Reviewerly/exHarmony huggingface dataset:
Runtime Analysis of Discrete Particle Swarm Optimization Applied to Shortest Paths Computation

We mathematically analyze a discrete particle swarm optimization (PSO) algorithm solving the single-source shortest path (SSSP) problem. Key features are an improved and extended study on Markov chains expanding the adaptability of this technique and its application on the well-known SSSP problem. The results are upper and lower bounds on the expected optimization time. For upper bounds, we combine return times within a Markov model with the well known fitness level method which is appropriate even for the non-elitist PSO algorithm. For lower bounds we prove that the recently introduced property of indistinguishability applies in this setting and we also combine it with a further Markov chain analysis. We prove a cubic upper and a quadratic lower bound and an exponential upper and lower bound on the expected runtime, respectively, depending on a PSO parameter.
Compound Variational Auto-encoder

Amortized variational inference (AVI) enables efficient training of deep generative models to scale to large datasets. The quality of the approximate inference is determined by various reasons, such as the ability of producing proper variational parameters for each datapoint in the recognition network and whether the variational distribution matches the true posterior, etc. This paper focuses on the inference sub-optimality of variational auto-encoders (VAE), where the goal is to reduce the difference caused by amortizing the variational distribution parameters over the entire training set instead of optimizing for each training example individually, which is also known as the amortization gap. This paper extends Bayesian inference in VAE from the latent level to both latent and weight levels by adopting Bayesian neural networks (BNN) in the encoder, so that each datapoint obtains its own distribution for better modeling. The hybrid design in the proposed compound VAE is empirically demonstrated to be capable of mitigating the amortization gap <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Topic Detection in Conversational Telephone Speech Using CNN with Multi-stream Inputs

Topic detection for conversational telephone speech (CTS) is addressed in this paper. The low accuracy of automatic speech recognition (ASR) will cause severe performance deterioration for topic detection. To make up for this, we adopt two ASR systems, HMM-BiLSTM and CTC systems, to provide complementary information for topic detection. After obtaining two sets of different recognized transcriptions, a CNN with multi-stream inputs is trained, and the pooling layer serves as document representations. Finally, element-wise summation of document representations from two streams is used as distributed representations of the documents, which are fed into agglomerative hierarchical clustering (AHC) algorithms to obtain clustering results. The experiments on a Japanese speech corpus demonstrate that the proposed approach can significantly improve the performance of topic detection.
Learning Low Rank and Sparse Models via Robust Autoencoders

Robust principal component analysis (RPCA), decomposes a data matrix into a superposition of a low-rank matrix and a sparse matrix under certain incoherent conditions. In this paper, we propose a nonlinear generalization of RPCA that uses two autoencoder networks to achieve such a decomposition, in which one autoencoder accounts for the low-rank component and the other for the sparse component. To this end, we provide a principled way of constructing these autoencoders for low-rank and sparse components. The generality of the proposed model is demonstrated by applying it onto three applications, namely 1) music/voice separation 2) image denoising and 3) video foreground separation. Experimental results indicate the effectiveness of the proposed model on these application domains.
Canonical Correlation Based Feature Extraction with Application to Anomaly Detection in Electric Appliances

This paper proposes a canonical correlation based feature extraction method with application to anomaly detection in electric appliances. Electric appliances in homes, offices or manufacturing factories are nowadays monitored by Internet of Things (IoT) platforms and systems. For unsupervised anomaly detection in such IoT systems, learning a model is challenging, since normal and anomaly behavior coexist in time-domain signals and are difficult to identify. For accurate model training, we propose to split odd and even frequency harmonics of electric current signals and transform using canonical correlation analysis to extract discriminative features. Evaluations on real-world data demonstrates that proposed approach outperforms the conventional unsupervised feature extraction methods.
An Implemented Open Source Blockchain Market for Smart Grids and Microgrids Using Autonomous Agents

This article presents a system for creating an implemented open energy market for future smart grid systems, microgrids, the Internet of Things, and the Industrial Internet of Things. We also investigate and analyze different aspects of blockchain technologies for this purpose in order to choose the appropriate methods, technologies, and approaches for realizing and implementing the system as an open source project. Based on this analysis, we present our resulting functional implementation of this energy market. Including measurements and a quantitative evaluation of the implementation itself. Showing the potential of the approach, where this implantation can be applied, showing its drawbacks, and finally our planned future work.
Big Data Mining in the Presence of Concept Drifting

