Reviewerly/exHarmony huggingface dataset:
Graph-based Robust Model Hashing

Deep neural networks (DNNs) intellectual property protection attracts extensive attention recently, and how to identify suspicious illegal copied version of original model is an important issue for protecting the profits of model owner. The existing techniques, such as model watermarking, are limited due to inevitable modifications to the models. In this paper, we propose a graph-based robust model hashing scheme that learns the compact hash representations of DNNs through processing comprehensive graph-level features. Thus, the hash distances between similar models are significantly smaller than those of dissimilar models, which can be utilized for model authentication. Specifically, model graph data is first constructed by analyzing the computational graph of the target model. Then, the graph-level features are extracted as non-linear substructures sets. After hash representation learning for the features, the target model can be represented as the hash code. Experimental results show that the proposed scheme is robust against different types of attacks on models, and can also achieve satisfactory performances of discrimination and generalizability.
Robust Blind Video Watermarking by Constructing Spread-Spectrum Matrix

Video watermarking is a quite important means to trace the distribution history of the video content by embedding the owner’s ID or some fingerprints (i.e., watermark) into each video frame. Therefore, it is hoped that the watermark of each frame can be extracted completely and correctly for video traceability and authentication. However, most existing video watermarking schemes are unavailable to extract the watermark information from each frame completely. Considering these limitations, we present a robust video watermarking method by constructing spread-spectrum matrix. Our idea is to constructing the spread-spectrum matrix based on the knowledge that human eyes are less sensitive to changes on high frequency component than low frequency component in order to improve watermark imperceptibility. Besides, we use the auto-convolution function (ACNF) to determine the location and scope of the embedded watermark and standard Hough transform (SHT) is used to determine the parameters of geometric distortion to improve watermark robustness. By evaluating various videos, the proposed video watermarking method guarantees good imperceptibility and a high watermark extraction accuracy when suffering from attacks such as video encoder compression, rotation, scaling, cropping, frame rate conversion and screenshot.
RKUPH Approval Service System Design Using SCSE Framework

The existence of IT services in supporting business processes is a must in today's modern organizations. The main goal to be achieved is to maintain the existence of the organization in accordance with the times. In practice, the development of IT services is highly dependent on the availability of human resources who have competence in software development. However, this limitation can be overcome by involving external parties (external outsourcing) as software developers. However, this approach has a weakness if the organization's service needs are not well defined, so it will be difficult for external parties who do not understand the organization's business processes; it has the potential to cause differences in specifications and development results. The PUPH Directorate has the same problem in realizing the transformation of forest management i.e., changing the RKUPH approval service from conventional to IT-based. To fulfill this, the PUPH Directorate must provide IT service design documents to develop IT services. Therefore, this study aims to design an IT service system that can then be used as a guide for application development by external parties. The SCSE framework was chosen as a guide in designing IT service systems. The resulting design evaluated against four SOA principles: coupling, cohesion, usability, and complexity. Based on the evaluation results, the values of the four principles are 0.009, 0.924, 0.010, and 3,000. It indicates that the resulting design are loosely coupling and high cohesion. Furthermore, adding web mockup activities and optimizing service blueprint activities in the SCSE framework makes it easier for organizations to develop business and IT service designs that align with organizational goals and strategies. In addition, the resulting design is easily understood by external parties so that the service system design that has been made becomes a control card for the development of IT services by external parties.
GraFF: A Multi-FPGA System with Memory Semantic Fabric for Scalable Graph Processing

FPGA has been a promising solution for graph processing in many scenarios. With a rapid growth in graph size, the on/off-chip memory capacity of a single FPGA is insufficient to hold large-scale graphs. To tackle such problem, in this position paper, we introduce GraFF, a Graph processing system with multiple FPGAs interconnected via a custom memory semantic Fabric. In order to efficiently exploit system parallelism, we first split the traversal of graph data into a series of independent fine-grained flits that are concurrently delivered among FPGAs as sheer memory semantic transactions. Then we relax FPGAs' synchronization from strict barrier boundaries between adjacent supersteps to fully parallelize graph traversing and computing. We build a prototype of GraFF with four custom FPGA nodes. Preliminary evaluation result based on the Breadth First Search (BFS) algorithm shows that the peak performance of GraFF reaches up to 6.23 GTEPS. Moreover, GraFF exhibits linear scalability when the number of FPGAs rises from one to four.
Introduction to Section VI

