Reviewerly/exHarmony huggingface dataset:
Indiscernibility Mask Key for Image Steganography

Our concern in this paper is to explore the possibility of using rough inclusions for image steganography. We present our initial research using indiscernibility relation as a steganographic key for hiding information into the stego carrier by means of a fixed mask. The information can be embedded into the stego-carrier in a semi-random way, whereas the reconstruction is performed in a deterministic way. The information shall be placed in selected bytes, which are indiscernible with the mask to a fixed degree. The bits indiscernible with other ratios (smaller or greater) form random gaps that lead to somehow unpredictable hiding of information presence. We assume that in our technique it can modify bits, the change of which does not cause a visual modification detectable by human sight, so we do not limit ourselves to the least significant bit. The only assumption is that we do not use the position when the mask we define uses it. For simplicity’s sake, in this work we present its operation, features, using the Least Significant Bit (LSB) method. In the experimental part, we have implemented our method in the context of hiding image into the image. The LSB technique in its simplest form is not resistant to stegoanalisys, so we used the well-known LSB matching method to mask the presence of our steganographic key usage. To verify the resistance to stegoanalisys we have conducted and discussed Chi-square and LSB enhancement test. The positive features of our method include its simplicity and speed, to decode a message we need to hide, or pass to another channel, a several-bit mask, degree of indiscernibility and size of the hidden file. We hope that our method will find application in the art of creating steganographic keys.
Text to Image Synthesis using Residual GAN

In the world of computer vision, a very intriguing problem is synthesizing or generating images (from the noise) of the reasonable quality from text descriptions. The applications of this problem are immense such as photo-editing, computer- aided design, etc. But the current AI systems are not up to the mark to reach the desired outcome. However, in recent years the progress in the field of text classification and image classification fields have paved the way for more advanced AI systems that can be used to achieve the desired goal by utilizing the discriminative power and strong generalization properties of attribute representations of recurrent neural networks and convolutional neural networks. Meanwhile, GANs have proved to produce reasonable images of birds, flowers, etc. In this work, we present GAN architecture to effectively aid the translation visual concepts from the text to image.
Unsupervised Image Style Embeddings for Retrieval and Recognition Tasks

We propose an unsupervised protocol for learning a neural embedding of visual style of images. Style similarity is an important measure for many applications such as style transfer, fashion search, art exploration, etc. However, computational modeling of style is a difficult task owing to its vague and subjective nature. Most methods for style based retrieval use supervised training with pre-defined categorization of images according to style. While this paradigm is suitable for applications where style categories are well-defined and curating large datasets according to such a categorization is feasible, in several other cases such a categorization is either ill-defined or does not exist. Our protocol for learning style based representations does not leverage categorical labels but a proxy measure for forming triplets of anchor, similar, and dissimilar images. Using these triplets, we learn a compact style embedding that is useful for style-based search and retrieval. The learned embeddings outperform other unsupervised representations for style-based image retrieval task on six datasets that capture different meanings of style. We also show that by fine-tuning the learned features with dataset-specific style labels, we obtain best results for image style recognition task on five of the six datasets.
An architectural approach to Cognitive Information System

The fast changes in information technology and business needs led to the evolution and development of Cognitive Information Systems (CIS). There have been few researches on the general model for analysis and design of CIS. This paper attempts to create a design scheme for incorporating the various model for CISs and Understanding-based management systems (UBMSS). The components that have been examined create elements of CIS analysis and design, however there were not described as modeling element and not described as an enabling tool to create a consistent and integrated system The most significant components for modeling are: semi-structured documents, business processes, constituents of knowledge management, the enterprise and, the information architecture including self-directing software components - Artificial Intelligence (AI) - that yield function. For CIS modeling the above-mentioned elements were combined into a unified framework that follows the object-oriented paradigm and architecture approaches. The aim of the research to describe a framework that presents an overarching model and assists our understanding of properties of CIS and UBMSS for being able to formulate a practical development method for CIS and cognitive management systems.
Data Anonymization in Social Networks State of the Art, Exposure of Shortcomings and Discussion of New Innovations

