Reviewerly/exHarmony huggingface dataset:
A microbial kinetic optimization approach

In order to solve some optimization problems with many local optimal solutions, a microbial dynamic optimization (MDO) algorithm is proposed by the kinetic theory of hybrid food chain microorganism cultivation with time delay. In this algorithm, it is assumed that multiple microbial populations are cultivated in a culture system. The growth of microbial populations is not only affected by the flow of culture fluid injected into the culture system, the concentration of nutrients and harmful substances, but also by the interaction between the populations. The influence of culture medium which is injected regularly will suddenly increase the concentration of nutrients and toxic substances, it will suddenly increase the impact on the population. These characteristics are used to construct absorption operators, grabbing operators, hybrid operators, and toxin operators; the global optimal solution of the optimization problem can be quickly solved by these operators and the population growth changes. The simulation experiment results show that the MDO algorithm has certain advantages for solving optimization problems with higher dimensions.
PerceptionGAN: Real-world Image Construction from Provided Text through Perceptual Understanding

Generating an image from a provided descriptive text is quite a challenging task because of the difficulty in incorporating perceptual information (object shapes, colors, and their interactions) along with providing high relevancy related to the provided text. Current methods first generate an initial low-resolution image, which typically has irregular object shapes, colors, and interaction between objects. This initial image is then improved by conditioning on the text. However, these methods mainly address the problem of using text representation efficiently in the refinement of the initially generated image, while the success of this refinement process depends heavily on the quality of the initially generated image, as pointed out in the Dynamic Memory Generative Adversarial Network (DM-GAN) paper. Hence, we propose a method to provide good initialized images by incorporating perceptual understanding in the discriminator module. We improve the perceptual information at the first stage itself, which results in significant improvement in the final generated image. In this paper, we have applied our approach to the novel StackGAN architecture. We then show that the perceptual information included in the initial image is improved while modeling image distribution at multiple stages. Finally, we generated realistic multi-colored images conditioned by text. These images have good quality along with containing improved basic perceptual information. More importantly, the proposed method can be integrated into the pipeline of other state-of-the-art text-based-image-generation models such as DM-GAN and AttnGAN to generate initial low-resolution images. We also worked on improving the refinement process in StackGAN by augmenting the third stage of the generator-discriminator pair in the StackGAN architecture. Our experimental analysis and comparison with the state-of-the-art on a large but sparse dataset MS COCO further validate the usefulness of our proposed approach. Contribution-This paper improves the pipeline for text to image generation by incorporating perceptual understanding in the initial stage of image generation.
Exploring Category Attention for Open Set Domain Adaptation

Great success has been achieved in the area of unsupervised domain adaptation, which learns to generalize from the labeled source domain to the unlabeled target domain. However, most of the existing techniques can only handle the closed-set scenario, which requires both the source domain and the target domain to have a shared category label set. In this work, we propose a two-stage method to deal with the more challenging task of open set domain adaptation, where the target domain contains categories unseen to the source domain. Our first stage formulates the alignment of two domains as a semi-supervised clustering problem, and initially associates each target-domain sample x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sup> ∈ X <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sup> with a source-domain category label ℓ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sup> ∈ L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sup> . To this end, we use the self-training strategy to learn a teacher network and a student network, both of which adopt the self-attention mechanism. Our second stage refines the resulting clusters by identifying the negative associations (x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sup> , ℓ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sup> ) and labeling the involved xt as unknown. For this purpose, we investigate the compatibility of each association by replacing the self-attention maps in the last convolutional layers with the newly proposed category attention maps (CAMs), which locate the informative feature pixels for a given category. Experimental results on three public datasets show the effectiveness and robustness our method in adaptation across various domain pairs.
Embeddings Based Parallel Stacked Autoencoder Approach for Dimensionality Reduction and Predictive Maintenance of Vehicles

Predictive Maintenance (PdM) of automobiles requires the storage and analysis of large amounts of sensor data. This requirement can be challenging in deploying PdM algorithms onboard the vehicles due to limited storage and computational power on the hardware of the vehicle. Hence, this study seeks to obtain low dimensional descriptive features from high dimensional data using Representation Learning. The low dimensional representation can then be used for predicting vehicle faults, in particular a component related to powertrain. A Parallel Stacked Autoencoder based architecture is presented with the aim of producing better representations when compared to individual Autoencoders with focus on vehicle data. Also, Embeddings are employed on Categorical Variables to aid the performance of the Artificial Neural Networks (ANN) models. This architecture is shown to achieve excellent performance, and in close standards to the previous state-of-the-art research. Significant improvement in powertrain failure prediction is obtained along with reduction in size of input data using our novel deep learning ANN architecture.
English to Sinhala Neural Machine Translation

