Reviewerly/exHarmony huggingface dataset:
CPU and RAM Energy-Based SLA-Aware Workload Consolidation Techniques for Clouds

Cloud computing offers hardware and software resources delivered as services. It provides solutions for dynamic as well as “pay as you go” provision of resources. Energy consumption of these resources is high which leads to higher operational costs and carbon emissions in data centers. A number of research studies have been conducted on energy efficiency of data centers, but most of them concentrate on single factor energy consumption, i.e., energy consumed by CPU only, and energy consumption by Random Access Memory (RAM) is neglected. However, recently the focus has been turned towards impact of energy consumption by RAM on data centers. Studies have shown that RAM consumes about 25% of joint energy consumed by a server's CPU and RAM. In this paper, two energy-aware virtual machine (VM) consolidation schemes are proposed that take into account a server's capacity in terms of CPU and RAM to reduce the overall energy consumption. The proposed schemes are compared with existing schemes using CloudSim simulator. The results show that the proposed schemes reduce the energy cost with improved Service Level Agreement (SLA).
Eye Gaze for Assistive Manipulation

A key challenge of human-robot collaboration is to build systems that balance the usefulness of autonomous robot behaviors with the benefits of direct human control. This balance is especially relevant for assistive manipulation systems, which promise to help people with disabilities more easily control wheelchair-mounted robot arms to accomplish activities of daily living. To provide useful assistance, robots must understand the user's goals and preferences for the task. Our insight is that systems can enhance this understanding by monitoring the user's natural eye gaze behavior, as psychology research has shown that eye gaze is responsive and relevant to the task. In this work, we show how using gaze enhances assistance algorithms. First, we analyze eye gaze behavior during teleoperated robot manipulation and compare it to literature results on by-hand manipulation. Then, we develop a pipeline for combining the raw eye gaze signal with the task context to build a rich signal for learning algorithms. Finally, we propose a novel use of eye gaze in which the robot avoids risky behavior by detecting when the user believes that the robot's behavior has a problem.
DeepAO: Efficient Screen Space Ambient Occlusion Generation via Deep Network

Ambient occlusion (abbr.AO) plays an important role in realistic rendering applications because AO produces more realistic ambient lighting, which is achieved by calculating the brightness of certain screen parts based on objects' geometry.However, the baseline computation of AO algorithm is time-consuming, which limits its application for real-time rendering.Currently, most AO algorithms are based on screen space to reduce the computational consumption, which leads to unrealistic results due to the usage of artificial features.To overcome these challenges, in this paper, we first create a well-crafted dataset with the pair of deferred shading buffer data and ground-truth AO shaded images.Then, we design an efficient deep neural network for the screen space AO image generation, based on which we further design a Compute Shader Library to compute the shaded AO images.Our extensive experimental results show that our method achieves competent performance than existing screen space ambient or volumetric ambient based AO methods both in visual quality and efficiency.
Actions and impediments for technical debt prevention

Background: Preventing the occurrence of technical debt (TD) in software projects can be cheaper than its payment. Prevention practices also help in catching inexperienced developers' 'not-so-good' solutions. However, little is known on how to prevent the occurrence of TD. Aims: To investigate, from the point of view of software practitioners, preventive actions that can be used to curb the occurrence of TD and the impediments that hamper the use of those actions. Method: We use data from the InsighTD Project, a family of industrial surveys specifically designed to study software engineering TD. We use a corpus of answers from 207 practitioners across different geographic locations to identify and analyze - both quantitatively and qualitatively - the TD preventive actions most used in practice. Results: We found that project planning, adoption of good practices, well-defined requirements, creating tests, and training are the most cited preventive actions that curb TD in software projects. We also identified seven preventive action categories and defined relationships among them and TD types. On the other hand, the main impediments to prevent TD are related to inappropriate project planning and lack of expertise of the team. Conclusions: Our list of preventive actions and impediments can help practitioners to implement policies for the sector and guide TD researches in a problem-driven way.
Examining the Potential of a Random Forest Derived Cloud Mask from GOES-R Satellites to Improve Solar Irradiance Forecasting

In order for numerical weather prediction (NWP) models to correctly predict solar irradiance reaching the earth’s surface for more accurate solar power forecasting, it is important to initialize the NWP model with accurate cloud information. Knowing where the clouds are located is the first step. Using data from geostationary satellites is an attractive possibility given the low latencies and high spatio-temporal resolution provided nowadays. Here, we explore the potential of utilizing the random forest machine learning method to generate the cloud mask from GOES-16 radiances. We first perform a predictor selection process to determine the optimal predictor set for the random forest predictions of the horizontal cloud fraction and then determine the appropriate threshold to generate the cloud mask prediction. The results show that the random forest method performs as well as the GOES-16 level 2 clear sky mask product with the ability to customize the threshold for under or over predicting cloud cover. Further developments to enhance the cloud mask estimations for improved short-term solar irradiance and power forecasting with the MAD-WRF NWP model are discussed.
Achieving Secure and Dynamic Range Queries Over Encrypted Cloud Data

