Reviewerly/exHarmony huggingface dataset:
Predicting Multi-Attribute Host Resource Utilization Using Support Vector Regression Technique

Studies on the resource workload demand in cloud computing environment aim at reducing resource wastage by optimizing the resource utilization in a cloud data center.Based on this goal, most of the existing approaches rely on resource management mechanisms such as resource allocation and Virtual Machine (VM) consolidation to reach an ideal solution for reducing resource wastage.Because of instability and high variability of the cloud resource usage and workloads, there is a demand for cloud providers to apply the prediction methods for forecasting the future cloud resource utilization.This paper employs a supervised statistical learning method, i.e., Support Vector Regression Technique (SVRT), to forecast the future usage of multi-attribute host resource.The method is particularly suitable to handle a non-linear cloud resource workload.To improve the prediction accuracy of SVRT, we decide Radial Basis Function as the kernel function of SVRT and apply Sequential Minimal Optimization Algorithm (SMOA) for the training and regression estimation of the prediction method.Besides, compared with the existing work, we consider the multi-attribute cloud resources other than the single resource.The method is employed under eight sets of real-world workloads, which are collected from BitBrain (BB), PlanetLab (PL) and Google Cluster Workload Traces (GCWT).Series of experiments conducted on the workload dataset show the effectiveness of our approach.Based on evaluation metrics, the final results show that the accuracy was enhanced by approximately 4%-16% and the error percentage was reduced by approximately 8%-60% compared with the state-of-the-art methods.
Camera Fingerprint Extraction via Spatial Domain Averaged Frames

Photo Response Non-Uniformity (PRNU) based camera attribution is an effective method to determine the source camera of a visual object (an image or a video). To apply this method, images or videos need to be obtained from a camera to create a “camera fingerprint” which then can be compared against the PRNU of the query media whose origin is under question. The fingerprint extraction process can be time consuming when a large number of video frames or images have to be denoised. This may need to be done when the individual images have been subjected to high compression or other geometric processing such as video stabilization. This paper investigates a simple, yet effective and efficient technique to create a camera fingerprint when so many still images need to be denoised. The technique utilizes Spatial Domain Averaged (SDA) frames. An SDA-frame is the arithmetic mean of multiple still images. When it is used for fingerprint extraction, the number of denoising operations can be significantly decreased with little or no performance loss. Experimental results show that the proposed method can work more than 50 times faster than conventional methods while providing similar matching results.
Blockchain technology for the management of food sciences researches

The management of scientific research projects, working on scale and involving geographically distant groups, uses frequently technological means that can loss information and access non-standardized or outdated data. The records complexity can make impossible to apply the project management discipline in a broad and efficient way. Researches involving products of vegetable origin demand collaborative studies with transparency and information traceability, increasing confidence in results. To address these issues, a solution for mapping the effectiveness of research management have been designed using blockchain technology, a decentralized solution aiming to the safety improvement, waste reduction and traceability of the scientific results along the process. The use of blockchain technology allows the creation of decentralized storage location for all information, according to personalized information and safe access permissions. Smart contracts were used to define research protocols for subsequent data collection, automatic processing and analysis, assuring that protocols were respected throughout the search chain and mapping the process efficiency. Blockchain can support the scientific research chain, specifically in this case for management of the functional foods research. The proposed architecture can be replicated for any type of research with the benefits of the technology present. In this way, data search through blockchain can be done, providing tools to the different network research groups to make validations and contributes to the traceability, safety and reliability of results.
An effective framework based on local cores for self-labeled semi-supervised classification

Abstract Semi-supervised self-labeled methods apply unlabeled data to improve the performance of classifiers which are trained by labeled data alone. Nevertheless, applying unlabeled data may deteriorate the prediction accuracy. One of the causes is that there are insufficient labeled data for training an initial classifier in self-labeled methods. However, existing solutions for this problem of lacking sufficient initial labeled data still have technical defects. For example, they fail to deal with non-spherical data and improve insufficient initial labeled data effectively, when initial labeled data are extremely scarce. In this paper, we propose an effective semi-supervised self-labeled framework based on local cores, aiming to solve the problem of lacking adequate initial labeled data in self-labeled methods and overcome existing technical defects above. Main ideas of our framework include two sides: (a) inadequate initial labeled data are improved by adding predicted local cores to them, where local cores are predicted by active labeling or co-labeling; (b) we use any semi-supervised self-labeled method to train a given classifier on improved labeled data and updated unlabeled data. In our framework, local cores roughly reveal the data distribution, which helps the proposed framework work on spherical or non-spherical data sets. In addition, local cores also help our framework improve insufficient initial labeled data effectively, even when initial labeled data are extremely scarce. Experiments show that the proposed framework is compatible with tested self-labeled methods, and can help self-labeled methods train a k nearest neighbor or support vector machine, when initial labeled data are insufficient.
LibDX: A Cross-Platform and Accurate System to Detect Third-Party Libraries in Binary Code

