Reviewerly/exHarmony huggingface dataset:
Image Captioning-based Smart Phone Forensics Analysis Model

One of the devices that police officers often come into contact with when conducting investigations and obtaining evidence is the smartphones. Mobile phones are now widely available and due to the increasing intelligence of this device, many criminals are using it to commit criminal activities and cover a wide range of industries. In order to effectively combat criminal activity, police officers need to analyse and forensically analyse the smartphones of suspects to try and find out what data is relevant to the case. Pictures and text on mobile phones are important data, often containing clues and evidence that can be used to investigate and analyse the cases. As the storage space of mobile phones is getting larger and larger, and there are more and more data in them, how to efficiently query the data associated with the case has become an important goal in police work. To help police officers efficiently conduct forensics analysis of smartphone image and text data, this paper describes a forensics framework based on image description, which is of practical importance in analysing images and text in mobile phones to help police officers effectively carry out investigation and forensics work.
Graph Attention Network for Short Text Type News

In the era of self-media, the spread of fake news is more widespread and rapid because anyone can become an editor of the news. It leads to a large number of short news texts. However, the unchecked dissemination and sharing of news information have led to a continuous emergence of fake news events. It not only misleads readers but also has a detrimental impact on society. Besides, short news texts are semantically sparse and need more contextual solid connections. It is difficult to extract text features and achieve high error detection efficiency. Therefore, this paper has proposed a heterogeneous graph attention network that includes multiple text-related features. The network breaks the traditional way of only connecting text features in graph neural networks, extracts various external knowledge and text feature information from the news to construct the graph network, and establishes connections for different text features to enhance semantic understanding. Additionally, connecting external knowledge bases is helpful in eliminating news entity word ambiguities. Then, the heterogeneous graph is embedded into a dual-attention mechanism at both node and pattern levels, capturing the importance of different adjacent nodes, reducing the weight of noisy nodes, and accurately identifying valid information. Experimental results demonstrate that the proposed method in this paper outperforms better than several baseline models, such as TextGCN in terms of accuracy.
ComFaaS: Comparative Analysis of Edge Computing with Function-as-a-Service

This research paper presents a comprehensive comparison between cloud computing and edge computing in the context of function-as-a-service (FaaS) applications. The project, ComFaaS, aims to evaluate the performance and efficiency of these computing paradigms by conducting benchmark programs with edge-server connections simulating event-triggered executions. The experimental setup involves a cloud computing model where programs are selected from the cloud, and an edge computing model where programs are requested by the edge. The results of this study provide valuable insights into the suitability and effectiveness of cloud and edge computing for real-world applications utilizing FaaS.
Neighbor-Discovery Recurrent Model for Point-of-Interest Recommendation

With the rapid development of Location-Based Social Networks (LBSNs), Point-of-Interest (POI) recommendation has become increasingly important. POI recommendation can help users to find interesting information from massive amounts of data. Various models have been proposed for this problem. However, due to the check-in data of sparseness, this problem is still challenging. In this paper, we propose a novel model named Neighbor-Discovery Recurrent Model (NDRM) to deal with the next POI recommendation problem. In our model, firstly we design a neighbor-discovery mechanism, where the category information of the location visited by users is utilized to find neighbors for the purpose of reducing the impact of data sparseness. Moreover, according to the check-in sequence of locations, we construct a graph structure and then use the Graph Convolutional Network (GCN) to capture the spatial feature. Finally, we evaluate the performance of the proposed NDRM on three real-world datasets. The experimental results show that our NDRM model outperforms the state-of-the-art models in next POI recommendation. In addition, it can also provide a reasonable and intuitive explanation for next POI recommendation.
Collaborative power fault localization method based on multi-source heterogeneous data

As industrial and societal processes continue to digitize, utilities are adding more smart grid sensors to collect additional data from the power system, substations, and other field locations, complicating current datasets. We propose a method for power system power failure detection using multivariate heterogeneous data that utilizes not only utility-owned power measurements, but also data from other relevant databases such as extensive temperature data. The method utilizes the power data collected from the communication power meter reading of a provincial power supply of the State Grid as well as the temperature data, which are subjected to multi-window statistical feature extraction in order to obtain a richer feature space. The fault localization method is divided into three stages. Firstly, an autoencoder (AE) based on integrated long and short-term memory network is utilized for reconstruction, and fault detection is performed based on the reconstruction error. Then matrix autoregressive prediction is performed on the abnormal sequence window, and specific fault localization is performed based on the prediction error. Finally, the trained multilayer perceptron is utilized for fault classification of fault data. The method has been used to detect faults in power related data sets collected from power systems.
Dynamic uncore frequency scaling based on performance events of Web applications

