Reviewerly/exHarmony huggingface dataset:
Proofs of the Undecidability of Stegananalysis Techniques

Steganalysis comprises a set of techniques that strive to find concealed information within diverse types of digital media. On the contrary, Steganography involves a group of methods that, by manipulation of a cover object, aims to hide information to make it imperceptible. Current Steganalysis techniques suffer from a certain degree of failure in the detection of a payload and, frequently, the impossibility to discover if a media hides some information. In this chapter, we prove that the detection of hidden material within a media, or a Steganalysis procedure, is an undecidable problem. Our proof comprises two sets of tests: first, we demonstrate the undecidability by the principle of Diagonalization of Cantor, and second, we applied a reduction technique based on the undecidability of malware detection. For this part, we outline the hypothesis that there exists a similitude between Steganography techniques and the generation of an innocuous computer virus. Both demonstrations proved that Steganalysis procedures are undecidable problems.
Interactive Labelled Object Treemap: Visualization Tool for Multiple Hierarchies

Jadeja, Mahipal Kanakia, Hitarth Muthu, RahulVisualizing multiple hierarchies is necessary for a variety of applications., i.e. for a given set of objects, to identify object types as well as the relationship between different objects (whether objects are related to one other or not). As the simplest solution, two separate tree diagrams (object tree and taxonomy tree) can be used to display object connections and object types. We have designed an interactive visualization technique that effectively conveys both the necessary information using a single representation. ‘Labeled Object treemap’ is a very well-studied technique for visualizing multiple hierarchies in which objects are connected if and only if their corresponding set labels are disjoint and object types can be identified easily by the background structure, treemap. In this paper, we have designed an interactive version of this technique. In our interactive solution, for the given set of object types, the user can provide any size of input tree in the given textbox. A unique set label is generated for each node, and nodes are distributed and displayed (as small red circles) according to their object types in the rectangle areas of the treemap. Four checkboxes are provided to offer various features. These features are discussed in detail in the paper. Our proposed interactive visualization technique has all the good features of the existing ‘labelled object ‘Treemap’ technique including single representation, high compactness, cluster identification, etc., and solves the issues of edge crossings and continuity.
Distributed Variational Bayes Based on Consensus of Probability Densities

This paper discusses a distributed design for the variational Bayes problem in a time-varying multi-agent network. In this circumstance, all the data are distributed stored and unavailable to all agents and each agent has to update based on its own computation and communication with neighbors. By virtue of distributed subgradient methods and consensus of probability densities idea, a distributed framework for variational Bayesian over exponential family is introduced, which can be implemented only based on local datasets. Simulations on synthetic datasets are presented to illustrate the effectiveness of the algorithm empirically, even though the communication network topology is time-varying.
Sphere Margins Softmax for Face Recognition

In conjunction with the cross-entropy loss, softmax function has gained great popularity in supervised deep convolutional neural networks (DCNNs). Although original softmax loss reduces training difficulty and makes multi classification problems easier to converge, the module does not explicitly encourage compactness within class and separability between classes, from this point of view it is not particularly suitable for face recognition tasks. In this paper, we reformulate the softmax loss with sphere margins (SM-Softmax) by normalizing both weights and extracted features of the last fully connected layer and have quantitatively adjustable angular margin by hyperparameter m <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> and m <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> . Extensive experiments on CASIA-WebFace and Labeled Face in the Wild (LFW) validate that our SM-Softmax gives better results than the present state-of-the-art methods while adopting the same experimental configuration (benchmark datasets and network structure) and the convergence speed in the early stage is accelerated significantly.
Cloud Computing Technologies

In today's world, cloud computing and e-commerce are complementary to each other in terms of their vast effectiveness. E-commerce allows the business to move and grow on the internet without having or buying any physical space. Cloud computing helps e-commerce by securing the investment of IT infrastructure. That is the only reason why most of the organizations are moving their business towards cloud computing. Many organizations are getting much more benefit from the usage of cloud computing. But before adopting this, they must understand its trade-off also. In this chapter, the authors present the requirements of e-commerce organizations for adoption of cloud computing, benefit after the adoption of cloud computing in the e-commerce business, and what difficulties e-commerce organizations feel after the usage of this technology.
An Improved State-Independent Fusion Algorithm Based on the Federated Kalman Filters

