Reviewerly/exHarmony huggingface dataset:
Universal quantum computing with thermal state bosonic systems

Recent development of mixed-state encoding (MSE) allows pure-state logical information to be encoded by a bosonic (continuous-variable) system in mixed physical state. Despite interest due to its counter-intuitiveness, the utility of the current MSE scheme is limited due to several operational drawbacks, namely redundant information carrier, probabilistic initialisation, and requirement of discrete-variable measurement. In this work, we present a simplified MSE that does not suffer from any of these drawbacks. Specifically, our protocol encodes each qubit by only one mixed-state bosonic mode, and the logical basis can be deterministically initialised from thermal equilibrium. All logical operations of this encoding can be performed with continuous-variable interaction and measurement only. Without the necessity of ground state cooling, our proposal could broaden the set of candidate systems for implementing quantum computers, and reduce the reliance on demanding refrigerating facility for current quantum computing architectures. Additionally, our protocol can enhance the noise tolerance of logical qubit even if the system can be efficiently cooled.
Virtual Replicas of Real Places: Experimental Investigations

As virtual reality (VR) technology becomes cheaper, higher-quality, and more widely available, it is seeing increasing use in a variety of applications including cultural heritage, real estate, and architecture. A common goal for all these applications is a compelling virtual recreation of a real place. Despite this, there has been very little research into how users perceive and experience such replicated spaces. This article reports the results from a series of three user studies investigating this topic. Results include that the scale of the room and large objects in it are most important for users to perceive the room as real and that non-physical behaviors such as objects floating in air are readily noticeable and have a negative effect even when the errors are small in scale.
Interface of mixed reality: from the past to the future

Mixed reality (MR) is an emerging technology which could potentially shape the future of our everyday lives by its unique approach to presenting information. Technology is changing rapidly and information can be presented on traditional computer screens following a WIMP (Windows, Icons, Menus, and Pointing) interface model, by using a head-mounted display to present virtual reality, or by MR which the process of presenting information through a combination of both virtual and physical elements. This paper classifies MR interfaces by applying a text mining method to a data base of 4296 relevant research papers published over the last two decades. The classification reveals the trends relating to each topic and the relations between them. This paper reviews the earlier studies and discusses the recent developments in each topic area and summarizes the advantages and disadvantages of the MR interface. Our objective is to assist researchers understand the trend for each topic and allows them to focus on the research challenges where technological advancements in the MR interface are most needed.
Three Techniques for Enhancing Chaos-Based Joint Compression and Encryption Schemes

In this work, three techniques for enhancing various chaos-based joint compression and encryption (JCAE) schemes are proposed. They respectively improved the execution time, compression ratio, and estimation accuracy of three different chaos-based JCAE schemes. The first uses auxiliary data structures to significantly accelerate an existing chaos-based JCAE scheme. The second solves the problem of huge multidimensional lookup table overheads by sieving out a small number of important sub-tables. The third increases the accuracy of frequency distribution estimations, used for compressing streaming data, by weighting symbols in the plaintext stream according to their positions in the stream. Finally, two modified JCAE schemes leveraging the above three techniques are obtained, one applicable to static files and the other working for streaming data. Experimental results show that the proposed schemes do run faster and generate smaller files than existing JCAE schemes, which verified the effectiveness of the three newly proposed techniques.
Forensic analysis of Google Allo messenger on Android platform

Purpose The purpose of this paper is to conduct a forensic analysis of Google Allo messenger on an Android-based mobile phone. The focus was on the analysis of the data stored by this application in the internal memory of the mobile device, with minimal use of third-party applications. The findings were compared with the already existing works on this topic. Android is the most popular operating system for mobile devices, and these devices often contain a massive amount of personal information about the user such as photos and contact details. Analysis of these applications is required in case of a forensic investigation and makes the process easier for forensic analysts. Design/methodology/approach Logical acquisition of the data stored by these applications was performed. A locked Android device was used for this purpose. Some scripts are presented to help in data acquisition using Android Debug Bridge (ADB). Manual forensic analysis of the device image was performed to see whether the activities carried out on these applications are stored in the internal memory of the device. A comparative analysis of an existing mobile forensic tool was also performed to show the effectiveness of the methodology adopted. Findings Forensic artifacts were recovered from Allo application. Multimedia content such as images were also retrieved from the internal memory. Research limitations/implications As this study was conducted for forensic analysis, it assumed that the mobile device used already has USB debugging enabled on it, although this might not be the applicable in some of the cases. This work provides an optimal approach to acquiring artifacts with minimal use of third-party applications. Practical implications Most of the mobile devices contain messaging application such as Allo installed. A large amount of personal information can be obtained from the forensic analysis of these applications, which can be useful in any criminal investigation. Originality/value This is the first study which focuses on the Google Allo application. The proposed methodology was able to extract almost as much as the data obtained using earlier approaches, but with minimal third-party application usage.
Nonlinear least-squares spline fitting with variable knots

