Reviewerly/exHarmony huggingface dataset:
Social <scp>IoT</scp> data mining and cyber‐crime forensics under complex cloud environment

Computer forensics is accompanied by the development of computer technology and the emergence of a new form of crime, network crime. The computer log provides a lot of evidence for the computer system to attack crime. Due to the particularity of information technology, it also brings huge information security risks in the whole information process. There are more and more new types of criminal activities which take computer network information system as criminal object and computer network information system as criminal tool. Therefore, it is of great practical significance to crack down on computer network crimes and ensure information security for national economic development and social stability. Different from the traditional social crime, cyber crime is a typical high‐tech crime. The evidence of network crime is transmitted and stored in the form of binary digital data through computers or related network devices in the network. The network forensics model based on data mining technology proposed in this paper applies data mining technology to the evidence analysis of network forensics, and makes full use of various mining modes of data mining technology.
Breaking the workflow: Design heuristics to support the development of usable digital audio production tools

The investigation that follows presents the results of a series of workshops with professional musicians and music producers. The work here elicits requirements for musicians in terms of software systems. The scope here explores how to design systems to support creativity and collaboration while maintaining a usable system - one which is effective, efficient and satisfies the user. The format models that of similar workshops, where a three-pronged approach is taken to focus on three different types of creativity: exploratory, combinatorial and transformational approaches. Participants describe a story that defines different user roles and expectations. Focus groups help to refine and combine the existing experiences and begin identify ways in which systems can be made more usable, and support more creative ways of working. We consider the broader consideration of usability, including defining and describing different user types and how their views of usability may differ or even be at odds. Our findings show that while existing systems are very good at supporting traditional usability metrics, they may not consider the broader implications of a considered and holistic user experience.
Serial-Serial AES as Custom Hardware Interface using NIOS II Processor

The use of embedded systems has grown widely recently. To ensure their security, cryptographic algorithms were involved to protect information's transfer against attacks. In this paper, a Serial-Serial AES for embedded system with optimized MixColumns and SubBytes transformations is designed. The proposed AES is interfaced as a custom hardware with the System designed around NIOS II processor via GPIOs (General-Purpose Input-Output). The NIOS II processor; is worn to assure the flexibility and facility of custom hardware interface. The Customizable AES is controlled through the NIOS II. The AES as custom hardware ameliorates the speed of data encryption, serves as accelerator, eases the considerable decrease in thermal power dissipation and therefore improves the performance of the whole of system.
A Light-Weight Crowdsourcing Aggregation in Privacy-Preserving Federated Learning System

Federated Machine Learning (FML) sheds light on secure distributed machine learning. However, generic FML methods may lead to privacy-leakage through the sharing of training information of individual models and have relatively poor performance when the training datasets for individual models are biased and diversified. This is a problem in combining models trained in different scenarios of IoT devices since the available training datasets are usually limited and biased. To tackle this problem, we propose a novel approach to precisely ensemble results from different models in distributed edge devices. Instead of passing the training information of individual models around that requires a relatively large amount of bandwidth and compromises data privacy, we suggest employing a trusted central agent that only collects different inference results from edge devices. Then based on a limited amount of labeled data, the agent runs a designed statistical iterative crowdsourcing algorithm to combine results for a more accurate aggregated prediction towards a user query. Our proposed system model, "Privacy-Preserving Federated Learning System", together with our light-weight Secure Crowdsourcing Aggregation (SC-Agg) algorithm, provide a more accurate prediction for outside queries at little cost without any prior knowledge of what query will be submitted. We experimentally verify that in our system, SC-Agg consistently outperforms the majority voting method and the best performing model of the ensemble in all testing scenarios. We believe that SC-Agg fits the real-world IoT applications better than other methods, such as the vanilla majority voting, for its robustness and better performance.
Semantic analysis of issues on Google play and Twitter

Mobile app users post their opinion about the apps, report bugs or request features on various platforms, the main one being App Stores. Previous research suggests that Twitter should be used as an additional resource to receive users' feedback, as app users tweet different issues. Although the classification and review summarization methods are developed previously for each platform separately, manual investigation of reviews or tweets is still required to identify the similar or different points that are discussed on App Store or Twitter. In this paper, we propose a framework to study the differences or similarities among app reviews from Google Play Store and tweets automatically by using the semantics of the words. The results from several experiments compared with expert evaluation, confirm that it can be applied to identify the similarities or differences among the extracted topics, n-grams, and users' comments.
Dynamic Hand Gesture Recognition Using 3D-Convolutional Neural Network

