Reviewerly/exHarmony huggingface dataset:
Descriptive Algorithmic Schemes as Image Analysis Machine Programs

This article is continued description of an original approach to the definition and description of a Turing machine (TM) for implementing descriptive image analysis methods based on an information structure for generating descriptive algorithmic schemes for automating image analysis. The fundamental problem, to which the subject of the study belongs, is the automation of extracting information from images that is necessary for making intelligent decisions. One of the important and promising areas of research in this problem is the automation of the choice of a method for solving the problem of image analysis. A necessary condition for such automation is a comparative analysis and optimization of the image analysis algorithms, which, in turn, requires estimates of the complexity and efficiency of algorithms and a universal calculator to obtain them. One of the strategic goals for the development of descriptive image analysis is the study of models of image analysis processes. To do this, it is proposed to define and build an ‘‘Image Analysis Machine,’’ i.e., TMs specialized for processing spatial information. A method for determining a TM for modeling descriptive algorithmic schemes for image analysis is proposed and described. This machine can also be used to evaluate the mathematical characteristics of image analysis algorithms.
Combining Information Measures for Improving Image Segmentation Quality

In this work, we present a new combined measure for improving the quality of digital image segmentation. The measure has two components. The first is information redundancy, and the second is variation of information. The new measure makes it possible to obtain an image partition that provides a compromise between the conflicting objectives of minimizing the number of information-important segments and minimizing the information dissimilarity between the original and segmented images. We found the conditions for the minimum of the combined measure. The conducted computational experiment showed an improvement in segmentation results when using the proposed combined measure in comparison with the previously used information redundancy measure.
Estimation of Unknown Words Using Speech and Eye Gaze When Reading Aloud Comics

This paper proposes a method for estimating speech balloons with unknown words for English learners based on speech and eye gaze information during reading aloud Japanese comics translated into English. In this method, a headset and eye tracker are used to record speech and eye gaze information. Then we extract 47 features from them together with text information. The features are used to train a support vector machine to estimate unknown words for each speech balloon. We evaluated the proposed method by measuring data from 20 Japanese university students. As a result, we confirmed that the proposed method performs better than the estimation using only text information and that the speech and eye gaze information are effective for estimating unknown words.
Visualization and Analysis of Whole Slide Histological Images

The use of modern approaches based on convolutional neural networks (CNNs) for segmentation of whole slide images (WSIs) helps pathologists obtain more stable and quantitative analysis results and improve diagnosis objectivity. But working with WSIs is extremely difficult due to their resolution, size and the presence of a large number of incompatible image storage formats from equipment manufacturers. In addition, the use of modern CNN-based image analysis methods is complicated by the need to use a set of tools with a low level of internal integration. In order to facilitate the interaction of histologists with whole slide images and modern image analysis methods we implemented PathScribe – a new universal cross-platform cloud-based tool for comfortable viewing and manipulating large collections of WSIs on almost any device, including tablets and smartphones. We also consider the important problem of automatic tissue type recognition on WSIs and propose a new CNN-based method of automatic tissue type recognition on WSIs with a 2 subsets of PATH-DT-MSU dataset which contain high-quality whole slide images of digestive tract tumors with tissue type area annotations. The proposed method achieved 0.929 accuracy on CRC-VAL-HE-7K dataset (9 classes) and 0.97 accuracy on PATH-DT-MSU-WSS1, WSS2 datasets (5 classes). The developed method allows to classify the areas corresponding to the gastric own mucous glands in the lamina propria and distinguish the tubular structures of a highly differentiated gastric adenocarcinoma with normal glands.
Revisiting Ensembling for Improving the Performance of Deep Learning Models

Ensembling is a very well-known strategy consisting in fusing several different models to achieve a new model for classification or regression tasks. Over the years, ensembling has been proven to provide superior performance in various contexts related to pattern recognition and artificial intelligence. Moreover, the basic ideas that are at the basis of ensembling have been a source of inspiration for the design of the most recent deep learning architectures. Indeed, a close analysis of those architectures shows that some connections among layers and groups of layers achieve effects similar to those obtainable by bagging, boosting and stacking, which are the well-known three basic approaches to ensembling. However, we argue that research has not fully leveraged the potential offered by ensembling. Indeed, this paper investigates some possible approaches to the combination of weak learners, or sub-components of weak learners, in the context of bagging. Based on previous results obtained in specific domains, we extend the approach to a reference dataset obtaining encouraging results.
NAME – A Rich XML Format for Named Entity and Relation Tagging

