Reviewerly/exHarmony huggingface dataset:
The SSIM index is not a metric and it is badly evaluate the similarity of images

The article explored some properties of a very popular feature of image structural similarity, called the SSIM index. According to https://scholar.google.com, the article [3], where it was first described, has made more than 20,800 citations during the last 14 years. This indicator is actively used by the scientific community in imaging research. It acquired the status of an unofficial international standard for assessing image quality in the presence of a template, often referred to as the image quality metric. This article debunks some of the myths that have arisen around this index. A theorem is proved which states that the SSIM index and any of its linear transformations are not metric functions. In many publications and in the Matlab application software package in the description of the SSIM function, it is said that the SSIM index is used to measure the image quality. However, this index, as well as any comparison function with a reference image (such as full-reference), in principle, cannot assess the quality of the analyzed images. They estimate only a certain degree of similarity between the template image and its distorted copy. The article also shows that the SSIM index cannot always correctly determine the similarity of images of the same scene, while the Pearson linear correlation coefficient makes it much faster and more accurate.
Low-dose CT image super resolution using a model-based framework with CNN prior

Improve low-dose CT(LDCT) imaging quality is a hot research issue in the medical imaging field. However, current image post-processing method mainly focus on denoising, ignoring the limitations of image resolution. To this end, a new model-based LDCT image super resolution based on CNN prior is proposed in this paper. Meanwhile, the Half Quadratic Splitting (HQS) algorithm is selected to accelerate high resolution LDCT calculation process. The convolutional neural network(CNN) prior is pre-trained and integrated into the HQS algorithm to regularize the model-based LDCT SISR problem. Experimental results demonstrate that our method can improve the resolution of low-dose CT images effectively.
Efficient Implementation of 2D Barcode Verification Algorithm for IoT Applications

This paper proposes a two-Dimensional Barcode-applied Verification Algorithm (2DBVA). The two-Dimensional Barcode (2DB) is used as an access tool for Internet-of-Things (IoT) applications. From a security aspect's point of view, 2DBVA has been evaluated in terms of security factors. To perform this evaluation, a Multiple-Layer Security Architecture (MLSA) has been used in order to attain the three security objectives which are: confidentiality, integrity, and availability. The 2DBVA aims to enable an authorized access to smart IoT applications such as smart city applications. The 2DBVA has adopted the 2DB to encrypt values being used as a smart-key. The performance of the proposed algorithm has considered a number of security factors in order to strengthen the security side of such an IoT application. To evaluate the 2DBVA accuracy, a total number of 166 2DBs has been generated. The proposed 2DBVA was implemented to securely encrypt and verify contents of 166 2DBs. Results have shown a high percentage of accuracy in terms of security for verified 2DBs used as smart keys for IoT applications.
FLEA-CBR – A Flexible Alternative to the Classic 4R Cycle of Case-Based Reasoning

This paper introduces FLEA-CBR, an alternative approach for composition of case-based reasoning (CBR) processes. FLEA-CBR extends the original 4R (Retrieve, Reuse, Revise, Retain) CBR cycle with a flexible order of execution of its main steps. Additionally, a number of combinatorial features for a more comprehensive and enhanced composition can be used. FLEA is an acronym for Find, Learn, Explain, Adapt and was initially created to solve the restrictiveness issues of case-based design (CBD) where many existing approaches consist of the retrieval phase only. However, the methodology can be transferred to other CBR domains too, as its flexibility allows for convenient adaptation to the given requirements and constraints. The main advantages of FLEA-CBR over the classic 4R cycle are the ability to combine and activate the main steps in desired or arbitrary order and the use of the explainability feature together with each of the steps as well as a standalone component, providing a deep integration of Explainable AI (XAI) into the CBR cycle. Besides the CBR methods, the methodology was also conceptualized to make use of the currently popular machine learning methods, such as recurrent and convolutional neural networks (RNN, ConvNet) or general adversarial nets (GAN), for all of its steps. It is also compatible with different case representations, such as graph- or attribute-based. Being a template for a distributed software architecture, FLEA-CBR relies on the autonomy of implemented components, making the methodology more stable and suitable for use in modern container-based environments. Along with the detailed description of the methodology, this paper also provides two examples of its usage: for the domain of CBR-based creativity and library service optimization.
Video Colorization Dataset and Benchmark