Hand gesture recognition in computer science and language translation is the means of recognizing hand gestures through mathematical methods. Gesture recognition has become one of growing fields of research. Hand gesture recognition has ample number of applications including human–computer interaction, sign language and virtual/augmented gaming technology. Users can perform gestures to control or interact with devices without physically touching them. There are many architectures designed in the field of gesture detection, but existing traditional solutions are not robust to detect hand gestures with high accuracy in real time in the presence of complex patterns in performing hand gestures. In this paper, we present a fast and efficient algorithm for classifying different dynamic hand gestures using 3D-convolution neural networks. Unlike 2D-convolution neural networks, 3D-convolution networks extract features along the temporal dimension for analysis of gestures performed in videos. The paper also focuses on improving accuracy and describes data preprocessing and optimization techniques for obtaining the model inference in real time at 30fps. Our method achieves a correct recognition accuracy of 90.7% for the evaluation made on the testing videos in Chalearn LAP Continuous Gesture dataset [1]. The detection process can be tested on laptops with standard specifications.
An exploratory study on improving automated issue triage with attached screenshots

Issue triage is a manual and time consuming process for both open and closed source software projects. Triagers first validate the issue reports and then find the appropriate developers or teams to solve them. In our industrial case, we automated the assignment part of the problem with a machine learning based approach. However, the automated system's average accuracy performance is 3% below the human triagers' performance. In our effort to improve our approach, we analyzed the incorrectly assigned issue reports and realized that many of them have attachments with them, which are mostly screenshots. Such issue reports generally have short descriptions compared to the ones without attachments, which we consider as one of the reasons for incorrect classification. In this study, we describe our proposed approach to include this new piece of information for issue triage and present the initial results.
Memory transformation networks for weakly supervised visual classification

The lack of labeled exemplars makes video classification based on supervised neural networks difficult and challenging. Utilizing external memory that contains task-related knowledge is a beneficial way to learn a category from a handful of samples; however, most existing memory-augmented neural networks still struggle to provide a satisfactory solution for multi-modal external data due to the high dimensionality and massive volume. In light of this, we propose a Memory Transformation Network (MTN) to convert external knowledge, by involving embedded and concentrated memories, so as to leverage it feasibly for video classification with weak supervision. Specifically, we employ a multi-modal deep autoencoder to project external visual and textual information onto a shared space to produce joint embedded memory, which can capture the correlation amongst different modalities to enhance the expressive ability. The curse of dimensionality issue can also be alleviated owing to the inherent dimension reduction ability of the autoencoder. Besides, an attention-based compression mechanism is employed to generate concentrated memory, which records useful information related to a specific task. In this way, the obtained concentrated memory is relatively lightweight to mitigate the time-consuming content-based addressing on large-volume memory. Our model outperforms the state-of-the-arts by 5.44% and 1.81% on average in two metrics over three real-world video datasets, demonstrating its effectiveness and superiority on visual classification with limited labeled exemplars.
A Virtual Reality Simulator to Assist in Memory Management Lectures

Virtual reality technology can assist the teaching-learning process via concrete concepts that follow a sequence of steps to accomplish a task in a three-dimensional space, as proven in the literature (e.g., operating a vehicle or disaster relief simulation). However, it is not clear whether virtual reality can also enhance situations involving concepts that are not inherently related to a three-dimensional space, such as the execution of a computer algorithm. This paper presents an immersive and interactive virtual reality simulator that can aid in the teaching of the memory management functionality of operating systems, including single, fixed and dynamic contiguous techniques, and the non-contiguous technique of paging. Learners are immersed inside a computer motherboard to learn memory management functionality of operating systems. They use a head-mounted display such as 3D virtual reality headsets and can interact with the environment using eye-gaze tracking and a joystick. We also present a case study in which 80 students were divided into two groups to evaluate the simulator. Our results indicate that using our simulator is a more effective approach for teaching memory management concepts than expositive classes. The functionality and usability tests results highlighted the positive aspects of the simulator and improvements that could be made.
An Ontology Based Authentication Framework for Healthcare Monitoring

