Reviewerly/exHarmony huggingface dataset:
A Quantum-Enhanced Radar Prototype

We have built and evaluated a prototype quantum radar in the laboratory which operates at microwave frequencies. Because the signal generation process relies on quantum mechanical principles, the system is considered to contain a quantum-enhanced radar transmitter. This transmitter generates a pair of entangled microwave signals and transmits one of them through free space, where the one-way signal is measured using a simple and rudimentary receiver. The type of entanglement used is called two-mode squeezed vacuum (TMSV), so we may call our radar a quantum two-mode squeezing radar (QTMS radar). At the heart of the transmitter is a device called a Josephson parametric amplifier (JPA), which generates the entangled microwave beams; these are then sent through a chain of amplifiers. One beam passes through 0.5 m of free space; the other is measured directly. The two measurement results are correlated in order to distinguish noise from signal. We compare this quantum-enhanced transmitter to a similar one using only conventional components, and find that there is a significant gain when the two systems broadcast signals at approximately the same power.
Active Student Engagement in Learning - Using Virtual Reality Technology to Develop Professional Skills for Petroleum Engineering Education

Abstract Virtual reality (VR) technology, which is the most immersive type of reality technology, can be used in simulating real workplace for occupational safety, trainings, and educational purposes. It encourages students to develop deeper understanding of content. In addition, it allows active learning that transitions students from being mere recipients of information to being participants actively engaged in the learning process. The implementation of this technology makes it possible to improve the effectiveness of the education by simplifying what is normally challenging to understand in physical classrooms. For example, VR can be used to help in visualizing and understanding the differences between different offshore oil and gas rigs. Field trips are one way of adding variety to instruction, thus optimizing teaching effctiveness while motivating student learning. Field trips help bridge formal and informal learning by connecting the students to the real-world experiences. Despite research confirming enhanced learning, sometimes it is not feasible to arrange the fild trips due to several logistical and safety reasons. This study set out to bring real-world petroleum facilities to the classroom with the help of VR technology in the Middle East campus. The goal is develop a tool that overcome the restrictions that are associated with the real field trips, increase the students’ interest and enhance their learning experience by using innovative teaching method in petroleum engineering courses. The developed VR-based field trips allowed petroleum engineering students to experience a range of field areas at a fraction of the cost of conventional real field trips, whilst forgoing restrictions due harsh climate and limited accessibility in relative comfort, addressing gender and disability equality within engineering education. Besides all this, the VR-based field trips allowed the students to access the reservoir, which is not possible in reality. Finally, the student feedback on the VR-based field trip demonstrate positive support of learning methods as it creates amazing experiences, and they feel it is exciting and engaging. In addition it also provided an effective way of emphasizing the learning process in petroleum engineering education and promotes active learning for new generations.
FAST: A Frequency-Aware Skewed Merkle Tree for FPGA-Secured Embedded Systems

Protection of external memory is important when an attacker could get physical accesses to the external memory bus. Compared to general-purpose systems, embedded systems are more vulnerable to physical attacks due to the portability. One of the attacks is a replay attack, which an attacker records data sent over a memory bus and replays it to pretend to be an authorized user. Traditionally, the replay attack is protected using a full, balanced Merkle Tree. Focusing on average-case performance and general-purpose systems, traversal and verification of Merkle Tree incur a huge latency overhead to each memory access. In contrast to general-purpose systems, embedded systems are normally application-specific, and program behaviors and memory access patterns are deterministic. Besides that, we also observed that not all memory locations are accessed equally frequently given a program. Based on these two observations, we propose FAST, a Frequency-Aware Skewed merkle Tree for application-specific embedded systems. After profiling a program in a simulation environment without involving any replay attack protection, we get a memory access frequency distribution. Afterward, we design an automatic and systematic approach to generate an application-specific optimal skewed Merkle Tree accordingly. We propose an efficient hardware architecture to accelerate FAST on FPGA, and by experimenting on five real-world benchmarks, our skewed Merkle Tree implementation outperforms baseline which uses a full balanced Merkle Tree by up to 3 times.
Novel Scheme of Reversible Watermarking With a Complementary Embedding Strategy

