Reviewerly/exHarmony huggingface dataset:
Image Hashing Based on SIFT Features

Image hashing maps high-dimensional image to low-dimensional binary code which can be used for fast image retrieval. A new image hashing method is proposed in this paper. Firstly, the SIFT features of an image are extracted. Secondly, the SIFT feature's 128 dimensional components are divided into 16 groups and the differential SIFT feature of each group is calculated in a sequential and cyclic way. Thirdly, the weight of each SIFT feature is calculated and is applied to the corresponding differential SIFT feature. Finally, the weighted differential SIFT features are summed to get the image's SIFT feature, and binarization is carried out on each group of the image's SIFT feature in a sequential and cyclic way to obtain the hash code of the image. The proposed method has the advantage that it can quickly generate hash codes for large-scale data sets and is robust to rotation and noise. The experimental results on caltech-256 showed that the proposed method can improve the effect of image retrieval.
Triaging digital device content at-scene:- Formalising the decision-making process

The prominence of technology usage in society has inevitably led to increasing numbers of digital devices being seized, where digital evidence often features in criminal investigations. Such demand has led to well documented backlogs placing pressure on digital forensic labs, where in an effort to combat this issue, the 'at-scene triage' of devices has been touted as a solution. Yet such triage approaches are not straightforward to implement with multiple technical and procedural issues existing, including determining when it is actually appropriate to triage the contents of a device at-scene. This work remains focused on this point due to the complexities associated with it, and to support first responders a nine-stage triage decision model is offered which is designed to promote consistent and transparent practice when determining if a device should be triaged.
Single Generative Networks for Stain Normalization and Quality Enhancement of Histological Images in Digital Pathology

Stain normalization of histopathology images is a promising technique commonly used in computer-aided diagnosis. This process eliminates the effects of staining intensity and color difference (batch effects) from various pathologic imaging systems. In this paper, we are focusing on stain normalization and visual quality enhancement. Although state-of-the-art methods, such as CycleGAN, perform well in image style transfer, they have been limiting by raw imaging quality. This paper propose a novel framework, single generative networks (SGNet), to train the staining model. We yield data pre-augmentation instantiated by clarity-brightness-saturation (CBS) adjustment, and introduce max pooling between the input and the intermediate features and positional normalization (PONO) to optimize network structure. The proposed approach is evaluated by using the placental pathological samples with villi, trophoblast cells and vascular area. Feature fusion results on placental sample demonstrate the proposed model outperforms existing methods, ESPCN, CycleGAN and SegCN-Net. Ablation studies also show the necessity of additional components. We test this network on low-quality images from different imaging systems. Experimental results preserve detailed structural information of tissues and show desirable performances on generalization ability of histological image, which increases the segmentation accuracy for digital pathology diagnosis. These findings have the potential for the establishment of histological staining criterion, massive pathological images with batch effects can be normalized with the aid of authoritative staining benchmark.
Predicting Red Light Running Violation Using Machine Learning Classifiers

Red light Running (RLR) violation remains as an important road safety concern at urban intersections. Existing method have mostly used statistical regression-based methods to explore factors contributing to RLR. However, it is well-known that statistical methods are based on predefined associations among variables and are unable to capture latent heterogeneity. This study aims to classify and predict RLR using spatial analysis and machine learning (ML) methods. Georeferenced RLR violation data for the year 2016 was collected for the city of Luzhou, China. To identify violation hotpots, frequency-based clustering was carried out using collect event tool in ArcMap geographic information system (GIS). Prior to RLR prediction via ML, data imbalance problem was addressed using a random over-sampling technique. Two widely used ML algorithms, i.e., Random Forest (RF) and Gradient Boosted Decision Tree (GBDT), were then used for prediction and classification of RLR. The performance of these models was assessed with accuracy and Cohen's kappa. The results showed that GBDT had an overall accuracy of 96% outperformed the RF.
Cross-Modal Visual Question Answering for Remote Sensing Data: The International Conference on Digital Image Computing: Techniques and Applications (DICTA 2021)

