Reviewerly/exHarmony huggingface dataset:
TutoriVR

Virtual Reality painting is a form of 3D-painting done in a Virtual Reality (VR) space. Being a relatively new kind of art form, there is a growing interest within the creative practices community to learn it. Currently, most users learn using community posted 2D-videos on the internet, which are a screencast recording of the painting process by an instructor. While such an approach may suffice for teaching 2D-software tools, these videos by themselves fail in delivering crucial details that required by the user to understand actions in a VR space. We conduct a formative study to identify challenges faced by users in learning to VR-paint using such video-based tutorials. Informed by results of this study, we develop a VR-embedded tutorial system that supplements video tutorials with 3D and contextual aids directly in the user's VR environment. An exploratory evaluation showed users were positive about the system and were able to use the proposed system to recreate painting tasks in VR.
Are architectural smells independent from code smells? An empirical study

Background. Architectural smells and code smells are symptoms of bad code or design that can cause different quality problems, such as faults, technical debt, or difficulties with maintenance and evolution. Some studies show that code smells and architectural smells often appear together in the same file. The correlation between code smells and architectural smells, however, is not clear yet; some studies on a limited set of projects have claimed that architectural smells can be derived from code smells, while other studies claim the opposite. Objective. The goal of this work is to understand whether architectural smells are independent from code smells or can be derived from a code smell or from one category of them. Method. We conducted a case study analyzing the correlations among 19 code smells, six categories of code smells, and four architectural smells. Results. The results show that architectural smells are correlated with code smells only in a very low number of occurrences and therefore cannot be derived from code smells. Conclusion. Architectural smells are independent from code smells, and therefore deserve special attention by researchers, who should investigate their actual harmfulness, and practitioners, who should consider whether and when to remove them.
Verifiable and Multi-Keyword Searchable Attribute-Based Encryption Scheme for Cloud Storage

In attribute-based searchable encryption (ABSE) scheme, data owners can encrypt their data with access policy for security consideration, and encrypt keywords to obtain keyword index for privacy keyword search, and data users can search interesting keyword on keyword indexes by keyword search trapdoor. However, many existing searchable encryption schemes only support single keyword search and most of the existing attribute-based encryption (ABE) schemes have high computational costs at user client. These problems significantly limit the application of attribute-based searchable encryption schemes in practice. In this paper, we propose a verifiable and multi-keyword searchable attribute-based encryption (VMKS-ABE) scheme for cloud storage, in our new scheme, multi-keyword can be searched and the search privacy is protected. That is, the cloud server can search the multi-keyword with keyword search trapdoor but it does not know any information about the keywords searched. In the proposed scheme, many computing tasks are outsourced to the cloud proxy server, which greatly reduces the computing burden at the user client. Besides, the scheme also supports the verification of the correctness of the outsourced private key. The proposed scheme is proved secure that the keyword index is indistinguishable under the adaptive keyword attacks in the general group model, and the ciphertext is selective secure under selective plaintext attacks in the random oracle model. The security and experimental results show that our scheme is suitable for practicability.
Sim2vec: Node similarity preserving network embedding

Networks such as social networks, computer networks, and citation networks are ubiquitous in the real world. Aiming to learn distributed representations for nodes in a network, network embedding has attracted widespread attention from quite a few researchers due to its critical application in complex network analysis. However, the majority of existing algorithms merely facilitate the local pairwise proximity or implicitly characterize the neighborhood similarity in a network, which are not expressive enough to characterize the diversity of node similarities. In this paper, we present an innovative network embedding framework, Sim2vec, which can encode more comprehensive node similarities among different nodes in a network into unified latent spaces based on deep neural networks. Specifically, we incorporate the adjacency similarity, the accessibility similarity and the neighborhood similarity between two nodes, and introduce their respective measures. To capture more integral structural information in a network, these node similarities are encoded into low-dimensional and dense vector spaces by jointly optimizing the carefully designed objective function. Extensive experiments conducted on several networks demonstrate that the proposed approach is effective and it outperforms the existing state-of-the-art approaches on a variety of tasks, including node classification, link prediction, node visualization and node clustering.
Quantum Block Image Encryption Based on Arnold Transform and Sine Chaotification Model

