Reviewerly/exHarmony huggingface dataset:
Investigating Differential Privacy Outcomes

Personal information is an important asset nowadays. Based on customers’ data, organizations are able to improve products and services, to customize solutions and to propose new ones. Privacy threats can arise from internal or external sources, having intentional or unintentional motivation. Differential Privacy (DP) is an approach that aims to utilize data while protecting the privacy of individuals. The general idea behind DP is to add noise in data in a way to keep privacy. In this paper, we provide a practical vision of DP. We introduce the D coefficient, which is an approach proposed to aid the understanding of the $$\varepsilon $$ parameter of DP. We also conduct experiments with the four data analysis algorithms, in a way to compare the accuracy of each algorithm when applying DP. We found that the D coefficient plays an important role of setting a tangible interpretation for the amount of privacy provided by each DP mechanism. Moreover, we confirmed the consistency of the DP mathematical definition, which gives more confidence and clarity in the trade off between privacy and data utility.
A Forest of Pipes: Presenting the 37-pipe Sheng from the Inside

In the vast majority of existing studio and live productions, the sheng is represented as an external sound object, i.e., a sound source (typically positioned frontally) with a given distance and width, placed in a real or virtual space, as is commonly done for other featured instruments in music production. The empirical research presented here reverses the listening perspective. It develops methods for setting the listener metaphorically inside the sheng, surrounded by the pipes. It is similar to the image of the “forest of pipes” sometimes used for a church organ. This idea, which (though complex) may be technically easier to apply to the church organ, poses significant difficulties for the sheng because of its size and the radiation properties of the pipes (mostly directed outwards). Alternative methods for the design of the microphone system must therefore be sought.
Joint Feature Euclidean Distance Sampling for 3d Object Detection

To improve detection accuracy, we propose a 3D object detection method based on the VoteNet. First, we design a joint feature Euclidean distance sampling method, which takes the spatial coordinates of voting points and the joint matrix of features as input, calculates the Euclidean distance between points, and then uses the Euclidean distance to pair the farthest points are sampled, which increases the probability that foreground points are sampled. Second, a Transformer is introduced into the voting module, which enhances the mutual attention between points and their neighbors, and aggregates local features of point clouds. Finally, channel attention is used to enhance the weight of seed points and foreground points in cluster points, and then the point cloud features of the two parts are fused to achieve the aggregation of global features and local features. Our method significantly improves the accuracy of 3D object detection.
Drones: Foreseeing…. A ‘Risky’ Business? Policing the Challenge that Flies Above

Drones are one of the most disruptive technologies of our time. They have the ability to do so much good, but history and the military legacy of airborne vehicles serves as an indicator of the potential risk to society that drones also present. This paper combines a law/policy, socio-legal approach that considers the history of technology that flies above society and invariably the linkage to warfare. Discussion is given to technological advancements alongside anticipatory measures and governance approaches that need to be factored in prior to unleashing certain technologies on society. This includes the means of policing drones alongside the police use of drones. Case studies are included to contextualise the (positive) use and misuse of drones as part of the conclusion. The findings are that drones are set to be a massive challenge and that there has been a lack of foresight, despite warnings, predictions and historical lessons which should have served as a premonitory indicator of the safety/security risks of drones. Ultimately, more protection is needed before the benefits of drones to society can be realised.
Evaluation of Contrastive Learning with Various Code Representations for Code Clone Detection

Code clones are pairs of code snippets that implement similar functionality. A particularly interesting case of clone detection is the detection of semantic clones, code snippets that have the same functionality but significantly differ in implementation. A promising approach to detecting semantic clones is contrastive learning, an machine learning paradigm popular in computer vision but not yet commonly adopted for code processing.Our work evaluates the most popular CL algorithms combined with three source code representations on two tasks. The first is code clone detection, which we evaluate on the POJ-104 dataset containing implementations of 104 algorithms. The second task is plagiarism detection. To evaluate the models on this task, we introduce gorshochek, a tool for transforming source code. We use it to create a dataset that mimics plagiarised code based on competitive programming solutions. We trained nine models for both tasks and compared them with six existing approaches, including traditional tools and modern pre-trained neural models.The results of our evaluation show that proposed models perform diversely in each task, however the performance of the graph-based models is generally above the others. Among CL algorithms, SimCLR and SwAV lead to better results, while Moco is the most robust approach.
A Brief Review on Motion Sickness for Autonomous Vehicle

