Reviewerly/exHarmony huggingface dataset:
GRL: Knowledge graph completion with GAN-based reinforcement learning

Knowledge graph completion intends to infer the entities that need to be queried through the entities and relations known in the knowledge graphs. It is used in many applications, such as question and answer systems, and searching engines. As the completion process can be represented as a Markov process, existing works would solve this problem with reinforcement learning. However, there are three issues blocking them from achieving high accuracy, which are reward sparsity, missing specific domain rules, and ignoring the generation of knowledge graphs. In this paper, we design a generative adversarial net (GAN)-based reinforcement learning model, named GRL, for knowledge graph completion. First, GRL employs the graph convolutional network to embed the knowledge graphs into the low-dimensional space. Second, GRL employs both GAN and long short-term memory (LSTM) to record trajectory sequences obtained by the agent from traversing the knowledge graph and generate new trajectory sequences if needed. At the same time, GRL applies domain-specific rules accordingly. Finally, GRL employs the deep deterministic policy gradient method to optimize both rewards and adversarial loss. The experiments show that GRL is able to both generate better policies and outperform traditional methods for several tasks.
Differentiable Gaussian Process Motion Planning

Modern trajectory optimization based approaches to motion planning are fast, easy to implement, and effective on a wide range of robotics tasks. However, trajectory optimization algorithms have parameters that are typically set in advance (and rarely discussed in detail). Setting these parameters properly can have a significant impact on the practical performance of the algorithm, sometimes making the difference between finding a feasible plan or failing at the task entirely. We propose a method for leveraging past experience to learn how to automatically adapt the parameters of Gaussian Process Motion Planning (GPMP) algorithms. Specifically, we propose a differentiable extension to the GPMP2 algorithm, so that it can be trained end-to-end from data. We perform several experiments that validate our algorithm and illustrate the benefits of our proposed learning-based approach to motion planning.
LipschitzLR: Using theoretically computed adaptive learning rates for fast convergence

We present a novel theoretical framework for computing large, adaptive learning rates. Our framework makes minimal assumptions on the activations used and exploits the functional properties of the loss function. Specifically, we show that the inverse of the Lipschitz constant of the loss function is an ideal learning rate. We analytically compute formulas for the Lipschitz constant of several loss functions, and through extensive experimentation, demonstrate the strength of our approach using several architectures and datasets. In addition, we detail the computation of learning rates when other optimizers, namely, SGD with momentum, RMSprop, and Adam, are used. Compared to standard choices of learning rates, our approach converges faster, and yields better results.
Mathematical Modeling of the Estimation Process of Functioning Efficiency Level of Information Web-Resources

Modeling of estimation processes of functioning efficiency level of information web-resources is considered in the paper. The application of stochastic models with thresholds is represented. An approach to estimation of the parameters of these models is proposed. A number of experimental researches has been carried out. The researches were executed using real data of a web-resource functioning. Based on the experiments, an efficiency of the proposed approach has been approved.
WarSampo knowledge graph: Finland in the Second World War as Linked Open Data

The Second World War (WW2) is arguably the most devastating catastrophe of human history, a topic of great interest to not only researchers but the general public. However, data about the Second World War is heterogeneous and distributed in various organizations and countries making it hard to utilize. In order to create aggregated global views of the war, a shared ontology and data infrastructure is needed to harmonize information in various data silos. This makes it possible to share data between publishers and application developers, to support data analysis in Digital Humanities research, and to develop data-driven intelligent applications. As a first step towards these goals, this article presents the WarSampo knowledge graph (KG), a shared semantic infrastructure, and a Linked Open Data (LOD) service for publishing data about WW2, with a focus on Finnish military history. The shared semantic infrastructure is based on the idea of representing war as a spatio-temporal sequence of events that soldiers, military units, and other actors participate in. The used metadata schema is an extension of CIDOC CRM, supplemented by various military history domain ontologies. With an infrastructure containing shared ontologies, maintaining the interlinked data brings upon new challenges, as one change in an ontology can propagate across several datasets that use it. To support sustainability, a repeatable automatic data transformation and linking pipeline has been created for rebuilding the whole WarSampo KG from the individual source datasets. The WarSampo KG is hosted on a data service based on W3C Semantic Web standards and best practices, including content negotiation, SPARQL API, download, automatic documentation, and other services supporting the reuse of the data. The WarSampo KG, a part of the international LOD Cloud and totalling ca. 14 million triples, is in use in nine end-user application views of the WarSampo portal, which has had over 690 000 end users since its opening in 2015.
Inverter Efficiency Analysis Model Based on Solar Power Estimation Using Solar Radiation