Quantum image encryption is an emerging technology for efficiently protecting visual information. A quantum block image encryption scheme is designed based on quantum Arnold transform (QArT) and lately proposed sine chaotification model (SCM) in this paper. First, in order to flexibly manipulate image blocks, a quantum block image representation (QBIR) model for block image is proposed, which encodes pixel gray values and position information of image blocks into two entangled qubit sequences. Then, QArT is applied to scramble the positions of image blocks. The final ciphertext image is obtained by quantum XOR operations, which is completed with a pseudorandom sequence generated from SCM. The introduction of SCM dramatically enlarges the key space to resist brute-force attack. Moreover, the generated sequence is dependent on the original image to resist the chosen-plaintext attack. The main quantum circuits are given and the numerical simulation results demonstrate that the proposed quantum image encryption scheme is valid and reliable for quantum image protection in terms of security and computational complexity.
Benefits and Challenges of Designing Cryogenic CMOS RF Circuits for Quantum Computers

Accurate and low-noise generation and amplification of microwave signals are required for the manipulation and readout of quantum bits (qubits). A fault-tolerant quantum computer operates at deep cryogenic temperatures (i.e., <; 100mK) and requires thousands of qubits for running practical quantum algorithms. Consequently, CMOS radio-frequency (RF) integrated circuits operating at cryogenic temperatures down to 4 K (Cryo-CMOS) offer a higher level of system integration and scalability for future quantum computers. In this paper, we extensively discuss the role, benefits, and constraints of Cryo-CMOS for qubits control and readout. The main characteristics of the CMOS transistors and their impacts on RF circuit designs are described. Furthermore, opportunities and challenges of low noise RF signal generation and amplification are investigated.
False confidence, non-additive beliefs, and valid statistical inference

Statistics has made tremendous advances since the times of Fisher, Neyman, Jeffreys, and others, but the fundamental and practically relevant questions about probability and inference that puzzled our founding fathers remain unanswered. To bridge this gap, I propose to look beyond the two dominating schools of thought and ask the following three questions: what do scientists need out of statistics, do the existing frameworks meet these needs, and, if not, how to fill the void? To the first question, I contend that scientists seek to convert their data, posited statistical model, etc., into calibrated degrees of belief about quantities of interest. To the second question, I argue that any framework that returns additive beliefs, i.e., probabilities, necessarily suffers from {\em false confidence}---certain false hypotheses tend to be assigned high probability---and, therefore, risks systematic bias. This reveals the fundamental importance of {\em non-additive beliefs} in the context of statistical inference. But non-additivity alone is not enough so, to the third question, I offer a sufficient condition, called {\em validity}, for avoiding false confidence, and present a framework, based on random sets and belief functions, that provably meets this condition. Finally, I discuss characterizations of p-values and confidence intervals in terms of valid non-additive beliefs, which imply that users of these classical procedures are already following the proposed framework without knowing it.
A similarity hybrid harmony search algorithm for the Team Orienteering Problem

The Harmony Search (HS) is a metaheuristic algorithm, which is inspired from the composition of music harmonies. The functionality and flexibility of HS contribute to the development of successful methodologies for different kind of scientific problems. The aim of this paper is to propose a variant of the classic HS algorithm in order to provide competitive solutions for the Team Orienteering Problem (TOP). We introduce the Similarity Hybrid Harmony Search (SHHS) algorithm as an alternative and innovative optimization method. The SHHS follows the standard procedure of HS with some modifications and includes a new strategy called “similarity process”. Two versions of the proposed method have been developed, the static version with predefined values for the parameters of the method and the dynamic one with dynamic adjustment of the parameters. The SHHS algorithm is applied to the known benchmark instances of TOP. The dynamic of the algorithm is tested through a complete solution analysis which gives the superiority of the dynamic version compared to the static one. The results of both versions of the proposed algorithm indicate the positive performance against other effective and robust optimization algorithms from the literature.
Hyperspectral image denoising with bilinear low rank matrix factorization

Hyperspectral images (HSIs) have rich spectral information, but the various noises generated during the imaging process destroy the visual quality of images and lower the application precision. Therefore, it's crucial to denoise HSI for making better use of it. At present, low-rank-based methods have shown potential in mixture noises removal. While their limitations in rank function approximation, which affects the description of the low rank property in HSI, still need to be broken through. This paper puts forward a bilinear low rank matrix factorization (BLRMF) HSI denoising method, where the bi-nuclear quasi-norm is employed for constraining the low rank characteristic in HSI. The bi-nuclear quasi-norm is a closer approximation to the rank function and can be calculated by the nuclear norms of two smaller factor matrices, which respectively describe the spatial low rank and the spectral low rank. The Alternating Direction Method of Multipliers (ADMM) is employed for solving the optimization problem. A large number of experiments on HSI denoising are conducted to verify the superiority of the BLRMF over the mainstream denoising methods.
Adaptive Convolution for Object Detection

