nils-herrmann/clean_scirepeval huggingface dataset:
3D Model-Based Face Recognition in Video

Face recognition in video has gained wide attention due to its role in designing surveillance systems. One of the main advantages of video over still frames is that evidence accumulation over multiple frames can provide better face recognition performance. However, surveillance videos are generally of low resolution containing faces mostly in non-frontal poses. Consequently, face recognition in video poses serious challenges to state-of-the-art face recognition systems. Use of 3D face models has been suggested as a way to compensate for low resolution, poor contrast and non-frontal pose. We propose to overcome the pose problem by automatically (i) reconstructing a 3D face model from multiple non-frontal frames in a video, (ii) generating a frontal view from the derived 3D model, and (iii) using a commercial 2D face recognition engine to recognize the synthesized frontal view. A factorization-based structure from motion algorithm is used for 3D face reconstruction. The proposed scheme has been tested on CMU’s Face In Action (FIA) video database with 221 subjects. Experimental results show a 40% improvement in matching performance as a result of using the 3D models.
Amulet: An Energy-Efficient, Multi-Application Wearable Platform

Wearable technology enables a range of exciting new applications in health, commerce, and beyond. For many important applications, wearables must have battery life measured in weeks or months, not hours and days as in most current devices. Our vision of wearable platforms aims for long battery life but with the flexibility and security to support multiple applications. To achieve long battery life with a workload comprising apps from multiple developers, these platforms must have robust mechanisms for app isolation and developer tools for optimizing resource usage.
 We introduce the Amulet Platform for constrained wearable devices, which includes an ultra-low-power hardware architecture and a companion software framework, including a highly efficient event-driven programming model, low-power operating system, and developer tools for profiling ultra-low-power applications at compile time. We present the design and evaluation of our prototype Amulet hardware and software, and show how the framework enables developers to write energy-efficient applications. Our prototype has battery lifetime lasting weeks or even months, depending on the application, and our interactive resource-profiling tool predicts battery lifetime within 6-10% of the measured lifetime.
Real-Time Robust Heart Rate Estimation From Wrist-Type PPG Signals Using Multiple Reference Adaptive Noise Cancellation

Heart rate (HR) monitoring using photoplethysmographic (PPG) signals recorded from wearers’ wrist greatly facilitates design of wearable devices and maximizes user experience. However, placing PPG sensors in wrist causes much stronger and complicated motion artifacts (MA) due to loose interface between sensors and skin. Therefore, developing robust HR estimation algorithms for wrist-type PPG signals has significant commercial values. In this paper, we propose a robust HR estimation algorithm for wrist-type PPG signals using multiple reference adaptive noise cancellation (ANC) technique—termed here as “MURAD.” The main challenge of using ANC for MA reduction is to devise a qualified reference noise signal (RNS) to the adaptive filter. We propose a novel solution by using four RNSs, namely, the three-axis accelerometer data and the difference signal between the two PPG signals. For each RNS, we get a different version of the cleaned PPG signal. Then, a set of probable HR values is estimated using all of the cleaned PPG signals, and then, the value that is closest to the estimated HR of the previous time window is chosen to be the HR estimate of the current window. Then, some peak verification techniques are employed to ensure accurate HR estimations. The proposed technique gives lower average absolute error compared to state-of-the art methods. So, MURAD method provides a promising solution to the challenge of HR monitoring using PPG in wearable devices during severe MA conditions.
A Survey of Passive Image Tampering Detection

Digital images can be easily tampered with image editing tools. The detection of tampering operations is of great importance. Passive digital image tampering detection aims at verifying the authenticity of digital images without any a prior knowledge on the original images. There are various methods proposed in this filed in recent years. In this paper, we present an overview of these methods in three levels, that is low level, middle level, and high level in semantic sense. The main ideas of the proposed approaches at each level are described in detail, and some comments are given.
Flow based botnet detection through semi-supervised active learning