In this paper, a new scheme of reversible watermarking is proposed using a complementary embedding strategy in the spatial domain.The proposed scheme consists of two stages: horizontal direction embedding and vertical direction embedding.A complementary embedding strategy is designed to increase the embedding capacity and decrease the distortion of the watermarked image in the vertical direction embedding.Specifically, in the horizontal direction, the proposed scheme embeds one secret data bit by increasing values of pixels in even rows and decreasing values of pixels in odd rows by one.In the vertical direction, it embeds another secret data bit by decreasing values of pixels in even rows and increasing values of pixels in odd rows by one.In addition, a histogram shrinkage technique is adopted to prevent overflow and underflow problems.Experimental results demonstrate that the proposed reversible watermarking scheme outperforms state-of-the-art methods in terms of both embedding capacity and watermarked image quality.
Parameterized Model Predictive Control of a Nonholonomic Mobile Manipulator: A Terminal Constraint-Free Approach

In this paper, a non-linear model predictive control of a mobile manipulator, consisting of a differential-drive mobile base and a 6 DOF arm, is presented. A novel approach to solve the online optimization problem that reduces the overall computational time is proposed, focusing on solving the trajectory tracking problem for handling and grasping operations of a mobile manipulator. The novelty is the introduction of parameterized control inputs and removing terminal constraints. Experimental results comparing our method with traditional non-linear MPC schemes are also reported. They show that the controller is able to achieve an average Cartesian error on the end-effector position of 2 cm.
A fast candidate viewpoints filtering algorithm for multiple viewshed site planning

The aim of site planning based on multiple viewshed analysis is to select the minimum number of viewpoints that maximize visual coverage over a given terrain. However, increasingly high-resolution terrain data means that the number of terrain points will increase rapidly, which will lead to rapid increases in computational requirements for multiple viewshed site planning. In this article, we propose a fast Candidate Viewpoints Filtering (CVF) algorithm for multiple viewshed site planning to lay a foundation for viewpoint optimization selection. Firstly, terrain feature points are selected as candidate viewpoints. Then, these candidate viewpoints are clustered and those belonging to each cluster are sorted according to the index of viewshed contribution (IVC). Finally, the candidate viewpoints with relatively low viewshed contribution rate are removed gradually using the CVF algorithm, through which, the viewpoints with high viewshed contribution are preserved and the number of viewpoints to be preserved can be controlled by the number of clusters. To evaluate the effectiveness of our CVF algorithm, we compare it with the Region Partitioning for Filtering (RPF) and Simulated Annealing (SA) algorithms. Experimental results show that our CVF algorithm is a substantial improvement in both computational efficiency and total viewshed coverage rate.
Sentiment Analysis using Sentiwordnet and Machine Learning Approach (Indonesia general election opinion from the twitter content)

The computational process of identifying and categorizing opinions that are expressed in the piece of text could be employed to determine information insight and the writer's opinion toward a particular topic. Most sentiment analysis employed for English text. Contrarily, a plethora method for sentiment analysis has been reported that the task stayed an interesting question for Indonesian text. The invention of machine learning models and broad accessibility of Twitter data on previous years have derived many researchers to take a machine learning model to resolve the sentiment analysis problem. The objective of this study is to build a sentiment analysis model using Sentiwordnet and machine learning for Indonesia general election opinion in Indonesian text from the twitter content. The data of the tweet was taken namely, the username, and the tweet itself. The theme of the tweet was the topic related to the 2019 general election figures, namely Joko Widodo and Prabowo Subianto. The period of data collection was November 13, 2018, to January 11, 2019, during the campaign period. The tweet was in Indonesia language. The result revealed sentiment analysis with the Naïve Bayes classification method showed 74.94% accuracy for Joko Widodo topic, while 71.37% accuracy for Prabowo topic.
Multi-dataset-multi-task Neural Sequence Tagging for Information Extraction from Tweets

Multi-task learning is effective in reducing the required data for learning a task, while ensuring competitive accuracy with respect to single task learning. We study effectiveness of multi-dataset-multi-task learning in training neural models for four sequence tagging tasks for Twitter data, namely, part of speech (POS) tagging, chunking, super sense tagging, and named entity recognition (NER). We utilize -- 7 POS, 10 NER, 1 Chunking, and 2 super sense -- tagged publicly available datasets. We use a multi-dataset-multi-task neural model based on pre-trained contextual text embeddings and compare it against single-dataset-single-task, and multi-dataset-single-task models. Even within a task, the tagging schemes may differ across datasets. The model learns using this tagging diversity across all datasets for a task. The models are more effective compared to single data/task models, leading to significant improvements for POS (1-2% acc., 7 datasets), NER (1-10% F1, 9 datasets), and chunking (4%). For super sense tagging there is 2% improvement in F1 for out of domain data. Our models and tools can be found at https://socialmediaie.github.io/
Early Online Classification of Encrypted Traffic Streams using Multi-fractal Features

