Reviewerly/exHarmony huggingface dataset:
The Contemporary Vision of Universal Strategic Planning for Facing (COVID-19) Crisis in the Field of Higher Education Via Virtual Learning - Training

This is an analytical study for Global Education vision 2030 -2020 )) of The contemporary vision of Global strategic plan for facing (covid-19)or the renew corona disease crisis via virtual learning - training for higher education.The research builds in the virtual university configuring its concept and how could it simulate the environments of reality ,and its tools with a new contemporary vision for facing (covid-19) crisis via Global strategic plan of virtual learning - training for higher education, and deals with virtual reality as an effective way to simulate reality whatever the circumstances and difficulties in the environment of university, through which it can be configured for different environments tell the reality of an individual can't be accessed or coexistence with them. For example, the environment of space can't be educated to student in the university environment to live by realistic, and here comes the role of virtual reality in an environment similar to the environment of space and enables the student to interact with it as if in the real environment.
Effects of Parameter Norm Growth During Transformer Training: Inductive Bias from Gradient Descent

The capacity of neural networks like the widely adopted transformer is known to be very high.Evidence is emerging that they learn successfully due to inductive bias in the training routine, typically a variant of gradient descent (GD).To better understand this bias, we study the tendency for transformer parameters to grow in magnitude ( 2 norm) during training, and its implications for the emergent representations within self attention layers.Empirically, we document norm growth in the training of transformer language models, including T5 during its pretraining.As the parameters grow in magnitude, we prove that the network approximates a discretized network with saturated activation functions.Such "saturated" networks are known to have a reduced capacity compared to the full network family that can be described in terms of formal languages and automata.Our results suggest saturation is a new characterization of an inductive bias implicit in GD of particular interest for NLP.We leverage the emergent discrete structure in a saturated transformer to analyze the role of different attention heads, finding that some focus locally on a small number of positions, while other heads compute global averages, allowing counting.We believe understanding the interplay between these two capabilities may shed further light on the structure of computation within large transformers.
Estimating Interpersonal Distance and Crowd Density with a Single-Edge Camera

For public safety and physical security, currently more than a billion closed-circuit television (CCTV) cameras are in use around the world. Proliferation of artificial intelligence (AI) and machine/deep learning (M/DL) technologies have gained significant applications including crowd surveillance. The state-of-the-art distance and area estimation algorithms either need multiple cameras or a reference object as a ground truth. It is an open question to obtain an estimation using a single camera without a scale reference. In this paper, we propose a novel solution called E-SEC, which estimates interpersonal distance between a pair of dynamic human objects, area occupied by a dynamic crowd, and density using a single edge camera. The E-SEC framework comprises edge CCTV cameras responsible for capturing a crowd on video frames leveraging a customized YOLOv3 model for human detection. E-SEC contributes an interpersonal distance estimation algorithm vital for monitoring the social distancing of a crowd, and an area estimation algorithm for dynamically determining an area occupied by a crowd with changing size and position. A unified output module generates the crowd size, interpersonal distances, social distancing violations, area, and density per every frame. Experimental results validate the accuracy and efficiency of E-SEC with a range of different video datasets.
A Semantic Feature-Wise Transformation Relation Network for Automatic Short Answer Grading

Automatic short answer grading (ASAG) is the task of assessing students' short natural language responses to objective questions.It is a crucial component of new education platforms, and could support more wide-spread use of constructed response questions to replace cognitively less challenging multiple choice questions.We propose a Semantic Feature-wise transformation Relation Network (SFRN) that exploits the multiple components of ASAG datasets more effectively.SFRN captures relational knowledge among the questions (Q), reference answers or rubrics (R), and labeled student answers (A).A relation network learns vector representations for the elements of QRA triples, then combines the learned representations using learned semantic feature-wise transformations.We apply translation-based data augmentation to address the two problems of limited training data, and high data skew for multi-class ASAG tasks.Our model has up to 11% performance improvement over state-of-the-art results on the benchmark SemEval-2013 datasets, and surpasses custom approaches designed for a Kaggle challenge, demonstrating its generality.
Self Question-answering: Aspect-based Sentiment Analysis by Role Flipped Machine Reading Comprehension

The pivot for the unified Aspect-based Sentiment Analysis (ABSA) is to couple aspect terms with their corresponding opinion terms, which might further burgeon easier sentiment predictions.In this paper, we investigate the unified ABSA task from the perspective of Machine Reading Comprehension (MRC) by observing that the aspect and the opinion terms can serve as the query and answer in MRC interchangeably.We propose a new paradigm named Role Flipped Machine Reading Comprehension (RF-MRC) to resolve.At its heart, the predicted results of either the Aspect Term Extraction (ATE) or the Opinion Terms Extraction (OTE) are regarded as the queries, respectively, and the matched opinion or aspect terms are extracted as answers.The queries and answers can be flipped for multi-hop detection.Finally, every matched aspect-opinion pair is predicted by the sentiment classifier.RF-MRC can solve the ABSA task without extra data annotation.Experiments on three widely used benchmarks and a challenging dataset demonstrate the superiority of the proposed framework.
Semantic Novelty Detection in Natural Language Descriptions

