Reviewerly/exHarmony huggingface dataset:
From Lexical Semantics to Traditional Ecological Knowledge: On Precipitation, Condensation and Suspension Expressions in Chinese

Precipitation, condensation and suspension are different meteorological events involving water in different forms. They are conceptualised and conventionalised with various verbal constructions in Sinitic languages. In this paper, we analyse data from three Mandarin varieties and 229 Sinitic languages, as well as materials from Old Chinese, to support the claim that there is an underlying shared conceptualisation scheme to account for all the variations, and that traditional ecological knowledge (TEK) can be extracted based on the directionality expressed by these linguistic constructions and PoS of weather words. Specifically, we found that across all Mandarin varieties and Sinitic languages, the weather verbs for precipitation (e.g., rain, snow and hail) typically represent downward movement and the weather phenomena words can typically act as verbs in Old Chinese. On the other hand, although the weather verbs for condensation (e.g., dew and frost) also tend to represent downward movement but the weather nouns typically do not have verbal usage in Old Chinese. Lastly, the weather verbs for suspension (e.g., fog and mist) are directionally uncertain and cannot function as a verb in Old Chinese either. The radical shared by Chinese characters denoting these phenomena provided the conceptual ground for morpho-semantic and grammatical behaviours based on Hantology. Our findings not only have important implications for linguistic ontology and lexical semantics, but also lend support to the emerging area of language-based reconstruction of TEK.
Graph Summarization for Computational Sensemaking on Complex Industrial Event Logs

Complex event logs in industrial applications can often be represented as graphs in order to conveniently model their multi-relational complex characteristics. Then, appropriate methods for analysis and mining are required, in order to provide insights that cover the relevant analytical questions and are understandable to humans. This paper presents a framework for such computational sensemaking on industrial event logs utilizing graph summarization techniques. We demonstrate the efficacy of the proposed approach on a real-world industrial dataset.
A Lifeline-Based Approach for Work Requesting and Parallel Particle Advection

Particle advection, a fundamental building block for many flow visualization algorithms, is very difficult to parallelize efficiently. That said, work requesting is a promising technique to improve parallel performance for particle advection. With this work, we introduce a new work requesting-based method which uses the Lifeline scheduling method. To evaluate the impact of this new algorithm, we ran 92 experiments, running at concurrencies as high as 8192 cores, data sets as large as 17 billion cells, and as many as 16 million particles, comparing against other work requesting scheduling methods. Overall, our results show that Lifeline has significantly less idle time than other approaches, since it reduces the number of failed attempts to request work.
Class Activation Map Generation by Multiple Level Class Grouping and Orthogonal Constraint

Class activation map (CAM) highlights regions of classes based on classification network, which is widely used in weakly supervised tasks. However, it faces the problem that the class activation regions are usually small and local. Although several efforts paid to the second step (the CAM generation step) have partially enhanced the generation, we believe such problem is also caused by the first step (training step), because single classification model trained on the entire classes contains finite discriminate information that limits the object region extraction. To this end, this paper solves CAM generation by using multiple classification models. To form multiple classification networks that carry different discriminative information, we try to capture the semantic relationships between classes to form different semantic levels of classification models. Specifically, hierarchical clustering based on class relationships is used to form hierarchical clustering results, where the clustering levels are treated as semantic levels to form the classification models. Moreover, a new orthogonal module and a two-branch based CAM generation method are proposed to generate class regions that are orthogonal and complementary. We use the PASCAL VOC 2012 dataset to verify the proposed method. Experimental results show that our approach improves the CAM generation.
Seismic Event Discrimination Using Deep CCA

Detection and discrimination of seismic events have important implications. Precise detection of earthquakes may help prevent collateral damage and even save lives. On the other hand, the ability to identify explosions reliably not only helps prevent false alarms but also is crucial for monitoring nuclear experiments. In this work, we present a method for automatic discrimination of seismic events. A neural network is trained to fuse information from multiple seismic channels into a correlated space. Then, we augment the minority class to avoid class imbalance. Finally, a tree-based classifier is used to estimate the nature of the suspected event. We apply the proposed approach to 1609 events collected in Israel and Jordan. Our framework demonstrates improved precision and recall scores.
A Comparison between Nonlinear Estimation based Algorithms for Mobile Robot Localizations