Cloud computing is motivating data owners to outsource their databases to the cloud. However, for privacy concerns, the sensitive data has to be encrypted before outsourcing, which inevitably posts a challenging task for effective data utilization. Existing work either focuses on keyword searches, or suffers from inadequate security guarantees or inefficiency. In this paper, we concentrate on multi-dimensional range queries over dynamic encrypted cloud data. We first propose a tree-based private range query scheme over dynamic encrypted cloud data (TRQED), which supports faster-than-linear range queries and protects single-dimensional privacy. Then, we discuss the defects of TRQED in terms of privacy-preservation. We modify the framework of the system by adopting a two-server model and put forward a safer range query scheme, called TRQED <inline-formula><tex-math notation="LaTeX">$^{+}$</tex-math></inline-formula> . By newly designed secure node query (SNQ) and secure point query (SPQ), we propose the perturbation-based oblivious R-tree traversal (ORT) operation to preserve both path pattern and stronger single-dimensional privacy. Finally, we conduct comprehensive experiments on real-world datasets and perform comparisons with existing works to evaluate the performance of the proposed schemes. Experimental results show that our TRQED and TRQED <inline-formula><tex-math notation="LaTeX">$^+$</tex-math></inline-formula> surpass the state-of-the-art methods in privacy-preservation level and efficiency.
Ultra-Large-Scale Repository Analysis via Graph Compression

We consider the problem of mining the development history—as captured by modern version control systems—of ultra-large-scale software archives (e.g., tens of millions software repositories corresponding). We show that graph compression techniques can be applied to the problem, dramatically reducing the hardware resources needed to mine similarly-sized corpus. As a concrete use case we compress the full Software Heritage archive, consisting of 5 billion unique source code files and 1 billion unique commits, harvested from more than 80 million software projects—encompassing a full mirror of GitHub. The resulting compressed graph fits in less than 100 GB of RAM, corresponding to a hardware cost of less than 300 U.S. dollars. We show that the compressed in-memory representation of the full corpus can be accessed with excellent performances, with edge lookup times close to memory random access. As a sample exploitation experiment we show that the compressed graph can be used to conduct clone detection at this scale, benefiting from main memory access speed.
A Closed-Form Prediction Update for Extended Target Tracking Using Random Matrices

This paper proposes a new class of state transition models that afford closed-form predictions for the tracking of extended targets. A key innovation is to employ a non-central inverse Wishart distribution to model the state transition density of the target extent. Importantly, this results in a simplified prediction update that is computationally efficient and improves target tracking performance when compared to state-of-the-art alternatives on standard simulation scenarios.
Generic SAO Similarity Measure via Extended Sørensen-Dice Index

As an essential component of many Natural Language Processing applications, semantic similarity measure has been studied for decades. Recent research results indicate that the Subject-Action-Object (SAO) structure in sentences is more desirable for describing the technological information, and SAO-based similarity measure outperforms classical text-based ones. The typical approach in the literature to finding the similarity between two SAO structures relies on a term matching technique, which produces the similarity score by the Sørensen-Dice index, i.e., the proportion of the total number of matching terms. However, in this paper, we observe that the entities in the SAO structures usually have a small number of terms, which makes the currently acknowledged methods have a high recurrence rate and poor accuracy. To settle this issue, we extend the Sørensen-Dice index, and present a new unified framework for the SAO similarity measure that can give a higher discrimination. The effectiveness of our measure is evaluated on the basis of patent data sets in the Nano-Fertilizer field. The results show that our measure can significantly improve the accuracy than the currently acknowledged ones. The proposed measure has an excellent flexibility and robustness, and can be easily used for patent similarity measure. In addition, the extended Sørensen-Dice index is of independent interest, and has potential applications for other similarity measures.
An Identity-Based Elliptic Curve Cryptography for Mobile Payment Security

Security breaches have been observed in different dimensions in mobile payment system. The violation of user’s privacy is a common phenomenon in mobile payment transactions. This study presents an improved security scheme for a mobile payment system using elliptic curve cryptography over a binary field with International Mobile Equipment Identity to ensure higher security. The scheme uses a payment gateway for registration and maps all input text to elliptic curve points using ASCII values. Payment details are stored on the gateway, which is encrypted but decrypted only with merchant’s decryption key. The proposed scheme was evaluated in terms of key size, security strength, computational power, memory capacity, encryption and decryption time and mobile phone battery. The result shows that the scheme provides integrity, confidentiality and privacy. The result also shows that the proposed scheme is time-efficient and computationally inexpensive for resource-constrained environment like mobile payment system.
Promoting Elementary Pupils’ Learning Motivation in Environmental Education with Mobile Inquiry-Oriented Ambience-Aware Fieldwork