In this paper, an improved optimal fusion algorithm based on independent fusion of states is proposed for federal filter, which can mainly solve the problem that the state with poor estimation accuracy pollutes other states during fusion, which causes the estimation accuracy of the federated filter system to decrease and the convergence rate to slow down. This improves the stability of the federated filter, thereby improving the robustness of the federal filter. Meanwhile, for the problem of complex fusion weighting matrix, large amount of calculation and poor stability in the fusion algorithm proposed by Carlson, the improved fusion algorithm, the improved fusion algorithm can reduce the complexity of fusion, reduce the operation time of the filtering system, and improve the stability of the filtering system.
Downsizing the Semantic Gap in Contextual Image Retrieval System Using Superintend Gross Silhouette Descriptor

Since shape is the most important feature for recognizing objects, it has to be extracted accurately in order to enhance the content based image retrieval system, but challenges prevailed in extracting shape features of an object in an image due to inability of shape descriptor which extracts a limited number of different shapes that are not invariant, alongside the inability to extracting features of overlapping objects, and the shape connotation gap problem between low level and high level features. In order to overcome these problems, this work proposes a Superintend Gross Silhouette Descriptor which uses pixel coordinates on spatial domain of the image for finding the real shape of the object by means of straight lines so it has the ability to detect the overlapped objects as well as the polygonal shapes. After being extracted, features would be trained using a random woodland classifier which classifies the features into a group of classes at maximum convergence for mitigating the shape connotation problem. At the time of retrieval, the features of the query image would be tested with trained features for measuring the similarity by the dynamite correlation coefficient method, which is a measure of the linear correlation so it would render the absolute value of the correlation coefficient which maintains the relationship strength among features.
Distributed Fusion Filter for Systems With Time-Correlated Multiplicative Noise

The distributed fusion state estimation problem for systems with time-correlated multiplicative noise is studied where the time-correlated multiplicative noise is described by a first-order Gauss-Markov process. Based on the projection theory, the distributed fusion filter weighted by scalars is given in the linear minimum variance (LMV) sense. The filtering error cross-covariance matrices between any two local filters are derived to obtain the fusion weights. The simulation research verifies the effectiveness of the proposed algorithms.
A Novel Robust Kalman Filter for Unmanned Ground Vehicles Positioning under GNSS Abnormal Measurements

For unmanned ground vehicles (UGV), reliable and precise navigation solution is a main challenge in complex environment, especially when measurements of global navigation satellite system (GNSS) are abnormal. In order to address this challenge, we propose an algorithmic solution strategy and present a novel robust Kalman filter for UGV positioning via fusing information from GNSS and inertial navigation system (INS). Firstly, we review the positioning requirements of UGVs by analyzing the technical needs of continuously determining a vehicle's location on road and precise navigation of lane level. Secondly, a new robust algorithm of Kalman filter is designed to reduce the positioning errors of GNSS/INS integrated navigation system when GNSS signals are abnormal. Thirdly, the application of the proposed algorithm to UGV positioning is illustrated. Simulation results with real data sets gathered from road tests show that the new robust filter can help us to evaluate the information quality of measurement, and can further autonomously adjust the Kalman gain and error covariance estimation matrices online. As a result, the accuracy and robustness of integrated navigation with the new filter can be improved in GNSS-challenged environments.
A Novel Collision Avoidance Method for Fixed-wing Unmanned Aerial Vehicles

In this paper, we study collision avoidance problems with fixed-wing unmanned aerial vehicles (UAVs). According to the spatial position and relative speed between the UAVs, several sufficient conditions for avoiding collisions are proposed. An emergency control algorithm is designed based on the sufficient conditions to ensure that the UAVs can be driven from a dangerous state to the safe area. In order to prevent frequent switching of states of the UAV between "safe mode" and "danger mode", a conflict buffer is additionally introduced for conflict resolution and smooth transition. The sufficient conditions can not only be applied to the collision avoidance problem with static and dynamic obstacles, but also can be directly extended to the internal collision avoidance between multiple vehicles. Meanwhile, control input saturation is explicitly considered for the emergency maneuver. Simulations are presented to illustrate effectiveness of the proposed algorithm.
Scene-Specific Multiple Cues Integration for Multiperson Tracking

Robust multiperson tracking requires the correct associations of online detection responses with existing trajectories. In this paper, we propose to integrate multiple cues to resolve the ambiguities in data association for multiperson tracking. Unlike most existing algorithms which integrate multiple cues in the same manner for different scenes, we learn scene-specific parameters to integrate multiple cues for different scenes, as the discriminative power of each cue may vary in different scenes. The scene-specific integration parameters are learned offline by supervised learning method. Min-cost multicommodity flow is employed to solve the data association task. The edge cost of the multicommodity network, which is crucial for the data association, is determined by integrating the multiple cues extracted from the detection response based on the learned scene-specific integration parameters. The experimental results on public multiperson tracking data set demonstrate the effectiveness of the proposed scene-specific integration method.
Target Tracking Based on SE-CNN and SiameseNet