Nonlinear estimation of uncertainties are generally solved by using Extended Kalman Filter (EKF), however performance of EKF algorithm is sluggish when system is highly nonlinear on scale for updating time interval. Tracking the ground truth, mobile robot model is highly nonlinear. In these conditions Unscented Kalman Filter (UKF) algorithm estimation performance is compared with EKF algorithm, while performing complexity of the same order. Difference between the algorithms are EKF performance based on Taylor approximations using jacobian matrices while UKF performance is based on the tuning of Unscented Transform (UT) parameters. In this paper, we are comparing mobile robots for both EKF and UKF navigation based algorithms for localization with respect to ground truth, Global Positioning System (GPS) and dead reckoning.
IVIST: Interactive VIdeo Search Tool in VBS 2020

This paper presents a new video retrieval tool, Interactive VIdeo Search Tool (IVIST), which participates in the 2020 Video Browser Showdown (VBS). As a video retrieval tool, IVIST is equipped with proper and high-performing functionalities such as object detection, dominant-color finding, scene-text recognition and text-image retrieval. These functionalities are constructed with various deep neural networks. By adopting these functionalities, IVIST performs well in searching users' desirable videos. Furthermore, due to user-friendly user interface, IVIST is easy to use even for novice users. Although IVIST is developed to participate in VBS, we hope that it will be applied as a practical video retrieval tool in the future, dealing with actual video data on the Internet.
Self-labeling methods for unsupervised transfer ranking

A lack of reliable relevance labels for training ranking functions is a significant problem for many search applications. Transfer ranking is a technique aiming to transfer knowledge from an existing machine learning ranking task to a new ranking task. Unsupervised transfer ranking is a special case of transfer ranking where there aren’t any relevance labels available for the new task, only queries and retrieved documents. One approach to tackling this problem is to impute relevance labels for (document-query) instances in the target collection. This is done by using knowledge from the source collection. We propose three self-labeling methods for unsupervised transfer ranking: an expectation-maximization based method (RankPairwiseEM) for estimating pairwise preferences across documents, a hard-assignment expectation-maximization based algorithm (RankHardLabelEM), which directly assigns imputed relevance labels to documents, and a self-learning algorithm (RankSelfTrain), which gradually increases the number of imputed labels. We have compared the three algorithms on three large public test collections using LambdaMART as the base ranker and found that (i) all the proposed algorithms show improvements over the original source ranker in different transferring scenarios; (ii) RankPairwiseEM and RankSelfTrain significantly outperform the source rankers across all environments. We have also found that they are not significantly worse than the model directly trained on the target collection; and (iii) self-labeling methods are significantly better than previous instance-weighting based solutions on a variety of collections.
Dubins Path Guidance for Fixed-Wing UAS Remote Sensing Applications

In this work, real-time optimal path planning is achieved for flight line tracking and loiter circles by taking into account dynamic constraints of a fixed-wing unmanned aircraft using Dubins path generation. Next, adaptive guidance algorithms are applied in order for the aircraft to precisely track these optimal paths. Flight line tracking is improved through the use of a trackline integral acceleration term, and optimal control techniques are applied using an augmented state space model. Improvements in aircraft tracking performance compared to traditional methods is quantified through simulations utilizing six-degree of freedom aircraft equations of motion for various flight line survey missions.
DeT: Defending Against Adversarial Examples via Decreasing Transferability

Deep neural networks (DNNs) have made great progress in recent years. Unfortunately, DNNs are found to be vulnerable to adversarial examples that are injected with elaborately crafted perturbations. In this paper, we propose a defense method named DeT, which can (1) defend against adversarial examples generated by common attacks, and (2) correctly label adversarial examples with both small and large perturbations. DeT is a transferability-based defense method, which to the best of our knowledge is the first such attempt. Our experimental results demonstrate that DeT can work well under both black and gray box attacks. We hope that DeT will be a benchmark in the research community for measuring DNN attacks.
Software Team Member Configurations: A Study of Team Effectiveness in Moodle

Many open source projects organize teams to collaboratively manage their software development activities (i.e. issue resolution processes). Therefore good configurations of software development teams can be an important factor, as effective allocation and completion of tasks may result in a more effective activity (i.e. changing configurations after an issue is reopened). To validate this assumption, we present an exploratory study on software team member configuration when resolving issues. We mine the JIRA issue tracking system to assess whether different team member configurations are quicker to resolve issues after being reopened. In a case study of Moodle, our results confirm that the combinations of team members in different roles correlate with reopened issues and the changing of team members is found to resolve those reopened issues. Moreover, the study shows that the issue type is an important factor when assigning team members.
Blind Single Image Reflection Suppression for Face Images using Deep Generative Priors

