Reviewerly/exHarmony huggingface dataset:
Constant overhead quantum fault tolerance with quantum expander codes

The threshold theorem is a seminal result in the field of quantum computing asserting that arbitrarily long quantum computations can be performed on a faulty quantum computer provided that the noise level is below some constant threshold. This remarkable result comes at the price of increasing the number of qubits (quantum bits) by a large factor that scales polylogarithmically with the size of the quantum computation we wish to realize. Minimizing the space overhead for fault-tolerant quantum computation is a pressing challenge that is crucial to benefit from the computational potential of quantum devices. In this paper, we study the asymptotic scaling of the space overhead needed for fault-tolerant quantum computation. We show that the polylogarithmic factor in the standard threshold theorem is in fact not needed and that there is a fault-tolerant construction that uses a number of qubits that is only a constant factor more than the number of qubits of the ideal computation. This result was conjectured by Gottesman who suggested to replace the concatenated codes from the standard threshold theorem by quantum error-correcting codes with a constant encoding rate. The main challenge was then to find an appropriate family of quantum codes together with an efficient classical decoding algorithm working even with a noisy syndrome. The efficiency constraint is crucial here: bear in mind that qubits are inherently noisy and that faults keep accumulating during the decoding process. The role of the decoder is therefore to keep the number of errors under control during the whole computation. On a technical level, our main contribution is the analysis of the SMALL-SET-FLIP decoding algorithm applied to the family of quantum expander codes . We show that it can be parallelized to run in constant time while correcting sufficiently many errors on both the qubits and the syndrome to keep the error under control. These tools can be seen as a quantum generalization of the BIT-FLIP algorithm applied to the (classical) expander codes of Sipser and Spielman.
Blockchain technology in supply chain management: insights from machine learning algorithms

Purpose This paper aims to retrieve key components of blockchain applications in supply chain areas. It applies natural language processing methods to generate useful insights from academic literature. Design/methodology/approach It first applies a text mining method to retrieve information from scientific journal papers on the related topics. The text information is then analyzed through machine learning (ML) models to identify the important implications from the existing literature. Findings The research findings are three-fold. While challenges are of concern, the focus should be given to the design and implementation of blockchain in the supply chain field. Integration with internet of things is considered to be of higher importance. Blockchain plays a crucial role in food sustainability. Research limitations/implications The research findings offer insights for both policymakers and business managers on blockchain implementation in the supply chain. Practical implications This paper exemplifies the model as situated in the interface of human-based and machine-learned analysis, potentially offering an interesting and relevant avenue for blockchain and supply chain management researchers. Originality/value To the best of the knowledge, the research is the very first attempt to apply ML algorithms to analyzing the full contents of blockchain-related research, in the supply chain sector, thereby providing new insights and complementing existing literature.
A Secured and Effective Load Monitoring and Scheduling Migration VM in Cloud Computing

Abstract Inside the united cloud-controlled condition, the dynamic and viewing acknowledge a critical movement wherein the host regulator deals with the advantages across has inside the laborer farm. Host regulator does a virtual machine and genuinely have the heads. The Virtual Machine the board solidifies creation, checking, and movement. In the occasion that Host Controller down, the associations supported by different hosts in Decentralized can’t be gotten to outside the Decentralized[1]. Decentralized Virtual Machine the board evades intertwined dissatisfaction by considering one among the hosts from Decentralized as host regulator that helps keep with expanding Decentralized in running state. Each host in Decentralized has different Virtual machine with very far past which it can’t give organization. to stay up the sting, the host’s in Decentralized does Virtual Machine advancement across different hosts. the data in development is as plaintext, the interloper can dismantle bundle headway and may control has traffic. The joining of the security structure has in Decentralized helps with guaranteeing the data being developed. This paper talks about two or three strategies for dynamic Host regulator choice, Virtual confirmation, and secure Virtual development over cloud conditions. An amazingly capable processing condition is obliged by technique for distributed computing wherein customers or different occupants are expected of a couple of sources to be equipped consider as a provider over the online. influencing adventure before the cloud carrier provider is that the way sufficiently and effectively the essential processing sources like virtual machines, association, parking lot outfit, and knowledge move limit, and various others. need to be directed all at once that no environment is in underneath use state in an exceptional natural factor. a far better than average undertaking planning technique is generally required for the dynamic choice of the heap observing to remain faraway from such a drag. Thru this proposed model we’ll be favouring the stainless figuring reliant on grasped booking procedure, which licenses you to accept the heap capably countless the virtual machine therefore the general reaction time (QoS)[2] must be unimportant. An examination of this proposed set of rules of undertaking booking approach is cultivated on the CloudSim test framework which shows that this will defeat the present strategies like customary First come first serve, SJF, and Genetic variation procedures.
Towards Explainable Classifiers Using the Counterfactual Approach - Global Explanations for Discovering Bias in Data