It is quite challenging to detect objects, especially, small objects, in complex scenes. To solve this problem, we propose a novel module named as adaptive convolution block (ACB), which adaptively adjusts the parameters of convolutional filters according to the current feature maps, and then, filter these feature maps with the obtained adaptive convolutional filters to generate enhanced features. Due to such adaptive convolution, the enhanced features can pay more attention to the concerned objects, suppress the interference information caused by irrelevant surroundings, and efficiently improve the detection accuracy. The proposed ACB is light weight and fast. By directly embedding the ACB into the single shot detection framework, we construct a novel real-time adaptive convolutional detector (ACD). Experiments on PASCAL VOC and MS COCO benchmarks confirm that our ACD outperforms the existing state-of-the-art single-stage detection models, and achieves a better tradeoff between accuracy and speed.
Near Real-Time Three Axis Head Pose Estimation Without Training

Head pose estimation methods evaluate the amount of head rotation according to two or three axes, aiming at optimizing the face acquisition process, or extracting neutral-pose frames from a video sequence. Most approaches to pose estimation exploits machine-learning techniques requiring a training phase on a large number of positive and negative examples. In this paper, a novel pose estimation method that exploits a quad-tree-based representation of facial features is described. The locations of a set of landmarks detected over the face image guide its subdivision into smaller and smaller quadrants based on the presence or lack of landmarks within each quadrant. The proposed pose descriptor is both effective and efficient, providing accurate yaw, pitch and roll axis estimates almost in real-time, without need for any training or previous knowledge about the subject. The experiments conducted on both the BIWI Kinect Head Pose Database and the challenging automated facial landmarks in the wild dataset, highlight a pose estimate precision exceeding the state-of-the-art with regard to methods not involving training and machine learning approaches.
Theory and Reality of Cryptocurrency Governance

:I analyze cryptocurrency ecosystems with Elinor Ostrom's meta-framework for self-governance. I conclude that Bitcoin falls short in its self-governing ambitions, while cryptocurrency software protocols and blockchain technologies have potentialities within "permissioned" peer-to-peer private or hybrid networks. However, regulation and supervision by trusted third parties are required.
Multiplicative Noise Removal for Texture Images Based on Adaptive Anisotropic Fractional Diffusion Equations

Multiplicative noise removal problems have attracted much attention in recent years. Unlike additive noise removal problems, multiplicative noise destroys almost all information of the original image, especially for texture images. In this paper, a fractional-order nonlinear diffusion model is proposed to denoise the texture images corrupted by multiplicative noise. In the model, a gray level indicator is introduced to remove multiplicative noise and preserve structure details for texture images. By virtue of the discrete Fourier transform, the model is solved by an iterative scheme in the frequency domain. Then an algorithm in the spatial domain is developed based on the definition of the Grünwald--Letnikov fractional-order derivative. Inspired by the discrepancy principle used for additive noise, we develop a new stopping criterion based on the mean and variance of the noise. Numerical examples are presented to demonstrate the effectiveness and efficiency of the proposed method. Experimental results show that the proposed model can handle multiplicative noise removal and texture preservation quite well.
RGB laser speckles based 3D profilometry

Laser speckle is becoming popular for encoding objects for 3D profile reconstructions. However, the measurement accuracy of laser speckles based stereophotogrammetry is often not high due to the subjective speckles formed on the sensor plane of the camera. In this letter, we propose a method to enhance the measurement accuracy by encoding the surface of the object using red, green, and blue (RGB) laser speckles which are generated when a combined RGB laser beam passes through a ground glass diffuser. A 3CCD color camera is utilized to capture the object from two different perspectives. Interestingly, there is no crosstalk between different channels. Hence, the proposed system is practically equivalent to three synchronized monochrome vision systems having separate red, green, and blue laser speckle projections. Correspondences located by the RGB speckle image correlation are more accurate, because the structured information in the color image is three times that in the monochrome image. Experimental results validate the higher accuracy of the proposed method against the conventional method using monochrome laser speckles. Moreover, the motion robustness of the proposed method is verified on the measurement of the moving objects.
An Augmented Reality Prototype for supporting IoT-based Educational Activities for Energy-efficient School Buildings

The use of Augmented Reality (AR) technologies is currently being investigated in numerous and diverse application domains. In this work, we discuss the ways in which we are integrating AR into educational in-class activities for the GAIA project, aiming to enhance existing tools that target behavioral changes towards energy efficiency in schools. We combine real-time IoT data from a sensing infrastructure inside a fleet of school buildings with AR software running on tablets and smartphones, as companions to a set of educational lab activities aimed at promoting energy awareness in a STEM context. We also utilize this software as a means to ease access to IoT data and simplify device maintenance. We report on the design and current status of our implementation, describing functionality in the context of our target applications, while also relaying our experiences from the use of such technologies in this application domain.
Ensemble clustering based on dense representation