With the development of the open-source movement, third-party library reuse is commonly practiced in programming. Application developers can reuse the code to save time and development costs. However, there are some hidden risks in misusing third-party libraries such as license violation and security vulnerability. The identification of libraries written in C or C++ is impeded by compilation process which hides most features of code. The same open-source package can be compiled into different binary code by different compilation processes. Therefore, this paper proposes LibDX, a platform-independent and fully-automated system, to detect reused libraries in binary files. With a well-designed feature extractor, LibDX can overcome compilation diversity between binary files. LibDX novelly introduces the logic feature block concept which is applied to deal with the feature duplication challenge in a large-scale feature database. We built a large test data set covering multiple platforms and evaluated LibDX with 9.5K packages including 25.8K C/C++ binary files. Our results show that LibDX achieves a precision of 92% and a recall of 97%, and outperforms state-of-the-art tools. We have validated the performance of the system with closed source commercial applications and found some license violation cases.
Fitness Landscape Analysis of Automated Machine Learning Search Spaces

The field of Automated Machine Learning (AutoML) has as its main goal to automate the process of creating complete Machine Learning (ML) pipelines to any dataset without requiring deep user expertise in ML. Several AutoML methods have been proposed so far, but there is not a single one that really stands out. Furthermore, there is a lack of studies on the characteristics of the fitness landscape of AutoML search spaces. Such analysis may help to understand the performance of different optimization methods for AutoML and how to improve them. This paper adapts classic fitness landscape analysis measures to the context of AutoML. This is a challenging task, as AutoML search spaces include discrete, continuous, categorical and conditional hyperparameters. We propose an ML pipeline representation, a neighborhood definition and a distance metric between pipelines, and use them in the evaluation of the fitness distance correlation (FDC) and the neutrality ratio for a given AutoML search space. Results of FDC are counter-intuitive and require a more in-depth analysis of a range of search spaces. Results of neutrality, in turn, show a strong positive correlation between the mean neutrality ratio and the fitness value.
Dense Layer Dropout Based CNN Architecture for Automatic Modulation Classification

Automatic modulation classification (AMC) is an important part of signal identification for cognitive radio as well as military communication. The problem has been approached traditionally using either likelihood-based or feature-based methods. Since the problem is a classification task, a deep learning (DL) based approach can be an attractive solution. A number of convolutional neural network (CNN) based DL algorithms were introduced for AMC recently. The complex baseband signals that are represented as In-phase and Quadrature (IQ) samples are applied to train the CNN. We propose a new CNN architecture that significantly improves the classification accuracy over existing results in the literature while keeping the number of trainable parameters low. In this architecture, dropouts are applied only in the dense layers.
Multiple perspectives of resilience: A holistic approach to resilience assessment using cognitive maps in practitioner engagement

Resilience has become a regulatory concept influencing investment decisions in the water and wastewater sector. However, current assessments predominantly focus on technical resilience and on engineering solutions. Here we propose an alternative, more holistic approach that captures multiple perspectives of resilience by eliciting and comparing cognitive maps of diverse agents both from within as well as external to a wastewater utility. We use Fuzzy Cognitive Mapping as a practical tool to elicit subjective views on resilience mechanisms and illustrate the methodology in co-production with professionals from the wastewater sector in the Belfast area (Northern Ireland). We find that the proposed participatory process facilitates a more "reflective", "inclusive" and "integrated" assessment than current approaches. Screening for risks and vulnerabilities using this new approach can foster an integrated system perspective by (i) systematically identifying connections between (sub)systems which are normally assessed separately, (ii) detecting feedbacks between system components which may reveal unintended consequences of resilience interventions and by (iii) obtaining a wider portfolio of potential interventions to increase overall resilience. We conclude that the suggested approach may be useful for strategic planning purposes within a utility and for improving cross-departmental communication among both internal and external agents.
Multilingual Acoustic Word Embedding Models for Processing Zero-resource Languages

