Reviewerly/exHarmony huggingface dataset:
Evaluation of Individualized HRTFs in a 3D Shooter Game

Previous research stresses the importance of Head-Related Transfer Function (HRTF) individualization approaches for accurately locating sound sources in virtual 3D spaces.However, in the realm of interactive experiences, methods for assessing whether individualized HRTFs bring a benefit to the player experience are rarely investigated.Methods to improve spatial audio rendering are needed now than ever since Virtual Reality (VR) is becoming a mainstream technology for interactive experiences.This paper proposes a method of using in-game metrics to test the hypothesis that individualized HRTFs improve the experience of both expert and novice players in a First-Person Shooter (FPS) game on a desktop environment.The FPS game provides players with a localization task across three different audio renderings using the same acoustic spaces: stereo panning (control condition), generic binaural rendering, and individualized binaural rendering.Collected metrics from the game include localization error, spatial quality attributes, and an extensive questionnaire.The individualized HRTFs for each participant were synthesized using a hybrid structural model.The model employs a deep learning architecture to synthesize a pinna-related response from a pinna image, and combines it with a measured generic head-and-torso response.The interaural time difference (ITD) is then adjusted to match that of an HRTF dataset subject minimizing a localization error metric.The results show that the 22 participants performed significantly better in the localization task with their individualized HRTF.Increased localization accuracy with respect to the generic HRTF was recorded both in azimuth and elevation perception, and especially in the case of expert game players.
Towards an Architecture to Support Data Access in Research Data Spaces

Using data from different data sources is a common procedure in data-driven research. As required data is often not available from centrally managed sources, the concept of data spaces has more and more frequently been utilized to integrate decentral data sources and supporting the access to these. However, decentrally organized data management leads to differences in data models, formats and technologies and the problem of matching data from different sources most often still requires significant manual work, either from the provider or the consumer of the data. Especially in research data spaces, properties of data sources are often dynamically modified or extended, and complex queries are formulated to access the data. In this paper, we present an architecture to approach this problem by automatically exploring the different data sources and searching for similar attributes. Matching attributes are then assembled to so-called vocabularies, that represent common concepts in the data space independently from their actual representations. This dynamic knowledge base is then used to efficiently query and access data in the data space. Finally, an application of the concept is presented to demonstrate the applicability of our architectural framework.
Interactive Visualization of Deep Learning Models in an Immersive Environment

The development of deep learning (DL) models has been prevalent among software engineers. However, it is difficult for non-experts to analyze and understand their behavior. Hence, we propose an interactive visualization system of DL models in an immersive environment. Because an immersive environment offers unlimited displays and visualization of high-dimensional data, it enables a comprehensive analysis on data propagations through the layers, and compares the multiple performance metrics. In this research, we implemented a prototype system, demonstrated it to machine learning engineers, and discussed the future benefits of visualizing DL models in an immersive environment. Accordingly, our concept received positive feedback; however, we inferred that most of the engineers consider the visualization technology as a unique introduction to the immersive environment.
Compositional and Bayesian inference analysis of the concentrations of air pollutants in Catalonia, Spain

While most countries have networks of stations for monitoring pollutant concentrations, they do not cover the whole territory continuously. Therefore, to be able to carry out a spatial and temporal study, the predictions for air pollution from unmeasured sites and time periods need to be used. The objective of this study is to predict the air pollutant concentrations of PM10, O3, NO2, SO2 and CO in sites throughout Catalonia (Spain) and time periods without a monitoring station. Compositional data (CoDa) studies the relative importance of pollutants. A novel feature in this article is combining CoDa with an indicator of total pollution. Predictions are then made using a combination of spatio-temporal models and the Bayesian Laplace Integrated Approach (INLA) inference method. The most relevant results obtained indicate that the log-ratio between NO2 and O3 has the highest variance and the best predictive accuracy in time and space. Total pollution levels are second in variance but have low spatial predictive accuracy. Third in variance is the low temporal accuracy found in the log-ratio between SO2 and the remaining pollutants. Globally, the combination of CoDa and the INLA method is suitable for making effective spatio-temporal predictions of air pollutants.
Web-Based Telegram Chatbot Management System: Create Chatbot Without Programming Language Requirements

