Reviewerly/exHarmony huggingface dataset:
Privacy-Preserving Distributed Online Optimization Over Unbalanced Digraphs via Subgradient Rescaling

In this article, we investigate a distributed online constrained optimization problem with differential privacy where the network is modeled by an unbalanced digraph with a row-stochastic adjacency matrix. To address such a problem, a distributed differentially private algorithm without introducing a trusted third-party is proposed to preserve the privacy of the participating nodes. Under mild conditions, we show that the proposed algorithm attains an O(log T) expected regret bound for strongly convex local cost functions, where T is the time horizon. Moreover, we remove the need for knowing the time horizon T in advance by adopting doubling trick scheme, and derive an O(√T) expected regret bound for general convex local cost functions. Our results coincide with the best theoretical regrets that can be achieved in the state-of-the-art algorithms. Finally, simulation results are conducted to validate the efficiency of our proposed algorithm.
A Systematic Literature Review on Outlier Detection in Wireless Sensor Networks

A wireless sensor network (WSN) is defined as a set of spatially distributed and interconnected sensor nodes. WSNs allow one to monitor and recognize environmental phenomena such as soil moisture, air pollution, and health data. Because of the very limited resources available in sensors, the collected data from WSNs are often characterized as unreliable or uncertain. However, applications using WSNs demand precise readings, and uncertainty in data reading can cause serious damage (e.g., health monitoring data). Therefore, an efficient local/distributed data processing algorithm is needed to ensure: (1) the extraction of precise and reliable values from noisy readings; (2) the detection of anomalies from data reported by sensors; and (3) the identification of outlier sensors in a WSN. Several works have been conducted to achieve these objectives using several techniques such as machine learning algorithms, mathematical modeling, and clustering. The purpose of this paper is to conduct a systematic literature review to report the available works on outlier and anomaly detection in WSNs. The paper highlights works conducted from January 2004 to October 2018. A total of 3520 papers are reviewed in the initial search process. Later, these papers are filtered by title, abstract, and contents, and a total of 117 papers are selected. These papers are examined to answer the defined research questions. The current paper presents an improved taxonomy of outlier detection techniques. This will help researchers and practitioners to find the most relevant and recent studies related to outlier detection in WSNs. Finally, the paper identifies existing gaps that future studies can fill.
A real-time power quality events recognition using variational mode decomposition and online-sequential extreme learning machine

In this paper, variational mode decomposition (VMD) and online-sequential extreme learning machine (OSELM) are integrated to detect and classify power quality events (PQEs) in real-time. Empirical Wavelet transform (EWT), empirical mode decomposition (EMD) and variational mode decomposition (VMD) are used to decompose the non-stationary power quality (PQ) signals into intrinsic mode functions (IMFs) or band-limited mode of oscillations. Four felicitous features are extracted by applying the Hilbert transform (HT) on the decomposed PQE signals. The synthetic, as well as practical PQE signals, are considered to test and examine the overall performance of the proposed method. OSELM is an efficient and advanced classifier which is implemented to recognize the single as well as multiple PQEs. The robust anti-noise performance, faster learning speed, lesser computational complexity, superior classification accuracy and short event detection time prove that the proposed VMD-OSELM method can be implemented in the electrical power system. Finally, a PC interface based hardware prototype is developed to verify the cogency of the proposed method in real-time. The feasibility of the proposed method is tested and validated by both the simulation and laboratory experiments.
Geographic-aware collaborative filtering for web service recommendation

The explosion of reusable Web services (e.g., open APIs, open data sources, and cloud/IoT services), has become a new opportunity for modern service-composition based applications development. However, this enormous growth of Web services increases the difficulty of selecting the best suitable Web services for a particular application. Hence, the design of an effective and efficient Web service recommendation, primarily based on user feedback, has become a challenge. In the mashup-API recommendation scenario, the most available feedback is the implicit invocation data, i.e., the binary data indicating whether or not a mashup has invoked an API. Various efforts are exploiting potential impact factors, such as the invocation context, to augment the implicit invocation data with the aim to improve service recommendation performance. One significant factor affecting the context of Web service invocations is geographical location, but it has been given less attention in the implicit-based service recommendation. In this paper, we propose a probabilistic matrix factorization based recommendation approach, which considers geographic location information in the derivation of the preference degree underlying a mashup-API interaction. The geographic information, which is integrated with functional descriptions, complements the mashup-API invocation data input for our matrix factorization model. We demonstrate the effectiveness of our approach by conducting extensive experiments on a real dataset crawled from ProgrammableWeb. The evaluation results show that augmenting the implicit data with geographical location information increases the precision of API recommendation for mashup services.
There are Open Learner Models About!