This paper proposes to study a fine-grained semantic novelty detection task, which can be illustrated with the following example.It is normal that a person walks a dog in the park, but if someone says "A man is walking a chicken in the park," it is novel.Given a set of natural language descriptions of normal scenes, we want to identify descriptions of novel scenes.We are not aware of any existing work that solves the problem.Although existing novelty or anomaly detection algorithms are applicable, since they are usually topic-based, they perform poorly on our fine-grained semantic novelty detection task.This paper proposes an effective model (called GAT-MA) to solve the problem and also contributes a new dataset.Experimental evaluation shows that GAT-MA outperforms 11 baselines by large margins.
Multiclass classification of dephasing channels

We address the use of neural networks (NNs) in classifying the environmental parameters of single-qubit dephasing channels. In particular, we investigate the performance of linear perceptrons and of two nonlinear NN architectures. At variance with time-series-based approaches, our goal is to learn a discretized probability distribution over the parameters using tomographic data at just two random instants of time. We consider dephasing channels originating either from classical $1/{f}^{\ensuremath{\alpha}}$ noise or from the interaction with a bath of quantum oscillators. The parameters to be classified are the color $\ensuremath{\alpha}$ of the classical noise or the Ohmicity parameter $s$ of the quantum environment. In both cases, we find that NNs are able to exactly classify parameters into 16 classes using noiseless data (a linear NN is enough for the color, whereas a single-layer NN is needed for the Ohmicity). In the presence of noisy data (e.g., coming from noisy tomographic measurements), the network is able to classify the color of the $1/{f}^{\ensuremath{\alpha}}$ noise into 16 classes with about $70%$ accuracy, whereas classification of Ohmicity turns out to be challenging. We also consider a more coarse-grained task and train the network to discriminate between two macroclasses corresponding to $\ensuremath{\alpha}\ensuremath{\lessgtr}1$ and $s\ensuremath{\lessgtr}1$, obtaining up to $96%$ and $79%$ accuracy using single-layer NNs.
Investigating Human Scale Spatial Experience

Spatial experience, or how humans experience a given space, has been a pivotal topic especially in urban-scale environments. On the human scale, HCI researchers have mostly investigated personal meanings or aesthetic and embodied experiences. In this paper, we investigate the human scale as an ensemble of individual spatial features. Through large-scale online questionnaires we first collected a rich set of spatial features that people generally use to characterize their surroundings. Second, we conducted a set of field interviews to develop a more nuanced understanding of the feature identified as most important: perceived safety. Our combined quantitative and qualitative analysis contributes to spatial understanding as a form of context information and presents a timely investigation into the perceived safety of human scale spaces. By connecting our results to the broader scientific literature, we contribute to the field of HCI spatial understanding.
Efficient verification of Boson Sampling

The demonstration of quantum speedup, also known as quantum computational supremacy, that is the ability of quantum computers to outperform dramatically their classical counterparts, is an important milestone in the field of quantum computing. While quantum speedup experiments are gradually escaping the regime of classical simulation, they still lack efficient verification protocols and rely on partial validation. Here we derive an efficient protocol for verifying with single-mode Gaussian measurements the output states of a large class of continuous-variable quantum circuits demonstrating quantum speedup, including Boson Sampling experiments, thus enabling a convincing demonstration of quantum speedup with photonic computing. Beyond the quantum speedup milestone, our results also enable the efficient and reliable certification of a large class of intractable continuous-variable multimode quantum states.
Sentiment Analysis of Online Course Evaluation Based on a New Ensemble Deep Learning Mode: Evidence from Chinese

In recent years, online course learning has gradually become the mainstream of learning. As the key data reflecting the quality of online courses, users’ comments are very important for improving the quality of online courses. The sentiment information contained in comments is the guide of course improvement. A new ensemble model is proposed for sentiment analysis. The model takes full advantage of Word2Vec and Glove in word vector representation, and utilizes the bidirectional long and short time network and convolutional neural network to achieve deep feature extraction. Moreover, the multi-objective gray wolf optimization (MOGWO) ensemble method is adopted to integrate the models mentioned above. The experimental results show that the sentiment recognition accuracy of the proposed model is higher than that of the other seven comparison models, with an F1score over 91%, and the recognition results of different emotion levels indicate the stability of the proposed ensemble model.
Multi-Self-Attention for Aspect Category Detection and Biomedical Multilabel Text Classification with BERT

