Reviewerly/exHarmony huggingface dataset:
Research on Deep Learning with Gesture Recognition and LSTM in Sign Language

Sign language is a tool for the hearing impaired to communicate with each other. It is a channel for expressing thoughts and emotions, and also one of the ways they communicate with ordinary people. However, not everyone can read sign language. For those who do not understand sign language, it is difficult to receive its meaning quickly. At the same time, it also causes inconvenience for the hearing impaired. Thus, gesture recognition combined with deep learning techniques of Long Short-Term Memory (LSTM) is used to translate sign language into sentences with the correct meaning in this study. Then, the convenience of communication between hearing-impaired and ordinary people can be enhanced. People can more easily understand the sign language expressions of hearing-impaired and improve their willingness to communicate and interact. The study of Sign Language Recognition (SLR) is to translate the gesture and continuity of the sign language for expressing the semantics, which provides a convenient tool for communication. In this study, we constructed a complete recognition model by combining a Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM) neural network to complete continuous recognition work. An ordered image sequence is extracted from the video and converted into a vector through the image database for training and learning sign language using the powerful image recognition capabilities of CNN. Next, the LSTM model is used to connect with the fully connected layer of CNN to complete the accomplished semantic recognition. In particular, the concept of Recurrent Neural Network (RNN) is suitable for time series data processing and the construction of sequence data learning. After making modifications to the traditional RNN architecture, the LSTM performs better in terms of memory and appropriate data length. We built gesture and sign language datasets and adopted the CNN-LSTM recognition method. As a result, a higher recognition rate was achieved with a smaller training set, which meets the needs of real-time SLR systems.
Investigation of the Effect of Solar Panel Temperature on Power Output Efficiency in Brass, Nigeria.

Background: Luminous efficacy is a property of light sources, which indicates what portion of the emitted electromagnetic radiation is usable for human vision relative to the overall efficiency of a light source for illumination. This also applies to solar panels. Purpose: To confirm the possibility of using solar panel as an alternative source of generating electricity and to highlight hindrances to the maximization of solar panel efficiency in the Niger Delta region of Nigeria using Brass as a case study. Materials and Methods: A Modern digital instrument, BK precision model 615 digital light meter and Alda AVD890C digital multimeter, were use for measurement of solar radiation, current and voltage respectively, under varying conditions of temperature. Result: An average Solar panel temperature of 26.2 oC was recorded in the morning hours. Towards noon, solar panel temperature increased up to 45 0C. Output current also increased from 0.0 to 20.0 x 10-1A. Solar panel temperatures between 26oC and 45oC appeared to favour increase in output current. Above 45oC, output current began to drop despite further increase in solar panel temperature. The best solar panel operating temperature in Brass is 45oC. Between the solar panel temperatures of 26oC and 32oC, output voltage remained relatively stable varying between 8.0V and 8.10V. Conclusion: Within limits, solar panel efficiency appears to be temperature dependent up to a maximum temperature, increasing up to 87.0% at 43.8oC. Higher temperatures appear to be counterproductive on solar panel efficiency.
Enhancing Population Diversity by Integrating Iterative Local Search with Deep Convolutional Generative Adversarial Networks (GANs)

Generative adversarial networks (GANs), which have powerful fitting ability and thus generate diverse samples, are efficient deep neural networks for generation tasks. GANs are rarely used to solve combinatorial optimization problems though they have great potential in the field of operational research. This paper presents a hybrid evolutionary algorithm (DCG-EA/I) driven by deep convolutional generative adversarial networks (DCGAN). First, the evolutionary individuals are encoded to fit the training of GANs. Then, an escaping strategy driven by DCGAN is proposed to expand evolutionary individuals space and enhance evolutionary population diversity. Moreover, we use a iterative local search 2-opt method to improve the quality of the solutions. Finally, a negative-as-positive mechanism is constructed so as to stabilize the training process of DCGAN and reduce the harm caused by mode collapse. The algorithm is tested on TSP standard library instances and real-world instances. Experimental results show that the proposed algorithm can mitigate the problem of local convergence and achieve competitive performances over other GAN-based algorithms.
FAVT: A Feature-Anchored Video Data Trading Mechanism