In a variety of Network-based Intrusion Detection System (NIDS) applications, one desires to detect groups of unknown attack (e.g., botnet) packet-flows, with a group potentially manifesting its atypicality (relative to a known reference “normal”/null model) on a low-dimensional subset of the full measured set of features used by the IDS. What makes this anomaly detection problem quite challenging is that it is a priori unknown which (possibly sparse) subset of features jointly characterizes a particular application, especially one that has not been seen before, which thus represents an unknown behavioral class (zero-day threat). Moreover, nowadays botnets have become evasive, evolving their behavior to avoid signature-based IDSes. In this work, we apply a novel active learning (AL) framework for botnet detection, facilitating detection of unknown botnets (assuming no ground truth examples of same). We propose a new anomaly-based feature set that captures the informative features and exploits the sequence of packet directions in a given flow. Experiments on real world network traffic data, including several common Zeus botnet instances, demonstrate the advantage of our proposed features and AL system.
Spectral efficient protocols for half-duplex fading relay channels

We study two-hop communication protocols where one or several relay terminals assist in the communication between two or more terminals. All terminals operate in half-duplex mode, hence the transmission of one information symbol from the source terminal to the destination terminal occupies two channel uses. This leads to a loss in spectral efficiency due to the pre-log factor one-half in corresponding capacity expressions. We propose two new half-duplex relaying protocols that avoid the pre-log factor one-half. Firstly, we consider a relaying protocol where a bidirectional connection between two terminals is established via one amplify-and-forward (AF) or decode-and-forward (DF) relay (two-way relaying). We also extend this protocol to a multi-user scenario, where multiple terminals communicate with multiple partner terminals via several orthogonalize-and-forward (OF) relay terminals, i.e., the relays orthogonalize the different two-way transmissions by a distributed zero-forcing algorithm. Secondly, we propose a relaying protocol where two relays, either AF or DF, alternately forward messages from a source terminal to a destination terminal (two-path relaying). It is shown that both protocols recover a significant portion of the half-duplex loss
Maximum Mean Discrepancy for Class Ratio Estimation: Convergence Bounds and Kernel Selection

First, we theoretically analyze the MMD-based estimates. Our analysis establishes that, under some mild conditions, the estimate is statistically consistent. More importantly, it provides an upper bound on the error in the estimate in terms of intuitive geometric quantities like class separation and data spread. Next, we use the insights obtained from the theoretical analysis, to propose a novel convex formulation that automatically learns the kernel to be employed in the MMD-based estimation. We design an efficient cutting plane algorithm for solving this formulation. Finally, we empirically compare our estimator with several existing methods, and show significantly improved performance under varying datasets, class ratios, and training sizes.
Optimizing restoration with segment routing

Segment routing is a new proposed routing mechanism for simplified and flexible path control in IP/MPLS networks. It builds on existing network routing and connection management protocols and one of its important features is the automatic rerouting of connections upon failure. Re-routing can be done with available restoration mechanisms including IGP-based rerouting and fast reroute with loop-free alternates. This is particularly attractive for use in Software Defined Networks (SDN) because the central controller need only be involved at connection set-up time and failures are handled automatically in a distributed manner. A significant challenge in restoration optimization in segment routed networks is the centralized determination of connections primary paths so as to enable the best sharing of restoration bandwidth over non-simultaneous network failures. We formulate this problem as a linear programming problem and develop an efficient primal-dual algorithm for the solution. We also develop a simple randomized rounding scheme for cases when there are additional constraints on segment routing. We demonstrate the significant capacity benefits achievable from this optimized restoration with segment routing.
Body temperature measurement for remote health monitoring system

