Reviewerly/exHarmony huggingface dataset:
Scalable Arabic text Classification Using Machine Learning Model

Recently, with the existence of internet we have witnessed great development in computer systems. Artificial intelligence means to developing computer systems that able to perform intelligent tasks. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</sup> Machine learning is one method of make such systems. In this paper, using SVM classifier, I build a Multi-label text classification model for Arabic text. This model is basically used to classify articles on their topics. The results show that using SVM classifier on the dataset generated the best results with 82.2% accuracy. The model was build using Python.
Measurement in-plane deformations in electronic speckle pattern interferometry using phase-shifting modulated by polarization

We propose to combine electronic speckle pattern interferometry with polarizing phase-shifting techniques to measure in-plane displacement fields. We present the technical and theoretical procedures for its implementation, added with the necessary calibration procedures employing a commercial polarimeter. Finally, we tested our proposal experimentally using a two-phase step algorithm to measure in-plane displacements on a latex sample.
Feasible high-dimensional measurement-device-independent quantum key distribution

Measurement-device-independent quantum key distribution (MDI-QKD) can resist all possible attacks from practical measurement devices. High-dimensional (HD) MDI-QKD in multiple degrees of freedom (DOFs) can efficiently increase the key generation rate and has strong noise and error resistance. However, previous HD MDI-QKD protocols in multiple DOFs required complete hyperentangled Bell state analysis (HBSA), which cannot be realized under current experimental conditions and largely limits the practicality of the HD MDI-QKD. In this paper, we propose an HD MDI-QKD protocol in multiple DOFs which adopts a linear-optical HBSA. This adoption of the linear-optical HBSA makes our HD MDI-QKD feasible under current experimental technology. The key generation rate of our MDI-QKD protocol is about eight times of the original MDI-QKD protocol. Our HD MDI-QKD protocol may have potential applications in current and future quantum communication fields.
Search4Code: Code Search Intent Classification Using Weak Supervision

Developers use search for various tasks such as finding code, documentation, debugging information, etc. In particular, web search is heavily used by developers for finding code examples and snippets during the coding process. Recently, natural language based code search has been an active area of research. However, the lack of real-world large-scale datasets is a significant bottleneck. In this work, we propose a weak supervision based approach for detecting code search intent in search queries for C# and Java programming languages. We evaluate the approach against several baselines on a real-world dataset comprised of over 1 million queries mined from Bing web search engine and show that the CNN based model can achieve an accuracy of 77% and 76% for C# and Java respectively. Furthermore, we are also releasing Search4Code, the first large-scale real-world dataset of code search queries mined from Bing web search engine. We hope that the dataset will aid future research on code search.
Artificial Intelligence (AI) and Augmented Reality (AR): Disambiguated in the Telemedicine / Telehealth Sphere

Abstract The world is navigating through unfamiliar and incomprehensible times – COVID-19, international economic crisis, and crumbling healthcare systems. The United States (US) healthcare industry is grappling with an increased workload and advancing digitization technological concerns. The failure of organizations to offer suitable cybersecurity controls within the critical infrastructure leads to advanced persistent threat (APT) that could have incapacitating effects on organizations. A keen understanding of cybersecurity is vital for leaders and the need is referenced in US policy that advances a national unity of effort to strengthen and maintain secure, functioning, and resilient critical infrastructure. Akin to the Presidential Policy Directive (PPD) 21 Critical Infrastructure Security and Resilience, leaders’ goals should be to reduce vulnerabilities, identify and disrupt threats, minimize consequences and hasten response and recovery efforts related to critical infrastructure. To address the concern, it is necessary to review how AI and AR serve as co-technologies to support security of patient care and monitoring, examine impacts on individuals’ and overall healthcare organizations, address how enhanced comprehension of AI and AR could guide medical professional leaders’ decisions and boost the overall patient experience. Therefore, this literature review examines AI and AR connections to cybersecurity for the healthcare environment.
DeepDNet: Deep Dense Network for Depth Completion Task

In this paper, we propose a Deep Dense Network for Depth Completion Task (DeepDNet) towards generating dense depth map using sparse depth and captured view. Wide variety of scene understanding applications such as 3D reconstruction, mixed reality, robotics demand accurate and dense depth maps. Existing depth sensors capture accurate and reliable sparse depth and find challenges in acquiring dense depth maps. Towards this we plan to utilise the accurate sparse depth as input with RGB image to generate dense depth. We model the transformation of random sparse input to grid-based sparse input using Quad-tree decomposition. We propose Dense-Residual-Skip (DRS) Autoencoder along with an attention towards edge preservation using Gradient Aware Mean Squared Error (GAMSE) Loss. We demonstrate our results on the NYUv2 dataset and compare it with other state of the art methods. We also show our results on sparse depth captured by ARCore depth API with its dense depth map. Extensive experiments suggest consistent improvements over existing methods.
A Cross-National Comparative Policy Analysis of the Blockchain Technology between the USA and China