This overview outlines key issues in learning with an open learner model (OLM). Originally, learner models remained hidden, as their primary role was to enable a system to personalize the educational interaction. Opening the model in an understandable form provides additional methods of prompting reflection, planning, and other metacognitive activities that are important in learning. Learner-system discussion, editing or other interaction about the learner model can allow users to contribute more directly to the modeling process to increase its accuracy, while at the same time, further facilitating reflection as they justify their viewpoints to themselves and the system. Social learning can be supported by opening learner models to peers, helping learners to gauge their relative progress, and effectively seek collaborators. OLMs can also provide useful information to others supporting the learning process, such as teachers and parents. OLMs are set to take an increasing role as new learning technologies develop.
Fuzzy Clustering-Based Adaptive Regression for Drifting Data Streams

Current models and algorithms have been increasingly required to learn in a nonstationary environment because the phenomenon of concept drift (or pattern shift) may occur, that is, the assumption that data are identically distributed may be invalid in data streams. Once the data pattern changes, a well-trained model built on the previous, now obsolete data cannot provide an accurate prediction for future data. To obtain reliable prediction, it is important to understand the existing patterns in the data stream and to know which pattern the current examples belong to during the modeling process. However, it is ambiguous to classify an example to a certain pattern in many real-world cases. In this paper, we propose a novel adaptive regression approach, called FUZZ-CARE, to dynamically recognize, train, and store patterns, and assign the membership degree of the upcoming examples belonging to these patterns. Membership degrees are presented by the membership matrix obtained from a kernel fuzzy c-means clustering, which is synchronously trained and adapted with regression parameters. Rather than designing a complicated procedure to continuously chase the newest pattern, which is a common approach in the literature, FUZZ-CARE abstracts useful past information to help predict newly arrived examples. It thus effectively avoids the risk of insufficient training due to the lack of new data and improves prediction accuracy. Experiments on six synthetic datasets and 21 real-world datasets validate the high accuracy and robustness of our approach.
Detecting Lithium (Li) Mineralizations from Space: Current Research and Future Perspectives

Optical and thermal remote sensing data have been an important tool in geological exploration for certain deposit types. However, the present economic and technological advances demand the adaptation of the remote sensing data and image processing techniques to the exploration of other raw materials like lithium (Li). A bibliometric analysis, using a systematic review approach, was made to understand the recent interest in the application of remote sensing methods in Li exploration. A review of the application studies and developments in this field was also made. Throughout the paper, the addressed topics include: (i) achievements made in Li exploration using remote sensing methods; (ii) the main weaknesses of the approaches; (iii) how to overcome these difficulties; and (iv) the expected research perspectives. We expect that the number of studies concerning this topic will increase in the near future and that remote sensing will become an integrated and fundamental tool in Li exploration.
Explaining Neural Networks Semantically and Quantitatively

This paper presents a method to pursue a semantic and quantitative explanation for the knowledge encoded in a convolutional neural network (CNN). The estimation of the specific rationale of each prediction made by the CNN presents a key issue of understanding neural networks, and it is of significant values in real applications. In this study, we propose to distill knowledge from the CNN into an explainable additive model, which explains the CNN prediction quantitatively. We discuss the problem of the biased interpretation of CNN predictions. To overcome the biased interpretation, we develop prior losses to guide the learning of the explainable additive model. Experimental results have demonstrated the effectiveness of our method.
A Physiology-Based QoE Comparison of Interactive Augmented Reality, Virtual Reality and Tablet-Based Applications

The availability of affordable head-mounted display technology has facilitated new, potentially more immersive, interactive multimedia experiences. These technologies were traditionally focused on entertainment; however, academia and industry are now exploring applications in other domains such as health, learning and training. Key to the success of these new multimedia experiences is the understanding of a user's perceived quality of experience (QoE). Subjective user ratings have been the primary mechanism to capture insights into a user's experience. Such ratings have generally been captured post experience and reflected using a mean opinion score (MOS). However, user perception is multifactorial and subjective ratings alone do not express the true measure of an experience. As a result, recent efforts to capture QoE have included exploring the use of implicit metrics (e.g., physiological measures). This article presents the results of an experimental QoE evaluation and comparison of immersive applications delivered across three multimedia platforms. The platforms compared were augmented reality, tablet and virtual reality. The QoE methodology employed considered explicit (post-test questionnaire) and implicit (heart rate and electrodermal activity) assessment methods. The results indicate comparatively higher levels of QoE for users of the augmented reality and tablet platforms.
Time for NIH to lead on data sharing

