Reviewerly/exHarmony huggingface dataset:
Curriculum Self-supervised Learning for Weakly-supervised Histopathological Image Segmentation

Histopathological image segmentation is the basic upstream task of pathomics cancer research. However, acquiring pixel-level annotations is extremely difficult and time-consuming. Moreover, it is also expertise-dependent. Researchers pay a lot of attention on how to reduce the annotation effort. One feasible solution is self-supervised learning. In this paper, we proposed a novel self-supervised learning strategy for histopathological image segmentation, which called Curriculum Self-supervised Learning (CSSL). In CSSL, we set up several pretext tasks which are highly related to the characteristics of histopathological images for the CNN model. And then, these pretext tasks are trained in a curriculum manner (easy to hard). By combining CSSL with Class Activation Map (CAM), we can achieve outstanding performance on a weakly-supervised tissue segmentation task, with no need of pixel-level annotation. We achieved Frequency-weighted Intersection over Union (FIoU) score of 0.6886 and Mean Intersection over Union (MIoU) score of 0.6985 for semantic segmentation, which exceeded the common self-supervised learning methods. Experiments were conducted to prove the effectiveness of proposed CSSL.
Multiscale Global Channel Network for Edge Detection

Neural network is used to fuse the spatial and channel information of each layer's local receptive fields to construct information features. But global long-range dependency is not effectively modeled, which leads to non-optimal discriminative feature representations. In this paper, we propose multi-scale global channel network (MSGC). We use self-attention mechanism to combine local features with their corresponding global dependencies, adaptively recalibrate the channel response, guide the network to ignore irrelevant information, and emphasize the correlation of relevant features. We evaluated the proposed method on BSDS500 dataset and NYUD dataset. MSGC achieves ODS F-measure of 0.815 on BSDS500, which is 0.9% higher than the existing technology.
Comparing Plan Recognition Algorithms Through Standard Plan Libraries

Plan recognition deals with reasoning about the goals and execution process of an actor, given observations of its actions. It is one of the fundamental problems of AI, applicable to many domains, from user interfaces to cyber-security. Despite the prevalence of these approaches, they lack a standard representation, and have not been compared using a common testbed. This paper provides a first step towards bridging this gap by providing a standard plan library representation that can be used by hierarchical, discrete-space plan recognition and evaluation criteria to consider when comparing plan recognition algorithms. This representation is comprehensive enough to describe a variety of known plan recognition problems and can be easily used by existing algorithms in this class. We use this common representation to thoroughly compare two known approaches, represented by two algorithms, SBR and Probabilistic Hostile Agent Task Tracker (PHATT). We provide meaningful insights about the differences and abilities of these algorithms, and evaluate these insights both theoretically and empirically. We show a tradeoff between expressiveness and efficiency: SBR is usually superior to PHATT in terms of computation time and space, but at the expense of functionality and representational compactness. We also show how different properties of the plan library affect the complexity of the recognition process, regardless of the concrete algorithm used. Lastly, we show how these insights can be used to form a new algorithm that outperforms existing approaches both in terms of expressiveness and efficiency.
Credibility-Based Fault Fusion Diagnosis Method for Power Transformer

The fault causes and phenomena of power transformers are complex; different monitoring parameters can reflect its state from different dimensions. In view of the diversity of monitoring data and the characteristics that the acquisition time of different monitoring data is not completely simultaneous, the power transformer fault fusion diagnosis method is proposed based on credibility theory in this paper. First, the monitoring data, including dissolved gas analysis data, partial discharge pulse signal, ultrasonic signal and vibration signal, are diagnosed, respectively. Then the credibility method is used to fuse the single diagnosis results to obtain the comprehensive state of transformers. The case analysis shows that the proposed method can not only make full use of various monitoring data to enhance the accuracy of transformer fault diagnosis but also apply to the fault diagnosis of transformers with different monitoring data types. This provides a new idea for the fusion diagnosis of power transformers.
Compilation of Core Competencies Scale for Vocational College Mathematics Teacher

The theoretical framework of core competencies model for vocational college mathematics teacher was research-informed which was based on both the mathematics education and vocational education. Moreover, the model was verified by means of expert interviews. Through a series of dimensional design, coding, item-selecting and evaluation, the core competencies scale for vocational college mathematics teacher was compilated. Exploratory factor analysis and confirmatory factor analysis were carried out to explore the construct validity. Results showed the scale high reliability and construct validity. The model was consistent with the theoretical framework and the data collected. It was a second-order factor model, summed up as core competencies, which had three factors-knowledge, skills and dispositions.
UNIDA AR-Book: Interactive Virtual Application of Darussalam Gontor Campus Putri University Based On Augmented Reality

