Reviewerly/exHarmony huggingface dataset:
Temporal Extensions to RDF

The Semantic Web aims at building a foundation of semantic-based data models and languages for not only manipulating data and knowledge, but also in decision making by machines. Naturally, time-varying data and knowledge are required in Semantic Web applications to incorporate time and further reason about it. However, the original specifications of RDF and OWL do not include constructs for handling time-varying data and knowledge. For simplicity, RDF model is confined to binary predicates, hence some form of reification is needed to represent higher-arity predicates. To this date, there are many proposals extending RDF and OWL for handling temporal data and knowledge. They all focus on the valid time. In this paper, we examine each of these proposals and develop a taxonomy to classify them according to the form of reification employed: explicit reification or implicit reification. The implicit reification proposals are further divided into three sub-categories according to semantic constructs they use. Some of these proposals stay compliant to the RDF and OWL standards whereas others add new constructs to RDF model and SPARQL query language. Additionally, we compare these proposed models with respect to characteristics, such as their syntax and semantics, their compliance to RDF and OWL specifications, their need for additional objects, etc. The comparison provides a useful guideline for the researchers and practitioners of the Semantic Web in managing temporal data and knowledge.
A Robust Morpheme Sequence and Convolutional Neural Network-Based Uyghur and Kazakh Short Text Classification

In this paper, based on the multilingual morphological analyzer, we researched the similar low-resource languages, Uyghur and Kazakh, short text classification. Generally, the online linguistic resources of these languages are noisy. So a preprocessing is necessary and can significantly improve the accuracy. Uyghur and Kazakh are the languages with derivational morphology, in which words are coined by stems concatenated with suffixes. Usually, terms are used as the representation of text content while excluding functional parts as stop words in these languages. By extracting stems we can collect necessary terms and exclude stop words. Morpheme segmentation tool can split text into morphemes with 95% high reliability. After preparing both word- and morpheme-based training text corpora, we apply convolutional neural network (CNN) as a feature selection and text classification algorithm to perform text classification tasks. Experimental results show that the morpheme-based approach outperformed the word-based approach. Word embedding technique is frequently used in text representation both in the framework of neural networks and as a value expression, and can map language units into a sequential vector space based on context, and it is a natural way to extract and predict out-of-vocabulary (OOV) from context information. Multilingual morphological analysis has provided a convenient way for processing tasks of low resource languages like Uyghur and Kazakh.
Branch Prediction Attack on Blinded Scalar Multiplication

In recent years, performance counters have been used as a side channel source to monitor branch mispredictions, in order to attack cryptographic algorithms. However, the literature considers blinding techniques as effective countermeasures against such attacks. In this article, we present the first template attack on the branch predictor. We target blinded scalar multiplications with a side-channel attack that uses branch misprediction traces. Since an accurate model of the branch predictor is a crucial element of our attack, we first reverse-engineer the branch predictor. Our attack proceeds with a first online acquisition step, followed by an offline template attack with a template building phase and a template matching phase. During the template matching phase, we use a strategy we call Deduce & Remove, to first infer the candidate values from templates based on a model of the branch predictor, and subsequently eliminate erroneous observations. This last step uses the properties of the target blinding technique to remove wrong guesses and thus naturally provides error correction in key retrieval. In the later part of this article, we demonstrate a template attack on Curve1174 where the double-and-add always algorithm implementation is free from conditional branching on the secret scalar. In that case, we target the data-dependent branching based on the modular reduction operations of long integer multiplications. Such implementations still exist in open source software and can be vulnerable, even if top level safeguards like blinding are used. We provide experimental results on scalar splitting, scalar randomization, and point blinding to show that the secret scalar can be correctly recovered with high confidence. Finally, we conclude with recommendations on countermeasures to thwart such attacks.
Cognitive Identity Management: Risks, Trust and Decisions using Heterogeneous Sources

This work advocates for cognitive biometric-enabled systems that integrate identity management, risk assessment and trust assessment. The cognitive identity management process is viewed as a multi-state dynamical system, and probabilistic reasoning is used for modeling of this process. This paper describes an approach to design a platform for risk and trust modeling and evaluation in the cognitive identity management built upon processing heterogeneous data including biometrics, other sensory data and digital ID. The core of an approach is the perception-action cycle of each system state. Inference engine is a causal network that uses various uncertainty metrics and reasoning mechanisms including Dempster-Shafer and Dezert-Smarandache beliefs.
Toward Evaluating Robustness of Deep Reinforcement Learning with Continuous Control

