Reviewerly/exHarmony huggingface dataset:
OpinionManager: Visual Exploration of Online Reviews in P2P Accommodation

User-generated online reviews are critical in P2P accommodations. They contain a wealth of information about the opinions and experiences of users, which help better understand consumer decisions and improve products and services. However, the huge volume of reviews makes it difficult for potential customers to gain useful insights and for managers to track customer opinions. To address these problems, we first use topic modeling techniques for customer opinion mining. Then, we build a deep learning network for sentiment analysis. Finally, we perform sentiment analysis of the reviews at the aspect level to obtain the sentiment vector representation of the accommodation. Moreover, we design a visual analytic system with a user-friendly interface to facilitate interactive analysis. Evaluation including user and case studies demonstrates the usefulness and effectiveness of this system.
Impact of Data Breaches to Organizations and Individuals

Information security breaches pose a growing threat to organizations and individuals, particularly those that are mainly associated with the e-commerce industry. Data security within an organization is important as Information systems are susceptible to cyber-attacks and usage of Information systems is increasing dramatically in every sector whether it is the FMCG sector or education sector. An information security breach can have a major impact on the financial standing of the breached company and its competitors.
Perfect Trees: Designing Energy-Optimal Symmetric Encryption Primitives

Energy efficiency is critical in battery-driven devices, and designing energyoptimal symmetric-key ciphers is one of the goals for the use of ciphers in such environments. In the paper by Banik et al. (IACR ToSC 2018), stream ciphers were identified as ideal candidates for low-energy solutions. One of the main conclusions of this paper was that Trivium, when implemented in an unrolled fashion, was by far the most energy-efficient way of encrypting larger quantity of data. In fact, it was shown that as soon as the number of databits to be encrypted exceeded 320 bits, Trivium consumed the least amount of energy on STM 90 nm ASIC circuits and outperformed the Midori family of block ciphers even in the least energy hungry ECB mode (Midori was designed specifically for energy efficiency).In this work, we devise the first heuristic energy model in the realm of stream ciphers that links the underlying algebraic topology of the state update function to the consumptive behaviour. The model is then used to derive a metric that exhibits a heavy negative correlation with the energy consumption of a broad range of stream cipher architectures, i.e., the families of Trivium-like, Grain-like and Subterranean-like constructions. We demonstrate that this correlation is especially pronounced for Trivium-like ciphers which leads us to establish a link between the energy consumption and the security guarantees that makes it possible to find several alternative energy-optimal versions of Trivium that meet the requirements but consume less energy. We present two such designs Trivium-LE(F) and Trivium-LE(S) that consume around 15% and 25% less energy respectively making them the to date most energy-efficient encryption primitives. They inherit the same security level as Trivium, i.e., 80-bit security. We further present Triad-LE as an energy-efficient variant satisfying a higher security level. The simplicity and wide applicability of our model has direct consequences for the conception of future hardware-targeted stream ciphers as for the first time it is possible to optimize for energy during the design phase. Moreover, we extend the reach of our model beyond plain encryption primitives and propose a novel energy-efficient message authentication code Trivium-LE-MAC.
Malta National Language Technology Platform: A vision for enhancing Malta’s official languages using Machine Translation

In this paper we introduce a vision towards establishing the Malta National Language Technology Platform; an ongoing effort that aims to provide a basis for enhancing Malta's official languages, namely Maltese and English, using Machine Translation.This will contribute towards the current niche of Language Technology support for the Maltese low-resource language, across multiple computational linguistics fields, such as speech processing, machine translation, text analysis, and multimodal resources.The end goals are to remove language barriers, increase accessibility, foster cross-border services, and most importantly to facilitate the preservation of the Maltese language.
<i>p</i>-Power Exponential Mechanisms for Differentially Private Machine Learning

Differentially private stochastic gradient descent (DP-SGD) that perturbs the clipped gradients is a popular approach for private machine learning. Gaussian mechanism GM, combined with the moments accountant (MA), has demonstrated a much better privacy-utility tradeoff than using the advanced composition theorem. However, it is unclear whether the tradeoff can be further improved by other mechanisms with different noise distributions. To this end, we extend GM ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p=2$ </tex-math></inline-formula> ) to the generalized <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> -power exponential mechanism ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> EM with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p&gt;0$ </tex-math></inline-formula> ) family and show its privacy guarantee. Straightforwardly, we can enhance the privacy-utility tradeoff of GM by searching noise distribution in the wider mechanism space. To implement <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> EM in practice, we design an effective sampling method and extend MA to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> EM for tightly estimating privacy loss. Besides, we formally prove the non-optimality of GM based on the variation method. Numerical experiments validate the properties of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> EM and illustrate a comprehensive comparison between <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> EM and the other two state-of-the-art methods. Experimental results show that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> EM is preferred when the noise variance is relatively small to the signal and the dimension is not too high.
Resultant Force Approach for Swarm UAV Behaviors

