nils-herrmann/clean_scirepeval huggingface dataset:
VBaaS: VNF Benchmark-as-a-Service

When rolling out Network Function Virtualization (NFV) services, resource monitoring becomes a critical task subject to different cost-accuracy tradeoffs depending on whether continuous monitoring or more static infrastructure resource views are taken. In this context, we propose Virtualized Network Functions (VNF) Benchmark-as-a-Service (VBaaS) to enable not only run-time resource evaluation but also test-before-deploy opportunities for VNFs and NFV Infrastructures. We describe the motivation behind VBaaS and its main value proposition for a number of use cases around the orchestration tasks of VNF Forwarding Graphs We present the main components of VBaaS along their system interactions and interfaces, discussing the main benefits of adopting VBaaS and open research issues. Addressing the identified challenges and finalizing our proof of concept VBaaS are our main ongoing work activities.
A Model-Based Sensor Database for Internet of Things

of a thesis at the University of Miami. Thesis supervised by Professor Dilip Sarkar. No. of pages in text. (39) The Internet of Things (IoTs) is becoming ubiquitous in our everyday lives, implying that more technologies will generate data. IoT devices use sensors to monitor various attributes of the environment such as temperature, humidity, light, etc. These sensors produce data periodically and storing this massive data in a database is becoming a huge challenge in the data storage infrastructure. Prior research has proposed compression algorithms and signature techniques to reduce data storage but do not specify how the data patterns are defined. Since similar patterns are exhibited everyday by the environment, this data generates the same information from everyday sensing. Therefore, in this study, we propose a system that stores data models rather than storing raw data points. Instead of storing each data point at a time, we develop and store data models with the corresponding time periods that captures the behavior of the sensor data. This helps in reducing data storage requirements. The data models developed are mathematical polynomial models that fit a sample data set. In addition, we propose a sensor database structure that addresses the issues of data redundancy as well as temporal constraints in the database.
Interactive Character Animation Using Simulated Physics: A State-of-the-Art Review

Physics simulation offers the possibility of truly responsive and realistic animation. Despite wide adoption of physics simulation for the animation of passive phenomena, such as fluids, cloths and rag-doll characters, commercial applications still resort to kinematics-based approaches for the animation of actively controlled characters. However, following a renewed interest in the use of physics simulation for interactive character animation, many recent publications demonstrate tremendous improvements in robustness, visual quality and usability. We present a structured review of over two decades of research on physics-based character animation, as well as point out various open research areas and possible future directions.
Green Data Centers: A Survey, Perspectives, and Future Directions

At present, a major concern regarding data centers is their extremely high energy consumption and carbon dioxide emissions. However, because of the over-provisioning of resources, the utilization of existing data centers is, in fact, remarkably low, leading to considerable energy waste. Therefore, over the past few years, many research efforts have been devoted to increasing efficiency for the construction of green data centers. The goal of these efforts is to efficiently utilize available resources and to reduce energy consumption and thermal cooling costs. In this paper, we provide a survey of the state-of-the-art research on green data center techniques, including energy efficiency, resource management, thermal control and green metrics. Additionally, we present a detailed comparison of the reviewed proposals. We further discuss the key challenges for future research and highlight some future research issues for addressing the problem of building green data centers. Green data center; Energy efficiency; Resource management; Thermal control; Green metrics, monitoring and experimental techniques
Superneurons: dynamic GPU memory management for training deep neural networks

Going deeper and wider in neural architectures improves their accuracy, while the limited GPU DRAM places an undesired restriction on the network design domain. Deep Learning (DL) practitioners either need to change to less desired network architectures, or nontrivially dissect a network across multiGPUs. These distract DL practitioners from concentrating on their original machine learning tasks. We present SuperNeurons: a dynamic GPU memory scheduling runtime to enable the network training far beyond the GPU DRAM capacity. SuperNeurons features 3 memory optimizations, Liveness Analysis, Unified Tensor Pool, and Cost-Aware Recomputation; together they effectively reduce the network-wide peak memory usage down to the maximal memory usage among layers. We also address the performance issues in these memory-saving techniques. Given the limited GPU DRAM, SuperNeurons not only provisions the necessary memory for the training, but also dynamically allocates the memory for convolution workspaces to achieve the high performance. Evaluations against Caffe, Torch, MXNet and TensorFlow have demonstrated that SuperNeurons trains at least 3.2432 deeper network than current ones with the leading performance. Particularly, SuperNeurons can train ResNet2500 that has 104 basic network layers on a 12GB K40c.
Calcium hydroxylapatite for jawline rejuvenation: consensus recommendations.