Video colorization is the process of assigning realistic, plausible colors to a grayscale video. Compared to its peer, image colorization, video colorization is a relatively unexplored area in computer vision. Most of the models available for video colorization are extensions of image colorization, and hence are unable to address some unique issues in video domain. In this paper, we evaluate the applicability of image colorization techniques for video colorization, identifying problems inherent to videos and attributes affecting them. We develop a dataset and benchmark to measure the effect of such attributes to video colorization quality and demonstrate how our benchmark aligns with human evaluations.
On Using Stateful LSTM Networks for Key-Phrase Detection

In this paper, we focus on LSTM (Long Short-Term Memory) networks and their implementation in a popular framework called Keras. The goal is to show how to take advantage of their ability to pass the context by holding the state and to clear up what the stateful property of LSTM Recurrent Neural Network implemented in Keras actually means. The main outcome of the work is then a general algorithm for packing arbitrary context-dependent data, capable of 1/ packing the data to fit the stateful models; 2/ making the training process efficient by supplying multiple frames together; 3/ on-the-fly (frame-by-frame) prediction by the trained model. Two training methods are presented, a window-based approach is compared with a fully-stateful approach. The analysis is performed on the Speech commands dataset. Finally, we give guidance on how to use stateful LSTMs to create a key-phrase detection system.
Cross-Training Deep Neural Networks for Learning from Label Noise

Although deep neural networks (DNNs) have achieved excellent generalization performance on well-labelled datasets, the real-world training sets usually suffer from label noise. Owing to the powerful representational ability, DNNs may be vulnerable to the noisy data thus resulting in a sharp deterioration of test performance. To tackle this problem, we propose a novel cross-training framework to leverage two synergic deep architectures, where each member plays the roles of both teacher of generating curriculums and student of updating model parameters via a novel loss function. It is convenient to train our proposed model directly without any prior information such as noise rate. The state-of-the-art experimental results on several benchmark datasets under different types of label noise have demonstrated the effectiveness and robustness of our method.
Warning Button Crime System in Supporting the Management of Public Services in the Legal Area of Polres Kota Tangerang

Abstract The integrated police service center (SPKT) is the place for managing public services in the form of receiving reports and handling the first reports and complaints from the public. The issue of technological development is also included in the criteria of police services in supporting the management of public services in order to obtain information and solve problems quickly and accurately. Therefore, there needs to be innovation in the public reporting service system in the area of Polres Kota Tangerang. The Warning Button Crime System is a tool that can make it easy for people to contact the police when a crime occurs. This tool consists of two parts, namely the transmitter side which is stored in the strategic area and the other side is the receiver stored in the police station. By utilizing the Arduino nano and uno microcontroller which is connected to the SIM 800L Module, it can function to send a message to the Polres Kota Tangerang, with the aim of being able to provide services easily to the public in notifying and reporting criminal acts or anything else to the police. So that the police are able to know more about the location of the incident correctly and can take preventive actions to overcome it. The management of changes made within this police institution can improve performance efficiently and effectively towards the achievement of objectives to ensure public security.
Knowledge transfer in ecologically grounded approaches to ubimus: InMesh 1.0

We approach the analysis of the dynamics of knowledge transfer and decision-making in ecologically grounded creative practice through a longitudinal study of the exchange processes involved in the elaboration of the installation InMesh 1.0. The paper outlines a theoretical framework for the analysis of knowledge transfer mechanisms in ubiquitous music projects. We focus on a domain-specific practice but rely on concepts that are also applicable to other contexts. We also describe a set of support strategies that enable remote exchanges for group-based aesthetic decision-making. Finally, we document and discuss the development of InMesh 1.0.
Automated Segmentation of Cervical Cells Using MSER Algorithm and Gradient Embedded Cost Function-Based Level-Set Method

Traditionally, cervical cells are screened by analyzing Pap smear slides. But this manual inspection requires expert pathologist making the entire process time consuming and prone to manual errors. Thus, it is needed urgently to develop an automated system for the screening process. Though extensive research work is going on for decades to develop the automated system, but the success is quite less owing to the fact in Pap smears, nuclei and cytoplasm are often found in clumps lacking any boundaries separating them. In this work, we have proposed a gradient embedded cost function for cytoplasm segmentation. We have used ISBI-15 dataset for the work and the result obtained is compared with the state-of-the-art techniques.
Multi-Agent Deep Collaboration Learning for Face Alignment Under Different Perspectives