Abstract The paper proposes summarized attribution-based post-hoc explanations for the detection and identification of bias in data. A global explanation is proposed, and a step-by-step framework on how to detect and test bias is introduced. Since removing unwanted bias is often a complicated and tremendous task, it is automatically inserted, instead. Then, the bias is evaluated with the proposed counterfactual approach. The obtained results are validated on a sample skin lesion dataset. Using the proposed method, a number of possible bias-causing artifacts are successfully identified and confirmed in dermoscopy images. In particular, it is confirmed that black frames have a strong influence on Convolutional Neural Network’s prediction: 22% of them changed the prediction from benign to malignant.
Always Keep your Target in Mind: Studying Semantics and Improving Performance of Neural Lexical Substitution

Lexical substitution, i.e. generation of plausible words that can replace a particular target word in a given context, is an extremely powerful technology that can be used as a backbone of various NLP applications, including word sense induction and disambiguation, lexical relation extraction, data augmentation, etc. In this paper, we present a large-scale comparative study of lexical substitution methods employing both rather old and most recent language and masked language models (LMs and MLMs), such as context2vec, ELMo, BERT, RoBERTa, XLNet. We show that already competitive results achieved by SOTA LMs/MLMs can be further substantially improved if information about the target word is injected properly. Several existing and new target word injection methods are compared for each LM/MLM using both intrinsic evaluation on lexical substitution datasets and extrinsic evaluation on word sense induction (WSI) datasets. On two WSI datasets we obtain new SOTA results. Besides, we analyze the types of semantic relations between target words and their substitutes generated by different models or given by annotators.
The Arabic ontology – an Arabic wordnet with ontologically clean content

We present a formal Arabic wordnet built on the basis of a carefully designed ontology hereby referred to as the Arabic Ontology. The ontology provides a formal representation of the concepts that the Arabic terms convey, and its content was built with ontological analysis in mind, and benchmarked to scientific advances and rigorous knowledge sources as much as this is possible, rather than to only speakers’ beliefs as lexicons typically are. A comprehensive evaluation was conducted thereby demonstrating that the current version of the top-levels of the ontology can top the majority of the Arabic meanings. The ontology consists currently of about 1,800 well-investigated concepts in addition to 16,000 concepts that are partially validated. The ontology is accessible and searchable through a lexicographic search engine (http://ontology.birzeit.edu) that also includes about 150 Arabic-multilingual lexicons, and which are being mapped and enriched using the ontology. The ontology is fully mapped with Princeton WordNet, Wikidata, and other resources.
Analysis of Digital Literacy Self-Efficacy Levels of Pre-service Teachers

This research attempts to analyze the pre-service teachers’ digital literacy self-efficacy in terms of various variables. The research used descriptive survey model, one of the survey models. The participants of the research consisted of 251 pre-service teachers learning at the departments of classroom teaching and social sciences teaching at a state university in Turkey. The participants were selected by convenience sampling method. The research employed Personal Information Form and Digital Literacy Self-Efficacy Scale as data collection tools. Descriptive statistical, t-test, one-way analysis of variance were used during data analysis. The research results revealed that the pre-service teachers’ digital literacy self-efficacy levels significantly differed across their gender, department, the presence of computer and internet at home; whereas, no significant difference was identified across the pre-service teachers’ digital literacy self-efficacy levels in terms of their class level, university entrance score type and the purposes of using technology.
Empirical models for estimating the global solar radiation of Jubail Industrial City, the Kingdom of Saudi Arabia

