Reviewerly/exHarmony huggingface dataset:
Unsupervised Bas-Relief Generation with Feature Transferring

The generation of relief based on 3D models has always been a research hotspot in computer graphics. Traditional algorithms are computationally complex. The latest methods based on deep learning bring new ideas to the problem but are rely on supervised training, which requires a large number of samples to be constructed manually. In this paper, an unsupervised bas relief generation method is proposed. In the method, the normal image of the model is modified by a style-transfer neural network firstly, and then it is reconstructed by Convolution Neural Network (CNN). Our method takes the orthogonal relationship between the normal vector and the local points as the optimization goal, thus avoiding the process of constructing "growth truth". At the same time, by adding constraints to the style-transfer network and the reconstruction network, the high jump in the original model is effectively eliminated, and the detailed features are well maintained. Experiments show that this method is simple and efficient, and can generate a series of pleasant stylized relief models with rich details and good saturation.
CLH: Approach for Detecting Deep Fake Videos

Deep Fakes are the media that takes the person’s image in an existing photograph, audio recording, or video and replaces them with another person’s likeness by making use of synthetic intelligence and device mastering. In this era, everybody can get easy access to software packages and tools to create deep fake videos. Existing techniques are constructed with the usage of the lip synchronization, mouth features artifacts and are commonly designed for detection of single frames. The proposed model, CLH (CNN+LSTM hybrid model) considers various parameters such as eye blinking, blurriness, skin tone, skin color, changes in lighting, lip syncing, and position to detect the fake videos. The CLH model employs “Convolutional Neural Networks (CNN)” and “Long Short-Term Memory (LSTM)” for detecting a deep fake video. The original videos and deep fake (high quality + low quality) videos were used in training the model. Datasets such as Celeb-DF, face forensics ++, Deep fake TIMIT, and fake videos developed by Facebook were used to train and evaluate the model, so that an efficient model is constructed. The proposed CLH model achieved a high accuracy of more than 90% and a low false positive rate of less than 5%. The CLH model is also compared with other models on the market and analyzed to understand the significance of the work.
Analysis and Modeling of Activity-Selection Behavior in Collaborative Knowledge-Building

People neither behave uniformly in their social lives nor is their behavior entirely arbitrary. Rather, their behavior depends on various factors such as their skills, motives, and backgrounds. Our analysis shows that such a behavior also prevails in the websites of Stack Exchange. We collect and analyze the data of over 5.3 million users from 156 Stack Exchange websites. In these websites, users’ diverse behavior shows up in the form of different activities that they choose to perform as well as how they stimulate each other for more contribution. Using the insights gained from the empirical analysis as well as the classical cognitive theories, we build a general cognitive model depicting the users’ interaction behavior emerging in collaborative knowledge-building setups. Further, the analysis of the model indicates that for any given collaborative system, there is an optimal distribution of users across its activities that leads to the maximum knowledge generation. We also apply the model on Stack Exchange websites and identify the under-represented activities.
Continual Neural Network Model Retraining

We propose incremental (re)training of a neural network model to cope with a continuous flow of new data. As such, this is a life-long learning process. We address two challenges of life-long retraining: catastrophic forgetting and efficient retraining. If we combine all past and new data it can easily become intractable to retrain the neural network model. On the other hand, if the model is retrained using only new data, it can easily suffer catastrophic forgetting and thus it is paramount to strike the right balance. Moreover, if we retrain all weights of the model every time new data is collected, retraining tends to require too many computing resources. To solve these two issues, we propose a novel retraining model that can select important samples and important weights utilizing multi-armed bandits. To further address forgetting, we propose a new regularization term focusing on synapse and neuron importance. We analyze multiple datasets to document the outcome of the proposed retraining methods. Various experiments demonstrate that our retraining methodologies mitigate the catastrophic forgetting problem while boosting model performance.
Developing a Database of Uzbek Language Concatenative Speech Synthesizer

Today, it is difficult to imagine our life without modern intellectual technologies In this regard, computer linguistics, especially synthesizers that generate speech signals from text, plays an important role. This article discusses the creation of a database for the concatenative Uzbek speech synthesizer. A number of tasks are performed to organize the reading of the Uzbek text based on sound fragments. The main elements of the database and their properties are analyzed.
Exipnos: An Efficient Verifiable Dynamic Symmetric Searchable Encryption Scheme with Forward and Backward Privacy