Swarm robotics is an approach in collective robotics inspired by the self-organized behavior of social animals. Through simple rules and local interactions, swarm robotics aims at designing robust, scalable, and flexible collective behaviors for the coordination of large numbers of robots. In this study, swarm behavior has been performed with limited communication and decentralized management in the simulation environment. We can list these swarm behaviors as follows; formation, coordinated navigation by protecting the formation, separation-reunion, passing through the passageway, leader selection, leader following. In our proposed method, each UAV is swarm determines the situation they are in according to its own position, the position of neighbor UAVs, the position of the goal, and if there is a strait, the position and size of the strait. UAVs calculate vectors that perform different tasks according to the situation they determine at the moment, and combine these vectors to decide their next move.
uHealth: Smart Clothing Enable d Intelligent Ubiquitous Health Monitoring System

The development of Internet of Things and artificial intelligence has greatly advanced the design and implementation of remote monitoring systems for intelligent healthcare services, of which the sensing and data analysis components play central roles. To this end, we propose a smart clothing-based ubiquitous health monitoring system, called uHealth, to collect physiological signals and behavioral data of the users and to support high-level applications. Specifically, we here introduce the design of smart clothing and illustrate the architecture of uHealth and its key components.
Emulation-accelerated Hamiltonian Monte Carlo algorithms for parameter estimation and uncertainty quantification in differential equation models

Abstract We propose to accelerate Hamiltonian and Lagrangian Monte Carlo algorithms by coupling them with Gaussian processes for emulation of the log unnormalised posterior distribution. We provide proofs of detailed balance with respect to the exact posterior distribution for these algorithms, and validate the correctness of the samplers’ implementation by Geweke consistency tests. We implement these algorithms in a delayed acceptance (DA) framework, and investigate whether the DA scheme can offer computational gains over the standard algorithms. A comparative evaluation study is carried out to assess the performance of the methods on a series of models described by differential equations, including a real-world application of a 1D fluid-dynamics model of the pulmonary blood circulation. The aim is to identify the algorithm which gives the best trade-off between accuracy and computational efficiency, to be used in nonlinear DE models, which are computationally onerous due to repeated numerical integrations in a Bayesian analysis. Results showed no advantage of the DA scheme over the standard algorithms with respect to several efficiency measures based on the effective sample size for most methods and DE models considered. These gradient-driven algorithms register a high acceptance rate, thus the number of expensive forward model evaluations is not significantly reduced by the first emulator-based stage of DA. Additionally, the Lagrangian Dynamical Monte Carlo and Riemann Manifold Hamiltonian Monte Carlo tended to register the highest efficiency (in terms of effective sample size normalised by the number of forward model evaluations), followed by the Hamiltonian Monte Carlo, and the No U-turn sampler tended to be the least efficient.
Diminished Reality in Architectural and Environmental design: Literature Review of Techniques, Applications, and Challenges

Diminished Reality in Architectural and Environmental design: Literature Review of Techniques, Applications, and Challenges Roghieh Eskandari and Ali Motamedi Pages 995-1001 (2021 Proceedings of the 38th ISARC, Dubai, UAE, ISBN 978-952-69524-1-3, ISSN 2413-5844) Abstract: The development of new visualization technologies, such as Mixed Reality (MR) and Augmented Reality (AR), has enabled many applications to improve our daily life. For example, AR has been used in landscape assessment by overlaying virtual objects on a real-world scene to enhance the user's experience. For many advanced visualization scenarios, the virtual removal of objects is done to prevent their intermingling with the existing objects, which could lead to inaccurate visualizations. Diminished Reality (DR) is the technique of virtually removing and seeing through undesired objects in an environment in real-time. The main objective of this paper is to review recent studies to provide an overview of the main procedures, techniques, and applications of DR. In this regard, this paper investigates definitions of DR, its enabling technologies, and its potential applications. It also discusses the current challenges of using DR systems in the Architecture, Engineering, and Construction (AEC) industry. Keywords: Diminished Reality; Augmented Reality; Construction Visualization; See-through vision; AEC industry; interior design; landscape simulation DOI: https://doi.org/10.22260/ISARC2021/0134 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
Vehicle Counter in Traffic Using Pixel Area Method with Multi-Region of Interest