The photovoltaic (PV) industry is an important part of the renewable energy industry. With the growing use of PV systems, interest in their operation and maintenance (O&amp;M) is increasing. In this regard, analyses of power generation efficiency and inverter efficiency are very important. The first step in efficiency analysis is solar power estimation based on environment sensor data. In this study, solar power was estimated using a univariate linear regression model. The estimated solar power data were cross-validated with the actual solar power data obtained from the inverter. The results provide information on the power generation efficiency of the inverter. The linear estimation model developed in this study was validated using a single PV system. It is possible to apply the coefficients presented in this study to other PV systems, even though the nature and error rates of the collected data may vary depending on the inverter manufacturer. To apply the proposed model to PV systems with different power generation capacities, reconstructing the model according to the power generation capacity is necessary.
Perceptual Hashing With Visual Content Understanding for Reduced-Reference Screen Content Image Quality Assessment

Numerous screen content images (SCIs) have been produced to meet the needs of virtual desktop and remote display, which put forward a very urgent requirement for security and management of SCIs. Perceptual hashing is an effective way to deal with this issue. However, since SCIs are generally composed of pictures, graphics and texts, their intrinsic characteristics are different from those of natural images. Thus the previous hashing methods for natural images are not suitable for SCIs. In this article, we propose a perceptual hashing method for SCIs from the perspective of visual content understanding. Specifically, considering that the visual content understanding of SCIs mainly comes from textual regions, while the contours of text always have thinner width and higher contrast, it is decided to generate hash in the gradient field. An input screen image is first performed by some joint preprocessing operations. Then the maximum gradient magnitude and corresponding orientation information are extracted from three color channels R, G and B. Normalized histogram and local frequency coefficient features are further obtained from the maximum gradient magnitude. Finally, a hash sequence is constructed by statistics that are derived from extracted features. Experiments validated on three SCIs databases were conducted to evaluate classification between robustness and discrimination. Receiver operating characteristics (ROC) results demonstrate that the proposed method is superior to the state-of-the-art algorithms. Besides, SIQAD and SCID databases were leveraged to present the application in reduced-reference screen content image quality assessment, and comparisons show that our hashing could provide accurate predictions than other metrics.
Advances in subword-based HMM-DNN speech recognition across languages

We describe a novel way to implement subword language models in speech recognition systems based on weighted finite state transducers, hidden Markov models, and deep neural networks. The acoustic models are built on graphemes in a way that no pronunciation dictionaries are needed, and they can be used together with any type of subword language model, including character models. The advantages of short subword units are good lexical coverage, reduced data sparsity, and avoiding vocabulary mismatches in adaptation. Moreover, constructing neural network language models (NNLMs) is more practical, because the input and output layers are small. We also propose methods for combining the benefits of different types of language model units by reconstructing and combining the recognition lattices. We present an extensive evaluation of various subword units on speech datasets of four languages: Finnish, Swedish, Arabic, and English. The results show that the benefits of short subwords are even more consistent with NNLMs than with traditional n-gram language models. Combination across different acoustic models and language models with various units improve the results further. For all the four datasets we obtain the best results published so far. Our approach performs well even for English, where the phoneme-based acoustic models and word-based language models typically dominate: The phoneme-based baseline performance can be reached and improved by 4% using graphemes only when several grapheme-based models are combined. Furthermore, combining both grapheme and phoneme models yields the state-of-the-art error rate of 15.9% for the MGB 2018 dev17b test. For all four languages we also show that the language models perform reasonably well when only limited training data is available.
Applicability of Industrial IoT in Diversified Sectors: Evolution, Applications and Challenges

Internet of Things (IoT) is defined as an interconnection among several devices which share data and all other relevant information across the network using unique identifiers without the involvement of humans. Any physical device can be easily operatable by using sensing devices, which reduces human workforce. The applications of IoT have been used in each and every field. With its ease of use of applicability in every sector, it is nowadays used in Industrial Internet of Things (IIoTs). In this paper, we present an overview of different emerging applications of IoT such as blockchain applications in Industry 4.0 and IIoT settings. As security is considered main aspect in any model; therefore, security metrics have been highlighted along with IoT-based architecture. Later section of paper describes the evolution of IIoT, revolution in industrial sector, collaboration of Industry 4.0 and IIoT, industrial automation, cybersecurity and data analytics, blockchain enrolled with IIoT, applications in public domain, etc.
A joint optimization framework to semi-supervised RVFL and ELM networks for efficient data classification

