nils-herrmann/clean_scirepeval huggingface dataset:
Combing Context and Commonsense Knowledge Through Neural Networks for Solving Winograd Schema Problems

Solving Winograd Schema Problems Quan Liu†, Hui Jiang‡, Zhen-Hua Ling†, Xiaodan Zhu, Si Wei§, Yu Hu†§ † National Engineering Laboratory for Speech and Language Information Processing University of Science and Technology of China, Hefei, Anhui, China ‡ Department of Electrical Engineering and Computer Science, York University, Canada ` National Research Council Canada, Ottawa, Canada § iFLYTEK Research, Hefei, China emails: quanliu@mail.ustc.edu.cn, hj@cse.yorku.ca, zhling@ustc.edu.cn, zhu2048@gmail.com siwei@iflytek.com, yuhu@iflytek.com
Applications of big data to smart cities

Many governments are considering adopting the smart city concept in their cities and implementing big data applications that support smart city components to reach the required level of sustainability and improve the living standards. Smart cities utilize multiple technologies to improve the performance of health, transportation, energy, education, and water services leading to higher levels of comfort of their citizens. This involves reducing costs and resource consumption in addition to more effectively and actively engaging with their citizens. One of the recent technologies that has a huge potential to enhance smart city services is big data analytics. As digitization has become an integral part of everyday life, data collection has resulted in the accumulation of huge amounts of data that can be used in various beneficial application domains. Effective analysis and utilization of big data is a key factor for success in many business and service domains, including the smart city domain. This paper reviews the applications of big data to support smart cities. It discusses and compares different definitions of the smart city and big data and explores the opportunities, challenges and benefits of incorporating big data applications for smart cities. In addition it attempts to identify the requirements that support the implementation of big data applications for smart city services. The review reveals that several opportunities are available for utilizing big data in smart cities; however, there are still many issues and challenges to be addressed to achieve better utilization of this technology.
Review of CMOS image sensors

The role of CMOS Image Sensors since their birth around the 1960s, has been changing a lot. Unlike the past, current CMOS Image Sensors are becoming competitive with regard to Charged Couple Device (CCD) technology. They offer many advantages with respect to CCD, such as lower power consumption, lower voltage operation, on-chip functionality and lower cost. Nevertheless, they are still too noisy and less sensitive than CCDs. Noise and sensitivity are the key-factors to compete with industrial and scientific CCDs. It must be pointed out also that there are several kinds of CMOS Image sensors, each of them to satisfy the huge demand in different areas, such as Digital photography, industrial vision, medical and space applications, electrostatic sensing, automotive, instrumentation and 3D vision systems. In the wake of that, a lot of research has been carried out, focusing on problems to be solved such as sensitivity, noise, power consumption, voltage operation, speed imaging and dynamic range. In this paper, CMOS Image Sensors are reviewed, providing information on the latest advances achieved, their applications, the new challenges and their limitations. In conclusion, the State-of-the-art of CMOS Image Sensors. q 2005
A FVF LDO regulator with damping-factor-control frequency compensation for SOC application

An output-capacitorless low-dropout regulator (OCL-LDO), which is based on flipped-voltage-follower (FVF) using damping-factor-control (DFC) frequency compensation for SOC application, is presented in this paper. The proposed LDO with 1.2V supply, 100mA load current was designed by SMIC 0.13um standard CMOS process. Simulation results have shown that the LDO can be stable for a load capacitance ranging from 0 to 80pF. The line regulation and load regulation are 3.3mV/V and 62uV/mA, respectively, and the quiescent current consumption is only 27uA. Under maximum load current changes, the overshoots and undershoots are less than 90mV and 140mV, and the recovery time is about 2.5us.
Metaheuristic Scheduling for Cloud: A Survey

Cloud computing has become an increasingly important research topic given the strong evolution and migration of many network services to such computational environment. The problem that arises is related with efficiency management and utilization of the large amounts of computing resources. This paper begins with a brief retrospect of traditional scheduling, followed by a detailed review of metaheuristic algorithms for solving the scheduling problems by placing them in a unified framework. Armed with these two technologies, this paper surveys the most recent literature about metaheuristic scheduling solutions for cloud. In addition to applications using metaheuristics, some important issues and open questions are presented for the reference of future researches on scheduling for cloud.
High-Performance Design of Hadoop RPC with RDMA over InfiniBand