Traffic density data plays an important role in decision making by the Intelligent Transportation System (ITS). This system uses this data in the process of adaptive traffic management. The inaccuracy of the data provided into the ITS system can result in errors in decision making. This study utilizes digital image engineering technology in the detection of four-wheeled vehicles in traffic traffic for the purpose of acquiring traffic density data. In this study, we propose a multi-ROI (pixel area methodRegion of Interest). This multi-ROI proposal is to be put forward to improve reading accuracy compared to just one ROI. With the use of this multi-ROI, the information obtained from the overall ROI can strengthen the accuracy of the data of vehicles passing in a lane. Our experimental results show that the use of multi-ROI with a certain amount of ROI can produce an accuracy rate of up to 88.66% compared to single-ROI which has an accuracy rate of 84.65%.
Python Language In The Development Of Computational Competencies In Humanities Education

This article examines the development of computational competencies in undergraduate and graduate students of humanities, studying philology through the use of free and open-source software such as the Python programming language on the example of quantitative text analysis using Python programming language libraries such as NumPy and SciPy, as well as Pandas.The leading platform for building Python programs to handle natural language data is NaturalLanguageToolkit, which is free and open-source software.For morphological analysis: obtaining the lemma or base of a given token and, if necessary, morphological parameters, we used the freeware program MyStem.Pandas is a package, an add-on to the NumPy library, that provides an efficient implementation of the DataFrame class.In Pandas we used structures like Series and Dataframe, and used the library matplotlib to visualize the result of cluster analysis as a dendrogram.The choice of training content concerning the application of these methods to arrays of textual data depends on the principles of professional orientation of training and ethno-cultural connotation of education, so the example of cluster analysis uses the Kalmykian folk epic "Dzhangar" and the sample questions to be proposed during the analysis of the example, choosing the object of subsequent application of the proposed method in the context of forming a professional picture of the world.In addition, a specific issue is the critical analysis of the processing of text analysis results obtained by applying the studied methods to further develop the teaching of computer science.
The Smart Glove Malaysian Sign Language Translator

The medium for deaf and mute people to communicate is by sign language that using mostly hand gesture to make the sign. There are somewhere between 138 and 300 different types of sign language used throughout the world today. In Malaysia there were not much people can understand the sign language. Based on the research negative attitudes of Malaysian toward Children with disabilities (CWDs) have been disclosed recently. Azizan (2015, July 5) reported that low opportunities are given to CWDs and people with disabilities (PWDs) when enroll to schools and universities. The lack of communication between normal and these special people is one of the factor that make people lack of awareness. Smart glove MSL translation examines the possibility of recognizing sign language gestures using Flex sensor and explores their use in Sign Language recognition. This project focused on one hand gesture sign language that commonly used in a daily life.   By using the 2 main sensor which is flex sensors and gyro sensor the parameter of some basic hand gesture movement can be measured. All the sensor is connected to the ESP32 which is have a feature to connect the project hardware to application software designed via Bluetooth or Wi-Fi. An experiment is carried out to measure the accuracy of the project, using 3 different people to use the glove and the data is recorded. The average accuracy of the project is 86.47% for every 11 sign gestures data collected. The output of this project is in form of a voice, text and picture that will be displayed on the smartphone screen
If You Like Me, Please Don’t “Like” Me: Inferring Vendor Bitcoin Addresses From Positive Reviews

Abstract Bitcoin and similar cryptocurrencies are becoming increasingly popular as a payment method in both legitimate and illegitimate online markets. Such markets usually deploy a review system that allows users to rate their purchases and help others to determine reliable vendors. Consequently, vendors are interested into accumulating as many positive reviews (likes) as possible and to make these public. However, we present an attack that exploits these publicly available information to identify cryptocurrency addresses potentially belonging to vendors. In its basic variant, it focuses on vendors that reuse their addresses. We also show an extended variant that copes with the case that addresses are used only once. We demonstrate the applicability of the attack by modeling Bitcoin transactions based on vendor reviews of two separate darknet markets and retrieve matching transactions from the blockchain. By doing so, we can identify Bitcoin addresses likely belonging to darknet market vendors.
Introduction to Data Visualization with Seaborn

In the previous chapter, you learned how to visualize data with the Pandas series and dataframe components. You learned various techniques for creating visualizations for Pandas data.
Analisis Kompleksitas Algoritma Biogeography Based Optimization (BBO) Pada Traveling Salesman Problem (TSP)