Abstract The growth of communication technology that keeps developing, the way we obtain and provide information are developing. One of the applications that get lots of interest is Telegram. This application makes out communication easier. The development of communication model through Telegram is developing as well, one of the models is the chatbot. Chatbot can be made answers a lot faster. Until recently, chatbots are mostly created with a particular programming language. This makes creating a chatbot only limited to those who understand the programming language. Although many applications offer to create a chatbot without programming by big vendors, but there hasn’t been any academic literature for this topic yet. Therefore, in this research, I will design and implement the Telegram chatbot management system. This application provides to create a chatbot without programming language requirements. The user just needs to set the keyword and choose what action will chatbot give. This application also provides a direct message, broadcast message, and display usage statistics. This application tested by creating actions set, buttons set, command, direct message, broadcast message, and display usage statistics. The user can do all of those without any programming language requirements.
Indonesian sentiment summarization for lecturer learning evaluation by using textrank algorithm

Abstract Learning evaluation is a very important process to assess lecturer performances. Evaluation is carried out at the end of the semester by students. Student can give comments, critics, suggestions related to the subject taught by the lecturers. To analyse student comments, the lecturer can read the entire comments one by one. The main focus in this research was to develop the system that will be applied in summarizing student comments on the implementation of course learning. The algorithm that was used to perform text summarization was the TextRank algorithm. TextRank is a graph-based ranking algorithm (a graph with a ranking model) for processing text from natural or human language documents. We used 100 data sentiments by students to lecturers who teach one course in one semester. It was directly taken from SIAK (academic information system) by using web services. Based on the evaluation of the summaries by the expert, it was found that the textRank algorithm is quite representative in performing summarization for sentiments with the accuracy of 82%.
Quantifying Use and Abuse of Personal Information

Once shared, our personal information on the Internet is no longer private. We routinely receive emails from companies that we have not had any known interaction with, and are receiving an increasingly large volume of spam phone calls. In this paper, we describe interim results from an experiment designed to quantify who is using and distributing our personally identifying information (PII). To do this, we set up 300 fake identities, each with an email address and around half with a live phone number, and performed one-time online interactions with 188 distinct companies. Over a 9-month span, we received around 20,000 artifacts and found that reputable companies, surprisingly, do not sell our information in ways that we could detect, that there was no observation of undue foreign interest during the election, and that the classic “extended vehicle warranty” scam is still in active use today.
A formal treatment of the role of verified compilers in secure computation

Secure multiparty computation (SMC) allows for complex computations over encrypted data. Privacy concerns for cloud applications makes this a highly desired technology and recent performance improvements show that it is practical. To make SMC accessible to non-experts and empower its use in varied applications, many domain-specific compilers are being proposed. We review the role of these compilers and provide a formal treatment of the core steps that they perform to bridge the abstraction gap between high-level ideal specifications and efficient SMC protocols. Our abstract framework bridges this secure compilation problem across two dimensions: 1) language-based source- to target-level semantic and efficiency gaps, and 2) cryptographic ideal- to real-world security gaps. We link the former to the setting of certified compilation, paving the way to leverage long-run efforts such as CompCert in future SMC compilers. Security is framed in the standard cryptographic sense. Our results are supported by a machine-checked formalisation carried out in EasyCrypt.
Computer Vision Based Privacy Protected Fall Detection and Behavior Monitoring System for the Care of the Elderly

The elderly population constitutes a large percentage of the society hence making elderly care a top priority. Falls have been identified as a leading issue among major problems faced by them. Concerning this, many monitoring devices have been developed, most of them focusing solely on one specific health care aspect or related to fall detection, and are based on sensors and wearable devices which are usually uncomfortable for daily use. Considering these aspects, the solution proposed in this research is a real time computer vision-based system that monitors behavior and detects anomalies through deep learning. The monitoring is mainly focused on detecting unusual behavior including falls, and monitoring routine activities to detect deviations. A device approach is used to deploy the deep learning models and consists of IP camera-based monitoring which uses a special privacy protected procedure that ensures the detection is done based on meta data and therefore no camera image or footage is stored. The research is mainly focused on four major components which are user identification, fall detection, routine variance detection and device configuration.
Bayesian active learning with abstention feedbacks

We study pool-based active learning with abstention feedbacks where a labeler can abstain from labeling a queried example with some unknown abstention rate. This is an important problem with many useful applications. We take a Bayesian approach to the problem and develop two new greedy algorithms that learn both the classification problem and the unknown abstention rate at the same time. These are achieved by simply incorporating the estimated average abstention rate into the greedy criteria. We prove that both algorithms have near-optimality guarantees: they respectively achieve a (1-1e) constant factor approximation of the optimal expected or worst-case value of a useful utility function. Our experiments show the algorithms perform well in various practical scenarios.
Unpaired Low-Dose CT Denoising using Conditional GAN with Structural Loss

