nils-herrmann/clean_scirepeval huggingface dataset:
C5: Cross-Cores Cache Covert Channel

Cloud computing relies on hypervisors to isolate virtual machines running on shared hardware. Since perfect isolation is difficult to achieve, sharing hardware induces threats. Covert channels were demonstrated to violate isolation and, typically, allow data exfiltration. Several covert channels have been proposed that rely on the processor’s cache. However, these covert channels are either slow or impractical due to the addressing uncertainty. This uncertainty exists in particular in virtualized environments and with recent L3 caches which are using complex addressing. Using shared memory would elude addressing uncertainty, but shared memory is not available in most practical setups. We build C5, a covert channel that tackles addressing uncertainty without requiring any shared memory, making the covert channel fast and practical. We are able to transfer messages on modern hardware across any cores of the same processor. The covert channel targets the last level cache that is shared across all cores. It exploits the inclusive feature of caches, allowing a core to evict lines in the private first level cache of another core. We experimentally evaluate the covert channel in native and virtualized environments. In particular, we successfully establish a covert channel between virtual machines running on different cores. We measure a bitrate of 1291bps for a native setup, and 751bps for a virtualized setup. This is one order of magnitude above previous cache-based covert channels in the same setup.
Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks

In this paper we propose a deep learning solution to age estimation from a single face image without the use of facial landmarks and introduce the IMDB-WIKI dataset, the largest public dataset of face images with age and gender labels. If the real age estimation research spans over decades, the study of apparent age estimation or the age as perceived by other humans from a face image is a recent endeavor. We tackle both tasks with our convolutional neural networks (CNNs) of VGG-16 architecture which are pre-trained on ImageNet for image classification. We pose the age estimation problem as a deep classification problem followed by a softmax expected value refinement. The key factors of our solution are: deep learned models from large data, robust face alignment, and expected value formulation for age regression. We validate our methods on standard benchmarks and achieve state-of-the-art results for both real and apparent age estimation.
Support vector machines under adversarial label contamination

Machine learning algorithms are increasingly being applied in security-related tasks such as spam and malware detection, although their security properties against deliberate attacks have not yet been widely understood. Intelligent and adaptive attackers may indeed exploit specific vulnerabilities exposed by machine learning techniques to violate system security. Being robust to adversarial data manipulation is thus an important, additional requirement for machine learning algorithms to successfully operate in adversarial settings. In this work, we evaluate the security of Support Vector Machines (SVMs) to well-crafted, adversarial label noise attacks. In particular, we consider an attacker that aims to maximize the SVM’s classification error by flipping a number of labels in the training data. We formalize a corresponding optimal attack strategy, and solve it by means of heuristic approaches to keep the computational complexity tractable. We report an extensive experimental analysis on the effectiveness of the considered attacks against linear and non-linear SVMs, both on synthetic and real-world datasets. We finally argue that our approach can also provide useful insights for developing more secure SVM learning algorithms, and also novel techniques in a number of related research areas, such as semi-supervised and active learning.
THERMAL MODELING OF ELECTRON BEAM ADDITIVE MANUFACTURING PROCESS – POWDER SINTERING EFFECTS

In recently developed Additive Manufacturing (AM) technologies, high-energy sources have been used to fabricate metallic parts, in a layer by layer fashion, by sintering and/or melting metal powders. In particular, Electron Beam Additive Manufacturing (EBAM) utilizes a high-energy electron beam to melt and fuse metal powders to build solid parts. EBAM is one of a few AM technologies capable of making full-density metallic parts and has dramatically extended their applications. Heat transport is the center of the process physics in EBAM, involving a high-intensity, localized moving heat source and rapid self-cooling, and is critically correlated to the part quality and process efficiency. In this study, a finite element model was developed to simulate the transient heat transfer in a part during EBAM subject to a moving heat source with a Gaussian volumetric distribution. The developed model was first examined against literature data. The model was then used to evaluate the powder porosity and the beam size effects on the high temperature penetration volume (melt pool size). The major findings include the following. (1) For the powder layer case, the melt pool size is larger with a higher maximum temperature compared to a solid layer, indicating the importance of considering powders for the model accuracy. (2) With the increase of the porosity, temperatures are higher in the melt pool and the molten pool sizes increase in the depth, but decrease along the beam moving direction. Furthermore, both the heating and cooling rates are higher for a lower porosity level. (3) A larger electron-beam diameter will reduce the maximum temperature in the melt pool and temperature gradients could be much smaller, giving a lower cooling rate. However, for the tested electron beampower level, the beam diameter around 0.4 mm could be an adequate choice.  In recent years, Additive Manufacturing (AM) using commercially available atomized metallic or alloyed powders with a high-energy beam heat source has been developed to fabricate complex shapes and multifunctional, custom designed components. Various types of mechanical components can be built with such rapid fabrication technologies through computer controlled machines . Titanium (Ti) and titanium alloys are materials with outstanding mechanical properties such as low density, high strengths, good chemical resistance and excellent biocompatibility. The combination of these properties in a special structure has many potential applications in the area of medical, aerospace, aeronautics and automotive systems. Generally, fabrications of these materials with conventional techniques are difficult due to their high melting point and extreme chemical affinity to atmospheric gas, especially at elevated temperatures .
Using galvanic skin response for cognitive load measurement in arithmetic and reading tasks

