Reviewerly/exHarmony huggingface dataset:
Heterogeneous Transfer Learning on Power Systems: A Merged Multi-modal Gaussian Graphical Model

Machine Learning (ML) is gaining increasing popularity to tackle uncertainty in physical systems, such as modern power systems. However, ML models can be hardly trained for newly-built power grids with limited data, especially when different power grids have different dimensionalities and distributions for measurement data. To tackle this problem, we propose a novel Heterogeneous Transfer Learning (HTL)-based method to boost the data volume of the target grid. Specifically, we propose a Merged Multi-Modal Gaussian Graphical Model ( M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> G <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> M) with a physical data merging process for knowledge transfer. To solve the maximum likelihood estimation of M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> G <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> M with imbalanced data from two grids, we propose a novel Expectation-Maximization algorithm. Finally, we quantify the negative transfer via the KL-Divergence to measure the distribution similarity between the source grid and the target grid for the transferring confidence. We demonstrate the advantages and the generalizability of our proposed models in diversified data sets for power systems and human action-sensing systems.
A Self-Sovereign Identity Architecture Based on Blockchain and the Utilization of Customer’s Banking Cards : The Case of Bank Scam Calls Prevention

The need to use a wide variety of online services is rising every day in today's digital age. In order to authenticate and authorize users to access services, identity management is essential. To solve several problems such as fragment identity repositories, aim for identity theft and data breaches and identity correlation, the evolution of identity models across siloes, centralized and presently self-sovereign identity based on distributed ledger is emerging. Self-sovereign identity returns the power to the proprietor of the identity and puts the user at the core of the model. In this paper, we propose a framework to identify scam bank service calls and avoid possible loss of personal accounts. Our framework is based on Hyperledger Indy, a public and permissioned Distributed Ledger Technology. The proposed framework strengthens the Know Your Customer process and enhances the trust of third party agencies delegated by banks to deliver many financial items.
Two-Stage Real-Time Multi-object Tracking with Candidate Selection

In recent years, multi-object tracking is usually treated as a data association problem based on detection results, also known as tracking-by-detection. Such methods are often difficult to adapt to the requirements of time-critical video analysis applications which consider detection and tracking together. In this paper, we propose to accomplish object detection and appearance embedding via a two-stage network. On the one hand, we accelerate network inference process by sharing a set of low-level features and introducing a Position-Sensitive RoI pooling layer to better estimate the classification probability. On the other hand, to handle unreliable detection results produced by the two-stage network, we select candidates from outputs of both detection and tracking based on a novel scoring function which considers classification probability and tracking confidence together. In this way, we can achieve an effective trade-off between multi-object tracking accuracy and speed. Moreover, we conduct a cascade data association based on the selected candidates to form object trajectories. Extensive experiments show that each component of the tracking framework is effective and our real-time tracker can achieve state-of-the-art performance.
Predatory Journal Classification Using Machine Learning

The prevalence of predatory journals has become more severe recently as this is harmful to science and technology development. For scholars publish papers more effectively and avoid publishers for profits, this research used a machine learning method to identify the predatory journals. The features like text content and keywords of the collected journals' websites were extracted from mainstream predatory journal websites and normal journal websites. This research proposed a predatory journal classification system based on a new model. The results show that our model's recall rate exceeds 90%, ensuring that the journals submitted by the researchers are not predatory.
Evaluation Method of Sensor Data Credibility based On Multi-Source Heterogeneous Information Fusion

The reliability of sensor node data is crucial for data processing, especially sensor network data for security monitoring. However, existing evaluation methods cannot effectively evaluate the data credibility when the sensors are occluded or damaged. In order to solve this issue, a credibility assessment method for sensor data is proposed in this paper by fusing multi-source heterogeneous sensor data. Experimental results on mine smoke data show that the proposed method could effectively improve the veracity of evaluation when the sensors are disturbed or blocked.
An Image Captioning Model Based on Bidirectional Depth Residuals and its Application