In this paper, we propose a multi-agent deep collaboration learning method (MADCL) for simultaneously detecting 2D facial landmarks and 3D facial landmarks projected from 3D to 2D, which aims at distinguishing the ambiguity caused by different perspectives. Above two facial annotations, there are a large number of public semantic areas and some very important private semantic areas. Our single agent captures and memorizes private features for iterations and multiple agents collaborate to learn public features. To achieve this, we design a collaboration learning mechanism to capture, memorize and share semantic information for enhancing the feature representation. Moreover, the input of traditional cascade regression methods is cropped directly from the raw facial image via the shape-indexed manner, which leads that the poor initial shapes likely bring about the predicted results getting worse and worse. We introduce the Markov decision process (MDP) to reason a better position of the initial shape by a reward function that reflects the shape quality. Authentic experimental results indicate that our MADCL consistently outperforms most state-of-the-art methods on two widely-evaluated challenging datasets.
Text Localization in Scene Images with SURF and Support Vector Machine for Multilingual Text

Detection of text is challenging due to complexities in the scene images with text and newer applications that use them. In our work we use general image repository that contains images suitable for various applications. We use Maximally Stable Extremal Regions (MSER)with Speed Up Robust Features (SURF)to find features. For training and testing Support Vector Machine (SVM)is used. Connected Component Analysis and filtering rules are used to get final localized text from the scene images. To evaluate our work, we have used existing data sets for English text and our own dataset for multilingual text.
Utility-Based Strategy for Balanced Cost and Availability at the Cloud Spot Market

In the Amazon EC2 cloud provider, the price of spot instances is much lower than on demand instances, however, at the cost of availability issues of the former. Recently, several strategies were proposed to analyze the Amazon EC2 spot pricing model employing techniques such as statistical and probabilistic modeling and neural networks. To the best of our knowledge, there is no work in the literature that can accurately capture the trade-off between price and availability in favor of user decision-making. In this work, we propose and evaluate a utility-based strategy that balances spot instance cost and availability to Amazon EC2 users. Our experiments show that the average availability reaches up to 98% for spot instances, using data gathered from Amazon (September to November 2016), and a bid value below 28% of the on demand price for the m4.10xlarge general purpose instance type.
Optimizing Parameters Values of Tree-Based Contrast Subspace Miner using Genetic Algorithm

Mining contrast subspace finds contrast subspaces or subspaces where a query object is most similar to a target class but different from other class in a two-class multidimensional data set. Tree-based contrast subspace miner (TB-CSMiner) which employs tree-based likelihood contrast scoring function has been recently introduced to mine contrast subspaces of a query object by constructing tree from a subspace that is data objects in a subspace space are divided into two nodes recursively with respect to the query object until the node contains only objects of same class or a minimum number of objects. A query object should fall in the node that has higher number of objects belong to the target class against the other class in a contrast subspace. The effectiveness of TB-CSMiner in finding contrast subspace of a query object relies on the values of several parameters involved which include the minimum number of objects in a node, the denominator of tree-based likelihood contrast scoring function, the number of relevant features for tree construction, and the number of random subspaces for contrast subspace search. It is difficult to identify the values of these parameters in a straightforward way based on the conventional analysis. As a consequence, this paper proposes a genetic algorithm based method for identifying the parameters values of TB-CSMiner in which sets of parameters values are treated as individuals and evolved to return the best set of parameters values. The experiment results show that the TB-CSMiner with parameters values identified through the genetic algorithm outperformed those identified through the conventional analysis in most of the cases.
Tele-Operation of Robot using Facial Feature Point Detection

In this paper, we develop an interface for remote control of robots with the operator's face movement using facial feature point detection. 14 feature points of the operator's face are detected using a camera. The position and posture of the face is obtained solving the perspective-n-point (PnP) problem. The interface allows three degrees of freedom (DOFs) of a robot, right and left, up and down, and zoom in and out. A laparoscope holder robot is controlled by four interfaces: the proposed method, pressing buttons on the robot, using a gyroscope with foot pedal and using an image processing of a maker. The operation time using the proposed interface is almost the same as the other three interfaces.
Machine learning based review on Development and Classification of Question-Answering Systems

Humans seek information continuously. In order to make information access easier, question answering (henceforth mentioned as QA) systems are developed with which user can interact in natural language and obtain relevant response. From using primitive methodologies to making the system intelligent and self-sufficient, many significant research advancements have been made in this domain since the 1960s. In this paper, we present a survey that aims to summarize the developmental trends in implementation of QA systems over the years. The paper mentions research classified under the identified important characteristics of any QA system. Consequently, an attempt is made to get a concise picture of the most current state of research in this domain. Following the review of this research, we have identified some areas having scope for future development like conversational QA systems, enhancing cognitive abilities of QA systems, etc.
Cinévoqué: Development of a Passively Responsive Framework for Seamless Evolution of Experiences in Immersive Live-Action Movies

