Reviewerly/exHarmony huggingface dataset:
Loss-based Active Learning for Named Entity Recognition

This paper addresses the practical issue of lacking training data when building named entity recognition (NER) systems. To this aim, we introduce a new active learning method for reducing the number of training samples required by the underlying NER system. Different from prior work that only focuses on training data, we define a new loss function that when estimating loss and uncertainty scores of training samples for selection, it takes also into account the uncertainty of the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex> unlabelled test instances most similar to the unlabelled training instances. Experimental results on both general domain and clinical benchmark datasets show that the proposed active learning method allows to train the NER system with between 5% to 7% less training data compared to state of the art uncertainty sampling methods, while retaining high NER effectiveness.
Deep Reinforcement Learning with Different Rewards for Scheduling in High-Performance Computing Systems

Scheduling is a challenging task for high-performance computing systems since it involves complex allocations of various types of resources among jobs with different characteristics. Because incoming jobs often vary with resource requests and may interact with other jobs, heuristics based scheduling algorithms tend to be suboptimal and require substantial amount of time to design and test under diverse conditions. As a result, reinforcement learning (RL) based approaches have been proposed to tackle various job scheduling challenges. We have also used deep neural networks for approximating the decisions in RL agents as table-based RL agent is not scalable for large-scale problem sizes. The performance of RL agents, however, has proven to been notoriously instable and sensitive to training hyperparameters and the reward signal. In this work, we aim to study how different reward signals might affect the RL agents’ performance. We trained RL agents with four different reward signals and simulations results under Alibaba workloads showed that trained RL agents’ improve the performance for 60-65% of the jobset compared to two popular heuristics.
AutoFuse: A Semi-supervised Autoencoder based Multi-Sensor Fusion Framework

The performance of existing methods for multisensor fusion are severely affected by the lack of significant amount of labeled data. In most practical scenarios, the amount of unlabeled data is huge in comparison to labeled data. To address this problem, a novel autoencoder based multi-sensor fusion framework for semi-supervised learning is proposed in this work. Here, both labeled and unlabeled data are used for learning the latent representation from each sensor. Subsequently, the latent representation of all the sensors are combined to perform classification. A joint optimization formulation is presented for learning the sensor-specific latent representation, their encoder and decoder weights and the classification weights together. This ensures discriminative features to be learnt from individual sensors that aids in classification. The requisite solution steps and the closed form updates for the joint learning of all the parameters are given. Experiment results presented on two datasets from different domains demonstrate the generalizability and superior performance of the proposed AutoFuse compared to state-of-the-art methods with relatively less complexity and the ability to work with partially annotated data.
Selection of OLAP Materialized Cube by Using a Fruit Fly Optimization (FFO) Approach: A Multidimensional Data Model

The Online Analytical Processing (OLAP) based multidimensional examination hassles for several stockpiling magnificence over huge data. For as much to recognize queries answering time companionable by OLAP framework users and understanding entire business perceive mandatory, OLAP data is structured as a data cube (a multidimensional model). The OLAP queries are responded in speedy and steady time by utilizing the cube materialization for assessments takers. But, this also involves unendurable expenses, regarding to stockpile memory and period, and as a data depot, OLAP has an average dimension and dimensionality which is to be significant on query processing. Consequently, cube assortment has got to be finished motivating to diminish inquiry management expenses, maintaining as a restraint the materializing gap. Several techniques and heuristics like deviationist and insatiable algorithms have been utilized to offer an estimated result. In this work, a Fruit Fly Optimization (FFO) approach is implemented in a lattice structure to obtain an optimal materialized data cube for reducing the query processing expenses. The results illustrate that FFO generates better performance than Particle Swarm Optimization (PSO) in terms of frequency and number of dimensions.
An Industrial B-Mode Phased Array Ultrasonic Imaging Reconstruction Algorithm Based on FRI Sampling

Firstly, a novel FRI sampling model has been proposed according to the characteristics of ultrasonic signals. The model has the advantages such as good stability, strong antinoise ability, simple circuit implementation, and fewer preconditions, compared to the traditional methods. Then, in order to verify the validity of the sampling model, the method is applied to B-type ultrasonic imaging, and a B-type phased array ultrasonic imaging algorithm based on FRI sampling model is proposed. Finally, the algorithm simulation experiment is designed, and the results show that the sampling point required by the proposed FRI sampling model is only 0.1% of the traditional B-type phased array ultrasonic imaging method, and the sampling frequency of the proposed ultrasonic imaging algorithm is only 0.0077% of the traditional B-type ultrasonic imaging method. Additionally, the experiment result indicates that this algorithm is more applicable to phased array ultrasonic imaging than the SOS filter is.
Meta-learning of Pooling Layers for Character Recognition