A draft policy is generally supportive but should start mandating data sharing
Adversarial Learning and Self-Teaching Techniques for Domain Adaptation in Semantic Segmentation

Deep learning techniques have been widely used in autonomous driving systems for the semantic understanding of urban scenes. However, they need a huge amount of labeled data for training, which is difficult and expensive to acquire. A recently proposed workaround is to train deep networks using synthetic data, but the domain shift between real world and synthetic representations limits the performance. In this work, a novel Unsupervised Domain Adaptation (UDA) strategy is introduced to solve this issue. The proposed learning strategy is driven by three components: a standard supervised learning loss on labeled synthetic data; an adversarial learning module that exploits both labeled synthetic data and unlabeled real data; finally, a self-teaching strategy applied to unlabeled data. The last component exploits a region growing framework guided by the segmentation confidence. Furthermore, we weighted this component on the basis of the class frequencies to enhance the performance on less common classes. Experimental results prove the effectiveness of the proposed strategy in adapting a segmentation network trained on synthetic datasets, like GTA5 and SYNTHIA, to real world datasets like Cityscapes and Mapillary.
A Survey on Blockchain for Enterprise Using Hyperledger Fabric and Composer

The potential of blockchain technology is immense and is currently regarded as a new technological trend with a rapid growth rate. Blockchain platforms like Bitcoin are public, open, and permission-less. They are also decentralized, immutable, and append-only ledger; those ledgers can store any type of data and are shared among all the participants of the network. These platforms provide a high degree of anonymity for their users’ identity and full transparency of the activities recorded on the ledger while simultaneously ensuring data security and tamper-resistance. All nodes on the network collectively work to validate the same set of data and to achieve group consensus. Blockchain platforms like Ethereum have the ability to develop smart contracts and embed business logic. This allows the use of blockchain beyond cryptocurrency as a business management solution. Besides the issues of scalability and the expensive nature of most blockchain systems, many attributes of traditional public blockchain are not desirable in a business or enterprise context such as anonymity, full transparency, and permissionless. Permissioned blockchain platforms like Hyperledger Fabric are designed and built with enterprise and business in mind, retaining the desirable qualities of blockchain for enterprise while replacing the qualities of blockchain that are undesirable for the enterprise. In this paper, we present a comprehensive review on the Hyperledger enterprise blockchain technologies.
Trusted Computing Meets Blockchain: Rollback Attacks and a Solution for Hyperledger Fabric

A smart contract on a blockchain cannot keep a secret because its data is replicated on all nodes in a network. To remedy this problem, it has been suggested combining blockchains with trusted execution environments (TEEs), such as Intel SGX, for executing applications that demand confidentiality. As a consequence, untrusted blockchain nodes cannot get access to the data and computations inside the TEE. This paper first explores issues that arise from the combination of TEEs with blockchains: Smart contracts executed inside TEEs are susceptible to rollback attacks, which should be prevented to maintain confidentiality for the application. However, in blockchains with non-final consensus protocols, such as the proof-of-work in Ethereum and others, the contract execution must handle rollbacks by design. This implies that TEEs for securing smart-contract execution cannot be directly used for such blockchains; this approach works only when the consensus decisions are final. Second, this work introduces an architecture and a prototype for smart-contract execution within Intel SGX for Hyperledger Fabric, a prominent enterprise blockchain platform. Our system resolves additional difficulties posed by the specific execute-order-validate architecture of Fabric, prevents rollback attacks on TEE-based execution as far as possible, and minimizes the trusted computing base. For increasing security, our design encapsulates each application on the blockchain within its own enclave that shields it from the host system. An evaluation shows that the overhead of moving the execution into SGX is within 10%-20% for a sealed-bid auction application.
Improving ant colony optimization algorithm with epsilon greedy and Levy flight

Abstract Ant colony optimization (ACO) algorithm is a meta-heuristic and reinforcement learning algorithm, which has been widely applied to solve various optimization problems. The key to improving the performance of ACO is to effectively resolve the exploration/exploitation dilemma. Epsilon greedy is an important and widely applied policy-based exploration method in reinforcement learning and has also been employed to improve ACO algorithms as the pseudo-stochastic mechanism. Levy flight is based on Levy distribution and helps to balance searching space and speed for global optimization. Taking advantage of both epsilon greedy and Levy flight, a greedy–Levy ACO incorporating these two approaches is proposed to solve complicated combinatorial optimization problems. Specifically, it is implemented on the top of max–min ACO to solve the traveling salesman problem (TSP) problems. According to the computational experiments using standard TSPLIB instances, greedy–Levy ACO outperforms max–min ACO and other latest TSP solvers, which demonstrates the effectiveness of the proposed methodology.
Ensemble Deep Learning for Multilabel Binary Classification of User-Generated Content