Multilabel classification is one of the most challenging tasks in natural language processing, posing greater technical difficulties than single-label classification. At the same time, multilabel classification has more natural applications. For individual labels, the whole piece of text has different focuses or component distributions, which require full use of local information of the sentence. As a widely adopted mechanism in natural language processing, attention becomes a natural choice for the issue. This paper proposes a multilayer self-attention model to deal with aspect category and word attention at different granularities. Combined with the BERT pretraining model, it achieves competitive performance in aspect category detection and electronic medical records’ classification.
A Hybrid Secure Cloud Platform Maintenance Based on Improved Attribute-Based Encryption Strategies

In the modern era, Cloud Platforms are the most needed port to maintain documents remotely with proper security norms.The concept of cloud environments is similar to the network channel.Still, the Cloud is considered the refined form of network, in which the data can easily be stored into the server without any range restrictions.The data maintained into the remote server needs a high-security feature, and the processing power of data should be high to retrieve the data back from the respective server.In the past, there were several security schemes available to protect the remote cloud server reasonably.However, the attack possibilities over the cloud platform remain; only all the researchers continuously work on this platform without any delay.This paper introduces a hybrid data security scheme called the Improved Attribute-Based Encryption Scheme (IABES).This IABES combines two powerful data security algorithms: Advanced Encryption Standard (AES) and Attribute-Based Encryption (ABE) algorithm.These two algorithms are combined to provide massive support to the proposed approach of data maintenance over the remote cloud server with high-end security norms.This hybrid data security algorithm assures the data cannot be attacked over the server by the attacker or intruder in any case because of its robustness.The essential generation process generates a credential for the users.It cannot be identified or visible to anyone as well as the generated certificates cannot be extracted even if the corresponding user forgets the credentials.The only way to get back the certification is resetting the credential.The obtained results prove the accuracy level of the proposed cypher security schemes compared with the regular cloud security management scheme, and the proposed algorithm essential generation process is unique.No one can guess or acquire it.Even the person may be the service provider or server administrator.For all, the proposed system assures data maintenance over the cloud platform with a high level of security and robustness in Quality of Service.
A comparative study of universal quantum computing models: Toward a physical unification

Quantum computing has been a fascinating research field in quantum physics. Recent progresses motivate us to study in depth the universal quantum computing models (UQCMs), which lie at the foundation of quantum computing and have tight connections with fundamental physics. Although being developed decades ago, a physically concise principle or picture to formalize and understand UQCM is still lacking. This is challenging given the diversity of still-emerging models, but important to understand the difference between classical and quantum computing. In this work, we carried out a primary attempt to unify UQCM by classifying a few of them as two categories, hence making a table of models. With such a table, some known models or schemes appear as hybridization or combination of models, and more importantly, it leads to new schemes that have not been explored yet. Our study of UQCM also leads to some insights into quantum algorithms. This work reveals the importance and feasibility of systematic study of computing models.
Path Planning for Automatic Guided Vehicles (AGVs) Fusing MH-RRT with Improved TEB

In this paper, an AGV path planning method fusing multiple heuristics rapidly exploring random tree (MH-RRT) with an improved two-step Timed Elastic Band (TEB) is proposed. The modified RRT integrating multiple heuristics can search a safer, optimal and faster converge global path within a short time, and the improved TEB can optimize both path smoothness and path length. The method is composed of a global path planning procedure and a local path planning procedure, and the Receding Horizon Planning (RHP) strategy is adopted to fuse these two modules. Firstly, the MH-RRT is utilized to generate a state tree structure as prior knowledge, as well as the global path. Then, a receding horizon window is established to select the local goal point. On this basis, an improved two-step TEB is designed to optimize the local path if the current global path is feasible. Various simulations both on static and dynamic environments are conducted to clarify the performance of the proposed MH-RRT and the improved two-step TEB. Furthermore, real applicative experiments verified the effectiveness of the proposed approach.
Subexponential rate versus distance with time-multiplexed quantum repeaters