Recently, Siamese convolution neural networks have achieved remarkable results in the field of target tracking because of their balanced accuracy and speed. At the same time, Siamese convolution neural networks can solve the problem that the deep neural network can't be updated in time and training data is insufficient. We propose an object tracking algorithm based on SE-CNN and Siamese convolution neural network. SE-CNN is added to the feature extraction submodule of SiameseRPN convolution neural network to enhance the quality of spatial encodings. During inference, a novel distractor-aware objective module is introduced to perform incremental learning. Benefit from the SE-CNN and distractor-aware objective module, Our algorithm performs well in the terms of accuracy and robustness in VOT2015, VOT2016, VOT2017 and OTB 2015.
An Application of Hadamard Transform to Test Stream Ciphers

In this chapter, we discuss results obtained on an application of the Hadamard transform to cryptanalysis, and in particular, we determine the probability to decipher different pseudo-random number generators used as components of stream ciphers.
Differential Evolution Algorithm of Basis Vector Scaling and Mirror Crossover

In order to avoid the problem of local optimality and low convergence accuracy, an improved differential evolution algorithm was proposed by combining the base vector scaling with the mirror crossover operation. Based on the basic difference algorithm, the base vector scaling coefficient is introduced to jump out of the local optimal value. In the process of crossover, mirror crossover is introduced to improve the probability of the better trial vector entering the selection step and promote the algorithm evolution. In order to verify the effectiveness of the improved differential evolution algorithm, the simulation results of 10 benchmark functions are compared with those of other algorithms in this paper. The results show that the proposed algorithm can avoid the algorithm from falling into local optimum and achieve better convergence performance.
Online Shaming : Ethical Tools for Human-Computer Interaction Designers

A set of tools – concepts, guidelines, and engineering solutions – are proposed to help human-computer interaction designers build systems that are ethical with regards to online shaming. Online sh ...
A Distance Measurement Method Based on Dual-parallel-line Projection for A Non-parameter Shooting System

Most images or videos are taken using mobile phones or other daily necessities. Unlike a professional device, those ready-to-use everyday items do not have a binocular or multi-ocular visual system. Thus, it is almost impossible to obtain the whole set of the internal parameters (the characteristics of a device, for example, a focal length) and the external parameters (the coordinates and rotational angles of a world coordinate system). For this reason, how to extract a distance is a difficult task and only few methods have been proposed to solve this problem. This paper presents a dual-parallel-line projection model to simply calculate the distance of a straight line for a target in a image. The model does not require any internal or external parameters and only needs very limited geometric information of an image. An image is processed using only a monocular vision system and an image perspective transformation. First, a monocular vision model is established to extract mathematical relationships between a photographer, target points, and target straight lines in a image. Then, a perspective transformation is carried out to change the angle of view on the plane to an angle of depression. An object with known length in the figure is calibrated to calculate the length of a target on the plane. Finally, auxiliary lines related to an object with known geometric information are added to the original image, and the results of the perspective transformation for those lines are calculated. The geometrical distance of a straight line between the photographer and the target is calculated using the mathematical relationship of the monocular vision model. Examples illustrate the validity of the method.
Fall Detection with Privacy as Standard

Current ambient assisted-living (AAL) systems used to detect falls in the elderly often rely on the person in question being either capable of pressing a panic button to alert others of their fall, or a wearable device which detects impacts. These systems can be invasive, negatively impacting on an individual's sense of independence and privacy which may in turn lead to a lower quality of life. This paper aims to examine a non-invasive means of detecting falls within the home, focusing on three separate approaches to detection, a waist-worn, computer-vision-based and novel in-shoe systems. The effectiveness of each individual system is explored and their effectiveness in detecting falls versus the privacy they provide are examined. The machine learning model which was trained for use with the waist-worn system achieved an accuracy of 74%, with a sensitivity of ~ 70%, using the limited dataset available for this preliminary study. The computer-vision system can accurately detect individuals in a scene as well as fall scenarios, however drastic lighting changes negatively impact the systems performance. Our in-shoe system achieved a zero false positive rate with an accuracy of ~ 67%.
Majority Logic Decoding With Subspace Designs

