Reviewerly/exHarmony huggingface dataset:
Supression of Speckle Noise in Ultrasound Images Using Lowpass Filters

Speckle noise is one of the most critical disturbances that alter the quality of Synthetic Aperture Radar (SAR) coherent images. Speckle noise is reduced by different types of low pass filters and homomorphic filters. The range of the performance metrice of each low pass filters and Homomorphism filters determines the quality of the filtered image. The preference of the filter is determined by the performance Metrics. The results of various analyses after comparison of the performance metrics was observed and was identified that Low pass Butterworth filter achieved PSNR of 71%.
Localized Background Subtraction Feature-Based Approach for Vehicle Counting

This paper presents vehicle counting using local features derived from background subtraction method combined with the traditional SVM classier. The main advantage of the proposed method is that it manages to overcome counting problem in different challenging situations such as during rainy day condition, which is yet a difficult problem in the standard background subtraction method. In this method, the background subtraction is performed within a small predefined region of a lane or road, thus increasing the computational speed. Next, two key features i.e. area covered by the motion indexed graph and number of edge pixel computed in the designated areas in the image are computed to form a 2D feature vector. Finally, we use SVM for classifying this vector into either vehicle or noise. Comparisons between the proposed method and other method show the potential of our approach.
Bringing luminance histograms of low-contrast digital images to two-level piecewise uniform distribution

The paper describes the problems of processing digital images for enhancing contrast to increase the distinguishability of details, performed by histogram methods that approximate the reduction of the histogram of image brightness to a given distribution. Using the uniform distribution algorithm does not take much time, but it does not regulate the brightness and contrast of the image processed. It is possible to achieve better distinguishability of details using a normal distribution, as compared to the uniform distribution, but in this case the solution requires numerical methods which take much more time. There has been proposed the algorithm of controlling the contrast of a digital image by bringing the luma histogram to a distribution determined by a piecewise constant differential distribution function with two levels of values. This algorithm helps to control the average brightness and contrast, without resorting to numerical methods, very dark and very light low-contrast images having greater distinguishability of details. The mathematical foundations of the proposed algorithm are presented. There have been studied the possibilities of increasing the detail distinguishability in the processed low-contrast image, as compared to the popular method based on bringing the brightness histogram to a uniform distribution.
An improved Image Description Method Using Recurrent Neural Network with Gated Recurrent Unit

Image description is a comprehensive task combining natural language processing and computer vision. This paper proposes a model improved by GRU for the lack of long-term memory due to use of ordinary RNN in m-RNN. The new model can predict the words at the next moment based on the information at the previous moment, and generates a complete image description sentence. Moreover, using the MSCOCO2014 dataset, our model was trained and tested, and the indicators such as BLEU, METEOR and CIDEr are used as evaluation criteria. The experimental comparison with the original model and other models is carried out to further verify the validity of our model.
Blockchain-Enabled Transactive Home Energy Management Systems in Distribution Networks

As home energy management systems (HEMSs) are implemented in homes as ways of reducing customer costs and providing demand response (DR) to the electric utility, homeowner's privacy can be compromised. As part of the HEMS framework, homeowners are required to send load forecasts to the distribution system operator (DSO) for power balancing purposes. Submitting forecasts allows a platform for attackers to gain knowledge on user patterns based on the load information provided. The attacker could, for example, enter the home to steal valuable possessions when the homeowner is away. In this paper, we propose a framework using a smart contract within a private blockchain to keep customer information private when communicating with the DSO. The results show the HEMS users' privacy is maintained, while the benefits of data sharing are obtained. Blockchain and its associated smart contracts may be a viable solution to security concerns in DR applications where load forecasts are sent to a DSO.
Measuring the Effectiveness of Twitter’s URL Shortener (t.co) at Protecting Users from Phishing and Malware Attacks