Abstract Empirical models provide precise and cost-effective information on solar energy assessment. The main objective of this work is to create and examine some regression models (linear, quadratic and logarithmic), for the estimation of the monthly global solar radiation potential in the Jubail Industrial City of Saudi Arabia. The industrial city has more solar energy potential, and the global horizontal irradiance is ranging from 4000 to 7000 Wh/m 2 /day. In order to initiate the green energy development in the city, site-specific hybrid models, which includes the ambient temperature, sunshine hours and relative humidity have been developed. Precise equation for the three proposed models is built up and examined for the industrial city. All the three empirical models provide good results. The statistical error estimation of the developed models reveals that the estimated and measured values appear exceptionally great and deliver similar outcomes. The values of the correlation coefficient and coefficient of determination of the quadratic model are 0.939 and 0.925, respectively, which are the highest among the three models. The mean absolute percentage error of the quadratic model is only 6.17%, and the other statistical errors are also comparatively low for the quadratic model. Therefore, the quadratic model gives an improved approximation on the estimation of solar energy parameters in which the errors between the measured and calculated values are smaller. Thus, the present regression models provide valuable data on the solar strength probable in the industrial city, and the data could be used for future energy planning.
Real-time RGBD-based Extended Body Pose Estimation

We present a system for real-time RGBD-based estimation of 3D human pose. We use parametric 3D deformable human mesh model (SMPL-X) as a representation and focus on the real-time estimation of parameters for the body pose, hands pose and facial expression from Kinect Azure RGB-D camera. We train estimators of body pose and facial expression parameters. Both estimators use previously published landmark extractors as input and custom annotated datasets for supervision, while hand pose is estimated directly by a previously published method. We combine the predictions of those estimators into a temporally-smooth human pose. We train the facial expression extractor on a large talking face dataset, which we annotate with facial expression parameters. For the body pose we collect and annotate a dataset of 56 people captured from a rig of 5 Kinect Azure RGB-D cameras and use it together with a large motion capture AMASS dataset. Our RGB-D body pose model outperforms the state-of-the-art RGB-only methods and works on the same level of accuracy compared to a slower RGB-D optimization-based solution. The combined system runs at 25 FPS on a server with a single GPU. The code will be available at saic-violet.github.io/rgbd-kinect-pose.
EDUCATION 4.0 TO SUPPORT ENTREPRENEURSHIP, SOCIAL DEVELOPMENT AND EDUCATION IN EMERGING ECONOMIES

As a complement to Industry 4.0, the term Education 4.0 has been defined as the evolution of traditional education considering the new digital age's technological tools, combining real and virtual world information. For emerging economies, the implementation of Education 4.0 is an essential asset for future economic and social development. In this context, the present work describes a set of conceptual Education 4.0 models to support three essential aspects in Latin American economies: entrepreneurship of small and medium enterprises, social welfare, and engineering education. These models highlight the human efforts and technological features required to adopt Education/Industry 4.0 in these aspects successfully.
Enhancing the performance of superconducting nanowire-based detectors with high-filling factor by using variable thickness

Abstract Current crowding at bends of superconducting nanowire single-photon detector (SNSPD) is one of the main factors limiting the performance of meander-style detectors with large filling factors. In this paper, we propose a new concept to reduce the influence of the current crowding effect, a so-called variable thickness SNSPD, which is composed of two regions with different thicknesses. A larger thickness of bends in comparison to the thickness of straight nanowire sections locally reduces the current density and reduces the suppression of the critical current caused by current crowding. This allows variable thickness SNSPD to have a higher critical current, an improved detection efficiency, and decreased dark count rate in comparison with a standard uniform thickness SNSPD with an identical geometry and film quality.
Multiple objects tracking in the UAV system based on hierarchical deep high-resolution network

Abstract Robust and high-performance visual multi-object tracking is a big challenge in computer vision, especially in a drone scenario. In this paper, an online Multi-Object Tracking (MOT) approach in the UAV system is proposed to handle small target detections and class imbalance challenges, which integrates the merits of deep high-resolution representation network and data association method in a unified framework. Specifically, while applying tracking-by-detection architecture to our tracking framework, a Hierarchical Deep High-resolution network (HDHNet) is proposed, which encourages the model to handle different types and scales of targets, and extract more effective and comprehensive features during online learning. After that, the extracted features are fed into different prediction networks for interesting targets recognition. Besides, an adjustable fusion loss function is proposed by combining focal loss and GIoU loss to solve the problems of class imbalance and hard samples. During the tracking process, these detection results are applied to an improved DeepSORT MOT algorithm in each frame, which is available to make full use of the target appearance features to match one by one on a practical basis. The experimental results on the VisDrone2019 MOT benchmark show that the proposed UAV MOT system achieves the highest accuracy and the best robustness compared with state-of-the-art methods.
Training Feedforward Neural Network Using Enhanced Black Hole Algorithm: A Case Study on COVID-19 Related ACE2 Gene Expression Classification