In this paper, we present a nonlinear least-squares fitting algorithm using B-splines with free knots. Since its performance strongly depends on the initial estimation of the free parameters (i.e. the knots), we also propose a fast and efficient knot-prediction algorithm that utilizes numerical properties of first-order B-splines. Using ℓp(p=1,2,∞) norm solutions, we also provide three different strategies for properly selecting the free knots. Our initial predictions are then iteratively refined by means of a gradient-based variable projection optimization. Our method is general in nature and can be used to estimate the optimal number of knots in cases in which no a-priori information is available. To evaluate the performance of our method, we approximated a one-dimensional discrete time series and conducted an extensive comparative study using both synthetic and real-world data. We chose the problem of electrocardiogram (ECG) signal compression as a real-world case study. Our experiments on the well-known PhysioNet MIT-BIH Arrhythmia database show that the proposed method outperforms other knot-prediction techniques in terms of accuracy while requiring much lower computational complexity.
Phonespoof: A New Dataset for Spoofing Attack Detection in Telephone Channel

The results of spoofing detection systems proposed during ASVspoof Challenges 2015 and 2017 confirmed the perspective in detection of unforseen spoofing trials in microphone channel. However, telephone channel presents much more challenging conditions for spoofing detection, due to limited bandwidth, various coding standards and channel effects. Research on the topic has thus far only made use of program codecs and other telephone channel emulations. Such emulations does not quite match the real telephone spoofing attacks. In order to asses spoofing detection methods in real scenario we present the PHONESPOOF dataset - spoofing data collected through realistic telephone channels. The PHONE-SPOOF data collection represents most threatening types of spoofing attacks and is publicly available dataset <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . This work <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> aimed to investigate robustness of the state-of-the-art deep learning based antispoofing systems under telephone spoofing attacks conditions based on the PHONESPOOF data. Moreover newly collected dataset makes it possible to analize language dependency issue for the Anti-Spoofing methods. In the work we also focused on the development of a unified LCNN-based approach for spoofing attack detection. The goal was to train a single system able to detect various types of spoofing attacks in telephone channel. The obtained results approve the effectiveness of such solution.
Real Differences between OT and CRDT in Correctness and Complexity for Consistency Maintenance in Co-Editors

OT (Operational Transformation) was invented for supporting real-time co-editors in the late 1980s and has evolved to become core techniques widely used in today's working co-editors and adopted in industrial products. CRDT (Commutative Replicated Data Type) for co-editors was first proposed around 2006, under the name of WOOT (WithOut Operational Transformation). Follow-up CRDT variations are commonly labeled as "post-OT" techniques capable of making concurrent operations natively commutative in co-editors. On top of that, CRDT solutions have made broad claims of superiority over OT solutions, and often portrayed OT as an incorrect and inefficient technique. Over one decade later, however, CRDT is rarely found in working co-editors; OT remains the choice for building the vast majority of today's co-editors. Contradictions between the reality and CRDT's purported advantages have been the source of much confusion and debate in co-editing researcher and developer communities. To seek truth from facts, we set out to conduct a comprehensive and critical review on representative OT and CRDT solutions and working co-editors based on them. From this work, we have made important discoveries about OT and CRDT, and revealed facts and evidences that refute CRDT claims over OT on all accounts. These discoveries help explain the underlying reasons for the choice between OT and CRDT in the real world. We report these results in a series of three articles. In this article (the second in the series), we reveal the differences between OT and CRDT in their basic approaches to realizing the same general transformation and how such differences had resulted in different technical challenges and consequential correctness and complexity issues. Moreover, we reveal hidden complexity and algorithmic flaws with representative CRDT solutions, and discuss common myths and facts related to correctness and complexity of OT and CRDT. We hope the discoveries from this work help clear up common myths and confusions surrounding OT and CRDT, and accelerate progress in co-editing technology for real world applications.
Comparative Analysis of Virtualization Methods in Big Data Processing