In convolutional neural network-based character recognition, pooling layers play an important role in dimensionality reduction and deformation compensation. However, their kernel shapes and pooling operations are empirically predetermined; typically, a fixed-size square kernel shape and max pooling operation are used. In this paper, we propose a meta-learning framework for pooling layers. As part of our framework, a parameterized pooling layer is proposed in which the kernel shape and pooling operation are trainable using two parameters, thereby allowing flexible pooling of the input data. We also propose a meta-learning algorithm for the parameterized pooling layer, which allows us to acquire a suitable pooling layer across multiple tasks. In the experiment, we applied the proposed meta-learning framework to character recognition tasks. The results demonstrate that a pooling layer that is suitable across character recognition tasks was obtained via meta-learning, and the obtained pooling layer improved the performance of the model in both few-shot character recognition and noisy image recognition tasks.
Explicit attacks on passive side channels of the light source in the BB84 decoy state protocol

Abstract Various imperfections of the hardware in quantum key distribution (QKD) lead to discrepancies between the theory and real-world devices. An eavesdropper can use these discrepancies to compromise the QKD security, thus legitimate sides need to estimate the influence of imperfections to prevent the loss of security. Here we investigate explicit eavesdropping attacks on passive side channels of the light source for a BB84 decoy states QKD protocol. We consider an optimal phase-covariant cloning attack, followed by unambiguous state discrimination and a joint measurement. Our calculations provide a hint that for state-of-the-art light sources the inherent presence of passive side channels still allows the protocol to be secure, albeit with the reduced secret key rates.
Mix-hops Graph Convolutional Networks for Skeleton-Based Action Recognition

Skeleton-based human action recognition has drawn considerable research interest since it can robustly accommodate dynamic circumstances and complex backgrounds. By modeling the human body skeletons as graph structure, graph convolution network (GCN) has achieved great success in this field. However, these methods based on GCN are difficult to capture global features and relations only through a single-layer network. To obtain the relations between distant skeleton joints of human body, it is necessary to stack multiple graph convolution layers. In addition, the topology of the graph needs to be set manually in graph convolution operation and it is shared in all layers and time dimensions. In this paper, a novel mix-hops graph convolutional network (MHGCN) is proposed to recognize human action from skeleton data. The proposed module can fuse local features with global features through a layer of graph convolutional network. Besides, the topological structure of graph in our model changes with the time dimension and it can be individually learned in an end-to-end way through the BP algorithm. The experiments on several benchmark datasets show remarkable performance for human action recognition, demonstrating the effectiveness of our method.
Cast-as-Intended: A Formal Definition and Case Studies

Verifiable voting systems allow voters to check whether their ballot is correctly recorded (individual verifiability) and allow anyone to check whether votes expressed in recorded ballots are correctly counted (universal verifiability). This suffices to ensure that honest voters’ votes are correctly counted, assuming ballots are properly generated.Achieving ballot assurance, i.e., assuring each voter that their vote is correctly encoded inside their ballot, whilst ensuring privacy, is a challenging aspect of voting system design. This assurance property is known as cast-as-intended. Unlike many properties of voting systems, it has yet to be formalised. We provide the first formal definition and apply our definition to MarkPledge, Prêt à Voter, Selene, ThreeBallot, and schemes based upon Benaloh challenges.
Improving Back-Translation with Iterative Filtering and Data Selection for Sinhala-English NMT

Neural Machine Translation (NMT) requires a large amount of parallel data to achieve reasonable results. For low resource settings such as Sinhala-English where parallel data is scarce, NMT tends to give sub-optimal results. This is severe when the translation is domain-specific. One solution for the data scarcity problem is data augmentation. To augment the parallel data for low resource language pairs, commonly available large monolingual corpora can be used. A popular data augmentation technique is Back-Translation (BT). Over the years, there have been many techniques to improve Vanilla BT. Prominent ones are Iterative BT, Filtering, and Data selection. We employ these in Sinhala - English extremely low resource domain-specific translation in order to improve the performance of NMT. In particular, we move forward from previous research and show that by combining these different techniques, an even better result can be obtained. Our combined model provided a +3.0 BLEU score gain over the Vanilla NMT model and a +1.93 BLEU score gain over the Vanilla BT model for Sinhala → English translation. Furthermore, a +0.65 BLEU score gain over the Vanilla NMT model and a +2.22 BLEU score gain over the Vanilla BT model were observed for English → Sinhala translation.
Fast computation of deep neural network and its real‐time implementation for image recognition

