Reviewerly/exHarmony huggingface dataset:
Effective-Resistance Preserving Spectral Reduction of Graphs

This paper proposes a scalable algorithmic framework for effective-resistance preserving spectral reduction of large undirected graphs. The proposed method allows computing much smaller graphs while preserving the key spectral (structural) properties of the original graph. Our framework is built upon the following three key components: a spectrum-preserving node aggregation and reduction scheme, a spectral graph sparsification framework with iterative edge weight scaling, as well as effective-resistance preserving post-scaling and iterative solution refinement schemes. By leveraging recent similarity-aware spectral sparsification method and graph-theoretic algebraic multigrid (AMG) Laplacian solver, a novel constrained stochastic gradient descent (SGD) optimization approach has been proposed for achieving truly scalable performance (nearly-linear complexity) for spectral graph reduction. We show that the resultant spectrally-reduced graphs can robustly preserve the first few nontrivial eigenvalues and eigenvectors of the original graph Laplacian and thus allow for developing highly-scalable spectral graph partitioning and circuit simulation algorithms.
Deep echo state network with reservoirs of multiple activation functions for time-series prediction

In this paper, an improved deep echo state network is proposed, named as multiple activation functions deep echo state network (MAF-DESN), where states are activated by multiple activation functions. A sufficient condition for MAF-DESN is given to guarantee that MAF-DESN possesses the echo state property. Finally, the MAF-DESN is applied to chaotic time-series predictions and compared to other ESN deformation models and popular LSTM. Simulation results show that under same network size condition, MAF-DESN possesses stronger explanatory power in chaotic far-infrared laser predictions (R-square=0.9537, others≤0.6487), and better fitting ability in daily foreign exchange rates (MAE=0.0040, others≥0.0047) and chaotic far-infrared laser (MAE=3.4042, others≥4.9021). In high-dimension-input task, MAF-DESN improved the performance when the results were compared (R-square=0.4274, others≤0.3975 and MAE=5.2221, others≥7.6876), while the train time of MAF-DESN did not increase when compared to DESN.
MobiEye

In recent years, machine learning research has largely shifted focus from the cloud to the edge. While the resulting algorithm- and hardware-level optimizations have enabled local execution for the majority of deep neural networks (DNNs) on edge devices, the sheer magnitude of DNNs associated with real-time video detection workloads has forced them to remain relegated to remote execution in the cloud. This problematic when combined with the strict latency requirements that are coupled with these workloads, and imposes a unique set of challenges not directly addressed in prior works. In this work, we design MobiEye, a cloud-based video detection system optimized for deployment in real-time mobile applications. MobiEye is able to achieve up to a 32% reduction in latency when compared to a conventional implementation of video detection system with only a marginal reduction in accuracy.
A Novel Breast Tumor Classification in Ultrasound Images, Using Deep Convolutional Neural Network

Recently, deep learning has shown great success in many computer vision applications. The ability to learn image features and use these features for object localization, classification and segmentation has paved the way for new medical image studies, improving the performance of automated computer-aided detection (CADe) systems. In this paper, a new approach is proposed for classification of breast tumors in ultrasound (US) images, based on convolutional neural networks (CNN). The database consists of 641 images, histopathologically classified in two categories (413 benign and 228 malignant lesions). To have a better estimate of model's classification performance, the data were split to perform fivefold cross validation. For each fold, 80% of data was used for training, and 20% for the evaluation. Different evaluation metrics were used as performance measurements. With the proposed network architecture, we achieved an overall accuracy of 86.12% for tumor classification and the area under the ROC curve (AUC) equal to 0.934. After applying image augmentation and regularization, the accuracy and the AUC increased to 92.01% and 0.9716%, respectively. The obtained results surpassed other machine learning methods based on manual feature selection, demonstrating the effectiveness of the proposed method for the classification of tumors in US imaging.
Linear canonical transform's differentiation properties and their application in solving generalized differential equations

In this paper, we introduce higher differentiation principles of the linear canonical transform (LCT). We also discuss the derived properties' application in the solution of two kinds of generalized differential equations associated with the LCT, including the generalized linear ordinary differential equation and the generalized wave equation.
Workflow Scheduling in Cloud Computing Using Memetic Algorithm

Cloud computing is a distributed computing system which can be used to efficiently solve large size computational problems. On of the main models used cloud computing is the ”pay as you go” model for the provided service. Workflow can be scheduled on virtual machines in data centers to use resources efficiently. Workflow scheduling known as one of the NP-complete problems, due to the dynamism and heterogeneity nature of cloud systems. In this paper, a Memetic Algorithm (MA) is proposed to solve the cloud workflow-scheduling problem considering cost and deadline as two objectives to optimize scheduling of scientific workflow in a cloud-computing environment. The proposed algorithm used hill climbing local search algorithm as an extra operator for Genetic Algorithm (GA) to improve individual solutions during global search. The experiment results show that the proposed MA decreases the makespan of the workflow and performs better compared to GA and Particle Swarm Optimization (PSO) algorithms.
Threat is in the Air