Authentication is one of the challenges designing healthcare applications for Predictive, Preventive and Personalized Medicine (PPPM (PPPM: Predictive, Preventive and Personalized Medicine.)). For user with limited interaction abilities and less memorization capabilities, it is no more feasible to involve usual knowledge-based or possession-based authentication methods. Remembering a password and entering it using a usual keyboard could be an impossible action not just a highly difficult task for a blind person or an old person with Alzahaimer. This paper proposes a new adaptable identification based authentication framework depending on the health and the mental situation of the user. The main idea consists in creating an ontology define and determinate the authentication methods from the different abilities and capabilities of the patient. The proposed PPPM system should be able to present the best suitable identification technique per patient.
Towards automated migration for blockchain-based decentralized application

Blockchain-based decentralized application is becoming more widely accepted because it publicly runs on the blockchain and cannot be modified implicitly. However, the fact that only a few developers can master both blockchain and front-end programming skills results in the error-prone DApps especially when smart contracts has undergone a migration. The existing techniques rarely pay attention to DApps' automated migration. In this paper, we first summarized 6 migration categories and proposed an approach to figure out where changes are and its categories. Besides, we designed a function call graph structure to ensure mapping relationship accurate and compared it with distinctions between two versions of ABI to offer revising suggestions. We have developed an automated tool to implement our approach in real-world DApps and acquired positive preliminary evaluation results which illustrated the practical value in realizing DApps' automated migration.
Multi-fault Diagnosis of Wind Turbine Based on Imprecise Probability

In this paper, an imprecise probability multi-fault diagnosis approach was proposed based on imprecise probability, which could simultaneously diagnose multi-fault events of the wind turbine. The approach excavated the dependence relationships between fault events and variables using the greedy search algorithm and constructed a credal network structure to express the relationships in an abstract way. The expanded imprecise Dirichlet model is used to obtain the imprecise conditional probability table at each evidence node in the network, thus to realize the parameter estimation of the credal network It was shown that based on the constructed model, features of different targets can adaptively be extracted from the same signal, and then these features are used to perform multi-fault diagnosis. The test results showed that the proposed approach realizes the correct diagnosis of faults under multiple working conditions.
Comparison of convolutional neural network in Python environment on CPU and GPU

the comparison of the convolutional neural network in Python environment is presented in this paper. The Anaconda platform provides free and easy to use tools for Python scripting language. After introduction to the environment, the experiment is described. First the used neural network architectures are shown. Used databases are defined later. Finally, the results are presented. The testing was done to determine the computational time needed for these architectures.
Super-Resolution of 3D MRI Corrupted by Heavy Noise With the Median Filter Transform

The acquisition of 3D MRIs is adversely affected by many degrading factors including low spatial resolution and noise. Image enhancement techniques are commonplace, but there are few proposals that address the increase of the spatial resolution and noise removal at the same time. An algorithm to address this vital need is proposed in this presented work. The proposal tiles the 3D image space into parallelepipeds, so that a median filter is applied in each parallelepiped. The results obtained from several such tilings are then combined by a subsequent median computation. The convergence properties of the proposed method are formally proved. Experimental results with both synthetic and real images demonstrate our approach outperforms its competitors for images with high noise levels. Moreover, it is demonstrated that our algorithm does not generate any hallucinations.
Support Correlation Filters Tracking using Mask Matrix

Support correlation filter tracking method uses cyclic sampling to transform the calculation into frequency domain, which solves the problems of sampling and large computation of support vector machine. However, the current method can not exploit the information of backgrounds because all samples are generated by cyclic sampling around the target in the tracking process. To solve this problem, this paper proposes a background awareness support correlation filter tracking method using mask matrix. In the tracking process, the mask matrix is used to extract the patchs densely from background as negative samples, so the background information is used effectively. Experiments on OTB100 database show that compared with Scale Kerneling Supported Correlation Filtering (SKSCF), the proposed algorithm achieves a gain of 4.2% in mean OP and 6.2% AUC score respectively.
ESP Students’ Digital Literacy Needs: The Teachers’ Perspectives