Mobile learning (M-learning) has been in high regard for motivating today's children to learn in schools. The present initiative, which harnesses M-learning in environmental education, aims to promote elementary pupils' learning motivation through engaging them in conducting mobile inquiry-oriented ambience-aware fieldwork (MIAF) in outdoor landscapes. Besides presenting the rationale and pedagogic design of the initiative, this paper reports and discusses the findings of a quasi-experiment which examined the motivational effectiveness of MIAF in comparison with the conventional inquiry-oriented fieldtrip-based learning approach's. The experiment was grounded upon the instructional motivation theory of ARCS (Attention, Relevance, Confidence and Satisfaction), involving a total of 145 elementary pupils. Results showed that, compared to the conventional approach, MIAF had significantly positive effects on the experimental group (versus the control group) upon the constructs of "A", "C", and "S", but not "R". This study offers researchers and practitioners in the domains of environmental education and M-learning new insights into adopting mobile devices in outdoor contexts, in particular, shedding light on designing and implementing inquiry-oriented fieldtrip-based learning in the natural environment.
Sequential Cross-Modal Hashing Learning via Multi-scale Correlation Mining

Cross-modal hashing aims to map heterogeneous multimedia data into a common Hamming space through hash function, and achieves fast and flexible cross-modal retrieval. Most existing cross-modal hashing methods learn hash function by mining the correlation among multimedia data, but ignore the important property of multimedia data: Each modality of multimedia data has features of different scales, such as texture, object, and scene features in the image, which can provide complementary information for boosting retrieval task. The correlations among the multi-scale features are more abundant than the correlations between single features of multimedia data, which reveal finer underlying structures of the multimedia data and can be used for effective hashing function learning. Therefore, we propose the M ulti-scale C orrelation S equential C ross-modal H ashing ( MCSCH ) approach, and its main contributions can be summarized as follows: (1) Multi-scale feature guided sequential hashing learning method is proposed to share the information from features of different scales through an RNN-based network and generate the hash codes sequentially. The features of different scales are used to guide the hash codes generation, which can enhance the diversity of the hash codes and weaken the influence of errors in specific features, such as false object features caused by occlusion. (2) Multi-scale correlation mining strategy is proposed to align the features of different scales in different modalities and mine the correlations among aligned features. These correlations reveal the finer underlying structure of multimedia data and can help to boost the hash function learning. (3) Correlation evaluation network evaluates the importance of the correlations to select the worthwhile correlations, and increases the impact of these correlations for hash function learning. Experiments on two widely-used 2-media datasets and a 5-media dataset demonstrate the effectiveness of our proposed MCSCH approach.
Statistical Significance Testing for Natural Language Processing

Data-driven experimental analysis has become the main evaluation tool of Natural Language Processing (NLP) algorithms. In fact, in the last decade, it has become rare to see an NLP paper, particularly
Ultrasonic signal denoising based on autoencoder

At present, denoising parameters in different signal processing algorithms require a specific signal waveform to be set. Human factors would significantly affect the denoising result. To solve this problem, we proposed a signal adaptive denoising method based on a denoising autoencoder to achieve denoising on ultrasonic signals. By applying this method to sample signals and comparing with the singular value decomposition (SVD), principal component analysis (PCA), and wavelet algorithms, it is found that this method can effectively suppress the noise at different noise intensities. Using the signal to noise ratio, root mean square error, and autocorrelation coefficient as evaluation parameters in the experiment, the overall denoising effect of the proposed method is better than that of PCA, and this method is better than the wavelet and SVD algorithms having a relatively weak noise intensity. In addition, by comparing the reconstructed signal curve of the proposed method and that of the wavelet algorithm, the proposed method can retain the information of signal saltation with a better performance. Finally, we apply this method for processing ultrasonic signals and verify its effectiveness from time and frequency domain diagrams.
Multi-Exposure Decomposition-Fusion Model for High Dynamic Range Image Saliency Detection