Neural Machine Translation (NMT) is the current state-of-the-art machine translation technique available among all other techniques. It has indeed shown promising results for resourceful languages. However, NMT heavily underperforms in low resource settings. English proficiency of Sri Lankans is very low and only a handful of people can understand and speak English. Since Sinhala language is the most widely used language in Sri Lanka, there is a huge demand for quality English to Sinhala translations in order to share knowledge among locals. Sinhala is a language with different morphology and syntax compared to English. Hence, translating English text to Sinhala is immensely challenging. In this paper, we introduce an effective NMT system along with Byte Pair Encoding (BPE) for the English-Sinhala language pair focusing on the Sri Lankan official government documents.
Visual-based People Counting and Profiling System for Use in Retail Data Analytics

Data on various key performance indicators (KPIs) are crucial in preventing problems and growing a business. In this paper, we propose the creation and analysis of the feasibility of using an intelligent video analytics (IVA) system to gather data on certain restaurant key performance indicators (KPIs). The main challenge lies in maximizing the use of an existing CCTV camera with a fixed viewpoint, which is tailored for security purposes instead of video analytics, by using its footage in the IVA. The researchers partnered with a restaurant in a high-traffic business district to create and test the system. The final system gathered data on foot traffic, customer gender classification, and customer group size. Neural networks such as YOLO, Deep SORT, and InceptionV3 were employed in the implementation. The results show that while it is possible to gather data on these three metrics through the system, the speed and accuracy can still be improved through downsizing the frames, down sampling the videos, and using other algorithms.
A Scalable Hardware Accelerator for Mobile DNA Sequencing

DNA sequencers are being miniaturized and increasingly targeted toward mobile applications. However, the intense bioinformatic computing needs of sequencers present a challenge for remote use with limited energy supply. This article presents a step toward realizing a low-power and high-speed bioinformatic engine, a hardware-accelerated basecaller, for mobile sequencing applications. The design is targeted for nanopore-based sequencers and is architected to easily scale to the complexity of this sensor. In addition to accelerating the CPU in real time with a custom field-programmable gate array (FPGA) through a high-speed serial link, the proposed framework envisages the challenging memory requirement of high-order nanopore sensors. The framework proposes a memory management scheme, which provisions the memory requirement problem in three dimensions: the basecalling speed, the circuit's area, and power consumption. The implementation results demonstrate a $142\times $ basecalling speed improvement over a 12-core CPU-only reference, as well as significant speedup compared with other existing solutions. Also, an energy efficiency improvement of three orders of magnitude is measured.
A Verifier-Based Password-Authenticated Key Exchange Using Tamper-Proof Hardware

Abstract Password-based authenticated key exchange (PAKE) allows two parties to compute a common secret key. PAKE offers the advantage of allowing two parties to pre-share only a password. However, when it is executed in a client–server environment, server corruption can expose the clients’ passwords. To be resilient against server compromises, verifier-based authenticated key exchange (VPAKE) is proposed, as an augmented version of PAKE. Thus far, there are two known major VPAKE constructions formally proven secure. However, both involve strong assumptions, such as random oracles. In this paper, we propose a simple and efficient VPAKE using tamper-proof hardware without random oracles to support resilient infrastructures. In particular, we transform Katz–Vaikuntanathan one-round PAKE into two-round VPAKE so as to instill resilience to server compromises. We provide a formal definition of VPAKE using tamper-proof hardware and security proof without random oracles. Finally, we provide a performance analysis and comparisons to previous VPAKE and PAKE protocols. Our transformation supports an efficient VPAKE protocol with six group element communications when the underlying Katz–Vaikuntanathan PAKE is instantiated by Cramer–Shoup ciphertext following the proposal by Benhamouda et al.
Representative Primary Capsule in Capsule Network Architecture for Fast Convergence