When using existing video data trading technology, the recorded information cannot provide adequate protection for video ownership after the video has been modified. In this regard, we propose a feature-anchored video data trading mechanism using blockchain to realize the security of video ownership in the video data trading process. The interference information filtering module is designed to prevent attackers from using interference pictures to attack the system and improve the detection effect. Convolutional Neural Network (CNN) is used to obtain the feature information of video data. The anchoring of video data ownership and video data feature information is achieved through video data asset structure on the blockchain. We design an NSG-based indexed slice archival storage method, and on this basis, perform feature matching and temporal alignment to achieve efficient detection of infringing video fragments from a large-scale copyright video database. We implement a three-stage trading method for video data through smart contracts, which ensures the coordination and security of the trading process between the video owner and the video purchaser, and ensures the security update of the video owner attribute.
Persistent Surveillance Method of Multiple Mobile Robots Based on Pheromone Positive Incentive Mechanism

This paper focuses on the persistent surveillance method of multiple mobile robots. Firstly, the existing calculation method of grid element value is improved by using pheromone. Then, aiming at two problems brought by the traditional persistent surveillance method: (1) when the initial positions of multiple robots are close to each other, they are easy to concentrate in a local area-this paper adjusts the weighting parameters ω <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> , good results are obtained; (2) sometimes, different robots are allocated to the same target point at the same time-this paper uses the method of allocating suboptimal target points and random allocation to improve this phenomenon, and compares the effects of suboptimal allocation and random allocation. The results show that suboptimal allocation is better than random allocation.
Quasistatic contact-rich manipulation via linear complementarity quadratic programming

Contact-rich manipulation is challenging due to dynamically-changing physical constraints by the contact mode changes undergone during manipulation. This paper proposes a versatile local planning and control framework for contact-rich manipulation that determines the continuous control action under variable contact modes online. We model the physical characteristics of contact-rich manipulation by quasistatic dynamics and complementarity constraints. We then propose a linear complementarity quadratic program (LCQP) to efficiently determine the control action that implicitly includes the decisions on the contact modes under these constraints. In the LCQP, we relax the complementarity constraints to alleviate ill-conditioned problems that are typically caused by measure noises or model miss-matches. We conduct dynamical simulations on a 3D physical simulator and demonstrate that the proposed method can achieve various contact-rich manipulation tasks by determining the control action including the contact modes in real-time.
Path Planning of Unmanned Autonomous Helicopter Based on Hybrid Satisficing Decision-Enhanced Swarm Intelligence Algorithm

With the increasing complexity of the air combat environment, the optimal flights path is no longer the only requirement for the path planning system of an unmanned autonomous helicopter (UAH). To enable the UAH path planning system to efficiently handle path planning problems in complex environments, a path planning method is proposed based on satisficing decision-enhanced hybrid swarm intelligence in this article. First, the UAH path planning is modeled as the multiobjective optimization problem. Besides the traditional flight cost of path planning, the performance of UAH and the safety constraints are both considered in this article to establish the fitness function of path planning. Then, a hybrid satisficing decision-enhanced swarm intelligence (HSD-SI) path planning algorithm is proposed based on the satisficing decision method. Through the collaboration and feedback between the hybrid algorithms, the satisfaction enhancement factor is dynamically adjusted so that the HSD-SI algorithm has multiple optimization properties. Thus, the UAH path planning system based on the HSD-SI algorithm can intelligently plan satisfactory flight paths according to the requirements of the mission. Simulation results verify the feasibility and effectiveness of the HSD-SI algorithm in dealing with the UAH path planning problems.
Deep Discriminative Hashing Networks on Point-wise Labels for Image Retrieval

With the continuous development of digital technology and the Internet, there are extremely large amounts of images generated every day. Hashing algorithms are gaining popularity for image retrieval tasks due to their high efficiency. However, most existing deep hashing methods adopt pairwise labels or triplet labels as their supervisory information, which is unfavorable when the data size is very large, because of the combinational property. We address this problem in this paper and propose a novel point-wise labels based hashing model named Deep Discriminative Hashing Networks (DDHNs). Moreover, we have designed a Hashing Discriminative Loss (HD-Loss) to improve the separability of the learned data representations. We validate that DDHNs outperform traditional hashing algorithms and existing deep hashing algorithms on multiple datasets. In particular, to testify the effect of HD-Loss, we have conducted ablation study and demonstrated that DDHNs perform better than DDHNs without HD-Loss on the used datasets.
A plug-in that simulates gaze and glance functions

