Reviewerly/exHarmony huggingface dataset:
Phase-space methods for representing, manipulating, and correcting Gottesman-Kitaev-Preskill qubits

The Gottesman-Kitaev-Preskill (GKP) encoding of a qubit into a bosonic mode is a promising bosonic code for quantum computation due to its tolerance for noise and all-Gaussian gate set. We present a toolkit for phase-space description and manipulation of GKP encodings that includes Wigner functions for ideal and approximate GKP states, for various types of mixed GKP states, and for GKP-encoded operators. One advantage of a phase-space approach is that Gaussian unitaries, required for computation with GKP codes, correspond to simple transformations on the arguments of Wigner functions. We use this fact and our toolkit to describe GKP error correction, including magic-state preparation, entirely in phase space using operations on Wigner functions. While our focus here is on the square-lattice GKP code, we provide a general framework for GKP codes defined on any lattice.
Temperature Monitoring System for Egg Incubators Using Raspberry Pi3 Based on Internet of Things (IoT)

The incubator is made as a substitute for hatching naturally at the same time. The success of the hatch machine is largely determined by the temperature stability in the incubator. In the use of small-scale hatcheries for native chicken hatching, they are still faced with the problem of low hatchability due to one of the obstacles, namely the power outage during the hatching process. To improve the monitoring performance of egg incubators, the writer wishes to conduct research "Monitoring to Control and Monitor Temperature in Egg Incubators" using a webcam camera to monitor temperature conditions and hatch eggs. The working system is the DHT11 sensor will detect the temperature, the webcam camera in real time will monitor the state of the eggs then the raspberry pi3 will automatically control the temperature and electrical energy on the incubator, the smartphone monitors and can also control the temperature with the state of the eggs in realtime, 12V battery as a replacement energy when the PLN goes out. From the results of the tests carried out, the authors can draw conclusions, namely: The webcam camera can display the condition of the eggs in the incubator room. Android smartphones can receive information with a webcam camera during the hatching process. The data logger can display recapitulated temperature and humidity data.
Reservoir Transformers

We demonstrate that transformers obtain impressive performance even when some of the layers are randomly initialized and never updated.Inspired by old and well-established ideas in machine learning, we explore a variety of non-linear "reservoir" layers interspersed with regular transformer layers, and show improvements in wall-clock compute time until convergence, as well as overall performance, on various machine translation and (masked) language modelling tasks.
CONVOLUTIONAL NEURAL NETWORK FOR ARABIC SPEECH RECOGNITION

This work is focused on single word Arabic automatic speech recognition (AASR). Two techniques are used during the feature extraction phase; Log frequency spectral coefficients (MFSC) and Gammatone-frequency cepstral coefﬁcients (GFCC) with their first and second-order derivatives. The convolutional neural network (CNN) is mainly used to execute feature learning and classification process. CNN achieved performance enhancement in automatic speech recognition (ASR). Local connectivity, weight sharing, and pooling are the crucial properties of CNNs that have the potential to improve ASR. We tested the CNN model using an Arabic speech corpus of isolated words. The used corpus is synthetically augmented by applying different transformations such as changing the pitch, the speed, the dynamic range, adding noise, and forward and backward shift in time. It was found that the maximum accuracy obtained when using GFCC with CNN is 99.77 %. The outcome results of this work are compared to previous reports and indicate that CNN achieved better performance in AASR.
Multiple Object Tracking Using Edge Multi-Channel Gradient Model With ORB Feature

Multiple object tracking based on tracking-by-detection is the most common method used in addressing illumination change and occlusion problems.In this paper, we present a tracking algorithm based on Edge Multi-channel Gradient Model.We first use the canny operator to extract the edges of the image, and establish a biologically inspired Multi-channel Gradient Model that integrate the spatio-temporalspectral information of the edge to detect moving multiple objects.Under this model, the ORB feature is introduced to solve the problem of matching the object with the object library.Therefore, we can achieve object consistency, and the threshold classification method can solve the problem of multiple object occlusion in the process of persistent multiple object tracking.The experimental results show that the proposed method can effectively deal with the problems of occlusion and illumination changes.Compared with other stateof-the-art algorithms, the proposed algorithm achieves better performance on MOTA, MOTP, and IDF1.In particular, it performs best on IDSW on MOT2015 dataset, with an average improvement ratio of 28.99% over the second-place algorithm.In addition, our algorithm has a better performance in running time, achieving a good compromise between the speed and the accuracy.
A Human Evaluation of AMR-to-English Generation Systems