While querying of structured geo-spatial data such as Google Maps has become commonplace, there remains a wealth of unstructured information in overhead imagery that is largely inaccessible to users. This information can be made accessible using machine learning for Visual Question Answering (VQA) about remote sensing imagery. We propose a novel method for Earth observation based on answering natural language questions about satellite images that uses cross-modal attention between image objects and text. The image is encoded with an object-centric feature space, with self-attention between objects, and the question is encoded with a language transformer network. The image and question representations are fed to a cross-modal transformer network that uses cross-attention between the image and text modalities to generate the answer. Our method is applied to the RSVQA remote sensing dataset and achieves a significant accuracy increase over the previous benchmark.
Bacterial Foraging Based Algorithm Front-end to Solve Global Optimization Problems

The Bacterial Foraging Algorithm (BFOA) is a well-known swarm collective intelligence algorithm used to solve a variety of constraint optimization problems with wide success. Despite its universality, implementing the BFOA may be complex due to the calibration of multiple parameters. Moreover, the Two-Swim Modified Bacterial Foraging Optimization Algorithm (TS-MBFOA) is a state-of-the-art modification of the BFOA which may lead to solutions close to the optimal but with more parameters than the original BFOA. That is why in this paper we present the design using the Unified Modeling Language (UML) and the implementation in the MATLAB platform of a front-end for the TS-MBFOA algorithm to calibrate the algorithm parameters faster and with no need for editing lines of code. To test our proposal, we solve a numerical optimization problem with constraints known as tension/compression spring, where 30 independent executions were conducted using the TS-MBFOA and then compared with an earlier version called MBFOA. The runtime configuration and the parameter tuning were fluent using our front-end, and the TS-MBFOA obtained the better results. To date, there is no other user-friendly implementation of this specific algorithm in an open-source code, and the front-end is flexible enough to include other numerical optimization problems with minimal effort.
A Deadline-Constrained Time-Cost-Effective Salp Swarm Algorithm for Resource Optimization in Cloud Computing

Nowadays, Cloud Computing has become the most attractive platform, which provides anything as a Service (XaaS). Many applications may be developed and run on the cloud without worrying about platforms. It is a big challenge to allocate optimal resources to these applications and satisfy user's quality of service requirements. Here, in this paper, a Deadline Constrained Time-Cost effective Salp Swarm Algorithm (DTC-SSA) is proposed to achieve optimized resource allocation. DTC-SSA assigns the user's task to an appropriate virtual machine (Vm) and achieves a trade-off between cost and makespan while satisfying the deadline constraints. Rigorous examination of the algorithm is conducted on the various scale and cloud resources. The proposed algorithm is compared with Particle Swarm Optimization (PSO), Grey Wolf Optimizer(GWO), Bat Algorithm(BAT), and Genetic Algorithm(GA). Simulation results prove that it outperforms others by minimizing makespan, execution cost, Response time, and improving resource utilization throughput.
Old frauds with a new sauce: digital assets and space transition

Purpose The purpose of this study is to describe the evolution of fraud schemes with historically conducted with fiat money in physical space to the crypto-assets in digital space as follows: ransomware, price manipulation, pump and dump schemes, misrepresentation, spoofing and Ponzi Schemes. To explain how fraud schemes have evolved alongside digital asset markets, this study applies the space transition theory. Design/methodology/approach The methodology used is a review of the media regarding six digital asset fraud schemes that have evolved from physical space to virtual space that are currently operational, as well as a review of the literature regarding the space transition theory. Findings This paper finds that the digital space and digital assets may facilitate pseudonymous criminal behavior in the present regulatory environment. Research limitations/implications The field is rapidly evolving, however this study finds that the conversion from physical to virtual space obfuscates the criminal activity, facilitating anonymity of the perpetrators, and creating new challenges for the legal and regulatory environment. Practical implications This paper finds that the digital space and digital assets may facilitate pseudonymous criminal behavior in the present regulatory environment. An understanding of the six crypto-asset fraud schemes described in the paper is useful for anti-financial crime professionals and regulators focusing on deterrence. Social implications The space transition theory offers an explanation for why digital space leads criminals to be better positioned to conduct financial crime in virtual space relative to physical space. This offers insights into behavior of digital asset fraudster behavior that could help limit the social damage caused by crypto-asset fraud. Originality/value To the authors’ knowledge, this paper is the first to detail the evolution of fraud schemes with fiat money in physical space to their corresponding schemes with digital assets in physical space. This study is also the first to integrate the space transition theory into an analysis of digital asset fraud schemes.
Three Layer Encryption Protocol: an Approach of Super Encryption Algorithm