The convolutional neural network gradually extracts the local features of the image through several convolutional layers. Studies have shown that the spatial correspondence of these features is particularly important. In the biology field, studies have shown that compared with human visual saliency, the Saccade path of the human's eye can better reflect human visual search behavior, and deepen the impact on dynamic visual attention. Therefore, to further improve the representation ability of the network, this paper constructs a network structure of Glance From top to bottom network (Gftb-Net), which can simulate the gaze of the human eye and stimulate the glance function and stimulate the brain's long-term and short-term memory mechanism, the network can selectively retain the characteristics of important features to further build interdependencies, adaptively change the feature information at the row and column level of the picture, and further improve the ability to collect global information. Based on the correlation between image rows and columns, a channel interaction strategy based on LSTM for collecting images to give weights between rows and columns is proposed. This method effectively combines the relationship between image rows and columns with spatial attention to extracting effective weight information. Then, the matrix dot product is combined with the attention vector after global pooling and standard deviation pooling, which effectively improves the representation ability between image rows and columns. The network's top1_accuracy has been reached at 79.25% in the Imagenet-100 classification task. Experiments have proven that the network is effective, and the plug-in can theoretically be used after any convolutional layer.
Evaluation Of Product Reviews Using Deep Learning Classifier Models

The world today is inclined towards digitization. The e-commerce industry which is a part of the digital world is doing great in the market as it makes the task of purchasing products conveniently done by the hands of the customers anywhere and at anytime without even stepping out of their house. Sentiment analysis can be used to analyse the customer reviews on the e-commerce platforms which help the businesses know what products are doing well in the market, what needs to be improved and what products need to be dropped. These reviews act as feedback to the business product owners and help them figure out important market decisions. In this paper, a comparative study has been done of the sentiment analysis of customer product reviews from Amazon using machine learning classification techniques like SVM as well as deep learning techniques like LSTM and CNN.
Using Strawberry algorithm to solve general mathematical model

It is usually difficult for people to solve real-life problems, although nature has had Its own way of looking at and addressing these challenges for millions of years. This is why these days when people fail to strategize in these circumstances, they turn to nature to solve problems. Thus, it is often difficult for algorithms to arrive at the correct solution to a problem through heuristic processes. On the other hand, these strategies are good for getting close to the required time. One of these algorithms is called the Strawberry Algorithm. Here we propose to study the influence of Strawberry Algorithm roots and runners. In this research, we came up with the best solution to find the roots of a linear equation using one of nature's algorithms, and from these algorithms we find the strawberry algorithm through the Visual Studio 2019 platform. It has been observed that the strawberry algorithm succeeded in finding solutions to the linear equation and was very effective in finding any linear equation and he compared the results of the algorithm with the genetic algorithm in terms of measuring efficiency in time and iteration so that the strawberry algorithm was better than the genetic algorithm.
PREDATOR: A Cache Side-Channel Attack Detector Based on Precise Event Monitoring

Recent work has demonstrated the security risk associated with micro-architecture side-channels. The cache timing side-channel is a particularly popular target due to its availability and high leakage bandwidth. Existing proposals for defending cache side-channel attacks either degrade cache performance and/or limit cache sharing, hence, should only be invoked when the system is under attack. A lightweight monitoring mechanism that detects malicious micro-architecture manipulation in realistic environments is essential for the judicious deployment of these defense mechanisms.In this paper, we propose PREDATOR, a cache side-channel attack detector that identifies cache events caused by an attacker. To detect side-channel attacks in noisy environments, we take advantage of the observation that, unlike non-specific noises, an active attacker alters victim’s micro-architectural states on security critical accesses and thus causes the victim extra cache events on those accesses. PREDATOR uses precise performance counters to collect detailed victim’s access information and analyzes location-based deviations. PREDATOR is capable of detecting five different attacks with high accuracy and limited performance overhead in complex noisy execution environments. PREDATOR remains effective even when the attacker slows the attack rate by 256 times. Furthermore, PREDATOR is able to accurately report details about the attack such as the instruction that accesses the attacked data. In the case of GnuPG RSA [20], PREDATOR can pinpoint the square/multiply operations in the Modulo-Reduce algorithm; and in the case of OpenSSL AES [45], it can identify the accesses to the T <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</inf> -Table.
CAMRI Loss: Improving Recall of a Specific Class without Sacrificing Accuracy