Remote health monitoring system has been an interesting topic recently among medical practitioners, engineers as well as IT professionals. However, the application of remote health monitoring system where doctor's can monitor patients' vital signs via web is practically new in Malaysia and other countries. Remote health monitoring system is beneficial to the patients and society where the implementation of such system will save hospital bill, waiting time and reduce traffics in the hospital. The objective of this project is to design and develop body temperature measurement device that can be observe by the doctor in real time as well as history data via internet with an alarm/indication in case of abnormalities. In the proposed health monitoring system, heart rate and body temperature wireless sensors were developed, however this paper only focus on body temperature wireless monitoring system. The temperature sensors will send the readings to a microcontroller using Xbee wireless communication. To send the real-time data to health monitoring database, wireless local area network (WLAN) has been used. Arduino with Ethernet shield based on IEEE 802.11 standard has been used for this purpose. Test results from a group of voluntary shows the real-time temperature reading successfully monitored locally (at home) and remotely (at doctor's computer) and the readings are comparable to commercial thermometer.
Real-time 6-DOF monocular visual SLAM in a large-scale environment

Real-time approach for monocular visual simultaneous localization and mapping (SLAM) within a large-scale environment is proposed. From a monocular video sequence, the proposed method continuously computes the current 6-DOF camera pose and 3D landmarks position. The proposed method successfully builds consistent maps from challenging outdoor sequences using a monocular camera as the only sensor, while existing approaches have utilized additional structural information such as camera height from the ground. By using a binary descriptor and metric-topological mapping, the system demonstrates real-time performance on a large-scale outdoor environment without utilizing GPUs or reducing input image size. The effectiveness of the proposed method is demonstrated on various challenging video sequences including the KITTI dataset and indoor video captured on a micro aerial vehicle.
45nm low power CMOS logic compatible embedded STT MRAM utilizing a reverse-connection 1T/1MTJ cell

This paper reports a 45nm spin-transfer-torque (STT) MRAM embedded into a standard CMOS logic platform that employs low-power (LP) transistors and Cu/low-k BEOL. We believe that this is the first-ever demonstration of embedded STT MRAM that is fully compatible with the 45nm logic technology. To ensure the switching margin, a novel Ȝreverse-connectionȝ 1T/1MT cell has been developed with a cell size of 0.1026 µm2. This cell is utilized to build embedded memory macros up to 32 Mbits in density. Device attributes and design windows have been examined by considering PVT variations to secure operating margins. Promising early reliability data on endurance, read disturb, and thermal stability have been obtained.
Privacy-Enhanced Web Personalization

Consumer studies demonstrate that online users value personalized content. At the same time, providing personalization on websites seems quite profitable for web vendors. This win-win situation is however marred by privacy concerns since personalizing people's interaction entails gathering considerable amounts of data about them. As numerous recent surveys have consistently demonstrated, computer users are very concerned about their privacy on the Internet. Moreover, the collection of personal data is also subject to legal regulations in many countries and states. Both user concerns and privacy regulations impact frequently used personalization methods. This article analyzes the tension between personalization and privacy, and presents approaches to reconcile the both.
HSI-Based Color Error-Aware Subpixel Rendering Technique

In this letter, a new subpixel rendering algorithm using the hue-saturation-intensity (HSI)-based color error analysis is proposed for matrix display devices. The proposed method converts an RGB input image into a HSI image, which is a color space based on human color perception, in order to analyze the sampling errors that are caused by subpixel rendering. Then, it computes the difference between the directionally displaced pixels and selects the direction with the minimum difference. Finally, the proposed method adaptively assigns weights to current and neighboring subpixels based on the directional difference. In experiments for various types of test images, the proposed method enhanced the average luminance sharpness by up to 0.224 and the average chrominance blending by up to 7.311 dB, as compared to the benchmark methods.
A Closed-Form, Consistent and Robust Solution to Uncalibrated Photometric Stereo Via Local Diffuse Reflectance Maxima