Blockchain technology can achieve decentralization, multi-party verification, anti-tampering, anonymity, traceability of transactions and distributed ledger applications. Countries around the world continue to seek blockchain business models, technologies and applications and have different visions and policies for the development of blockchain. This study provides a comparative policy framework for the theoretical analysis of blockchain technology between the USA and China. Using the innovative policy tools proposed by Rothwell and Zegveld, these two countries are analyzed from the viewpoint of twelve policy tools. The results show that the USA and China both prefer to use “Environmental-side” policies. The USA has focused more on “Legal and regulatory”, “Public services” and “Procurement”. China has the highest proportion of policies in “Political tools”, followed by “Legal and regulatory”, while “Scientific and technical”, “Education” and “Overseas agent” come in third. Blockchain technology has developed vigorously among industries and its applications have gradually diversified. The results can provide a reference for policy planning for various stakeholders.
Measuring Human Perception to Improve Handwritten Document Transcription

In this paper, we consider how to incorporate psychophysical measurements of human visual perception into the loss function of a deep neural network being trained for a recognition task, under the assumption that such information can reduce errors. As a case study to assess the viability of this approach, we look at the problem of handwritten document transcription. While good progress has been made towards automatically transcribing modern handwriting, significant challenges remain in transcribing historical documents. Here we describe a general enhancement strategy, underpinned by the new loss formulation, which can be applied to the training regime of any deep learning-based document transcription system. Through experimentation, reliable performance improvement is demonstrated for the standard IAM and RIMES datasets for three different network architectures. Further, we go on to show feasibility for our approach on a new dataset of digitized Latin manuscripts, originally produced by scribes in the Cloister of St. Gall in the the 9th century.
Collective Anomaly Detection for Multivariate Data using Generative Adversarial Networks

Generative adversarial network (GAN) is used to model complex high-dimensional distributions of real-world scenarios. It has been applied to anomaly detection and making great achievements. However, most of the existing GAN-based anomaly detection methods cannot detect collective anomalies that change the behavior of multipoint data instances. Moreover, although many GAN-based methods for time-series anomaly detection have been proposed, there are few studies to handle collective anomalies in time-series data. Besides, there is still much room to improve the methods in terms of computational cost and the accuracy for detecting anomaly. We thus aim to propose a GAN-based method to detect multi-dimensional collective anomalies with high accuracy. To correctly detect collective anomalies, we especially introduce an encoder into a GAN-based anomaly detection method to obtain the latent states of the real data. We furthermore adopt a sequence to sequence technique to both encoder and generator, recurrent neural network, and fully connected neural network for the discriminator. We conducted experiments using two types of datasets: artificial and natural, and verified the effectiveness of our GAN model.
Learning Goals from Failure

We introduce a framework that predicts the goals behind observable human action in video. Motivated by evidence in developmental psychology, we leverage video of unintentional action to learn video representations of goals without direct supervision. Our approach models videos as contextual trajectories that represent both low-level motion and high-level action features. Experiments and visualizations show our trained model is able to predict the underlying goals in video of unintentional action. We also propose a method to "automatically correct" unintentional action by leveraging gradient signals of our model to adjust latent trajectories. Although the model is trained with minimal supervision, it is competitive with or outperforms baselines trained on large (supervised) datasets of successfully executed goals, showing that observing unintentional action is crucial to learning about goals in video.
PAAS

Conventional tile-based 360° video streaming methods, including deep reinforcement learning (DRL) based, ignore the interactive nature of 360° video streaming and download tiles following fixed sequential orders, thus failing to respond to the user's head motion changes. We show that these existing solutions suffer from either the prefetch accuracy or the playback stability drop. Furthermore, these methods are constrained to serve only one fixed streaming preference, causing extra training overhead and the lack of generalization on unseen preferences. In this paper, we propose a dual-queue streaming framework, with accuracy and stability purposes respectively, to enable the DRL agent to determine and change the tile download order without incurring overhead. We also design a preference-aware DRL algorithm to incentivize the agent to learn preference-dependent ABR decisions efficiently. Compared with state-of-the-art DRL baselines, our method not only significantly improves the streaming quality, e.g., increasing the average streaming quality by 13.6% on a public dataset, but also demonstrates better performance and generalization under dynamic preferences, e.g., an average quality improvement of 19.9% on unseen preferences.
Multi-Label Arabic Text Classification Based On Deep Learning