Deep reinforcement learning has achieved great success in many previously difficult reinforcement learning tasks, yet recent studies show that deep RL agents are also unavoidably susceptible to adversarial perturbations, similar to deep neural networks in classification tasks. Prior works mostly focus on model-free adversarial attacks and agents with discrete actions. In this work, we study the problem of continuous control agents in deep RL with adversarial attacks and propose the first two-step algorithm based on learned model dynamics. Extensive experiments on various MuJoCo domains (Cartpole, Fish, Walker, Humanoid) demonstrate that our proposed framework is much more effective and efficient than model-free based attacks baselines in degrading agent performance as well as driving agents to unsafe states.
Dynamic search fireworks algorithm with chaos

As a relatively new algorithm for swarm intelligence, fireworks algorithm imitates the explosion process of fireworks. A different amplitude in dynamic search fireworks algorithm is presented for an improvement of enhanced fireworks algorithm. This paper integrates chaos with the dynamic search fireworks algorithm so as to further improve the performance and achieve global optimization. Three different variants of dynamic search fireworks algorithm with chaos are introduced and 10 chaotic maps are used to tune either the amplification coefficient [Formula: see text] or the reduction coefficient [Formula: see text]. Twelve benchmark functions are verified in use of the dynamic search fireworks algorithm with chaos (dynamic search fireworks algorithm). The dynamic search fireworks algorithm significantly outperformed the Fireworks Algorithm, enhanced fireworks algorithm, and dynamic search fireworks algorithm based on solution accuracy. The highest performance was seen when dynamic search fireworks algorithm was used with a Gauss/mouse map to tune Ca. Additionally, the dynamic search fireworks algorithm was compared with the firefly algorithm, harmony search, bat algorithm, and standard particle swarm optimization (SPSO2011). Study results indicated that the dynamic search fireworks algorithm has the highest accuracy solution among the five algorithms.
Learning to Move with Affordance Maps

The ability to autonomously explore and navigate a physical space is a fundamental requirement for virtually any mobile autonomous agent, from household robotic vacuums to autonomous vehicles. Traditional SLAM-based approaches for exploration and navigation largely focus on leveraging scene geometry, but fail to model dynamic objects (such as other agents) or semantic constraints (such as wet floors or doorways). Learning-based RL agents are an attractive alternative because they can incorporate both semantic and geometric information, but are notoriously sample inefficient, difficult to generalize to novel settings, and are difficult to interpret. In this paper, we combine the best of both worlds with a modular approach that {\em learns} a spatial representation of a scene that is trained to be effective when coupled with traditional geometric planners. Specifically, we design an agent that learns to predict a spatial affordance map that elucidates what parts of a scene are navigable through active self-supervised experience gathering. In contrast to most simulation environments that assume a static world, we evaluate our approach in the VizDoom simulator, using large-scale randomly-generated maps containing a variety of dynamic actors and hazards. We show that learned affordance maps can be used to augment traditional approaches for both exploration and navigation, providing significant improvements in performance.
Statistical Error Propagation Affecting the Quality of Experience Evaluation in Video on Demand Applications

In addition to the traditional Quality of Service (QoS) metrics of latency, jitter and Packet Loss Ratio (PLR), Quality of Experience (QoE) is now widely accepted as a numerical proxy for the actual user experience. The literature has reported many mathematical mappings between QoE and QoS, where the QoS parameters are measured by the network providers using sampling. Previous research has focussed on sampling errors in QoS measurements. However, the propagation of these sampling errors in QoS through to the QoE values has not been evaluated before. This is important: without knowing how sampling errors propagate through to QoE estimates there is no understanding of the precision of the estimates of QoE, only of the average QoE value. In this paper, we used industrially acquired measurements of PLR and jitter to evaluate the sampling errors. Additionally, we evaluated the correlation between these QoS measurements, as this correlation affects errors propagating to the estimated QoE. Focusing on Video-on-Demand (VoD) applications, we use subjective testing and regression to map QoE metrics onto PLR and jitter. The resulting mathematical functions, and the theory of error propagation, were used to evaluate the error propagated to QoE. This error in estimated QoE was represented as confidence interval width. Using the guidelines of UK government for sampling in a busy hour, our results indicate that confidence intervals around estimated the Mean Opinion Score (MOS) rating of QoE can be between MOS = 1 to MOS = 4 at targeted operating points of the QoS parameters. These results are a new perspective on QoE evaluation and are of potentially great significance to all organisations that need to estimate the QoE of VoD applications precisely.
Point-spread Function Estimation for Adaptive Optics Imaging of Astronomical Extended Objects

