Reviewerly/exHarmony huggingface dataset:
Local Dimming Algorithm of Automotive LCD Instrument Based on Otsu and Maximum Entropy

In order to reasonably reduce the energy consumption of automotive LCD instrument and improve the display quality, a new local dimming algorithm is proposed in this paper. Firstly, the gray image of the input image is obtained by using the maximum principle, and then, the LED backlight brightness value is obtained by using Otsu method and maximum entropy method. The BMA backlight smoothing algorithm is improved by combining bilinear interpolation algorithm, and the dimming image is obtained by using pixel compensation algorithm based on logarithm. Then, for low brightness images, high brightness images, low contrast images, and high contrast images, typical algorithms are used to simulate and compare, and the image processing effects of various local dimming algorithms are analyzed. Finally, the entropy weight method is used to objectively evaluate the local dimming algorithm. The results show that the new local dimming algorithm reduces the energy consumption and improves the display quality, which verifies the effectiveness of the proposed algorithm.
MIML library: A modular and flexible library for multi-instance multi-label learning

MIML library is a Java software tool to develop, test, and compare classification algorithms for multi-instance multi-label (MIML) learning. The library includes 43 algorithms and provides a specific format and facilities for data managing and partitioning, holdout and cross-validation methods, standard metrics for performance evaluation, and generation of reports. In addition, algorithms can be executed through xml configuration files without needing to program. It is platform-independent, extensible, free, open-source, and available on GitHub under the GNU General Public License.
Individualization and digitalization of creative development of students in chemistry lessons

Background. In the context of increasing robotization and digitalization of most of the routine and algorithmic types of human activity, the problem of the creative development of each child and the individualization of education in a public school, especially in the field of natural science education, arises. The aim. To study the prerequisites and opportunities for digitalization of the creative development of students as a means of increasing the reflexivity of their mental activity in solving creative problems and individualizing the educational process in a public school based on the subject of chemistry. Methods. At the first stage of the study, a survey of chemistry teachers was used. At the second stage, diagnostic tasks in chemistry were developed to verify the identified stereotypes; at the third stage, ways were studied to overcome stereotyped ideas in chemistry by schoolchildren; and at the fifth stage, the possibility of digitizing mental actions using the CREO_DATUM computer program was studied. Results. Of the 70 different variants of erroneous ideas in the school chemistry course, 18 thinking stereotypes turned out to be the most common. The possibility of overcoming stereotypes is not associated with additional classes and explanations of teachers, but with the independent solution of creative tasks by students, since in this process the reflection of schoolchildren develops, which is a condition for overcoming stereotypes. The optimal time in the classroom for students to complete short creative tasks is 10–12 minutes. It is sufficient to measure such creative actions of students as divergence, criticality, uniqueness. To digitize the mental activity of students when solving creative problems in chemistry and to automate the construction of graphs for the creative development of each student, the CREO_DATUM program was developed and tested. Conclusion. Long-term studies of the possibility of using subject-creative tasks based on the material of a school chemistry course for the development of creativity and reflexivity of students serve as a rationale for designing digital means for individualizing the creative development of students.
A State Transition Algorithm Based on Jump Operator Applied to Image Threshold Segmentation

This paper proposes an improved state transition algorithm (JUSTA) based on the OTSU multi-threshold segmentation algorithm. We introduce a state transition algorithm (STA) into image threshold segmentation and propose a new jump operator to improve the performance of STA according to the characteristics of threshold solution space and a multi-threshold segmentation algorithm. The jump operator enhances the local search ability of the algorithm by adding synchronization characteristics. At the same time, the variation factor modified by quadratic function and the dynamic mutation probability is introduced to improve the ability to jump out of the current state. A large number of experimental studies show that the JUSTA algorithm has obvious advantages in stability and rapidity in finding the best threshold of image threshold segmentation model compared with genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony algorithm (ABC) and state transition algorithm (STA).
Artificial Intelligence in Cloud Technology and Wireless Network Technology using Health Care System