Ensemble clustering has emerged as a powerful tool for improving the stability and accuracy of the clustering task. Although various approaches have been proposed for improving the performance of algorithms, most of them ignored two crucial messages provided by base clusterings. First, some samples of input data may be outliers that locate the boundary of the clusters and can be easily partitioned into different clusters. Second, must-link information exists amongst some instances. In this paper, we develop a novel ensemble method that utilizes a dense representation model to construct a pairwise similarity matrix, and further obtain the final ensemble clusterings result via Ncut. In particular, a robust loss function is used in the proposed model, which can weaken the effect caused by outliers. As the model is convex but non-smooth, we propose a customized re-weighted optimization method and theoretically prove that the final solution provided by it is the global optimal solution for the original problem. Furthermore, by analysing the particular structure of input clusterings, we introduce a slimming strategy, which can utilize the must-link information amongst instances to reduce the size of input data and further reduce the time cost for constructing similarity matrix. Numerous experimental results on real datasets demonstrate the advantages of proposed method over the state-of-the-art algorithms.
Designing a Distributed Ledger Technology System for Interoperable and General Data Protection Regulation–Compliant Health Data Exchange: A Use Case in Blood Glucose Data

Distributed ledger technology (DLT) holds great potential to improve health information exchange. However, the immutable and transparent character of this technology may conflict with data privacy regulations and data processing best practices.The aim of this paper is to develop a proof-of-concept system for immutable, interoperable, and General Data Protection Regulation (GDPR)-compliant exchange of blood glucose data.Given that there is no ideal design for a DLT-based patient-provider data exchange solution, we proposed two different variations for our proof-of-concept system. One design was based purely on the public IOTA distributed ledger (a directed acyclic graph-based DLT) and the second used the same public IOTA ledger in combination with a private InterPlanetary File System (IPFS) cluster. Both designs were assessed according to (1) data reversal risk, (2) data linkability risks, (3) processing time, (4) file size compatibility, and (5) overall system complexity.The public IOTA design slightly increased the risk of personal data linkability, had an overall low processing time (requiring mean 6.1, SD 1.9 seconds to upload one blood glucose data sample into the DLT), and was relatively simple to implement. The combination of the public IOTA with a private IPFS cluster minimized both reversal and linkability risks, allowed for the exchange of large files (3 months of blood glucose data were uploaded into the DLT in mean 38.1, SD 13.4 seconds), but involved a relatively higher setup complexity.For the specific use case of blood glucose explored in this study, both designs presented a suitable performance in enabling the interoperable exchange of data between patients and providers. Additionally, both systems were designed considering the latest guidelines on personal data processing, thereby maximizing the alignment with recent GDPR requirements. For future works, these results suggest that the conflict between DLT and data privacy regulations can be addressed if careful considerations are made regarding the use case and the design of the data exchange system.
Achieving Differential Privacy and Fairness in Logistic Regression

Machine learning algorithms are used to make decisions in various applications. These algorithms rely on large amounts of sensitive individual information to work properly. Hence, there are sociological concerns about machine learning algorithms on matters like privacy and fairness. Currently, many studies focus on only protecting individual privacy or ensuring fairness of algorithms. However, how to meet both privacy and fairness requirements simultaneously in machine learning algorithms is under exploited. In this paper, we focus on one classic machine learning model, logistic regression, and develop differentially private and fair logistic regression models by combining functional mechanism and decision boundary fairness in a joint form. Theoretical analysis and empirical evaluations demonstrate our approaches effectively achieve both differential privacy and fairness while preserving good utility.
DeepAttest

Emerging hardware architectures for Deep Neural Networks (DNNs) are being commercialized and considered as the hardware-level Intellectual Property (IP) of the device providers. However, these intelligent devices might be abused and such vulnerability has not been identified. The unregulated usage of intelligent platforms and the lack of hardware-bounded IP protection impair the commercial advantage of the device provider and prohibit reliable technology transfer. Our goal is to design a systematic methodology that provides hardware-level IP protection and usage control for DNN applications on various platforms. To address the IP concern, we present DeepAttest, the first on-device DNN attestation method that certifies the legitimacy of the DNN program mapped to the device. DeepAttest works by designing a device-specific fingerprint which is encoded in the weights of the DNN deployed on the target platform. The embedded fingerprint (FP) is later extracted with the support of the Trusted Execution Environment (TEE). The existence of the pre-defined FP is used as the attestation criterion to determine whether the queried DNN is authenticated. Our attestation framework ensures that only authorized DNN programs yield the matching FP and are allowed for inference on the target device. DeepAttest provisions the device provider with a practical solution to limit the application usage of her manufactured hardware and prevents unauthorized or tampered DNNs from execution.