Reviewerly/exHarmony huggingface dataset:
Hierarchical Fusion for Practical Ghost-free High Dynamic Range Imaging

Ghosting artifacts and missing content due to the over-/under-saturated regions caused by misalignments are generally considered as the two key challenges in high dynamic range (HDR) imaging for dynamic scenes. However, previous CNN-based methods directly reconstruct the HDR image from the input low dynamic range (LDR) images, with implicit ghost removal and multi-exposure image fusion in an end-to-end network structure. In this paper, we decompose HDR imaging into ghost-free image fusion and ghost-based image restoration, and propose a novel practical Hierarchical Fusion Network (HFNet), which contains three sub-networks: Mask Fusion Network, Mask Compensation Network, and Refine Network. Specifically, LDR images are linearly fused in Mask Fusion Network ignoring the misaligned regions. Then the ghost regions of fusion image are restored with mask compensation. Finally, all these results are refined in the third network. This strategy of divide and rule makes the proposed method significantly more tiny than previous methods. Experiments on different datasets show that superior performance of HFNet with 9x fewer FLOPs, 4x fewer parameters and 3x faster inference speed than the existing methods while providing comparable accuracy. And it achieves state-of-the-art quantitative and qualitative results while applied with similar FLOPs.
Anomaly Detection for Big Data Security: A Benchmark

Among the many challenges in the Big Data Security field is that we have to secure a high volume and velocity flow of data, this cannot be done using classic static security methods, we need a different approach, one that can analyze the traffic in real time and gives fast feedback and one of these approaches is Anomaly detection. Anomaly detection or outlier detection has been an active research area for the past years, it is an automatic and reactive method that can be used to flag any suspicious behavior in the traffic of Big Data systems. In this paper we present a benchmark study of the most used Anomaly detection algorithms in the literature. We focus on analyzing their performance on detecting anomalies in Big Data. The performance of the algorithms will be evaluated on the UNSW-NB15 Dataset. This study aims to determine what is the best algorithm or group of algorithms that performs very well in a Big Data context.
Website-Based English Learning System

Technological sophistication can be an alternative for teachers in teaching and learning activities, especially conventional learning is still widely used by teachers who act as knowledge transferers, while students are more passive as recipients of knowledge. The teacher teaches in the classroom using a textbook then the students pay attention to the explanation given by the teacher with 2x40 minutes for each subject/learning material. This causes a lack of interest in learning English for students. The purpose of the research is to provide a new learning method that is presented to students as an effort to increase students' interest in learning English in schools as well as supporters/supporters in learning English at the school. The method used in developing the model is the prototype method and for testing using the Black box testing method, while for UX (User Experience) testing, it is using the EQU (User Experience Quistionare). The results achieved are that students can access the English language learning system at school by utilizing the school's computer laboratory facilities and can be accessed again when they come home from school
Topological and Semantic Map Generation for Mobile Robot Indoor Navigation

The current tendency in mobile robot indoor navigation is to move from the representation environment as a geometric grid map to a topological and semantic map closer to the way how humans reason. The topological and semantic map enables a robot to understand the environment. This paper presents a topological and semantic segmentation algorithm that divides a grid map into single rooms or similar meaningful semantic units with a collision-free path to connect them. First, a topological map is build based on the distance transform of the grid map. Then a semantic map is build based on the distance transform of the grid map and a circular kernel. Finally, we filter and prune the topological map by merging the nodes which represent the same room. The segmented performance of the proposed planning framework is verified on multiple maps. The experiment results show that the proposed method can accurately segment rooms and generate topological semantic maps.
Efficient Federated Learning Framework Based on Multi-Key Homomorphic Encryption

Federated learning is one of the cutting-edge research area in machine learning in era of big data. In federated learning, multiple clients rely on an untrusted server for model training in a distributed environment. Instead of sending local data directly to the server, the client achieves the effect of traditional centralized learning by sharing optimized parameters that represent the local model. However, the data used to train the model by the client holds private information of individuals. A potential adversary can steal the clients’ model parameters by corrupting the server, then recover clients’ local training data or reconstruct their local models. In order to solve the aforementioned problems, we construct an efficient federated learning framework based on multi-key homomorphic encryption, which can effectively restrict the adversary from accessing the clients’ model. In this framework, using homomorphic encryption ensures that all operations, including the server-side aggregation process, are secure and do not reveal any private information about the training data. At the same time, we consider multi-key scenario, where each client does not need to share the same public key and private key, but each of them has its own public-private key pair. It is convenient for the client to join the model update or be offline at any time, which greatly increases the flexibility and scalability of the system. Security and efficiency analysis indicates that the proposed framework is secure and efficient.
Optimisation of the combined application planning and execution time utilising repeated PRM replanning for point-to-point sequences