Galvanic Skin Response (GSR) has recently attracted researchers' attention as a prospective physiological indicator of cognitive load and emotions. However, it has commonly been investigated through single or few measures and in one experimental scenario. In this research, aiming to perform a comprehensive study, we have assessed GSR data captured from two different experiments, one including text reading tasks and the other using arithmetic tasks, each imposing multiple cognitive load levels. We have examined temporal and spectral features of GSR against different task difficulty levels. ANOVA test was applied for the statistical evaluation. Obtained results show the strong significance of the explored features, especially the spectral ones, in cognitive workload measurement in the two studied experiments.
Neural Clinical Paraphrase Generation with Attention

Paraphrase generation is important in various applications such as search, summarization, and question answering due to its ability to generate textual alternatives while keeping the overall meaning intact. Clinical paraphrase generation is especially vital in building patient-centric clinical decision support (CDS) applications where users are able to understand complex clinical jargons via easily comprehensible alternative paraphrases. This paper presents Neural Clinical Paraphrase Generation (NCPG), a novel approach that casts the task as a monolingual neural machine translation (NMT) problem. We propose an end-to-end neural network built on an attention-based bidirectional Recurrent Neural Network (RNN) architecture with an encoderdecoder framework to perform the task. Conventional bilingual NMT models mostly rely on word-level modeling and are often limited by out-of-vocabulary (OOV) issues. In contrast, we represent the source and target paraphrase pairs as character sequences to address this limitation. To the best of our knowledge, this is the first work that uses attention-based RNNs for clinical paraphrase generation and also proposes an end-to-end character-level modeling for this task. Extensive experiments on a large curated clinical paraphrase corpus show that the attention-based NCPG models achieve improvements of up to 5.2 BLEU points and 0.5 METEOR points over a non-attention based strong baseline for word-level modeling, whereas further gains of up to 6.1 BLEU points and 1.3 METEOR points are obtained by the character-level NCPG models over their word-level counterparts. Overall, our models demonstrate comparable performance relative to the state-of-the-art phrase-based non-neural models.
Broadband and compact impedance transformers for microwave circuits

Most microwave circuits and antennas use impedance transformers. In a conventional power amplifier, large number of inductors and capacitor or transmission line sections are use to realize an impedance transformer. This article presents an asymmetric broadside-coupled microstrip TLT (transmission line transformer) on the GaAs substrate. This matching network can be easily implemented using any monolithic microwave integrated circuit (MMIC) process supporting multilevel metallization
THE GEOMETRY OF OPTIMAL TRANSPORTATION

A classical problem of transporting mass due to Monge and Kantorovich is solved. Given measures μ and ν on Rd, we find the measure-preserving map y(x) between them with minimal cost — where cost is measured against h(x − y) with h strictly convex, or a strictly concave function of |x − y|. This map is unique: it is characterized by the formula y(x) = x − (∇h)−1(∇ψ(x)) and geometrical restrictions on ψ. Connections with mathematical economics, numerical computations, and the Monge-Ampère equation are sketched. ∗Both authors gratefully acknowledge the support provided by postdoctoral fellowships: WG at the Mathematical Sciences Research Institute, Berkeley, CA 94720, and RJM from the Natural Sciences and Engineering Research Council of Canada.
Reinforcement Learning of Cooperative Persuasive Dialogue Policies using Framing

In this paper, we apply reinforcement learning for automatically learning cooperative persuasive dialogue system policies using framing, the use of emotionally charged statements common in persuasive dialogue between humans. In order to apply reinforcement learning, we describe a method to construct user simulators and reward functions specifically tailored to persuasive dialogue based on a corpus of persuasive dialogues between human interlocutors. Then, we evaluate the learned policy and the effect of framing through experiments both with a user simulator and with real users. The experimental evaluation indicates that applying reinforcement learning is effective for construction of cooperative persuasive dialogue systems which use framing.
New target for rice lodging resistance and its effect in a typhoon