Motion sickness (MS) is an unpleasant sensation such as headache and nausea which occurs during travelling by vehicle. Extensive studies had been carried out regarding the factor and the mitigation methods of MS, especially for the vehicle’s passengers. Nowadays, a revolution from the automotive industry resulting from the development of the autonomous vehicles. One of the key concerns in autonomous vehicle research is that its possibility to have a higher chance to contribute to MS among the occupants compared to the conventional vehicle. Hence, this paper presents reviews on the MS reduction methods, focusing on the application towards the autonomous vehicle. Considering the importance of MS reduction in improving the occupant’s comfort level, it is concluded that this issue requires more attention among autonomous vehicle researchers.
A CNN Based Air-Writing Recognition Framework for Linguistic Characters

Air writing is a practice of writing the linguistic characters in free space utilizing the six degrees of freedom of hand motion. We propose a system that uses a generic webcam to detect and recognize the virtually written characters by a user as per their will. This system performs detection using morphological operations for masking the tracker or writing object. This system gives the user the freedom to select a writing object of any color, shape, or material for tracking purposes. The trajectory of the object’s mask is tracked and rendered on a virtual window. The air-written character is recognized using the Convolutional Neural Network (CNN). The CNN is trained on a dataset of English handwritten characters of 26 different classes (A-Z). The accuracy achieved by the proposed system for an isolated character is 99.75%.
An Efficient Method for Parameter Estimation and Separation of Multi-Component Lfm Signals

This paper proposes an efficient fast parameter estimation and separation algorithm for multicomponentlinear frequency modulation signals based on the fourth-order origin moment of fractionalspectrum and fractional domain filtering. Aiming at the problems of low estimation accuracyand large computational complexity of traditional two-dimensional search and time-frequencyanalysis methods, the algorithm applies an efficient fractional time-frequency analysis methodto determine the initial interval and search center of the optimal rotation order. Moreover, thealgorithm proposed employs the good impulse performance and anti-noise performance of thefourth-order origin moment of fractional spectrum to formulate a reasonable search strategy of theoptimal order, which reduces the amount of calculation while ensuring the accuracy of the parameters.A step-by-step separation and filtering technique for signals in the fractional Fourier domainis further implemented for the problem of interference between multi-component signals, whichsuppresses the influence of strong components on the parameter estimation of weak components.The error analysis of the linear frequency modulation signal is carried out, and the Cramer-RaoLower Bound for the unbiased estimation of the parameters is derived. The final simulation resultsprove the accuracy and effectiveness of the method in noisy environments.
IT Project Risk Management Model

The chapter develops a knowledge-based IT project risk management model, which, unlike the existing ones, allows to take into account the dynamic and incremental way of executing IT projects, whereby the requirements for the information product and the ways of their realization are refined taking into account new information and experience gained. According to the model, the inputs to the risk management decision-making process should be stored in the knowledge base of the project, based on which the rules for the operation of the expert system are formulated. It is suggested to use such components of the knowledge base as the risk database and the risk management knowledge repository. The Risk Database contains information on the tasks of IT project implementation, a concise description of problem situations, directions for solving the problem, quantification of risks, and the effectiveness of risk management measures. Updated risk database information, along with implicit knowledge (project stakeholder experience and qualifications, domain laws, etc.), is used to replenish the risk management knowledge repository to derive conclusions and patterns reflecting key project risk management policies.
Mobile Technologies as a Support Tool for the Development of Communicative Competencies

The paper deals with the development of communicative competencies using mobile technologies. The authors focus exclusively on primary education, that is, teaching in primary schools. The paper consists of two main parts. The first theoretical part presents the theoretical basis of key competencies in education as well as communicative competencies and mobile technologies in teaching. Subsequently, the authors focus on the current state of the researched issue and describe the current phase of their own research. The second part of the paper outlines the proposal of their main research and the planned pilot study. The authors plan to conduct quantitative-qualitative research. As a part of the main research, lectures will take place in both the experimental and control group. The experimental group will work on developing its communicative competencies with the support of mobile technologies while the control group will work without them. At the end of the paper the authors also consider the possibilities of applying the distance learning model in research. The conclusion of the paper presents questions and current problems that the authors are currently solving and which can have a significant impact on the course of the research.
An Efficient Algorithm to Select Reference Views for Virtual View Synthesis