Darussalam Gontor University is a newly established campus with fairly rapid infrastructure development. The design of the prototype building using a brochure owned by UNIDA Gontor Putri is considered unable to visually represent the buildings that have been or will be built properly. This study aims to implement Augmented Reality Book (AR-Book) technology as an introduction media and a tool for the layout of campus buildings, as well as a medium for documentation or archives that can be stored in the long term. Building data is obtained from observations by collecting data by observing directly at the location and visualizing in the form of photos or videos taken using cameras and drones. This media is based on android mobile using marker based augmented reality technology to visualize the 3-dimensional shape of the created building object. Media is built following the stages of the Software Development Life Cycle (SDLC) model. Testing is carried out through functional testing of the application using the blackbox method to test the overall function of the features in the application so that it can run well, testing the level of suitability of the application with android devices. The blackbox test results show that all features are functioning properly and there are no errors. Subsequent research this media can be developed into the form of a campus tour by utilizing virtual reality technology.
Multimodal Diarization Systems by Training Enrollment Models as Identity Representations

This paper describes a post-evaluation analysis of the system developed by ViVoLAB research group for the IberSPEECH-RTVE 2020 Multimodal Diarization (MD) Challenge. This challenge focuses on the study of multimodal systems for the diarization of audiovisual files and the assignment of an identity to each segment where a person is detected. In this work, we implemented two different subsystems to address this task using the audio and the video from audiovisual files separately. To develop our subsystems, we used the state-of-the-art speaker and face verification embeddings extracted from publicly available deep neural networks (DNN). Different clustering techniques were also employed in combination with the tracking and identity assignment process. Furthermore, we included a novel back-end approach in the face verification subsystem to train an enrollment model for each identity, which we have previously shown to improve the results compared to the average of the enrollment data. Using this approach, we trained a learnable vector to represent each enrollment character. The loss function employed to train this vector was an approximated version of the detection cost function (aDCF) which is inspired by the DCF widely used metric to measure performance in verification tasks. In this paper, we also focused on exploring and analyzing the effect of training this vector with several configurations of this objective loss function. This analysis allows us to assess the impact of the configuration parameters of the loss in the amount and type of errors produced by the system.
Large-scale Text Multiclass Classification Using Spark ML Packages

Abstract Machine learning is widely used in the field of natural language processing. Text classification is an important branch of natural language processing, mainly utilized to determine the topic of news information and judge the emotional tendency reflected by social data. However, with the expansion of data scale, traditional machine learning algorithms will encounter various performance bottlenecks due to the limitation of single node resource. The machine learning library ML provided by the distributed memory computing framework spark can efficiently process large-scale datasets. Based on Spark ML, this paper uses logistic regression to classify the preprocessed data originated from standard dataset 20newsgroups. The classification accuracy and F1-score can reach 89.64% and 89.64% respectively. Meanwhile, 5-fold cross validation experiments prove that the result is reliable and procedure design is feasible.
A Bi-Channel Math Word Problem Solver With Understanding and Reasoning

This paper addresses the problem of solving arithmetic word problems that are stated in Chinese with some commonsense implicit quantity relations. The addition of commonsense quantity relations is a critical step in building the machine solver for solving arithmetic word problems. This paper proposes a channel-based method to overcome the challenges of the problem and forms a Bi-channel model. The first model is the Syntax-Semantic model that convert a math problem into a set of equations. The second model adds the set of quantity equations from commonsense into the equation set. The last section is to convert the machine solution into a humanoid solution. The experimental results on 286 arithmetic word problems from the textbooks show that the proposed method has good potential.
Securely Boosting Chain Growth and Confirmation Speed in PoW Blockchains