Server power consumption has become a problem due to the increased computational processing and communication traffic associated with the spread of ICT, big data, and cloud services. CPU uncore frequency scaling is one of the power-saving technologies for servers and has been researched in the HPC field. However, previous research focused on batch-processing applications. They did not treat real-time processing (like a Web application) or consider a request rate fluctuation or response time requirement. A Web application requires only a small amount of computation for one request. If a Web server increases processing performance each time a request arrives without considering the load amount and performance requirements, the web server consumes more energy than necessary. Therefore, we proposed a dynamic uncore frequency scaling method for HTTP request processing used in Web applications. Our method feature is detecting application performance degradation by monitoring performance events. Thereby, this method reduces power consumption while suppressing server processing delays. We experimented by comparing our method to the high-performance setting (baseline). Although the SLA violation ratio did not change between the baseline and the proposed method, the proposed method consumed 20% less energy than the baseline. These results indicate that our method is effective for a Web application.
Visualization of Online Product Reviews Written in Japanese Based on Entity Sentiment Analysis for Enhanced Customer Experience

Product reviews on e-commerce sites provide useful information for potential customers. However, these reviews can sometimes be too subjective, requiring much time to extract useful information from them. Sentiment analysis of product reviews has been a tested and effective way to summarize a huge number of product reviews, however, this technique can suppress much information and, as a result, may lack insights for the customers. Therefore, in this paper, we propose an approach based on entity sentiment analysis where the entities are dynamically generated by natural language processing of product reviews written in Japanese, and their corresponding sentiment values are tallied to produce a visual representation. A preliminary user study has confirmed the effectiveness of the proposed approach.
Skeleton-based Motion Recognition Model by using Graph Convolutional Network and Deep Neural Network

Human motion recognition is a prominent research topic in the field of computer vision and pattern recognition. This technology has found numerous applications in various fields, such as human-computer interaction, intelligent video surveillance, motion analysis, and medical assistance. Graph convolutional network (GCNs) have achieved outstanding performance in skeleton-based action recognition. However existing GCN-based methods still have some issues. First the adjacency matrix neglecting the dependencies between the non-adjacent joints. Second they rely entirely on human joints ignore modeling of the surrounding environment for modeling complicated dependencies. In this paper, we present a novel approach to skeleton-based motion recognition using a combination of GCNs and deep neural network (DNNs). Our proposed method utilizes GCN to process skeleton data and DNN to process human image data. Subsequently, we fuse the extracted features to make predictions for the final label. We have conducted experiments on the fall detection dataset, and our results indicate that the proposed approach achieves state-of-the-art accuracy.
An Anti-Degenerative Approach for FCM Neural Network Classifier in Complex Classification Tasks

In this paper, an anti-degenerative approach based on the floating centroids method (FCM) is introduced to enhance the robustness of a neural network classifier. Although a gradient-based floating centroids method (GDFC) has adopted the gradient descent method instead of evolutionary computation to optimize the FCM, this method excessively pursues maximizing inter-class distances and minimizing the intra-class distances to cause the floating centroids to degenerate into the fixed centroids, resulting in pseudo-floating centroids phenomenon. Therefore, we propose an anti-degenerative approach to solve this problem. It can soft clean up the mapping sample features on both sides of the decision boundary and forcefully recall the mapping samples close to the partition space so that a more flexible centroid distribution is generated. Experimental results show that the proposed method not only achieves excellent performance on accuracy and average f-measure in some complex classification tasks but also facilitates data visualization and dimensionality reduction.
A novel numeric embedding scheme for hiding full-color images into audio

Watermarking is a viable means for safeguarding the proprietary rights of digital media. This study presents a discrete Fourier transform (DFT)-based phase modulation (PM) scheme, which enables efficient and effective blind audio embedding at a rate of 508.85 numeric values per second while simultaneously preserving the original perceptual quality. With this payload capacity, it becomes possible to hide a full-color image of pixels in an audio signal with a duration of just 24.15 seconds. Our analysis and evaluation demonstrate that the proposed PM performs exceptionally well in terms of imperceptibility, thanks to the careful selection of the embedding frequency range. Additionally, it shows remarkable resilience against common signal processing attacks.
Building a Multi-Objective Recommender System Using Machine Learning Based on Light Gradient-Boosting Machine