Motion planning and collision avoidance are well studied topics in modern industrial robotics and they are already relatively simple to adopt using packages like ROS MoveIt or Battelle PathPlan. Using probabilistic planning algorithms, collision-free trajectories can be found in as little time as hundredths of a second. However, the obtained trajectories can vary significantly for each planning request. This can result in notably longer trajectories than necessary, increasing the total time required for planning and execution. Optimisation-based motion planners such as PRM* and RRT* can find shorter paths, at the cost of a higher planning time. Our focus is put on applications that require both short planning and short trajectory execution times using existing planning tools, this with the goal of minimising the total time required for the entire application. In this paper, an easy-to-implement approach is proposed to perform path optimisation for trajectory planning applications, without altering the used planner itself, which the authors dubbed repeated PRM. This optimisation is performed by planning, selecting and eliminating trajectories based on minimal motion time during the robot’s current movement. Testing is done in a virtual environment using ROS MoveIt with a 6-DOF Stäubli TX2-90XL, utilising the OMPL PRM motion planning algorithm, eight random pose targets and eight collision objects. Tests are performed on pose sequences of one to eight poses with 30 simulations each. The average of all total times required to perform the pose sequences with the repeated PRM approach are compared to these of sequential PRM and PRM* methods. After testing, the repeated PRM method shows an average impact on the total planning and execution time with a reduction of up to 26,35 % compared to sequential PRM depending on the length of the pose sequence, and up to 57,60 % compared to sequential PRM*, as the additional computational time required for PRM* significantly increases its total required time. The variation of the total required times of the found trajectory sequences also improves by an average of 56,16 % and 45,56 % compared to PRM and PRM* respectively.
Gesture Learning For Self-Driving Cars

Human-computer interaction (HCI) is crucial for safety as autonomous vehicles (AVs) become commonplace. Yet, little effort has been put toward ensuring that AVs understand human communications on the road. In this paper, we present Gesture Learning for Advanced Driver Assistance Systems (GLADAS), a deep learning-based self-driving car hand gesture recognition system developed and evaluated using virtual simulation. We focus on gestures as they are a natural and common way for pedestrians to interact with drivers. We challenge the system to perform in typical, everyday driving interactions with humans. Our results provide a baseline performance of 94.56% accuracy and 85.91% F1 score, promising statistics that surpass human performance and motivate the need for further research into human-AV interaction.
A Recurrent Refinement Network for Satellite Video Super-Resolution

Deep learning-based methods have shown superior performance in VSR tasks. However, satellite video frames are characterized by large width, low resolution, and lack of features. Consequently, the conventional VSR method is not suitable for satellite video. In this paper, a recurrent refinement network is proposed. Considering that the vast majority of remote sensing images belong to the static background, a single-image SR (SISR) method is first used to obtain high-resolution features for a specific target frame. To further complement the missing details, the network learns the complementary information enhanced by an Encoder-Decoder structure from adjacent frames to refine the results of SISR. To measure the contribution of different adjacent frames to the recovery of the target frame, a temporal attention mechanism is introduced in the final fusion stage. The experiment on the video data of Jilin-1 demonstrates the effectiveness of our method.
Review of secure the online multimedia data using chaotic map and steganography techniques

The security of data is considered as important issue in any organization. The highly level of security is required to prevent data attacked, damaged or stolen. The online data transfer is rapidly on demand to reduce the costs and time for the organization. This study aims to discuss security approaches that could be adopted in order to secure data transfer online. The literature in the security domain is discussed to determine the most suitable security techniques for the transfer data depends on the data nature, i.e. textual, video, images, or audio data. The discuss shows that techniques such as encrypted data are suitable to safe the textual transfer data. The chaotic map and steganography techniques are suitable to protect the images, video, and audio data. Steganography works to hiding the data in other plain media such as images. The chaotic map works on remapping the data pixel map in order to maximize the complexity of handling the original data by the attackers. It is recommended to offer integration between the security techniques to improve data transfer protection. For example, the data encryption would be supported by other techniques such as a chaotic map, and the steganography can be supported by techniques such as chaotic map. This paper offers important evidences about the security techniques that would be adopted which depends on the nature of data transfer as well as the integration between the techniques to enhance the protection of data transfer. The users or organizations can adopt security techniques according to their data in the environment of working. In the future, the privacy techniques of data transfer would be investigated to prevent the illegal accessing of the data.
A Comparative Analysis of Knowledge Graph Query Performance