Privacy is a concern of social network users. Social networks are a source of valuable data for scientific or commercial analysis. Therefore, anonymizing social network data before releasing it becomes an important issue. The nodes in the network represent the individuals and the links among them denote their relationships. Nevertheless, publishing a social graph directly by simply removing the names of people who contributed to this graph raises important privacy issues. In particular, some inference attacks on the published graph can lead to de-anonymizing certain nodes, learning the existence of a social relation between two nodes or even using the structure of the graph itself to deduce the value of certain sensitive attributes. In this paper, we present a brief yet systematic review of the existing anonymization techniques for privacy preserving publishing of social network data. We identify the challenges in privacy preserving publishing of social network data comparing to the extensively studied relational case. We survey the existing anonymization methods for privacy preservation in three categories: graph modification approaches, generalization approaches and differential privacy methods.
A Game Theoretic Approach for Digital Forensic Tool Selection †

Digital forensic investigations are getting harder and more time consuming everyday because of various problems including rapid advances in technology, wide variety of available devices in investigations, and large amount of data to be analyzed. In order to tackle with these issues, digital forensic tools are developed by open-source communities and software companies. These software products are released as a complete toolkit or standalone tools targeting specific tasks. In either case, digital forensic investigators use these tools based on their familiarity because of previous training experiences, available funding from their agencies/businesses, tool’s ease of use, etc. Moreover, using additional tools to verify the findings is a common practice in digital forensic investigations. This is particularly common when the previously selected tools do not generate an expected output. In this paper, we propose a game theoretic approach to the tool selection problem in order to help investigators to make a decision on which digital forensic tool to use. We particularly focused on file carving tool usage when building and analyzing our model because of the available data on these tools. Our results show how important it is to investigate the dynamics of strategy changes between the tools during an investigation to increase the efficiency of the investigation using game theoretic modeling.
Cross-modal subspace learning via kernel correlation maximization and discriminative structure-preserving

How to measure the distance between heterogeneous data is still an open problem. Many research works have been developed to learn a common subspace where the similarity between different modalities can be calculated directly. However, most of existing works focus on learning a latent subspace but the semantically structural information is not well preserved. Thus, these approaches cannot get desired results. In this paper, we propose a novel framework, termed Cross-modal subspace learning via Kernel correlation maximization and Discriminative structure-preserving (CKD), to solve this problem in two aspects. Firstly, we construct a shared semantic graph to make each modality data preserve the neighbor relationship semantically. Secondly, we introduce the Hilbert-Schmidt Independence Criteria (HSIC) to ensure the consistency between feature-similarity and semantic-similarity of samples. Our model not only considers the inter-modality correlation by maximizing the kernel correlation but also preserves the semantically structural information within each modality. The extensive experiments are performed to evaluate the proposed framework on the three public datasets. The experimental results demonstrate that the proposed CKD is competitive compared with the classic subspace learning methods.
Improving the Performance of CBIR Using XGBoost Classifier with Deep CNN-Based Feature Extraction

The main challenge faced by CBIR systems is the semantic gap, namely the existence of semantic differences between low-level pixel images captured by machines and high-level semantics perceived by humans. This is caused by the CBIR system which is very dependent on the feature extraction used. The success of deep learning techniques, especially Convolutional Neural Networks (CNN) in solving the problem of computer vision applications has inspired researchers to overcome the problem of the semantic gap. Until now, what is known by researchers, Deep CNN research for CBIR uses softmax and SVM classifiers. However, the XGBoost classifier achieves extraordinary performance even though compared to SVM. By applying the advantages of Deep CNN techniques and XGBoost classifier, the Deep CNN model is proposed as a feature extractor and XGBoost to form a classification model replacing the softmax and SVM classifier. From the proposed Deep CNN model, two Fully Connected (FC) layers (FC1 and FC2) are taken as feature representations. In this study, Features Vector (FV) taken from the Deep CNN model that have been produced are used as extractor features (FV.FC1 and FV.FC2). The performance of the Deep CNN for CBIR tasks generated using softmax, SVM, and XGBoost classifier observed. The performance evaluated is accuracy, precision, recall, and f1-score. Based on experimental results on the Wang, GHIM-10k, and Fruit-360 dataset, XGBoost classifier can increase accuracy, precision, recall, and f1-score in all datasets. The best performance of the CBIR system is using XGBoost classifier with the best feature extractor taken from FV.FC2. Whereas the FV.FC1 feature extractor does not produce significant performance when compared to the resources needed.
Towards an Immersive Guided Virtual Reality Microfabrication Laboratory Training System

In this paper, we present a 3D virtual reality-based interactive laboratory training system that provided training on how to operate a variety of machines in a microfabrication lab environment. The training system focused on providing fully immersive guided learning features that helped users to learn the lab operations independently. The system consisted of a hint system that automatically highlights lab tools, VR hand controller assistance, and an auto scoring system. Ten participants were tested using the system. Preliminary results showed clear improvement in learning speed, independent learning ability, and error reductions during this immersive guided VR learning environment.
Multi-modal Multi-agent Path Finding with Optimal Resource Utilization