Proof-of-Work (PoW) blockchains add blocks, and consequently the chain weight, randomly. The blocks added also have a significant network delay owing to their large size. Large delay combined with randomness causes forks that are responsible for many security problems. One can reduce fork occurrences by designing a system with large block intervals and size but this design compromises performance aspects such as confirmation time guarantees. The trade-off between security and performance in PoW blockchain is a well discussed topic in the literature. In this paper, we aim to reduce the conflict between security and performance through our novel concept of Links. Links are small, fast and frequent structures that can be incorporated on any new or existing PoW blockchains. Links help reduce the confirmation time of its underlying blockchain while preserving its consistency security guarantees. Our novel lower-bound growth rate for PoW systems with links in the presence of a general adversary in a partially synchronous network, shows that links provide a more steady chain growth rate than classic PoW systems without links. On a well-established, secure system like Bitcoin, we use the derived growth rate to show that when links are incorporated to Bitcoin, they help reduce its confirmation time from 60 minutes by half to 30 minutes, while retaining the consistency threshold guarantees of the original Bitcoin system. We provide a proof of concept emulation of links in a Bitcoin test bed consisting of 210 nodes having real world latencies between them. We benchmark a system with links against Bitcoin to show links cause negligible network overheads and no compromises on security in terms of orphaned weight due to forks. Our theoretical analyses and experiments can easily be extended to other Nakamoto style PoW systems.
Aerial Based Traffic Tracking and Vehicle Count Detection Using Background Subtraction

With the advent of increasing population, the traffic density is also increasing. Thus, it becomes deemed necessary for the urban planners to analyse the traffic condition via video surveillance for a successful improvisation to the existing town planning. The objective of this study was to develop a high altitude video surveillance setup with centroid tracker and compare different variants of background subtraction comprising of K-Nearest Neighbour, Mixture of Gaussian and Geometric Multi-Grid on a vision-based system for road vehicle counting and tracking. This project uses Python as its programming language and Open Computer Vision (OpenCV) as an open-source library for developing a high altitude video surveillance system for vehicle counting and directional motion detection. The designed system was able to achieve high count precision even in difficult scenarios related to occlusions or the presence of shadows. The principle of the system was to install a camera on the pedestrian bridges and track the vehicular traffic congestion by incorporating a unique ID. Moving objects were tracked using different background subtraction algorithm and object tracking was conducted using the centroid tracker. The video processing model was combined with a motion detection procedure, which correctly classified the positioning of moving vehicles depending on the space and time when the experiment was conducted on the site location. From the results it was revealed that both K-Nearest Neighbour and Mixture of Gaussian showed better accuracy with 93% and 100% depending upon the traffic density modalities. Using the proposed setup design, the identification of severe shadows based on solidity can be computed through the nature of the shape and this classification allows its accuracy to be estimated.
Medical equipment recognition using deep transfer learning

With technological advancement, visual search has become an effective tool for searching important information by providing images. We propose a practical medical equipment recognition that can be used in visual search through deep transfer learning. We evaluated three deep learning models, i.e., VGG-16, ResNet-50, and Inception-v3, to recognise ten different classes of medical equipment. A data set consisting of 2,666 images had been collected and augmented to measure the models’ effectiveness. The models pre-trained with the ImageNet data set were transferred to the final models, and the last layers were replaced and trained with the collected data set. A grid search method was then used to find the best combination of hyperparameters, such as optimiser, batch size, epoch number, dropout rate, and learning rate. We tested the models using photos captured using smartphones. The results showed that Inception-v3 outperformed the other two models with the highest accuracy of 0.9454. This is the first study that uses deep transfer learning for recognising medical equipment to our best knowledge. Such recognition technology can potentially be implemented in visual search for helping consumers to check the validity of medical equipment.
Multi-objective Explanations of GNN Predictions

Graph Neural Network (GNN) has achieved state-of-the-art performance in various high-stake prediction tasks, but multiple layers of aggregations on graphs with irregular structures make GNN a less interpretable model. Prior methods use simpler subgraphs to simulate the full model, or counterfactuals to identify the causes of a prediction. The two families of approaches aim at two distinct objectives, “simulatability” and “counterfactual relevance”, but it is not clear how the objectives can jointly influence the human understanding of an explanation. We design a user-study to investigate such joint effects, and use the findings to design a multi-objective optimization (MOO) algorithm to find Pareto optimal explanations that are well-balanced in simulatability and counterfactual. Since the target model can be of any GNN variants and may not be accessible due to privacy concerns, we design a search algorithm using zero-th order information without accessing the architecture and parameters of the target model. Quantitative experiments on nine graphs from four applications demonstrate that the Pareto efficient explanations dominate single-objective baselines that use first-order continuous optimization or discrete combinatorial search. The explanations are further evaluated in robustness and sensitivity to show their capability of revealing convincing causes, while being cautious about the possible confounders. The diverse dominating counterfactuals can certify the feasibility of algorithmic recourse, that can potentially promote algorithmic fairness where humans are participating in the decision-making using GNN.
Media Library Resources as Means of Developing Semantic Reading

