nils-herrmann/clean_scirepeval huggingface dataset:
High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks

Recent advances in deep learning for object recognition in natural images has prompted a surge of interest in applying a similar set of techniques to medical images. Most of the initial attempts largely focused on replacing the input to such a deep convolutional neural network from a natural image to a medical image. This, however, does not take into consideration the fundamental differences between these two types of data. More specifically, detection or recognition of an anomaly in medical images depends significantly on fine details, unlike object recognition in natural images where coarser, more global structures matter more. This difference makes it inadequate to use the existing deep convolutional neural networks architectures, which were developed for natural images, because they rely on heavily downsampling an image to a much lower resolution to reduce the memory requirements. This hides details necessary to make accurate predictions for medical images. Furthermore, a single exam in medical imaging often comes with a set of different views which must be seamlessly fused in order to reach a correct conclusion. In our work, we propose to use a multi-view deep convolutional neural network that handles a set of more than one highresolution medical image. We evaluate this network on large-scale mammography-based breast cancer screening (BI-RADS prediction) using 103 thousand images. We focus on investigating the impact of training set sizes and image sizes on the prediction accuracy. Our results highlight that performance clearly increases with the size of training set, and that the best performance can only be achieved using the images in the original resolution. This suggests the future direction of medical imaging research using deep neural networks is to utilize as much data as possible with the least amount of potentially harmful preprocessing.
Foreground-Adaptive Background Subtraction

Background subtraction is a powerful mechanism for detecting change in a sequence of images that finds many applications. The most successful background subtraction methods apply probabilistic models to background intensities evolving in time; nonparametric and mixture-of-Gaussians models are but two examples. The main difficulty in designing a robust background subtraction algorithm is the selection of a detection threshold. In this paper, we adapt this threshold to varying video statistics by means of two statistical models. In addition to a nonparametric background model, we introduce a foreground model based on small spatial neighborhood to improve discrimination sensitivity. We also apply a Markov model to change labels to improve spatial coherence of the detections. The proposed methodology is applicable to other background models as well.
Translating Neuralese

Several approaches have recently been proposed for learning decentralized deep multiagent policies that coordinate via a differentiable communication channel. While these policies are effective for many tasks, interpretation of their induced communication strategies has remained a challenge. Here we propose to interpret agents’ messages by translating them. Unlike in typical machine translation problems, we have no parallel data to learn from. Instead we develop a translation model based on the insight that agent messages and natural language strings mean the same thing if they induce the same belief about the world in a listener. We present theoretical guarantees and empirical evidence that our approach preserves both the semantics and pragmatics of messages by ensuring that players communicating through a translation layer do not suffer a substantial loss in reward relative to players with a common
Game design principles for engaging cooperative play: core mechanics and interfaces for non-mimetic simulation of fire emergency response

Core mechanics are the activities that players repeat to play a game, the central aspects of play constrained by rules. Interfaces mediate play experiences, impacting engagement with core mechanics. We design core mechanics for gathering, integrating, and sharing information, based on team coordination practices of fire emergency responders. We connect these mechanics with interfaces that impact player engagement. Mechanics and interfaces combine into a non-mimetic simulation game, which eschews fire and smoke, in favor of re-creating information flows and team structures.
 We describe the iteration of mechanics and interface components as shaped by practice, pilot games, participatory redesign sessions, and long-term user studies. The result is integrated core mechanics that we develop from work practice and interface components that support engagement with them. From this data, we construct game design principles for engaging cooperative play: information distribution, modulating visibility, providing the right information in the right time, making predictable, and understandable representations for shared mental models.
Data Security and Privacy in the IoT

The Internet of Things (IoT) paradigm refers to the network of physical objects or "things" embedded with electronics, software, sensors, and connectivity to enable objects to exchange data with servers, centralized systems, and/or other connected devices based on a variety of communication infrastructures. IoT makes it possible to sense and control objects creating opportunities for more direct integration between the physical world and computer-based systems. IoT will usher automation in a large number of application domains, ranging from manufacturing and energy management (e.g. SmartGrid), to healthcare management and urban life (e.g. SmartCity). However, because of its finegrained, continuous and pervasive data acquisition and control capabilities, IoT raises concerns about the security and privacy of data. Deploying existing data security solutions to IoT is not straightforward because of device heterogeneity, highly dynamic and possibly unprotected environments, and large scale. In this talk, after outlining key challenges in data security and privacy, we present initial approaches to securing IoT data, including efficient and scalable encryption protocols, software protection techniques for small devices, and fine-grained data packet loss analysis for sensor networks.
Identifying Factors That Encourage and Hinder Knowledge Sharing in a Longstanding Online Community of Practice.