We present NAME XML, a schema for named entities and relations in documents. The standout features are: option to reference a variety of document formats (such as PAGE XML or plain text), support of entity hierarchies, custom entity types via ontologies, more expressivity due to disambiguation of base entities and entity attributes (e.g. “person” and “person name”), and relations between entities that can be directed or undirected. We describe the format in detail, show examples, and discuss real-world use cases.
A User Behaviour-Based Video Segmentation Strategy for High-Concurrency Environment

With the development of the media industry, maximizing concurrency while ensuring the user’s viewing experience has become an important research topic for each major video website. Currently, one solution is to slice the video, which conserves server bandwidth. Therefore, the design of segment durations has been widely discussed in academia. The research shows that the longer the segment duration is, the better the user experience; however, the increase in video segment duration has a negative impact on the concurrency of the video website. In this paper, a variable length segmentation strategy based on user behaviour is proposed for video segmentation. In the early stage of video browsing, the video loading is ensured, and a short video segmentation strategy is adopted. When the number of users becomes stable, the segment duration is slowly increased to a fixed value to ensure fluency for the users watching the videos. In this paper, the JMeter test tool is used to test the concurrency volume. Through experimental verification, the variable length video segmentation algorithm proposed in this paper is shown to effectively improve the concurrency of the video websites.
Towards Fairer and More Efficient Federated Learning via Multidimensional Personalized Edge Models

Federated learning (FL) is an emerging technique that trains massive and geographically distributed edge data while maintaining privacy. However, FL has inherent challenges in terms of fairness and computational efficiency due to the rising heterogeneity of edges, and thus usually results in sub-optimal performance in recent state-of-the-art (SOTA) solutions. In this paper, we propose a Customized Federated Learning (CFL) system to eliminate FL heterogeneity from multiple dimensions. Specifically, CFL tailors personalized models from the specially designed global model for each client jointly guided by an online trained model-search helper and a novel aggregation algorithm. Extensive experiments demonstrate that CFL has full-stack advantages for both FL training and edge reasoning and significantly improves the SOTA performance w.r.t. model accuracy (up to 7.2% in the non-heterogeneous environment and up to 21.8% in the heterogeneous environment), efficiency, and FL fairness.
The Effect of Self-Directed Learning Ability on Perceived Achievement in Non Face to Face and Face to Face Classes: Mediating Effect of Learning Satisfaction

Objectives This study examined university students’ self-directed learning ability, learning satisfaction, and perceived achievement were difference between non face to face and face to face classes, and analyzed whether learning satisfaction mediated between self-directed learning ability and perceived achievement. Methods The response results of 210 same students who participated in non face to face class and face to face classes in 2022 were analyzed. The collected data were analyzed using SPSS 27.0 for descriptive statistics, correlation analysis, reliability analysis, and multiple regression analysis. The mediating effects were analyzed using Process Macro 4.0. Results First, there was no significant difference in self-directed learning ability and learning satisfaction in non face to face and face to face classes. The perceived achievement was significantly higher in face to face classes than in non face to face classes. Second, self-directed learning ability had a significant effect on learning satisfaction and perceived achievement. Learning satisfaction showed a mediating effect between self-directed learning ability and perceived achievement. Conclusions The results of this study prove that when self-directed learning ability increases, learning satisfaction increases and perceived achievement can be improved. This study is the result of students who have experienced both non face to face and face to face classes, so it can be used as basic data for improving student performance and characteristics of classes to be considered in post COVID university education.
An Improved Relation Extraction Method Based on Information Control Injection and Attention-Guided Densely Connected Graph Convolutional Network

The extraction of entities and multi-type relations in overlapping triples has been a challenging problem. This paper proposes a relation extraction method based on information control injection and attention-guided densely connected graph convolutional networks by dividing the model into two stages inspired by the GraphRel [1] approach. Considering that the shallow graph convolutional network (GCN) used by GraphRel can only capture local structural information on large graphs, this paper uses the multi-head attention mechanism to form different weight matrices. It extracts deeper structural information in the text by combining it with a densely connected graph convolutional network (DCGCN). In addition, the features extracted by DCGCN are not entirely correct. Therefore, this paper uses KL-divergence to control the degree of information injection to obtain a better feature representation. To verify the model's effectiveness, we conducted extensive experiments on two widely used public datasets: NYT and WebNLG. The results show that compared to GraphRel, the F1 value of the model improves by 24.8% on the NYT dataset and 35.9% on the WebNLG dataset. More importantly, the model significantly improved in extracting overlapping relations.
FedPDD: A Privacy-preserving Double Distillation Framework for Cross-silo Federated Recommendation

