Reviewerly/exHarmony huggingface dataset:
Deep Multi-Semantic Fusion-Based Cross-Modal Hashing

Due to the low costs of its storage and search, the cross-modal retrieval hashing method has received much research interest in the big data era. Due to the application of deep learning, the cross-modal representation capabilities have risen markedly. However, the existing deep hashing methods cannot consider multi-label semantic learning and cross-modal similarity learning simultaneously. That means potential semantic correlations among multimedia data are not fully excavated from multi-category labels, which also affects the original similarity preserving of cross-modal hash codes. To this end, this paper proposes deep multi-semantic fusion-based cross-modal hashing (DMSFH), which uses two deep neural networks to extract cross-modal features, and uses a multi-label semantic fusion method to improve cross-modal consistent semantic discrimination learning. Moreover, a graph regularization method is combined with inter-modal and intra-modal pairwise loss to preserve the nearest neighbor relationship between data in Hamming subspace. Thus, DMSFH not only retains semantic similarity between multi-modal data, but integrates multi-label information into modal learning as well. Extensive experimental results on two commonly used benchmark datasets show that our DMSFH is competitive with the state-of-the-art methods.
Unveiling quantum entanglement and correlation of sub-Ohmic and Ohmic baths for quantum phase transitions in dissipative systems

By employing the spin-boson model in a dense limit of environmental modes, quantum entanglement and correlation of sub-Ohmic and Ohmic baths for dissipative quantum phase transitions are numerically investigated based on the variational principle. With several measures borrowed from quantum information theory, three different types of singularities are found for the first-order, second-order, and Kosterlitz-Thouless phase transitions, respectively, and the values of transition points and critical exponents are accurately determined. Besides, the scaling form of the quantum discord in the Ohmic case is identified, quite different from that in the sub-Ohmic regime. In a two-spin model, two distinct behaviors of the quantum discord are uncovered: one is related to the quantum entanglement between two spins and the other is decided by the correlation function in the position space rather than the entanglement.
Automated Hardening of Deep Neural Network Architectures

Abstract Designing optimal neural network (NN) architectures is a difficult and time-consuming task, especially when error resiliency and hardware efficiency are considered simultaneously. In our paper, we extend neural architecture search (NAS) to also optimize a NN’s error resilience and hardware related metrics in addition to classification accuarcy. To this end, we consider the error sensitivity of a NN on the architecture-level during NAS and additionally incorporate checksums into the network as an external error detection mechanism. With an additional computational overhead as low as 17% for the discovered architectures, checksums are an efficient method to effectively enhance the error resilience of NNs. Furthermore, the results show that cell-based NN architectures are able to maintain their error resilience characteristics when transferred to other tasks.
A Novel Anomaly Detection for Streaming Data using LSTM Autoencoders

The high-volume and velocity data stream generated from devices and applications from different domains grows steadily and is valuable for big data research. One of the most important topics is anomaly detection for streaming data, which has attracted attention and investigation in plenty of areas, e.g., the sensor data anomaly detection, predictive maintenance, event detection. Those efforts could potentially avoid large amount of financial costs in the manufacture. However, different from traditional anomaly detection tasks, anomaly detection in streaming data is especially difficult due to that data arrives along with the time with latent distribution changes, so that a single stationary model doesn’t fit streaming data all the time. An anomaly could become normal during the data evolution, therefore it is necessary to maintain a dynamic system to adapt the changes. In this work, we propose a LSTMs-Autoencoder anomaly detection model for streaming data. This is a mini-batch based streaming processing approach. We experimented with streaming data that containing different kinds of anomalies as well as concept drifts, the results suggest that our model can sufficiently detect anomaly from data stream and update model timely to fit the latest data property.. Index Terms: About four key words or phrases in alphabetical order, separated by commas.
Construction of an Intelligent Evaluation Model of Mental Health Based on Big Data

