Reviewerly/exHarmony huggingface dataset:
Super-resolution analysis via machine learning: a survey for fluid flows

This paper surveys machine-learning-based super-resolution reconstruction for vortical flows. Super resolution aims to find the high-resolution flow fields from low-resolution data and is generally an approach used in image reconstruction. In addition to surveying a variety of recent super-resolution applications, we provide case studies of super-resolution analysis for an example of two-dimensional decaying isotropic turbulence. We demonstrate that physics-inspired model designs enable successful reconstruction of vortical flows from spatially limited measurements. We also discuss the challenges and outlooks of machine-learning-based super-resolution analysis for fluid flow applications. The insights gained from this study can be leveraged for super-resolution analysis of numerical and experimental flow data.
Sentiment analysis of Kazakh text and their polarity

Sentiment analysis is one of the most important and interesting tasks in natural languages. A number of resources and tools have been developed for sentiment analysis of English, Turkish, Russian and other languages. Unfortunately, there were no data and tools available for sentiment analysis in Ka zakh. The Dictionary of Kazakh sentiment words has been created during this study. In this work, we described the rule-based method using a dictionary of emotional words for sentiment analysis of texts in the Kazakh language, based on the morphological rules and ontological model. We studied the texts in Kazakh and determined the parts of speech that define the text mood. Based on the conducted studies, a lot of phrases were identified as determining the text polarity. This paper is an extended version of the paper published in [in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), LNCS, 2017, pp. 669–677]. In addition to the original material, the paper includes additional rules for determining sentiment on a 5-point scale.
Four Basic Argument Forms

This paper provides a theoretical rationale for distinguishing four basic argument forms. On the basis of a survey of classical and contemporary definitions of argument, a set of assumptions is formulated regarding the linguistic and pragmatic aspects of arguments. It is demonstrated how these assumptions yield four different argument forms: (1) first-order predicate arguments, (2) first-order subject arguments, (3) second-order subject arguments, and (4) second-order predicate arguments. These argument forms are then further described and illustrated by means of concrete examples, and it is explained how they are visually represented in the Periodic Table of Arguments.
Measurement of kinematic fields via DIC for impact engineering applications

Two dynamic Brazilian tests are analyzed via digital image correlation for acquisition rates equal to 5 and 10 million frames per second. Displacements of the order of 1 mm, velocities of the order of ± 250 m/s, and accelerations as high as ± 4 × 107 m/s2 are measured with spacetime DIC. Uncertainty quantifications enable spacetime DIC to be compared with instantaneous analyses. The gains provided by the temporal regularization are very significant for the acceleration fields. The observed levels are consistent with a priori estimates.
Composite Quantization

This paper studies the compact coding approach to approximate nearest neighbor search. We introduce a composite quantization framework. It uses the composition of several (M) elements, each of which is selected from a different dictionary, to accurately approximate a D-dimensional vector, thus yielding accurate search, and represents the data vector by a short code composed of the indices of the selected elements in the corresponding dictionaries. Our key contribution lies in introducing a near-orthogonality constraint, which makes the search efficiency is guaranteed as the cost of the distance computation is reduced to O(M) from O(D) through a distance table lookup scheme. The resulting approach is called near-orthogonal composite quantization. We theoretically justify the equivalence between near-orthogonal composite quantization and minimizing an upper bound of a function formed by jointly considering the quantization error and the search cost according to a generalized triangle inequality. We empirically show the efficacy of the proposed approach over several benchmark datasets. In addition, we demonstrate the superior performances in other three applications: combination with inverted multi-index, inner-product similarity search, and query compression for mobile search.
A Fast 4K Video Frame Interpolation Using a Hybrid Task-Based Convolutional Neural Network

Visual quality and algorithm efficiency are two main interests in video frame interpolation. We propose a hybrid task-based convolutional neural network for fast and accurate frame interpolation of 4K videos. The proposed method synthesizes low-resolution frames, then reconstructs high-resolution frames in a coarse-to-fine fashion. We also propose edge loss, to preserve high-frequency information and make the synthesized frames look sharper. Experimental results show that the proposed method achieves state-of-the-art performance and performs 2.69x faster than the existing methods that are operable for 4K videos, while maintaining comparable visual and quantitative quality.
Detecting Malicious False Frame Injection Attacks on Surveillance Systems at the Edge Using Electrical Network Frequency Signals