Cross-platform recommendation aims to improve recommendation accuracy by gathering heterogeneous features from different platforms. However, such cross-silo collaborations between platforms are restricted by increasingly stringent privacy protection regulations, thus data cannot be aggregated for training. Federated learning (FL) is a practical solution to deal with the data silo problem in recommendation scenarios. Existing cross-silo FL methods transmit model information to collaboratively build a global model by leveraging the data of overlapped users. However, in reality, the number of overlapped users is often very small, thus largely limiting the performance of such approaches. Moreover, transmitting model information during training requires high communication costs and may cause serious privacy leakage. In this paper, we propose a novel privacy-preserving double distillation framework named FedPDD for cross-silo federated recommendation, which efficiently transfers knowledge when overlapped users are limited. Specifically, our double distillation strategy enables local models to learn not only explicit knowledge from the other party but also implicit knowledge from its past predictions. Moreover, to ensure privacy and high efficiency, we employ an offline training scheme to reduce communication needs and privacy leakage risk. In addition, we adopt differential privacy to further protect the transmitted information. The experiments on two real-world recommendation datasets, HetRec-MovieLens and Criteo, demonstrate the effectiveness of FedPDD compared to the state-of-the-art approaches.
FAQ: Mitigating the Impact of Faults in the Weight Memory of DNN Accelerators through Fault-Aware Quantization

Permanent faults induced due to imperfections in the manufacturing process of Deep Neural Network (DNN) accelerators are a major concern, as they negatively impact the manufacturing yield of the chip fabrication process. Fault-aware training is the state-of-the-art approach for mitigating such faults. However, it incurs huge retraining overheads, specifically when used for large DNNs trained on complex datasets. To address this issue, we propose a novel Fault-Aware Quantization (FAQ) technique for mitigating the effects of stuck-at permanent faults in the on-chip weight memory of DNN accelerators at a negligible overhead cost compared to fault-aware retraining while offering comparable accuracy results. We propose a lookup table-based algorithm to achieve ultra-low model conversion time. We present extensive evaluation of the proposed approach using five different DNNs, i.e., ResNet-18, VGG11, VGG16, AlexNet and MobileNetV2, and three different datasets, i.e., CIFAR-10, CIFAR-100 and ImageNet. The results demonstrate that FAQ helps in maintaining the baseline accuracy of the DNNs at low and moderate fault rates without involving costly fault-aware training. For example, for ResNet-18 trained on the CIFAR-10 dataset, at 0.04 fault rate FAQ offers (on average) an increase of 76.38% in accuracy. Similarly, for VGG11 trained on the CIFAR-10 dataset, at 0.04 fault rate FAQ offers (on average) an increase of 70.47% in accuracy. The results also show that FAQ incurs negligible overheads, i.e., less than 5% of the time required to run 1 epoch of retraining. We additionally demonstrate the efficacy of our technique when used in conjunction with fault-aware retraining and show that the use of FAQ inside fault-aware retraining enables fast accuracy recovery. For example, for ResNet18 trained on the CIFAR-100 dataset, at 0.1 fault rate retraining without FAQ enables (on average) 4.36% accuracy recovery while retraining with FAQ enables (on average) 66.97% accuracy recovery compared to no mitigation approach.
Decision Support System in The Distribution of Direct Village Cash Assistance (BLT DD) in Bunga Mas Kelurahan, Seluma Regency Using Simple Multi Attribute Rating Technique (SMART) Algorithm

Dana Desa as described in Article 1 paragraph 8 of the Regulation of the Minister of Finance No. 190/PMK.7/2021 is a fund sourced from the state revenue and expenditure budget intended for Villages which is transferred through the district/city regional revenue and expenditure budget and is used to finance government administration, development implementation, community development, and community empowerment. Village BLT itself is budgeted for at least 40% of the ceiling value of the Village Fund received by each Village as regulated in Presidential Regulation No. 104 of 2021. For BLT Village recipients, it is regulated in Article 33 paragraph (1) of the Minister of Finance Number 190/PMK.07/ 2021. However, there are not a few mistakes and injustices in receiving BLT Desa. Decision Support System (DSS) is a system that can provide a reference in making decisions based on mathematical calculations of the values ​​of the determining criteria. Therefore, it is necessary to create a "Decision Support System in the Distribution of Direct Cash Assistance (BLTDD) in Bunga Mas Village, Seluma Regency Using the Simple Multi Attribute Rating Technique (SMART) Algorithm" through the calculation of the criteria values ​​that have been regulated in the Minister of Finance Regulation no. 190/PMK.7/2021 and this system will contain Techniques from the Simple Multi-Attribute Rating Technique (SMART) algorithm for the calculation method..
Application Of The Template Matching Method In Orchid Image Classification In Bengkulu