In this paper, mental health data were used to evaluate the educational effects, in which the high and low scorers of three emotions, autism, positivity, and anxiety, are compared separately to explore the subtle differences in the long-term trends of the sensing traits of people with opposite characteristics. Based on the fusion of multiple kinds of sensing traits, the differences in physical and mental health assessment of positive and negative emotions by different fusion trait approaches are explored, and speech and behavioural traits are fused to build a physical and mental health assessment system for positive and negative emotions. Energy gravity uses physical distance to estimate the residual energy of nodes and considers the energy distribution of downstream nodes. The main work is to combine the data of mental health of higher education students using data mining techniques, to analyze the feasibility study of mental health education of college students. Relevant definitions, classifications, tasks, processes, and application areas of data mining techniques are introduced, and the basic principles of data mining are analyzed in detail. Taking the mental health assessment data of new students as the research object, the decision tree algorithm is used to construct a decision tree model for students with depressive symptoms, and an association rule algorithm is used to data mine the relationship between factors of psychological dimensions. Finally, it can find out the hidden laws and knowledge behind the data information and analyze the relationship that exists between psychological problems and students.
Quantum solvability of noisy linear problems by divide-and-conquer strategy

Noisy linear problems have been studied in various science and engineering disciplines. A class of "hard" noisy linear problems can be formulated as follows: Given a matrix $\hat{A}$ and a vector $\mathbf{b}$ constructed using a finite set of samples, a hidden vector or structure involved in $\mathbf{b}$ is obtained by solving a noise-corrupted linear equation $\hat{A}\mathbf{x} \approx \mathbf{b} + \boldsymbolη$, where $\boldsymbolη$ is a noise vector that cannot be identified. For solving such a noisy linear problem, we consider a quantum algorithm based on a divide-and-conquer strategy, wherein a large core process is divided into smaller subprocesses. The algorithm appropriately reduces both the computational complexities and size of a quantum sample. More specifically, if a quantum computer can access a particular reduced form of the quantum samples, polynomial quantum-sample and time complexities are achieved in the main computation. The size of a quantum sample and its executing system can be reduced, e.g., from exponential to sub-exponential with respect to the problem length, which is better than other results we are aware. We analyse the noise model conditions for such a quantum advantage, and show when the divide-and-conquer strategy can be beneficial for quantum noisy linear problems.
Text detection in Arabic news video based on MSER and RetinaNet

In this paper, we propose a novel approach for text detection in Arabic news videos. Firstly, we apply MSER method and morphological operators (open and close) to extract candidate regions of text in image. Then, we use a deep learning method called RatinaNet. It is based in two stages. The first one aims to extract features using residual network (ResNet) and a pyramidal feature network (FPN). In the second step, we use two fully convolutional networks (FCN), one is for the classification task and the other for the bounding box regression task. For training and testing stages, we have used the AcTiVD [18] dataset. Experiments results proves the efficiency and performance of the proposed method.
Multi-CartoonGAN with Conditional Adaptive Instance-Layer Normalization for Conditional Artistic Face Translation

In CycleGAN, an image-to-image translation architecture was established without the use of paired datasets by employing both adversarial and cycle consistency loss. The success of CycleGAN was followed by numerous studies that proposed new translation models. For example, StarGAN works as a multi-domain translation model based on a single generator–discriminator pair, while U-GAT-IT aims to close the large face-to-anime translation gap by adapting its original normalization to the process. However, constructing robust and conditional translation models requires tradeoffs when the computational costs of training on graphic processing units (GPUs) are considered. This is because, if designers attempt to implement conditional models with complex convolutional neural network (CNN) layers and normalization functions, the GPUs will need to secure large amounts of memory when the model begins training. This study aims to resolve this tradeoff issue via the development of Multi-CartoonGAN, which is an improved CartoonGAN architecture that can output conditional translated images and adapt to large feature gap translations between the source and target domains. To accomplish this, Multi-CartoonGAN reduces the computational cost by using a pretrained VGGNet to calculate the consistency loss instead of reusing the generator. Additionally, we report on the development of the conditional adaptive layer-instance normalization (CAdaLIN) process for use with our model to make it robust to unique feature translations. We performed extensive experiments using Multi-CartoonGAN to translate real-world face images into three different artistic styles: portrait, anime, and caricature. An analysis of the visualized translated images and GPU computation comparison shows that our model is capable of performing translations with unique style features that follow the conditional inputs and at a reduced GPU computational cost during training.
Simulation of Vertical Temperature Distribution in Green Building Space Based on the Niche Genetic Algorithm

At present, green buildings often emphasize the indoor space environment, and they have different demands for health, comfort and user production efficiency. In general, green buildings make full use of sunlight and save energy. However, the vertical temperature distribution of buildings is changeable, and the temperature difference is large in different periods, leading to the unsatisfactory effect of heat utilization. Therefore, a method of simulating vertical temperature distribution in green building space based on a niche genetic algorithm was proposed. Based on the sharing mechanism, the niche idea was used to reflect the sharing function of the similarity among individuals so that the spatial fitness of each level could be obtained. The temperature internal force values of the connecting components and the internal and external vertical components were calculated, and then the calculation for the vertical temperature distribution of green building space was completed. Simulation results show that the temperature simulation error in the proposed method is small, so this method can provide a solid theoretical basis for different production needs.
Leveraging query logs and machine learning for parametric query optimization