Despite the strong interests among practitioners, there is a knowledge gap with regard to online communities of practice. This study examines knowledge sharing among critical-care and advanced-practice nurses, who are engaged in a longstanding online community of practice. Data were collected about members’ online knowledge contribution as well as motivations for sharing or not sharing knowledge with others. In sum, 27 interviews with members and content analysis of approximately 400 messages were conducted. Data analysis showed that the most common types of knowledge shared were “Institutional Practice” and “Personal Opinion”. Five factors were found that helped motivate knowledge sharing: (a) self-selection type of membership, (b) desire to improve the nursing profession, (c) reciprocity, (d) a non-competitive environment, and (e) the role of the listserv moderator. Regarding barriers for knowledge sharing, four were found: (a) no new or additional knowledge to add, (b) unfamiliarity with subject, (c) lack of time, and (d) technology. These results will be informative to researchers and practitioners of online communities of practice.
A 3.1 mW 8b 1.2 GS/s Single-Channel Asynchronous SAR ADC With Alternate Comparators for Enhanced Speed in 32 nm Digital SOI CMOS

An 8b 1.2 GS/s single-channel Successive Approximation Register (SAR) ADC is implemented in 32 nm CMOS, achieving 39.3 dB SNDR and a Figure-of-Merit (FoM) of 34 fJ per conversion step. High-speed operation is achieved by converting each sample with two alternate comparators clocked asynchronously and a redundant capacitive DAC with constant common mode to improve the accuracy of the comparator. A low-power, clocked capacitive reference buffer is used, and fractional reference voltages are provided to reduce the number of unit capacitors in the capacitive DAC (CDAC). The ADC stacks the CDAC with the reference capacitor to reduce the area and enhance the settling speed. Background calibration of comparator offset is implemented. The ADC consumes 3.1 mW from a 1 V supply and occupies 0.0015 mm2.
1W < 0.9dB IL DC-20GHz T/R switch design with 45nm SOI process

In this paper, we discuss a DC-20GHz single-pole double-throw (SPDT) transmit/receive switch (T/R switch) design in 45nm SOI process. This circuit is dedicated to fully integrated CMOS RF front end modules for X/Ku band satellite communication applications. The switch exhibits a measured insertion loss of 0.59dB, return loss of 23dB, and isolation of 17dB at 14GHz. The input 1dB compression point is 31.5dBm, and one-tone IIP3 is 63.8dBm. This state of the art performance is comparable or even better than existing commercial GaAs SPDT in this frequency range. The core area is only 90um × 100um, which is very helpful for low cost large element phase array designs.
A Wireless Sensor Network Deployment for Rural and Forest Fire Detection and Verification

Forest and rural fires are one of the main causes of environmental degradation in Mediterranean countries. Existing fire detection systems only focus on detection, but not on the verification of the fire. However, almost all of them are just simulations, and very few implementations can be found. Besides, the systems in the literature lack scalability. In this paper we show all the steps followed to perform the design, research and development of a wireless multisensor network which mixes sensors with IP cameras in a wireless network in order to detect and verify fire in rural and forest areas of Spain. We have studied how many cameras, sensors and access points are needed to cover a rural or forest area, and the scalability of the system. We have developed a multisensor and when it detects a fire, it sends a sensor alarm through the wireless network to a central server. The central server selects the closest wireless cameras to the multisensor, based on a software application, which are rotated to the sensor that raised the alarm, and sends them a message in order to receive real-time images from the zone. The camera lets the fire fighters corroborate the existence of a fire and avoid false alarms. In this paper, we show the test performance given by a test bench formed by four wireless IP cameras in several situations and the energy consumed when they are transmitting. Moreover, we study the energy consumed by each device when the system is set up. The wireless sensor network could be connected to Internet through a gateway and the images of the cameras could be seen from any part of the world.
MOCHA: Morphable Locality and Compression Aware Architecture for Convolutional Neural Networks