Low-dose CT has been a popular diagnostic imaging for its high availability and less radiation than normal-dose CT. Reducing the noise and reconstructing a noise-free CT image is a hotspot for researchers. The existing methods cannot deal with unpaired data well yet while the paired data is difficult to obtain. With unpaired data, we proposed an effective method using conditional generative adversarial network with Structural Loss to solve this problem. Our method is simpler and more efficient on computation than other unpaired methods and our experiments showed that it is comparable to other deep learning methods on denoising performance.
Bornon: Bengali Image Captioning with Transformer-Based Deep Learning Approach

Image captioning using encoder–decoder-based approach where CNN is used as the Encoder and sequence generator like RNN as Decoder has proven to be very effective. However, this method has a drawback, that is, sequence needs to be processed in order. To overcome this drawback, some researchers have utilized the transformer model to generate captions from images using English datasets. However, none of them generated captions in Bengali using the transformer model. As a result, we utilized three different Bengali datasets to generate Bengali captions from images using the transformer model. Additionally, we compared the performance of the transformer-based model with a visual attention-based encoder–decoder approach. Finally, we compared the result of the transformer-based model with other models that employed different Bengali image captioning datasets.
FAST and FLANN for feature matching based on SURF

The aim of this paper is to Reduce Speeded Up Robust Features' (SURF) time-consuming problem and get a high accuracy in image registration, the Features From Accelerated Segment Test (FAST) algorithm and the Fast Library for Approximate N earest Neighbors (FLANN) are proposed respectively for extracting the feature points and increasing the accuracy. First, the FAST algorithm is applied to extract the features of the image, and then the SURF algorithm is used to construct the descriptor to realize the rapid extraction of image features with rotation invariance. And then an improved feature matching algorithm FLANN is proposed to accurately match the feature points. The experimental results show that our method is about two times faster than the traditional SURF algorithm and also has a higher accuracy. The main outcomes of this paper are the usage of FAST for a time-consuming problem and FLANN for a high accuracy in image registration.
Quantum coherence, correlations and nonclassical states in the two-qubit Rabi model with parametric oscillator

Quantum coherence and quantum correlations are studied in the strongly interacting system composed of two qubits and an oscillator with the presence of a parametric medium. To analytically solve the system, we employ the adiabatic approximation approach. It assumes each qubit's characteristic frequency is substantially lower than the oscillator frequency. To validate our approximation, a good agreement between the calculated energy spectrum of the Hamiltonian with its numerical result is presented. The time evolution of the reduced density matrices of the two-qubit and the oscillator subsystems are computed from the tripartite initial state. Starting with a factorized two-qubit initial state, the quasi-periodicity in the revival and collapse phenomenon that occurs in the two-qubit population inversion is studied. Based on the measure of relative entropy of coherence, we investigate the quantum coherence and its explicit dependence on the parametric term both for the two-qubit and the individual qubit subsystems by adopting different choices of the initial states. Similarly, the existence of quantum correlations is demonstrated by studying the geometric discord and concurrence. Besides, by numerically minimizing the Hilbert-Schmidt distance, the dynamically produced near maximally entangled states are reconstructed. The reconstructed states are observed to be nearly pure generalized Bell states. Furthermore, utilizing the oscillator density matrix, the quadrature variance and phase-space distribution of the associated Husimi $Q$-function are computed in the minimum entropy regime and conclude that the obtained nearly pure evolved state is a squeezed coherent state.
OLTP In Real Life: A Large-scale Study of Database Behavior in Modern Online Retail