Timely and accurate flow classification is important for identifying flows with different service requirements, optimized network management, and for helping network operators simultaneously operate networks at higher utilization while providing end users good quality of experience (QoE). With most services starting to use end-to-end encryption (HTTPS and QUIC), traditional Deep Packet Inspection (DPI) and port-based approaches are no longer applicable. Furthermore, most flow-level-based approaches ignore the complex non-linear characteristics of internet traffic (e.g., self similarity). To address this challenge, in this paper, we present and evaluate a classification framework that combines multi-fractal feature extraction based on time series data (which captures these non-linear characteristics), principal component analysis (PCA) based feature selection, and man-in-the-middle (MITM) based flow labeling. Our detailed evaluation shows that the method is able to quickly and effectively classify traffic belonging to the six most popular traffic types (video streaming, web browsing, social networking, audio communication, text communication, and bulk download) and to distinguish between video-on-demand (VoD) and live streaming sessions delivered from the same services. Our results show that good accuracy can be achieved with only information about the timing of the packets within a flow.
Hyperspectral Imaging Applications to Geometallurgy: Utilizing Blast Hole Mineralogy to Predict Au-Cu Recovery and Throughput at the Phoenix Mine, Nevada

Abstract The Phoenix mine and predecessor operations in north-central Nevada have produced an aggregate of 5.2 Moz of gold and 550 million pounds of copper from an Eocene-aged Au-Cu porphyry-related skarn. The complex skarn mineralogy intimately associated with ore-grade mineralization poses significant challenges to blasting, mining, comminution, and process operations. These challenges are rooted in highly variable silicate mineralogy, which manifests as fine-grained, submillimeter grain-size, generally green colored rocks that inhibit accurate identification in the field. Prior to this study, all mineralogical data utilized in Phoenix mine ore control were sourced from blast hole cuttings mapped by ore control geologists in the field—the standard practice at many modern mine sites. At Phoenix, a direct link between mineralogy and mill performance was recognized; however, mineralogical data captured in the field was not sufficient to optimize process operations. To address this, it was determined that analytical work was necessary to quantify fine-grained mineralogy of variable ore types. A visible-near and short-wave infrared (VNIR-SWIR) hyperspectral imaging system provided the ideal tool, as it allows near real-time mineralogical data acquisition and semiquantitative determination of mineral abundances. Multiple iterative studies were conducted to prove that hyperspectral imaging of Phoenix ore types provides results suitable for process optimization. This six-month study described here included hyperspectral imaging of 3,008 blast hole cuttings samples from three pits, and 877 crusher feed, rougher feed, and rougher tails samples. Hyperspectral feature extractions derived from mill samples were paired with associated mill performance data and used to build predictive Au-Cu recovery, grade, and throughput models using multiple linear regression, partial least squares, and deep learning techniques with R-correlation values to observed data of 0.56 to 0.71. Blast hole hyperspectral data were then applied to recovery, grade, and throughput models to calculate predicted recoveries and throughputs that were spatially kriged with excellent correlations to geologic features. The application of VNIR-SWIR hyperspectral imaging to blast hole cuttings is a powerful predictive and diagnostic geometallurgical tool in operations where silicate mineralogy has a strong impact on process operations.
A survey of real locomotion techniques for immersive virtual reality applications on head-mounted displays

Locomotion is a fundamental activity in Virtual Reality (VR) and has been the focus of a large body of research since the implementation of the first systems. In real locomotion techniques, users physically move in the real world to affect movement in the Virtual Environment (VE). Real locomotion has been found to perform better than other forms of locomotion for many tasks. To overcome the challenges imposed by restricted physical space, researchers have devised ingenious interaction techniques for real locomotion. In this paper, we present a survey of interaction techniques for real locomotion in VR. Our analysis is focused on the interaction techniques – the combination of devices, user's actions, and system's responses. We categorize interaction techniques for real locomotion in VR into: unmediated, warnings, reorientation/resetting, scaling, redirection, and dynamic VE. These categories represent fundamentally different approaches to real locomotion and user action feedback. We further characterize techniques in each of these categories according to category-specific parameters. Finally, it is important to state that this paper was developed with the aim of helping newcomers to the field to understand and implement the techniques here presented.
Tackling Deceptive Optimization Problems Using Opposition-based DE with Center-based Latin Hypercube Initialization