We demonstrated the new target for lodging resistance in rice (Oryza sativa L.) by the analysis of physiological function of a locus for lodging resistance in a typhoon (lrt5) with the near isogenic line under rice “Koshihikari” genetic background (tentatively named S1). The higher lodging resistance of S1 was observed during a typhoon in September 2004 (28 days after heading), when most other plants in “Koshihikari” became lodged. Visual observations showed that bending of the upper stems triggered lodging during the typhoon; the upper stem of “Koshihikari” buckled completely, whereas that of S1 remained straight. In addition to the strong rain and winds during the typhoon, the weight of the buckled upper plant parts increased the pressure on adjacent plants and caused a domino effect in “Koshihikari”. Young’s modulus, an indicator of the rigidity of the culm, was significantly higher in S1 than in “Koshihikari”. In the upper culm, the starch content in S1 was 4.8 times the value in “Koshihikari”, and senescence was delayed in the upper leaves of S1. These results suggest that the rigidity of the upper culm by the higher starch content (as a result of delayed senescence in the upper leaves) may be responsible for the higher lodging resistance during a typhoon in rice.
Multi-user interaction using handheld projectors

Recent research on handheld projector interaction has expanded the display and interaction space of handheld devices by projecting information onto the physical environment around the user, but has mainly focused on single-user scenarios. We extend this prior single-user research to co-located multi-user interaction using multiple handheld projectors. We present a set of interaction techniques for supporting co-located collaboration with multiple handheld projectors, and discuss application scenarios enabled by them.
Diversifying search results

We study the problem of answering ambiguous web queries in a setting where there exists a taxonomy of information, and that both queries and documents may belong to more than one category according to this taxonomy. We present a systematic approach to diversifying results that aims to minimize the risk of dissatisfaction of the average user. We propose an algorithm that well approximates this objective in general, and is provably optimal for a natural special case. Furthermore, we generalize several classical IR metrics, including NDCG, MRR, and MAP, to explicitly account for the value of diversification. We demonstrate empirically that our algorithm scores higher in these generalized metrics compared to results produced by commercial search engines.
Fingerprint Recognition and Matching using Matlab

--------------------------------------------------------ABSTRACT----------------------------------------------------------Fingerprints are considered to be the best and fastest method for biometric identification. They are secure to use, unique for every person and do not change in one’s lifetime. Human fingerprints are rich in details called minutiae, which can be used as identification marks for security purposes. This paper is a study and implementation of a fingerprint recognition using image processing tool in Matlab. The approach mainly involves extraction of minutiae points from sample fingerprint images and then performing matching based on the number of minutiae pairing among two fingerprints in question. For each task, some classical and up-todate methods in literatures are analyzed. Based on the analysis, an integrated solution for fingerprint recognition is developed for demonstration. It finally generates a percentage score which tells whether two fingerprints match or not.
ECG signal analysis and arrhythmia detection on IoT wearable medical devices

Healthcare is one of the most rapidly expanding application areas of the Internet of Things (IoT) technology. IoT devices can be used to enable remote health monitoring of patients with chronic diseases such as cardiovascular diseases (CVD). In this paper we develop an algorithm for ECG analysis and classification for heartbeat diagnosis, and implement it on an IoT-based embedded platform. This algorithm is our proposal for a wearable ECG diagnosis device, suitable for 24-hour continuous monitoring of the patient. We use Discrete Wavelet Transform (DWT) for the ECG analysis, and a Support Vector Machine (SVM) classifier. The best classification accuracy achieved is 98.9%, for a feature vector of size 18, and 2493 support vectors. Different implementations of the algorithm on the Galileo board, help demonstrate that the computational cost is such, that the ECG analysis and classification can be performed in real-time.
Dynamic Bin Packing for On-Demand Cloud Resource Allocation

Dynamic Bin Packing (DBP) is a variant of classical bin packing, which assumes that items may arrive and depart at arbitrary times. Existing works on DBP generally aim to minimize the maximum number of bins ever used in the packing. In this paper, we consider a new version of the DBP problem, namely, the MinTotal DBP problem which targets at minimizing the total cost of the bins used overtime. It is motivated by the request dispatching problem arising from cloud gaming systems. We analyze the competitive ratios of the modified versions of the commonly used First Fit, Best Fit, and Any Fit packing (the family of packing algorithms that open a new bin only when no currently open bin can accommodate the item to be packed) algorithms for the MinTotal DBP problem. We show that the competitive ratio of Any Fit packing cannot be better than μ + 1, where μ is the ratio of the maximum item duration to the minimum item duration. The competitive ratio of Best Fit packing is not bounded for any given μ. For First Fit packing, if all the item sizes are smaller than 1/β of the bin capacity (β> 1 is a constant), the competitive ratio has an upper bound of β/β-1·μ+3β/β-1 + 1. For the general case, the competitive ratio of First Fit packing has an upper bound of 2μ + 7. We also propose a Hybrid First Fit packing algorithm that can achieve a competitive ratio no larger than 5/4 μ + 19/4 when μ is not known and can achieve a competitive ratio no larger than μ + 5 when μ is known.
Column-stores vs. row-stores: how different are they really?