View synthesis is one of the key techniques for generating immersive media.Virtual view synthesis techniques require considerable input views to provide a wide viewing space to users, such as 360 virtual reality.However, the computational complexity is increased, and the synthesized virtual image is blurred when all input views are used as reference views.We analyzed the algorithm complexity and synthesized image quality according to the number of reference pictures.Based on the results, we propose a systematic algorithm to compose an optimal subset of the reference pictures to reduce the hole area and increase the accuracy of the overlapped pixel data in the virtual view.The algorithm consists of the screening and optimal composing steps, which involve logical and geometric measurements.The experimental results demonstrate that the proposed method reduces algorithm complexity and improves the virtual view quality.
The Influence of the Project Mature Organization Factors on the Agile Transformation

In the conditions of a dynamic environment and constant market pressures for the implementation of internal and external changes, organizations have a great challenge, how to remain flexible, and at the same time stable and competitive. One of the approaches that enables the harmonization of these contradictions is the concept of creating an agile organization, whose basic values are competence, cooperation with clients, continuous communication, transparency and flexibility. As a relatively new way of thinking, organizational agility enables efficient and effective placement of goods and services, as well as a quick response to competition activities. In this paper, using the Delphi method, the analysis of the influence of factors of project-mature organization, defined on the basis of theoretical assumptions, on agile transformation was performed.
The Face and AI

Our faces have become an entrypoint for artificial intelligence. The portraits you post of yourself on social media will be saved, transmitted, analyzed, and processed countless times in the cloud. Nobody will tell you this or ask for your permission. There are processes that nobody notices. The seeds of AI for facial recognition were planted in Silicon Valley and they blossomed into the world. This chapter will explain how that happened.
A Local Correspondence-aware Hybrid CNN-GCN Model for Single-image Human Body Reconstruction

Reconstructing a 3D human body mesh from a monocular image is a challenging inverse problem because of occlusion and complicated human articulations. Recent deep learning-based methods have made significant progress in single-image human reconstruction. Most of these works are either model-based methods, which estimate the sparse parameters of a statistical human body model, or model-free methods, which directly recover the vertices' coordinates of a predefined mesh. However, model-based methods always suffer detail losses due to the limited parameter space, and model-free methods are hard to directly recover satisfactory results from images due to the use of a shared global feature for all vertices and the domain gap between 2D regular images and 3D irregular meshes. To resolve these issues, we propose a hybrid model, which combines the advantages of both model-based approach and model-free approach to estimate a 3D human mesh in a coarse-to-fine manner. Initially, we utilize a convolutional neural network (CNN) to estimate the parameters of a Skinned Multi-Person Linear Model (SMPL), which allows us to generate a coarse human mesh. After that, the vertex coordinates of the coarse human mesh are further refined by a graph convolutional neural network (GCN). Unlike previous GCN-based methods, whose vertex coordinates are recovered from a shared global feature, we propose a LOcal CorRespondence-Aware (LOCRA) module to extract local special features for each vertex. To make the local features related to the human pose, we also add a keypoint-related loss to supervise the training process of the LOCRA module. Experiments demonstrate that our hybrid model with the LOCRA module outperforms existing methods on multiple public benchmarks. Our code will be publicly available.
Mode of Operation for Modification, Insertion, and Deletion of Encrypted Data

Due to the development of 5G communication, many aspects of information technology (IT) services are changing. With the development of communication technologies such as 5G, it has become possible to provide IT services that were difficult to provide in the past. One of the services made possible through this change is cloud-based collaboration. In order to support secure collaboration over cloud, encryption technology to securely manage dynamic data is essential. However, since the existing encryption technology is not suitable for encryption of dynamic data, a new technology that can provide encryption for dynamic data is required for secure cloud-based collaboration. In this paper, we propose a new encryption technology to support secure collaboration for dynamic data in the cloud. Specifically, we propose an encryption operation mode which can support data updates such as modification, addition, and deletion of encrypted data in an encrypted state. To support the dynamic update of encrypted data, we invent a new mode of operation technique named linked-block cipher (LBC). Basic idea of our work is to use an updatable random value so-called link to link two encrypted blocks. Due to the use of updatable random link values, we can modify, insert, and delete an encrypted data without decrypt it.
Towards an Inclusive Co-design Toolkit: Perceptions and Experiences of Co-design Stakeholders

