Reviewerly/exHarmony huggingface dataset:
Adaptive control system to producing a novel spun parts in sheet metal spinning process

Abstract The problem in Sheet metal spinning is limited to an axisymmetric part. This research aims to design and build a flexible mechanism by a combination of mechanical with the electrical system to achieve the forming of polygon spun parts with a different number of sides without using a specific mandrel. This system based on developing a synchronous spinning machine, in which the rotational motion and feeding of the tradition lathe machine were measured and control to maintain the speed synchronization with the trajectory of the forming tool rollers (a special tool of forming), where this tool controlled by the electrical motors. The speed of the electric motor controlled by variable frequency drives VFD, pulse-controlled, and software. The machine can be applied to produce several shapes by changing pulse-controlled inside software code, which means that’s the machine is much more flexible than any other way. In this survey three spun parts (triangle, square and hexagonal) cross-section parts were produced, and aluminum alloy sheets (1050) of circular shape with 1.5 mm in thickness and 150 mm diameter will be used in this process. Finally, the output from this process will compare with a CAD model to examine the process validity by comparing the dimensional accuracy. The result shows that the average dimensional accuracy error for the triangle, rectangular, and hexagonal spun parts are 14.15, 6.47, and 3.78 respectively which consider an acceptable percentage in the spinning process.
WiP: Privacy Enabled Biometric Authentication Based on Proof of Decryption Techniques

Biometric authentication systems are widely used for authenticating users, especially in the areas like law enforcement, healthcare, airport security etc. Two major concerns arise in any biometric authentication system: (i) Privacy of user’s biometrics, which do not change much over time (ii) Trust assumption between user and server. To address the former issue privacy enabled biometric authentication schemes are designed, wherein as part of the authentication, encrypted biometrics are sent to the server and server then computes the authentication result on encrypted biometrics. The latter issue is addressed by using trusted third party or trusted execution environment (TEE), which is not secure. To overcome this, we propose a novel method, where server can authenticate the user in a privacy preserving manner without the need for any trusted party or TEE. We propose 3 novel proof of decryption based techniques: (i) HMAC (Hash based MAC) of the authentication result on encrypted data (ii) VC (Verifiable Computing) based approach and (iii) Blinding techniques. Using these approaches we eliminate the need for trust assumptions between user and server in semi-honest setting i.e. they execute the protocol correctly but are trying to learn more about data (server) or tamper with the authentication (user). The proposed protocol is agnostic to any authentication method used by server, hence our contribution is two-fold. We analyze security, complexity and practicality of each of these approaches and compare with the state-of-the-art.
Document Image Classification Method Based on Graph Convolutional Network

Automatic and reliable document image classification is an essential part of high-level business intelligence. Previous studies mainly focus on applying Convolutional Neural Network (CNN)-based methods like GoogLeNet, VGG, ResNet, etc. These methods only rely on visual information of images but textual and layout features are ignored, thereby their performances in document image classification tasks are limited. Using multi-modal content can improve classification performances since most document images found in business systems carry explicit semantic and layout information. This paper presents an innovative method based on the Graph Convolutional Network (GCN) to learn multiple input image features, including visual, textual, and positional features. Compared with the CNN-based methods, the proposed approach can make full use of the multi-modal features of the document image to lead the model competitive with other state-of-the-art methods with much fewer parameters. In addition, the proposed model does not require large-scale pre-training. Experiments show that the proposed method achieves an accuracy of 93.45% on the popular RVL-CDIP document image dataset.
Aplication of Hybrid Method for Superior cattle selection using Decision Support System

Merauke is one of seventeen districts selected for strengthening beef cattle breeding activities to provide livestock germs, especially for local livestock, through domestic procurement. The sustainability of domestic beef production supplies must be supported by the availability of quality beef breeds. Superior beef cattle are one of the determining factors and have a strategic value in the effort to develop beef cattle. Criteria for evaluating the quality of cattle breeds, including body size or morphology, health and defects in livestock, are in accordance with Indonesian national standards for good beef cattle breeding guidelines. Selection of cattle breeds is done by the traditional breeders community only by visually paying attention to the size of cattle. This study aims to develop decision support tools for farmers in selecting prospective high-quality beef cattle, to analyze the criteria data for cattle using a combination of two methods; AHP and SMART, that alternative solutions can be obtained that will be recommended to farmers. The local beef cattle of Merauke that are used as research samples are PO cattle, system provides information on bull superior which farmers can use as supporting information in making decisions.
Shallow Node Representation Learning using Centrality Indices