Age-associated volume loss is now known to play an important role in the structural changes of the aging face. In the lower face, this manifests as drooping of the corners of the mouth and jowl leading to a loss of the oval jawline of youth. Jawline reshaping by replacing volume has therefore become an indispensable component of modern facial rejuvenation.



Calcium hydroxylapatite (CaHA; Radiesse® , Merz Pharmaceuticals GmbH, Frankfurt, Germany) is an injectable filler with a cosmetic indication for tissue augmentation. The ability of calcium hydroxylapatite to provide immediate and long-lasting volume enhancement makes it an ideal agent for restoring an oval jawline.



This consensus statement has been developed to assist clinicians who would like to gain more experience in the use of volumizing agents to achieve an optimal outcome with this procedure.



Using the recently developed Merz Aesthetics Scale® for jawline, the consensus provides a treatment protocol for individuals at each stage of oval loss and presents a series of before and after images to illustrate the improvements that can be achieved. Specific recommendations for calcium hydroxylapatite including type of anesthesia, injection techniques, volume for injection, use in combination with other procedures, and expected duration of corrections are provided. Techniques for minimizing and managing expected problems and potential complications are also described.



Calcium hydroxylapatite is appropriate for treating patients at any stage of oval loss.
Computing CNN Loss and Gradients for Pose Estimation with Riemannian Geometry

Pose estimation, i.e. predicting a 3D rigid transformation with respect to a fixed co-ordinate frame in, SE(3), is an omnipresent problem in medical image analysis. Deep learning methods often parameterise poses with a representation that separates rotation and translation. Available frameworks do not provide means to calculate loss on a manifold, regression is usually performed using the L2-norm independently on the rotation’s and the translation’s parameterisations. This is a metric for linear spaces that does not take into account the Lie group structure of SE(3). We propose a general Riemannian formulation of the pose estimation problem, and train CNNs directly on SE(3) equipped with a left-invariant Riemannian metric. The loss between the ground truth and predicted pose (elements of the manifold) is calculated as the Riemannian geodesic distance, which couples together the translation and rotation components. Network weights are updated by back-propagating the gradient with respect to the predicted pose on the tangent space of the manifold SE(3). We thoroughly evaluate the effectiveness of our loss function by comparing its performance with popular and most commonly used existing methods, on tasks such as image-based localisation and intensity-based 2D/3D registration. We also show that hyper-parameters, used in our loss function to weight the contribution between rotations and translations, can be intrinsically calculated from the dataset to achieve greater performance margins.
A religious upbringing reduces the influence of genetic factors on disinhibition: evidence for interaction between genotype and environment on personality.

Information on personality, on anxiety and depression and on several aspects of religion was collected in 1974 Dutch families consisting of adolescent and young adult twins and their parents. Analyses of these data showed that differences between individuals in religious upbringing, in religious affiliation and in participation in church activities are not influenced by genetic factors. The familial resemblance for different aspects of religion is high, but can be explained entirely by environmental influences common to family members. Shared genes do not contribute to familial resemblances in religion. The absence of genetic influences on variation in several dimensions of religion is in contrast to findings of genetic influences on a large number of other traits that were studied in these twin families. Differences in religious background are associated with differences in personality, especially in Sensation Seeking. Subjects with a religious upbringing, who are currently religious and who engage in church activities score lower on the scales of the Sensation Seeking Questionnaire. The most pronounced effect is on the Disinhibition scale. The resemblances between twins for the Disinhibition scale differ according to their religious upbringing. Receiving a religious upbringing seems to reduce the influence of genetic factors on Disinhibition, especially in males.
Can you program ethics into a self-driving car?

IT'S 2034. A drunken man walking along a sidewalk at night trips and falls directly in front of a driverless car, which strikes him square on, killing him instantly. Had a human been at the wheel, the death would have been considered an accident because the pedestrian was clearly at fault and no reasonable person could have swerved in time. But the "reasonable person" legal standard for driver negligence disappeared back in the 2020s, when the proliferation of driverless cars reduced crash rates by 90 percent. Now the standard is that of the reasonable robot. The victim's family sues the vehicle manufacturer on that ground, claiming that, although the car didn't have time to brake, it could have swerved around the pedestrian, crossing the double yellow line and colliding with the empty driverless vehicle in the next lane. A reconstruction of the crash using data from the vehicle's own sensors confirms this. The plaintiff's attorney, deposing the car's lead software designer, asks: "Why didn't the car swerve?"
Automated cancer diagnosis based on histopathological images : a systematic survey