Data security is one of the critical things that must be considered along with the ease of the data distribution process in Era Society 5.0. Maintaining data security is not an easy thing to do. One way that can be used to secure data is through cryptographic techniques. In cryptography, two basic techniques are often used, namely substitution techniques and transposition techniques. The problem in this study is related to the weakness of the level of data security in the classical cryptography algorithm, substitution, and transposition techniques. This study aims to develop a cryptographic approach to super encryption by doing a hybrid or a combination of three cryptographic algorithms. The approach proposed in this study is also known as the three-layer encryption protocol. The algorithms that focus on this research are Autokey Cipher, Columnar Transposition, and Hill Cipher. Evaluation of the results of the application of the proposed three-layer protocol approach using the Avalanche Effect approach. As a result, the proposed three-layer protocol technique can produce a better level of security than single-layer encryption.
An Energy-Efficient Deep Reinforcement Learning FPGA Accelerator for Online Fast Adaptation with Selective Mixed-precision Re-training

Recently, deep reinforcement learning (DRL) has shown human-level performances in sequential decision-making problems including a gaming agent and robot control [1]. Especially, DRL supports autonomous adaptation of edge devices to unknown environments thanks to its distinct characteristics. Fig. 1 shows the basic components of the DRL system. It consists of a DRL agent, a replay buffer, and the environment. Unlike traditional deep learning which requires labeled data, DRL training utilizes experiences stored in the replay buffer. The stored experiences are generated by repetitive interaction between the DRL agent and the environment. This trial-and-error-based training method enables the agent to adapt to sudden environmental changes.
Implementation of RESTful API Web Services Architecture in Takeaway Application Development

The Covid-19 that hit the world had an impact on the economy, especially in the trade sector, one of which was experienced by Small and Medium Enterprises (SMEs). Hanura Takeaway (Haway) is an SME engaged in the delivery of goods and food. To facilitate transactions for goods and food delivery services, it is necessary to develop applications that simplify the transaction process. In developing web services, it is necessary to exchange data that is accessed via standard internet protocols. Therefore, we need a web service in developing this application. Implementing a RESTful API web service will certainly facilitate the development of software applications outside the system or with different programming languages or platforms. This research will develop web service architecture using RESTful API in Takeaway application. To optimize the URI, several parameters are used, including filtering, sorting, selection and pagination. The Takeaway application consists of a website as a backend and an Android-based as a frontend. From the test results based on the function method using the Postman application, it shows that the REST API Sever built on the server has been running well. In testing the response time using the Apache JMeter application, the application shows a good response time. Meanwhile, the comparison of responses and requests to SOAP and REST architectures shows that REST takes faster time.
FGPA: Fine-Grained Pipelined Acceleration for Depthwise Separable CNN in Resource Constraint Scenarios

Depthwise Separable Convolution can effectively reduce parameters and operations with little loss in precision, which becomes more and more popular in many innovative neural networks such as MobileNet and Xception. Due to limited computing resources and storage space, how to deploy Depthwise Separable CNN(DSCNN) in a more efficient manner for resource constraint scenarios is still an open and important problem to be explored. In this paper, a Fine-Grained Pipelined Acceleration scheme (FGPA) is proposed. Different from existing works, we partition the DSCNN accelerator into multiple computational modules that support fine-grained operations according to resource constraint and data dependency analysis, which is able to support starting next layer computation before previous layer completion. Along with the proposed scheduling strategy Start as Soon as Possible (SSP), we can accelerate DSCNN in a pipelined parallel manner, and all the intermediate data between layers can be kept on-chip memory to avoid the stream-out cost. In this way, we can not only improve the utilization of on-chip memory and computing resources but also reduce the network computational delay. As a case study, MobileNetV1 is implemented on Zynq XC7Z045. Experimental results indicate that the proposed DSCNN accelerator can achieve 99.68 GOPS and 0.17 GOPS/DSP under 250MHz clock frequency.
Multi-scale Convolutional Neural Network for Melanoma Histopathology Image Classification