Abstract The convolution is widely used for deep neural networks to extract the key features, which requires many additions and multiplications. In this study, the fast computational algorithm is presented to reduce the number of arithmetic when the accuracy is kept. The order of deep convolution is alternative to save the computational operators. To verify the performance, the proposed algorithm is embedded to the typical deep neural network VggNet. The structure of VggNet is further modified using the proposed summation and concatenation techniques to improve the computational accuracy and to reduce the processing time. Compared with the original VggNet, the simulations show that the operational FLOPs can be greatly reduced at least 50% with various datasets testing. Besides, the training time with epoch per batch can save about 10%–20%. The proposed fast algorithm can lessen the parameters and the mode size over 90%. The recognition accuracy can be improved with 1%–4% from various datasets testing. Based on the fast network, real‐time FPGA had been realized, which the hardware performance can achieve 371 GOPs with 642 DSP cores. The processing speed can achieve near to 1 k frames per second, and the real‐time recognition rate can achieve over 90%.
Towards Richer Realizations of Holographic CBR

Holographic Case-Based Reasoning is a framework developed to build cognitively appealing case-based reasoners with proactive and interconnected cases. Improved realizations of the Holographic CBR framework are developed using the principles of dynamic memory proposed by Roger Schank and tested on their cognitive appeal, efficiency, and solution quality compared to other relevant systems.
Annotated dataset of history-related tweets

In this article, we present a dataset containing history-related content obtained from social media. It contains hashtags and tweets that include these hashtags, as well as the results of third party tools applied to the tweets that include extracted entities, years, and url categories, and the categories for the history-related hashtags we used to crawl the tweets. We collected the tweets from Twitter official API using hashtag-based crawling. The crawling process had been performed from March 2016 to July 2018. During the crawling, we applied a bootstrapping approach which is an iterative process of collecting tweets using a small set of seed hashtags, and a manual inspection of newly acquired hashtags that co-occur with the seed hashtags to include those they are related to history. Finally, we collected 147 history-related hashtags and 2,370,252 tweets. We then defined 6 categories for the collected hashtags after their manual investigation. The presented dataset could be useful for further analysis on how people refer to history in Twitter, for collecting new history-related tweets, for training classifiers to detect history-related tweets, or for further investigations of the proposed hashtag categories.
Dynamic Perturbation of Weights for Improved Data Reconstruction in Unsupervised Learning

The concept of weight pruning has shown success in neural network model compression with marginal loss in classification performance. However, similar concepts have not been well recognized in improving unsupervised learning. To the best of our knowledge, this paper proposes one of the first studies on weight pruning in unsupervised autoencoder models using non-imaging data points. We adapt the weight pruning concept to investigate the dynamic behavior of weights while reconstructing data using an autoencoder and propose a deterministic model perturbation algorithm based on the weight statistics. The model perturbation at periodic intervals resets a percentage of weight values using a binary weight mask. Experiments across eight nonimaging data sets ranging from gene sequence to swarm behavior data show that only a few periodic perturbations of weights improve the data reconstruction accuracy of autoencoders and additionally introduce model compression. All data sets yield a small portion of (<5 %) weights that are substantially higher than the mean weight value. These weights are found to be much more informative than a substantial portion (>90%) of the weights with negative values. In general, the perturbation of low or negative weight values at periodic intervals has improved the data reconstruction loss for most data sets when compared to the case without perturbation. The proposed approach may help explain and correct the dynamic behavior of neural network models in a deterministic way for data reconstruction and obtaining a more accurate representation of latent variables using autoencoders.
PePa Ping Dataset

Among all Internet Quality of Service (QoS) indicators, Round-trip time (RTT), jitter and packet loss have been thoroughly studied due to their great impact on the overall network's performance and the Quality of Experience (QoE) perceived by the users. Considering that, we managed to generate a real-world dataset with a comprehensive contextualization of these important quality indicators by passively monitoring the network in user-space. To generate this dataset, we first developed a novel Periodic Passive Ping (PePa Ping) methodology for Android devices. Contrary to other works, PePa Ping periodically obtains RTT, jitter, and number of lost packets of all TCP connections. This passive approach relies on the implementation of a local VPN server residing inside the client device to manage all Internet traffic and obtain QoS information of the connections established. The collected QoS indicators are provided directly by the Linux kernel, and therefore, they are exceptionally close to real QoS values experienced by users' devices. Additionally, the PePa Ping application continuously measured other indicators related to each individual network flow, the state of the device, and the state of the Internet connection (either WiFi or Mobile). With all the collected information, each network flow can be precisely linked to a set of environmental data that provides a comprehensive contextualization of each individual connection.
Exploring Classification Capability of CNN Features