Images of an object under different illumination are known to provide strong cues about the object surface. A mathematical formalization of how to recover the normal map of such a surface leads to the so-called uncalibrated photometric stereo problem. In the simplest instance, this problem can be reduced to the task of identifying only three parameters: the so-called generalized bas-relief (GBR) ambiguity. The challenge is to find additional general assumptions about the object, that identify these parameters uniquely. Current approaches are not consistent, i.e., they provide different solutions when run multiple times on the same data. To address this limitation, we propose exploiting local diffuse reflectance (LDR) maxima, i.e., points in the scene where the normal vector is parallel to the illumination direction . We demonstrate several noteworthy properties of these maxima: a closed-form solution, computational efficiency and GBR consistency. An LDR maximum yields a simple closed-form solution corresponding to a semi-circle in the GBR parameters space (see Fig. 2); because as few as two diffuse maxima in different images identify a unique solution, the identification of the GBR parameters can be achieved very efficiently; finally, the algorithm is consistent as it always returns the same solution given the same data. Our algorithm is also remarkably robust: It can obtain an accurate estimate of the GBR parameters even with extremely high levels of outliers in the detected maxima (up to 80 % of the observations). The method is validated on real data and achieves state-of-the-art results.
An architecture methodology for secure video conferencing

This paper describes how to enhance the security of VoIP applications using hardware security features on computing platforms such as notebooks, tablets and smartphones. Specifically we explain how to develop such applications using the protection offered by a processor based security technology, which provides the ability for software developers to maintain control of the security by creating trusted domains within applications. Using this processor security technology, sensitive code and data can be hosted without the risk of being observed or modified by malware present in other parts of the system. The resulting VoIP applications would better meet the strong security needs of corporate and government sectors for real time digital information sharing. We include results of a research project sponsored by the United States Department of Homeland Security and the United States Air Force Academyi where the team studied ways to enhance the security of a video conferencing application and implemented an experimental Video Chat application using security technologies provided by the processor and media processing hardware.
A Model Seeker: Extracting Global Constraint Models from Positive Examples

Sequence Generator Projection Constraint Conjunction 1 scheme(612,34,18,34,1) id alldifferent*18 2 scheme(612,34,18,2,2) id alldifferent*153 3 ) id alldifferent*34 4 scheme(612,34,18,1,18) absolute value symmetric alldifferent([1..18])*34 5 scheme(612,34,18,17,1) absolute value alldifferent*36 6 repart(612,34,18,34,9) id sum ctr(0)*306 7 repart(612,34,18,34,9) id twin*1 8 repart(612,34,18,34,9) id elements([i,-i ])*1 9 first(9,[1,3,5,7,9,11,13,15,17]) id strictly increasing*1 10 vector(612) id global cardinality([-18.. -1-17,0-0,1..18-17])*1 11 repart(612,34,18,34,9) id sum powers5 ctr(0)*306 12 repart(612,34,18,34,9) id sum cubes ctr(0)*306 13 repart(612,34,18,34,3) sign global cardinality([-1-3,0-0,1-3])*102 14 scheme(612,34,18,34,1) sign global cardinality([-1-17,0-0,1-17])*18 15 repart(612,34,18,17,9) sign global cardinality([-1-2,0-0,1-2])*153 16 repart(612,34,18,2,9) sign global cardinality([-1-17,0-0,1-17])*18 17 ) sign global cardinality([-1-9,0-0,1-9])*34 18 repart(612,34,18,34,9) sign sum ctr(0)*306 19 repart(612,34,18,34,9) sign twin*1 20 repart(612,34,18,34,9) absolute value twin*1 21 repart(612,34,18,34,9) sign elements([i,-i ])*1 22 scheme(612,34,18,34,1) sign among seq(3,[-1])*18 23 repart(612,34,18,34,9) absolute value elements([i,i ])*1 24 first(9,[1,3,5,7,9,11,13,15,17]) absolute value strictly increasing*1 25 first(6,[1,4,7,10,13,16]) absolute value strictly increasing*1 26 scheme(612,34,18,34,1) absolute value nvalue(17)*18 Selected Example Results
Bubble-flux: precise online QoS management for increased utilization in warehouse scale computers