Pathologists need to comprehensively observe histopathological images of different scales in clinical melanoma diagnosis because histopathological images have various distinctive features at different scales. In clinical diagnosis, the combination of 40X and 10X pathological images is the most commonly used by pathologists. ResNet50 is a popular convolutional neural network structure, which is often used in automatic diagnosis. However, the current research on deep learning-based histopathological image recognition mainly focuses on single-scale networks, and there are few studies on multi-scale neural networks. Therefore, we propose a novel multi-scale convolutional network based on ResNet50. It can merge features of different scales by simultaneously input images of 40x and 10x. Furthermore, we collected 2,241 Whole slide images and constructed a multi-center melanoma dataset for model training and testing. In the experiment, the accuracy of the multi-scale network is 96.4%, and the accuracy of single-scale networks is 90.3%∼95.4%. The multi-scale network has achieved significantly better performance than single-scale networks. Experimental results show that, compared with single-scale networks, multi-scale neural networks can learn more sufficient features and obtain higher diagnostic accuracy.
Towards sustainable community: Effort to reduce flood risk and increase well-being in a densely populated flood-prone area

Abstract Dealing with flooding in a densely populated flood-prone area poses complex challenges. Almost all residents realize that living in the area is hazardous. However, they choose to stay there. Therefore, flood risk management should be applied in the area. This study aims to: 1) develop facts of the local community in a flood-prone area in decreasing the flood risk while improving well-being through modifying drainage channels used for fish and vegetable farming; 2) evaluate all benefits of drainage investments include the social and economic benefits. The research method consists of a quantitative approach through the distribution of questionnaires and a qualitative approach through in-depth interviews and field surveys. In this study, a concerted community effort was developed as a design parameter. At the same time, the observation parameters include knowledge of the causes of flooding, knowledge of flood risk, community involvement, and government flood control programs. The results showed that the strength of concerted community effort was significantly influenced by the knowledge of flood risk and the local community involvement. This effort can decrease the flood risk by up to 30% and serve direct financial benefits of IDR 48 million in a year.
Prediction of human behavior with synthetic data

The truly relevant dataset creation tasks are aimed at assessing human action. The approach to detect and recognize a person falling allows to promptly warn about dangerous incidents for further analysis of the consequences that can help in the tasks of healthcare industry. The problem can be solved using modeled data based on digital human simulation. The result of a proposed modular pipeline for synthetic data generation of digital human interaction with the 3D environment was demonstrated in this paper. The research includes the following contributions: the synthetic dataset based on procedural generation of realistic movements and fall which taking into account physics model of a digital human; registering basic rgb and segmentation rendering maps while simulating a digital human fall; in segmentation maps, we present hitting coordinate masks with the interaction of the human model and 3D scene. The pipeline is implemented using Unreal Engine that provides automatic “playback” of various scenarios for simulation. We used the generated synthetic data to train the Mask R-CNN framework. It is shown that a fallen person can be recognized with an accuracy of 97.6% and the type of person’s impact can be classified, including hitting the head when falling with training the model on simulation data. The proposed method also allows covering a variety of scenarios that can have a positive effect at a CNN training stage in the tasks of data creation for human action estimation.
Using the QR code as a means of automating the process of accounting for attendance at educational classes

Abstract Nowadays, the technology of QR codes is one of the promising areas of development of the IT industry, which has found application in various industries, business areas, medicine, etc. In the field of education, QR codes are used to increase the interactivity of classes, provide additional multimedia content, conduct surveys and other control activities. This work presents the key features of QR codes, their architecture and main components. The use of QR codes in automating the process of accounting for students’ attendance is considered. A web application has been developed for teachers, the interface of which allows them to generate QR codes for academic disciplines. A mobile application with an integrated QR-code scanner was developed for students. All attendance data is recorded in the teacher’s electronic attendance register. A conceptual data model of the system is presented, as well as the main algorithms of its operation related to the generation and scanning of QR codes. The practice of using the developed system demonstrates the effectiveness of monitoring attendance data by promptly entering it into an electronic journal.
Closed-loop Fast Marching Tree (CL-FMT*) with Application to Helicopter Landing Trajectory Planning

Motion planning for complex dynamic systems such as helicopters is a challenging problem due to non-holonomic and nonlinear differential constraints. Approaches for optimal kinodynamics motion planning have only been demonstrated for simple dynamic systems such as Dubins car or linear systems. In this paper we present Closed-loop FMT* (CL-FMT*) which is an extension of FMT* [7] that uses closed-loop prediction. With closed-loop prediction the computationally intense two-point-boundary-value steering procedure can be avoided. We describe the effectiveness of CL-FMT* compared to CL-RRT*. We then demonstrate the use of CL-FMT* for helicopter landing trajectory planning with realistic dynamics constrains.