Deep neural networks like the Convolutional Neural Networks (CNN) is a popular state of the art method for various computer vision applications used for image classification and detection. However, they do not consider spatial relationships between various objects in the image. The Capsule Network (CapsNet) architecture proposed by Geoffrey Hinton consists of capsules instead of neurons. The capsule network is developed to overcome the drawbacks of CNN such as back-propagation, translation variance and pooling layers. This paper presents a comparison between the CNN and capsule network with respect to accuracy and training time to identify suitable applications which can be solved using capsule network. For smaller datasets, capsule network perform better than CNN. For this reason, the capsule network is implemented for safety critical system like autonomous vehicles. In order to train a fully autonomous vehicle, huge dataset covering all possible road driving scenario is required. Unavailability of such a huge dataset makes capsule network an apt choice for solving the problem. This paper finally proposes a new design for the capsule network which helps the model to achieve faster convergence leading to reduced training time for all kinds of datasets. The enhanced dynamic routing agreement based capsule network model uses a new representative primary capsule in place of original primary capsules for fast convergence. The proposed model achieves 2.8 times faster convergence on the MNIST dataset.
An ant colony based mapping of quantum circuits to nearest neighbor architectures

Although this decade is witnessing tremendous advancements in fabrication technologies for quantum circuits, this industry is facing several design challenges and technological constraints. Nearest Neighbor (NN) enforcement is one such design constraint that demands the physical qubits to be adjacent. In the last couple of years, this domain has made progress starting from designing advanced algorithms to improved synthesis methodologies, even though developing efficient design solutions remains an active area of research. Here, we propose such a synthesis technique that efficiently transforms quantum circuits to NN designs. To find the NN solution, we have taken help of an ant colony algorithm which completes the circuit conversion in two phases: in the first phase, it finds the global qubit ordering for the input circuit and, in the second phase, a heuristic driven look-ahead scheme is executed for local reordering of gates. The proposed algorithm is first fitted into a 1D design and, later, mapped to 2D and 3D configurations. The combination of such heuristic and the meta-heuristic schemes has resulted promising solutions in the transformation of quantum circuits to NN-compliant architectures. We have tested our algorithm over a wide spectrum of benchmarks and comparisons with state-of-the-art design approaches showed considerable improvements.
Automatic Word Spacing of Korean Using Syllable and Morpheme

In Korean, spacing is very important to understand the readability and context of sentences. In addition, in the case of natural language processing for Korean, if a sentence with an incorrect spacing is used, the structure of the sentence is changed, which affects performance. In the previous study, spacing errors were corrected using n-gram based statistical methods and morphological analyzers, and recently many studies using deep learning have been conducted. In this study, we try to solve the spacing error correction problem using both the syllable-level and morpheme-level. The proposed model uses a structure that combines the convolutional neural network layer that can learn syllable and morphological pattern information in sentences and the bidirectional long short-term memory layer that can learn forward and backward sequence information. When evaluating the performance of the proposed model, the accuracy was evaluated at the syllable-level, and also precision, recall, and f1 score were evaluated at the word-level. As a result of the experiment, it was confirmed that performance was improved from the previous study.
Facial expression recognition based on facial part attention mechanism

In the context of facial expression recognition (FER), a lot of methods focus on feature extraction of the whole facial image, but sometimes it is equally important to concentrate on local area features that embody stronger emotions. This paper proposes an FER method based on the facial part attention mechanism (FPA). We adopt the attention mechanism to extract emotional rich local area features, which are complementary to the whole facial features for better FER. First, this paper proposes a cluster-based facial landmarks selection method to select facial landmarks that have commonality and best reflect local area emotions. Then, we design an FPA convolutional neural network, which consists of two parts. The first part is the object network, which is used to extract the whole facial features; the second part is the part attention network, and the attention component is used to extract the local part features. Finally, the two parts are merged to train the final classifier. The method was tested on the Real-World Affective Faces database and the recognition accuracy of 87.26% was obtained, which proved the effectiveness of the method.
A Novel Approach for Mining Closed Clickstream Patterns