With the spread of wireless application, huge amount of data is generated every day. Thanks to its elasticity, machine learning is becoming a fundamental brick in this field, and many of applications are developed with the use of it and the several techniques that it offers. However, machine learning suffers on different problems and people that use it often are not aware of the possible threats. Often, an adversary tries to exploit these vulnerabilities in order to obtain benefits; because of this, adversarial machine learning is becoming wide studied in the scientific community. In this paper, we show state-of-the-art adversarial techniques and possible countermeasures, with the aim of warning people regarding sensible argument related to the machine learning.
Robust Signal Classification Using Siamese Networks

We propose a noise-robust signal classification approach using siamese convolutional neural networks (CNNs), which employ a linked parallel structure to rank similarity between inputs. Siamese networks have powerful capabilities that include effective learning with few samples and noisy inputs. This paper focuses on the advantages that siamese CNNs exhibit for classification of quite similar wireless signal emitters across signal-to-noise ratio (SNR) and dataset size. Without any a priori information, candidate siamese and baseline CNNs were trained on compressed spectrogram images to distinguish modulated signal pulses with randomized symbols and identical signal parameters, save for slight frequency offsets commonly exhibited in commercial RF emitter reference oscillator uncertainty distributions. Compared with baseline CNN approaches the proposed methods demonstrate improved classification performance under poor SNR. Moreover, this advantage holds the potential for superior, low-SNR, semi-supervised classification using embeddings from within the networks.
Adaptive Activation Function Generation for Artificial Neural Networks through Fuzzy Inference with Application in Grooming Text Categorisation

The activation function is introduced to determine the output of neural networks by mapping the resulting values of neurons into a specific range. The activation functions often suffer from ‘gradient vanishing’, ‘non zero-centred function outputs’, ‘exploding gradients’, and ‘dead neurons’, which may lead to deterioration in the classification performance. This paper proposes an activation function generation approach using the Takagi-Sugeno-Kang inference in an effort to address such challenges. In addition, the proposed method further optimises the coefficients in the activation function using the genetic algorithm such that the activation function can adapt to different applications. This approach has been applied to a digital forensics application of online grooming detection. The evaluations confirm the superiority of the proposed activation function for online grooming detection using an unbalanced data set.
Neural Product Retrieval at Walmart.com

For an E-commerce website like Walmart.com, search is one of the most critical channel for engaging customer. Most existing works on search are composed of two steps, a retrieval step which obtains the candidate set of matching items, and a re-rank step which focuses on fine-tuning the ranking of candidate items. Inspired by latest works in the domain of neural information retrieval (NIR), we discuss in this work our exploration of various product retrieval models which are trained on search log data. We discuss a set of lessons learned in our empirical result section, and these results can be applied to any product search engine which aims at learning a good product retrieval model based on search log data.
Fire Control System Operation Status Assessment Based on Information Fusion: Case Study

In traditional fault diagnosis strategies, massive and disordered data cannot be utilized effectively. Furthermore, just a single parameter is used for fault diagnosis of a weapons fire control system, which might lead to uncertainty in the results. This paper proposes an information fusion method in which rough set theory (RST) is combined with an improved Dempster-Shafer (DS) evidence theory to identify various system operation states. First, the feature information of different faults is extracted from the original data, then this information is used as the evidence of the state for a diagnosis object. By introducing RST, the extracted fault information is reduced in terms of the number of attributes, and the basic probability value of the reduced fault information is obtained. Based on an analysis of conflicts in the existing DS evidence theory, an improved conflict evidence synthesis method is proposed, which combines the improved synthesis rule and the conflict evidence weight allocation methods. Then, an intelligent evaluation model for the fire control system operation state is established, which is based on the improved evidence theory and RST. The case of a power supply module in a fire control computer is analyzed. In this case, the state grade of the power supply module is evaluated by the proposed method, and the conclusion verifies the effectiveness of the proposed method in evaluating the operation state of a fire control system.
Deep Facial Action Unit Recognition and Intensity Estimation from Partially Labelled Data