Ensuring the quality of service (QoS) for latency-sensitive applications while allowing co-locations of multiple applications on servers is critical for improving server utilization and reducing cost in modern warehouse-scale computers (WSCs). Recent work relies on static profiling to precisely predict the QoS degradation that results from performance interference among co-running applications to increase the number of "safe" co-locations. However, these static profiling techniques have several critical limitations: 1) a priori knowledge of all workloads is required for profiling, 2) it is difficult for the prediction to capture or adapt to phase or load changes of applications, and 3) the prediction technique is limited to only two co-running applications.
 To address all of these limitations, we present Bubble-Flux, an integrated dynamic interference measurement and online QoS management mechanism to provide accurate QoS control and maximize server utilization. Bubble-Flux uses a Dynamic Bubble to probe servers in real time to measure the instantaneous pressure on the shared hardware resources and precisely predict how the QoS of a latency-sensitive job will be affected by potential co-runners. Once "safe" batch jobs are selected and mapped to a server, Bubble-Flux uses an Online Flux Engine to continuously monitor the QoS of the latency-sensitive application and control the execution of batch jobs to adapt to dynamic input, phase, and load changes to deliver satisfactory QoS. Batch applications remain in a state of flux throughout execution. Our results show that the utilization improvement achieved by Bubble-Flux is up to 2.2x better than the prior static approach.
Robust Hyperspectral Classification Using Relevance Vector Machine

The curse of dimensionality is the main reason for the computational complexity and the Hughes phenomenon in supervised hyperspectral classification. Previous studies seldom consider in a simultaneous fashion the real situation of insufficiency of available training samples, particularly for small land covers that often contain the key information of the scene, and the problem of complexity. In this paper, the capabilities of a feature reduction technique used for discrimination are combined with the advantages of a Bayesian learning-based probabilistic sparse kernel model, the relevance vector machine (RVM), to develop a new supervised classification method. In the proposed method, the hyperdimensional data are first transformed to a lower dimensionality feature space using the feature reduction technique to maximize separability between classes. The transformed data are then processed by a multiclass RVM classifier based on the parallel architecture and one-against-one strategy. To verify the effectiveness of the method, experiments were carried out on real hyperspectral data. The results are compared with the most efficient supervised classification techniques such as the support vector machine using appropriate performance indicators. The results show that the proposed method performs better than the other approaches particularly for small and scattered landcover classes which are harder to be precisely classified. In addition, this method has the advantages of low computational complexity and robustness to the Hughes phenomenon.
What do donations buy ? A model of philanthropy based on prestige and warm glow *

Charities publicize the donations they receive, generally according to dollar categories rather than the exact amount. Donors in turn tend to give the minimum amount necessary to get into a category. These facts suggest that donors have a taste for having their donations made public. This paper models the effects of such a taste for ‘‘prestige’’ on the behavior of donors and charities. I show how a taste for prestige means that charities can increase donations by using categories. The paper also discusses the effect of a taste for prestige on competition between charities.  1998 Elsevier
A Cloud-based Intrusion Detection Service framework

Intrusion Detection System (IDS) have become increasingly popular over the past years as an important network security technology to detect cyber attacks in a wide variety of network communication. IDS monitors' network or host system activities by collecting network information, and analyze this information for malicious activities. Cloud computing, with the concept of Software as a Service (SaaS) presents an exciting benefit when it enables providers to rent their services to users in perform complex tasks over the Internet. In addition, Cloud based services reduce a cost in investing new infrastructure, training new personnel, or licensing new software. In this paper, we introduce a novel framework based on Cloud computing called Cloud-based Intrusion Detection Service (CBIDS). This model enables the identification of malicious activities from different points of network and overcome the deficiency of classical intrusion detection. CBIDS can be implemented to detect variety of attacks in private and public Clouds.