Reviewerly/exHarmony huggingface dataset:
Homomorphic Encryption for Quantum Annealing with Spin Reversal Transformations

Homomorphic encryption has been an area of study in classical computing for decades. The fundamental goal of homomorphic encryption is to enable (untrusted) Oscar to perform a computation for Alice without Oscar knowing the input to the computation or the output from the computation. Alice encrypts the input before sending it to Oscar, and Oscar performs the computation directly on the encrypted data, producing an encrypted result. Oscar then sends the encrypted result of the computation back to Alice, who can decrypt it. We describe an approach to homomorphic encryption for quantum annealing based on spin reversal transformations and show that it comes with little or no performance penalty. This is in contrast to approaches to homomorphic encryption for classical computing, which incur a significant additional computational cost. This implies that the performance gap between quantum annealing and classical computing is reduced when both paradigms use homomorphic encryption. Further, homomorphic encryption is critical for quantum annealing because quantum annealers are native to the cloud - a third party (such as untrusted Oscar) performs the computation. If sensitive information, such as health-related data subject to the Health Insurance Portability and Accountability Act, is to be processed with quantum annealers, such a technique could be useful.
A Modified Grey Wolf Optimization for Energy Efficiency and Resource Wastage Balancing in Cloud Data-Centers

Virtual Machine Placement (VMP) process is one of the significant issues in Cloud data-centers. This operation can manage VMs to be placed on the best available Physical Machine (PM) and it has a notable impact on the performance, resource utilization, and power consumption of the data-centers. Meta-heuristics swarm intelligence methods are a common approach of solving these types of optimization problems. Grey Wolf Optimization (GWO) is a meta-heuristic algorithm that has proved its advantages. In this paper, we propose a Modified Grey Wolf Optimizer (MGWO) to balance power consumption and memory resource wastage using virtual machine placement. The suggested algorithm compared with two existing greedy algorithms, First Fit (FF) and First Fit Decreasing (FFD). The simulation results revealed the effectiveness of the proposed MGWO to provide a tradeoff between energy efficiency and memory resource wastage. The average of resource wastage of the MGWO is less than 10 percent, considering the tradeoff between energy consumption and the memory wastage parameter. In addition, the convergence test of the MGWO has been compared with a Genetic Algorithm (GA). The proposed solution is useful for defining some constraints for controlling resource wastage, such as cores and memory wastage.
A teacher-student model with the bilingual word embedding for zero-shot machine translation

Neural machine translation has achieved state-of-art performance with sufficient data, but it still suffers from the data scarcity problem for low-resource language pairs. Teacher-student model that transfers knowledge from a pivot→target model to a source→target model with the source↔pivot parallel data has shown its effectiveness. In this paper, we improve the model with the bilingual word embedding and transformer architecture. Experiments are carried out on three commonly used translation datasets and the result shows the improvement over a baseline teacher-student model.
A Lightweight Deep Network for 3D Medical Image Segmentation

Medical image segmentation is an important preprocessing task for computer-aided diagnosis and computer-aided surgery. As medical images such as computed tomography scans provide 3D volumetric data, 3D deep learning (DL) models (e.g., 3DU-Net) have been proposed for medical image segmentation. Compared with conventional 2D DL, 3D DL models have more network parameters, which may lead to overfitting when small training sets are available. In addition, 3D image processing has a high computational cost and consequently a long computation time. To reduce both the number of network parameters and computation time, we propose a lightweight formulation of 3DU-Net based on three-component decomposition. The proposed method can substantially reduce the number of parameters and computation time for 3D image segmentation while maintaining a comparable accuracy with the conventional 3DU-Net.
Mass authentication information injection method for effective security management of AMI device

AMI is a technology that provides electricity usage and billing information in real time by two-way communication between consumers and a supplier which is a power company. The two-way communication environment can be used as a variety of security threat paths and security vulnerabilities may occur due to the increased adoption of multi-devices by multiple manufacturers. Security threats to AMI can be a major threat to cybersecurity across the country, so countermeasures are needed. In order to solve these problems, an electronic certificate-based AMI device authentication system was established, but a system for managing and injecting keys and certificates for a large number of devices is required. Management technology for security authentication information such as keys and certificates of AMI devices is an important factor in ensuring AMI security safety. Therefore, in this paper, we propose a method to inject mass security authentication information for AMI devices to systematically, effectively manage and safely inject authentication information.
Fingerprinting using database steganography