Research on facial action unit (AU) analysis typically require facial images that are labelled with those action units. While unlabelled facial images abound, labelling those images with action units or intensity is costly and time-consuming. Our approach makes it possible to analyze facial AUs when only some of the images have been labelled. We use many facial images to learn a deep framework that is able to take advantage of the facial representations. A restricted Boltzmann machine uses the available AU annotations to learn the AU label or intensity distribution. We train a support vector machine for AU recognition and a support vector regression for AU intensity estimation by maximizing the log likelihood of the AU mapping functions, taking into account the learned multiple AU distribution for all training data, while simultaneously diminishing errors between the predicted action units and ground-truth action unit occurrence or intensities for all labelled data. We perform experiments on two databases. The results demonstrate the superiority of a deep neural network for learning facial features, as well as the benefit of action unit label or intensity constraints for action unit occurrence recognition or intensity estimation in fully or semi-supervised scenarios.
On the strong consistency of feature‐weighted <i>k</i>‐means clustering in a nearmetric space

Weighted k ‐means ( W K ‐means) is a well‐known method for automated feature weight learning in a conventional k ‐means clustering framework. In this paper, we analytically explore the strong consistency of the W K ‐means algorithm under independent and identically distributed sampling of the data points. The choice of dissimilarity measure plays a key role in data partitioning and detecting the inherent groups existing in a dataset. We propose a proof of strong consistency of the W K ‐means algorithm when the dissimilarity measure used is assumed to be a nearmetric. The proof can be further extended to those dissimilarity measures which are an increasing function of a nearmetric. Through detailed experiments, we demonstrate that W K ‐means‐type algorithms, equipped with a nearmetric, can be pretty effective especially when some of the features are unimportant in revealing the cluster structure of the dataset.
A Study on Computer Vision Techniques for Self-driving Cars

Self-driving cars have become inevitable to be present in a near future. A big number of large companies, startups and research groups have been working for years to fulfil the vision of an absolute unmanned transportation system. These systems have the capacity to model how our future societies and livelihood will be shaped. The utopian dream may still be years away, but current researchers have been shaping up for this tomorrow little by little. From a rise and advances in the field of deep learning, there has been a major push received, especially in the field of computer vision and its possibly uncountable applications. Autonomous vehicles have grown a lot over the past decade from development of better computer vision algorithms for solving common driving tasks, to coming up with large datasets which support training of such systems. In this paper, we have aimed to give a brief introduction to research trends being followed in the overlapping areas of self-driving cars and computer vision. Some state of the art algorithms for solving some common problems which an autonomous system can be benefitted from, such as object detection and semantic scene segmentation, are also discussed.
An Efficient Memory Zeroization Technique Under Side-Channel Attacks

Protection of secured data content in volatile memories (processor caches, embedded RAMs etc) is essential in networking, wireless, automotive and other embedded secure applications. It is utmost important to protect secret data, like authentication credentials, cryptographic keys etc., stored over volatile memories which can be hacked during normal device operations. Several security attacks like cold boot, disclosure attack, data remanence, physical attack, cache attack etc. can extract the cryptographic keys or secure data from volatile memories of the system. The content protection of memory is typically done by assuring data deletion in minimum possible time to minimize data remanence effects. In today's state-of-the-art SoCs, dedicated hardwares are used to functionally erase the private memory contents in case of security violations. This paper, in general, proposes a novel approach of using existing memory built-in-self-test (MBIST) hardware to zeroize (initialize memory to all zeros) on-chip memory contents before it is being hacked either through different side channels or secuirty attacks. Our results show that the proposed MBIST based content zeroization approach is substantially faster than conventional techniques. By adopting the proposed approach, functional hardware requirement for memory zeroization can be waived.
Hoax News Detection on Twitter using Term Frequency Inverse Document Frequency and Support Vector Machine Method

Twitter is one of the social media that is currently popularly used around the world. It's just that twitter has some problems that adversely affect its users. Hoax is one of the negative things that often occur in social media, news in the hoax is still doubted the truth or the fact. In this final project, the authors built a system to detect hoax news on twitter. The purpose of the research is to minimize the hoax news spread on twitter. The use of the Term Frequency Inverse Document Frequency (TF-IDF) weighting system in the system gives a weighted value to a tweet taken from the occurrence of a hoax news sent by someone on Twitter. Data classification uses the Support Vector Machine (SVM) method of the system to predict the possibility of a twitter account user spreading a hoax news based on the user's behavior. Testing data is done based on the contents of content tweets. Datasets are arranged based on attributes used such as the number of retweets, URLs, number of hashtags, provocations, feuds, anxieties, and unbalanced news. Processed data is divided into training data and testing data. The result of data using all features get the highest accuracy is 78,33%. The contribution of this research is that it can detect news that has a tendency towards hoax and can filter which is classified as hoax or not hoax.
HardScope