This paper analyzes the complexity of the Biogeography Based Optimization (BBO) algorithm. The algorithm's intelligence is measured by how much time and space it takes to run. The characteristic of a good algorithm are small running time and less memory usage. The quantity used to explain the abstract model of time / space measurement is the complexity of the algorithm. There are two kinds of algorithm complexity, namely time complexity and space complexity. Time complexity is expressed as the number of computational stages needed to run the algorithm as a function of input size n. Space complexity is expressed as the amount of memory used by the data structure contained in the algorithm as a function of the input size of n. By using the magnitude of the time / space complexity of the algorithm, we can determine the rate of increase in time (space) required by the algorithm as the size of the input n increases. The steps for analyzing algorithm complexity are completing the BBO algorithm on the TSP, finding the relationship between iteration and time (second), looking for the relationship between the number of cities and processing time (second), and analyzing the algorithm complexity. The results showed that the BBO algorithm on the TSP is a quadratic algorithm O (n ^ 2), which is the number of cities increasing, so the implementation time of the algorithm also increases. Biogeography Based Optimization (BBO) algorithm applied to the Traveling Salesman Problem (TSP).
Optimal Differential Privacy Composition for Exponential Mechanisms and the Cost of Adaptivity

Composition is one of the most important properties of differential privacy (DP), as it allows algorithm designers to build complex private algorithms from DP primitives. We consider precise composition bounds of the overall privacy loss for exponential mechanisms, one of the fundamental classes of mechanisms in DP. We give explicit formulations of the optimal privacy loss for both the adaptive and non-adaptive settings. For the non-adaptive setting in which each mechanism has the same privacy parameter, we give an efficiently computable formulation of the optimal privacy loss. Furthermore, we show that there is a difference in the privacy loss when the exponential mechanism is chosen adaptively versus non-adaptively. To our knowledge, it was previously unknown whether such a gap existed for any DP mechanisms with fixed privacy parameters, and we demonstrate the gap for a widely used class of mechanism in a natural setting. We then improve upon the best previously known upper bounds for adaptive composition of exponential mechanisms with efficiently computable formulations and show the improvement.
Learning Geometry Information of Target for Visual Object Tracking with Siamese Networks

Recently, Siamese architecture has been widely used in the field of visual tracking, and has achieved great success. Most Siamese network based trackers aggregate the target information of two branches by cross-correlation. However, since the location of the sampling points in the search feature area is pre-fixed in cross-correlation operation, these trackers suffer from either background noise influence or missing foreground information. Moreover, the cross-correlation between the template and the search area neglects the geometry information of the target. In this paper, we propose a Siamese deformable cross-correlation network to model the geometric structure of target and improve the performance of visual tracking. We propose to learn an offset field end-to-end in cross-correlation. With the guidance of the offset field, the sampling in the search image area can adapt to the deformation of the target, and realize the modeling of the geometric structure of the target. We further propose an online classification sub-network to model the variation of target appearance and enhance the robustness of the tracker. Extensive experiments are conducted on four challenging benchmarks, including OTB2015, VOT2018, VOT2019 and UAV123. The results demonstrate that our tracker achieves state-of-the-art performance.
An FPGA-Based LOCO-ANS Implementation for Lossless and Near-Lossless Image Compression Using High-Level Synthesis

In this work, we present and evaluate a hardware architecture for the LOCO-ANS (Low Complexity Lossless Compression with Asymmetric Numeral Systems) lossless and near-lossless image compressor, which is based on JPEG-LS standard. The design is implemented in two FPGA generations, evaluating its performance for different codec configurations. The tests show that the design is capable of up to 40.5 MPixels/s and 124 MPixels/s per lane for Zynq 7020 and UltraScale+ FPGAs, respectively. Compared to the single thread LOCO-ANS software implementation running in a 1.2 GHz Raspberry Pi 3B, each hardware lane achieves 6.5 times higher throughput, even when implemented in an older and cost-optimized chip like the Zynq 7020. Results are also presented for a lossless only version, which achieves a lower footprint and approximately 50% higher performance than the version that supports both lossless and near-lossless. Interestingly, these great results were obtained applying High-Level Synthesis, describing the coder with C++ code, which tends to establish a trade-off between design time and quality of results. These results show that the algorithm is very suitable for hardware implementation. Moreover, the implemented system is faster and achieves higher compression than the best previously available near-lossless JPEG-LS hardware implementation.