Rudolph (1967) introduced one-step majority logic decoding for linear codes derived from combinatorial designs. The decoder is easily realizable in hardware and requires that the dual code has to contain the blocks of so called geometric designs as codewords. Peterson and Weldon (1972) extended Rudolph's algorithm to a two-step majority logic decoder correcting the same number of errors as Reed's celebrated multi-step majority logic decoder. Here, we study the codes from subspace designs. It turns out that these codes have the same majority logic decoding capability as the codes from geometric designs, but their majority logic decoding complexity is sometimes drastically improved. For a known infinite series of subspace designs the reduction of complexity is exponential.
Conducting an Inventory with Shared Print in Mind

A physical inventory of the open stacks collection at Indiana University was conducted to determine the rate of error in the corresponding bibliographic records. The inventory was started to address some errors that were found when materials were pulled for offsite storage, but took on an increased importance as participation in shared print programs increased. This article describes the methodology used to conduct the inventory, as well as the rate of error found in the records for a large, open stack collection.
Developing Evaluation of Teachers’ Activity in Conditions of Attestation: Regional Aspect

The problem of pedagogical activity’s perfection makes matters worse requirements in working out of set of instruments for adequate evaluation of results of teacher’s work, development his personal qualities. There is experience of estimation of competence results of professional activity about 60000 teachers of Sverdlovsk region in a process of their attestation for the first and the highest category in 2013-2017 years. It is used indexes of forming activity’s components as a quality of criterion-estimates system in expert sheets. It is revealed comparatively low level of developing analytical and creative components and component self-perfecting in a structure of pedagogical activity in results of analysis of forming of components of pedagogical activity. It is allowed to implement further correction of activity of teacher’s methodical unions, and also information-methodical services of region.
Other Shape Reconstruction Techniques

In the previous chapters, the photometric stereo and structured light methods were introduced. Although these two methods have been well studied in the computer vision field, other optical phenomena are used to provide clues for shape reconstruction. In this chapter, shape reconstruction techniques that utilize special optical phenomena (e.g., polarization, fluorescence, cast shadow, and projector defocus) are introduced.
Communication-Aware Virtual Machine Placement in Cloud

Cloud computing is a model for enabling ubiquitous, on-demand access to a shared pool of configurable computing resources which can be rapidly provisioned and released with minimal management effort. In cloud environment, resources are offered to the users in the form of virtual machines for hosting various types of application. These applications may be small application where entire component of application reside in a single virtual machine or composite application consisting of number of components, where each component reside in different virtual machine and communicate with each other. The virtual machines belonging to the same application have to communicate efficiently for better application performance. There is a need for a mechanism that increases the application performance and server utilization. This paper presents a communication aware placement of virtual machines for multi-tier web applications. The experiment is conducted using suitable number of Xen Cloud Platform enabled servers. The results shows improvements in performance.
A method of face repair based on encoder-decoder and dual discrimination network

Neural networks have made significant achievements in the field of image restoration. To efficiently repair facial images with large areas damaged, a decoder-encoder structured convolutional neural network is used as a generative model and skip-connection is added between some of its layers to enhance the structure prediction ability of the generated model and well suppressed the problem that the repair network is easy to over-fitting. The global discrimination network mostly uses the image’s edge structure and feature information to ensure that the repaired image, which is the output from the repair network, conforms to visual connectivity, while the local discriminators, not only recognize local consistency but also optimize more details. The network structure proposed in this paper combines the encoder-decoder, skip-connection, and dual discriminator networks to improve the effect of face completion. The experimental results on the CelebA show that the proposed method is superior to other methods in repairing images with large areas of damage.
Path Planning of UGV using Sampling-Based Method and PSO in 2D Map Configuration: A Comparative Analysis

The navigation system of an unmanned ground vehicle is a core research area in the field of robotics. It must be robust enough to guide UGV from source to destination through an obstacle free path. Nowadays many research are going on path planning algorithms both in a static and dynamic environment. In this paper a comparative work has been presented between sampling-based technique algorithm (PRM, bidirectional RRT) and particle swarm optimization algorithm applied in a static simple and complex environment. Sampling-based techniques use random sampling for the selection of points in the configuration space while checking not to be in obstacle configuration. The results showed that both types of techniques generated obstacle-free path in different periods. The time required by sampling-based technique is relatively less than the PSO technique. In sampling-based technique the path has vertexes at the sampled points while in PSO path consists of smooth curve which can be changed in the algorithm.