In the scientific field of big data, creating a recommender system that effectively balances diverse user preferences represents a significant challenge. As a response to this challenge, this paper presents a comprehensive methodology to build a multi-objective recommender system using the power of machine learning, particularly focusing on the Light Gradient-Boosting Machine (LightGBM). Using the OTTO product dataset from a Kaggle competition, we delve into the process of data preprocessing, feature engineering, and model selection, in an effort to build an optimal, multi-objective recommender system. Our single-model approach, grounded in LightGBM, allows us to adeptly handle the vast and complex dataset. This method, while conceived for the OTTO competition, contributes a broader insight into the development of recommender systems that cater to multiple objectives. Through rigorous evaluation, our work demonstrates that this approach substantially enhances performance in terms of accuracy and processing speed, providing not only an effective solution for the competition's problem statement but also a significant advancement in the field of recommender system technology.
Semi-supervised learning with CNN for low-light images enhancement

In recent years, deep neural networks have been applied to dark image enhancement to improve the brightness and contrast. However, it is a challenging task to collect the dataset that contains normal and low-light image pairs of the same scene for supervised learning. To solve the problem, in this paper, we propose a semi-supervised learning framework for image enhancement. The proposed network has two branches, a supervised one and an unsupervised one. The supervised branch is trained with paired data and pixel-to-pixel loss, while the unsupervised one is trained with unpaired data and the loss function is defined with statistic property of normal images. In both branches, a quadratic function is used to map low-light pixel to enhanced ones, and the CNN model learns pixel-wise parameters of the quadratic function instead of inferring the normal images directly. Extensive experimental results demonstrate that the proposed network outperforms the state-of-the-art methods with a PSNR gain of 8.42%.
Bitcoin-Powered IoT Networks for Climate Change Research: a Peer-to-Peer Micro-Payment System to Enhance Data Collection

Climate data forms the basis of our understanding of how the climate system works. Climate change research is now impacting strategy and policy decision-making in a wide variety of areas, ranging from water management to public lands, health, and navigation. For climate change research, data needs to be collected for prolonged periods of time and in the most extensive number of locations possible, guaranteeing the highest accuracy and completeness possible. IoT networks can play an important role in collecting these data, helping to monitor and even tackle climate change, however just a minority of the existing IoT devices that could serve for this purpose are currently exploited.
Automatic Segmentation Method for the Nuclei Contour of High Resolution Rat Brain

The brain consists of massive nuclei with different functions. In neuroscience research, the precise recognition and delineation of nucleus boundaries is the crux of brain atlas illustration. Here, we propose a method based on Markov Random Field. This method introduces fractional differentiation into texture feature extraction and a new potential energy function is defined based on kernel region information. To handle the problem of large data volume, we propose a strategy of dual MRF processing with down sampling for pre classification. Finally, the fuzzy entropy criterion is used to optimize the segmentation results. Experiments on model data and real tissue slice data show that this method can not only segment the regions with obvious differences in the density of the nuclei with insignificant differences in the density of nuclei, but also divide the nuclei without insignificant density differences by the cell shapes and textures. In addition, this method can not only segment a specific target kernel in one calculation, but also simultaneously partition multiple target kernels. The segmentation algorithm shows great potential for illustrating high-resolution 3D brain atlases.
Self-Attention based Deep Hash Learning Method for Efficient Image Retrieval

Hash method with efficient retrieval efficiency and lower storage space, in the most recent has been widely used in image retrieval task. For complex image retrieval task, the manually extracted features often has limitations, and the deep convolution neural network can extract better features. However, traditional convolution neural networks usually focus on local features, while ignoring the ability to learn global features. In addition, traditional hash methods require constructing sample pairs to learn distance metrics, resulting in high computational costs. To address the above problem, in this paper a new self-attention based deep hash (SADH) learning method is proposed, which introduces a labeled hashing center and trains the network using self-attention mechanism. The method aims to fit the image hash codes to the relevant hashing center. The distance between the hashing center and the image hash codes is calculated using a loss function, resulting in generated hash code with strong discriminative power. Experimental evaluations on standard datasets demonstrate that this method outperforms the state-of-the-art retrieval methods in terms of retrieval accuracy.
A Blockchain-Based Ticket Sales Platform