In recent years, learning embeddings for nodes of a graph has become one of the most efficient w ays t o solve different graph problems such as link prediction, clustering and classification. I n t his p aper, w e p ropose a n ovel m ethod, called SECI, for learning embeddings of nodes, with application to link prediction. SECI samples from the network using breadth-first search and depth-first s earch, a nd i nterpolates b etween these two using centrality indices. The intuition behind SECI is that for nodes that have a low centrality score only a very small neighborhood is explored; and for dominant nodes that have a high centrality score a large neighborhood is explored. We evaluate the empirical performance of SECI over several realworld networks and show that it outperforms well-known existing algorithms.
Graph Summarization with Latent Variable Probabilistic Models

This study addresses the issue of summarizing a static graph, known as graph summarization, effectively and efficiently. The resulting compact graph is referred to as a summary graph. Based on the minimum description length principle (MDL), we propose a novel graph summarization algorithm called the graph summarization with latent variable probabilistic models (GSL) for a static graph. MDL asserts that the best statistical decision strategy is the one that best compresses the data. The key idea of GSL is encoding the original and summary graphs simultaneously using latent variable probabilistic models with two-part coding, that is, first encoding a summary graph, then encoding the original graph given the summary graph. When encoding these graphs, we can use various latent variable probabilistic models. Therefore, we can encode a more complex graph structure than the conventional graph summarization algorithms. We demonstrate the effectiveness of GSL on both synthetic and real datasets.
Detection of Denial of Service Attacks Using Echo State Networks

Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks are major threats to cybersecurity in communication networks. These cyber attacks are evolving and becoming more difficult to identify and, hence, a number of intrusion detection approaches have been proposed. Various machine learning techniques have proved useful in detecting such anomalies. We rely on supervised machine learning and apply echo state networks to detect known DoS and DDoS attacks. Echo state networks belong to a reservoir computing approach used to train recurrent neural networks. Their performance is compared to bidirectional long short-term memory using datasets collected by the Canadian Institute for Cybersecurity and the RIPE and Route Views data collection sites. Performance is evaluated based on accuracy, F-Score, false alarm rate, and training time. Experimental results indicate that echo state networks have comparable performance and shorter training time.
Visualization of Student-Item Interaction Matrix

One type of visualization of data from digital learning environments focuses on students' interaction with the educational content. Students may, for example, answer questions, read texts, or solve problems. We can represent these interactions as a matrix, where rows correspond to students, columns to educational items, and values to some aspect of student activity (e.g., the correctness of answers, response times, the order of actions). Visualizing this matrix is useful for several purposes. For teachers, it can provide an understanding of the skill and behavior of their students. For system developers, it can provide insight into the behavior of both students and adaptive algorithms, and it can also help detect suspicious activity. For researchers, it can provide an understanding of the properties of datasets used in experiments and valuable warnings about biases that are present in data. However, suitable visualization of the student-item interactions is nontrivial. To facilitate the design of the visualization, we provide a systematic discussion of approaches to student-item matrix visualization. Using data from an introductory programming exercise, we also provide specific illustrations of different visualization designs.
DIRECT: Discrete Image Rescaling with Enhancement from Case-specific Textures

This paper addresses image rescaling, the task of which is to downscale an input image followed by upscaling for the purposes of transmission, storage, or playback on heterogeneous devices. The state-of-the-art image rescaling network (known as IRN) tackles image downscaling and upscaling as mutually invertible tasks using invertible affine coupling layers. In particular, for upscaling, IRN models the missing high-frequency component by an input-independent (case-agnostic) Gaussian noise. In this work, we take one step further to predict a case-specific high-frequency component from textures embedded in the downscaled image. Moreover, we adopt integer coupling layers to avoid quantizing the downscaled image. When tested on commonly used datasets, the proposed method, termed DIRECT, improves high-resolution reconstruction quality both subjectively and objectively, while maintaining visually pleasing downscaled images.
Interactive media facades—research prototypes, application areas and future directions

This chapter discusses the application of digital technologies for creating interactive media facades. It introduces different approaches for creating dynamic building skins that can respond to external stimuli from within the building's environment. These approaches are demonstrated through media façade projects spanning from technologies that allow passersby to directly interact with content displayed on the façade to buildings that adapt their visual appearance based on activities taking place inside the building. In addition to a discussion of state-of-the-art projects, the chapter presents research studies that propose new interaction techniques and new application areas for interactive media facades. As a practical contribution, the chapter presents a framework to guide the design and implementation of interactive media facades. As a conceptual contribution, the chapter outlines criticalities, limitations, challenges and opportunities for future research and practice in this domain.
Improved Anomaly Detection Based on Image Reconstruction and Global Template Features for Industrial Products