Multiple approaches have been developed to address data privacy concerns, as cloud services increasingly gain traction. One of these methods is Searchable Encryption (\(\mathsf {SE}\)), which enables a user to search over encrypted data. When applied to a dynamic dataset, it is important that \(\mathsf {SE}\) achieves two essential properties upon updating a dynamic dataset: (1) Forward Privacy, which guarantees that an updated document would not be linked to previous searches and (2) Backward Privacy, which prevents information leakage from deleted data.
A Natural Language Query Method for Linked Data

Efficient data retrieval is one of the key issues in the development of Web of Data based on Resource Description Framework (RDF). Formal query language like SPARQL is an effective way to retrieve structured data, but users are accustomed to natural language-based retrieval. Due to its grammatical complexity and prerequisite knowledge of ontology schema, the formal query language is hard to be applied in natural language-based retrieval directly without help from new approaches or tools. Therefore, how to automatically convert keyword search into formal query-based retrieval is an important part of the realization of Web of Data. The natural language query method for linked data automatically converts natural language queries into SPARQL queries to improve the effectiveness and efficiency of the system. In this paper, the SPARQL query is generated by transforming language elements among abstract models, followed by constructing the ontology-based query semantic graphs and illuminating semantic ambiguity. The experimental results show that the proposed approach has a higher recall rate, better precision, and lower time consumption.
Image Periodization for Convolutional Neural Networks

In last few years, convolutional neural networks (CNNs) have achieved great success on many image classification tasks, which shows the effectiveness of deep learning methods. The training process of CNN is usually based on a large number of training data, and the CNN can learn local image translation invariance property by using the convolution operation and pooling operation, which is very important for image classification. However, the performance of CNN is limited when the location of the key object varies greatly in the image or the number of data is insufficient. Addressing this problem, in this paper, we propose a novel method named image periodization for CNN on image classification tasks. We extend the original image periodically and resample it to generate new images, while we design a circular convolutional layer to replace the original convolutional layer. Our method can be used as a data augmentation method, and it can provide complete translation invariance property for CNNs. Our method can be easily plugged into common CNNs, and the experiment results show consistent improvement on different CNN-based models.
Improvement of Fuzzy Graph Drawing Using Partition Tree

Obtaining useful information from ambiguous information is a necessity in various fields. Ambiguous information can be handled quantitatively by using fuzzy theory, and representing it in an easy-to-understand manner is critical. One solution is to visualize an ambiguous relationship by using fuzzy graph representation, which has the essential characteristic of expressing variable relationships in between its nodes. We previously proposed an algorithm to draw intelligible and comprehensive fuzzy graphs. This study describes an improved drawing method for that graph drawing algorithm. As a result, highly related nodes were arranged closer to one another, and the display area was reduced. This method can be used as an effective means of expressing the results of ambiguous information analysis.
Underwater Slam Algorithm Based on Image Sonar Salient Target Detection

This paper proposes an underwater SLAM algorithm based on image sonar salient target detection to optimize the cumulative error of the autonomous underwater vehicle (AUV) inertial navigation system. It is helpful for long-range AUV detection in marine archaeology, rescue search, and hydrogeology. The application of science and other fields is of great significance. Due to the complexity of the underwater environment and the unpredictability of submerged targets, the implementation of the SLAM algorithm based on image information-assisted positioning and navigation has always been a challenging task. Due to the variability of underwater acoustic channels, the unknownness of underwater targets, and the limitation of the detected image quality, this paper adopts the saliency target detection method of image sonar data to enhance the perception of the external environment. It constructs a complex and changeable underwater SLAM system, improving error-free autonomous vehicle navigation. This experiment shows that the extraction of landmark features based on the significant target detection of image sonar can accurately construct an underwater SLAM system and reduce the navigation error of an autonomous underwater vehicle (AUV).
The comparison of the proposed recommended system with actual data

Recommendation systems for the museum have been active in the past decade. It used to be a difficult task to make the personalized recommended list for museum-goer. However, with the current technology, research can provide the list for visitors via technology such as mobile applications. We have proposed a recommendation system based on social filtering and statistical methods in the previous paper. This paper applies the F1-score to evaluate our recommendation methods on the actual visitor loggers from Chao sampradaya national museum. We compare the social filtering method with the statistical method and benchmark with the random recommendation. In comparison, the statistical method gives the same result as social filtering when the time is limited. The longer time the visitor spends in the museum, the better result from the social filtering. However, in terms of calculation complexity, the statistical method outperforms social filtering.
Exploration and mapping of an indoor environment using Multirotor Aerial Vehicle