Multi-label text classification is a natural extension of text classification in which each document can be assigned with a possible widespread set of labels. Natural Language Processing (NLP) helps to understand and manipulate text in natural language by using the computer. Arabic Text Classification is challenging recently because the Arabic language is under-resourced although it has many users. The aim of this paper is to build a model to classify Arabic news and help users get and display the most relevant news to their interests. In this paper, we demonstrate the efficiency of using deep learning models in solving Arabic multi-label text classification problem. Multilayer Perceptron (MLP) and Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) have been used; we build two models via python. All data has been cleaned to improve the quality of experimental data. The result of test data in LSTM was 82.03 whereas in the MLP model was 80.37, and both models were evaluated using F1 score.
Learning from robotic artefacts: A quest for strong concepts in Human-Robot Interaction

This paper is a methodological replication of Barendregt et al. [11], who urged Child-Computer Interaction field to embrace Intermediate Level Knowledge as a meaningful and valid way of generating knowledge. We extend this epistemological gap to the Human-Robot Interaction (HRI). Currently, artefact-centered papers—papers that present the development of an artefact—seem to be one of the primary ways that the HRI field generates knowledge. In this paper, we made an analysis of all papers presented at the HRI Conference from 2006 to 2020. Our results indicate that the 41,2 % of the papers were artefact-centered; and the impact of them, measured in the number of citations, was significantly lower than other kinds of papers. We used 23 artefact-centered papers to formulate two strong concepts and investigate how the foundational design epistemology about intermediate-level knowledge and RtD can contribute to other design-related disciplines to produce useful and valuable knowledge.
Don't Look Back, Look into the Future

Deterministic database systems have been shown to significantly improve the availability and scalability of a distributed database system deployed on a shared-nothing architecture across WAN while ensuring strong consistency. However, their scalability and performance advantages highly depend on the quality of data partitioning due to the reduced flexibility in transaction processing. Although a deterministic database system can employ workload driven data (re-)partitioning and live data migration algorithms to partition data, we found that the effectiveness of these algorithms is limited in complex real-world environments due to the unpredictability of machine workloads. In this paper, we present Hermes, a deterministic database system prototype that, for the first time, does not rely on sophisticated data partitioning to achieve high scalability and performance. Hermes employs a novel transaction routing mechanism that jointly optimizes the balance of machine workloads, data (re-)partitioning, and live data migration by looking into the queued transactions to be executed in the near future. We conducted extensive experiments which show that Hermes is able to yield 29% to 137% increase in transaction throughput as compared to the state-of-the-art systems under complex real-world workloads.
A novel probability iterative closest point with normal vector algorithm for robust rail profile registration

Regular inspection of rail wear is very important to ensure the safety of railway transportation. Rail profile registration is a key step in rail wear measurement. However, the accuracy of rail profile registration cannot be guaranteed due to soil and corrosion on the rail waist. To solve this problem, a new rail profile registration method based on probability iterative closest point algorithm with normal vector direction (N-PICP) is proposed in this paper. First, rail profile data is obtained using a line structured light measurement system. Then, the rail profile data is divided into rail head and rail waist according to the distance between adjacent points. The data of the rail waist is used to register with the corresponding part of the standard profile data by N-PICP. Finally, the rail head data is also converted according to the calculated transformation matrix. The experimental results demonstrate that the proposed method can realize the registration of the rail profile with a high precision and strong robustness.
GEOQKD: quantum key distribution from a geostationary satellite

Due to the distance limitation of quantum communication via ground-based fibre networks, space-based quantum key distribution (QKD) is a viable solution to extend such networks over continental and, ultimately, over global distances. Compared to LEO, QKD from GEO would offer substantial advantages, i.e. large coverage, continuous link to ground stations (cloud cover limited), 24/24h operation (background limited), no tracking required, however, coming with large link losses seen the space-ground distance. TNO, together with Eutelsat and CGI-NL, performed a detailed study on the feasibility of QKD from GEO, including a high level system design of payload and ground segment. We conclude that QKD from GEO is technically feasible, and a favourable solution if the satellite needs to act as an untrusted node (that is, no security assumptions required for the space segment). However, the optimal solution, generating a higher value-for-money, is to use a hybrid system, implementing an untrusted and trusted mode BBM92 QKD protocol. In order to arrive at a minimum required secure bit rate of ~1 bit/s in untrusted mode, a 2x ~0.5m diameter telescope in the space segment is required with &lt;0.25µrad pointing accuracy, a &lt;1GHz entangled photon source, in combination with ~2m diameter telescopes on ground. Details on our assumptions and results and drawings of the high level system design will be presented, as well as the development roadmap describing the required technology improvements and building blocks for the overall hybrid approach, which is applicable to non-GEO applications as well.
Novel nano-scale absolute linear displacement measurement based on grating projection imaging