Quantum communications capacity using direct transmission over length-$L$ optical fiber scales as $R\ensuremath{\sim}{e}^{\ensuremath{-}\ensuremath{\alpha}L}$, where $\ensuremath{\alpha}$ is the fiber's loss coefficient. The rate achieved using a linear chain of quantum repeaters equipped with quantum memories, probabilistic Bell state measurements (BSMs), and switches used for spatial multiplexing, but no quantum error correction, was shown to surpass the direct-transmission capacity. However, this rate still decays exponentially with the end-to-end distance, viz., $R\ensuremath{\sim}{e}^{\ensuremath{-}s\ensuremath{\alpha}L}$, with $s<1$. We show that the introduction of temporal multiplexing---i.e., the ability to perform BSMs among qubits at a repeater node that were successfully entangled with qubits at distinct neighboring nodes at different time steps---leads to a subexponential rate-vs-distance scaling, i.e., $R\ensuremath{\sim}{e}^{\ensuremath{-}t\sqrt{\ensuremath{\alpha}L}}$, which is not attainable with just spatial or spectral multiplexing. We evaluate analytical upper and lower bounds to this rate and obtain the exact rate by numerically optimizing the time-multiplexing block length and the number of repeater nodes. We further demonstrate that incorporating losses in the optical switches used to implement time multiplexing degrades the rate-vs-distance performance, eventually falling back to exponential scaling for very lossy switches. We also examine models for quantum memory decoherence and describe optimal regimes of operation to preserve the desired boost from temporal multiplexing. Quantum memory decoherence is seen to be more detrimental to the repeater's performance over switching losses.
DCGAN based spectrum sensing data enhancement for behavior recognition in self-organized communication network

Communication behavior recognition is an issue with increasingly importance in the anti-terrorism and national defense area. However, the sensing data obtained in actual environment is often not sufficient to accurately analyze the communication behavior. Traditional means can hardly utilize the scarce and crude spectrum sensing data captured in a real scene. Thus, communication behavior recognition using raw sensing data under small-sample condition has become a new challenge. In this paper, a data enhanced communication behavior recognition (DECBR) scheme is proposed to meet this challenge. Firstly, a preprocessing method is designed to make the raw spectrum data suitable for the proposed scheme. Then, an adaptive convolutional neural network structure is exploited to carry out communication behavior recognition. Moreover, DCGAN is applied to support data enhancement, which realize communication behavior recognition under small-sample condition. Finally, the scheme is verified by experiments under different data size. The results show that the DECBR scheme can greatly improve the accuracy and efficiency of behavior recognition under small-sample condition.
A Comparative Analysis of Big Data Frameworks: An Adoption Perspective

The emergence of social media, the worldwide web, electronic transactions, and next-generation sequencing not only opens new horizons of opportunities but also leads to the accumulation of a massive amount of data. The rapid growth of digital data generated from diverse sources makes it inapt to use traditional storage, processing, and analysis methods. These limitations have led to the development of new technologies to process and store very large datasets. As a result, several execution frameworks emerged for big data processing. Hadoop MapReduce, the pioneering framework, set the ground for forthcoming frameworks that improve the processing and development of large-scale data in many ways. This research focuses on comparing the most prominent and widely used frameworks in the open-source landscape. We identify key requirements of a big framework and review each of these frameworks in the perspective of those requirements. To enhance the clarity of comparison and analysis, we group the logically related features, forming a feature vector. We design seven feature vectors and present a comparative analysis of frameworks with respect to those feature vectors. We identify use cases and highlight the strengths and weaknesses of each framework. Moreover, we present a detailed discussion that can serve as a decision-making guide to select the appropriate framework for an application.
Bio-Inspired Computing-A Dive into Critical Problems, Potential Architecture and Techniques

The integration and development of electronics in the recent years have impacted a major development on the world and humans, one among that is nanotechnology. Nanotechnology has achieved a greater progress in biomedical engineering in diagnosis and treatment, leading to the introduction of nanomaterials for drug delivery, prostheses and implanting. This work describes the Bio-Nano-tools that are developed based on iron oxide properties, automated tools used in the tumor detection, satin bowerbird optimization (SBO) technique employed in diagnosis of skin cancer. This work also highlights the post introduction development of nanomaterials like combination of nanotechnology with Artificial Intelligence (AI) and its impact, advancement of nanomaterials based on their operations, shapes and characteristics that leading to the growth of nanostructures with operations control properties. The paper also highlights the improvement of silicon neuromorphic photonic processors and parallel simulators in the development of bio inspired computing. We are hopeful that this review article provides future directions in Bio-Inspired Computing.&#x0D; HIGHLIGHTS&#x0D; &#x0D; In processing of medical images, noise plays a challenging role. So, reduction of noise is important, with the data that is analyzed in our review, it is shown that noise reduction can be achieved using Gradient and Feature Adaptive Contour (GFAC) model, with effective results&#x0D; There are many algorithms that are used for skin cancer detection, as highlighted in our review. Amongst all the methods, the particle swarm optimization (PSO) algorithm shows impressive results when compared to other models in terms of feature extraction in dermoscopy images&#x0D; Satin bowerbird optimization (SBO) algorithm helps in improving the CNN efficiency. The optimal justification of the hyper parameter numbers in convolutional neural network (CNN) for skin cancer diagnosis can be achieved using an SBO algorithm&#x0D;