This part introduction presents an overview of the key concepts discussed in the subsequent chapters. The part explores the concept of talent development as it occurs in connection with K-12 schooling but also beyond K-12 schooling, during childhood and adolescence, and also as gifted individuals pursue post-secondary opportunities. It chronicles the types of curriculum activities within each major area of the curriculum and some beyond it that aid in the development of high-level talent, stressing the importance of psychosocial skill development as well as cognitive development. The part then explores relationships between the organization of learning experiences at K-12 versus in college as they relate to the characteristics and needs of advanced learners. It also extends the possible applications of the ICM beyond K-12 and addresses areas of affective growth and career development.
Model Caption Generator Using Visual Geometry, Residual, and Inception Architecture

Image extraction is to be an essential task upon image classification. One of the challenges of these topics is an improves the extraction model. After that, they combined it with a recurrent neural network to generate a word fitting to an image's area. Interpretation of geology images takes a long time. It needs many geologists, especially in the description of the content of image rocks. Based on these problems, this study proposed a model that can be conducted for the geologist tasks. It enabled to make a caption for an image of geology rocks. The study uses VGG16, ResNet, and InceptionV3 concatenate to LSTM and word2vec that successfully captioned images of the foreground object like cars, people, animals, and many others. Even though the model can extract the image, the outcome does not align with the research objective. The study confirmed that the outcome has a value BLEU score of 4-gram of 0.367, 0.344, and 0.273, respectively. The study outcomes still mistake identifying objects of background and do not correctly caption relate to rock contents. The study concluded that the proposed new model is to be open challenges to achieve a result precisely to geologist descriptions.
Knowledge Requirement for E-government Implementation in Indonesia: A Mixed Method

Knowledge is an organizational intangible asset it describes organizational core value. Knowledge defines in individual and organizational artefact. Indonesia began assessing e-government implementation in 2018 and faced various problems and challenges which can be solved by skill and experience in e-government implementation. This research aimed to identify knowledge requirements of e-government implementation in Indonesia and divided it into 4 domains. A mixed methods approach was used to discover some findings related to the required knowledge. Content analysis is used to codify the knowledge require based on the regulation. Then, correspondence analysis is used to analyze the contingency value of each knowledge and domain. The result shows there are 75 knowledge required for e-Government implementation which is divided into 4 domains. BIPLOT diagram represents the association and similarity between each knowledge and the domain.
Patient Record System for People Living with HIV/AIDS (PLWHA) at The Srikandi Sejati Foundation

Public service is held to aid in the form of services from government, organizations, non-governmental organization, and others. As a form of public service, health care service becomes a service that is needed by people as an essential service. Srikandi Sejati Foundation is as an organization that serves the public in the form of assistance services for key populations (trans women) in suppressing and controlling HIV/AIDS, especially for Indonesian trans women. With the increasing number of patients that are assisted by Srikandi Sejati Foundation, a system is needed to manage and process the patients' data so it can be used to create reports and executive supervision for People Living With HIV/AIDS (PLWHA) through the program that was held by Srikandi Sejati Foundation with EpiC Indonesia, as well as monitoring the performance and operations of the foundation. The proposed system was built using the prototyping method and the result was a website that can be accessed by staffs and executives. The website is expected to assist the management of Srikandi Sejati Foundation's in managing patient data, reporting, visualizing, and monitoring in real time.
Building Dialogue Understanding Models for Low-resource Language Indonesian from Scratch

Using off-the-shelf resources from resource-rich languages to transfer knowledge to low-resource languages has received a lot of attention. The requirements of enabling the model to achieve the reliable performance, including the scale of required annotated data and the effective framework, are not well guided. To address the first question, we empirically investigate the cost-effectiveness of several methods for training intent classification and slot-filling models from scratch in Indonesia (ID) using English data. Confronting the second challenge, we propose a Bi-Confidence-Frequency Cross-Lingual transfer framework (BiCF), which consists of “BiCF Mixing”, “Latent Space Refinement” and “Joint Decoder”, respectively, to overcome the lack of low-resource language dialogue data. BiCF Mixing based on the word-level alignment strategy generates code-mixed data by utilizing the importance-frequency and translating-confidence. Moreover, Latent Space Refinement trains a new dialogue understanding model using code-mixed data and word embedding models. Joint Decoder based on Bidirectional LSTM (BiLSTM) and Conditional Random Field (CRF) is used to obtain experimental results of intent classification and slot-filling. We also release a large-scale fine-labeled Indonesia dialogue dataset (ID-WOZ 1 ) and ID-BERT for experiments. BiCF achieves 93.56% and 85.17% (F1 score) on intent classification and slot filling, respectively. Extensive experiments demonstrate that our framework performs reliably and cost-efficiently on different scales of manually annotated Indonesian data.
Condition Assessment Method of Electronic Transformer Based on High-dimensional Random Matrix