A novel network model “bidirectional depth residuals gated recurrent unit network (BDR-GRU) ” is designed and implemented for improving the effectiveness of Image Captioning. BDR-GRU is designed based on encoder and decoder architecture. Moreover, the network can run on an NVIDIA JETSON TX2 processor, which makes the algorithm applied to mobile robots. In the encoding stage, the convolution neural network is used to obtain the multi-dimensional vector information of the image, and the BDR-GRU network is used to complete the sentence generation in the decoding stage. The BDR-GRU network model is a new recurrent neural network model, which is improved on the basics of the GRU network. Firstly, the layer of the GRU network is increased from a single layer to multiple layers. Secondly, the bidirectional derivation structure is redesigned to enhance the ability of derivation. Finally, the residual mechanism between levels is designed to prevent the disappearance of gradient and over-fitting caused by the increase of the layers. Experiments are carried out on TX2 processor and have been done to verify the effectiveness of our design, and the results are compared with img-gLSTM network model, neural talk model, attention model, and unidirectional GRU model, then the results are analyzed. The experimental results show that the CIDEr evaluation value of our network model is 12.7% higher than that of the img-gLSTM network and 14.6% higher than that of the Neural Talk network, other evaluation indicators also improve significantly. The experimental results prove the significance of our BDR-GRU model.
Speaker Recognition in Emotional Environment using Excitation Features

Speaker Recognition is known as the task of recognizing the person speaking from his/her speech. Speaker recognition has many applications including transaction authentication, access control, voice dialing, web services, etc. Emotive speaker recognition is important because in real life, human beings extensively express emotions during conversations, and emotions alter the human voice. A text-independent speaker recognition system is proposed in the work. The system designed is for emotional environment. The proposed system in this work is trained using the speech samples recorded in neutral environment and the system evaluation is performed in an emotional environment. Here, excitation source features are used to represent speaker-specific details contained in speech signal. The excitation source signal is obtained after separating the segmental level features from the voice samples. The excitation source signal is almost considered as a noise so identifying a speaker in an emotive environment is a challenging task. Excitation features include Linear Prediction (LP) residual, Glottal Closure Instance (GCI), LP residual phase, residual cepstrum, Residual Mel-Frequency Cepstral Coefficient (R-MFCC), etc. A decrease in performance is observed when the system is trained with neutral speech samples and tested with emotional speech samples. Different emotions considered for emotional speaker identification are happy, sad, anger, fear, neutral, surprise, disgust, and sarcastic For the classification of speakers the algorithms used are Gaussian Mixture Model (GMM), Support Vector Machine (SVM), K-Nearest Neighbor(KNN), Random Forest and Naive Bayes.
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Let R=F2+uF2 (u2=0) and S=F2+uF2+u2F2 (u3=0) be two finite commutative chain rings. This paper studies F2RS-cyclic codes, which are described as S[x]-submodules of the S[x]-module F2[x]∕〈xr−1〉×R[x]∕〈xs−1〉×S[x]∕〈xt−1〉. We study their generator polynomials and the minimal generating sets. We classify each case of the generating sets separately and determine the size of each such case. Free F2RS-cyclic codes and separable codes are discussed, and the structural properties of dual codes of free F2RS-cyclic codes are investigated. Moreover, we determine the relationship between the generator polynomials of free F2RS-cyclic codes and their duals. As applications, we provide several examples of optimal and near-optimal binary codes which are obtained from the Gray images of F2RS-cyclic codes.
Design of Mobile Learning Service Platform Based on Data Mining

Abstract The current mobile learning service platforms are mostly self-centered, and they provide exactly the same learning content, and their personalized service capabilities are insufficient to meet the different learning needs of different students at the same time. To this end, combined with data mining technology, the research on a mobile learning service platform that can provide personalized services is conducted in this paper. The platform design includes four parts, namely frame design (development frame design and network architecture), main hardware design (microprocessor, switch, memory), main software design (user registration and login program, personalized mining analysis program, online learning procedures, online communication procedures) and system testing. The test results show that the concurrent performance and compatibility performance of the system meets the requirements after testing, allowing multiple users to use different mobile clients to access the platform at the same time.
Fuzzy Based Adaptive Dimension Parking Algorithm Including Obstacle Avoidance for Autonomous Vehicle Parking