Bengkulu is one of the provinces in Indonesia, precisely located in the western part of Indonesia, namely on the island of Sumatra. Bengkulu has a wide variety of beautiful flowers, including orchids which are unique to Bengkulu. Bengkulu orchids are included as rare and endangered flowers, and the lack of public knowledge of the shape and characteristics of these flowers makes the author decide to create a system that can identify the type of orchid. In classifying the image texture, it can be done using the template matching method. The template matching method is a simple method used to identify patterns in an image. The template matching method is often used to identify character images of letters, numbers, fingerprints and other matching applications. In this study, we propose the use of a template matching algorithm to classify orchid images based on species and family. The test results use 30 test image data. The minimum correlation value with the correct label on the orchid image is 0.7967 and the classification image is 0.7899. From the experimental results it can be concluded that the template matching algorithm is quite good in orchid image classification as evidenced by a test accuracy of 76 percent.
Graduation Book Information System Of Muhammadyah University Of Bengkulu

A system is an entity consisting of two or more components that interact with each other to achieve a goal. Information means the result of a process that is organized, meaningful and useful to the person who receives it. Graduation is an activity for the inauguration ceremony of graduation, handing over diplomas, release, and returning students to parents and as a sign that students have completed their studies. The graduation book system is closely related to internal control issues and as a tool to monitor and protect company assets. Students register to submit to the study program staff with complete files, then the staff verifies whether it is complete or not. Make a list of graduation registrants for student names then submit them to the head of study program, then the head of study program directs to BAAK. BAAK analyzes and retypes then creates a judicial decree. Then the next stage is BAU as the graduation equipment committee to make a graduation book by re-distributing the google form to request data by registering so complicated, the author hopes by proposing a graduation book information system by making it easier for students as well as BAAK and BAU of the University of Muhammadiyah Bengkulu. The research objectives that the author wants to achieve are: Produce a graduation book system or application, providing convenience to students in obtaining web-based information. Research through case studies conducted on the Graduation Book Information System of the University of Muhammadiyah Bengkulu by conducting interviews and providing questionnaires to parties and through literature studies. System development methods use analysis (system requirement analysis), design (design), coding (code generation), testing (testing), database design. From this research, a Graduation Book System application was produced consisting of the main page, graduation candidate page, operator page and admin page. With the Graduation Book Information System, it can make it easier to access the system that has often experienced obstacles in viewing graduation information, such as graduation schedules and graduation requirements.
VA4SM: A Visual Analytics Tool for Software Maintenance

Source code can contain a large amount of data to be interpreted by the human reader. This can be a daunting task since the cognitive ability of an individual may not be as efficient as computers. Visual analytics (VA) can be applied in software maintenance to facilitate code comprehension and other maintenance tasks. In this research, we propose a prototype tool called VA4SM, for visualizing important source code information such as static dependencies, structure and code metrics. We discuss the key features of VA4SM and improvements for future work.
Enabling Secure Data Integrity in Regenerating-Coding-Based Cloud Environment

A well-liked approach to distributed, effective, and profitable computation is called "cloud computing," in which shared, programmable resources are made available as a service through the Internet.The capacity to offer processing, storage, networks, along with other essential computing resources is referred to as cloud infrastructure as a service (IaaS) and is defined by the U.S. National Institute of Standards and Technology.The Future Internet (FI) architectures provide structures for assembling applications atop federated service primitives in order to handle networked cloud computing environments (NCE).This research introduces a hierarchical framework to investigate inter-domain resource mapping in a networked cloud environment.It suggests an Iterated Local Search (ILS) metaheuristic-based request splitting method and a Networked Cloud Mapping (NCM) method depending upon the approaches used to solve the intra-domain VNE problem.The suggested framework may be used to achieve a high degree of productivity across a significant volume of VN requests across networked cloud sizes including the least amount of processing time, according to thorough assessments.
A Data Center Energy Storage Economic Analysis Model Based on Information Decision Theory and Demand Response

Internet data center has the characteristics of high power load and power consumption, and its equipped with energy storage battery is expected to become the ideal controllable load of virtual power plant. Due to the high power consumption of data centers, the operation cost can be reduced by using peak-valley TOU or market-based demand response. Therefore, a value evaluation model of energy storage is proposed in this paper. Firstly, in order to minimize the construction cost and energy consumption cost of energy storage system, this model studies the configuration of energy storage system in large data centers. Secondly, aiming at the uncertain parameters of the model, the information gap decision theory (IGDT) is introduced to model it. The robust model and opportunity model are established to provide a reasonable decision basis for scheduling operation. Finally, a large data center is used as an application example to verify the correctness and effectiveness of the proposed model and algorithm.