Acoustic word embeddings are fixed-dimensional representations of variable-length speech segments. In settings where unlabelled speech is the only available resource, such embeddings can be used in "zero-resource" speech search, indexing and discovery systems. Here we propose to train a single supervised embedding model on labelled data from multiple well-resourced languages and then apply it to unseen zero-resource languages. For this transfer learning approach, we consider two multilingual recurrent neural network models: a discriminative classifier trained on the joint vocabularies of all training languages, and a correspondence autoencoder trained to reconstruct word pairs. We test these using a word discrimination task on six target zero-resource languages. When trained on seven well-resourced languages, both models perform similarly and outperform unsupervised models trained on the zero-resource languages. With just a single training language, the second model works better, but performance depends more on the particular training-testing language pair.
What Does a Network Layer Hear? Analyzing Hidden Representations of End-to-End ASR Through Speech Synthesis

End-to-end speech recognition systems have achieved competitive results compared to traditional systems. However, the complex transformations involved between layers given highly variable acoustic signals are hard to analyze. In this paper, we present our ASR probing model, which synthesizes speech from hidden representations of end-to-end ASR to examine the information maintained after each layer calculation. Listening to the synthesized speech, we observe gradual removal of speaker variability and noise as the layer goes deeper, which aligns with the previous studies on how deep network functions in speech recognition. This paper is the first study analyzing the end-to-end speech recognition model by demonstrating what each layer hears. Speaker verification and speech enhancement measurements on synthesized speech are also conducted to confirm our observation further.
Predicting the Quality of Compressed Videos With Pre-Existing Distortions

Because of the increasing ease of video capture, many millions of consumers create and upload large volumes of User-Generated-Content (UGC) videos to social and streaming media sites over the Internet. UGC videos are commonly captured by naive users having limited skills and imperfect techniques, and tend to be afflicted by mixtures of highly diverse in-capture distortions. These UGC videos are then often uploaded for sharing onto cloud servers, where they are further compressed for storage and transmission. Our paper tackles the highly practical problem of predicting the quality of compressed videos (perhaps during the process of compression, to help guide it), with only (possibly severely) distorted UGC videos as references. To address this problem, we have developed a novel Video Quality Assessment (VQA) framework that we call 1stepVQA (to distinguish it from two-step methods that we discuss). 1stepVQA overcomes limitations of Full-Reference, Reduced-Reference and No-Reference VQA models by exploiting the statistical regularities of both natural videos and distorted videos. We also describe a new dedicated video database, which was created by applying a realistic VMAF-Guided perceptual rate distortion optimization (RDO) criterion to create realistically compressed versions of UGC source videos, which typically have pre-existing distortions. We show that 1stepVQA is able to more accurately predict the quality of compressed videos, given imperfect reference videos, and outperforms other VQA models in this scenario.
A novel high precise laser 3D profile scanning method with flexible calibration

To improve the accuracy of laser-contour profile devices, we propose a 3D laser scanning system with high accuracy that includes camera and laser plane calibration, and laser-stripe center extraction. First, a calibration device was designed and manufactured, from which the fiducial points can be easily and accurately detected to calibrate the laser plane. Then through the perspective invariance and the projective invariant cross-ratio properties, a flexible and accurate calibration algorithm is presented. A polynomial fitting method is employed to overcome the discontinuity and thickness of the laser stripe center. Finally, a cubic spline interpolation is utilized to restore the continuous gray value of the transitional regions of the light stripe. Experimental results with different test pieces demonstrate that the minimum geometric accuracy is 18µm and the repeatability is less than ± 3µm, which indicates that the proposed method is feasible to be used in industrial high precision measurement.
Causal effect analysis of logistics processes risks in manufacturing industries using sequential multi-stage fuzzy cognitive map: a case study

Any shortage and deviation in the implementation of logistics processes can lead to a poor performance and losing the market share of the manufacturer. To overcome this problem, the identification of existing risks in all logistics sub-processes, and taking corrective/preventive measures, is of great importance. In this study, a risk prioritization approach is presented based on a sequential multi-stage fuzzy cognitive map (SMFCM) and process failure mode and effects analysis (PFMEA) to prioritize logistics processes risks. This approach, in addition to considering a process-oriented view, prioritizes the logistics risks according to the amount of each risk’s impact on other risks through the internal and external-stage causal relationships as well as the values of risk factors. The score obtained from this approach can create enough distinction among the priorities of different risks in comparison with conventional risk priority number (RPN). Also, the results based on this score have a lower dependency on experts’ opinions by applying the Extended Delta Rule (EDR) learning algorithm. In fact, the intelligent nature of this approach enables decision-makers to identify critical risks and examine the status of the system at any time. The results of the implementation of the SMFCM-PFMEA approach in a manufacturing company active in the automotive spare parts industry confirm the superiority of this compared with the conventional score.
Interactive Learning for Multimedia at Large