Abstract Anomaly detection in industry applications is a challenging problem when negative (defective) samples are unavailable, especially in the case where there are missing parts or foreign objects occupied a relatively large region. Conventional reconstruction-based approaches cannot guarantee the restored image being a normal one, leading to poor segmentation results. In this work, we propose an unsupervised anomaly detection approach to tackle the problem of large-area anomaly detection by incorporating global template features into an Auto-Encoder like reconstruction model. In particular, our model infers the value of each pixel based on both the surrounding local-neighborhood information and the global information encoded at the same pixel position. During the reconstruction phase, any abnormal features are then replaced with normal ones, avoiding over-reconstruction of large-area abnormalities. The experimental results in comparison with other methods demonstrate its effectiveness for industrial anomaly detection.
Personalized Recommendation Based On Entity Attributes and Graph Features

With the rapid increase in the amount of website data, it has been a more difficult task for users to get the infor-mation they are interested in. Personalized recommendation is an important bridge to find the information which users really need on the website. Many recent studies have introduced additional attribute information about users and/or items to the rating matrix for alleviating the problem of data sparsity. In order to make full use of the attribute information and scoring matrix, deep learning based recommendation methods are proposed, especially the autoencoder model has attracted much attention because of its strong ability to learn hidden features. However, most of the existing autoencoder- based models require that the dimension of the input layer is equal to the dimension of the output layer, which may increase model complexity and certain information loss when using attribute information. In addition, as users' awareness of privacy protection increases, user attribute information is difficult to obtain. To address the above problems, in this paper, we propose a hybrid personalized recommendation model, which uses a semi-autoencoder to jointly embed the item's score vector and internal graph features (short for Co-Agpre). Specifically, we regard the user-item historical interaction matrix as a bipartite graph, and the Laplacian of the user-item co-occurrence graph is utilized to obtain the graph features of the item for solving the problem of sparse attributes. Then a semi-autoencoder is introduced to learn the hidden features of the item and perform rating prediction. The proposed model can flexibly use information from different sources to reduce the complexity of the model. Experiments on two real-world datasets demonstrate the effectiveness of the proposed Co-Agpre compared with state-of-the-art methods.
Detecting Anomalies using Overlapping Electrical Measurements in Smart Power Grids

As cyber-attacks against critical infrastructure be-come more frequent, it is increasingly important to be able to rapidly identify and respond to these threats. This work investigates two independent system with overlapping electrical measurements with the goal to more rapidly identify anomalies. The independent systems include HIST, a SCADA historian, and ION, an automatic meter reading system (AMR). While prior research has explored the benefits of fusing measurements, the possibility of overlapping measurements from an existing elec-trical system has not been investigated. To that end, we explore the potential benefits of combining overlapping measurements both to improve the speed/accuracy of anomaly detection and to provide additional validation of the collected measurements. In this paper, we show that merging overlapping measurements provide a more holistic picture of the observed systems. By applying Dynamic Time Warping more anomalies were found – specifically, an average of 349 times more anomalies, when considering anomalies from both overlapping measurements. When merging the overlapping measurements, a percent change of anomalies of up to 785% can be achieved compared to a non-merge of the data as reflected by experimental results.
Context Representation and Reasoning in Robotics-An Overview

Information (including knowledge) representation is a crucial factor in artificial intelligence and especially in robotics. Information includes data about the environment, its inhabitants and all the objects that are within it, as well as attributes and relations between those objects, including humans. This information constitutes what is called “context” of a robot or a machine and can be represented in various ways. This contextual information can be used to answer queries, and even to create other pieces of relevant information, using reasoning. In this paper, we review basic research work focusing on representation of this kind of information and the reasoning mechanisms that use it to perform inferences and produce (new) results. We focus on the main aspects that concern this research field, that is context definition, representation and reasoning. Finally, we propose a few research directions that could be explored.
Temporal based Network Packet Anomaly Detection using Machine Learning