There has been a significant amount of excitement and recent work on column-oriented database systems ("column-stores"). These database systems have been shown to perform more than an order of magnitude better than traditional row-oriented database systems ("row-stores") on analytical workloads such as those found in data warehouses, decision support, and business intelligence applications. The elevator pitch behind this performance difference is straightforward: column-stores are more I/O efficient for read-only queries since they only have to read from disk (or from memory) those attributes accessed by a query.
 This simplistic view leads to the assumption that one can obtain the performance benefits of a column-store using a row-store: either by vertically partitioning the schema, or by indexing every column so that columns can be accessed independently. In this paper, we demonstrate that this assumption is false. We compare the performance of a commercial row-store under a variety of different configurations with a column-store and show that the row-store performance is significantly slower on a recently proposed data warehouse benchmark. We then analyze the performance difference and show that there are some important differences between the two systems at the query executor level (in addition to the obvious differences at the storage layer level). Using the column-store, we then tease apart these differences, demonstrating the impact on performance of a variety of column-oriented query execution techniques, including vectorized query processing, compression, and a new join algorithm we introduce in this paper. We conclude that while it is not impossible for a row-store to achieve some of the performance advantages of a column-store, changes must be made to both the storage layer and the query executor to fully obtain the benefits of a column-oriented approach.
An Experimental Study of Graph Connectivity for Unsupervised Word Sense Disambiguation

Word sense disambiguation (WSD), the task of identifying the intended meanings (senses) of words in context, has been a long-standing research objective for natural language processing. In this paper, we are concerned with graph-based algorithms for large-scale WSD. Under this framework, finding the right sense for a given word amounts to identifying the most ¿important¿ node among the set of graph nodes representing its senses. We introduce a graph-based WSD algorithm which has few parameters and does not require sense-annotated data for training. Using this algorithm, we investigate several measures of graph connectivity with the aim of identifying those best suited for WSD. We also examine how the chosen lexicon and its connectivity influences WSD performance. We report results on standard data sets and show that our graph-based approach performs comparably to the state of the art.
Dry and Noncontact EEG Sensors for Mobile Brain–Computer Interfaces

Dry and noncontact electroencephalographic (EEG) electrodes, which do not require gel or even direct scalp coupling, have been considered as an enabler of practical, real-world, brain-computer interface (BCI) platforms. This study compares wet electrodes to dry and through hair, noncontact electrodes within a steady state visual evoked potential (SSVEP) BCI paradigm. The construction of a dry contact electrode, featuring fingered contact posts and active buffering circuitry is presented. Additionally, the development of a new, noncontact, capacitive electrode that utilizes a custom integrated, high-impedance analog front-end is introduced. Offline tests on 10 subjects characterize the signal quality from the different electrodes and demonstrate that acquisition of small amplitude, SSVEP signals is possible, even through hair using the new integrated noncontact sensor. Online BCI experiments demonstrate that the information transfer rate (ITR) with the dry electrodes is comparable to that of wet electrodes, completely without the need for gel or other conductive media. In addition, data from the noncontact electrode, operating on the top of hair, show a maximum ITR in excess of 19 bits/min at 100% accuracy (versus 29.2 bits/min for wet electrodes and 34.4 bits/min for dry electrodes), a level that has never been demonstrated before. The results of these experiments show that both dry and noncontact electrodes, with further development, may become a viable tool for both future mobile BCI and general EEG applications.
Linear Object Classes and Image Synthesis From a Single Example Image

The need to generate new views of a 3D object from a single real image arises in several fields, including graphics and°object recognition. While the traditional approach relies on the use of 3D models, we have recently introduced [11, 6, 5] techniques that are applicable under restricted conditions but simpler. The approach exploits image transformations that are specific to the relevant object class and learnable from example views of other "prototypical" objects of the same class. In this paper, we introduce such a new technique by extending the notion of linear class first proposed by Poggio and Vetter . For linear object classes it is shown that linear transformations can be learned exactly from a basis set of 2D prototypical views. We demonstrate the approach on artificial objects and then show preliminary evidence that the technique can effectively "rotate" high-resolution face images from a single 2D view.