Concept drift in big data mining is an absolute, highly demanding research issue in this digital era. A concept in "concept drift" involved in the field of data mining (DM) and machine learning (ML) studies is referred to the relationship between input features and class variables. In real-life classification problems, a concept can be changed or new concepts may appear over the time. Innumerable data mining classification models, new methods are propounded every day to enhance the solution to this raised issue. In this paper, we have extended our vision to attempt an investigation in resolving the issue of concept drift detection, and carried out analysis for creating a better method to adapt to the newly arriving concepts. We have addressed the issue of concept drifting for mining big data, and presented an evolutionary, concept-adaptive, rule-based approach that classifies novel class instances in "concept drifting". The proposed method clusters the big data and extracts decision rules from each cluster for classifying instances with existing classes. For classifying new unlabelled instances, the proposed method first examines and ensures if the new instances belong to the clusters in existence or not. If the new instance does not appear to belong to the existing clusters, we have considered this instance as a novel class instance. Then, we have extracted new classification rules from the new instances data and added these new rules with existing rules. Performance evaluation tests of the proposed method have been conducted using a number of datasets provided by UCI (University of California, Irvine) machine learning repository. The calculated result proves proposed approach as an effective and efficient means of detecting novel class, which implies concept drift identification at the same time.
Experience-based Efficient Scheduling algorithm (EXES) for serving requests in cloud using SDN controller

Efficient servicing of requests in cloud environment has become need of the hour. Cloud services work based on zones in various locations and multiple service requests may be simultaneously considered as a batch and allocated to various zones. Experience-based Efficient Scheduling or EXES focuses o n achieving minimum possible waiting time for a batch of requests, under the constraint that overall allocation cost should be less than or equal to a budget limit. Migration of tasks is also possible to balance loads if budget permits and we gain in energy. For each task in a batch and all available zones, a priority value is computed based on previous interaction experience of the zone and the site that generated this task. The zone that produces highest priority for a task, is allocated the task. An SDN controller is in charge of the entire process of priority computation and assigning tasks to zones. Priority is given to requests generating from sites that consumed lesser execution time compared to other sites that have generated requests in request queue of the zone. To the best of authors’ knowledge, no existing scheduling scheme in cloud has considered batch processing based on service process experience of zones.
Semi-supervised and Population Based Training for Voice Commands Recognition

We present a rapid design methodology that combines automated hyper-parameter tuning with semi-supervised training to build highly accurate and robust models for voice commands classification. Proposed approach allows quick evaluation of network architectures to fit performance and power constraints of available hardware, while ensuring good hyper-parameter choices for each network in real-world scenarios. Leveraging the vast amount of unlabeled data with a student/teacher based semi-supervised method, classification accuracy is improved from 84% to 94% in the validation set. For model optimization, we explore the hyper-parameter space through population based training and obtain an optimized model in the same time frame as it takes to train a single model.
Simulation of optical vortex measurement for out-of-plane displacement by using fringe-based optical-flow

Fringe-based optical flow is a novel method for measuring the deformation of an object with a high sensitivity. When it is used for out-of-plane displacement measurement, there would be greatly error in low fringe-frequency area. To solve the problem, a new method is proposed, in which an optical vortex beam is used as the object light and a spherical wave as the reference light in double-optical-path interferometry. The interference system and principle are introduced. Some simulations are completed. The results show that there is an unmeasurable area with the circular closed fringe patterns formed by two plane waves. But with the non-closed circular fringe patterns formed by the OV object light and the spherical wave reference light, the measured phase differences are in good agreement with the standard values. And then the measurement errors would be decreased greatly. Besides, the method by using patterns obtained by the interference of an OV light and a spherical wave have a certain degree of anti-noise performance.
Image-Mediated Data Augmentation for Low-Resource Human Activity Recognition

Data augmentation is a kind of technique that widely used to solve many machine learning tasks, such as image classification, it enlarge the training dataset size virtually and avoid overfitting. Some former work has demonstrated the effectiveness of data augmentation while using simple techniques, such as cropping, rotating, and flipping the input images. However, there are few augmentation techniques for time series. In this paper, we propose a novel data augmentation technique for human activity recognition (HAR) task, whose data set is consist of time series. Our technique is named image-mediated data augmentation (IMDA), as the name suggests, we create new samples by series-to-image-to-series conversion. When the series data is converted into an image, the augmentation can be easily applied with traditional techniques for images. Then we convert this newly generated images back to the series data. So that we finished the data augmentation towards time series. Experimental result shows that IMDA improves classification accuracy from 90.50% to 91.55% on the HAR dataset. It also show that IMDA largely improved classification accuracy when the quantity of samples in the training set was quite small. The experimental result which basic on simulated low-resource settings shows that IMDA improves the classification accuracy by up to 11.67%. Therefore, our technique is more valuable for low-resource tasks.
Enabling Superior and Controllable Video Streaming QoE with 5G Network Orchestration