Most current state-of-the art systems for generating English text from Abstract Meaning Representation (AMR) have been evaluated only using automated metrics, such as BLEU, which are known to be problematic for natural language generation.In this work, we present the results of a new human evaluation which collects fluency and adequacy scores, as well as categorization of error types, for several recent AMR generation systems.We discuss the relative quality of these systems and how our results compare to those of automatic metrics, finding that while the metrics are mostly successful in ranking systems overall, collecting human judgments allows for more nuanced comparisons.We also analyze common errors made by these systems.
DART: A Lightweight Quality-Suggestive Data-to-Text Annotation Tool

We present a lightweight annotation tool, the Data AnnotatoR Tool (DART), for the general task of labeling structured data with textual descriptions.The tool is implemented as an interactive application that reduces human efforts in annotating large quantities of structured data, e.g. in the format of a table or tree structure.By using a backend sequence-to-sequence model, our system iteratively analyzes the annotated labels in order to better sample unlabeled data.In a simulation experiment performed on annotating large quantities of structured data, DART has been shown to reduce the total number of annotations needed with active learning and automatically suggesting relevant labels.
An Efficient Privacy-Preserving Credit Score System Based on Noninteractive Zero-Knowledge Proof

Credit system is generally associated with the banking and financial institutions, although it has far reaching implications for residents of countries, such as U.S., particularly for those with a poor credit history. Specifically, a credit score computation (CSC) quantifies an individual’s credit value or credit risk, which is used by banking and financial institutions, as well as other entities (e.g., during purchasing of insurance policies and application of rental properties), to facilitate their decision-making (e.g., whether to approve the insurance policy purchase or the level of premium). Although a number of CSC models have been proposed in the literature for supporting different application scenarios, privacy protection of CSC is rarely considered despite the potential for leakage of user private information (e.g., registration, hobbies, credit, relationships, and inquiry). Such information can then be abused for other nefarious activities, such as identity theft and credit card fraud. Thus, in this article, we first analyze the privacy strength of existing CSC models, prior to presenting the formal definition of a privacy-preserving CSC system alongside its security requirements. Then, we propose a concrete construction based on Paillier encryption with three proposed noninteractive zero-knowledge schemes. To demonstrate feasibility of our proposal, we evaluate both its security and performance.
Ten Simple Rules for Digital Government Transformation with Blockchain Smart Contracts

The digital transformation makes our government sustainable accomplishment.Smart contracts are the primary purpose of digital government transformation.In this paper, we present ten simple rules to enable users to be careful and use this technology correctly.The organisation can be achieved a successful transformation to the smart organisation.Our rules are presented in nonchronological order that you should do before you even think about blockchain with smart contracts.This paper also encourages a regular user to apply in the government that still have used a traditional transaction.The recommendation can follow the suggested solution in order to have a successful migration from the traditional organisation towards suitable blockchain smart contracts.
Robust Active Contour Model Using Patch-Based Signed Pressure Force and Optimized Fractional-Order Edge

Active contour models (ACMs) are the most widely used method for image segmentation. However, global fitting ACMs cannot effectively segment inhomogeneous images and local fitting ACMs suffer from noise and the initial position of the contour. To overcome these shortcomings, we propose a novel ACM that consists mainly of a local fitting term, an edge-based term and an external force term. The Jensen-Shannon divergence (JSD) based local fitting term is implemented to address intensity inhomogeneity. The edge-based term is formulated on Caputo-Fabrizio (CF) based fractional-order Gaussian derivatives and applied to compute the weighted area of the region inside the contour. The patch-based external force is designed to improve the robustness of the developed ACM to noise and the initial position of the contour. To further improve the robustness of the proposed model to noise, the input image is first replaced with its local robust statistics. Experimental results demonstrate that the developed model is not only robust to noise and the initial contour but also effective in dealing with intensity inhomogeneity.
ATG-PVD: Ticketing Parking Violations on a Drone

In this paper, we introduce a novel suspect-and-investigate framework, which can be easily embedded in a drone for automated parking violation detection (PVD). Our proposed framework consists of: 1) SwiftFlow, an efficient and accurate convolutional neural network (CNN) for unsupervised optical flow estimation; 2) Flow-RCNN, a flow-guided CNN for car detection and classification; and 3) an illegally parked car (IPC) candidate investigation module developed based on visual SLAM. The proposed framework was successfully embedded in a drone from ATG Robotics. The experimental results demonstrate that, firstly, our proposed SwiftFlow outperforms all other state-of-the-art unsupervised optical flow estimation approaches in terms of both speed and accuracy; secondly, IPC candidates can be effectively and efficiently detected by our proposed Flow-RCNN, with a better performance than our baseline network, Faster-RCNN; finally, the actual IPCs can be successfully verified by our investigation module after drone re-localization.
A Simple and Secure Reformation-Based Password Scheme