In traditional cancer diagnosis, pathologists examine biopsies to make diagnostic assessments largely based on cell morphology and tissue distribution. However, this is subjective and often leads to considerable variability. On the other hand, computational diagnostic tools enable objective judgments by making use of quantitative measures. This paper presents a systematic survey of the computational steps in automated cancer diagnosis based on histopathology. These computational steps are: 1.) image preprocessing to determine the focal areas, 2.) feature extraction to quantify the properties of these focal areas, and 3.) classifying the focal areas as malignant or not or identifying their malignancy levels. In Step 1, the focal area determination is usually preceded by noise reduction to improve its success. In the case of cellular-level diagnosis, this step also comprises nucleus/cell segmentation. Step 2 defines appropriate representations of the focal areas that provide distinctive objective measures. In Step 3, automated diagnostic systems that operate on quantitative measures are designed. After the design, this step also estimates the accuracy of the system. In this paper, we detail these computational steps, address their challenges, and discuss the remedies to overcome the challenges, emphasizing the importance of constituting benchmark data sets. Such benchmark data sets allow comparing the different features and system designs and prevent misleading accuracy estimation of the systems. Therefore, this allows determining the subsets of distinguishing features, devise new features, and improve the success of automated cancer diagnosis.
A Survey on the Security of Blockchain Systems

Since its inception, the blockchain technology has shown promising application prospects. From the initial cryptocurrency to the current smart contract, blockchain has been applied to many fields. Although there are some studies on the security and privacy issues of blockchain, there lacks a systematic examination on the security of blockchain systems. In this paper, we conduct a systematic study on the security threats to blockchain and survey the corresponding real attacks by examining popular blockchain systems. We also review the security enhancement solutions for blockchain, which could be used in the development of various blockchain systems, and suggest some future directions to stir research efforts into this area.
Can You Hear Me Now ? The Impact of Voice in an Online Gaming Community

This paper reports the results of a controlled field experiment in which voice communication was introduced into an existing online community (online gaming guilds within the popular game ‘‘World of Warcraft’’), comparing a mix of voice and text with text only. Quantitative results suggest increases in liking and trust due to the addition of voice, as well as insulation from unexpected negative impacts of text-only play. The findings are discussed with respect to social capital, cyberbalkanization, and the general computer-mediated communication literature, with special attention paid to social information processing theory.
A 1.2V 64Gb 341GB/S HBM2 stacked DRAM with spiral point-to-point TSV structure and improved bank group data control

With the recent increasing interest in big data and artificial intelligence, there is an emerging demand for high-performance memory system with large density and high data-bandwidth. However, conventional DIMM-type memory has difficulty achieving more than 50GB/s due to its limited pin count and signal integrity issues. High-bandwidth memory (HBM) DRAM, with TSV technology and wide IOs, is a prominent solution to this problem, but it still has many limitations: including power consumption and reliability. This paper presents a power-efficient structure of TSVs with reliability and a cost-effective HBM DRAM core architecture.
Design aspects of network assisted device-to-device communications

Device-to-device (D2D) communications underlaying a cellular infrastructure has been proposed as a means of taking advantage of the physical proximity of communicating devices, increasing resource utilization, and improving cellular coverage. Relative to the traditional cellular methods, there is a need to design new peer discovery methods, physical layer procedures, and radio resource management algorithms that help realize the potential advantages of D2D communications. In this article we use the 3GPP Long Term Evolution system as a baseline for D2D design, review some of the key design challenges, and propose solution approaches that allow cellular devices and D2D pairs to share spectrum resources and thereby increase the spectrum and energy efficiency of traditional cellular networks. Simulation results illustrate the viability of the proposed design.
The Performance of VIX Options Pricing Models : Empirical Evidence Beyond Simulation

Several VIX options pricing models exist in the literature. However, no empirical study examines the pricing efficiency of these models using market-based data. We fill this gap by using three representative models to price VIX options. We find that (1) Black-Scholestype models by Whaley (1993) and Carr and Lee (2007) price deep-in-the-money options very well and price in-the-money options reasonable well in terms of both percentage and dollar errors; however, these models do not price out-of-the-money options very closely to their actual value. (2) Grunbichler and Longstaff (1996)’s mean-reverting model produces smaller dollar errors than the other models for out-of-the-money options, which is contrary to previous simulation-based findings, but their model generates high dollar and percentage errors for in-the-money options. (3) Carr and Lee’s (2007) model is inferior to Whaley (1993) for all options, and (4) no model generates low percentage errors for out-of-themoney options.
Experiments in Automatic Statistical Thesaurus Construction