The goal of single image reflection removal is to suppress unwanted merging of radiances from different surfaces in the scene. This is an inherently ill-posed and challenging problem. Conventional approaches use different assumptions and constraints on the background and reflected layers to solve this problem. Recently, deep learning-based approaches have been applied to this task. These methods require extensive amount of realistic data for training. In this paper, we propose to incorporate class-specific prior models for reducing the ill-posedness of the reflection separation task. Specifically, we use a pre-trained deep face-generative model for reflection supression from face images. We design an optimization scheme that effectively leverages the deep generative model and leads to a constrained solution space. Our method does not require training data corresponding to reflection separation task. We evaluate our proposed approach using both synthetic and real world facial images containing reflections and compare with existing state-of-the-art techniques. The results demonstrate advantages of our approach over the current state-of-the-art in single image reflection separation from faces.
Parameterized Trajectory Planning for Dynamic Soaring

Methods for parameterized trajectory planning for dynamic soaring are discussed. Two parameterizations based on flight path are described: the first uses cubic splines, with parameters defining the locations of a set of control points; the second uses skewed/flattened sinusoids, where parameters define skewness, flatness, amplitude, and frequency. Both parameterizations are continuous to at least C2, allowing smooth trajectories to be planned and flown. A trajectory following controller tracks the planned trajectories. Both parameterizations are compared with a collocation method and show faster convergence as well as improved performance in cases where wind fields are not known precisely. A deep neural network is developed to permit fast computation of trajectories under changing wind conditions. Convergence of trajectories using this deep neural network method is shown in simulation.
BeaconBlocks: Augmenting Proof-of-Stake with On-Chain Time Synchronization

Blockchain protocols based on Proof-of-Stake (PoS) algorithms aim to provide an alternative to the energy-consuming Proof-of-Work mining procedure. Following a PoS algorithm, nodes have to agree on the miner next eligible to contribute a block and on the point in time he is allowed to broadcast it. The latter requirement raises to the need for synchronous clocks. In this paper we describe BeaconBlocks, a new scheme for constructing PoS protocols. A major difference to former work is incorporating time synchronization as an essential element of the protocol itself, gaining independence of the nodes' clocks and allowing the protocol to resist attacks on clock synchronization infrastructure. To this end, we describe both a mechanism for obtaining the correct time during node startup and for retaining synchronicity of estimated time during a node's lifetime. In contrast to prior work, our approach for miner selection exhibits an interleaved unslotted structure. We show that fairness is achieved when miners follow our scheme and we provide a discussion of attack possibilities, allowing developers to choose secure parameters when adopting the scheme.
Co-occurrence Weight Selection in Generation of Word Embeddings for Low Resource Languages

This study aims to increase the performance of word embeddings by proposing a new weighting scheme for co-occurrence counting. The idea behind this new family of weights is to overcome the disadvantage of distant appearing word pairs, which are indeed semantically close, while representing them in the co-occurrence counting. For high-resource languages, this disadvantage might not be effective due to the high frequency of co-occurrence. However, when there are not enough available resources, such pairs suffer from being distant. To favour such pairs, a weighting scheme based on a polynomial fitting procedure is proposed to shift the weights up for distant words while the weights of nearby words are left almost unchanged. The parameter optimization for new weights and the effects of the weighting scheme are analysed for the English, Italian, and Turkish languages. A small portion of English resources and a quarter of Italian resources are utilized for demonstration purposes, as if these languages are low-resource languages. Performance increase is observed in analogy tests when the proposed weighting scheme is applied to relatively small corpora (i.e., mimicking low-resource languages) of both English and Italian. To show the effectiveness of the proposed scheme in small corpora, it is also shown for a large English corpus that the performance of the proposed weighting scheme cannot outperform the original weights. Since Turkish is relatively a low-resource language, it is demonstrated that the proposed weighting scheme can increase the performance of both analogy and similarity tests when all Turkish Wikipedia pages are utilized as a corpus. The positive effect of the proposed scheme has also been demonstrated in a standard sentiment analysis task for the Turkish language.
Electromagnetic Side Channel Attack against Embedded Encryption Chips

Correlative electromagnetic side channel attack (CEMA) is a common and effective method in side channel attacks. However, a large amount of data acquisition and processing restricts the time of key cracking. This paper proposes a fast side channel attack method, which encrypts the selected plaintexts and filters the collected electromagnetic data on frequency domain, and then performs CEMA analysis. Finally, only 150 pieces of electromagnetic data are used to successfully crack all the keys of the advanced encryption standard (AES) cryptographic algorithm, and the cracking time is reduced to 1/50 of the traditional method.
Dam: A Practical Scheme to Mitigate Data-Oriented Attacks with Tagged Memory Based on Hardware