Deceptiveness is among hard to tackle characteristics of the optimization problems. So far, a few papers are published in this area, while there are many real-world deceptive optimization problems. In a deceptive problem, the landscape does not provide useful information in order to progress toward the global optimum. In another word, it tends towards the deceptive attractors. As a result, finding the global optimum is a challenging task in this family of optimization problems. The goal of this paper is to propose a population-based algorithm for solving these problems. opposition-based learning (OBL) is a well-established approach to enhance meta-heuristic algorithms. Based on OBL concept, the opposite of a candidate solution is generated. Then, based on the objective values of the candidate solution and its opposite, the OBL selects the best. In another word, the proposed algorithm generates opposite of good and bad candidates in the population to break out the deceptiveness of objective function. opposition-based DE is using OBL during population initialization and also during its iterations. The ODE version proposed in this paper is different from the original ODE algorithm; in fact the scheme to generate the opposite candidate is redefined differently. Another approach used in this paper is Latin Hypercube Sampling (LHS). LHS is a statistical method to generate random samples using a multidimensional distribution. This paper combines a modified LHS and OBL with differential evolution algorithm to tackle deceptiveness in the landscape. In order to evaluate the efficiency of the proposed algorithm, some shifted benchmark functions with various characteristics are utilized. The results verify the performance superiority of the proposed algorithm.
Maneuvering target tracking of underwater autonomous vehicles based on bearing-only measurements assisted by inequality constraints

This paper addresses the problem of enabling a constant velocity observer to track a maneuvering target using inequality constraints. In this paper, an observer measures the bearing of sound emitted from the target at each time step. We consider an observer which can access the inequality constraints imposed on target's speed, range, target's course, or target location. As far as we know, this paper is unique in making a constant velocity observer, which measures the bearing angle of a maneuvering target, track the target assisted by range, course, speed, or target location constraints. The observer restarts the filter using inequality constraints to reduce the estimation error considerably, even after the filter diverges. As the observer restarts the filter, stored target information is reused by running the filter backward in time. We verify the effectiveness of our filter restart approach by comparing it with the interacting multiple model (IMM) Kalman filter using MATLAB simulations.
Incremental Frequent Itemsets Mining With FCFP Tree

Frequent itemsets mining (FIM) as well as other mining techniques has been being challenged by large scale and rapidly expanding datasets. To address this issue, we propose a solution for incremental frequent itemsets mining using a Full Compression Frequent Pattern Tree (FCFP-Tree) and related algorithms called FCFPIM. Unlike FP-tree, the FCFP-Tree maintains complete information of all the frequent and infrequent items in the original dataset. This allows the FCFPIM algorithm not to waste any scan and computational overhead for the previously processed original dataset when new dataset are added and support changes. Therefore, much processing time is saved. Importantly, FCFPIM adopts an effective tree structure adjustment strategy when the support of some items changes due to the arrival of new data. FCFPIM is conducive to speeding up the performance of incremental FIM. Although the tree structure containing the lossless items information is space-consuming, a compression strategy is used to save space. We conducted experiments to evaluate our solution, and the experimental results show the space-consuming is worthwhile to win the gain of execution efficiency, especially when the support threshold is low.
Insulator identification and self-shattering detection based on mask region with convolutional neural network

As a component of a power transmission line, the state of an insulator impacts the reliability and safety of the power grid. Self-shattering is an important factor that may cause insulator anomalies. We present a method for detecting insulator self-shattering using mask regions with a convolutional neural network, namely a mask region convolutional neural network. The method can locate fault insulators while finding the fault image with insulator self-shattering. It can also find the insulators and distinguish between normal and self-shattering even if there are multiple insulators in an image. The insulator self-shattering detection program is written in TensorFlow and the Keras deep learning framework. Experiments are conducted on 810 real-world images. The testing results show that the mean average precision can be up to 1 for single-target images and 0.948 for multitarget images.
Intrinsically-Typed Mechanized Semantics for Session Types

Session types have emerged as a powerful paradigm for structuring communication-based programs. They guarantee type soundness and session fidelity for concurrent programs with sophisticated communication protocols. As type soundness proofs for languages with session types are tedious and technically involved, it is rare to see mechanized soundness proofs for these systems.
Learning a Better Negative Sampling Policy with Deep Neural Networks for Search

In information retrieval, sampling methods used to select documents for neural models must often deal with large class imbalances during training. This issue necessitates careful selection of negative instances when training neural models to avoid the risk of overfitting. For most work, heuristic sampling approaches, or policies, are created based off of domain experts, such as choosing samples with high BM25 scores or a random process over candidate documents. However, these sampling approaches are done with the test distribution in mind. In this paper, we demonstrate that the method chosen to sample negative documents during training plays a critical role in both the stability of training, as well as overall performance. Furthermore, we establish that using reinforcement learning to optimize a policy over a set of sampling functions can significantly improve performance over standard training practices with respect to IR metrics and is robust to hyperparameters and random seeds.