Cloud computing systems have become widely used for Big Data processing, providing access to a wide variety of computing resources and a greater distribution between multi-clouds. This trend has been strengthened by the rapid development of the Internet of Things (IoT) concept. Virtualization via virtual machines and containers is a traditional way of organization of cloud computing infrastructure. Containerization technology provides a lightweight virtual runtime environment. In addition to the advantages of traditional virtual machines in terms of size and flexibility, containers are particularly important for integration tasks for PaaS solutions, such as application packaging and service orchestration. In this paper, we overview the current state-of-the-art of virtualization and containerization approaches and technologies in the context of Big Data tasks solution. We present the results of studies which compare the efficiency of containerization and virtualization technologies to solve Big Data problems. We also analyze containerized and virtualized services collaboration solutions to support automation of the deployment and execution of Big Data applications in the cloud infrastructure.
Priority and collision avoidance system for traffic lights

In this paper, a collision avoidance system is presented to detect red light running and warn nearby vehicles and pedestrians in real time in order to prevent possible accidents. No complex infrastructure-based solution such as those based on radars or cameras is here required. Instead, a new solution based on smartphones carried by drivers and pedestrians is proposed so that it is the device inside the vehicle violating a traffic light, the one that self-reports the offence in order to generate alerts and warn nearby vehicles and pedestrians to prevent accidents. The proposal could also be used by road authorities to collect data on traffic lights that are most frequently violated in order to define an action plan to investigate causes and look for solutions. It includes a classifier for learning and estimating driver behaviour based on collected data, which is used to predict whether he/she is about to run a red light or detect whether that has already happened. In the first case, the system broadcasts warnings directly to close vehicles and pedestrians through Wi-Fi, while in the second case, the proposal warns vehicles and pedestrians in the neighbourhood through a server. The solution also includes a prioritization system based on changing traffic lights at intersections according to the needs and characteristics of the traffic at all times, giving the top priority to emergency vehicles. Furthermore, the proposal involves the use of cryptographic schemes to protect authenticity and integrity of messages sent from traffic lights, smartphones and servers, and privacy and anonymity to promote the use of the system. A beta version with some parts of the proposal has been implemented and the obtained results are promising.
Assessing the Robustness of Mixture Models to Measurement Noninvariance

Recent work reframes direct effects of covariates on items in mixture models as differential item functioning (DIF) and shows that, when present in the data but omitted from the fitted latent class model, DIF can lead to overextraction of classes. However, less is known about the effects of DIF on model performance-including parameter bias, classification accuracy, and distortion of class-specific response profiles-once the correct number of classes is chosen. First, we replicate and extend prior findings relating DIF to class enumeration using a comprehensive simulation study. In a second simulation study using the same parameters, we show that, while the performance of LCA is robust to the misspecification of DIF effects, it is degraded when DIF is omitted entirely. Moreover, the robustness of LCA to omitted DIF differs widely based on the degree of class separation. Finally, simulation results are contextualized by an empirical example.
{StreamBox-TZ}: Secure Stream Analytics at the Edge with {TrustZone}

While it is compelling to process large streams of IoT data on the cloud edge, doing so exposes the data to a sophisticated, vulnerable software stack on the edge and hence security threats. To this end, we advocate isolating the data and its computations in a trusted execution environment (TEE) on the edge, shielding them from the remaining edge software stack which we deem untrusted. This approach faces two major challenges: (1) executing high-throughput, low-delay stream analytics in a single TEE, which is constrained by a low trusted computing base (TCB) and limited physical memory; (2) verifying execution of stream analytics as the execution involves untrusted software components on the edge. In response, we present StreamBox-TZ (SBT), a stream analytics engine for an edge platform that offers strong data security, verifiable results, and good performance. SBT contributes a data plane designed and optimized for a TEE based on ARM TrustZone. It supports continuous remote attestation for analytics correctness and result freshness while incurring low overhead. SBT only adds 42.5 KB executable to the TCB (16% of the entire TCB). On an octa core ARMv8 platform, it delivers the state-of-the-art performance by processing input events up to 140 MB/sec (12M events/sec) with sub-second delay. The overhead incurred by SBT's security mechanism is less than 25%.
Research on Digital Camouflage Pattern Generation Algorithm Based on Adversarial Autoencoder Network

In the past, most of the digital camouflage used textural features to extract the configuration features of spots in gray images, unable to effectively utilize the position relationship between color information. In order to overcome this shortcoming, a new digital camouflage pattern design model was proposed based on the model of adversarial autoencoder network. Firstly, the complexity and performance of several main color extraction algorithms were analyzed and compared, and combined with AFK-MC 2 algorithm and color similarity coefficient, a fast camouflage main color clustering method was proposed. Then a deep convolution adversarial autoencoder network was designed to extract and describe the configuration features of the spots in background pattern. In order to diffuse pixel spot and achieve the effect of spatial color blending, a morphological processing algorithm was proposed to process the generated camouflage patterns. Finally, two sets of grassland and woodland datasets were established, respectively. The influence of the number of latent variables of network on the training process was tested on the dataset, and the number of camouflage feature descriptions was determined to be greater than or equal to 10. In order to verify the effectiveness of the generated camouflage, the spots in background region and target region were randomly selected, and the Euclidean distance between the feature parameters of these spots was calculated. Both the visual and experimental results demonstrate that the generated spots have high fusion with the background.
Batch-Shaped Channel Gated Networks.