The aim of this paper is twofold. First, black hole algorithm (BHA) is proposed as a new training algorithm for feedforward neural networks (FNNs), since most traditional and metaheuristic algorithms for training FNNs suffer from the problem of slow coverage and getting stuck at local optima. BHA provides a reliable alternative to address these drawbacks. Second, complementary learning components and Levy flight random walk are introduced into BHA to result in a novel optimization algorithm (BHACRW) for the purpose of improving the FNNs' accuracy by finding optimal weights and biases. Four benchmark functions are first used to evaluate BHACRW's performance in numerical optimization problems. Later, the classification performance of the suggested models, using BHA and BHACRW for training FNN, is evaluated against seven various benchmark datasets: iris, wine, blood, liver disorders, seeds, Statlog (Heart), balance scale. Experimental result demonstrates that the BHACRW performs better in terms of mean square error (MSE) and accuracy of training FNN, compared to standard BHA and eight well-known metaheuristic algorithms: whale optimization algorithm (WOA), biogeography-based optimizer (BBO), gravitational search algorithm (GSA), genetic algorithm (GA), cuckoo search (CS), multiverse optimizer (MVO), symbiotic organisms search (SOS), and particle swarm optimization (PSO). Moreover, we examined the classification performance of the suggested approach on the angiotensin-converting enzyme 2 (ACE2) gene expression as a coronavirus receptor, which has been overexpressed in human rhinovirus-infected nasal tissue. Results demonstrate that BHACRW-FNN achieves the highest accuracy on the dataset compared to other classifiers.
What makes instance discrimination good for transfer learning?

Contrastive visual pretraining based on the instance discrimination pretext task has made significant progress. Notably, recent work on unsupervised pretraining has shown to surpass the supervised counterpart for finetuning downstream applications such as object detection and segmentation. It comes as a surprise that image annotations would be better left unused for transfer learning. In this work, we investigate the following problems: What makes instance discrimination pretraining good for transfer learning? What knowledge is actually learned and transferred from these models? From this understanding of instance discrimination, how can we better exploit human annotation labels for pretraining? Our findings are threefold. First, what truly matters for the transfer is low-level and mid-level representations, not high-level representations. Second, the intra-category invariance enforced by the traditional supervised model weakens transferability by increasing task misalignment. Finally, supervised pretraining can be strengthened by following an exemplar-based approach without explicit constraints among the instances within the same category.
Semantics-guided synthesis

This paper develops a new framework for program synthesis, called semantics-guided synthesis (SemGuS), that allows a user to provide both the syntax and the semantics for the constructs in the language. SemGuS accepts a recursively defined big-step semantics, which allows it, for example, to be used to specify and solve synthesis problems over an imperative programming language that may contain loops with unbounded behavior. The customizable nature of SemGuS also allows synthesis problems to be defined over a non-standard semantics, such as an abstract semantics. In addition to the SemGuS framework, we develop an algorithm for solving SemGuS problems that is capable of both synthesizing programs and proving unrealizability, by encoding a SemGuS problem as a proof search over Constrained Horn Clauses: in particular, our approach is the first that we are aware of that can prove unrealizabilty for synthesis problems that involve imperative programs with unbounded loops, over an infinite syntactic search space. We implemented the technique in a tool called MESSY, and applied it to both SyGuS problems(i.e., over expressions) and synthesis problems over an imperative programming language.
Measuring Driver Situation Awareness Using Region-of-Interest Prediction and Eye Tracking

With increasing progress in autonomous driving, the human does not have to be in control of the vehicle for the entire drive. A human driver obtains the control of the vehicle in case of an autonomous system failure or when the vehicle encounters an unknown traffic situation it cannot handle on its own. A critical part of this transition to human control is to ensure a sufficient driver situation awareness. Currently, no direct method to explicitly estimate driver awareness exists. In this paper, we propose a novel system to explicitly measure the situation awareness of the driver. Our approach is inspired by methods used in aviation. However, in contrast to aviation, the situation awareness in driving is determined by the detection and understanding of dynamically changing and previously unknown situation elements. Our approach uses machine learning to define the best possible situation awareness. We also propose to measure the actual situation awareness of the driver using eye tracking. Comparing the actual awareness to the target awareness allows us to accurately assess the awareness the driver has of the current traffic situation. To test our approach, we conducted a user study. We measured the situation awareness score of our model for 8 unique traffic scenarios. The results experimentally validate the accuracy of the proposed driver awareness model.