One of the forces of learning in the 21st century is digital lifestyle which can be acquired by obtaining digital literacy. There are many advantages that digital literacy offers, especially in an education setting. However, there was limited literature on students’ digital literacy needs analysis to support the curriculum in ESP context. This study aimed at finding English for Specific Purposes (ESP) students’ digital literacy needs to provide information for constructing ESP curriculum. The data were obtained from 55 ESP teachers through a questionnaire and an interview. Then, the data were analyzed by using content analysis. The result of the study revealed that ESP students needed to have a high degree of expertise on three components of digital literacy proposed by Hague and Payton (2010), namely functional skills, critical thinking and evaluation, and ability to find and select information. In addition, ESP students required to possess a more technical understanding of five components of digital literacy, namely creativity, cultural and social understanding, collaboration, effective communication, and e-safety. It is suggested that ESP teachers and the university policymaker need to consider the students’ digital literacy needs in constructing the ESP curriculum. Moreover, other researchers were suggested to conduct studies using different frameworks of digital literacy.
A Haptic Feedback Device Reduces Dizziness in Users Watching a Virtual Reality Video

The purpose of this study was to investigate methods by which to reduce the dizziness that people experience when watching virtual reality (VR) videos. VR technology is being used in increasing numbers of fields, but a phenomenon that causes dizziness is common. A head-mounted display (HMD) is the device generally worn for viewing virtual reality videos. HMDs put the screen close to the eyes, and users often feel pressure and also experience dizziness. The feeling of dizziness is a result of the unique constitution of each person. In this work, different subjects were selected based on their level of physical fitness. This research showed that haptic feedback caused by vibration during the viewing process can help reduce dizziness. We designed a vibration device, which was mounted on the HMD headband. Through sensing audio from the video, the vibrator is triggered, and the feeling of vibration is feed back to the haptic device. In the study, we used four vibration points, A1, A2, O1, and O2 near the areas of the brain affected by the detection points. The head is a highly sensitive organ that experiences a tactile sensation due to the vibration of the headband when an individual is watching a VR movie, thereby causing inconsistent visual and body perceptions. We found in the current research that the dizziness felt when watching VR video is caused by the inconsistency between visual and cognitive experiences. Typically, the tactile sensation can reduce such cognitive sensations and lower the degree of dizziness experienced by the wearer.
Probabilistic TSDF Fusion Using Bayesian Deep Learning for Dense 3D Reconstruction with a Single RGB Camera

In this paper, we address a 3D reconstruction problem using depth prediction from a single RGB image. With the recent advances in deep learning, depth prediction shows high performance. However, due to the discrepancy between training environment and test environment, 3D reconstruction can be vulnerable to the uncertainty of depth prediction. To consider the uncertainty of depth prediction for robust 3D reconstruction, we adopt Bayesian deep learning framework. Conventional Bayesian deep learning requires a large amount of time and GPU memory to perform Monte Carlo sampling. To address this problem, we propose a lightweight Bayesian neural network consisting of U-net structure and summation-based skip connections, which is performed in real-time. Estimated uncertainty is utilized in probabilistic TSDF fusion for dense 3D reconstruction by maximizing the posterior of TSDF value per voxel. As a result, global TSDF robust to erroneous depth values can be obtained and then dense 3D reconstruction from the global TSDF is achievable more accurately. To evaluate the performance of depth prediction and 3D reconstruction using our method, we utilized two official datasets and demonstrated the outperformance of the proposed method over other conventional methods.
Aircraft Detection from Remote Sensing Images using Single Shot Scale-invariant Face Detector with Viridis Saliency Map

Aircraft detection from remote sensing images is a useful task and essential in many ways, such as surveillance, tracking, and information collecting - however, it is still a challenging task because of a complicated of background, and a small scale of the aircraft compared to the remote sensing image size. Many researches aim to improve detection accuracy, but due to the problem mentioned above, it makes many proposed methods perform less effectively. Therefore in this paper, we proposes a practical saliency map that can significantly reduce noise from the background by using Viridis colormap, Gaussian blur, Otsu's thresholding method, and closing morphological operation to reduce noise from the background and create a binary image. Then, we find a contour of all objects in the binary image and fill a hole inside them to get the better binary image. After that, we combine a grayscale image with the binary mask to create the saliency map. Finally, we use the four channels image, which composes of three channels from an RGB input image and one channel from the saliency map, as the input for training with the CNN model based on the S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> FD network. The experimental results show that applying the saliency map and the RGB input image can improve the recall rate from 94.99% to 98% compared to using only the RGB input image with the S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> FD network. Thus this method performs well and sufficient to detect the aircraft in the high complexity of the background from remote sensing images.