Reviewerly/exHarmony huggingface dataset:
Performance-to-Power Ratio Aware Resource Consolidation Framework Based on Reinforcement Learning in Cloud Data Centers

Dynamic consolidation of virtual machines (VMs) is presented as a significant technique of energy conservation in cloud environments, which can eliminate the hotspot of overloaded hosts and switch the under loaded hosts to sleep mode through live migration of virtual machines. However, since the fact that migrating VM consumes a certain amount of extra resources, the process of reallocation can cause Service Level Agreement (SLA) violations. In this paper, a novel proactive framework which considers both predicted resource utilization and Performance-to-power Ratio (PPR) of heterogeneous hosts is proposed to perform dynamic VM consolidation to achieve balance of performance and energy. More precisely, a workload predictor is proposed based on the modified Weighted Moving Average (WMA) algorithm, representing the support for dynamic resource allocation; a cluster controller is proposed based on reinforcement learning for exploring the optimal matching relationship between resource requests and host at different PPR levels; a resource allocator is designed based on greedy strategy for achieving the trade-off between energy consumption and application performance across the cluster. Moreover, the framework is implemented based on distributed architecture and off-line learning pattern, which are able to not only scale up quickly but also improve the computing efficiency of the system. To validate the effectiveness of the proposed method, we have performed experimental evaluation on CloudSim with real-world workload traces of PlanetLab, and the simulation results demonstrate that it reduces the energy consumption up to 45.25% and effectively deals with high Quality of Service (QoS) requirements and heterogeneous distributed infrastructures in comparison with other competitive approaches.
Real-time Power Line Detection Network using Visible Light and Infrared Images

In the electrical industry, power line detection is an important task for safety of autonomous drone, which assists inspection of the electrical facilities. In this paper, we proposed real-time power line detection network, which is based on deep learning. Building on existing fast segmentation convolutional neural network (Fast-SCNN), which showed competitive performance with fast processing time for image segmentation, we modified its classifier part by inserting additional convolution layers and transposed convolution layer to improve the performance. Furthermore, unlike previous studies that utilized one-kind of sensor image, stacked four-channel image of visual light (VL) and contrast enhanced infrared (IR) were introduced as an input of our modified network. Experimental results on our model showed that the mean intersection over union of multi sensor image (VL+IR) was 6.7% value higher than the Fast-SCNN with single sensor image (VL) and real-time processing speed of 24 frames per second on the drone captured outdoor dataset.
NEEXP is Contained in MIP

We study multiprover interactive proof systems. The power of classical multiprover interactive proof systems, in which the provers do not share entanglement, was characterized in a famous work by Babai, Fortnow, and Lund (Computational Complexity 1991), whose main result was the equality MIP = NEXP. The power of quantum multiprover interactive proof systems, in which the provers are allowed to share entanglement, has proven to be much more difficult to characterize. The best known lower-bound on MIP* is NEXP ⊆ MIP* due to Ito and Vidick (FOCS 2012). As for upper bounds, MIP* could be as large as RE, the class of recursively enumerable languages. The main result of this work is the inclusion NEEXP = NTIME[2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2poly(n)</sup> ] ⊆ MIP*. This is an exponential improvement over the prior lower bound and shows that proof systems with entangled provers are at least exponentially more powerful than classical provers. In our protocol the verifier delegates a classical, exponentially large MIP protocol for NEEXP to two entangled provers: the provers obtain their exponentially large questions by measuring their shared state, and use a classical PCP to certify the correctness of their exponentially-long answers. For the soundness of our protocol, it is crucial that each player should not only sample its own question correctly but also avoid performing measurements that would reveal the other player's sampled question. We ensure this by commanding the players to perform a complementary measurement, relying on the Heisenberg uncertainty principle to prevent the forbidden measurements from being performed.
Novelty Detection and Online Learning for Chunk Data Streams

Datastream analysis aims at extracting discriminative information for classification from continuously incoming samples. It is extremely challenging to detect novel data while incrementally updating the model efficiently and stably, especially for high-dimensional and/or large-scale data streams. This paper proposes an efficient framework for novelty detection and incremental learning for unlabeled chunk data streams. First, an accurate factorization-free kernel discriminative analysis (FKDA-X) is put forward through solving a linear system in the kernel space. FKDA-X produces a Reproducing Kernel Hilbert Space (RKHS), in which unlabeled chunk data can be detected and classified by multiple known-classes in a single decision model with a deterministic classification boundary. Moreover, based on FKDA-X, two optimal methods FKDA-CX and FKDA-C are proposed. FKDA-CX uses the micro-cluster centers of original data as the input to achieve excellent performance in novelty detection. FKDA-C and incremental FKDA-C (IFKDA-C) using the class centers of original data as their input have extremely fast speed in online learning. Theoretical analysis and experimental validation on under-sampled and large-scale real-world datasets demonstrate that the proposed algorithms make it possible to learn unlabeled chunk data streams with significantly lower computational costs and comparable accuracies than the state-of-the-art approaches.
Blockchain Technology: Adoption Perspectives in Tourism