Self-exploration of unknown environments using mobile robots is an important aspect in order to achieve complete automation of the tasks done by robots. The key idea of the exploration is to find a pose at which the robot will gain more information about the environment. By moving from such a pose to another the robot builds a map using the information gained during motion. A frontier based exploration algorithm for 3D spaces is introduced. The algorithm is an extension of the classic frontier exploration algorithm. The space is represented in an octree structure for faster access and efficient memory management. At every iteration of the exploration algorithm, the Multirotor Aerial Vehicle (MAV) performs a 360° rotation to increase the information gain about the environment, this rotation is dependent on the sensor’s field of view. If a rotating sensor which can cover a 360° of the environment is used, the 360° rotation can be skipped without affecting the methodology. The distance moved by the MAV is specified at each iteration of the exploration process depending on the environment. Simulations were conducted to compare the effect of different parameters in the algorithm.
Self-Adaptive Hashing for Fine-Grained Image Retrieval

The main challenge of fine-grained image hashing is how to learn highly discriminative hash codes to distinguish the within and between class variations. On the one hand, most of the existing methods treat sample pairs as equivalent in hash learning, ignoring the more discriminative information contained in hard sample pairs. On the other hand, in the testing phase, these methods ignore the influence of outliers on retrieval performance. In order to solve the above issues, this paper proposes a novel Self-Adaptive Hashing method, which learns discriminative hash codes by mining hard sample pairs, and improves retrieval performance by correcting outliers in the testing phase. In particular, to improve the discriminability of hash codes, a pair-weighted based loss function is proposed to enhance the learning of hash functions of hard sample pairs. Furthermore, in the testing phase, a self-adaptive module is proposed to discover and correct outliers by generating self-adaptive boundaries, thereby improving the retrieval performance. Experimental results on two widely-used fine-grained datasets demonstrate the effectiveness of the proposed method.
CODS: Cloud-assisted Object Detection for Streaming Videos on Edge Devices

The advance of hardware has allowed edge devices to carry out varying applications, including computation-intensive machine learning tasks. This paper targets a real-time application of detecting objects on streaming videos with a tightened requirement on processing overhead. While the edge device still lacks the computation power to apply the object detection algorithm on every video frame, this paper integrates cloud-side servers in the solution as much prior work has attempted. However, our design is different from the conventional off-loading solutions that delegate the computation tasks to the cloud servers. We present a solution named CODS where the computation tasks are still conducted by the edge devices while the cloud server provides useful guidelines for choosing appropriate analysis algorithms. We implement our solution with the Jetson Nano device and evaluate it with videos from representative datasets. The results show that CODS is quite effective and superior to baseline alternatives.
Identifying Protection Motivation Theory Factors that Influence Smartphone Security Measures

Because smartphones are ubiquitous in our society, and undergraduate students use smartphones extensively, it is necessary to understand how undergraduates can be protected from malicious hacker attacks, viruses, and malware. This quantitative study explores the influence of Protection Motivation Theory (PMT) constructs on security behaviors of undergraduate students. The primary focus of this quantitative study is to answer our primary research question: to what extent do the independent PMT variables (perceived threat severity, perceived threat vulnerability, self-efficacy, response efficacy, and response cost) explain undergraduate students’ employment of smartphone security measures? The findings suggest that although all hypotheses were not supported by the analysis; however, self-efficacy and perceived threat vulnerability showed a statistically positive association with several smartphone security measures. However, none of the constructs showed a statistically significant association with using unsecured Wi-Fi in public places. The practical implications of the findings are for industry to focus on better design of smartphone hardware and software (such as antivirus apps) to protect users, and to increase self-efficacy among smartphone undergraduate users through training and education.
A New Approach for Modification of Grasshopper Optimization Algorithm in Machine Learning Applications

This paper presents one novel approach for providing a modified version of the well known Grasshopper optimization algorithm. Since the Grasshopper optimization is highly demanded in the field of optimization, this idea tries to maximize the searching abilities of this algorithm. The new technique utilizes the binary crossover along with polynomial mutation for enhancement of quality of outcomes. These two concepts when added with the normal algorithm grasshopper optimization try to the increase in exploration as well as exploitation of available solution space. The simulation is done using standard unimodal in addition to multimodal functions for proving the superiority of the results. From the quality of results obtained after simulation, it can be clearly concluded that the newly planned modified grasshopper optimization algorithm is found fruitful in variety of real world engineering applications.
Computer Vision Based Wellness Analysis of Geriatrics