5G promises to offer a single network platform that can be optimized for several vertical industries and applications. It is envisioned that this optimization will be achieved through advanced network orchestration functionalities. Video streaming accounts for the vast majority of mobile traffic, hence optimizing performance and Quality of Experience (QoE) for video traffic becomes crucial for the next generation (5G) mobile networks. In this paper, we focus on video streaming applications based on MPEG-DASH (Dynamic Adaptive Streaming over HTTP), a technology that dynamically adapts to changing network conditions by requesting content in chunks encoded at different bitrates, and which is used by major streaming services like Netflix and YouTube. The adaptation process of DASH clients may be speeded up by using the recently standardized MPEG-DASH SAND (Server and Network-assisted DASH) protocol, which envisions the presence of a DANE (DASH-Aware Network Element) in the network providing real-time network bandwidth information to DASH clients. However, when DASH clients do not request the content at the bitrate recommended by the DANE, i.e. they ignore SAND messages or purposely request content at higher bitrates, bandwidth may end up being used in suboptimal way and end users will likely end up receiving poor playback experience, especially on shared bottlenecks. In this paper, we address this issue by combining SAND with network orchestration, where we use advanced SDN (Software Defined Networking) capabilities to have the network enforce the bandwidths selected by the DANE for each individual DASH client, and we demonstrate this concept in the state of the art 5G testbed of the H2020 TRIANGLE project.
Non-interactive Zero Knowledge Proofs in the Random Oracle Model

The Fiat-Shamir (FS) transform is a well known and widely used technique to convert any constant-round public-coin honest-verifier zero-knowledge (HVZK) proof or argument system $$\mathsf {HVZK}=(\mathcal {P},\mathcal {V})$$ in a non-interactive zero-knowledge (NIZK) argument system $$\mathsf {NIZK}=(\mathsf {NIZK}.\mathsf {Prove}, \mathsf {NIZK}.\mathsf{Verify})$$ . The FS transform is secure in the random oracle (RO) model and is extremely efficient: it adds an evaluation of the RO for every message played by $$\mathcal {V}$$ . While a major effort has been done to attack the soundness of the transform when the RO is instantiated with a "secure" hash function, here we focus on a different limitation of the FS transform that exists even when there is a secure instantiation of the random oracle: the soundness of $$\mathsf {NIZK}$$ holds against polynomial-time adversarial provers only. Therefore even when $$\mathsf {HVZK}$$ is a proof system, $$\mathsf {NIZK}$$ is only an argument system. In this paper we propose a new transform from 3-round public-coin HVZK proof systems for several practical relations to NIZK proof systems in the RO model. Our transform outperforms the FS transform protecting the honest verifier from unbounded adversarial provers with no restriction on the number of RO queries. The protocols our transform can be applied to are the ones for proving membership to the range of a one-way group homomorphism as defined by [Maurer - Design, Codes and Cryptography 2015] except that we additionally require the function to be endowed with a trapdoor and other natural properties. For instance, we obtain new efficient instantiations of NIZK proofs for relations related to quadratic residuosity and the RSA function. As a byproduct, with our transform we obtain essentially for free the first efficient non-interactive zap (i.e., 1-round non-interactive witness indistinguishable proof system) for several practical languages in the non-programmable RO model and in an ideal-PUF model. Our approach to NIZK proofs can be seen as an abstraction of the celebrated work of [Feige, Lapidot and Shamir - FOCS 1990].
IT and Educational Environment of an Engineering University

The paper is devoted to the description of the experience of creating electronic information and educational environment (hereinafter - EIOS) in an engineering university, the application of distance educational technologies in engineering education abroad and in Russia. It also describes the main problems of creation and introduction of the system of e-learning in engineering education in preparation of engineers of the XXI century. In Russia, the use of EIOS in the educational process is provided for by Federal state educational standards (FSES) and Secondary vocational education (SVE) under all programs of bachelor, specialist, master, and postgraduate training. Also describes first results of the Project “National Platform for Open Education”.
Experiences of studying Attention through EEG in the Context of Review Tasks

Context: Electroencephalograms (EEG) have been used in a few cases in the context of software engineering (SE). EEGs allow capturing emotions and cognitive functioning. Such human factors have already shown to be important to understand software engineering tasks. Therefore, it is essential to gain experience in the community to utilize EEG as a research tool. Objective: To report experiences of using EEG in the context of a software engineering education (review of master theses proposals). We provide our reflections and lessons learned of (1) how to plan an EEG study, (2) how to conduct and execute (e.g., tools), (3) how to analyze. Method: We carried out an experiment using an EEG headset to measure the participants' attention rate. The experiment task includes reviewing three master thesis project plans. Results: We describe how we evolved our understanding of experimentation practices to collect and analyze psychological and cognitive data. We also provide a set of lessons learned regarding the application of EEG technology for research. Conclusions: We believe that that EEG could benefit software engineering research to collect cognitive information under certain conditions. The lessons learned reported here should be used as inputs for future experiments in software engineering, where human aspects are of interest.
On the Adversarial Robustness of Subspace Learning