Parametric query optimization (PQO) must address two problems: identify a relatively small number of plans to cache for a parameterized query (populateCache), and efficiently select the best cached plan to use for executing any instance of the parameterized query (getPlan). Our approach decouples these two decisions. We formulate populateCache as an optimization problem with the goal of identifying a set of plans that minimizes the optimizer estimated cost of queries in the log, and present an efficient algorithm. For getPlan, we leverage query logs to train machine learning (ML) models to choose the lowest optimizer-estimated cost plan from the cached plans. We conduct extensive experiments using complex parameterized queries from benchmarks and real workloads. Our algorithm for populateCache achieves low geometric mean sub-optimality (1.2) even for complex queries using relatively few plans, and scales well to large query logs. The mean latency of our ML model based getPlan technique (~ 210μ sec ) is between one to four orders of magnitude faster compared to prior PQO techniques. The mean sub-optimality is low (1.05), and the 95 th percentile sub-optimality (1.3) is between 1.1× and 25× lower compared to prior techniques. Finally, we present an efficient algorithm for getPlan that leverages execution time information in query logs to circumvent inaccuracies of the query optimizer's cost estimates.
The Role of Administrative Leadership in Supporting Culture of Quality and Excellence Based on Kaqa Excellence Model: An Implementation Study

Based on the King Abdullah Quality Award (KAQA) excellence model, this study seeks to understand management leadership's role in supporting quality and excellence. The study applied in Saudi Electricity Company, estimated to have (33957) employees in all Saudi Arabian branches (KSA). The current study used a constructed questionnaire with 32 items spanning six dimensions. The first dimension assesses management leadership's role in determining the company's strategic direction, while the second assesses management leadership’s role in monitoring the management system and institutional performance. On the other hand, the third dimension is about the enhancement of the relationship with all in concern. The fourth dimension aims to assess management leadership's role in promoting a company culture of quality, excellence, and innovation. The fifth dimension is the measurement of a management leadership role in implementing governance and social responsibility. The last dimension is the measurement of a management leadership role in managing changes, risks, and crises. The current study found statistically significant differences between participants in an administrative leadership role in monitoring, reviewing management system, and institutional performance. Thus, the organization should evaluate administrative leadership's role in management. Administrative leadership also fosters quality, excellence, and creativity. This demonstrates the need for leadership to review, develop, and improve the company's strategic direction.
Information as an Essential Component of the Biological Structures and Their Informational Organization

In this paper it is shown that information is involved in operations, independently of their source or support, chemical, physical or mathematical ones. Particularly, it is shown that info-structuration and info-destructuration are participatory processes typically in biological organisms, which can be represented schematically as a relation between the interacting components, highlighting that the info-structuration is an information-absorbing process and info-destructuration is an info-releasing one. Moreover, functions and functionality of an organism, as a combination of multiple interacting components, are actually expressed as information. It is also shown on this basis that can be distinguished and defined seven informational systems both in the human/sub-human bodies and in the living unit, which is the eukaryotic cell. An info-circuit of a sense perception can be described in terms of typical info-communication systems, as composed by information source (captured sensation), a coder (the involved sensor), a transmission channel (nervous system in animals or pathways in the cell), the decoder (brain in human and sub-human organisms or specialized reaction in the cell) and the displaying “screen" (prefrontal cortex in the brain or sensitive interpreter in the cell). Therefore, it is deduced that a proto-consciousness could be defined not only for the human and sub-human organisms, as rudimentary as the brain circuits are less complex in comparison with the brain, but also in the cell, marked by sentient-decisional pathways/micro-circuits and genetic transcription/translation (body info-(re) structuration) and/or replication (reproduction) processes.
Edge Detection-Based Feature Extraction for the Systems of Activity Recognition