The evolution of modern technologies has made real-time and personalized monitoring immensely helpful to the geriatrics. With the increasing trendin the growth of the elderly population, it has become cumbersome for caregivers to keep a check on seniors' day by day exercises physically and ceaselessly. Trends in their daily activities, such as eating, sleeping, sitting, standing, walking, drinking etc. can provide caregivers proper information with regard to seniors' health. Hence, the proposed idea is to devise a self-operating model to continuously scan the seniors' activities and to provide them with descriptive analysis using Image Processing techniques and Deep Learning algorithms. The proposed methodology consists of the Human activity recognition and the classification of the Human activity using ResNet-101, VGG-16 and Inception V3 which are Convolutional Neural Network Architectures and to propose the best model based on its performance. The model will detect the elderly's activity, recognize the activity and finally provide the descriptive wellness analysis.
Surgery remember@: an innovation to reduce surgical cancellations

The use of mobile phones has dramatically increased all over the world. Such revolution in the communication amongst individuals has a great impact in patient care, supporting their self-management and promoting shared responsibility with health services. Given that improved communication facilitates compliance with scheduled procedures and reduces surgical cancellations, the current work aims to develop a communication tool named Surgery Remember@ to mitigate surgical suspensions due to patient absenteeism. The present article is a study of technological production divided into four chapters: literature review; analysis of the hospital administrative profile; software development; and process mapping for software implementation. Taking into account that in the last three years the problem of absenteeism was the main cause of institutional surgical cancellations; the development of Surgery Remember@ endeavours to reduce surgery cancellations, improving efficiency and reducing costs. It is known that sending messages three days before the surgical procedure makes it possible to replace patients in the event of cancellations, optimizing the human and material resources in the operating room. The confirmation of the pre-aesthetic consultation is also positive, for it allows the verification of perioperative assistance improvement. Hence, besides being viable and easy to implement, the software developed allows the addition of other features based on user requirements, proving to be an asset to reduce surgery cancellations.
Runtime analysis of evolutionary algorithms

tutorial Runtime analysis of evolutionary algorithms: basic introduction Authors: Per Kristian Lehre University of Birmingham, UK University of Birmingham, UKView Profile , Pietro S. Oliveto University of Sheffield, UK University of Sheffield, UKView Profile Authors Info & Claims GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2021 Pages 399–425https://doi.org/10.1145/3449726.3461422Published:08 July 2021Publication History 0citation81DownloadsMetricsTotal Citations0Total Downloads81Last 12 Months23Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Security and Privacy in Big Data Computing

The rapid growth of digitization in the present era leads to an exponential increase of information which demands the need of a Big Data paradigm. Big Data denotes complex, unstructured, massive, heterogeneous type data. The Big Data is essential to the success in many applications; however, it has a major setback regarding security and privacy issues. These issues arise because the Big Data is scattered over a distributed system by various users. The security of Big Data relates to all the solutions and measures to prevent the data from threats and malicious activities. Privacy prevails when it comes to processing personal data, while security means protecting information assets from unauthorized access. The existence of cloud computing and cloud data storage have been predecessor and conciliator of emergence of Big Data computing. This article highlights open issues related to traditional techniques of Big Data privacy and security. Moreover, it also illustrates a comprehensive overview of possible security techniques and future directions addressing Big Data privacy and security issues.
Ancient Chinese Recognition Method Based on Attention Mechanism

Characters and symbols play an important role of historical development and cultural transmission. Automatic ancient character recognition has become a meaningful and typical task. However, the existing recognition methods mostly focus on the detection and classification of modern Chinese, there are lack of the research on ancient Chinese, especially pre-Qin characters. And the methods are mainly computer graphics, topology, support vector machines (SVM) and convolutional neural networks (CNN), these methods lack attention to character features. Thus, based on ancient Chinese characters dataset of Tsinghua Bamboo Slips, the method proposed in this paper add attention mechanism to recognition algorithms to replace traditional convolution in order to improve recognition accuracy. Besides, we propose a data augmentation method specifically for character images, as much as possible without changing the writing form of Chinese characters. Experimental results demonstrated that our method has achieved a top5 accuracy of 99.98% which is higher compared with other methods.