Today, machine learning based on neural networks has become mainstream, in many application domains. A small subset of machine learning algorithms, called Convolutional Neural Networks (CNN), are considered as state-ofthe- art for many applications (e.g. video/audio classification). The main challenge in implementing the CNNs, in embedded systems, is their large computation, memory, and bandwidth requirements. To meet these demands, dedicated hardware accelerators have been proposed. Since memory is the major cost in CNNs, recent accelerators focus on reducing the memory accesses. In particular, they exploit data locality using either tiling, layer merging or intra/inter feature map parallelism to reduce the memory footprint. However, they lack the flexibility to interleave or cascade these optimizations. Moreover, most of the existing accelerators do not exploit compression that can simultaneously reduce memory requirements, increase the throughput, and enhance the energy efficiency. To tackle these limitations, we present a flexible accelerator called MOCHA. MOCHA has three features that differentiate it from the state-of-the-art: (i) the ability to compress input/ kernels, (ii) the flexibility to interleave various optimizations, and (iii) intelligence to automatically interleave and cascade the optimizations, depending on the dimension of a specific CNN layer and available resources. Post layout Synthesis results reveal that MOCHA provides up to 63% higher energy efficiency, up to 42% higher throughput, and up to 30% less storage, compared to the next best accelerator, at the cost of 26-35% additional area.
Bayesian Opponent Exploitation in Imperfect-Information Games

Two fundamental problems in computational game theory are computing a Nash equilibrium and learning to exploit opponents given observations of their play (opponent exploitation). The latter is perhaps even more important than the former: Nash equilibrium does not have a compelling theoretical justification in game classes other than two-player zero-sum, and for all games one can potentially do better by exploiting perceived weaknesses of the opponent than by following a static equilibrium strategy throughout the match. The natural setting for opponent exploitation is the Bayesian setting where we have a prior model that is integrated with observations to create a posterior opponent model that we respond to. The most natural, and a well-studied prior distribution is the Dirichlet distribution. An exact polynomial-time algorithm is known for best-responding to the posterior distribution for an opponent assuming a Dirichlet prior with multinomial sampling in normal-form games; however, for imperfect-information games the best known algorithm is based on approximating an infinite integral without theoretical guarantees. We present the first exact algorithm for a natural class of imperfect-information games. We demonstrate that our algorithm runs quickly in practice and outperforms the best prior approaches. We also present an algorithm for a uniform prior.
A simplified design of quasi-Yagi antennas using the new microstrip-to-CPS transitions

A new, systematic, simplified design procedure for quasi-Yagi antennas is presented. The design is based on the simple impedance matching among antenna components: i.e., transition, feed, and antenna. This new antenna design is possible due to the newly developed ultra-wideband transition. As design examples, wideband quasi- Yagi antennas are successfully designed and implemented in Ku- and Ka-bands with frequency bandwidths of 53.2% and 29.1%, and antenna gains of 4-5 dBi and 5.2-5.8 dBi, respectively. The design method can be applied to other balanced antennas and their arrays.
A Resource-Limited Hardware Accelerator for Convolutional Neural Networks in Embedded Vision Applications

In this brief, we introduce an architecture for accelerating convolution stages in convolutional neural networks (CNNs) implemented in embedded vision systems. The purpose of the architecture is to exploit the inherent parallelism in CNNs to reduce the required bandwidth, resource usage, and power consumption of highly computationally complex convolution operations as required by real-time embedded applications. We also implement the proposed architecture using fixed-point arithmetic on a ZC706 evaluation board that features a Xilinx Zynq-7000 system on-chip, where the embedded ARM processor with high clocking speed is used as the main controller to increase the flexibility and speed. The proposed architecture runs under a frequency of 150 MHz, which leads to 19.2 Giga multiply accumulation operations per second while consuming less than 10 W in power. This is done using only 391 DSP48 modules, which shows significant utilization improvement compared to the state-of-the-art architectures.
Collaborative Topic Regression with Social Regularization for Tag Recommendation

Recently, tag recommendation (TR) has become a very hot research topic in data mining and related areas. However, neither co-occurrence based methods which only use the item-tag matrix nor content based methods which only use the item content information can achieve satisfactory performance in real TR applications. Hence, how to effectively combine the item-tag matrix, item content information, and other auxiliary information into the same recommendation framework is the key challenge for TR. In this paper, we first adapt the collaborative topic regression (CTR) model, which has been successfully applied for article recommendation, to combine both item-tag matrix and item content information for TR. Furthermore, by extending CTR we propose a novel hierarchical Bayesian model, called CTR with social regularization (CTR-SR), to seamlessly integrate the item-tag matrix, item content information, and social networks between items into the same principled model. Experiments on real data demonstrate the effectiveness of our proposed models.
Fun with Asynchronous Vision Sensors and Processing