The electronic applications of financial institutions like banks and insurance companies use either token-based, biometric-based, or knowledge-based password scheme to keep the confidential information of their customers safe from hackers. The knowledge-based password scheme’s resistance, particularly its reformation-based password scheme against shoulder surfing attacks, is comparatively better than the other two because its password can be entered in crowded places without fear of shoulder surfers. However, the available reformation based passwords involve mental computation making their usability difficult. Furthermore, they also need an extra device like earphones during password entry causing to create a gap for information leakage. Moreover, most of the passwords store passwords’ actual content on a server database that causes penetration in the financial institutions’ database. In this article, a reformation-based password scheme involving no mental computation and using no extra device is proposed. The proposed scheme works on the password characters’ indices, which change dynamically after each login process. It gets the password characters’ indices from the end-user and obtains his password characters’ indices from the database. Next, the textual passwords are formed from the user-provided indices and those obtained from the database. The textual passwords are then compared, and if found match, then login is succeeded, otherwise failed. Our proposed password scheme’s experimental results on the password data set showed better security and usability compared to state-of-art password schemes.
Transmuted Lower Record Type Fréchet Distribution with Lifetime Regression Analysis Based on Type I-Censored Data

This paper introduces a new lifetime distribution by mixing the first two lower record values and various distributional properties are examined.Statistical inference on distribution parameters are discussed with five estimators.A Monte Carlo simulation study is carried out to evaluate the risk behavior of these estimators for different sample of sizes.The distribution modeling analysis is provided based on real data to demonstrate the fitting ability of the proposed model.In addition, a lifetime regression model is described by re-parameterization on the log lifetimes.The superiority of proposed regression model is revealed in well-known models.
Robust CNN Compression Framework for Security-Sensitive Embedded Systems

Convolutional neural networks (CNNs) have achieved tremendous success in solving complex classification problems. Motivated by this success, there have been proposed various compression methods for downsizing the CNNs to deploy them on resource-constrained embedded systems. However, a new type of vulnerability of compressed CNNs known as the adversarial examples has been discovered recently, which is critical for security-sensitive systems because the adversarial examples can cause malfunction of CNNs and can be crafted easily in many cases. In this paper, we proposed a compression framework to produce compressed CNNs robust against such adversarial examples. To achieve the goal, our framework uses both pruning and knowledge distillation with adversarial training. We formulate our framework as an optimization problem and provide a solution algorithm based on the proximal gradient method, which is more memory-efficient than the popular ADMM-based compression approaches. In experiments, we show that our framework can improve the trade-off between adversarial robustness and compression rate compared to the existing state-of-the-art adversarial pruning approach.
Contextual Transformation Networks for Online Continual Learning

Continual learning methods with fixed architectures rely on a single network to learn models that can perform well on all tasks. As a result, they often only accommodate common features of those tasks but neglect each task's specific features. On the other hand, dynamic architecture methods can have a separate network for each task, but they are too expensive to train and not scalable in practice, especially in online settings. To address this problem, we propose a novel online continual learning method named ``Contextual Transformation Networks” (CTN) to efficiently model the \emph{task-specific features} while enjoying neglectable complexity overhead compared to other fixed architecture methods. Moreover, inspired by the Complementary Learning Systems (CLS) theory, we propose a novel dual memory design and an objective to train CTN that can address both catastrophic forgetting and knowledge transfer simultaneously. Our extensive experiments show that CTN is competitive with a large scale dynamic architecture network and consistently outperforms other fixed architecture methods under the same standard backbone. We will release our implementation upon acceptance.
O3ERS: An explainable recommendation system with online learning, online recommendation, and online explanation

Explainable recommendation systems (ERSs) have attracted increasing attention from researchers, which generate high-quality recommendations with intuitive explanations to help users make appropriate decisions. However, most of the existing ERSs are designed with an offline setting, which can hardly adjust their models using the online feedback instantly for improved performance. To overcome the limitations of ERSs with the offline setting, we propose a novel online setting for ERSs and devise an effective model called O3ERS in this online setting, which can perform online learning with good scalability and rigorous theoretical guides for better online recommendations and online explanations. O3ERS also addresses two challenging problems in real scenarios, namely, the sparsity and delay of online explanations' feedback as well as the partialness and insufficiency of online recommendations' feedback. Specifically, O3ERS not only instantly leverages the knowledge learned from the recommendations' feedback to adjust the sparse and delayed explanations' feedback for better explanations but also utilizes a novel exploitation–exploration strategy that incorporates the explanations' feedback to adjust the partial and insufficient recommendations' feedback for better recommendations. Our theoretical analysis and empirical studies on one simulated and two real-world datasets show that our model outperforms the state-of-the-art models in online scenarios remarkably.
Deep Sparse Auto-Encoder Features Learning for Arabic Text Recognition