This paper explores the classification capability of features by three ways, respectively: decision tree/random forest, hierarchical clustering and WordNet. To simulate the human judgment process, first, a decision tree is first constructed to reflect the importance of features. The model performs worse when top 5 features and top 10 features are used separately than when the all features are used, showing that the top k feature set omits some information that are important to classification. Second, hierarchical clustering is used to show the relationships between high-level features of different classes. The ward linkage method is selected to construct the hierarchical clustering tree. The parts of the adjacent classes with higher feature overlap are mapped back to the original image to visually show common features captured by the CNN network. Finally, to study the semantic value of neural network classification, the WordNet semantic structure is applied to fit the image classification process. However, results are relatively poor, demonstrating the inconsistency between the WordNet classification and machine learning classification.
Geochemical Exploration Techniques

Geochemistry plays a major role in exploration for geothermal resources, both during surface exploration and after exploration wells have been drilled and discharged. The main tasks of the geochemist during surface exploration are the selection of sampling locations, sample collection, chemical analysis of the samples and interpretation of the analytical results. This chapter presents a review of the whole process.
Impact of different compression rates for hyperspectral data compression based on a convolutional autoencoder

This work addresses the problem of hyperspectral data compression and compares the reconstruction accuracy for different compression rates. Through data compression, the enormous amount of data created by hyperspectral sensors can be transmitted effectively. Remote sensing-related applications such as disaster management, land cover classification, and object recognition are improved with real-time information. We propose a 1D-Convolutional Autoencoder structure for lossy hyperspectral data compression and the necessary adjustments for realizing compression ratios of C<sub>R</sub> = 4, C<sub>R</sub> = 8, C<sub>R</sub> = 16, and C<sub>R</sub> = 32. Unlike many other contributions, we not only evaluate the reconstruction accuracy based on standard metrics like Signal to Noise Ratio and Spectral Angle Mapper but also on a target application, namely land cover classification. The reconstruction accuracy of the 1D-Convolutional Autoencoder is compared to machine learning-based lossy compression methods, namely Deep Autoencoder, Nonlinear Principal Component Analysis, and the Principal Component Analysis. The compression performances are compared using two data sets with different amounts of spectral signatures. The 1D-Convolutional Autoencoder performance surpasses the benchmark methods for all compression rates using the standard metrics. In addition, the 1D-Convolutional Autoencoder achieves the highest classification results for the land cover classification for all compression rates and is able to compress hyperspectral data efficiently. Furthermore, the robustness and generalization capability of the 1D-Convolutional Autoencoder is demonstrated by using unknown data for the evaluation.
Deep Learning for Accurate Corner Detection in Computer Vision-Based Inspection

This paper describes application of deep learning for accurate detection of corner points in images and its application for an inspection system developed for the worker training and assessment. In our local built and construction industry, workers need to be certified for their technical skills through a training and assessment process. Assessment involves trainees to understand a task given with a technical drawing, e.g. electrical wiring and trunking wall assembly, and implement it accurately in a given period of time. Typically experts manually/visually evaluate the finished assembly and decide if it’s done correctly. In this study, we employed computer vision techniques for the assessment process in order to reduce significant man hour. Computer vision based system measures dimensions, orientation and position of the wall assembly and produces a report accordingly. However, analysis depends on accurate detection of the objects and their corner points which are used as reference points for measurements. Corner detection is widely used in image processing and there are numerous algorithms available in the literature. Conventional algorithms are founded upon pixel based operations and they return many redundant or false corner points. In this study, we employed a hybrid approach using deep learning and Minimum Eigen value corner detection for this purpose and achieved highly accurate corner detection. This subsequently improved the reliability of the inspection system.
User Identification Leveraging Whispered Sound for Wearable Devices

The increasingly popular usage of wearable devices provides users with the ability to continuously track their health conditions or physical activities. Such system is however vulnerable to user spoofing, in which a user distributes his/her device to other users such that the data collected from these users could be claimed to be his/her own. Thus, it is critical to identify the user for many wearable devices, allowing the sensing data to be labeled properly. In this paper, we propose a user identification system by leveraging the users whispered sound to mitigate user spoofing for wearable devices. Our system exploits the contact microphone placed into contact with the body to capture the users whispered sound for user identification. Given the captured acoustic data, our system first identifies frames which contain whispered events. Our system then calculates acoustic features from the identified whispered frames to determine whether the voice is collected when the microphone is on the body. Moreover, to make our system robust, we assign different quality weights to the whispers phonemes by considering their consistency (i.e., intra users differences) and distinctiveness (i.e., inter users differences) simultaneously. Our experiments demonstrate that our system is robust and accurate across various scenarios.