A well constructed thesaurus has long been recognized as a valuable tool in the effective operation of an information retrieval system. This paper reports the results of experiments designed to determine the validity of an approach to the automatic construction of global thesauri (described originally by Crouch in  and  based on a clustering of the document collection. The authors validate the approach by showing that the use of thesauri generated by this method results in substantial improvements in retrieval effectiveness in four test collections. The term discrimination value theory, used in the thesaurus generation algorithm to determine a term's membership in a particular thesaurus class, is found not to be useful in distinguishing a “good” from an “indifferent” or “poor” thesaurus class). In conclusion, the authors suggest an alternate approach to automatic thesaurus construction which greatly simplifies the work of producing viable thesaurus classes. Experimental results show that the alternate approach described herein in some cases produces thesauri which are comparable in retrieval effectiveness to those produced by the first method at much lower cost.
Software Aging

Programs, like people, get old. We can 't prevent aging, but we can understand its causes, take steps to limits its effects, temporarily reverse some of the damage it has caused, and prepare for the day when the software is no longer viable. A sign that the Software Engineering profession has matured will be that we lose our preoccupation with the jirst release and focus on the long term health of our products. Researchers and practitioners must change their perception of the problems of software development. Only then will Sojlware Engineering deserve to be called Engineering. 1 What nonsense! I can easily imagine the reaction of some computer scientists to the title of this paper. " Software is a mathematical product; mathematics doesn't decay with time. If a theorem was correct 200 years ago, it will be correct tomorrow. If a program is correct today, it will be correct 100 years from now. If it is wrong 100 years from now, it must have been wrong when it was written. It makes no sense to talk about software aging. " Like many such statements, the imagined quote is true but not really relevant. Software products do exhibit a phenomenon that closely resembles human aging. Old software has begun to cripple its once-proud owners; many products are now viewed as a burdensome legacy from the past. A steadily increasing amount of effort is going into the support of these older products. Like human aging, software aging is inevitable, but like human aging, there are things that we can do to slow down the process and, sometimes, even reverse its effects. Software aging is not a new phenomenon, but it is gaining in significance because of the growing economic importance of software and the fact that increasingly , software is a major part of the " capital " of many high-tech firms. Man y old software products have become essential cogs in the machinery of our society. The aging of these products is impeding the further development of the systems that include them. The authors and owners of new software products often look at aging software with disdain. They believe that, if the product had been designed using to-day's techniques, it wouldn't be causing problems. Such remarks remind me of a young jogger scoffing at an 86 year old man (who, unknown to the jogger, was a champion swimmer into his 50's) …
A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution

We address the problem of image upscaling in the form of single image super-resolution based on a dictionary of lowand highresolution exemplars. Two recently proposed methods, Anchored Neighborhood Regression (ANR) and Simple Functions (SF), provide state-ofthe-art quality performance. Moreover, ANR is among the fastest known super-resolution methods. ANR learns sparse dictionaries and regressors anchored to the dictionary atoms. SF relies on clusters and corresponding learned functions. We propose A+, an improved variant of ANR, which combines the best qualities of ANR and SF. A+ builds on the features and anchored regressors from ANR but instead of learning the regressors on the dictionary it uses the full training material, similar to SF. We validate our method on standard images and compare with state-of-the-art methods. We obtain improved quality (i.e. 0.2-0.7dB PSNR better than ANR) and excellent time complexity, rendering A+ the most efficient dictionary-based super-resolution method to date.
Speech recognition with primarily temporal cues.

Nearly perfect speech recognition was observed under conditions of greatly reduced spectral information. Temporal envelopes of speech were extracted from broad frequency bands and were used to modulate noises of the same bandwidths. This manipulation preserved temporal envelope cues in each band but restricted the listener to severely degraded information on the distribution of spectral energy. The identification of consonants, vowels, and words in simple sentences improved markedly as the number of bands increased; high speech recognition performance was obtained with only three bands of modulated noise. Thus, the presentation of a dynamic temporal pattern in only a few broad spectral regions is sufficient for the recognition of speech.
Pattern classification with missing data: a review

Pattern classification has been successfully applied in many problem domains, such as biometric recognition, document classification or medical diagnosis. Missing or unknown data are a common drawback that pattern recognition techniques need to deal with when solving real-life classification tasks. Machine learning approaches and methods imported from statistical learning theory have been most intensively studied and used in this subject. The aim of this work is to analyze the missing data problem in pattern classification tasks, and to summarize and compare some of the well-known methods used for handling missing values.