We present a method that trains large capacity neural networks with significantly improved accuracy and lower dynamic computational cost. We achieve this by gating the deep-learning architecture on a fine-grained-level. Individual convolutional maps are turned on/off conditionally on features in the network. To achieve this, we introduce a new residual block architecture that gates convolutional channels in a fine-grained manner. We also introduce a generally applicable tool $batch$-$shaping$ that matches the marginal aggregate posteriors of features in a neural network to a pre-specified prior distribution. We use this novel technique to force gates to be more conditional on the data. We present results on CIFAR-10 and ImageNet datasets for image classification, and Cityscapes for semantic segmentation. Our results show that our method can slim down large architectures conditionally, such that the average computational cost on the data is on par with a smaller architecture, but with higher accuracy. In particular, on ImageNet, our ResNet50 and ResNet34 gated networks obtain 74.60% and 72.55% top-1 accuracy compared to the 69.76% accuracy of the baseline ResNet18 model, for similar complexity. We also show that the resulting networks automatically learn to use more features for difficult examples and fewer features for simple examples.
Evaluating MAP-Elites on constrained optimization problems

Constrained optimization problems are often characterized by multiple constraints that, in the practice, must be satisfied with different tolerance levels. While some constraints are hard and as such must be satisfied with zero-tolerance, others may be soft, such that non-zero violations are acceptable. Here, we evaluate the applicability of MAP-Elites to "illuminate" constrained search spaces by mapping them into feature spaces where each feature corresponds to a different constraint. On the one hand, MAP-Elites implicitly preserves diversity, thus allowing a good exploration of the search space. On the other hand, it provides an effective visualization that facilitates a better understanding of how constraint violations correlate with the objective function. We demonstrate the feasibility of this approach on a large set of benchmark problems, in various dimensionalities, and with different algorithmic configurations. As expected, numerical results show that a basic version of MAP-Elites cannot compete on all problems (especially those with equality constraints) with state-of-the-art algorithms that use gradient information or advanced constraint handling techniques. Nevertheless, it has a higher potential at finding constraint violations vs. objectives trade-offs and providing new problem information. As such, it could be used in the future as an effective building-block for designing new constrained optimization algorithms.
An Empirical Study of README contents for JavaScript Packages

Contemporary software projects often utilize a README.md to share crucial information such as installation and usage examples related to their software. Furthermore, these files serve as an important source of updated and useful documentation for developers and prospective users of the software. Nonetheless, both novice and seasoned developers are sometimes unsure of what is required for a good README file. To understand the contents of README, we investigate the contents of 43,900 JavaScript packages. Results show that these packages contain common content themes (i.e., ‘usage’, ‘install’ and ‘license’). Furthermore, we find that application-specific packages more frequently included content themes such as ‘options’, while library-based packages more frequently included other specific content themes (i.e., ‘install’ and ‘license’).
Latent Code and Text-based Generative Adversarial Networks for Soft-text Generation

Md. Akmal Haidar, Mehdi Rezagholizadeh, Alan Do Omri, Ahmad Rashid. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Near-Neighbor Methods in Random Preference Completion

This paper studies a stylized, yet natural, learning-to-rank problem and points out the critical incorrectness of a widely used nearest neighbor algorithm. We consider a model with n agents (users) {xi}i∈[n] and m alternatives (items) {yl}l∈[m], each of which is associated with a latent feature vector. Agents rank items nondeterministically according to the Plackett-Luce model, where the higher the utility of an item to the agent, the more likely this item will be ranked high by the agent. Our goal is to identify near neighbors of an arbitrary agent in the latent space for prediction.We first show that the Kendall-tau distance based kNN produces incorrect results in our model. Next, we propose a new anchor-based algorithm to find neighbors of an agent. A salient feature of our algorithm is that it leverages the rankings of many other agents (the so-called “anchors”) to determine the closeness/similarities of two agents. We provide a rigorous analysis for one-dimensional latent space, and complement the theoretical results with experiments on synthetic and real datasets. The experiments confirm that the new algorithm is robust and practical.