Memory-unsafe programming languages like C and C++ leave many (embedded) systems vulnerable to attacks like control-flow hijacking. However, defenses against control-flow attacks, such as (fine-grained) randomization or control-flow integrity are in-effective against data-oriented attacks and more expressive Data-oriented Programming (DOP) attacks that bypass state-of-the-art defenses.
Towards an Ontology Based-Approach for Human Resource Management

Human Resources (HRs) as one of the most valuable asset of any organizations play a crucial role in their success. Within the context of project management, HR Management (HRM) is perceived as an entire knowledge area with defined processes, Tools and Techniques (T&T) in PMBOK 5th Guide. Although this guide shows a strong focus on HRM, it does not illustrate, in a clear way, how to perform each process with the required competencies and the related T&T. Hence, by using only PMBOK processes, the project manager cannot be assisted to select the suitable team according to their skills, also the project team members are not able to interchange their competencies. To address these issues, we propose to use an ontological approach in order to build a common shared representation in the HRM domain. This approach fosters the interoperability among HRs as well as their efficient use of T&T; further it can provide whom using PMBOK a better understanding and guidance for managing HRs with evidence.
Open set SAR target classification

Deep learning has shown significant performance advantages in object recognition problems. In particular, convolutional neural networks (CNN's) have been a preferred approach when recognizing objects in imagery. In general, however, CNN's have been applied to closed set recognition problems - those problems where all the objects of interest are in both the training and test sets. This effort addresses target classification using synthetic aperture radar (SAR) as the imaging sensor. In addition, this effort investigates the open set classification problem where targets in the test set are not in the training set. In this open set problem, the objective is to correctly classify test target types represented in the training set while rejecting those not in the training set as unknown. This open set problem is addressed using a hybrid approach of CNN's combined with a novel support vector machine (SVM) approach called SV-means.
Decoding earth's plate tectonic history using sparse geochemical data

Accurately mapping plate boundary types and locations through time is essential for understanding the evolution of the plate-mantle system and the exchange of material between the solid Earth and surface environments. However, the complexity of the Earth system and the cryptic nature of the geological record make it difficult to discriminate tectonic environments through deep time. Here we present a new method for identifying tectonic paleo-environments on Earth through a data mining approach using global geochemical data. We first fingerprint a variety of present-day tectonic environments utilising up to 136 geochemical data attributes in any available combination. A total of 38301 geochemical analyses from basalts aged from 5–0 Ma together with a well-established plate reconstruction model are used to construct a suite of discriminatory models for the first order tectonic environments of subduction and mid-ocean ridge as distinct from intraplate hotspot oceanic environments, identifying 41, 35, and 39 key discriminatory geochemical attributes, respectively. After training and validation, our model is applied to a global geochemical database of 1547 basalt samples of unknown tectonic origin aged between 1000–410 Ma, a relatively ill-constrained period of Earth's evolution following the breakup of the Rodinia supercontinent, producing 56 unique global tectonic environment predictions throughout the Neoproterozoic and Early Paleozoic. Predictions are used to discriminate between three alternative published Rodinia configuration models, identifying the model demonstrating the closest spatio-temporal consistency with the basalt record, and emphasizing the importance of integrating geochemical data into plate reconstructions. Our approach offers an extensible framework for constructing full-plate, deep-time reconstructions capable of assimilating a broad range of geochemical and geological observations, enabling next generation Earth system models.
Cooperative Server-Client HTTP Adaptive Streaming System for Live Video Streaming

In this work, we propose a cooperative server-client HTTP adaptive streaming system to provide a high-quality live video streaming service. In the proposed system, the server adaptively encodes the video segment to improve the bandwidth utilization of clients according to the bandwidth information collected from clients. Additionally, the client selects the segment bitrate by considering the bandwidth utilization, quality difference, and buffered playback time. The performance of the proposed system is verified through a simulation. The simulation results show that the proposed system can provide higher bandwidth utilization and lower quality difference than existing HTTP adaptive streaming systems.
Tablepedia: Automating PDF Table Reading in an Experimental Evidence Exploration and Analytic System

Web research, data science, and artificial intelligence have been rapidly changing our life and society. Researchers and practitioners in the fields take a large amount of time to read literature and compare existing approaches. It would significantly improve their efficiency if there was a system that extracted and managed experimental evidences (say, a specific method achieves a score of a specific metric on a specific dataset) from tables of paper PDFs for search, exploration, and analytic. We build such a demonstration system, called Tablepedia, that use rule-based and learning-based methods to automate the “reading” of PDF tables. It has three modules: template recognition, unification, and SQL operations. We implement three functions to facilitate research and practice: (1) finding related methods and datasets, (2) finding top-performing baseline methods, and (3) finding conflicting reported numbers. A pointer to a screencast on Vimeo: https://vimeo.com/310162310