In the reign of Internet Technology, Cloud Technology has emerged as one of the powerful tools in computing with many advantages in the bag. Cloud technology is defined as online access to all types of computing applications. The applications range from access to data, servers, software, databases to storage & retrieval of data. The data can be accessed from anywhere, anytime. The user doesn't need to own the hardware's in this case. Clouds have multiple data centers located at various places. On-demand data service is the best part of cloud technology. This technology is made use in many industries. This research work will study how cloud technology and wireless networking technology are applied in the optimization of hospital financial management. The healthcare industry faces various challenges in financial management such as budgeting, growth planning & cost-effectiveness, etc. Considering this scenario, it is evident that there is a solid need to optimize its financial management. The Cloud Computing Technology driven by Artificial Intelligence (AI) is deployed in carrying out repetitive tasks & automation, resulting in increased productivity. In this research, the proposed system makes use of wireless networking systems & cloud technology for optimization of hospital financial management. The model works with the implementation of hybrid of Fuzzy Neural Network. The proposed model is analyzed with the existing Fuzzy Logic and Neural Network models and is found that the proposed model has obtained an accuracy of 97.56% with minimum resources for successful financial management at hospitals.
Optimization of elliptic curve scalar multiplication using constraint based scheduling

Elliptic Curve Cryptography is public key cryptography that features smaller keys, ciphertexts, and signatures and is faster than RSA at the same security level. Scalar multiplication is the main and the most compute-intensive operation in the generation of keys. Point Addition, Doubling and Inversion are the basic operations for scalar multiplication. Inversion is a very expensive operation as compared to multiplication, addition and squaring in the finite fields with an affine coordinate system. López-Dahab coordinates are the best alternative to reduce the inversion overhead in scalar computation. Area, Delay and Power trade-offs are the main constraints in hardware implementations of scalar multiplication. In this paper, optimization of elliptic curve scalar multiplication using constraint-based scheduling for the López-Dahab coordinate system is proposed. Data dependency graphs of point addition and doubling are modified for optimization of area and delay. The proposed architecture is implemented on Altera Stratix-II FPGA. The constraint is applied on the field multiplication operation and the considerable area is reduced. The proposed architecture computes scalar multiplication in 11.43 μs and takes 9856 ALMs. The performance comparison with state of the art shows that area is reduced by 41.21%, delay is reduced by 2.4% and Area-Delay-Product is improved.
Solving pickup and drop-off problem using hybrid pointer networks with deep reinforcement learning

In this study, we propose a general method for tackling the Pickup and Drop-off Problem (PDP) using Hybrid Pointer Networks (HPNs) and Deep Reinforcement Learning (DRL). Our aim is to reduce the overall tour length traveled by an agent while remaining within the truck's capacity restrictions and adhering to the node-to-node relationship. In such instances, the agent does not allow any drop-off points to be serviced if the truck is empty; conversely, if the vehicle is full, the agent does not allow any products to be picked up from pickup points. In our approach, this challenge is solved using machine learning-based models. Using HPNs as our primary model allows us to gain insight and tackle more complicated node interactions, which simplified our objective to obtaining state-of-art outcomes. Our experimental results demonstrate the effectiveness of the proposed neural network, as we achieve the state-of-art results for this problem as compared with the existing models. We deal with two types of demand patterns in a single type commodity problem. In the first pattern, all demands are assumed to sum up to zero (i.e., we have an equal number of backup and drop-off items). In the second pattern, we have an unequal number of backup and drop-off items, which is close to practical application, such as bike sharing system rebalancing. Our data, models, and code are publicly available at Solving Pickup and Dropoff Problem Using Hybrid Pointer Networks with Deep Reinforcement Learning.
Recent Advances in Sign Language Recognition using Deep Learning Techniques

Communication is essential to express and receive information, knowledge, ideas, and views among people, but it has been quite a while to be an obstruction for people with hearing and mute disabilities. Though there is sign language to communicate with non-sign people it is difficult for everyone to interpret and understand. As reported by World Health Organization (WHO), five percent of the world population of over 300 million people suffers from hearing disability. Technology has grown rapidly in the last few decades with the presence of deep learning and artificial intelligence, but somehow physically impaired people are not able to get the maximum benefit of it due to lack of awareness, accessibility problems, cost, and other reasons. This survey paper discusses the recent development and assistance provided for signers to communicate with non-signers.
Faint Moving Small Target Detection based on Optical Flow Method

Moving target detection algorithm plays a vital role in computer vision research. Moving object detection mainly processes video images to identify moving objects differently from the background. Moving target detection algorithm has an excellent application role, such as: used for security and forbidden area security. This paper presents an effective method for detecting moving targets. The authors combine the corner detection method with LK optical flow method. Afterimage preprocessing, image corner detection, finally, we use LK optical flow method to detect the movement of the moving object, and we can judge the movement direction of the moving object only by two frames of pictures. This method can judge the direction of moving objects only by two pictures frames and has an excellent performance in speed detection. In particular, in detecting small moving targets, the results of this method are noticeable.
TD-Sampler: Learning a Training Difficulty Based Sampling Strategy for Few-Shot Object Detection