This paper focuses on point-spread function (PSF) estimation for astronomical images containing only single extended objects captured by adaptive optics systems. The problem is very different from, and much more challenging than, PSF estimation with point-like source images. We propose a new PSF estimation framework based on deep-learning technology. In this framework, PSFs can be estimated "end-to-end" using the original degraded images. Moreover, such a framework can precisely address different sources of blur without requiring accurate prior information about the PSF, image or imaging system. Therefore, the method is practical. We test the proposed method on both simulated and real data, and the favorable results show that the method is valid and performs much better than classical methods do.
Exponential Family Graph Embeddings

Representing networks in a low dimensional latent space is a crucial task with many interesting applications in graph learning problems, such as link prediction and node classification. A widely applied network representation learning paradigm is based on the combination of random walks for sampling context nodes and the traditional Skip-Gram model to capture center-context node relationships. In this paper, we emphasize on exponential family distributions to capture rich interaction patterns between nodes in random walk sequences. We introduce the generic exponential family graph embedding model, that generalizes random walk-based network representation learning techniques to exponential family conditional distributions. We study three particular instances of this model, analyzing their properties and showing their relationship to existing unsupervised learning models. Our experimental evaluation on real-world datasets demonstrates that the proposed techniques outperform well-known baseline methods in two downstream machine learning tasks.
One Homonym per Translation

The study of homonymy is vital to resolving fundamental problems in lexical semantics. In this paper, we propose four hypotheses that characterize the unique behavior of homonyms in the context of translations, discourses, collocations, and sense clusters. We present a new annotated homonym resource that allows us to test our hypotheses on existing WSD resources. The results of the experiments provide strong empirical evidence for the hypotheses. This study represents a step towards a computational method for distinguishing between homonymy and polysemy, and constructing a definitive inventory of coarse-grained senses.
Prognosing Human Activity Using Actions Forecast and Structured Database

The goal of this work is to forecast human activities that may require robot assistance.Each activity consists of consecutive actions.Each action is bounded by initial and final state and is created by the motion trajectory.The states are defined in the training phase.The vision and depth sensors are used for data collection.The data are processed and the structured database is built.This base is used for making prediction.The method allows us to forecast the trajectories of nominally possible motion goals (prognosing of an action).The probability functions support the selection of possible motion goal.Then the possible motion trajectory is created which predicts the ongoing action.The activity is predicted on the basis of already completed action sequences and using knowledge about possible sequences stored in the database.The core of the reasoning process are: the probability functions, the action graphs (describing the activities) and the structured database.The approach was evaluated using four datasets: CAD 60, CAD-120, WUT-17, and WUT-18.The efficiency of the presented solution compared to the other existing state-of-the-art methods is also investigated.
Non-Local Recurrent Neural Memory for Supervised Sequence Modeling

Typical methods for supervised sequence modeling are built upon the recurrent neural networks to capture temporal dependencies. One potential limitation of these methods is that they only model explicitly information interactions between adjacent time steps in a sequence, hence the high-order interactions between nonadjacent time steps are not fully exploited. It greatly limits the capability of modeling the long-range temporal dependencies since one-order interactions cannot be maintained for a long term due to information dilution and gradient vanishing. To tackle this limitation, we propose the Non-local Recurrent Neural Memory (NRNM) for supervised sequence modeling, which performs non-local operations to learn full-order interactions within a sliding temporal block and models the global interactions between blocks in a gated recurrent manner. Consequently, our model is able to capture the long-range dependencies. Besides, the latent high-level features contained in high-order interactions can be distilled by our model. We demonstrate the merits of our NRNM approach on two different tasks: action recognition and sentiment analysis.
Meta-Amortized Variational Inference and Learning

Despite the recent success in probabilistic modeling and their applications, generative models trained using traditional inference techniques struggle to adapt to new distributions, even when the target distribution may be closely related to the ones seen during training. In this work, we present a doubly-amortized variational inference procedure as a way to address this challenge. By sharing computation across not only a set of query inputs, but also a set of different, related probabilistic models, we learn transferable latent representations that generalize across several related distributions. In particular, given a set of distributions over images, we find the learned representations to transfer to different data transformations. We empirically demonstrate the effectiveness of our method by introducing the MetaVAE, and show that it significantly outperforms baselines on downstream image classification tasks on MNIST (10-50%) and NORB (10-35%).
ANALISIS PERFORMA METODE K-NEAREST NEIGHBOR UNTUK IDENTIFIKASI JENIS KACA