High dynamic range (HDR) imaging techniques have witnessed a great improvement in the past few decades. However, saliency detection task on HDR content is still far from well explored. In this paper, we introduce a multi-exposure decomposition-fusion model for HDR image saliency detection inspired by the brightness adaption mechanism. The proposed model is composed of three modules. Firstly, a decomposition module converts the input raw HDR image into a stack of LDR images by uniformly sampling the exposure time range. Secondly, a saliency region proposal network is employed to generate the candidate saliency maps for each LDR image in the exposure stack. Finally, an uncertainty weighting based fusion algorithm is applied to generate the overall saliency map for the input HDR image by merging the obtained LDR saliency maps. Extensive experiments show that our proposed model achieves superior performance compared with the state-of-the-art methods on the existing HDR eye fixation databases. The source code of the proposed model are made publicly available at https://github.com/sunnycia/DFHSal.
Locally lateral manifolds of normalised Gabor features for face recognition

Due to inherent characteristics of multiscale and orientation, normalised Gabor features have been successfully used in face recognition. Various previous works have showcased the strength and feasibility of this approach, especially on its robustness against local variations. However, the projected features are numerous and substantial in dimension, which is largely due to the convolution of multiscale and orientation of wavelets. Such features, when used in practical face recognition, would require relatively lengthy classification process, particularly when it involves computationally extensive local classifier or experts, such as ensembles of local cosine similarity (ELCS) classifier. The authors address this issue by simultaneously reducing the size of Gabor features laterally and locally using a manifold learning method called locally linear embedding (LLE). This method is thus denoted as locally lateral normalised local Gabor feature vector with LLE (LGFV/LN/LLE). Results on several publicly available face datasets reveal the superiority of the authors’ approach in terms of improvements in feature compression of LGFV features by up to a reduction of 95% of total dimensionality while increasing the average classification accuracy by 26%. Altogether, the authors show that their LGFV/LN/LLE augmented by ELCS classifiers delivers equivalent result when compared against the state‐of‐the‐art.
A New Splitting Criterion for Better Interpretable Trees

A new splitting criterion for classification trees that generates better decision rules in terms of interpretability is proposed in this paper.The criterion is designed to find homogeneous rules that describe a significant number of instances with a short length.The proposed criterion considers only one side of a split to generate highly homogeneous rules and concurrently utilizes a function of sample ratios with an adjustable hyperparameter to control the coverage of rules.The distinctive feature of the proposed method is that it is applied adaptively at every split.We also introduce an efficient heuristic algorithm to determine an appropriate hyperparameter value for every split.Experimental results evaluated over 17 benchmark datasets show that the proposed criterion combined with the proposed heuristic constructs a better interpretable decision tree.It is verified through quantitative and qualitative analysis that the constructed tree produces highly interpretable rules, and its predictive performance is comparable to that of other popular criteria.
Small Object Detection in Aerial Imagery using RetinaNet with Anchor Optimization

Deep Learning has successfully solved many computer vision problems sometimes in conjunction with traditional computer vision methods and sometimes by replacing them. In this paper, we aim to solve the problem of object detection by employing different methods from deep learning as well as computer vision. Significant amount of work is done in the domain of generic object detection, where usually objects (foreground) cover majority of image space as compared to background. In this paper we will focus on detecting small objects which constitute a tiny area as compared to background such as aerial imagery where desired objects such as people, cars etc. tend to appear relatively small. Such images have an intrinsic imbalanced class problem because background samples dominate object samples. We propose to use an anchor optimization method which will help reduce unnecessary region proposals as well as it can generate customized anchors depending upon the dataset. It can be used in conjunction with any single stage object detection framework. Its empirically noted that this anchor optimization technique improves accuracy over baseline frameworks.
One-Shot Voice Conversion Using Star-Gan

Our efforts are made on one-shot voice conversion where the target speaker is unseen in training dataset or both source and target speakers are unseen in the training dataset. In our work, StarGAN is employed to carry out voice conversion between speakers. An embedding vector is used to represent speaker ID. This work relies on two datasets in English and one dataset in Chinese, involving 38 speakers. A user study is conducted to validate our framework in terms of reconstruction quality and conversion quality. The results show that our framework is able to perform one-shot voice conversion and also outperforms state-of-the-art methods when the speaker in the test is seen in the training dataset. The exploration experiment demonstrates that our framework can be updated with incremental training when the data from new speakers is available.
Crnn-Ctc Based Mandarin Keywords Spotting

Deep learning based approaches have greatly improved the performance of spoken keyword spotting (KWS). However, KWS of different languages should have their own corresponding modeling units to optimize the performance. In this paper, we propose an end-to-end Mandarin KWS system using Convolutional Recurrent Neural Network with the Connectionist Temporal Classification (CTC) loss function (CRNN-CTC). The tonal syllables are adopted as modeling units. Experiments on AISHELL-2 datasets showed that the proposed approach on the tasks of 13 keywords and 20 keywords can achieve a false rejection rate of 5.35% with 0.26 FA/hour and 6.37% with 0.17 FA/hour, respectively.