The multi-agent path finding (MAPF) problem is a combinatorial search problem that aims at finding paths for multiple agents (e.g., robots) in an environment (e.g., an autonomous warehouse) such that no two agents collide with each other. We study a general version of MAPF, called mMAPF, that involves further challenges, such as multi-modal transportation modes, a set of waypoints to visit for each agent, and consumption of different types of resources. We introduce a declarative method to solve mMAPF, using answer set programming that provides a flexible formal framework to address all these challenges while optimizing multiple objectives.
LINGUISTIC ASPECTS AS CREATIVITY EXPRESSION IN COMPUTER-MEDIATED BUSINESS COMMUNICATION

Business communication online has developed over time due to the constant change of modern communication technologies. Researchers affirm that digital technologies have both positive and negative impact on business communication. Creativity in business communication turns to be crucial in the realization of business opportunities. However, it is linked not only to imagination or original ideas. It also means innovation and innovativeness, the use of innovative digital technologies that help to fuel great ideas, enhance critical thinking, open new ways to business ambitions. The study is devoted to the analysis of linguistic aspects in computer-mediated business communication. Two types of digital business discourse – e-mail and websites of insurance companies – are analyzed with the focus on their linguistic features. From the linguistic point of view computer-mediated business communication differs from conventional business communication and the use of lexicon (e.g. special formal and standard vocabulary) is critical in making this communication successful. Grammatical features of business language are also of great relevance. Stylistic features employed in business communication are also crucial since they help to provide emphasis, achieve clarity and freshness of expression. Linguistic elements of computer-mediated communication in e-mails and analysed websites do not always coincide. The analysis has shown that websites tend to be less formal and compared to e-mails are more exposed to creative linguistic expressions.
Quantum Hopfield Neural Networks: A New Approach and Its Storage Capacity

At the interface between quantum computing and machine learning, the field of quantum machine learning aims to improve classical machine learning algorithms with the help of quantum computers. Examples are Hopfield neural networks, which can store patterns and thereby are used as associative memory. However, the storage capacity of such classical networks is limited. In this work, we present a new approach to quantum Hopfield neural networks with classical inputs and outputs. The approach is easily extendable to quantum inputs or outputs. Performance is evaluated by three measures of error rates, introduced in this paper. We simulate our approach and find increased storage capacity compared to classical networks for small systems. We furthermore present classical results that indicate an increased storage capacity for quantum Hopfield neural networks in large systems as well.
System Design of Embedded Event Data Recorder for Evidence Collection

Paper comes with design of various event data recorder designed using microcontrollers & microprocessors. Data of various events are recorder by considering events of extreme condition. Analysis was made case wise while moving vehicle through different geographical locations. Graphical and tabular analysis was made to explain various cases. To analyze data, four cases are consider. Each case elaborates and focuses on certain aspects while traveling vehicle from location A to location B. Case wise analysis is also provided to know the exact cause of accident. Data analysis is done by measuring the mismatch between previous and current reading. This table will show delta changes in sensor values from which prediction regarding accident can be done.
Interpret Neural Networks by Extracting Critical Subnetworks

In recent years, deep neural networks have achieved excellent performance in many fields of artificial intelligence. The requirements for the interpretability and robustness of neural networks are also increasing. In this paper, we propose to understand the functional mechanism of neural networks by extracting critical subnetworks. Specifically, we denote the critical subnetworks as a group of important channels across layers such that if they were suppressed to zeros, the final test performance would deteriorate severely. This novel perspective can not only reveal the layerwise semantic behavior within the model but also present more accurate visual explanations appearing in the data through attribution methods. Moreover, we propose two adversarial example detection methods based on the properties of sample-specific and class-specific subnetworks, which provides the possibility for increasing the model robustness.
A Strategy of Data Synchronization in Distributed System with Read Separating from Write