Participatory design holds great potential for the creation of inclusive technology but existing toolkits and resources to support co-design are not always accessible to designers and co-designers with disabilities. In this paper we present two studies to assist in facilitating the creation of a sustainable, accessible, inclusive co-design toolkit for individuals with intellectual disabilities i) exploration of the perceptions and experiences of lecturers (n = 5) and students (n = 5) involved in co-design activities via individual interviews and ii) a protocol and initial findings from focus groups with men and women with intellectual disabilities to inform on best co-design practices (n = 15). Positive reflections were reported on the co-design experience by all participants. Communication was highlighted as a theme that requires further attention and specific support during co-design processes with third level designers and co-designers with intellectual disabilities.
Supporting Peer-Learning with Augmented Reality in Neuroscience and Medical Education

XR (VR and AR) is being integrated into higher education to facilitate the teaching of complex concepts and presenting learning material difficult to explain using other means. An interesting pedagogical approach is to enable students to help each other in achieving their learning goals. Collaborative- and peer learning are among the pedagogies that can enable students to work together towards their learning outcomes. Such pedagogies can also be valuable for hybrid learning. VR is commonly preferred for multiuser collaboration. Nonetheless the role of AR for collaboration is increasing. In this paper we discuss our approach and on-going work towards developing a framework for supporting collaborative and peer learning through AR and how an AR-based neuroanatomy app can be used for peer-learning.
Sclabel: Finding Performance Bugs Through Clone Detection Based on Labeled Code Templates

Performance bugs are defects in source code that slow down program execution, and produced causes of them are complex and diverse. Many tools have been proposed for detecting performance bugs, but they can only find certain types of results which having similar code patterns, and need dynamic analysis or control flow graph of the program. Therefore, conventional tools are lack of generality and high cost of space and time. As we know, many state-of-the-art clone detection techniques have been proposed for general similar code detection, but they can only detect highly similar code (e.g., tools based on text, token or AST) or rely on training dataset (e.g., tools based on machine learning), making them impractical for detecting performance bugs in real-world projects.
Public Tasks During Contagious Disease Pandemics: A Rights-Based Perspective

During a contagious disease pandemic the primary duty of a state is to protect the rights of its inhabitants without endangering its longer-term protective capacities. The effective and efficient mitigation of an epidemic requires a temporary rule of the plan. Besides, trust must be built up and sustained. The protection of life and health must not be confined to the protection from the contagious disease but must be balanced against other important (societal) aims. In addition, a state has duties to join efforts to combat the pandemic internationally and to support, if possible, other countries in containing the disease.
STAF: Spatio-Temporal Attention Framework for Understanding Road Agents Behaviors

On-road behavior analysis is a key task required for robust autonomous vehicles. Unlike traditional perception tasks, this paper aims to achieve a high-level understanding of road agent activities. This allows better operation under challenging contexts to enable better interaction and decision-making in such complex environments. In this paper, we tackle the problem of discriminating spatio-temporal features that capture the visual instants that require more attention.We propose a new approach called STAF (Spatio- Temporal Attention Framework) through Long Short Term Memory (LSTM) layers that uses a multi-head attention mechanism on its past cell state to focus on attributes that are relevant over time. Experiments have been carried out on two different scenarios over data from Joint Attention in Autonomous Driving (JAAD) and Honda Research Institute Driving Dataset (HDD), both datasets devoted to understanding the behavior of road agents. The evaluation and results obtained proof that the proposed "STAF" is outperforming stateof- the-art algorithms-based LSTM (Ramanishka [41], Rasouli [40], and LSTM-baseline). For example, STAF outperforms LSTM with mean Average Precision (mAP) of 13%.
FX Asian Option Pricing Using Quantum Computers

This work presents an algorithm for calculating Asian-style currency options using a gate-base quantum computer. We have described the algorithm in detail, starting from loading the distribution of the underlying asset, through the comparator, calculating the average, payout function, and ending on QAE. We show a novel approach in distribution loading which allow extending our solution to a local volatility model (the previous papers use constant values for interest rate and volatility for the underlying asset). We compared the results obtained on the quantum computer with those obtained classically for different strikes and conducted an error analysis. This work provides a starting point to further work on the deployment Quantum Monte Carlo method in a financial institution.