E-commerce is a multi-trillion US dollar industry and a major user of online transaction processing (OLTP) systems. Yet, we do not have a good understanding of the real-world properties that characterize hardware usage in database systems serving online retail. One key reason is the difficulty of obtaining monitoring logs from production retail datacenters, as large retailers typically consider such data to be highly sensitive. Additionally, resource-usage patterns in OLTP systems and their correlations with the workload are generally challenging to model (not just in retail), due to high levels of transaction concurrency and complexity in OLTP applications. Relying on synthetic benchmarks alone to simulate and study these systems is not sufficient. Instead, this paper takes an empirical approach.We present the first large-scale study of OLTP systems in real life, using traces from two large retailers. The first trace represents six months worth of logs from a large online retailer in Latin America, B2W Digital (B2W). The second trace represents 8 days of hardware logs from Alibaba, the largest online retailer in China. Our analysis reveals important characteristics of real systems serving retail. First, we identify the statistical distributions that best fit hardware-usage data and find that system behavior in modern retail workloads departs from certain conventional assumptions in the literature, particularly the assumption that resource utilization follows a ‘heavy-tailed’ distribution. Next, we examine how these resource-usage patterns change with different retail seasons and correlate with workload demand and transaction type. Finally, we discuss practical implications for OLTP systems management, specifically in hardware provisioning and capacity planning.
Topic Driven Adaptive Network for cross-domain sentiment classification

As a hot spot these years, cross-domain sentiment classification aims to learn a reliable classifier using labeled data from a source domain and evaluate the classifier on a target domain. In this vein, most approaches utilized domain adaptation that maps data from different domains into a common feature space. To further improve the model performance, several methods targeted to mine domain-specific information were proposed. However, most of them only utilized a limited part of domain-specific information. In this study, we first develop a method of extracting domain-specific words based on the topic information derived from topic models. Then, we propose a Topic Driven Adaptive Network (TDAN) for cross-domain sentiment classification. The network consists of two sub-networks: a semantics attention network and a domain-specific word attention network, the structures of which are based on transformers. These sub-networks take different forms of input and their outputs are fused as the feature vector. Experiments validate the effectiveness of our TDAN on sentiment classification across domains. Case studies also indicate that topic models have the potential to add value to cross-domain sentiment classification by discovering interpretable and low-dimensional subspaces.
Computationally Secure Quantum Oblivious Transfer

Abstract Oblivious transfer (OT) is one of the cornerstones of secure multiparty computation. It is clear that unconditionally secure OT is impossible. Any protocol for OT requires computational assumptions, such as number‐theoretic cryptographic assumptions or the assumptions of existence of one‐way functions. On the other hand, it is broadly believed that one‐way functions alone do not promise secure OT. However, in this paper, a novel computationally secure quantum oblivious transfer (QOT) protocol is proposed with the help of quantum channels under the minimum assumption requirements: the existence of one‐way functions.
Block Scrambling Image Encryption Used in Combination with Data Augmentation for Privacy-Preserving DNNs

In this paper, we propose a novel learnable image encryption method for privacy-preserving deep neural networks (DNNs). The proposed method is carried out on the basis of block scrambling used in combination with data augmentation techniques such as random cropping, horizontal flip and grid mask. The use of block scrambling enhances robustness against various attacks, and in contrast, the combination with data augmentation enables us to maintain a high classification accuracy even when using encrypted images. In an image classification experiment, the proposed method is demonstrated to be effective in privacy-preserving DNNs.
Enterprise Accounting and Financial Risk Analysis System Based on Decision Tree and SVM

With the continuous development of the market economy, competition in the industry is becoming more and fiercer, leading to the financial situation of many companies are in trouble. Financial risk analysis plays an important role in helping companies to develop smoothly. This paper based on machine learning algorithm and the application of composition analysis, is to research and develop enterprise financial management system for analysis. It is important to start from improving the financial management level of the enterprise, so as to reveal the potential financial risks of the enterprise, and timely prevent the financial crisis of the enterprise from the source, and timely solve hot issues. The author consults a large number of literatures, analyzes and summarizes the advantages and disadvantages of the research results in the field of risk analysis, and applies the implementation of decision tree and SVM financial risk analysis. This paper mainly implements the design of financial risk analysis system from the two directions of financial management and risk analysis.
De-Identification of Clinical Notes Using Contextualized Language Models and a Token Classifier

The de-identification of clinical notes is crucial for the reuse of electronic clinical data and is a common Named Entity Recognition (NER) task. Neural language models provide a great improvement in Natural Language Processing (NLP) tasks, such as NER, when they are integrated with neural network methods. This paper evaluates the use of current state-of-the-art deep learning methods (Bi-LSTM-CRF) in the task of identifying patient names in clinical notes, for de-identification purposes. We used two corpora and three language models to evaluate which combination delivers the best performance. In our experiments, the specific corpus for the de-identification of clinical notes and a contextualized embedding with word embeddings achieved the best result: an F-measure of 0.94.