The aim of the paper is to ascertain whether the blockchain technology represents a functional tool for better governance of the tourism system. Innovation adoption has been widely debated among scholars in order to identify variables and models that boost adoption processes within touristic firms. Although the blockchain technology is at its first stage of development, thanks to instruments such as cryptocurrencies, smart contracts, and decentralized applications, it has already a significant impact on tourism business deals. The paper is based on selective literature on the topic and aims at providing a brief review of its progress. It also offers a number of suggestions for research directions yet to be adequately investigated. The innovative factor of the paper lies in offering a complete overview of the current extent of application of the theories of blockchain technology in tourist sector.
Diagnostics-Driven Nonstationary Emulators Using Kernel Mixtures

Weakly stationary Gaussian processes (GPs) are the principal tools in the statistical approaches to the design and analysis of computer experiments (or uncertainty quantification). Such processes are fitted to computer model output using a set of training runs to learn the parameters of the process covariance kernel. The stationarity assumption is often adequate, yet can lead to poor predictive performance when the model response exhibits nonstationarity, for example, if its smoothness varies across the input space. In this paper, we introduce a diagnostic-led approach to fitting nonstationary GP emulators by specifying finite mixtures of region-specific covariance kernels. Our method first fits a stationary GP and, if traditional diagnostics exhibit nonstationarity, those diagnostics are used to fit appropriate mixing functions for a covariance kernel mixture designed to capture the nonstationarity, ensuring an emulator that is continuous in parameter space and readily interpretable. We compare our approach to the principal nonstationary GP models in the literature and illustrate its performance on a number of idealized test cases and in an application to modeling the cloud parameterization of the French climate model.
Robust and Efficient Corner Detector Using Non-Corners Exclusion

Corner detection is a traditional type of feature point detection method. Among methods used, with its good accuracy and the properties of invariance for rotation, noise and illumination, the Harris corner detector is widely used in the fields of vision tasks and image processing. Although it possesses a good performance in detection quality, its application is limited due to its low detection efficiency. The efficiency is crucial in many applications because it determines whether the detector is suitable for real-time tasks. In this paper, a robust and efficient corner detector (RECD) improved from Harris corner detector is proposed. First, we borrowed the principle of the feature from accelerated segment test (FAST) algorithm for corner pre-detection, in order to rule out non-corners and retain many strong corners as real corners. Those uncertain corners are looked at as candidate corners. Second, the gradients are calculated in the same way as the original Harris detector for those candidate corners. Third, to reduce additional computation amount, only the corner response function (CRF) of the candidate corners is calculated. Finally, we replace the highly complex non-maximum suppression (NMS) by an improved NMS to obtain the resulting corners. Experiments demonstrate that RECD is more competitive than some popular corner detectors in detection quality and speed. The accuracy and robustness of our method is slightly better than the original Harris detector, and the detection time is only approximately 8.2% of its original value.
Resolving the shortest path problem using the haversine algorithm

Route search requires a useful and accurate algorithm to calculate the distance between two objects, especially on a round surface such as the earth. This algorithm provides a considerable circle distance between two locations on a non-flat surface. The distance of the two objects is determined based on longitude and latitude. Haversine is an algorithm that can determine the distance between two objects on the surface of a sphere. Haversine has now been developed by using a simple formula, which with computer calculations, can provide a very accurate level of precision between two points. Research with Haversine will be applied to determine the closest location. A graph is a diagram that contains specific information if interpreted correctly. The graph is used to describe various kinds of existing structures. The purpose is as a visualization of objects to make them easier to understand. The Haversine algorithm can determine the distance of two coordinates of the earth. By applying this method, the distance between the two coordinates can be determined.
Evaluation of orthogonal and biorthogonal wavelets for video steganography