Knowledge Graphs (KGs) continue to gain widespread momentum for use in different domains. A variety of Data Management Systems (DMSs) have accordingly been developed in response to this growing deployment for storing KGs and querying their content. The performance of services offered by DMSs is crucial to unlocking the full potential of KGs for different purposes ranging from semantic search to reasoning and data integration. However, the efficiency of representative DMS types in supporting archetypal KG queries has not received adequate research attention. In this paper, we aim to provide a fine-grained, comparative analysis of four major DMS types, namely, row-, column-, graph-, and document-stores, against major query types, namely, subject-subject, subject-object, treelike, and optional joins. In particular, we analyze the performance of row-store Virtuoso, column-store Virtuoso, Blazegraph, and MongoDB using well-known benchmark datasets and queries. Our experimental results yield insight into the performance of the selected DMSs when executing different query types. The results highlight, however, that no single DMS proves superior in all benchmark scenarios, suggesting that a DMS should be selected and tailored to the query types being executed.
Application of the Fractal Dimension Calculation Technique to Determine the Shape of Selected Monchepluton Intrusion Crystals (NE Fennoscandia)

Thirty-eight samples of minerals from Paleoproterozoic Layered PGE Intrusion Monchepluton, located in NE Fennoscandia, were tested. An automated computational technique was used which involved counting the sides superimposed on vectorized graphics using separated crystal boundaries. The results were obtained for olivine, orthopyroxene, clinopyroxene, and plagioclase. On this basis, an interpretation of the mineral box fractal dimension was made, along with an interpretation of its nature in the rock in which it was found. The performed calculations were applied to the sampling positions, and maps of changes in fractal dimensions were prepared. The nature of the minerals studied was correlated with the type of rocks present in Monchepluton. Then, the fractal dimensions were scaled to a percentage scale determining the mean value for the euhedral crystal as 100%, and a map was obtained representing the advancement of secondary processes after summing these data for all four investigated minerals. This method was analyzed and its advantages and limitations shown.
Application of digital technology in the work of a pathologist: guidelines for learning how to use speech recognition systems

Natural language processing is one of the branches of computational linguistics. It is a branch of computer science that includes algorithmic processing of speech and natural language scripts. The algorithms facilitate the development of human-to-machine translation and automatic speech recognition systems (ASRS). ASRS use is twofold: accurately converting operators speech into a coherent and meaningful text and using natural language for interaction with a computer. Currently, these systems are widely used in medical practice, including anatomic pathology. Successful ASRS implementation pivots on creation of standardized templated descriptions for organic inclusion in the diagnosis dictation, likewise on physician training for using such systems in practice. In the past decade, there have been several attempts to standardize surgical pathology reports and create templates undertaken by physicians worldwide. After studying domestic and foreign literature, we created a list of the essential elements that must be included in the template for macro-and microscopic descriptions comprising the final diagnosis. These templates will help in decision-making and accurate diagnosis as they contain all the imperative elements in order of importance. This approach will significantly reduce the need for re-examination of both fixed macroscopic material and the preparation of additional histological sections. The templates built into ASRS reduce the time spent on documentation and significantly reduce the workload for pathologists. For the successful use of ASRS, we have developed an educational course, Digital Speech Recognition in an Anatomical Pathology Practice, for postgraduate education of both domestic and foreign doctors. A brief description of the course is presented in this article, and the course itself is available on the Internet.
Crime Predictive Model in Cybercrime based on Social and Economic Factors Using the Bayesian and Markov Theories

If financial institutions cannot detect incidents effectively, it cannot succeed in responding to incidents. This implies that the detection of incidents, is the most important aspect of incident response. A stochastic process with a first order dependence in discrete state and time is described as Markov chain, in the same way, Bayesian theory is a mathematical framework for reasoning and performing inference using probability. These two theories when based on socioeconomic factors can be used to predict cybercrime occurrence in Management Information Systems (MIS). The advancement of technology in banking has made banking business processes very convenient, but as the technology advances, cybercrimes of different nature emerges and equally at its peak. In as much as there are different measures already in place to combat these crimes, there still lies so many vulnerabilities which cannot be evitable in any information systems. Financial institutions need to develop predictive models that can be used to combat this cybercrime activities.In this paper, the application of Markov chain and Bayesian inference was used to analyze the nature of cybercrime and the probability of its occurrence, and the results were used to analyze the possibility of occurrence of cybercrimes based on the factors considered.
Target-guided Adaptive Base Class Reweighting for Few-Shot Learning