Success of deep learning on image applications heavily relies on tremendous data. Since collecting sufficient data is difficult for certain applications, e.g., medical image processing, few-shot learning has recently become a more attractive research field in deep learning community. For few-shot object detection, previous methods popularly finetunes a pretrained detector for data-scarce novel classes using a small number of data-abundant base class data. To effectively adopt all base class data in the finetuning stage while overcoming incurred imbalanced learning, we propose a training difficulty (TD) criterion for training data in this paper. Training data with a bigger TD more probably changes a detector&#x0027;s activation pattern significantly, compared to data with a smaller TD. Since the TD criterion requires time-consuming backpropagation information, we learn a TD predic-tor with the detector in the pretraining stage to accelerate TD computation. Based on the TD predictor, we propose a sampling strategy which prefers a batch of training data with more novel class data and lower probability of incurring significant changes on the detector&#x0027;s activation pattern. Experimental results on two popular datasets Pascal VOC and MSCOCO show that for novel class data, our proposed method, TD-Sampler, achieves state-of-the-art on Pascal VOC, and improves mAP averagely by 22.9&#x0025; on MSCOCO compared to the previous best method. Ablation study further shows that averaged accuracy drop of ours without TD based sampling is 21.3&#x0025; compared to ours. Therefore, the TD based sampling is a crucial component of our proposed method.
Safe Overfitting of Boosted Tree Algorithm in Heat Transfer Modeling

Overfitting generally considered to be an avoidable pitfall in any kind of machine learning setting. Both academic literature and applied machine learning is saturated with tools and techniques preventing overfit and estimating the likelihood of its occurrence, and the potential damages it causes during model generalization. However in this paper it will be outlined, that if certain strict conditions are met, overfitting may still occur, but the generated machine learning model is still effective and applicable, as long as it is based on an actual physical phenomena behind the data, and there is an abundance of learnable events provided. In this paper, a heat transfer example is shown as the empirical proof of this concept.
Qiskit As a Simulation Platform for Measurement-based Quantum Computation

Universal quantum computation is anticipated to provide solutions to classically intractable problems. Measurement-based quantum computation (MBQC) is one of the leading candidates to achieve universal quantum computation. In MBQC, the development of the underlying quantum algorithms is governed by single-qubit measurements on a highly entangled cluster state of qubits. Recent advances in MBQC mainly explore the physical aspects of MBQC (i.e. Physical development of cluster states) and as most of the simulators that exist today are for the gate-based model of quantum computation, and to date, there is no MBQC-dedicated simulator for MBQC-based algorithmic development. This paper exploits a well-maintained, and popular gate-based simulation framework developed by IBM called Qiskit to design MBQC-based quantum algorithms. We implement various gates, including Hadamard, and CNOT gates, to demonstrate the universality of MBQC in Qiskit. We also demonstrate MBCQ-based algorithm development in Qiskit by implementing MBQC-based teleportation protocol and Grover’s algorithm. The objective of this exercise is to demonstrate that the existing gate-based simulators can be used for developing MBQC-based quantum designs, which would help the research community in exploring the advantages that MBQC can offer compared to other quantum computation models.
On Unifying the Compliance Management of Applications Based on IaC Automation

Infrastructure-as-Code (IaC) technologies are used to automate the deployment of cloud applications. They promote the usage of code to define and configure the IT infrastructure of cloud applications allowing them to benefit from conventional software development practices, which facilitates the rapid deployment of new versions of application infrastructures without sacrificing quality or stability. On the other hand, enterprise applications need to conform to compliance regarding external regulations and internal policies. Many of these compliance rules affect the application architecture on which IaC code operates. However, managing the architectural compliance of IaC-based application deployments faces a number of challenges, such as configuration drift and the heterogeneity of IaC technologies. Therefore, in this work, we present a vision on how to uniformly manage the compliance of the infrastructure of applications that utilize heterogeneous IaC technologies for deployment automation. To this end, we introduce an initial design for the IaC-based Architectural Compliance Management Framework and discuss how it addresses the corresponding challenges.
Design of Intentional Eye-blink Signal Acquisition and Control System Based on Pressure Sensor