With the becoming of the age of data science, data as a primary resource requires protection, not only with respect to privacy. Still, many data driven technologies ask for the sharing of data with external experts, labs and other collaborators. Data fingerprinting and watermarking describes a set of techniques in order to protect the ownership of data even after sharing and enabling the identification of so-called data leaks, i.e. they make it possible to identify the leaking data recipient of a data set. In this work we provide a novel approach for watermarking whole databases based on steganographic methods that are robust against changes in the stored data set.
DOVE: Decomposition Oriented Video super-rEsolution

Video super-resolution (VSR) has attracted a lot of attention that converts a low resolution (LR) video into a high resolution (HR) one. The original LR video is typically produced either by the downscaling processing or low-resolution sensor. Considering that the resolution degradation or limitation makes different impacts on different low-frequency (LF) and high-frequency (HF) components of the LR video signal, we propose a Decomposition Oriented Video super-rEsolution (DOVE) method in this paper. More specifically, a three-stream VSR network is designed in which the proposed LF and HF stream is responsible for modeling LF and HF components in the feature space. Moreover, a multi-frame refinement stream takes features of coarsely aligned frames as input and generates finely aligned counterparts progressively to guide the learning of LF and HF streams at the intermediate feature level. Furthermore, non-local channel attention is devised to capture long-range dependencies on a global scale both in the channel domain. Experimental results indicate that separating the learning of LF and HF components helps better estimate the HR frame from LR frames and superior VSR performance is achieved when compared with that of recent state-of-the-art methods.
Myanmar Dialogue Act Recognition Using Bi-LSTM RNN

Spoken language understanding (SLU) is an essential element of any dialogue system to understand the language where dialogue act (DA) recognition is also critical aspects of pre-processing step for speech understanding and dialogue system. This paper proposes a deep learning-based DA model which use a deep recurrent neural network (RNN) with bi-directional long short-term memory (Bi-LSTM). The model mainly consists of a word-encode layer, a Bi-LSTM layer, and a softmax layer. For corpus preparation, we collected and annotated a large dialog act annotation corpus, which is called MmTravel (Myanmar Travel) corpus, on travel domain human-human conversations dataset (consists of 80k utterances). This paper reports analysis and comparison of proposed model Bi-LSTM with RNN, LSTM, and baseline SVM model. Experiments on the dataset is shown that our proposed DA model performs better than our previous work, support vector machine (SVM) models, which achieve an improvement of more than 2% accuracy increase in classification on the dataset.
Autoencoder Embedding, Segmentation and Classification of Apparel Images using a Deep ConvNet Approach

The Fashion sector is among the most visually diverse and resourceful ones. Specifically, it requires the techniques of image processing and computer vision techniques in order to determine the widely prevalent fashion trends and public preferences, to generate advertisement policies, categorize apparel for online stores and search engines. It becomes imperative to generate models leveraging Computer Vision to classify this apparel and reconstruct lossy images that may arise from non-standardized sources while requiring little human intervention. This paper explores varied methods in Deep Learning that allow extraction of features in each image in the form of embeddings, pixel-wise segmentation of the apparel, followed by classification to the closest and most fitting class. The paper sets a base for a complete vision based solution for fashion items, which includes instance segmentation using Mask RCNN, feature extraction using embeddings and classification of all the fashion items present in a frame. Also, the performance of existing models are compared based on this dataset with thirteen main classes by ensuring minimum loss with the combination of certain hyperparameters.
Self-Calibration Kalman Filter with Linearly-Varying Unknown Input

State estimation for stochastic systems with unknown inputs has been a research hotspot in recent years. Many research results including the augmented state Kalman filter, the two-stage Kalman filter, the optimal two-stage Kalman filter and the robust two-stage Kalman filter (RTSKF) have been developed by various researchers. Considering that unknown inputs sometimes vary linearly in practical engineering, this paper addresses the problem of state estimation for linear systems with linearly-varying unknown inputs. A self-calibration Kalman filter with linearly-varying unknown input (SCKF-LVUI) is proposed where the unknown input is estimated by exploring the information from the state equation and state estimates at previous steps. The derivation of the SCKF-LVUI is given and the state estimate are calculated as well as the corresponding covariance matrix. Furthermore, a simulation example is conducted and demonstrates that the presented SCKF-LVUI has high estimation accuracy and can be conveniently applied in engineering applications.
Formal Verification of a Database Management System