There is urgent demand for new absolute Linear Displacement Measurement (LDM) technology with high resolution and high precision, during the development of high-end numerical control technology. In previous research, we found that displacement measurement based on the image processing method performs well, however, the volume increases when using an imaging lens, and the system is susceptible to factors such as virtual focus. In this paper, the lensless LDM image optical path is established based on grating projection imaging, then the absolute grating coding method is operated based on M−sequence pseudo-random coding. A linear displacement subdivision algorithm is then deployed based on the digital image recognition algorithm. An LDM device with a measuring range of 250 mm was designed to test the performance of the proposed method. The device shows a measurement resolution of 1 nm and measurement accuracy of 1.76 μm in the range of 250 mm. This work lays a foundation for further research on high performance LDM technology.
Arabic Multi-Topic Labelling using Bidirectional Long Short-Term Memory

The number of text documents on the internet is rapidly increasing. As a result, there is a growing demand for methods that can automatically organize and identify electronic documents (instances). The multi-label classification task has been used in a variety of applications and is commonly used in real-world problems. It simultaneously assigns multiple labels to each text. In the Arabic language, there have been few and inadequate research studies on the multi-label text classification issue. In this paper, I proposed a deep learning model using bidirectional long short-term memory (BiLSTM) for multi-class topic classification using Mowjaz Multi-Topic Labelling Task dataset. The BiLSTM model consists of 4 layers only which can be considered as light weight model, these layers are input layer, bidirectional LSTM layer, and two dense layers. The results show that the model successfully to classify topics with F1-Socre of 0.8089 on the testing dataset.
Cryo-CMOS Electronics For Quantum Computing: Bringing Classical Electronics Closer To Qubits In Space And Temperature

The core of a quantum computer is a quantum processor, which is composed of quantum bits (qubits). Qubits can be implemented in many flavors, but they are fragile, and their state has a tendency to decay in time. To be usable for computation, qubits must be corrected in real time by a classical controller. The controller must also see that desired computations are executed. Today, the control of qubits is performed at room temperature (RT) by racks of instruments, while the qubits operate at several tens of milli-Kelvin. To ensure compactness and, eventually, scalability, we have proposed and implemented CMOS circuits designed to operate at a few degrees Kelvin. This makes complex thermalization circuits unnecessary, potentially enabling superconductive interconnects, which would enable virtually zero resistance and low thermal conductivity. Cryogenic CMOS, or cryo-CMOS, technology was chosen to achieve classical control due to its scalable nature and overall miniaturization opportunities. Cryo-CMOS circuits and systems need to be carefully designed to ensure strict power budgets, while achieving quite advanced specifications in terms of noise and bandwidth. We review the requirements of a classical controller and cryo-CMOS circuits and systems developed for spin qubits in our lab. Results and perspectives are presented with a discussion about a road map for the future.
Shotgun reconstruction in the hypercube

Abstract Mossel and Ross raised the question of when a random coloring of a graph can be reconstructed from local information, namely, the colorings (with multiplicity) of balls of given radius. In this article, we are concerned with random 2‐colorings of the vertices of the ‐dimensional hypercube, or equivalently random Boolean functions. In the worst case, balls of diameter are required to reconstruct. However, the situation for random colorings is dramatically different: we show that almost every 2‐coloring can be reconstructed from the multiset of colorings of balls of radius 2. Furthermore, we show that for , almost every ‐coloring can be reconstructed from the multiset of colorings of 1‐balls.
Secure multitarget tracking over decentralized sensor networks with malicious cyber attacks

This paper is concerned with the multitarget tracking over decentralized sensor networks where the network can potentially be compromised by malicious cyber attacks. We consider the hybrid cyber attacks, including denial of service (DoS), false data injection (FDI), and extra packet injection (EPI) attack. We first establish the feature model of DoS, FDI and EPI attacks for decentralized multitarget tracking. Then, we propose a decentralized multitarget tracking algorithm against DoS, FDI and EPI attacks, which consists of three phases: prediction, adaptation and combination. The adaptation phase is to update the estimate of each node with its own measurements and all its neighbors' measurements. The combination phase is to fuse the estimate of each node with all its neighbors' estimates. By incorporation of the neighbors' measurements and fusing the neighbors' estimates, it can dramatically reduce the adverse effect of cyber attacks and provide reliable tracking performance. Numerical experiments are provided to demonstrate the effectiveness of the proposed algorithm.