In the present computer networking scenario, due to increase in the amount of ubiquitous network usage with computers, mobile computing, sensing systems and IoT applications, there is a tendency for large-scale cyber-attacks to occur. With the enormous rise in cyber-attacks in today's world, it becomes imperative to monitor the network packet flow in order to identify attacks by intruders, which otherwise can lead to disruptions and losses in computing resources. These large dimensional and voluminous network packets have to be carefully examined and investigated for network anomalies, and will be a tedious and complicated task. Hence, to make this process simpler, a network anomaly detection model that considers the temporal variations in packet transmissions to identify the threats and to protect these networks against malicious activities is proposed. In this model, we propose a machine learning based anomaly detection system (ADS) that is capable of classifying malicious and benign traffic. At the same time, identifies the time varying patterns by using unsupervised learning that provides the patterns and hidden features in the captured packets. Kohonen's self-organizing map neural network (KSOM-NN) is used as an unsupervised learning method to classify the pre-processed packet features into clusters and thereafter the classification methods are used and verified with the labelled dataset. Similarly, a statistical analysis and a threshold value of packet loss, jitter, and other parameters for normal and anomalous packets are observed for further investigations. Above models were trained and tested with the UNSW-NB15 dataset. Totally the patterns are thoroughly analyzed for anomalies with the help of supervised and unsupervised machine learning algorithms. Improved performance with respect to anomaly detection rate and accuracy is observed.
Monitoring of digital transformation risks as a key policy to prevent future financial crises

The digital transformation of finance has been significantly facilitated by the COVID-19 pandemic, and it has become the dominant trend and the driving force of development in the upcoming years. The digital transformation brings not only benefits to financial markets, people, companies, and institutions, but it also results in dramatic changes of the underlying risks. The nature, mechanisms, and scale of financial crises are bound to change substantially. The paper develops a new, forward-looking approach to financial crises research. We build further upon the multidisciplinary research agenda on digital transformation by Verhoef et al. (2021). Achieving a bright digital future requires knowing and managing the adverse effects of digitalisation (Clim, 2019; Dickson, 2019; Gimpel &amp; Schmied, 2019). Our literature search has not found any studies on digital transformation risks as a key policy to prevent future financial crises. The purpose of this paper is to examine the existing system of risks monitoring, to analyse changes in risks due to the digital transformation in finance, and to provide policymakers with insights regarding the related evolution of risks. This paper is a policy analysis type of research containing a systematic overview of risk assessment reports at the global and the EU levels.
Knowledge Inference Models Used in Adaptive Learning

This chapter provides an overview of adaptive learning and examines the student model component used in adaptive learning systems. Established and more recent approaches to student modeling that infer student knowledge (i.e. what students know at any given moment during the learning experience) are discussed, as student knowledge is the most common learner characteristic widely assessed in large-scale adaptive systems. This chapter concludes with a discussion of the limitations of the current generation of adaptive learning systems, and areas of potential for future progress.
A Bottom-Up Approach applied to Dependency Parsing in Malayalam Language

Malayalam is the official language of Kerala and Lakshadweep islands which falls in Dravidian languages. Malayalam is where all the words cannot be freely moved and is known as a less word order language. Parsing or syntax analysis is the process of analysing each word in natural language sentence, computer languages or data structures, and verifies whether the tokens can be derived from the grammar with the help of Context Free Grammar(CFG). This paper aims to develop a dependency parser for Malayalam Language using bottom-up parsing algorithm. The system adopted a systematic approach for stemming and pos tagging. The experimental result confirms that the system produces an adequate parsing result for malayalam sentences.
Classifying stable and unstable videos with deep convolutional networks

Since the invention of cameras, video shooting has become a passion for human. However, the quality of videos recorded with devices such as handheld cameras, head cameras, and vehicle cameras may be low due to shaking, jittering and unwanted periodic movements. Although the issue of video stabilization has been studied for decades, there is no consensus on how to measure the performance of a video stabilization method. In many studies in the literature, different metrics have been used for comparison of different methods. In this study, deep convolutional neural networks are used as a decision maker for video stabilization. VGG networks with different number of layers are used to determine the stability status of the videos. It was observed that VGG networks showed a classification performance up to 96.537% using only two consecutive scenes. These results show that deep learning networks can be utilized as a metric for video stabilization.
Evolutionary Algorithms for Optimal Control Problem of Mobile Robots Group Interaction

An optimal control problem of mobile robots group interaction on a plane with hourglass-shaped phase constraints is presented. Hourglass-shaped phase constraints can be represented as checkpoints that must be traversed by any or all of controlled objects. The modern evolutionary algorithms are used for searching the control that provides passage of all checkpoints by all robots of the group in minimum time. In a computational experiment the performance of hybrid evolutionary algorithm for solving this task is considered for mobile robots being launched simultaneously.