Abstract The operation status of the transformer in the substation is changeable, and the evaluation index can not be determined, which leads to an increase in evaluation error. The evaluation method of the electronic transformer based on a high-dimensional random matrix is studied and analyzed. A comprehensive evaluation of the needs and standards and analysis of the evaluation characteristics are targeted to clear the evaluation index and calculate the threshold, build a high-dimensional random matrix state assessment model, and use the wide-area measurement processing method to achieve state assessment. The test results show that the final evaluation error of the method is only 0.82, which is well controlled below 1. It indicates that in the process of practical application, the evaluation speed is fast, the accuracy is high, and the links are simplified, which has practical application value.
Researches Advanced in Human Pose Estimation Based on Deep Learning

Abstract Human pose estimation has always been a popular research topic in the discipline of computer vision, which aims to predict the spatial position of the human body’s key points (parts/joints) from a given image or video. Benefiting from the powerful feature representation ability of the convolutional neural networks, the pose estimation methods based on the deep learning have become the mainstream framework of this task, which obtain the human pose estimation results by extracting the human pose feature information at different scales with specific neural networks and using the corresponding methods to process it. This paper, based on the depth study of human body posture estimation can be divided into single-task and multi-person missions. The time and method of correlation are classified and summarized, respectively from the network architecture, algorithm and feature extraction of performance that are analyzed. The current problems existing in the research field and methods are summarized, and the research prospect for the future has been prospected.
Creating a Learning Environment for the Fifth Industrial Revolution

It has been argued that we have moved into the age of personalization. One can see this while ordering drinks at a local Starbucks where options are limitless. This personalization has been called the Fifth Industrial Revolution, a time noted for a deep, multi-level cooperation between people and machines. With emphasis on innovation, purpose, and inclusivity, this revolution calls for changes in the classroom setting to focus on relationships between human needs and lived experiences. So how do we prepare our students for this reality? Methods of instruction that create an engaging and collaborative learning community need to be considered when designing classroom experiences. The five facets of personalized learning will be examined through the lens of student research and application. Examples of products and the process in which they were created will be shared including Padlet, Mentimeter, and Google Jamboard. The learning instruments introduced in this paper will demonstrate ways to encourage individual and collaborative reflection in stimulating learning environments. Situated Learning Theory will be used to explain the necessity of learning in context for pre-service teachers.
DDAM: <u>D</u> ata <u>D</u> istribution- <u>A</u> ware <u>M</u> apping of CNNs on Processing-In-Memory Systems

Convolution neural networks (CNNs) are widely used algorithms in image processing, natural language processing and many other fields. The large amount of memory access of CNNs is one of the major concerns in CNN accelerator designs that influences the performance and energy-efficiency. With fast and low-cost memory access, Processing-In-Memory (PIM) system is a feasible solution to alleviate the memory concern of CNNs. However, the distributed manner of data storing in PIM systems is in conflict with the large amount of data reuse of CNN layers. Nodes of PIM systems may need to share their data with each other before processing a CNN layer, leading to extra communication overhead. In this article, we propose DDAM to map CNNs onto PIM systems with the communication overhead reduced. Firstly, A data transfer strategy is proposed to deal with the data sharing requirement among PIM nodes by formulating a Traveling-Salesman-Problem (TSP). To improve data locality, a dynamic programming algorithm is proposed to partition the CNN and allocate a number of nodes to each part. Finally, an integer linear programming (ILP)-based mapping algorithm is proposed to map the partitioned CNN onto the PIM system. Experimental results show that compared to the baselines, DDAM can get a higher throughput of 2.0× with the energy cost reduced by 37% on average.
Inferencing on Edge Devices: A Time- and Space-aware Co-scheduling Approach

Neural Network (NN)-based real-time inferencing tasks are often co-scheduled on GPGPU-style edge platforms. Existing works advocate using different NN parameters for the same detection task in different environments. However, realizing such approaches remains challenging, given accelerator devices’ limited on-chip memory capacity. As a solution, we propose a multi-pass, time- and space-aware scheduling infrastructure for embedded platforms with GPU accelerators. The framework manages the residency of NN parameters in the limited on-chip memory while simultaneously dispatching relevant compute operations. The mapping decisions for memory operations and compute operations to the underlying resources of the platform are first determined in an offline manner. For this, we proposed a constraint solver-assisted scheduler that optimizes for schedule makespan. This is followed by memory optimization passes, which take the memory budget into account and accordingly adjust the start times of memory and compute operations. Our approach reports a 74%–90% savings in peak memory utilization with 0%–33% deadline misses for schedules that suffer miss percentage in ranges of 25%–100% when run using existing methods.
Design and Simulation of Complete Coverage Indoor Robot Path Planning Algorithm Based on BIM