Read separating from write is a strategy that NewSQL adopts to incorporate the advantages of traditional relation database and NoSQL database. Under this architecture, baseline data is split into multiple partitions stored at distributed physical nodes, while delta data is stored at single transaction node. For reducing the pressure of transaction node and improving the query performance, delta data needs to be synchronized into storage nodes. The current strategies trigger the procedure of data synchronization per partition, meaning that unchanged partitions will also participate in data synchronization, which consumes extra network cost, local IO and space resources. For improving the efficiency of data synchronization meanwhile mitigating space utilization, the fine-grained data synchronization strategy is proposed, whose main idea includes that fine-grained logical partitions upon original coarse-grained partitions is established, providing more correct synchronized unit; the delta data sensing strategy is introduced, which records the mapping between changed partitions and its delta data; instead of partition driven, the data synchronization through the delta-broadcasting mechanism is driven, constraining that only changed partitions can participate in data synchronization. The fine-grained data synchronization strategy on Oceanbase is implemented, which is a distributed database with read separating from write, and the results show that our strategy is better than other strategies in efficiency of data synchronizing and space utilization.
3D analysis of catchment basins by incorporating modified dilution correction equations in geochemical anomaly delineation

Analysis of stream sediment geochemical data has undergone a series of developments during last decades. As a matter of fact, many factors can potentially influence the chemical composition of these sediments and the existing techniques are not able to fully explain compositional variations occurring along with their transport. By applying some recently developed equations, this paper takes into account the topographical relief and the morphological surface areas of outcropping lithological units to both estimate local geochemical background values and delineate promising zones in terms of mineral exploration. Specifically, through the combination of a three-dimensional sample catchment analysis with dilution correction equation, the process of delineating catchment basins featured by economically relevant Cu occurrences has been improved. The effectiveness of the technique has been evaluated through the application of the prediction-area plot obtaining better results than applying the two-dimensional approach.
Distributed Iterative Gating Networks for Semantic Segmentation

In this paper, we present a canonical structure for controlling information flow in neural networks with an efficient feedback routing mechanism based on a strategy of Distributed Iterative Gating (DIGNet). The structure of this mechanism derives from a strong conceptual foundation, and presents a light-weight mechanism for adaptive control of computation similar to recurrent convolutional neural networks by integrating feedback signals with a feed forward architecture. In contrast to other RNN formulations, DIGNet generates feedback signals in a cascaded manner that implicitly carries information from all the layers above. This cascaded feedback propagation by means of the propagator gates is found to be more effective compared to other feedback mechanisms that use feedback from output of either the corresponding stage or from the previous stage. Experiments reveal the high degree of capability that this recurrent approach with cascaded feedback presents over feed-forward baselines and other recurrent models for pixel-wise labeling problems on three challenging datasets, PASCAL VOC 2012, COCO-Stuff, and ADE20K.
Elastic-Move: Passive Haptic Device with Force Feedback for Virtual Reality Locomotion

With advances in virtual reality, users can experience in the immersive virtual environment by a head-mounted display. One of the most widely used interaction techniques in VR application is locomotion. Most VR applications currently use teleportation as a locomotion method. Because teleportation does not involve any visible translational motion, it can avoid VR sickness. However, teleportation still has some disadvantages. Compared to the joystick which uses dash to move in the virtual environment, teleportation would reduce user’s immersion and make them lose the sense of direction due to the lack of moving process. In this paper, we introduce the ”Elastic-Move”, an approach for cost-effectively using passive force feedback to reduce VR sickness and allow users to maintain immersion and sense of direction. We propose two haptic devices with force feedback: ”Elastic-Rope” and ”Elastic-Box”. Elastic-Rope is consisting of elastic ropes and controllers, and Elastic-Box is consisting of eight springs and a steel frame. We conducted an experiment that compared the degree of VR sickness by simulator sickness questionnaire(SSQ) survey while moving in the virtual environment among Dash, Teleport, Elastic-Rope, and Elastic-Box. As a result, Elastic-Rope and Elastic-Box reduce the VR sickness of users. Besides, Elastic-Box can be transposed in numerous applications because it is a low-cost and versatile device. This work suggests that passive force feedback can effectively reduce VR sickness.
VLSI based orthogonal diagonal cross hair search (ODCHS) algorithm implementation for efficient image compression

In this paper, an efficient image codec is proposed using Magnetic Resonance Images (MRI). During the past few years, frequency domain analyzes such as Discrete Cosine Transform and Discrete Wavelet Transform (DWT) have been widely used in the field of image compression due to their well localized property of its coefficients in both frequency and space domain. This work also deals with image compression based on frequency domain transformation. As the medical images are very important for diagnosis, they require lossless compression to store them. However, the coefficients of DWT are real numbers; lossless compression cannot be achieved. To overcome this limitation, a variant of DWT named Lifting Wavelet Transform (LWT) is utilized in the proposed system. The proposed codec is applied on the decomposed image. The codec has also been synthesized using FPGA and the results are compared with simulation results and verified.