Hadoop RPC is the basic communication mechanism in the Hadoop ecosystem. It is used with other Hadoop components like MapReduce, HDFS, and HBase in real world data-centers, e.g. Facebook and Yahoo!. However, the current Hadoop RPC design is built on Java sockets interface, which limits its potential performance. The High Performance Computing community has exploited high throughput and low latency networks such as InfiniBand for many years. In this paper, we first analyze the performance of current Hadoop RPC design by unearthing buffer management and communication bottlenecks, that are not apparent on the slower speed networks. Then we propose a novel design (RPCoIB) of Hadoop RPC with RDMA over InfiniBand networks. RPCoIB provides a JVM-bypassed buffer management scheme and utilizes message size locality to avoid multiple memory allocations and copies in data serialization and deserialization. Our performance evaluations reveal that the basic ping-pong latencies for varied data sizes are reduced by 42%-49% and 46%-50% compared with 10GigE and IPoIB QDR (32Gbps), respectively, while the RPCoIB design also improves the peak throughput by 82% and 64% compared with 10GigE and IPoIB. As compared to default Hadoop over IPoIB QDR, our RPCoIB design improves the performance of the Sort benchmark on 64 compute nodes by 15%, while it improves the performance of CloudBurst application by 10%. We also present thorough, integrated evaluations of our RPCoIB design with other research directions, which optimize HDFS and HBase using RDMA over InfiniBand. Compared with their best performance, we observe 10% improvement for HDFS-IB, and 24% improvement for HBase-IB. To the best of our knowledge, this is the first such design of the Hadoop RPC system over high performance networks such as InfiniBand.
Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review

Digital pathology and microscopy image analysis is widely used for comprehensive studies of cell morphology or tissue structure. Manual assessment is labor intensive and prone to interobserver variations. Computer-aided methods, which can significantly improve the objectivity and reproducibility, have attracted a great deal of interest in recent literature. Among the pipeline of building a computer-aided diagnosis system, nucleus or cell detection and segmentation play a very important role to describe the molecular morphological information. In the past few decades, many efforts have been devoted to automated nucleus/cell detection and segmentation. In this review, we provide a comprehensive summary of the recent state-of-the-art nucleus/cell segmentation approaches on different types of microscopy images including bright-field, phase-contrast, differential interference contrast, fluorescence, and electron microscopies. In addition, we discuss the challenges for the current methods and the potential future work of nucleus/cell detection and segmentation.
19.7 A 79GHz binary phase-modulated continuous-wave radar transceiver with TX-to-RX spillover cancellation in 28nm CMOS

The demand for inexpensive and ubiquitous accurate motion-detection sensors for road safety, smart homes and robotics justifies the interest in single-chip mm-Wave radars: a high carrier frequency allows for a high angular resolution in a compact multi-antenna system and a wide bandwidth allows fora high depth resolution. With the objective of single-chip radar systems, CMOS is the natural candidate to replace SiGe as a leading technology [1-6].
Show, Attend and Translate: Unsupervised Image Translation with Self-Regularization and Attention

Image translation between two domains is a class of problems aiming to learn mapping from an input image in the source domain to an output image in the target domain. It has been applied to numerous domains, such as data augmentation, domain adaptation and unsupervised training. When paired training data is not accessible, image translation becomes an ill-posed problem. We constrain the problem with the assumption that the translated image needs to be perceptually similar to the original image and also appears to be drawn from the new domain, and propose a simple yet effective image translation model consisting of a single generator trained with a self-regularization term and an adversarial term. We further notice that existing image translation techniques [56, 28] are agnostic to the subjects of interest and often introduce unwanted changes or artifacts to the input. Thus we propose to add an attention module to predict an attention map to guide the image translation process. The module learns to attend to key parts of the image while keeping everything else unaltered, essentially avoiding undesired artifacts or changes. The predicted attention map also opens door to applications such as unsupervised segmentation and saliency detection. Extensive experiments and evaluations show that our model while being simpler, achieves significantly better performance than existing image translation methods.
A Small-Size Dual-Feed Broadband Circularly Polarized U-Slot Patch Antenna

A small-size broadband circularly polarized U-slot patch antenna with dual-feed is proposed. By introducing an additional feeding probe near the vertical slot of the conventional singly fed square U-slot patch antenna printed on a high-permittivity substrate, two series resonances in close proximity are excited. The two resonant frequencies are found to be independent of the orientation of U-slot with respect to the patch, and broadband circular polarization is achieved by introducing a nonquadrature phase difference between two feeding ports. Experimental results show that the overlapped bandwidth of VSWR ≤ 1.5 and AR ≤ 3 dB is over 20% with a small overall size of 0.33λ<sub>0</sub> × 0.33λ<sub>0</sub> × 0.068λ<sub>0</sub>, where λ<sub>0</sub> is the free-space wavelength at the center frequency within the operating band.
Self-Organizing Scheme Based on NFV and SDN Architecture for Future Heterogeneous Networks