This paper provides a personal perspective on our group’s efforts in building event-based vision sensors, algorithms, and applications over the period 2002-2012. Some recent advances from other groups are also briefly described. When Mahowald and Mead built the first silicon retina with asynchronous digital output around 1992 , conventional CMOS active pixel sensors (APS) were still research chips. It required the investment by industry of about a billion dollars to bring CMOS APS to high volume production. So it is no surprise that while the imager community has been consumed by the megapixel race to make nice photos, cameras that mimic more closely how the eye works have taken a long time to come to a useful form. These “silicon retinas” are much more complex at the pixel level than APS cameras and they pay the price in terms of fill factor and pixel size; machine vision cameras with capability of synchronous global electronic shutter are about 5um. Silicon retina pixels are roughly 10 times the area of a machine vision camera pixel. So why are silicon retinas still interesting? Mostly because of the high cost at the system level of processing the highly redundant data from conventional cameras, and the fixed latencies imposed by the frame intervals. High performance activity driven event-based sensors could greatly benefit applications in real time robotics, where just as in nature, latency and power are very important [2,5,9,10].
A Systematic Review of Information Security Frameworks in the Internet of Things (IoT)

By 2020, it is estimated that the number of connected devices is expected to grow exponentially to 50 billion. Internet of things has gained extensive attention, the deployment of sensors, actuators are increasing at a rapid pace around the world. There is tremendous scope for more streamlined living through an increase of smart services, but this coincides with an increase in security and privacy concerns. There is a need to perform a systematic review of Information security governance frameworks in the Internet of things (IoT).  The aim of this paper to evaluate systematic review of information security management frameworks which are related to the Internet of things (IoT). It will also discuss different information security frameworks that cover IoT models and deployments across different verticals. These frameworks are classified according to the area of the framework, the security executives and senior management of any enterprise that plans to start using smart services needs to define a clear governance strategy concerning the security of their assets, this system review will help them to make a better decision for their investment for secure IoT deployments.  A set of standard criteria has been established to analyze which security framework will be the best fit among these classified security structures in particularly for Internet of Things (IoT). The first step to evaluate security framework by using standard criteria methodology is to identify resources, the security framework for IoT is selected to be assessed according to CCS. The second step is to develop a set of Security Targets (ST). The ST is the set of criteria to apply for the target of evaluation (TOE). The third step is data extraction, fourth step data synthesis, and final step is to write-up study as a report.  After reviewing four information security risk frameworks, this study makes some suggestions related to information security risk governance in Internet of Things (IoT). The organizations that have decided to move to smart devices have to define the benefits and risks and deployment processes to manage security risk. The information security risk policies should comply with an organization's IT policies and standards to protect the confidentiality, integrity and availability of information security. The study observes some of the main processes that are needed to manage security risks. Moreover, the paper also drew attention on some suggestions that may assist companies which are associated with the information security framework in Internet of things (IoT).
An Analysis of Temporal-Difference Learning with Function Approximation 1

We discuss the temporal-difference learning algorithm, as applied to approximating the cost-to-go function of an infinite-horizon discounted Markov chain, using a function approximator involving linear combinations of fixed basis functions. The algorithm we analyze performs on-line updating of a parameter vector during a single endless trajectory of an ergodic Markov chain with a finite or infinite state space. We present a proof of convergence (with probability 1), a characterization of the limit of convergence, and a bound on the resulting approximation error. In addition to proving new and stronger results than those previously available, our analysis is based on a new line of reasoning that provides new intuition about the dynamics of temporal-difference learning. Finally, we prove that on-line updates, based on entire trajectories of the Markov chain, are in a certain sense necessary for convergence. This fact reconciles positive and negative results that have been discussed in the literature, regarding the soundness of temporal-difference learning.
Traffic sign recognition using MSER and Random Forests

We present a novel system for the real-time detection and recognition of traffic symbols. Candidate regions are detected as Maximally Stable Extremal Regions (MSER) from which Histogram of Oriented Gradients (HOG) features are derived, and recognition is then performed using Random Forests. The training data comprises a set of synthetically generated images, created by applying randomised distortions to graphical template images taken from an on-line database. This approach eliminates the need for real training images and makes it easy to include all possible signs. Our proposed method can operate under a range of weather conditions at an average speed of 20 fps and is accurate even at high vehicle speeds. Comprehensive comparative results are provided to illustrate the performance of the system.
THE THEORY OF PROJECT MANAGEMENT : EXPLANATION TO NOVEL METHODS

In a series of prior papers, the authors have explored the theoretical foundation of project management. In this paper, this theoretical foundation is consolidated and used for explaining the novel features of two project management methods, which radically deviate from the conventional doctrine of project management: Last Planner and Scrum. Both methods have emerged since mid-nineties as practical responses to the failure of conventional project management methods, Scrum in the field of software projects, Last Planner in the field of construction projects. It is shown that both methods reject the underlying theoretical foundation of conventional project management and instead subscribe, implicitly or explicitly, to alternative theories, which better match the situation in question.