Over the past few years, the importance of video surveillance in securing national critical infrastructure has significantly increased, with applications including the detection of failures and anomalies. Accompanied by the proliferation of video is the increasing number of attacks against surveillance systems. Among the attacks, False Frame Injection (FFI) attacks that replay video frames from a previous recording to mask the live feed has the highest impact. While many attempts have been made to detect FFI frames using features from the video feeds, video analysis is computationally too intensive to be deployed on-site for real-time false frame detection. In this paper, we investigated the feasibility of FFI attacks on compromised surveillance systems at the edge and propose an effective technique to detect the injected false video and audio frames by monitoring the surveillance feed using the embedded Electrical Network Frequency (ENF) signals. An ENF operates at a nominal frequency of 60 Hz/50 Hz based on its geographical location and maintains a stable value across the entire power grid interconnection with minor fluctuations. For surveillance system video/audio recordings connected to the power grid, the ENF signals are embedded. The time-varying nature of the ENF component was used as a forensic application for authenticating the surveillance feed. The paper highlights the ENF signal collection from a power grid creating a reference database and ENF extraction from the recordings using conventional short-time Fourier Transform and spectrum detection for robust ENF signal analysis in the presence of noise and interference caused in different harmonics. The experimental results demonstrated the effectiveness of ENF signal detection and/or abnormalities for FFI attacks.
The Complexity of Planning Revisited — A Parameterized Analysis

The early classifications of the computational complexity of planning under various restrictions in STRIPS (Bylander) and SAS+ (Bäckström and Nebel) have influenced following research in planning in many ways. We go back and reanalyse their subclasses, but this time using the more modern tool of parameterized complexity analysis. This provides new results that together with the old results give a more detailed picture of the complexity landscape. We demonstrate separation results not possible with standard complexity theory, which contributes to explaining why certain cases of planning have seemed simpler in practice than theory has predicted. In particular, we show that certain restrictions of practical interest are tractable in the parameterized sense of the term, and that a simple heuristic is sufficient to make a well-known partial-order planner exploit this fact.
Data Clustering with Actuarial Applications

Data clustering refers to the process of dividing a set of objects into homogeneous groups or clusters such that the objects in each cluster are more similar to each other than to those of other clusters. As one of the most popular tools for exploratory data analysis, data clustering has been applied in many scientific areas. In this article, we give a review of the basics of data clustering, such as distance measures and cluster validity, and different types of clustering algorithms. We also demonstrate the applications of data clustering in insurance by using two scalable clustering algorithms, the truncated fuzzy c-means (TFCM) algorithm and the hierarchical k-means algorithm, to select representative variable annuity contracts, which are used to build predictive models. We found that the hierarchical k-means algorithm is efficient and produces high-quality representative variable annuity contracts.
SURel: A Gold Standard for Incorporating Meaning Shifts into Term Extraction

We introduce SURel, a novel dataset for German with human-annotated meaning shifts between general-language and domain-specific contexts.We show that meaning shifts of term candidates cause errors in term extraction, and demonstrate that the SURel annotation reflects these errors.Furthermore, we illustrate that SURel enables us to assess optimisations of term extraction techniques when incorporating meaning shifts.
A novel Spatio-Temporal Fuzzy Inference System (SPATFIS) and its stability analysis

Modeling an online time series problem is often a challenging task because of the intrinsic dynamical characteristics of the underlying data distribution and the uncertainty stemming from the data. Hence, we propose a novel Spatio-Temporal Fuzzy Inference System (SPATFIS). One of the prime features of SPATFIS lies in the inclusion of memory type neurons which incorporates both spatial and temporal information of the sequences with a dual recurrent structure in its input and defuzzification layers. SPATFIS also proposes a new self-adaptive learning mechanism to add, eliminate and unify its fuzzy rules. This helps it to attain a parsimonious rule base. Furthermore, stability is rigorously inspected and SPATFIS is proved to be stable using Lyapunov’s Input to State Stability theorem. The stability analysis encompasses both the structure and the parameter learning phases. To evaluate the efficacy of SPATFIS numerically, it is compared against state-of-the-art self-adaptive neuro-fuzzy systems with benchmark time series problems from the literature. We also evaluate SPATFS’ performance under prequential First-Test-Then-Train protocol to show its suitability in handling data stream. The experimental results distinctly indicate SPATFIS to be significantly faster while retaining competitive accuracy and a compact rule base. A thorough statistical analysis is conducted afterwards to further affirm its advantages.
Multi-Modal Curriculum Learning over Graphs