Assertion based program verification is a well-known formal approach to (dis)prove correctness of algorithms associated to software systems. Assertions are input and output properties a correct program must satisfy. These properties are traditionally written in a specification language based on classical logic. Associated classical reasoning (inference) systems are then used to (dis)prove program correctness. However, when programs manipulate mutable data structures such as pointers, classical logical operators have been unable to successfully model syntactically unrelated expressions. In this article, we study separation logics, which are equipped with specially-purposed operators to model mutable data structures. We describe the use of this logic as a specification language in the verification of a database management system (DMS). In particular, we detect several bugs in two DMS libraries regarding heap manipulation. We describe these bugs in detail and propose solutions.
A Combination of Landsat-8 Satellite Imaging with Geothermal Manifestation Point at Wayratai Geothermal Prospect Area to identify “the zone of interest” on Geo-track and Geo-tourism planning

Abstract In this paper, we will identify the zone of interest on Wayratai geothermal prospect area based on the Satellite Imagery Map and Geothermal Manifestation Point to make a geo-track and geo-tourism planning in the future. Based on Satellite Imagery Map, we composite several bands, such as band (6,4,2) to describe an alteration rock and composite band (4,3,2) to describe a vegetation variation. After that, we process several bands to get the land surface temperature in this area, then we correlated the satellite data with surface measurement (with X-Ray Thermogun) on several geothermal manifestations. Based on both correlation, it shows a positive correlation, such as hot spring on the East Area with temperature 70-80°C is correlated by a high value of LST (indicated by dark-red zone), fumarole with temperature 74-78°C, and steaming ground on the West Area with temperature 26-59°C. Based on this data, then we make a geo-tourism planning on this area with make geo-track to connect all of “zone of interest” on satellite imagery with regional geology spot with a circular route, so in the future, Wayratai geothermal prospect area can be one of a geo-tourism spot on Lampung Province.
DualGCN: Representation Learning for Property Graphs via Dual-level Convolutional Networks

Representation learning based on graph neural networks (GNNs) has attracted a surge of convolutional in recent years. Most of the existing state-of-the-art approaches only focus on node properties and inadequately utilize edge properties, yet property graphs, where both nodes and edges have high-dimensional properties, are ubiquitous in real world. In this paper, we propose a novel property graph representation learning method, named DualGCN (GCN with dual-level convolutional layers), to incorporate node and edge properties, even graph structure into the procedure of learning graph representations. DualGCN consists of node-level and edge-level convolutional layers and alternately stacks the two types of layers to demystify the relationship between node and edge properties. Leveraging the proposed DualGCN, the node and edge embeddings can be mutually reinforced and improved. Besides, we use 2-hop neighbors and assign the central nodes or edges duple weights to improve the quality of graph structure information. As a result, our experiments with multiple realworld property graph datasets demonstrate that DualGCN consistently outperforms the existing baseline and state-of-the-art methods by a significant margin in node classification, link prediction and graph classification.
Scale and the Gaze of a Machine

Scale suffuses the work we do and, recently, has us considering an aspect of scale best suited to those with ethnographic training. We've been asked to help with scaling up one of the latest blockbusters in high tech – deep learning. Advances in deep learning have enabled technology to be programmed to not only see who we are by using facial ID systems and hear what we say by using natural language systems; machines are now even programmed to recognize what we do with vision‐based activity recognition. However, machines often define the objects of their gaze at the wrong scale. Rather than “look for” people or objects, with deep learning, machines typically look for patterns at the smallest scale possible. In multiple projects, we've found that insights from anthropology are needed to inform both the scale and uses of these systems.
A Novel System Architecture for an Improved Self-care Solution – Conceptual Design and Key Components

The high penetration rate that mobile devices enjoy in to day’s society has facilitated the creation of new digital services, with those offered by operators and content providers standing out. However, even this has failed to encourage consumers to express positive opinions on telecommunication services, especially when compared with other sectors. One of the main reasons of the mistrust shown is the low level of quality of customer service provided an area that generates high costs for the operators themselves, due to the high number of people employed at call centers in order to handle the volume of calls received. To face these challenges, operators launched self-care applications in order to provide customers with a tool that would allow them to autonomously manage the services they have subscribed. In this paper, we present an architecture that provides customized information to customers – a solution that is separate from mobile operating systems and communication technologies.
Improving Pronunciation Clarity of Dysarthric Speech Using CycleGAN with Multiple Speakers