The article considers the concept of electronic educational manuals aimed at developing semantic reading. Modern reality offers the reader a huge amount of digital information. The difficulty, especially for schoolchildren, consists in logical comprehension and critical evaluation of the information received. These qualities directly depend on the skill of semantic reading. The author analyses content of the types of activities in electronic educational resources available of institution of additional education. Comparison of the content of the types of activities offered by electronic manuals and the recommendations of the scientific and pedagogical community on the development of semantic reading skills led to the conclusion that there are practically no types of activities aimed at the development of semantic reading in the electronic educational resources. The author carried out theoretical analysis of the properties of electronic resources that can become factors of its development. The article defines the property of interactivity as a reserve for the development of semantic reading. Interactivity is divided into “managing”, in which no new information is created, except for information about the management itself, and “complementary”, where the new information is created. The “complementary” interactivity is the reserve for the development of semantic reading. For experimental verification of this statement, there has been created the electronic educational resource — the workbook “Questions of the Computer”. The influence of this resource has been studied in the course of classes in additional education programs. The article confirms the assumption about the effectiveness of using the property of “complementary” interactivity for the development of semantic reading skills when working with electronic educational resources. The author gives recommendations on the creation of electronic educational resources and acquisition of media libraries of institutions of additional education, taking into account the need to develop students’ semantic reading skills.
A Comparative Study of Algorithms of Software Effort Estimation for the Robotic and Communication Systems Based on Improved Accuracy

The engineering systems of robotics, communication networks, and electronics status, require a software effort estimation to decrease the error of effort and cost estimation since huge sizes of datasets are used in these systems. It supports the different tasks in scheduling, planning, and so on yet it is difficult to estimate the necessary duration to fix a required task. However, the computational complexity level is increased with improving of abovementioned systems. In this paper, several software effort estimation techniques are considered for mechatronics and communications systems. These techniques are artificial neural networks, Fuzzy logic rule, genetic algorithm, and others. The analyses and investigations revealed that the hybrid technique is the best one, which is described as the statistical representations cascaded to artificial neural networks. the hybrid technique has a higher accuracy with desirable complexity.
Application of face recognition in port unrestricted scene

Traditional face recognition technology needs to collect data in a specified environment with stable facial features. However, due to the large scope and wide area of port operation, traditional identification methods have been unable to meet the requirements of port unrestricted scenarios. In this paper, the algorithms based on RetinaFace face detection and ArcFace face recognition are applied to port staff attendance in unrestricted scenarios. In order to obtain a more stable and accurate face representation method, this paper studies face detection, face image pretreatment, face recognition, loss function and other aspects, and verifies the feasibility of this model in port unrestricted scene attendance through the experimental results on PubFig dataset and the analysis of real-time face recognition effect.
We Need Community Effort to Achieve PDC Adoption!

The Center for Parallel and Distributed Computing Curriculum Development and Educational Resources (CDER) released a PDC guideline in 2012 and soon after IEEE/ACM included PDC topics in their CS curriculum guidelines in 2013. CDER is currently working to update the PDC guidelines. The primary strategy to achieve adoption of PDC in early CS course is the courseware approach where pedagogical materials are developed and made available for instructors to integrate PDC content into their courses. content. Despite the creation of many new materials, adoption of PDC content has been fairly slow. In this position paper, we present a framework to classify the marks one need to hit to develop courseware that is adoptable, portable, engaging, and easy to find. We review some of the efforts to improve PDC adoption in the US and we hypothesize that these efforts have not consistently exhibited the attributes necessary for wide adoption. Our position is that while the courseware that have been developed by the community are essential stepping stones, we will not see PDC adoption until a larger community can be brought to bear on the problem. We need to enable composing and remixing the existing materials and develop new materials to cover the needs of a diverse range of institutions.
Institutions and Economic Transformation in Africa

Economic transformation suggests sustainable and inclusive economic development. It includes the changes in the structures of the economy in favour of more productive sectors to ensure consistent economic progress. This has been the earnest desire of most African countries. However, achieving economic transformation will require strong institutions, among others. The channels through which institutions can influence the process of economic development using specific country analyses are still under-researched. Thus, this study examines the importance of institutions in the development and sustenance of some important sectors: finance, education, technology, industry, and trade, believed to drive economic transformation. Drawing evidence from Nigeria, the study submits that institutions have varying influence on all the selected sectors. Thus, the role of specific sectoral planning based on a strong institutional framework cannot be overemphasised in a country's quest for economic transformation.