In this paper, we investigate the adversarial robustness of subspace learning problems. Different from the scenario addressed by classic robust algorithms that assume fractions of data are corrupted, we consider a more powerful adversary who can observe the whole data and modify all of them. The goal of the adversary is to maximize the distance between the subspace learned from the original data set and that learned from the modified data. We characterize the optimal rank-one attack strategy and show that the optimal strategy depends on the smallest singular value of the original data matrix and the adversary's energy budget.
Domain-Specific Image Classification Using Ensemble Learning Utilizing Open-Domain Knowledge

Image classification is a fundamental research problem with a lot of applications in computer vision. The labels in most existing research in image classification are concepts from natural objects or scenes (e.g., cat, dog, sky, mug). Most image classification tasks conduct training and testing using pre-labeled datasets due to its supervised fashion. However, in the real-world scenarios, images need to be classified into the labels from more abstract concepts (e.g., immodest images, offensive images). These concepts usually come from a specific domain and are defined by some guidelines. We call this type of problem domain-specific image classification. Lack of labeled images for training, especially for domain-specific images, is a common challenge in image classification. In this paper, we propose a novel framework to address domain-specific image classification with insufficient labeled data for training. Since a label from an abstract concept usually includes multiple specific concepts or labels, we decompose each tag with the abstract concept into multiple labels with each label corresponding to a specific concept using domain knowledge. Then we use each specific concept as a base learner, and combine the multiple base learners to form an ensemble learning. For each base learner, we utilize open-domain knowledge (e.g., search engines) to obtain labeled data to train each base learner with deep neural networks. Finally, an ensemble learner is trained using another neural network by incorporating these base learners. Using immodest images as an example of an abstract concept, experiments have been conducted to show that the proposed neural-network-based ensemble learning performs better than traditional ensemble learning. Also, the combined strong leaner with all base learners performs better than a subset of base learners. In addition, this ensemble learning framework reduces the dependency of in-house labeled data by utilizing public-domain knowledge and still reaches the comparable results.
Blockchain Revolution in Global Environmental Governance: Too Good to Be True?

Is a blockchain revolution happening now or has its hype been seriously overstated? In the data-driven world, blockchain technology can potentially bring many benefits to people and organisations that suffer from a lack of information infrastructure or access to reliable integrated data. So, there is no surprise that leading actors of global environmental governance, including the United Nations and the World Bank, have started to support initiatives to develop blockchain platforms. However, the successful implementation of this technology and its ability to contribute to effective environmental policies depend on a range of factors that are often difficult to manage. In this paper, we aim to systematise theoretical and practical approaches to blockchain platforms in environmental management as well as to analyse existing barriers to this adoption. Based on our study, we concluded that the main obstacles preventing a blockchain revolution in global environmental governance are not connected to technological development, but rather to modern information cultures and worldviews.
GEMM-eMFIS (FRI/E): A Novel General Episodic Memory Mechanism For Fuzzy Neural Networks

In fields such as finance, medicine, engineering, and science, making real-time predictions during transient periods characterized by sudden and large changes is a hard challenge for machine learning. Humans keep memory of these transient events, abstractly learn the most relevant rules and reuse them when similar events occur, which stems from episodic memory that allows storage and recall of similar events. This paper proposes a novel online general episodic memory mechanism (GEMM) and demonstrates its integration into the Neuro-Fuzzy system (NFS) architecture called evolving Mamdani Fuzzy Inference System (eMFIS) with Fuzzy Rule Interpolation and Extrapolation (FRI/E). Our proposition, called GEMM-eMFIS(FRI/E), learns from past events by storing and retrieving them from an episodic memory cache during event-driven transient behavior, thereby boosting performance while using a few rules only. GEMM-eMFIS(FRI/E) further has several in-built mechanisms that enable it to learn effectively from continuous stream of online data. They include associative-dissociative learning theory to keep its rule base updated, 2-stage incremental clustering; (2SIC) to determine cluster width, interpolation and extrapolation of rules to deal with concept shifts and drifts in the time-variant data, and rule pruning and merging to keep the rule base compact. GEMM-eMFIS (FRI/E) is benchmarked against other NFS' on various time-variant datasets such as stock index prices and rainfall runoff with 3%-5% improvement during transient period and shows strong forecasting performances with 4%-5% more interpretability with lesser rules.