Nowadays, the industry makes various types of goods that have glass-based materials, float car window panes, non-float building windows, lamps, jars, and tableware. These glasses have the same production material, the difference between one and the other is the composition of the production material. K-Nearest Neighbor (KNN) algorithm which is one of the classification methods in data mining and also a supervised learning algorithm in machine learning is a method for classifying objects based on learning data that is the closest distance to the object.. This study discusses the performance measurement (accuracy, precision, recall and f-measure) of the KNN method with a variety of values on 1000 glass type production data objects obtained from the central UCI Machine Learning Repository dataset. The conclusion of this research is the results of the value of K = 3 to K = 9, the best performance values obtained at K = 3, where the level of accuracy reaches 64%, 63% precision, 71% recall, and F-Measure of 67%.
Collision detection and avoidance method for two cooperative robot manipulators

Abstract The paper examines a collisions detection and avoidance problem for two cooperative robot manipulators. The proposed collision detection method is based on the division of workspace into small discrete volume spaces. Each of the discrete volumes have the status “occupied” or “free”. The control system generates restriction signals when the motion along the calculated path will bring the manipulator into occupied volume spaces. A step-by-step algorithm of the collision detection system is given.
Modification of Manual Raindrops Type Observatory Ombrometer with Ultrasonic Sensor HC-SR04

Water, in any way it comes, is important for the life of all living things.Indonesia is an area of tropical equatorial with a variation of rain, which is quite high.The regularity of the distribution of rainfall is one of the aspects most important to the activity of the community.As the development of technology, the intensity of rainfall can be measured manually using Ombrometer Observatory tool.The manual tool for measuring the rain precipitation, Ombrometer Observatorium, is used to take data manually.Samples should be taken at 7.00 a.m.everyday using a measuring cup to know the height of the water contained.However, the type is prone to error at the high rainfall intensity, since the drainage of the samples is conducted every 24 hours.Therefore, much water is wasted.To solve the problem, a modification of a rainfall gauge was made, that is Ombrometer Observatory with ultrasonic sensor HC-SR04.The height of the water in the container is sent through a server of which the data is stored in the database every ten minutes to reduce the risk of evaporation.It also minimizes the error in measuring the rainfall intensity.The results have been compared to the ones by BMKG (Meteorology, Climatology, and Geophysics Agency).The correlation value of the measurement ratio reached 0.9739 or 97.39%.
Deep Learning for Automatic Diacritics Restoration in Romanian

In this paper we address the issue of automatic diacritics restoration (ADR) for Romanian using deep learning strategies.We compare 6 separate architectures with various mixtures of recurrent and convolutional layers. The input consists in sequences of consecutive words stripped of their diacritic symbols. The network's task is to learn to restore the diacritics for the entire sequence. No additional linguistic or semantic information is used as input to the networks.The best results were obtained with a CNN-based architecture and achieved an accuracy of 97% at word level. At diacritic-level the accuracy of the same architecture is 89%.
Sequence Classification Restricted Boltzmann Machines With Gated Units

For the classification of sequential data, dynamic Bayesian networks and recurrent neural networks (RNNs) are the preferred models. While the former can explicitly model the temporal dependences between the variables, and the latter have the capability of learning representations. The recurrent temporal restricted Boltzmann machine (RTRBM) is a model that combines these two features. However, learning and inference in RTRBMs can be difficult because of the exponential nature of its gradient computations when maximizing log likelihoods. In this article, first, we address this intractability by optimizing a conditional rather than a joint probability distribution when performing sequence classification. This results in the “sequence classification restricted Boltzmann machine” (SCRBM). Second, we introduce gated SCRBMs (gSCRBMs), which use an information processing gate, as an integration of SCRBMs with long short-term memory (LSTM) models. In the experiments reported in this article, we evaluate the proposed models on optical character recognition, chunking, and multiresident activity recognition in smart homes. The experimental results show that gSCRBMs achieve the performance comparable to that of the state of the art in all three tasks. gSCRBMs require far fewer parameters in comparison with other recurrent networks with memory gates, in particular, LSTMs and gated recurrent units (GRUs).