In recent years, information security over the internet has gained enormous immersion due to a technological escalation in communication via digital media. Nowadays, videos are the most common form of media over the internet, and they have gained the attention of video steganography research community for the security of the communication. This paper implemented video steganography using a widely-known transform technique, discrete wavelet transform (DWT) which consists of different orthogonal and biorthogonal filters used for the decomposition of a frame/image. The aim of this paper is to provide a comparative evaluation for orthogonal and biorthogonal DWT filters with different matrices such as MSE, PSNR. The performance of the different filters has been evaluated both quantitatively and qualitatively to investigate the best among them. Also, the best of orthogonal and biorthogonal filters have been further evaluated with other metrics such as SSIM, BER and histogram analysis. Further, the embedding algorithm has been compared with the existing video steganography techniques. The experimental results illustrate that bior 6.8 (biorthogonal filter) outperforms the other orthogonal and biorthogonal filters in terms of visual distortion.
One-body entanglement as a quantum resource in fermionic systems

We show that one-body entanglement, which is a measure of the deviation of a pure fermionic state from a Slater determinant (SD) and is determined by the mixedness of the single-particle density matrix (SPDM), can be considered as a quantum resource. The associated theory has SDs and their convex hull as free states, and number conserving fermion linear optics operations (FLO), which include one-body unitary transformations and measurements of the occupancy of single-particle modes, as the basic free operations. We first provide a bipartitelike formulation of one-body entanglement, based on a Schmidt-like decomposition of a pure $N$-fermion state, from which the SPDM [together with the $(N\ensuremath{-}1)$-body density matrix] can be derived. It is then proved that under FLO operations the initial and postmeasurement SPDMs always satisfy a majorization relation, which ensures that these operations cannot increase, on average, the one-body entanglement. It is finally shown that this resource is consistent with a model of fermionic quantum computation which requires correlations beyond antisymmetrization. More general free measurements and the relation with mode entanglement are also discussed.
Verbal Explanations for Deep Reinforcement Learning Neural Networks with Attention on Extracted Features

In recent years, there has been increasing interest in transparency in Deep Neural Networks. Most of the works on transparency have been done for image classification. In this paper, we report on work of transparency in Deep Reinforcement Learning Networks (DRLNs). Such networks have been extremely successful in learning action control in Atari games. In this paper, we focus on generating verbal (natural language) descriptions and explanations of deep reinforcement learning policies. Successful generation of verbal explanations would allow better understanding by people (e.g., users, debuggers) of the inner workings of DRLNs which could ultimately increase trust in these systems. We present a generation model which consists of three parts: an encoder on feature extraction, an attention structure on selecting features from the output of the encoder, and a decoder on generating the explanation in natural language. Four variants of the attention structure full attention, global attention, adaptive attention and object attention - are designed and compared. The adaptive attention structure performs the best among all the variants, even though the object attention structure is given additional information on object locations. Additionally, our experiment results showed that the proposed encoder outperforms two baseline encoders (Resnet and VGG) on the capability of distinguishing the game state images.
xYOLO: A Model For Real-Time Object Detection In Humanoid Soccer On Low-End Hardware

With the emergence of onboard vision processing for areas such as the internet of things (IoT), edge computing and autonomous robots, there is increasing demand for computationally efficient convolutional neural network (CNN) models to perform real-time object detection on resource constraints hardware devices. Tiny-YOLO is generally considered as one of the faster object detectors for low-end devices and is the basis for our work. Our experiments on this network have shown that Tiny-YOLO can achieve 0.14 frames per second (FPS) on the Raspberry Pi 3 B, which is too slow for soccer playing autonomous humanoid robots detecting goal and ball objects. In this paper we propose an adaptation to the YOLO CNN model named xYOLO, that can achieve object detection at a speed of 9.66 FPS on the Raspberry Pi 3 B. This is achieved by trading an acceptable amount of accuracy, making the network approximately 70 times faster than Tiny-YOLO. Greater inference speed-ups were also achieved on a desktop CPU and GPU. Additionally we contribute an annotated Darknet dataset for goal and ball detection.
A simple saliency detection approach via automatic top-down feature fusion