Eye-blink control means that users blink intentionally to realize human-computer interaction. An intentional eye-blink signal acquisition and control system based on flexible pressure sensor is designed, which aims to control the object through simple eye movements such as blinking and winking. The system uses two pressure sensors to collect the blink signals at the orbicularis oculi muscles of the left and right eyes respectively. The software part realizes the acquisition and recognition of six intentional eye-blink signals, and transmits blink-control instruction signals through Bluetooth, so as to realize the control of the remote-controlled vehicle. The advantage of this system is that it is not affected by illumination and does not need GUI interface for control. The availability of the system is qualified after system test.
Multi-speaker emotional speech synthesis with limited datasets: Two-stage non-parallel training strategy

Emotional speech synthesis is the synthesis of text into speech with various emotions. At present, mainstream deep learning-based emotional speech synthesis networks rely on single-speaker emotional speech datasets for training, but such specially designed high-quality datasets are difficult to obtain in reality. In this paper, we propose a novel two-stage training strategy for end-to-end emotional speech synthesis. In the first stage, a text-to-speech Mel-spectrogram alignment is trained using the single-speaker neutral speech dataset. In the second stage, a limited multi-speaker speech emotional dataset and a part of the single-speaker neutral speech dataset are used for non-parallel mixed training, decoupling emotion category, speaker identity and text information to achieve multi-speaker emotional speech synthesis. Experiments show that using this strategy can synthesize emotional speech on a limited emotional speech dataset, and the effect of synthesizing emotional speech is better than the mainstream speech conversion model under the same resource.
A Systematic Approach to Lyapunov Analyses of Continuous-Time Models in Convex Optimization

First-order methods are often analyzed via their continuous-time models, where their worst-case convergence properties are usually approached via Lyapunov functions.In this work, we provide a systematic and principled approach to find and verify Lyapunov functions for classes of ordinary and stochastic differential equations.More precisely, we extend the performance estimation framework, originally proposed by Drori and Teboulle [14], to continuous-time models.We retrieve convergence results comparable to those of discrete-time methods using fewer assumptions and inequalities, and provide new results for a family of stochastic accelerated gradient flows.
WBATimeNet: A deep neural network approach for VM Live Migration in the cloud

Live Migration (LM) of Virtual Machines (VMs) is an important activity for most cloud platforms, including Azure. LM impacts the availability of VMs, due to which workloads running on them may get affected adversely. Azure cloud infrastructure consists of many nodes and their associated VMs. Many VMs (up to a few million) are candidates to undergo LM at any given time. During the process of LM, it is essential to ensure very low or no adverse impact on the running workload of the customer. Thus, of the millions of VMs, predicting which ones to live migrate based on their low utilization of resources is a critical task. To solve this, we propose a novel deep learning network WBATimeNet, which uses Multivariate Time Series data of Memory, CPU, and Disk to predict which VM should be live migrated. WBATimeNet is a deep neural network-based architecture that uses White-box Adversarial Training to address the high variability and uncertainty of time series data. The experimental results illustrate that WBATimeNet outperforms baseline models by a large margin and helps maintain the increased availability of VMs in Azure during the LM process.
PCTNet: 3D Point Cloud and Transformer Network for Monocular Depth Estimation

Estimating dense depth map from one image is a challenging task for computer vision. Because the same image can correspond to the infinite variety of 3D spaces. Neural networks have gradually achieved reasonable results on this task with the continuous development of deep learning. But the depth estimation method based on monocular cameras still has a gap in accuracy compared with multi-view or sensor-based methods. Thus, this paper proposes to supplement a limited number of sparse 3D point clouds combined with transformer processing to increase the accuracy of the monocular depth estimation model. The sparse 3D point clouds are used as supplementary geometric information and the 3D point clouds are input into the network with the RGB image. After five times integration, the multi-scale features are extracted, and then the swin transformer block is used to process the output feature map of the main network, further improving the accuracy. Experiments demonstrate that our network achieves better results than the best method on the current most commonly used dataset for monocular depth estimation, NYU Depth V2. However, the qualitative results are also better than the best method.
A Criminal Record Keeper System using Blockchain

In India, as per records, it has been observed that with each succeeding year, criminal activities are surging. Crime is an act executed against the law of constitution and is a menace to our society. Thus, crime and criminals need to be monitored conveniently. Unlike USA or other countries, in India, criminal records are not public, which means neither private organizations nor common people can access criminal records for employee verification. A centralized system, which gives access to criminal data, would be a feasible solution to this problem. This study aims to develop a Flutter-based blockchain-enabled permissionless system that would help in the remote access of criminal records. The system would be developed using Ethereum, which offers vivid features such as truffle for enabling Ethereum virtual environment, Ganache for personal testing of smart contracts, InterPlanetary file system for encrypted storage of data and easy tracking of files, and MetaMask, which acts as a digital wallet.