Curriculum Learning (CL) is a recently proposed learning paradigm that aims to achieve satisfactory performance by properly organizing the learning sequence from simple curriculum examples to more difficult ones. Up to now, few works have been done to explore CL for the data with graph structure. Therefore, this article proposes a novel CL algorithm that can be utilized to guide the Label Propagation (LP) over graphs, of which the target is to “learn” the labels of unlabeled examples on the graphs. Specifically, we assume that different unlabeled examples have different levels of difficulty for propagation, and their label learning should follow a simple-to-difficult sequence with the updated curricula. Furthermore, considering that the practical data are often characterized by multiple modalities, every modality in our method is associated with a “teacher” that not only evaluates the difficulties of examples from its own viewpoint, but also cooperates with other teachers to generate the overall simplest curriculum examples for propagation. By taking the curriculums suggested by the teachers as a whole, the common preference (i.e., commonality) of teachers on selecting the simplest examples can be discovered by a row-sparse matrix, and their distinct opinions (i.e., individuality) are captured by a sparse noise matrix. As a result, an accurate curriculum sequence can be established and the propagation quality can thus be improved. Theoretically, we prove that the propagation risk bound is closely related to the examples’ difficulty information, and empirically, we show that our method can generate higher accuracy than the state-of-the-art CL approach and LP algorithms on various multi-modal tasks.
A spatial and temporal features mixture model with body parts for video-based person re-identification

The video-based person re-identification is to recognize a person under different cameras, which is a crucial task applied in visual surveillance system. Most previous methods mainly focused on the feature of full body in the frame. In this paper we propose a novel Spatial and Temporal Features Mixture Model (STFMM) based on convolutional neural network (CNN) and recurrent neural network (RNN), in which the human body is split into $N$ parts in horizontal direction so that we can obtain more specific features. The proposed method skillfully integrates features of each part to achieve more expressive representation of each person. We first split the video sequence into $N$ part sequences which include the information of head, waist, legs and so on. Then the features are extracted by STFMM whose $2N$ inputs are obtained from the developed Siamese network, and these features are combined into a discriminative representation for one person. Experiments are conducted on the iLIDS-VID and PRID-2011 datasets. The results demonstrate that our approach outperforms existing methods for video-based person re-identification. It achieves a rank-1 CMC accuracy of 74\% on the iLIDS-VID dataset, exceeding the the most recently developed method ASTPN by 12\%. For the cross-data testing, our method achieves a rank-1 CMC accuracy of 48\% exceeding the ASTPN method by 18\%, which shows that our model has significant stability.
C4Synth: Cross-Caption Cycle-Consistent Text-to-Image Synthesis

Generating an image from its description is a challenging task worth solving because of its numerous practical applications ranging from image editing to virtual reality. All existing methods use one single caption to generate a plausible image. A single caption by itself, can be limited and may not be able to capture the variety of concepts and behavior that would be present in the image. We propose two deep generative models that generate an image by making use of multiple captions describing it. This is achieved by ensuring 'Cross-Caption Cycle Consistency' between the multiple captions and the generated image(s). We report quantitative and qualitative results on the standard Caltech-UCSD Birds (CUB) and Oxford-102 Flowers datasets to validate the efficacy of the proposed approach.
MalDetect: A Structure of Encrypted Malware Traffic Detection

Recently, TLS protocol has been widely used to secure the application data carried in network traffic. It becomes more difficult for attackers to decipher messages through capturing the traffic generated from communications of hosts. On the other hand, malwares adopt TLS protocol when accessing to internet, which makes most malware traffic detection methods, such as DPI (Deep Packet Inspection), ineffective. Some literatures use statistical method with extracting the observable data fields exposed in TLS connections to train machine learning classifiers so as to infer whether a traffic flow is malware or not. However, most of them adopt the features based on the complete flow, such as flow duration, but seldom consider that the detection result should be given out as soon as possible. In this paper, we propose MalDetect, a structure of encrypted malware traffic detection. MalDetect only extracts features from approximately 8 packets (the number varies in different flows) at the beginning of traffic flows, which makes it capable of detecting malware traffic before the malware behaviors take practical impacts. In addition, observing that it is inefficient and time-consuming to re-train the offline classifier when new flow samples arrive, we deploy Online Random Forest in MalDetect. This enables the classifier to update its parameters in online mode and gets rid of the re-training process. MalDetect is coded in C++ language and open in Github. Furthermore, MalDetect is thoroughly evaluated from three aspects: effectiveness, timeliness and performance.
Research Framework for Immersive Virtual Field Trips

Virtual field trips have been thought of and implemented for several decades. For the most part, these field trips were delivered through desktop computers and often as interactive but strictly two-dimensional experiences. The advent of immersive technologies for both creating content and experiencing places in three dimensions provides ample opportunities to move beyond the restrictions of two dimensional media. We propose here a framework we developed to assess immersive learning experiences, specifically immersive virtual field trips (iVFTs). We detail the foundations and provide insights into associated empirical evaluations.
A Predictor of Moving Objects for First-Person Vision