It is widely accepted that the top sides of convolutional neural networks (CNNs) convey high-level semantic features, and the bottom sides contain low-level details. Therefore, most of recent salient object detection methods aim at designing effective fusion strategies for side-output features. Although significant progress has been achieved in this direction, the network architectures become more and more complex, which will make the future improvement difficult and heavily engineered. Moreover, the manually designed fusion strategies would be sub-optimal due to the large search space of possible solutions. To address above problems, we propose an Automatic Top-Down Fusion (ATDF) method, in which the global information at the top sides are flowed into bottom sides to guide the learning of low layers. We design a novel valve module and add it at each side to control the coarse semantic information flowed into a specific bottom side. Through these valve modules, each bottom side at the top-down pathway is expected to receive necessary top information. We also design a generator to improve the prediction capability of fused deep features for saliency detection. We perform extensive experiments to demonstrate that ATDF is simple yet effective and thus opens a new path for saliency detection.
A Novel Deep Progressive Image Compression Framework

In Internet applications, compressing the image without perceptually distinguishable distortions and loading the images without notable delays in the client end can significantly improve the user experience. Compressing the image at high bit rates can maintain the high quality of the decoded image but in cost of long transmitting and decoding time, resulting in bad user experience. The progressive coding scheme can resolve the conflict between the high quality requirement and the large loading delay. This paper proposes a novel efficient progressive image coding framework based on deep convolutional neural networks. The proposed framework is composed of a uniform encoder network and two progressive decoder networks. The encoder network decomposes the input image into two scales of representations, that can be transmitted and reconstructed progressively into a basic quality preview image and a high-quality image by two individual decoder networks respectively. All the networks are jointly learned when achieving the rate distortion optimization of both scales. Experiments results show that the proposed method has much better coding performance than the commercial codecs WebP and JPEG, which are commonly used in Internet applications. Meanwhile, the proposed codec consumes much less time to load the image compared with WebP.
Hardware-assisted Remote Runtime Attestation for Critical Embedded Systems

Remote attestation, as a challenge-response protocol, enables a trusted entity, called verifier, to ask for an untrusted device, called prover, to provide assurance about its internal integrity. Due to its strong guarantees, remote attestation is becoming increasingly popular for critical embedded systems which can be used for medical, military or industrial control purposes. Previous proposals, which used checksums on static code regions to assure the load-time integrity, miss the runtime attacks that affect only dynamic memory regions. To address these attacks, this paper proposes a new scheme that attests the runtime integrity according to the control and data features of the program. The runtime check can be performed in real time with the help of a novel hardware security module (HSM) design which is connected to the prover's system bus. Proposed HSM detects runtime issues by checking compliance of the bits seen on the address and data bus with the static model loaded into its memory. Our attestation scheme is capable of reporting sophisticated runtime attacks such as code-reuse and non-control data attacks.
Ensemble Approach for Natural Language Question Answering Problem

Machine comprehension, answering a question depending on a given context paragraph is a typical task of Natural Language Understanding. It requires to model complex dependencies existing between the question and the context paragraph. There are many neural network models attempting to solve the problem of question answering. One of the best models have been selected, studied and compared with each other. All the selected models are based on the neural attention mechanism concept. Additionally, studies on a SQuAD dataset were performed. The subsets of queries were extracted and then each model was analyzed how it deals with specific group of queries. The ensemble model based on Mnemonic Reader, BiDAF and QANet was created and tested on SQuAD dataset. It outperforms the best Mnemonic Reader model.
MRI denoising using BM3D equipped with noise invalidation denoising technique and VST for improved contrast

Denoising of medical scanned images such as X-ray, MRI etc. is important stage in the medical use. To remove the noise from “magnetic resonance images” (MRI) is the attention of researchers to generate the MR images with high “signal-to-noise ratio” as well as with high spatial resolution. In this denoising technique, block-matching and 3-dimensional filtering (BM3D) method is used to denoise the MR images. Main steps used in BM3D are grouping, 3-dimensional discrete wavelet transformation and wavelet shrinkage. In the proposed method, noise invalidation denoising technique (NIDe) is used rather than hard thresholding. NIDe gives the threshold value automatically based on the data and noise characteristics and threshold value changes according to the characteristics of data i.e. wavelet coefficient of image. Before denoising MR images, variance stabilization transform (VST) discard the noise variance dependency of the MRI intensities. Combining block-matching and 3-dimensional filtering technique and VST make able the use of the BM3D technique for Magnetic Resonance Image denoising. After BM3D i.e. final denoised MR image, “contrast limited adaptive histogram equalization” technique is applied to increase the contrast of MR images which are denoised. Performance metrics such as “Peak Signal to Noise ratio”, “Root Mean Square Error”, “Mutual Information”, “Edge Entropy” and “Structural Similarity Index Method” are found out for “T1 weighted”, “T2 weighted” and “PD weighted” magnetic resonance images.