One of the most recent challenging issues of pattern recognition and artificial intelligence is Arabic text recognition. This research topic is still a pervasive and unaddressed research field, because of several factors. Complications arise due to the cursive nature of the Arabic writing, character similarities, unlimited vocabulary, use of multi-size and mixed-fonts, etc. To handle these challenges, an automatic Arabic text recognition requires building a robust system by computing discriminative features and applying a rigorous classifier together to achieve an improved performance. In this work, we introduce a new deep learning based system that recognizes Arabic text contained in images. We propose a novel hybrid network, combining a Bag-of-Feature (BoF) framework for feature extraction based on a deep Sparse Auto-Encoder (SAE), and Hidden Markov Models (HMMs), for sequence recognition. Our proposed system, termed BoF-deep SAE-HMM, is tested on four datasets, namely the printed Arabic line images Printed KHATT (P-KHATT), the benchmark printed word images Arabic Printed Text Image (APTI), the benchmark handwritten Arabic word images IFN/ENIT, and the benchmark handwritten digits images Modified National Institute of Standards and Technology (MNIST).
Breast Cancer Image Multi-Classification Using Random Patch Aggregation and Depth-Wise Convolution based Deep-Net Model

Adapting the profound, deep convolutional neural network models for large image classification can result in the layout of network architectures with a large number of learnable parameters and tuning of those varied parameters can considerably grow the complexity of the model. To address this problem a convolutional Deep-Net Model based on the extraction of random patches and enforcing depth-wise convolutions is proposed for training and classification of widely known benchmark Breast Cancer histopathology images. The classification result of these patches is aggregated using majority vote casting in deciding the final image classification type. It has been observed that the proposed Deep-Net model implementation results when compared with classification results of the VGG Net(16 layers) learned features, outclasses in terms of accuracy when applied to breast tumor Histopathology images. The objective of this work is to examine and comprehensively analyze the sub-class classification performance of the proposed model across all optical magnification frontiers.
Object Detection for Sweeping Robots in Home Scenes (ODSR-IHS): A Novel Benchmark Dataset

Object detection plays an important role in computer vision.It has a variety of applications, including security detection, vehicle recognition, and service robots.With the continuous improvement of public databases and the development of deep learning, object detection has witnessed significant breakthroughs.However, the object detection of sweeping robots during operations should consider various factors, including the camera angle, indoor scenery, and identification of object category.To the best of our knowledge, no corresponding database on these conditions has been developed.In this study, we review the development of object detection based on deep learning in computer vision.Then, we propose a large-scale publicly available benchmark dataset called object detection for sweeping robots in home scenes (ODSR-IHS).The dataset has 6,000 images and 16,409 instances of 14 object categories.Finally, we evaluate several state-of-the-art methods on the ODSR-IHS dataset and transplant them to the hardware to establish a benchmark dataset for object recognition research on sweeping robots.
Integration of Blockchain and IoT (B-IoT): Architecture, Solutions, &amp; Future Research Direction

Abstract Internet of Things (IoT) plays a very important role in every field. With increase in its features it is almost used in every sector. As it is used everywhere so key elements of security are very much critical components which are to be preserved. For formulating these concepts, a new technology called blockchain can be used. In order to have secure transaction between various items, this technology is tremendously used without involving any third party. In this paper, we present introductory part of IoT enabled with blockchain, their key features, architecture layout, characteristic features of both the technologies, their futuristic solutions for different real-world problems, different communicational models etc. Both the technologies possess a number of characteristic features in every field but certain limitations too exist which gives futuristic direction for research.
“This is really interesting. I never even thought about this.” Methodological strategies for studying invisible information work.

Significant information work (Corbin &amp; Strauss, 1985; 1988) is often required to grapple with increasing quantities, types, and sources of information. Much of this work is invisible both to researchers and to the people undertaking it. As there are many axes along which informational work can be made invisible, researchers require flexible and creative methods in order to bring hidden information work to light. Each drawing on our own information practices research study, we introduce and reflect on four methodological strategies that have been effective in recognizing and revealing hidden aspects of informational work: (1) consider the local and the translocal; (2) attend to the material and the textual; (3) consider visual methods; and (4) (re)consider the participant’s role and expertise. We conclude by reflecting on the benefits and pitfalls of bringing visibility to invisible information work and conclude with a call for further research focused on the invisible.