Future cellular networks will be of high capacity and heterogeneity. The structure and architecture will require high efficiency and scalability in network operation and management. In this paper, we address main requirements and challenges of future cellular networks and introduce network function virtualisation (NFV) with software defined networking (SDN) to realize the self-organizing (SO) scheme. NFV integrates the hardware appliances together in industry standard servers. And SDN performs as core controller of the network. The proposed SO scheme is based on soft fractional frequency reuse (SFFR) framework. The scheme takes different traffic demands into consideration and allocates the power adaptively. Finally the system is proved to be more scalable, energy-saving, and intelligent.
A survey on intelligent routing protocols in wireless sensor networks

This paper surveys intelligent routing protocols which contribute to the optimization of network lifetime in wireless sensor networks (WSNs). Different from other surveys on routing protocols for WSNs, this paper first puts forward new ideas on the definition of network lifetime. Then, with a view to prolonging network lifetime, it discusses the routing protocols based on such intelligent algorithms as reinforcement learning (RL), ant colony optimization (ACO), fuzzy logic (FL), genetic algorithm (GA), and neural networks (NNs). Intelligent algorithms provide adaptive mechanisms that exhibit intelligent behavior in complex and dynamic environments like WSNs. Inspired by such an idea, some intelligent routing protocols have recently been designed for WSNs. Under each category, it discusses the representative routing algorithms and further analyzes the performance of network lifetime defined in three aspects. This paper intends to give assistance in the optimization of network lifetime in WSNs, together with offering a guide for the collaboration between WSNs and computational intelligence (CI). & 2013
ESC: Dataset for Environmental Sound Classification

One of the obstacles in research activities concentrating on environmental sound classification is the scarcity of suitable and publicly available datasets. This paper tries to address that issue by presenting a new annotated collection of 2000 short clips comprising 50 classes of various common sound events, and an abundant unified compilation of 250000 unlabeled auditory excerpts extracted from recordings available through the Freesound project. The paper also provides an evaluation of human accuracy in classifying environmental sounds and compares it to the performance of selected baseline classifiers using features derived from mel-frequency cepstral coefficients and zero-crossing rate.
Mobile game-based learning in secondary education: engagement, motivation and learning in a mobile city game

Using mobile games in education combines situated and active learning with fun in a potentially excellent manner. The effects of a mobile city game called Frequency 1550, which was developed by The Waag Society to help pupils in their first year of secondary education playfully acquire historical knowledge of medieval Amsterdam, were investigated in terms of pupil engagement in the game, historical knowledge, and motivation for History in general and the topic of the Middle Ages in particular. A quasi-experimental design was used with 458 pupils from 20 classes from five schools. The pupils in 10 of the classes played the mobile history game whereas the pupils in the other 10 classes received a regular, project-based lesson series. The results showed those pupils who played the game to be engaged and to gain significantly more knowledge about medieval Amsterdam than those pupils who received regular projectbased instruction. No significant differences were found between the two groups with respect to motivation for History or the MiddleAges. The impact of location-based technology and gamebased learning on pupil knowledge and motivation are discussed along with suggestions for future research.
Development of Embedded STT-MRAM for Mobile System-on-Chips

Spin-transfer-torque magnetoresistive random access memory (STT-MRAM) has been considered a promising technology for future mobile system-on-chip memories due to negligible static leakage power, high-speed read/write operations, and unlimited read/write endurance. In this paper, we address key challenges and recent advances for enabling embedded STT-MRAM on a deeply scaled CMOS logic platform.
HTTP/2-Based Methods to Improve the Live Experience of Adaptive Streaming

HTTP Adaptive Streaming (HAS) is today the number one video technology for over-the-top video distribution. In HAS, video content is temporally divided into multiple segments and encoded at different quality levels. A client selects and retrieves per segment the most suited quality version to create a seamless playout. Despite the ability of HAS to deal with changing network conditions, HAS-based live streaming often suffers from freezes in the playout due to buffer under-run, low average quality, large camera-to-display delay, and large initial/channel-change delay. Recently, IETF has standardized HTTP/2, a new version of the HTTP protocol that provides new features for reducing the page load time in Web browsing. In this paper, we present ten novel HTTP/2-based methods to improve the quality of experience of HAS. Our main contribution is the design and evaluation of a push-based approach for live streaming in which super-short segments are pushed from server to client as soon as they become available. We show that with an RTT of 300 ms, this approach can reduce the average server-to-display delay by 90.1% and the average start-up delay by 40.1%.
NeuroStylist: Neural Compatibility Modeling for Clothing Matching