Sentiment analysis usually refers to the analysis of human-generated content via a polarity filter. Affective computing deals with the exact emotions conveyed through information. Emotional information most frequently cannot be accurately described by a single emotion class. Multilabel classifiers can categorize human-generated content in multiple emotional classes. Ensemble learning can improve the statistical, computational and representation aspects of such classifiers. We present a baseline stacked ensemble and propose a weighted ensemble. Our proposed weighted ensemble can use multiple classifiers to improve classification results without hyperparameter tuning or data overfitting. We evaluate our ensemble models with two datasets. The first dataset is from Semeval2018-Task 1 and contains almost 7000 Tweets, labeled with 11 sentiment classes. The second dataset is the Toxic Comment Dataset with more than 150,000 comments, labeled with six different levels of abuse or harassment. Our results suggest that ensemble learning improves classification results by 1.5 % to 5.4 % .
Using application knowledge to reduce cold starts in FaaS services

In Function-as-a-Service platforms (FaaS), which have become very popular lately, code is deployed in the unit of single functions and the cloud provider handles resource management. There, a key problem is the so-called cold start problem: when a request comes in and no idle container can be found for the execution of the target function, then a new container needs to provisioned. In that case, the request incurs an extra latency - the cold start latency.
Frequency and Temporal Convolutional Attention for Text-Independent Speaker Recognition

Majority of the recent approaches for text-independent speaker recognition apply attention or similar techniques for aggregation of frame-level feature descriptors generated by a deep neural network (DNN) front-end. In this paper, we propose methods of convolutional attention for independently modelling temporal and frequency information in a convolutional neural network (CNN) based front-end. Our system utilizes convolutional block attention modules (CBAMs) [1] appropriately modified to accommodate spectrogram inputs. The proposed CNN front-end fitted with the proposed convolutional attention modules outperform the no-attention and spatial-CBAM baselines by a significant margin on the VoxCeleb [2], [3] speaker verification benchmark. Our best model achieves an equal error rate of 2.031% on the VoxCeleb1 test set, which is a considerable improvement over comparable state of the art results. For a more thorough assessment of the effects of frequency and temporal attention in real-world conditions, we conduct ablation experiments by randomly dropping frequency bins and temporal frames from the input spectrograms, concluding that instead of modelling either of the entities, simultaneously modelling temporal and frequency attention translates to better real-world performance.
Data-Driven Personas for Enhanced User Understanding: Combining Empathy with Rationality for Better Insights to Analytics

Persona is a common human-computer interaction technique for increasing stakeholders’ understanding of audiences, customers, or users. Applied in many domains, such as e-commerce, health, marketing, software development, and system design, personas have remained relatively unchanged for several decades. However, with the increasing popularity of digital user data and data science algorithms, there are new opportunities to progressively shift personas from general representations of user segments to precise interactive tools for decision-making. In this vision, the persona profile functions as an interface to a fully functional analytics system. With this research, we conceptually investigate how data-driven personas can be leveraged as analytics tools for understanding users. We present a conceptual framework consisting of (a) persona benefits, (b) analytics benefits, and (c) decision-making outcomes. We apply this framework for an analysis of digital marketing use cases to demonstrate how data-driven personas can be leveraged in practical situations. We then present a functional overview of an actual data-driven persona system that relies on the concept of data aggregation in which the fundamental question defines the unit of analysis for decision-making. The system provides several functionalities for stakeholders within organizations to address this question.
EaSTFLy: Efficient and secure ternary federated learning

Privacy-preserving machine learning allows multiple parties to perform distributed data analytics while guaranteeing individual privacy. In this area, researchers have proposed many schemes that combine machine learning with privacy-preserving technologies. But these works have shortcomings in terms of efficiency. Meanwhile, federated learning has received widespread attention due to its ability to update parameters without collecting users’ raw data, but this method is short in communications and privacy. Recently, ternary gradients federated learning(TernGrad) has been proposed to reduce the communications, but it is still to various security and privacy threats. In this paper, firstly, we analyze the privacy leakages of TernGrad. Then, we present our solution-EaSTFLy to solve the privacy issue. More concretely, in EaSTFLy, we combine TernGrad with secret sharing and homomorphic encryption to design our privacy-preserving protocols against semi-honest adversary. In addition, we optimize our protocols via SIMD. Compared to prior works on floating-point gradients, our protocols are more efficient in communication and computation overheads, and the accuracy is as high as the plaintext ternary federated learning. To our best knowledge, this is the first research combining ternary federated learning with privacy-preserving technologies. Finally, we evaluate our experiments to show improvements.