In this paper, we propose a method that improves pronunciation clarity of dysarthric speech using CycleGAN based non-parallel voice conversion. This method converts dysarthric speech into healthy speech using CycleGAN. We considered the use of single and multiple speakers as healthy speech. The subjective evaluations showed the effectiveness of using multiple speakers as healthy speech.
Study on the Influence of Multiple Image Inputs of a Multi-View Fusion Neural Network Based on Grad-CAM and Masked Image Inputs

Neural network models are used successfully in many applications like traffic sign recognition in the automotive context, cancer detection in medicine engineering, machine monitoring in the manufacturing industry, et cetera. However, the decisions of a neural network model for a particular input sample in a classification task are mostly nontransparent. We propose techniques to determine which input image of a Multi-View Fusion Neural Network has the most influence on the prediction of the model for a particular image sample pair and which regions in the input images are important. In addition, a trained Multi-View Fusion Neural Network is studied regarding the question of influence. The results are convincing and show that the studied model learned similar concepts like a human.
Contingencies in Behavioral Mode : Enhancing Player’s Adaptive Ability

Abstract Metamorph is a stealth action RPG with bvoth open world and mission-based settings. Adopting the elements of cyberpunk and capitalistic system with immense number of AIs, this game is set in a futuristic-style city with four main districts, each with different culture and building design. The player will play as Centauri, a cyborg who joins Private Sector Resistance in order to stop the Private Sector that produces war machines which are in form of droids with artificial intelligence from colonizing the world. There is a wide range of enemy types, two gameplay styles and various game mechanics in order to complete the missions, this game focuses on player’s adaptive and cognitive abilities throughout the levels.
Agile Stage-Gate Hybrid Framework for New Product Development

The search for appropriate New Product Development (NPD) models or different approaches has become a new emerging topic both for scholars and practitioners. One of the latest results of these researches on NPD management is agile, to manage the increasing complexity of the NPD process. Physical product development, however, is much different from software development. Agile software development has been utilized to answer the challenges of software system development in spite of the lack of empirical evidence from academic research. The purpose of this chapter is to provide a systematic approach regarding the implementation of the Stage-Gate model and agile software in NPD process. Authors derived recommendations for practitioners who want to study Stage-Gate and agile methods in NPD and a research agenda for academics that highlights the need for further research in this area. In addition, the resulting systematic overview is useful as a reference work for researchers in the field of NPD and helps them identify both related work and new research opportunities in this field.
The Upper Confidence Bound Algorithm: Asymptotic Optimality

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Improving the Accuracy of Photovoltaic Generation Forecasting by Considering Particulate Matter Variables

Particulate matter contributes to changing the environment and thus reduces solar irradiance. The reduction in solar irradiance is expected to reduce the power output of photovoltaic (PV) generation. Therefore, analyzing the influence of particulate matter on PV generation is required to understand and efficiently utilize PV systems. Furthermore, it is important to consider these influences when developing forecasting algorithms to improve the algorithm's accuracy. We first analyzed the influence of particulate matter on PV output by examining the correlation of particulate matter concentration on PV power output based on the Pearson correlation method. Then, the PV forecasting model including particulate matter variables and interaction variables was developed based on an artificial neural network (ANN). The results reveal that the particulate matter variables can help improve the accuracy of PV forecasting.
Discriminative Localized Sparse Representations for Breast Cancer Screening

Breast cancer is the most common cancer among women both in developed and developing countries. Early detection and diagnosis of breast cancer may reduce its mortality and improve the quality of life. Computer-aided detection (CADx) and computer-aided diagnosis (CAD) techniques have shown promise for reducing the burden of human expert reading and improve the accuracy and reproducibility of results. Sparse analysis techniques have produced relevant results for representing and recognizing imaging patterns. In this work we propose a method for Label Consistent Spatially Localized Ensemble Sparse Analysis (LC-SLESA). In this work we apply dictionary learning to our block based sparse analysis method to classify breast lesions as benign or malignant. The performance of our method in conjunction with LC-KSVD dictionary learning is evaluated using 10-, 20-, and 30-fold cross validation on the MIAS dataset. Our results indicate that the proposed sparse analyses may be a useful component for breast cancer screening applications.