Nowadays, as a beauty-enhancing product, clothing plays an important role in human's social life. In fact, the key to a proper outfit usually lies in the harmonious clothing matching. Nevertheless, not everyone is good at clothing matching. Fortunately, with the proliferation of fashion-oriented online communities, fashion experts can publicly share their fashion tips by showcasing their outfit compositions, where each fashion item (e.g., a top or bottom) usually has an image and context metadata (e.g., title and category). Such rich fashion data offer us a new opportunity to investigate the code in clothing matching. However, challenges co-exist with opportunities. The first challenge lies in the complicated factors, such as color, material and shape, that affect the compatibility of fashion items. Second, as each fashion item involves multiple modalities (i.e., image and text), how to cope with the heterogeneous multi-modal data also poses a great challenge. Third, our pilot study shows that the composition relation between fashion items is rather sparse, which makes traditional matrix factorization methods not applicable. Towards this end, in this work, we propose a content-based neural scheme to model the compatibility between fashion items based on the Bayesian personalized ranking (BPR) framework. The scheme is able to jointly model the coherent relation between modalities of items and their implicit matching preference. Experiments verify the effectiveness of our scheme, and we deliver deep insights that can benefit future research.
Human photoreceptor topography.

We have measured the spatial density of cones and rods in eight whole-mounted human retinas, obtained from seven individuals between 27 and 44 years of age, and constructed maps of photoreceptor density and between-individual variability. The average human retina contains 4.6 million cones (4.08-5.29 million). Peak foveal cone density averages 199,000 cones/mm2 and is highly variable between individuals (100,000-324,000 cones/mm2). The point of highest density may be found in an area as large as 0.032 deg2. Cone density falls steeply with increasing eccentricity and is an order of magnitude lower 1 mm away from the foveal center. Superimposed on this gradient is a streak of high cone density along the horizontal meridian. At equivalent eccentricities, cone density is 40-45% higher in nasal compared to temporal retina and slightly higher in midperipheral inferior compared to superior retina. Cone density also increases slightly in far nasal retina. The average human retina contains 92 million rods (77.9-107.3 million). In the fovea, the average horizontal diameter of the rod-free zone is 0.350 mm (1.25 degrees). Foveal rod density increases most rapidly superiorly and least rapidly nasally. The highest rod densities are located along an elliptical ring at the eccentricity of the optic disk and extending into nasal retina with the point of highest density typically in superior retina (5/6 eyes). Rod densities decrease by 15-25% where the ring crosses the horizontal meridian. Rod density declines slowly from the rod ring to the far periphery and is highest in nasal and superior retina. Individual variability in photoreceptor density differs with retinal region and is similar for both cones and rods. Variability is highest near the fovea, reaches a minimum in the midperiphery, and then increases with eccentricity to the ora serrata. The total number of foveal cones is similar for eyes with widely varying peak cone density, consistent with the idea that the variability reflects differences in the lateral migration of photoreceptors during development. Two fellow eyes had cone and rod numbers within 8% and similar but not identical photoreceptor topography.
Human physique and sexual attractiveness: sexual preferences of men and women in Bakossiland, Cameroon.

Men and women living in a rural community in Bakossiland, Cameroon were asked to rate the attractiveness of images of male or female figures manipulated to vary in somatotype, waist-to-hip ratio (WHR), secondary sexual traits, and other features. In Study 1, women rated mesomorphic (muscular) and average male somatotypes as most attractive, followed by ectomorphic (slim) and endomorphic (heavily built) figures. In Study 2, amount and distribution of masculine trunk (chest and abdominal) hair was altered progressively in a series of front-posed male figures. A significant preference for one of these images was found, but the most hirsute figure was not judged as most attractive. Study 3 assessed attractiveness of front-posed male figures which varied only in length of the non-erect penis. Extremes of penile size (smallest and largest of five images) were rated as significantly less attractive than three intermediate sizes. In Study 4, Bakossi men rated the attractiveness of back-posed female images varying in WHR (from 0.5-1.0). The 0.8 WHR figure was rated markedly more attractive than others. Study 5 rated the attractiveness of female skin color. Men expressed no consistent preference for either lighter or darker female figures. These results are the first of their kind reported for a Central African community and provide a useful cross-cultural perspective to published accounts on sexual selection, human morphology and attractiveness in the U.S., Europe, and elsewhere.
Avoiding Discrimination through Causal Reasoning

Recent work on fairness in machine learning has focused on various statistical discrimination criteria and how they trade off. Most of these criteria are observational: They depend only on the joint distribution of predictor, protected attribute, features, and outcome. While convenient to work with, observational criteria have severe inherent limitations that prevent them from resolving matters of fairness conclusively. Going beyond observational criteria, we frame the problem of discrimination based on protected attributes in the language of causal reasoning. This viewpoint shifts attention from “What is the right fairness criterion?” to “What do we want to assume about our model of the causal data generating process?” Through the lens of causality, we make several contributions. First, we crisply articulate why and when observational criteria fail, thus formalizing what was before a matter of opinion. Second, our approach exposes previously ignored subtleties and why they are fundamental to the problem. Finally, we put forward natural causal non-discrimination criteria and develop algorithms that satisfy them.