Autonomous vehicle parking and obstacle avoidance navigation have drawn increased attention in recent times for autonomous vehicle-related solutions. Existing autonomous vehicle parking algorithms generally fail to mimic the human-like tendency to adapt naturally, and most of these designs are practically fixed. They do not preserve adaptive nature with machine dynamics, especially vehicles related. In this paper, a novel fuzzy-based adaptive dimension parking algorithm (FADPA) is proposed that integrates obstacle avoidance capabilities to a standalone parking controller that is made adaptive to vehicle dimensions in order to provide human-like intelligence for parking problems. This algorithm adopts fuzzy membership thresholds with respect to vehicle dimensions to enhance the vehicle's path during parking with taking care of obstacles. It is generalized for all segments of cars, and different simulation results are presented to show the effectiveness of the proposed algorithm.
Identifying and Mitigating Humanitarian Challenges to COVID-19 Contact Tracing

COVID-19 contact tracing has rapidly emerged as a dynamic field of endeavor, with different countries taking different approaches, both politically and technologically.In this paper we examine the situation of Australia's development of a COVID-19 contact tracing application (which is in reality a proximity tracing application) as a case-study.Both technological and societal elements are considered, in particular, the delivery of poor protection, or the perception of poor protection, of privacy and civil liberties to negatively impact the adoption of such an application, and thus hamper its potential.The rest of the paper explores this digital-politic nexus and tensions within crisis response, and examines the trade-off can be improved through increasing public trust of such technologies by improving their actual and perceived privacy and human rights properties without reducing their medical effectiveness.Lessons for humanitarian organizations are extracted from this.
Mixture of Deterministic and Stochastic Quantization Schemes for Lightweight CNN

There has been a breakthrough in the field of image classification and object detection, owing to the development of GPU and deep learning. However, because of the huge computation of deep learning, it is hard to use the deep learning algorithms in an embedded platform or a mobile device. Therefore, many compression studies have been conducted, and one of the most popular methods is a parameter quantization. In this paper, we propose an adaptive quantization scheme that reduces the loss of accuracy due to the quantization by properly mixing deterministic and stochastic quantization methods, while retaining the characteristics of the hardware-friendly fixed-point quantization method. By applying the proposed method to the weight parameters of image classification and object detection networks, the proposed method shows better mean average precision (mAP) of up to 0.44% in image classification and 0.91 % in object detection.
Universal Text Scanner Solution

Receipt data extraction and digitization is difficult even today attributing to the fact that receipts have a lot of variations mainly in the form of being crumpled, soiled and the overall scanning quality of the images being low. The major problems the industries are facing today in the domain are:(i)The lack of generalization in standard OCR solutions and other custom pipelines built from open source api like tesseract etc.(ii)High cost, yet low accuracy of commercially available solutions.(iii)Requirement for organization to supply large volumes of hand annotated images for training. In the paper we explain a strategy to overcome these limitations and to build a holistic pipeline for text detection and extraction deployable in real word. We have surveyed traditional methods as well as known recent CNN based architectures and moved on to explain the application of the novel architecture Connectionist Text Proposal Network(CTPN),to solve for the specific task of text detection in scanned text heavy images. We also compared the CTPN outcomes against outcomes on the state-of-art-trained SSD on sample dataset and it justified how the CTPN is a more suitable algorithm for this use case.
A User-Centered Approach to Design a Mobile Application for Chronic Pain Management

This paper presents a user-centered approach for the design/re-design of a mobile application for chronic pain management, with a focus on chronic low back pain, sometimes referred to as "invisible disability". Within the framework of a multidisciplinary project involving physicians, patients and computer scientists, the work presented here describes firstly an identification and representation process of the needs/expectations of the users of such an application (patients/physicians), based on a data-driven persona development method and, secondly, an evaluation process of the user experience (UX) of the current version of the application, using among others usability tests. The aim of the approach is to establish (re-)design guidelines in order to improve this kind of application.
A Survey of Intelligent transportation systems based Modern Object Detectors Under Night-time Conditions

Object detection has progressed rapidly during the last few years and has become a highly significant area in computer vision. Amidst the rise of autonomous vehicles and smart traffic management systems, accurate vehicle detection under various lighting conditions has become paramount. This paper compares four state-of-the-art models Faster R-CNN, RetinaNet, YOLOv3 and YOLOv4 on how precise they detect vehicles under day and night-time scenarios. Experiments measure the 50% and 75% threshold average precision differences of multiple vehicle classes across two datasets. The results reveal a worse accuracy average of 15-20% and a maximum difference of 33% at night when compared to more illuminated day images.
A store location-based recommender system using user’s position and web searches