Due to the inefficiency of gradient-based iterative ways in network training, randomization-based neural networks usually offer non-iterative closed form solutions. The random vector functional link (RVFL) and extreme learning machine (ELM) are two popular randomized networks which provide us unified frameworks for both regression and multi-class classification. Currently, existing studies on RVFL and ELM focused mainly on supervised tasks even though we usually have only a small number of labeled samples but a large number of unlabeled samples. Therefore, it is necessary to make both models appropriately utilize both labeled and unlabeled samples; that is, we should develop their semi-supervised extensions. In this paper, we propose a joint optimization framework to semi-supervised RVFL and ELM networks. In the formulated JOSRVFL (jointly optimized semi-supervised RVFL) and JOSELM, the output weight matrix and the label indicator matrix of the unlabeled samples can be jointly optimized in an iterative manner. We provide a novel approach to optimize the JOSRVFL and JOSELM objective functions. Extensive experiments on benchmark data sets and Electroencephalography-based emotion recognition tasks showed the excellent performance of the proposed JOSRVFL and JOSELM models. Moreover, because the direct input–output connections help to regularize the randomization, JOSRVFL could obtain superior performance to JOSELM in most cases.
AttriChain: Decentralized traceable anonymous identities in privacy-preserving permissioned blockchain

In this paper, we propose a framework named AttriChain that supports transactions with anonymous sender identity and threshold traceability in permissioned blockchain. AttriChain realizes on-chain access control and distributed identity governance by allowing users to create transactions anonymously using their transaction keys and their attributes. It provides unlinkability and anonymity of transactions belonging to legitimate users whereas provides accountability for those whom other members in the network regard as questionable or malicious. The design of threshold traceability of anonymous transactions allows network members to trace transactions back to the sender without violating the decentralized property of blockchain. In AttriChain, the spontaneous tracing of misbehaviors in the network distributes the power of identity governance to multiple independent tracing members instead of depending on single-point management. It significantly increases user privacy and autonomy in a permissioned network and facilitates auditing at the same time. We define the functionality and the protocol of AttriChain in the universal composable model. We also provide an implementation of an AttriChain prototype for performance analysis.
Assembly Line Anomaly Detection and Root Cause Analysis Using Machine Learning

Anomaly detection is becoming widely used in Manufacturing Industry to enhance product quality.At the same time, it plays a great role in several other domains due to the fact that anomaly may reveal rare but represent an important phenomenon.The objective of this paper is to detect anomalies and identify the possible variables that caused these anomalies on historical assembly data for two series of products.Multiple anomaly detection techniques were performed; HBOS, IForest, KNN, CBLOF, OCSVM, LOF, and ABOD.Moreover, we used AUROC and Rank Power as performance metrics, followed by Boosting ensemble learning method to ensure the best anomaly detectors robustness.The techniques that gave the highest performance are KNN, ABOD for both product series datasets with 0.95 and 0.99 AUROC respectively.Finally, we applied a statistical root cause analysis on the detected anomalies with the use of Pareto chart to visualize the frequency of the possible causes and its cumulative occurrence.The results showed that there are seven rejection causes for both product series, whereas the first three causes are responsible for 85% of the rejection rates.Besides, assembly machines engineers reported a significant reduction in the rejection rates in both assembly machines after tuning the specification limits of the rejection causes identified by this research results.
Rethinking Experience Replay: a Bag of Tricks for Continual Learning

In Continual Learning, a Neural Network is trained on a stream of data whose distribution shifts over time. Under these assumptions, it is especially challenging to improve on classes appearing later in the stream while remaining accurate on previous ones. This is due to the infamous problem of catastrophic forgetting, which causes a quick performance degradation when the classifier focuses on learning new categories. Recent literature proposed various approaches to tackle this issue, often resorting to very sophisticated techniques. In this work, we show that naive rehearsal can be patched to achieve similar performance. We point out some shortcomings that restrain Experience Replay (ER) and propose five tricks to mitigate them. Experiments show that ER, thus enhanced, displays an accuracy gain of 51.2 and 26.9 percentage points on the CIFAR-10 and CIFAR-100 datasets respectively (memory buffer size 1000). As a result, it surpasses current state-of-the-art rehearsal-based methods.
Risk estimation of SARS-CoV-2 transmission from bluetooth low energy measurements

Digital contact tracing approaches based on Bluetooth low energy (BLE) have the potential to efficiently contain and delay outbreaks of infectious diseases such as the ongoing SARS-CoV-2 pandemic. In this work we propose a machine learning based approach to reliably detect subjects that have spent enough time in close proximity to be at risk of being infected. Our study is an important proof of concept that will aid the battery of epidemiological policies aiming to slow down the rapid spread of COVID-19.
A deep learning approach to building an intelligent video surveillance system