Interactive learning has been suggested as a key method for addressing analytic multimedia tasks arising in several domains. Until recently, however, methods to maintain interactive performance at the scale of today’s media collections have not been addressed. We propose an interactive learning approach that builds on and extends the state of the art in user relevance feedback systems and high-dimensional indexing for multimedia. We report on a detailed experimental study using the ImageNet and YFCC100M collections, containing 14 million and 100 million images respectively. The proposed approach outperforms the relevant state-of-the-art approaches in terms of interactive performance, while improving suggestion relevance in some cases. In particular, even on YFCC100M, our approach requires less than 0.3 s per interaction round to generate suggestions, using a single computing core and less than 7 GB of main memory.
Adaptive Knowledge Distillation Based on Entropy

Knowledge distillation (KD) approach is widely used in the deep learning field mainly for model size reduction. KD utilizes soft labels of teacher model, which contain the dark- knowledge that one-hot ground-truth does not have. This knowledge can improve the performance of already saturated student model. In case of multiple-teacher models, generally, the same weighted average (interpolated training) of multiple-teacher's labels is applied to KD training. However, if the knowledge characteristics among teachers are somewhat different, the interpolated training can be at risk of crushing each knowledge characteristics and can also raise noise component. In this paper, we propose an entropy based KD training, which utilizes the teacher model labels with lower entropy at a larger rate among the various teacher models. The proposed method shows a better performance than the conventional KD training scheme in automatic speech recognition.
Solving Missing-Annotation Object Detection with Background Recalibration Loss

This paper focuses on a novel and challenging detection scenario: A majority of true objects/instances is unlabeled in the datasets, so these missing-labeled areas will be regarded as the background during training. Previous art [1] on this problem has proposed to use soft sampling to re-weight the gradients of RoIs based on the overlaps with positive instances, while their method is mainly based on the two-stage detector (i.e. Faster RCNN) which is more robust and friendly for the missing label scenario. In this paper, we introduce a superior solution called Background Recalibration Loss (BRL) that can automatically re-calibrate the loss signals according to the pre-defined IoU threshold and input image. Our design is built on the one-stage detector which is faster and lighter. Inspired by the Focal Loss [2] formulation, we make several significant modifications to fit on the missing-annotation circumstance. We conduct extensive experiments on the curated PASCAL VOC [3] and MS COCO [4] datasets. The results demonstrate that our proposed method outperforms the baseline and other state-of-the-arts by a large margin.
Two-Step Acoustic Model Adaptation for Dysarthric Speech Recognition

This paper introduces a model adaptation approach for a speaker-dependent dysarthric speech recognition system. The dysarthria we focus on in this paper is caused by athetoid cerebral palsy, which causes involuntary muscle movements in those with the disease. For this reason, the dysarthric people's speech is often unstable and difficult for conventional automatic speech recognition (ASR) systems to recognize. A model-adaptation approach, which adapts an ASR model to dysarthric speech, is one possible solution. However, because the difference in speaking styles between dysarthric and non-dysarthric people is so significant, the conventional adaptation method is not able to sufficiently adapt the model to the dysarthric speech. In our proposed two-step model-adaptation approach, an ASR model is first adapted to the general speaking style of multiple dysarthric speakers, and then the adapted model is further adapted for the target speaker. From our experiments on an ASR task, our two-step adaptation approach showed better performance than a conventional one-step adaptation approach.
Condition monitoring of power transmission and transformation equipment based on industrial internet of things technology

Nowadays, Internet of Things (IoT) technology is an advanced and emerging information based technology and it possesses avast development potential. The application of IoT technology in power system is beneficial for the intelligent transformation of the power system. The power system provides a vast application space for the IoT technology. The purpose of this paper is to strongly promote the application and deployment process of the IoT in various industries and play a long-term important role in promoting the informatization transformation of China’s industry. This paper studies the state monitoring system of power transmission and transformation equipment based on panoramic data and introduces the information model into the IoT of power transmission and transformation equipment. According to the equipment panorama information required by the life cycle management of power transmission a comprehensive panorama information modeling scheme is proposed for smart substation equipment, and the network performance is monitored through simulation analysis. In particular, the end-to-end delay of the application layer changed greatly, with a decrease of 0.015 s. This study provides a reference for the development of state monitoring system of power transmission and transformation equipment in China.