In real-world applications of multi-class classification models, misclassification in an important class (e.g., stop sign) can be significantly more harmful than in other classes (e.g., speed limit). In this paper, we propose a loss function that can improve the recall of an important class while maintaining the same level of accuracy as the case using cross-entropy loss. For our purpose, we need to make the separation of the important class better than the other classes. However, existing methods that give a class-sensitive penalty for cross-entropy loss do not improve the separation. On the other hand, the method that gives a margin to the angle between the feature vectors and the weight vectors of the last fully connected layer corresponding to each feature can improve the separation. Therefore, we propose a loss function that can improve the separation of the important class by setting the margin only for the important class, called Class-sensitive Additive Angular Margin Loss (CAMRI Loss). CAMRI loss is expected to reduce the variance of angles between features and weights of the important class relative to other classes due to the margin around the important class in the feature space by adding a penalty to the angle. In addition, concentrating the penalty only on the important classes hardly sacrifices the separation of the other classes. Experiments on CIFAR-10, GTSRB, and AwA2 showed that the proposed method could improve up to 9% recall improvement on cross-entropy loss without sacrificing accuracy.
E-Skills in Cybersecurity

Today’s digital work environments require that organizations increase their cyber resilience. This calls for organizational solutions to find skilled cybersecurity professionals and efficient solutions and procedures that secure digital technologies. Some recent developments deepen the understanding of cybersecurity skills, build skills frameworks, taxonomies and certification systems to base trainings on work-life needs. This study is part of the project ECHO research activities, and it identifies skills that have been used as base skills to develop the ECHO E-skills and Training Toolkit. This case study collected recruitment advertisements from the web service Monster to understand what the Finnish labor market looks at the most desired e-skills that employers want from their future employees. The results of this study are based on a sample (n = 178) of Finnish job advertisements collected in the summer of 2021. This section looks at the job advertisements both by sector and level of expertise and then discusses the relevant skills categories of Technical, Situation awareness, Problem-solving, and Sector specific e-skills. The results show that companies are actively looking for Technical e-skills and Problem-solving e-skills. The most sought after Technical e-skills were programming, software (SW) and information technology (IT). The most important Problem-solving e-skills are, according to the results communication, interaction, self-driven, teamwork, and cooperation and collaboration, and situation awareness e-skills were surprisingly very little addressed to.
Integration of Multiple Formal Matrix Models in Coq

Matrices are common tools in mathematics and computer science, and matrix formalization in proof assistants can provide strong support for verifying system behaviors related to matrix operations. The Coq community has proposed at least five formal matrix models, although the Coq standard library does not implement them. Developers who require a formal matrix library could have difficulty choosing what model to use. More importantly, once a choice is made, switching to another model later can be expensive. Although these matrix models have formalized matrix theory to a certain scale, demonstrating the ability to support the development of matrix theory, they have not identified the absolute advantages over other models, making it difficult for developers to choose. Moreover, the models have different data structures with completely different signatures of function and theorem, forcing developers to virtually completely rewrite matrix-related scripts when switching to a new matrix model. To address these problems, herein we undertake the following. First, we propose a unified matrix interface and integrate existing formal matrix models in the Coq community based on this interface. Secondly, we construct bijective functions between the different models to form isomorphisms, thus establishing connections between these models. We also provide technical comparison conclusions to help developers make choices. Hence, matrix formalization developers have a reference guide in the early stage and can switch to other models at a low cost at a later stage or use multiple models simultaneously with conversion assistance.
Probabilistic Update Policy to Deal with Bandits for Staged Problems Applied to Cases

Some decision problems are represented in sequential stages within which an action is executed without knowing its effect until the action of the last stage is completed. A dynamic case management modeling and notation problem has been remodeled with these features to improve its automation. This chapter describes a bandit-based application with a probabilistic learning policy tested with simulated data and a stage graph and proposes its application in case automation. The results of the simulations and an initial model of the application as a graph of stages are presented.
Adversarial Input Detection Based on Critical Transformation Robustness

Recent studies have shown that ad-hoc image transformations are effective for defending certain adversarial attacks. It is desirable to determine which transformations are more effective than others for adversarial defenses before these transformations are being deployed in practice. We propose in this paper the notion of Critical Transformation Robustness (CTR), which can indicate potentially the detection performance of an input transformation through the difference between its CTR on clean inputs and that on adversarial ones. Then, based on this new notion, we further present a general training framework that can deliver an effective and efficient adversarial detector, which features in a customized combination of specific input transformations for defending the given, possibly mixed, adversarial attacks. We evaluate our training framework on 3 typical image datasets with 17 types of popular input transformations for detecting a mixture of 22 types of adversarial attacks. Experimental results show that the CTR differences between the clean and adversarial inputs can essentially guide the selection of an effective combination of input transformations with their nearly-optimal parameter values. Furthermore, compared with the state-of-the-art input transformation-based adversarial detection methods, the detectors generated by our training framework exhibit on average 73.4% - 87.5% higher performance on the mixed adversarial attacks.