Predicting the motion of objects captured by a moving camera is important for first-person vision tasks. In this paper, we present an accurate model to forecast the position of moving objects by disentangling global and object motion without the need of camera calibration or planarity assumptions. Our predictor uses past observations to model online the motion of objects by selectively tracking a spatially balanced set of keypoints and estimating scene transformations between pairs of frames. We show that we can forecast up to 60% more accurately than state-of-the-art predictors while being resilient to noisy observations. Moreover, the proposed predictor is robust to frame-rate reduction and outperforms alternative approaches while processing only 33% of the frames with moving cameras. We also show the benefit of integrating the proposed predictor in a multi-object tracker.
Task-Oriented Conversation Generation Using Heterogeneous Memory Networks

How to incorporate external knowledge into a neural dialogue model is critically important for dialogue systems to behave like real humans.To handle this problem, memory networks are usually a great choice and a promising way.However, existing memory networks do not perform well when leveraging heterogeneous information from different sources.In this paper, we propose a novel and versatile external memory networks called Heterogeneous Memory Networks (HMNs), to simultaneously utilize user utterances, dialogue history and background knowledge tuples.In our method, historical sequential dialogues are encoded and stored into the context-aware memory enhanced by gating mechanism while grounding knowledge tuples are encoded and stored into the context-free memory.During decoding, the decoder augmented with HMNs recurrently selects each word in one response utterance from these two memories and a general vocabulary.Experimental results on multiple real-world datasets show that HMNs significantly outperform the state-of-the-art datadriven task-oriented dialogue models in most domains.
SYNTHESYS+ Abridged Grant Proposal

European natural history collections are a critical infrastructure for meeting the most important challenge humans face over the next 30 years – creating a sustainable future for ourselves and the natural systems on which we depend – and for answering fundamental scientific questions about ecological, evolutionary, and geological processes. Since 2004 SYNTHESYS has been an essential instrument supporting this community, underpinning new ways to access and exploit collections, harmonising policy and providing significant new insights for thousands of researchers, while fostering the development of new approaches to face urgent societal challenges. SYNTHESYS+ is a fourth iteration of this programme, and represents a step change in the evolution of this community. For the first time SYNTHESYS+ brings together the European branches of the global natural science organisations (GBIF https://www.gbif.org/, TDWG https://www.tdwg.org/, GGBN http://www.ggbn.org/ggbn_portal/ and CETAF https://cetaf.org/) with an unprecedented number of collections, to integrate, innovate and internationalise our efforts within the global scientific collections community. Major new developments addressed by SYNTHESYS+ include the delivery of a new virtual access programme, providing digitisation on demand services to a significantly expanded user community; the construction of a European Loans and Visits System (ELViS) providing, for the first time, a unified gateway to accessing digital, physical and molecular collections; and a new data processing platform (the Specimen Data Refinery), applying cutting edge artificial intelligence to dramatically speed up the digital mobilisation of natural history collections. The activities of SYNTHESYS+ form a critical dependency for DiSSCo - the Distributed System of Scientific Collections (https://dissco.eu/), which is the European Research Infrastructure for natural science collections, under the ESFRI umbrella. DiSSCo will undertake the maintenance and sustainability of SYNTHESYS+ products at the end of the programme.
Fine-Tuning Meta-Heuristic Algorithm for Global Optimization

This paper proposes a novel meta-heuristic optimization algorithm called the fine-tuning meta-heuristic algorithm (FTMA) for solving global optimization problems. In this algorithm, the solutions are fine-tuned using the fundamental steps in meta-heuristic optimization, namely, exploration, exploitation, and randomization, in such a way that if one step improves the solution, then it is unnecessary to execute the remaining steps. The performance of the proposed FTMA has been compared with that of five other optimization algorithms over ten benchmark test functions. Nine of them are well-known and already exist in the literature, while the tenth one is proposed by the authors and introduced in this article. One test trial was shown to check the performance of each algorithm, and the other test for 30 trials to measure the statistical results of the performance of the proposed algorithm against the others. Results confirm that the proposed FTMA global optimization algorithm has a competing performance in comparison with its counterparts in terms of speed and evading the local minima.
Behavioral modeling of integrated phase-change photonic devices for neuromorphic computing applications

The combination of phase-change materials and integrated photonics has led to the development of new forms of all-optical devices, including photonic memories, arithmetic and logic processors, and synaptic and neuronal mimics. Such devices can be readily fabricated into photonic integrated circuits, so potentially delivering large-scale all-optical arithmetic-logic units and neuromorphic processing chips. To facilitate in the design and optimization of such large-scale systems, and to aid in the understanding of device and system performance, fast yet accurate computer models are needed. Here, we describe the development of a behavioral modeling tool that meets such requirements, being capable of essentially instantaneous modeling of the write, erase, and readout performance of various integrated phase-change photonic devices, including those for synaptic and neuronal mimics.