Human activity recognition (HAR) is a fascinating and significant challenging task. Generally, the accuracy of HAR systems relies on the best features from the input frames. Mostly, the activity frames have the hostile noisy conditions that cannot be handled by most of the existing edge operators. In this paper, we have designed an adoptive feature extraction method based on edge detection for HAR systems. The proposed method calculates the direction of the edges under the presence of nonmaximum conquest. The benefits are in ease that depends upon the modest procedures, and the extension possibility is to determine other types of features. Normally, it is practical to extract extra low-level information in the form of features when determining the shapes and to get the appropriate information, the additional cultured shape detection procedure is utilized or discarded. Basically, this method enlarges the percentage of the product of the signal-to-noise ratio (SNR) and the highest isolation along with localization. During the processing of the frames, again some edges are demonstrated as a footstep function; the proposed approach might give better performance than other operators. The appropriate information is extracted to form feature vector, which further be fed to the classifier for activity recognition. We assess the performance of the proposed edge-based feature extraction method under the depth dataset having thirteen various kinds of actions in a comprehensive experimental setup.
Multichannel 3D ink painting rendering model based on the LMS algorithm

Aiming at the problem that the ink concentration control ability is poor and the model construction efficiency is low in the current application of the 3D ink painting rendering model, a multichannel 3D ink painting rendering model based on the LMS algorithm is designed. The local search algorithm is used to obtain the characteristic information of ink painting. The LMS algorithm is used to filter the collected original ink painting information. Through iterative calculation, the dynamic rendering of 3D ink painting is realized. An experiment was designed to verify the effect of this method. The experimental results show that the proposed multichannel 3D ink painting rendering model has high rendering efficiency and better application effect than the current methods.
DeepIC: Coding for Interference Channels via Deep Learning

The two-user interference channel is a model for multi one-to-one communications, where two transmitters wish to communicate with their corresponding receivers via a shared wireless medium. Two most common and simple coding schemes are Time Division (TD) and Treating Interference as Noise (TIN). Interestingly, it is shown that there exists an asymptotic scheme, called Han-Kobayashi scheme, that performs better than TD and TIN. However, Han-Kobayashi scheme has impractically high complexity and is designed for asymptotic settings, which leads to a gap between information theory and practice. In this paper, we focus on designing practical codes for interference channels. As it is challenging to analytically design practical codes with feasible complexity, we apply deep learning to learn codes for interference channels. We demonstrate that DeepIC, a convolutional neural network-based code with an iterative decoder, outperforms TD and TIN by a noticeable margin for two-user Additive White Gaussian Noise channels with moderate amount of interference.
A knowledge base for robots to model the real-world as a hypergraph

Robotic systems operating in the real world would benefit from a clear semantic model to understand the real world. Such semantics are typically captured in a knowledge structure. The speed at which robots need to update their real-world knowledge (because of new sensory observations) prevented the use of complex knowledge structures, due to which they typically rely on hierarchical structures and triples. However, the shift from automated to autonomous robotics implied that more knowledge about the real-world is necessary, thereby requiring many work-arounds to maintain such a hierarchical knowledge base. To remove such work-arounds, this article adopts the richer hypergraph model for creating an actual knowledge base implemented on a real robotic system. Experiments show that the use of a hypergraph, without any work-arounds, still allows real-time updates of the knowledge-base with actual information extracted from the robot’s sensors.
Road Traffic Accidents Detection Based On Crash Estimation

This paper proposes a framework to detect road accidents in real-time using generic CCTV cameras installed on roads. The framework focuses on how to achieve high performance on congested roads by introducing a new technique called track-compensated frame interpolation (TCFI) for tracking the vehicles, focuses on achieving higher accuracy by introducing a new approach for crash detection called crash estimation algorithm and focuses on how the system will deal with the massive number of CCTV cameras by dropping footages that are highly unlikely to contain an accident at an early stage and implemented the modules based on the pipelining technique. The framework is formed up of four stages; first, vehicles are detected using YOLO neural network, then tracked for several frames using MOSSE tracker, followed by a filtration process based on a new approach of crash estimation; finally, for every vehicle, we process its tracked footage through ViF descriptor then use the output as a feature vector for an SVM model that classifies accidents. The system achieves 93% accuracy with processing time beats all previous systems.
Marginal metric utility for autonomic cloud application management

Managing Cloud applications with variable resource requirements over time is an insipid task that could benefit from autonomic application management. The management platform will then need to know what the application owner considers a good deployment for the current execution context, which is normally captured by a utility function. However, it is often difficult to define such a function directly by first principles in a way that would perfectly capture the application owner's preferences. This paper proposes a methodology for defining the utility function only from the monitoring measurements taken to assess the state and context of the running application.