Nowadays, gathering information about commercial products can be performed either online or offline. While online searches can be virtually undertaken through online shopping websites, offline searches should be done physically at stores. However, there is a specific emerging trend where users can check some product opportunities online before getting to the stores and then possibly buying some items whose properties have already been evaluated over the Web. Product properties can be studied online while on site evaluation provides a direct contact with these goods at the stores. The objective of the approach developed in this paper is to discover user preferences when searching and exploring online shopping websites and then recommend the stores that better match their interests. First, users' internet behaviours are extracted from an online shopping website. Secondly, a Voronoi high-dimensional structure supports the derivation of similarities between the users and stores. Third, a distance matrix between the user and the selected stores is generated. Finally, a ranked list of the most appropriate stores is provided to the users based on their product interest and their locations. The whole approach has been successfully tested by a panel of 30 volunteers in the 6th District of the city of Tehran.
Exploring Effective Methods to Improve the Performance of Tiny Object Detection

In this paper, we present our solution of the 1st Tiny Object Detection (TOD) Challenge. The purpose of the challenge is to detect tiny person objects (2–20 pixels) in large-scale images. Due to the extreme small object size and low signal-to-noise ratio, the detection of tiny objects is much more challenging than objects in other datasets such as COCO and CityPersons. Based on Faster R-CNN, we explore some effective and general methods to improve the detection performance of tiny objects. Since the model architectures will not be changed, these methods are easy to implement. Accordingly, we obtain the 2nd place with the $$A P_{50}^{{\text {tiny}}}$$ score of 71.53 in the challenge.
Evaluation of the Quality of Survey Data and its Visualization Using Dashboards

Surveys are a popular tool for decision-making support. However, the quality of survey results significantly affects the quality of decisions made on their basis. Firstly based on Shannon’s formula, we evaluated the average amount of information received from the group survey. Then we analyzed the information loss caused by omissions and duplication of answers to the questionnaire. Next on the modeled data, we showed the existence of a sufficient respondents’ number to reduce the effect of data distortion. Finally, we proposed to use various types of dashboards for visual assessing the appropriateness of data.
To Infinity and Beyond: Financing Platforms With Uncapped Crypto Tokens

Centralized platforms (e.g., Uber) rely primarily on sales commission (aka, service fees) to generate revenues whereas decentralized blockchain-based startups (e.g., Filecoin) often forego these in favor of token retention. We show that both levers help to overcome moral hazard and incentivize platform building, but they aren't perfect substitutes and imply a strategic trade-off: the commission approach generally leads to higher long-term profits for the platform founders, whereas token retention can lead to higher service levels, benefiting the service providers and users. Furthermore, the two levers also require different ICO designs when raising capital: commission works best when paired with uncapped ICOs (i.e., unlimited token supply) whereas token retention works best with capped ICOs under certain conditions. These findings offer some guidance and explanations for the operating and ICO design choices of decentralized platforms.
A TPCM-Based Trusted PXE Boot Method For Servers

Information level protection standard 2.0 requires trusted verification of system bootstrappers, system programs, etc. of server equipment based on trusted root. According to the requirements of information level protection standard, this paper puts forward a network trusted start-up scheme based on the trusted platform control module to guarantee the security and trust of the server's BIOS firmware, PXE boot file and Linux system file. When publishing BIOS firmware, PXE startup file, Linux system file, the state-secret algorithm SM3 is used to calculate the summary value as the benchmark value, and stored in the trusted platform control module, BIOS firmware, Linux boot file. When the server starts up with PXE, the BIOS firmware is measured by the Trusted Platform Control Module, the BIOS Start Environment Measures PXE Boot File, and the PXE Boot File measures the Linux system file. The trusted platform control module is the trust root level measurement level, the first level of trust level, the trust chain, the implementation of a trusted server operating environment. The method proposed in this paper is tested on the domestic autonomous controllable Sunway server, and the experimental results show that the method proposed in this paper is feasible.