Cinematic Virtual Reality’s (CVR) inherent feature of allowing the user to choose their Point of View (POV) within a 360° space brings forth new challenges to storytelling. The approaches used in traditional films do not translate directly to this medium, as it is uncertain if the user would follow all the Points of Interest (POIs) consistently. Our framework, Cinévoqué, aims to address this issue by using the real-time data generated during a VR film to passively alter the narrative and parts of the experience to suit the user’s viewing behavior. In this poster, we discuss the technical approaches used to implement this framework and create responsive live-action CVR.
Beyond Bounding Box: Fine-Grained Vehicle Detection via Single Stage Detector with Hierarchical output

Vehicle detection is a crucial module of the camera-based forward collision alert, which is usually used to calculate range and time-to-collision. Conventional methods based on bounding box are too coarse to handle challenging situations such as vehicle pose variations. In this paper, we propose a novel vehicle detector with a fine-grained output representation. The detector exploits a hierarchical tree-like output representation by introducing two subclasses and virtual control points, which not only discriminates each face of a vehicle but also locates their boundaries accurately. Our detector adopts popular single stage multi-scale CNN framework, which is equipped with the hierarchical output, and is beyond the bounding box methods. Experiments on our large-scale self-collected dataset show that our method achieves satisfactory performance.
The Structural Equation Model Diagram as a Visualisation Tool

Tables of values rarely provide enough information to make deep understandings about the underlying data. For millennia, charts, graphs, illustrations and diagrams have been used to visualise the underlying structure of correlated data. This paper demonstrates in brief how diagrams can be used to visualise complex interrelationships using Structural Equation Modeling of IQ.
Image Analysis and Processing in the Encrypted Domain

In this research project, we are interested by finding solutions to the problem of image analysis and processing in the encrypted domain. For security reasons, more and more digital data are transferred or stored in the encrypted domain. However, during the transmission or the archiving of encrypted images, it is often necessary to analyze or process them, without knowing the original content or the secret key used during the encryption phase. We propose to work on this problem, by associating theoretical aspects with numerous applications. Our main contributions concern: data hiding in encrypted images, correction of noisy encrypted images, recompression of crypto-compressed images and secret image sharing.
Puzzle—More Logic Puzzle Apps Solved by Mathematical Programming

This paper considers six logic puzzles (i.e., single-player games) that are available as smartphone apps—namely Starbattle, V3ck, Monodot, Knight Moves, Circuit Scramble, and Binary Sudoku. For each puzzle, an integer linear programming formulation is presented (a MathProg implementation is available as well). The purpose is to provide interesting examples and exercises for teaching mathematical programming in operations research and management science lectures. The puzzles lead to a broad variety of exercises, ranging from graph models to specific constraint types such as logical and inequality constraints.
Detecting Curves in Very Noisy Images Using Fourier-Argand Moments

Detection of curves (e.g., ridges) from very noisy images is an important yet challenging task in photon-starved imaging applications (e.g., nuclear imaging modalities, fluorescence/electron microscopy, radioastronomy, first photon/light-in-flight imaging).In this paper, we exploit the consistency of the image along the curve, i.e., the fact that the image changes slowly when we move along the curve-"locally" laminar images. We compute a sequence of complex scalars that we call Fourier-Argand moments, and show that the direction of variation of a laminar image is purely encoded in the phase of these moments. In particular, focusing on ridges located at the center of the image, we show that using these moments altogether in a frequency estimation algorithm provides a very accurate and highly robust estimate of the direction of the ridge: we demonstrate this accuracy for noise levels as high as -10dB.We then show how to detect curves-i.e., local ridges-by computing the Fourier Argand moments within a sliding window across the image, and design a consistency map whose thresholding allows to keep only the pixels on the curve.Numerical experiments on both synthetic images and real images (low light photography) demonstrate the accuracy and robustness to noise of the proposed method, compared to a state of the art method.
Investigation of Countermeasures to Anti-Forensic Methods

Anti-forensic methods have become a deterrent to law enforcement agencies around the world as they seek to prosecute those individuals involved in cybercrimes. These methods and techniques contribute to complicating the process of investigation. This can result in the delay of evidence acquisition and compromise the integrity of the evidence. With the rise in the use of anti-forensic methods, investigators need to work continuously to stay ahead of the rising trend in internet crimes. The following work investigates some of these anti-forensic methods and seeks to summarize some of the countermeasures that can be employed by law enforcement agencies.