Concerts or fan meetings usually attract lots of attention, generating huge demand for tickets for these events. But, existing ticket purchase systems are unable to efficiently and transparently accommodate this. We often hear scandals of bot-controlled clients gobbling up tickets within seconds after sales open or celebrities acquiring prime tickets exceeding individual quota. These problems arise because existing systems are centralized. This can be a single point of failure and the controlling authority can ban or give privileges to certain users. This work sets out to remedy these problems using a blockchain-base solution that, by nature, is highly decentralized. We have created and deployed EVM (Ethereum Virtual Machine)-based smart contracts for ticket sales platform on two EVM-compatible blockchains, Ethereum (ETH) and Avalanche (AVAX). These contracts inherit heavily from the ERC-721 standard for NFT (Non-Fungible Token). The platforms on both blockchains engender the desirable transparency. However, the platform on Avalanche is much more efficient and economical to deploy and utilize. We have open-sourced the code for our platform on Github. Visit the following link to fork or check it out: https://github.com/JesperBerben/TicketNFT In addition, you can now interact with our platform on Avalanche which has been deployed and verified at the address linked to below: https://snowtrace.io/address/0x17fd3f6cf6cbff75604b63f1ca12c6db29730a9c
SCO-DUMA: Reading Comprehension with Looking Origin and Comparing Options

Multiple-choice reading comprehension (MCRC) is a task of choosing the correct option from candidates given an article and a question. Recent researchs propose a new model with combining a part-of-speech (POS) embedding layer in the pre-training language model and a DUal Multi-head Attention (DUMA) matching layer to get the state-of-the-art. However, there is still distance between the model's performance and human's performance. We propose two mechanisms base on DUMA to futher advance the performance. In the interaction layer, we propose a option interaction mechanism and a gated self-comparing mechanism to imitate the human's behavior of re-reading passage and comparing the options. Our mechanisms on ALBERT-Base has a 1.4% improvement on DREAM and a 2.1% improvement on RACE.
Agile Leadership Cost Estimation Model in Software Development Project (Case Study: Public Service Applications)

In software projects, there is usually a planning phase at the beginning. This planning includes time, cost, and allocation of other resources to ensure that the project can proceed as expected. Leadership theories have been continuously updated over time, including in the field of software development projects. The dynamism of technology and project uncertainties pose challenges for project managers to estimate time, cost, human resources, technology, and other supporting resources. Qualitative analysis is used to combine several variables related to leadership management in the activities of the software development project team. The result obtained is an agile leadership cost estimation model in software development projects, tested on 4 public service application development projects. The model is obtained from the combination of critical success factors in leadership management and 3 supporting elements (IQ, MQ, and EQ). The author then multiplied the leadership weight in 19 activities by the pay rate per hour according to the standard project manager wage. The result shows a deviation of 10.71 percent from the project manager tariff using the Term of References document against Kelly Services Indonesian Salary. In the future, this research will be tested for accuracy in time and other resource allocation against the planned project allocation.
Proposed Technique for Data Security with the AES Algorithm in Robot Operating System (ROS)

Currently, the Robot Operating System (ROS), is extensively used in various fields such as education, research, commercial and industry. This is due to the fact that ROS offers a range of packages that handle communication and hardware operation. However, it is widely known that ROS's security is still not robust. Transitioning from ROS to ROS2, which has enhanced security, might be somewhat challenging because some tasks might be in actual use in the industry or ongoing research projects. The issue of data security is also of paramount importance. There are currently various methods to hack into systems or intercept data in computer networks. After studying numerous related research works, we propose an encryption method for transmitted and received data using the AES algorithm, known as Layer-2. This method has been experimented with streaming images of four resolutions: <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{320}\times \mathbf{240}, \mathbf{640}\times \mathbf{480},\mathbf{1280}\times \mathbf{720}$</tex> , and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1920}\times \mathbf{1080}$</tex> . By comparing the frame rate and CPU usage during both unencrypted and encrypted streaming, it was found that the software developed according to the concept of this research can stream in an encrypted format at the same frame rate as unencrypted streaming, with an average maximum increase in CPU usage of 19.98% on the publishing side. The encryption concept in this research is designed based on a text-based protocol combined with the function overloading feature of object-oriented programming. This allows for a quick add-on to operating ROS software with minimal modifications. Furthermore, it can be adapted for use with other data transmission methods, such as Publish/Subscribe models like NATS, MQTT, etc.