As the carrier of building information, BIM has a wealth of indoor environmental information to plan path. This paper proposes a new design idea for the complete coverage path planning algorithm: use the IFC file to obtain the absolute coordinates of the building component, and then realize grid modeling of robots in building interior environments. On this basis, an improved BFS-based complete coverage path planning algorithm is designed to plan the robot’s coverage path offline through boundary coverage and dead zone escape. Finally, this paper designs a simulation test to verify the feasibility and robustness of the method, and the factors affecting the algorithm repetition rate and the true coverage rate are analyzed in detail. The results show that the real coverage is related to the size information loss during environmental modeling. The real coverage is stable above 90%, and the repetition rate is around 10%. The algorithm has good application prospects and can be applied to the path planning of intelligent construction processes, etc.
A Semi-Supervised Method for PatchMatch Multi-View Stereo with Sparse Points

Recently, the deep-learning-based PatchMatch method has been rapidly developed in 3D reconstruction, based on which boundary regions are filled with other parts that most closely match edge parts, but limited PatchMatch data hinder the generalization of the method to unknown settings. If various large-scale PatchMatch datasets are generated, the process would require considerable time and resources when performing neighborhood point-matching calculations using random iterative algorithms. To solve this issue, we first propose a new, sparse, semi-supervised stereo-matching framework called SGT-PatchMatchNet, which can reconstruct reliable 3D structures with a small number of 3D points using the ground truth of surface frame values. Secondly, in order to solve the problem of the luminosity inconsistency of some pixels in other views, a photometric similar-point loss function is proposed to improve the performance of 3D reconstruction, which causes the neighborhood information to project the depth value of the predicted depth to meet the same 3D coordinates. Finally, in order to solve the problem of the edge blurring of the depth map obtained using the network model, we propose a robust-point consistency loss function to improve the integrity and robustness of the occlusion and edge areas. The experimental results show that the proposed method not only has good visual effects and performance indicators but can also effectively reduce the amount of computation and improve the calculation time.
Recent advances in algebraic geometry and Bayesian statistics

This article is a review of theoretical advances in the research field of algebraic geometry and Bayesian statistics in the last two decades. Many statistical models and learning machines which contain hierarchical structures or latent variables are called nonidentifiable, because the map from a parameter to a statistical model is not one-to-one. In nonidentifiable models, both the likelihood function and the posterior distribution have singularities in general, hence it was difficult to analyze their statistical properties. However, from the end of the 20th century, new theory and methodology based on algebraic geometry have been established which enable us to investigate such models and machines in the real world. In this article, the following results in recent advances are reported. First, we explain the framework of Bayesian statistics and introduce a new perspective from the birational geometry. Second, two mathematical solutions are derived based on algebraic geometry. An appropriate parameter space can be found by a resolution map, which makes the posterior distribution be normal crossing and the log likelihood ratio function be well-defined. Third, three applications to statistics are introduced. The posterior distribution is represented by the renormalized form, the asymptotic free energy is derived, and the universal formula among the generalization loss, the cross validation, and the information criterion is established. Two mathematical solutions and three applications to statistics based on algebraic geometry reported in this article are now being used in many practical fields in data science and artificial intelligence.
A Closed-loop Hybrid Supervision Framework of Cryptocurrency Transactions for Data Trading in IoT

The Device-as-a-service (DaaS) Internet of Things (IoT) business model enables distributed IoT devices to sell collected data to other devices, paving the way for machine-to-machine (M2M) economy applications. Cryptocurrencies are widely used by various IoT devices to undertake the main settlement and payment task in the M2M economy. However, the cryptocurrency market, which lacks effective supervision, has fluctuated wildly in the past few years. These fluctuations are breeding grounds for arbitrage in IoT data trading. Therefore, a practical cryptocurrency market supervision framework is very imperative in the process of IoT data trading to ensure that the trading is completed safely and fairly. The difficulty stems from how to combine these unlabeled daily trading data with supervision strategies to punish abnormal users, who disrupt the data trading market in IoT. In this article, we propose a closed-loop hybrid supervision framework based on the unsupervised anomaly detection to solve this problem. The core is to design the multi-modal unsupervised anomaly detection methods on trading prices to identify malicious users. We then design a dedicated control strategy with three levels to defend against various abnormal behaviors, according to the detection results. Furthermore, to guarantee the reliability of this framework, we evaluate the detection rate, accuracy, precision, and time consumption of single-modal and multi-modal detection methods and the contrast algorithm Adaptive KDE [ 19 ]. Finally, an effective prototype framework for supervising is established. The extensive evaluations prove that our supervision framework greatly reduces IoT data trading risks and losses.