The widespread deployment of unsafe programming languages such as C and C++, leaves many programs vulnerable to memory corruption attacks. With the continuous improvement of control-flow hijacking defense methods, recent works on data-oriented attacks including Data-oriented Exploits (DOE), Data-oriented Programming (DOP), and Block-oriented Programming (BOP) have been showed that these attacks can cause significant threat even in the presence of control-flow defense mechanism. Moreover, DFI (Date Flow Integrity) is a software-only approach for mitigating data-oriented attacks, while it incurs a 104% performance overhead. There are no suitable defense methods for such attacks as yet. In this paper, we propose Dam, a practical scheme to mitigate data-oriented attacks with tagged memory based on hardware. Dam is a novel approach using the idea of tagged memory to break data-flow stitching and gadgets dispatcher of generating data-oriented attacks rather than complete DFI. By enforcing security checking on memory access, Dam eliminates two requirements in constructing a valid data-oriented attack. We have implemented Dam by extending lowRISC, a RISC-V based SoC (System of a Chip) that implements tagged memory. And our evaluation results show that our scheme has an average performance cost of 6.48%, while Dam provides source compatibility and strong security.
Anomaly Detection Based on Unsupervised Disentangled Representation Learning in Combination with Manifold Learning

Identifying anomalous samples from highly complex and unstructured data is a crucial but challenging task in a variety of intelligent systems. In this paper, we present a novel deep anomaly detection framework named AnoDM (standing for Anomaly detection based on unsupervised Disentangled representation learning and Manifold learning). The disentanglement learning is currently implemented by β-VAE for automatically discovering interpretable factorized latent representations in a completely unsupervised manner. The manifold learning is realized by t-SNE for projecting the latent representations to a 2D map. We define a new anomaly score function by combining β-VAE's reconstruction error in the raw feature space and local density estimation in the t-SNE space. AnoDM was evaluated on both image and time-series data and achieved better results than models that use just one of the two measures and other deep learning methods.
Identification of the El Niño Effect on Lake Toba’s Water Level Variation

Abstract Lake Toba is located in the province of North Sumatra, Indonesia which is the largest lake in southeast Asia and is the largest volcanic-type lake in the world. The lake’s countenance level is decreased and this can have a negative impact on utilization of power plants and agricultural irrigation. This study will see whether the water level is decreased due to the influence of El Niño activity. The methods used are statistical and descriptive analysis. The data used is the water level of Lake Toba in period of 1957-2016, precipitation data uses NOAA Climate Prediction Center (CPC) data with a resolution of 0.50° × 0.50° from January 1957 to December 2013 (56 years) to analyze rainfall anomalies. El Niño’s activities have an effect on Lake Toba water level, where the moderate El Niño &gt; 12 months duration resulted in a reduction of 1.5-2 m, while the strong El Niño with a duration of &gt; 10 months resulted in a decline of 2-2.5 m.
Learning versus optimal intervention in random Boolean networks

Abstract Random Boolean Networks (RBNs) are an arguably simple model which can be used to express rather complex behaviour, and have been applied in various domains. RBNs may be controlled using rule-based machine learning, specifically through the use of a learning classifier system (LCS) – an eXtended Classifier System (XCS) can evolve a set of condition-action rules that direct an RBN from any state to a target state ( attractor ). However, the rules evolved by XCS may not be optimal, in terms of minimising the total cost along the paths used to direct the network from any state to a specified attractor. In this paper, we present an algorithm for uncovering the optimal set of control rules for controlling random Boolean networks. We assign relative costs for interventions and ‘natural’ steps. We then compare the performance of this optimal rule calculator algorithm (ORC) and the XCS variant of learning classifier systems. We find that the rules evolved by XCS are not optimal in terms of total cost. The results provide a benchmark for future improvement.
A-MAL: Automatic Motion Assessment Learning from Properly Performed Motions in 3D Skeleton Videos

Assessment of motion quality has recently gained high demand in a variety of domains. The ability to automatically assess subject motion in videos that were captured by cheap devices, such as Kinect cameras, is essential for monitoring clinical rehabilitation processes, for improving motor skills and for motion learning tasks. The need to pay attention to low-level details while accurately tracking the motion stages, makes this task very challenging. In this work, we introduce A-MAL, an automatic, strong motion assessment learning algorithm that only learns from properly-performed motion videos without further annotations, powered by a deviation time-segmentation algorithm, a parameter relevance detection algorithm, a novel time-warping algorithm that is based on automatic detection of common temporal points-of-interest and a textual-feedback generation mechanism. We demonstrate our method on motions from the Fugl-Meyer Assessment (FMA) test, which is typically held by occupational therapists in order to monitor patients' recovery processes after strokes.