For few-shot learning, minimizing the empirical risk cannot reach the optimal hypothesis from image to its label due to the effect of overfitting. Therefore, most of the existing work leverages a set of base classes with sufficient labeled samples to pre-train a general encoder for feature representation, which is then applied for all few-shot classification tasks without considering the uniqueness of the target task. We suppose that different base classes help solve a target task in varying degrees, and some classes even introduce a negative effect. To this end, we propose a Target-guided Base Class Reweighting (TBR) approach, which uses a reweighting-in-the-loop optimization algorithm to assign a set of weights for base classes adaptively given a target task. Specifically, TBR learns the parameter of the encoder via minimizing weighted empirical risk on base class data, then optimizes the weights according to the the encoder's performance on support set of the target task. Such an alternating optimization procedure brings reweighting into the loop which makes the encoder more sensitive to the novel classes of the target task. Extensive experiments demonstrate that the proposed method can improve the performance of model-based approaches on two few-shot classification benchmarks.
Tool for SPARQL Querying over Compact RDF Representations

We present an architecture for the efficient storing and querying of large RDF datasets. Our approach seeks to store RDF datasets in very little space while offering complete SPARQL functionality. To achieve this, our proposal was built over HDT, an RDF serialization framework, and its interaction with the Jena query engine. We propose a set of modifications to this framework in order to incorporate a range of space-efficient compact data structures for data storage and access, while using high-level capabilities to answer more complicated SPARQL queries. As a result, our approach provides a standard mechanism for using low-level data structures in complicated query situations requiring SPARQL searches, which are typically not supported by current solutions.
Virtual Materiality: Realistic Clay Sculpting in VR

Art is a creative, intuitive process which engages us on an emotional level. It involves experimentation, spontaneity, and play. Just as a painter may use the same brush with different mark-making techniques on the surface of their canvas, a clay sculptor may use one knife to scratch, texture, slice or create bas reliefs. To an artist, the tool and the medium as a whole are subject to spontaneous, nuanced decision making and use in multitudinous ways which are core to the art. In this work-in-progress paper, we hope to inspire renewed interest in intuitive digital art creation. We argue that the modern state-of-the-art 3D creation software fails to recreate the tactile feeling and complexity of the real-world tools because they treat their 'tools' as rigid operators. To explore ways to better accommodate the creative process, we present a Virtual Reality (VR) application, titled Virtual Materiality, that provides a set of digital sculpting tools that feel more like traditional analog tools. In this work, we generalize a 'physical sculpting tool', enabling users to deform our virtual clay in a more realistic, mass-conserving manner. Future work is planned to incorporate more interactions that one might expect with real clay, and to evaluate the efficacy of this new digital sculpting modality.
Rapid prototyping of chatbots for data exploration

This paper reports on the experience we carried out in the last years for the definition of a model-based technique for the automatic generation of chatbots for data exploration. We illustrate how this technique has been integrated within a no-code platform offering visual notations to index relational data sources and bind them to conversation flows for data exploration.
Polar Ray: A Single-stage Angle-free Detector for Oriented Object Detection in Aerial Images

Oriented bounding boxes are widely used for object detection in aerial images. Existing oriented object detection methods typically follow the general object detection paradigm by adding an extra rotation angle on the horizontal bounding boxes. However, the angular periodicity incurs the difficulty in angle regression and rotation sensitivity on bounding boxes. In this paper, we propose a new anchor-free oriented object detector, Polar Ray Network (PRNet), where object keypoints are represented by polar coordinates without angle regression. Our PRNet learns a set of polar rays from the object center to boundary with predefined equal-distributed angles. We introduce a dynamic PointConv module to optimize the regression of polar ray by incorporating object corner features. Furthermore, a classification feature guidance module is presented to improve the classification accuracy by incorporating more spatial contents from polar rays. Experimental results on two public datasets, i.e., DOTA and HRSC2016, demonstrate that the proposed PRNet significantly outperforms existing anchor-free detectors, and shows highly competitiveness with the state-of-the-art two-stage anchor-based methods.