Closed sequential pattern (CSP) mining is an optimization technique in sequential pattern mining because they produce more compact representations. Additionally, the runtime and memory usage required for mining CSPs is much lower than the sequential pattern mining. This task has fascinated numerous researchers. In this study, we propose a novel approach for closed clickstream pattern mining using C-List (CCPC) data structure. Closed clickstream pattern mining is a more specific task of CSP mining that has been lacking in research investment; nevertheless, it has promising applications in various fields. CCPC consists of two key steps: It initially builds the SPPC-tree and the C-List for each frequent 1-pattern and then determines all frequently closed clickstream 1-patterns; next, it constructs the C-List for each frequent k-pattern and mines the remaining frequently closed k-patterns. The proposed method is optimized by modifying the SPPC-tree structure and a new property is added into each node element in both the SPPC-tree and C-Lists to quickly prune nonclosed clickstream. Experimental results conducted on several datasets show that the proposed method is better than the previous techniques and improves the runtime and memory usage in most cases, especially when using low minimum support thresholds on the huge databases.
Multi-kernel Covariance Terms in Multi-output Support Vector Machines

This paper proposes a novel way to learn multi-task kernel machines by combining the structure of classical Support Vector Machine (SVM) optimization problem with multi-task covariance functions developed in Gaussian process (GP) literature. Specifically, we propose a multi-task Support Vector Machine that can be trained on data with multiple target variables simultaneously, while taking into account the correlation structure between different outputs. In the proposed framework, the correlation structure between multiple tasks is captured by covariance functions constructed using a Fourier transform, which allows to represent both auto and cross-correlation structure between the outputs. We present a mathematical model and validate it experimentally on a rescaled version of the Jura dataset, a collection of samples representing the amount of seven chemical elements into several locations. The results demonstrate the utility of our modeling framework.
Security and a Framework for Identity

We have a framework for a fundamental, hence broad, notion of identity, which allows very diverse and general aspects of security to be addressed. We use the causal theory of reference to explain how not just singular denoting expressions but also artifacts such as fingerprints and classifiers (broadly, “denoting devices”) denote individuals. Our current focus is on identity in criminal justice settings. The identity relation between denoting devices is an equivalence relation inducing equivalence classes that contain devices all of which denote the same individual. This is how information on individuals is typically fused. We see a case as a constellation of situations, to which investigators refer and in which information is recorded and transferred. We discuss how argumentation schemes support identity hypotheses, and how evidence for identity hypotheses from multiple sources is combined in Dempster-Shafer theory. Our framework can be applied in principle to any approach or technique for establishing identity hypotheses. The specific techniques of a domain (e.g., machine learning) are left to domain experts; we just incorporate them into our framework.
Word2Vec Duplicate Bug Records Identification Prediction Using Tensorflow

Bug duplication reporting is one of the most widespread software problems that cause inconvenience for the internal software stakeholders. It is useful for developers to eliminate redundant bug records where the fewer bugs duplicated records in bug reports documentation the more efficiently allocated resources are set to fix and enhance the software features. In this paper, the word embedding (Word2Vec) approach is used on four different software components from the Mozilla Core dataset with different sentence types through the duplicated bug category records to compare whether two given bug record descriptions are categorized as related bugs records. Besides, this paper proposes three different similarity measures and explores the accuracy of each measure. The study results show that the approach's accuracy is proportional to the existence of similar words within any of the two given two bug records descriptions. Additionally, we found that percentage of similarity accuracy is improved by finding the closest word using the Euclidean distance method than traversing for more index adjacent values within the trained word vector array.
Improving face recognition performance using TeCS<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msup><mml:mrow /><mml:mn>2</mml:mn></mml:msup></mml:math> dictionary

Human mind processes the different primitive components of image signals such as color, shape, texture, and symmetry in a parallel and complex fashion. Deep neural networks aim to learn all these components from the image in an unsupervised manner. However, learning the primitive features is not formally assured in a deep learning formulation, and, adding these features explicitly would improve the performance. Especially in face recognition, humans intuitively and implicitly employ the usage of primitive features such as color, shape, texture, and symmetry of faces. Inspired by this observation, this paper presents a novel approach in building a learning based TeCS2 space. This space consists of meta-level features obtained from dictionary learning and combining it with task specific deep learning classifiers (such as DenseNet) for face recognition. Confidence based fusion mechanism is presented to supplement the task specific deep learning classifier with the proposed TeCS2 features. The effectiveness of the proposed framework is evaluated on four benchmark face recognition datasets: (i) Disguised Faces in the Wild (DFW), (ii) Labeled faces in the wild (LFW), (iii) IIITD Plastic Surgery dataset, and (iv) Point and Shoot Challenge (PaSC).