavior, organizational citizenship behavior, organizational commitment, job satisfaction, task performance, turnover intention, counterproductive work behavior and absenteeism. In fact, organizations can achieve a sustainable employee performance by ensuring the high level of employee engagement. This study contributes to academic literature by extending the knowledge of engagement-performance relationship under the tenet of Social Exchange Theory (SET). Moreover, it may provide a roadmap for future researchers towards understanding the linkage between employee engagement and employee performances.
Blockchain-based secure firmware update for embedded devices in an Internet of Things environment

Embedded devices are going to be used extremely in Internet of Things (IoT) environments. The small and tiny IoT devices will operate and communicate each other without involvement of users, while their operations must be correct and protected against various attacks. In this paper, we focus on a secure firmware update issue, which is a fundamental security challenge for the embedded devices in an IoT environment. A new firmware update scheme that utilizes a blockchain technology is proposed to securely check a firmware version, validate the correctness of firmware, and download the latest firmware for the embedded devices. In the proposed scheme, an embedded device requests its firmware update to nodes in a blockchain network and gets a response to determine whether its firmware is up-to-date or not. If not latest, the embedded device downloads the latest firmware from a peer-to-peer firmware sharing network of the nodes. Even in the case that the version of the firmware is up-to-date, its integrity, i.e., correctness of firmware, is checked. The proposed scheme guarantees that the embedded device’s firmware is up-to-date while not tampered. Attacks targeting known vulnerabilities on firmware of embedded devices are thus mitigated.
A Variable Threshold Voltage CMOS Comparator for Flash Analog to Digital Converter

This paper presents a variable threshold voltage CMOS comparator for flash analog to digital converter. The proposed comparator has single-ended type of architecture. The comparator is designed and analyzed by Cadence Virtuoso Analog Design Environment using UMC 180nm technology. The proposed comparator consumes peak power of 34. 97 ?W from 1. 8 V power supply. It achieves the power delay product (PDP) of 8 fJ and propagation delay of 230 ps. The designed comparator eliminates the requirement of resistive ladder network for reference voltage generation. This makes it highly suitable in the design of flash analog to digital converter.
A review of facial nerve anatomy.

An intimate knowledge of facial nerve anatomy is critical to avoid its inadvertent injury during rhytidectomy, parotidectomy, maxillofacial fracture reduction, and almost any surgery of the head and neck. Injury to the frontal and marginal mandibular branches of the facial nerve in particular can lead to obvious clinical deficits, and areas where these nerves are particularly susceptible to injury have been designated danger zones by previous authors. Assessment of facial nerve function is not limited to its extratemporal anatomy, however, as many clinical deficits originate within its intratemporal and intracranial components. Similarly, the facial nerve cannot be considered an exclusively motor nerve given its contributions to taste, auricular sensation, sympathetic input to the middle meningeal artery, and parasympathetic innervation to the lacrimal, submandibular, and sublingual glands. The constellation of deficits resulting from facial nerve injury is correlated with its complex anatomy to help establish the level of injury, predict recovery, and guide surgical management.
Smart Locks: Lessons for Securing Commodity Internet of Things Devices

We examine the security of home smart locks: cyber-physical devices that replace traditional door locks with deadbolts that can be electronically controlled by mobile devices or the lock manufacturer's remote servers. We present two categories of attacks against smart locks and analyze the security of five commercially-available locks with respect to these attacks. Our security analysis reveals that flaws in the design, implementation, and interaction models of existing locks can be exploited by several classes of adversaries, allowing them to learn private information about users and gain unauthorized home access. To guide future development of smart locks and similar Internet of Things devices, we propose several defenses that mitigate the attacks we present. One of these defenses is a novel approach to securely and usably communicate a user's intended actions to smart locks, which we prototype and evaluate. Ultimately, our work takes a first step towards illuminating security challenges in the system design and novel functionality introduced by emerging IoT systems.
Outlier Rejection for Absolute Pose Estimation with Known Orientation

Estimating the pose of a camera is a core problem in many geometric vision applications. While there has been much progress in the last two decades, the main difficulty is still dealing with data contaminated by outliers. For many scenes, e.g. with poor lightning conditions or repetitive textures, it is common that most of the correspondences are outliers. For real applications it is therefore essential to have robust estimation methods. In this paper we present an outlier rejection method for absolute pose estimation. We focus on the special case when the orientation of the camera is known. The problem is solved by projecting to a lower dimensional subspace where we are able to efficiently compute upper bounds on the maximum number of inliers. The method guarantees that only correspondences which cannot belong to an optimal pose are removed. In a number of challenging experiments we evaluate our method on both real and synthetic data and show improved performance compared to competing methods.