Abstract Recent advances in the field of object detection and face recognition have made it possible to develop practical video surveillance systems with embedded object detection and face recognition functionalities that are accurate and fast enough for commercial uses. In this paper, we compare some of the latest approaches to object detection and face recognition and provide reasons why they may or may not be amongst the best to be used in video surveillance applications in terms of both accuracy and speed. It is discovered that Faster R-CNN with Inception ResNet V2 is able to achieve some of the best accuracies while maintaining real-time rates. Single Shot Detector (SSD) with MobileNet, on the other hand, is incredibly fast and still accurate enough for most applications. As for face recognition, FaceNet with Multi-task Cascaded Convolutional Networks (MTCNN) achieves higher accuracy than advances such as DeepFace and DeepID2+ while being faster. An end-to-end video surveillance system is also proposed which could be used as a starting point for more complex systems. Various experiments have also been attempted on trained models with observations explained in detail. We finish by discussing video object detection and video salient object detection approaches which could potentially be used as future improvements to the proposed system.
PlotThread: Creating Expressive Storyline Visualizations using Reinforcement Learning

Storyline visualizations are an effective means to present the evolution of plots and reveal the scenic interactions among characters. However, the design of storyline visualizations is a difficult task as users need to balance between aesthetic goals and narrative constraints. Despite that the optimization-based methods have been improved significantly in terms of producing aesthetic and legible layouts, the existing (semi-) automatic methods are still limited regarding 1) efficient exploration of the storyline design space and 2) flexible customization of storyline layouts. In this work, we propose a reinforcement learning framework to train an AI agent that assists users in exploring the design space efficiently and generating well-optimized storylines. Based on the framework, we introduce PlotThread, an authoring tool that integrates a set of flexible interactions to support easy customization of storyline visualizations. To seamlessly integrate the AI agent into the authoring process, we employ a mixed-initiative approach where both the agent and designers work on the same canvas to boost the collaborative design of storylines. We evaluate the reinforcement learning model through qualitative and quantitative experiments and demonstrate the usage of PlotThread using a collection of use cases.
Joint 3D Layout and Depth Prediction from a Single Indoor Panorama Image

In this paper, we propose a method which jointly learns the layout prediction and depth estimation from a single indoor panorama image. Previous methods have considered layout prediction and depth estimation from a single panorama image separately. However, these two tasks are tightly intertwined. Leveraging the layout depth map as an intermediate representation, our proposed method outperforms existing methods for both panorama layout prediction and depth estimation. Experiments on the challenging real-world dataset of Stanford 2D–3D demonstrate that our approach obtains superior performance for both the layout prediction tasks (3D IoU: \(85.81\%\) v.s. \(79.79\%\)) and the depth estimation (Abs Rel: 0.068 v.s. 0.079).
Single-Trace Attacks on Message Encoding in Lattice-Based KEMs

In this article, we propose single-trace side-channel attacks against lattice-based key encapsulation mechanisms (KEMs) that are the third-round candidates of the national institute of standards and technology (NIST) standardization project. Specifically, we analyze the message encoding operation in the encapsulation phase of lattice-based KEMs to obtain an ephemeral session key. We conclude that a singletrace leakage implies a whole key recovery: the experimental results realized on a ChipWhisperer UFO STM32F3 target board achieve a success rate of 100% for CRYSTALS-KYBER and SABER regardless of an optimization level and those greater than 79% for FrodoKEM. We further demonstrate that the proposed attack methodologies are not restricted to the above algorithms but are widely applicable to other NIST post-quantum cryptography (PQC) candidates, including NTRU Prime and NTRU.
Label Embedding Online Hashing for Cross-Modal Retrieval

Supervised cross-modal hashing has gained a lot of attention recently. However, most existing methods learn binary codes or hash functions in a batch-based scheme, which is inefficient in an online scenario, i.e., data points come in a streaming fashion. Online hashing is a promising solution; however, there still exist several challenges, e.g., how to effectively exploit semantic information, how to discretely solve the binary optimization problem, how to efficiently update hash codes and hash functions. To address these issues, in this paper, we propose a novel supervised online cross-modal hashing method, i.e., Label EMbedding ONline hashing, LEMON for short. It builds a label embedding framework including label similarity preserving and label reconstructing, which may generate discriminative binary codes and reduce the computational complexity. Furthermore, it not only preserves the pairwise similarity of incoming data, but also establishes a connection between newly coming data and existing data by the inner product minimization on a block similarity matrix. In the light of this, it can exploit more similarity information and make the optimization less sensitive to incoming data, leading to effective binary codes. In addition, we design a discrete optimization algorithm to solve the binary optimization problem without relaxation. Therefore, the quantization error can be reduced. Moreover, its computational complexity is only relevant to the size of incoming data, making it very efficient and scalable to large-scale datasets. Extensive experimental results on three benchmark datasets demonstrate that LEMON outperforms some state-of-the-art offline and online cross-modal hashing methods in terms of accuracy and efficiency.