In this paper we investigate how effective Twitter’s URL shortening service (t.co) is at protecting users from phishing and malware attacks. We show that over 10,000 unique blacklisted phishing and malware URLs were posted to Twitter during a 2-month timeframe in 2017. This lead to over 1.6 million clicks which came directly from Twitter users – therefore exposing people to potentially harmful cyber attacks. However, existing research does not explore if blacklisted URLs are blocked by Twitter at time of click.
Using mineral chemistry for determination of crystallization conditions and tectonic setting of diabasic intrusive rocks from Deh-Zahir Area (West of Rafsanjan)

Using mineral chemistry for determination of crystallization conditions and tectonic setting of diabasic intrusive rocks from Deh-Zahir Area (West of Rafsanjan)
IVP: A Three Level Confusion-Diffusion Network Supporting Implicit Data Integrity

We present a cryptographic construction called IVP and study its security properties. IVP is a three level confusion-diffusion network that supports confidentiality and data integrity without requiring any message expansion of the content, such as, for example, for the computation of a MAC. We demonstrate that IVP is in the recently proposed class of cryptographic constructions called `Random Oracles according to Observer functions' (RO <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ). These constructions support a new notion of data integrity called `implicit' data integrity, which is based on the fact that user data usually demonstrate some patterns. If some ciphertext becomes corrupted, then the resulting plaintext no longer demonstrates such patterns. Thus, defense against data corruption attacks becomes possible by hardening the computation of ciphertext values, the plaintext of which demonstrates patterns. The encryption key is considered unknown.We show that IVP supports implicit data integrity and is secure in input perturbing and oracle replacing adversary models. The security of IVP is associated with a pattern which is frequently encountered among client and server data. This is the pattern of encountering 4 or more 16-bit words being equal to each other in a set of 32 words. The cryptographic strength of IVP is 30.215 bits, which is sufficient for defending against on-line data corruption and content replay attacks. Computationally, IVP is much lighter than other authenticated encryption approaches requiring only two additional rounds of AES, beyond the AES standard encryption rounds in the critical path. These correspond to some minimal computation overhead.
A Change Detection Approach for Processing Outdoor Scenes on Hadoop Clusters

Processing video data is becoming more useful in a wide range of applications. However, video data are demanding for computing resources, such as processor, memory, and disk. This is because the data size is huge in nature and growing exponentially. Change detection is a commonly used method in a variety of video processing applications, so it has been attracting the attention of many researchers. The goal of improving the speed of change detection could be to satisfy real-time performance or to process larger data in a timely manner. This study proposes an approach based on MapReduce and sampling to improve the performance of using change detection to process large video data on Hadoop clusters. The experiments, conducted on an outdoor scene dataset, show significant improvement in the execution time.
Low Bitrate Image Coding based on Dual Tree Complex Wavelet Transform

The rapid growth in Multimedia application over the Internet has put considerable pressure on existing hardware system for information transmission and retrieval over the bandlimited channel. To deal with huge data volume of multimedia application, image coding is a viable solution. This paper proposed image coding scheme based on dual tree complex wavelet transform (DT-CWT). DT-CWT exploit the directionality and shift invariant property to compact the pixel energy in few wavelet coefficients. The transform coefficients are encoded using simple Embedded Block truncation coding scheme (EBCOT) and experiment results demonstrates that the rate-distortion results outperforms to the state-of-the art JPEG 2000 standard at low bit-rate, particularly for images with strong directional features.
First Index-Free Manifold Ranking-Based Image Retrieval with Output Bound

Image retrieval keeps attracting a lot of attention from both academic and industry over past years due to its variety of useful applications. Due to the rapid growth of deep learning approaches, more better feature vectors of images could be discovered for improving image retrieval. However, most (if not all) existing deep learning approaches consider the similarity between 2 images locally without considering the similarity among a group of similar images globally, and thus could not return accurate results. In this paper, we study the image retrieval with manifold ranking (MR) which considers both the local similarity and the global similarity, which could give more accurate results. However, existing best-known algorithms have one of the following issues: (1) They require a bulky index, (2) some of them do not have any theoretical bound on the output, and (3) some of them are time-consuming. Motivated by this, we propose an algorithm, namely Monte Carlo-based MR (MCMR) for image retrieval, which does not have the above issues. We are the first one to propose an index-free manifold ranking-based image retrieval with the output theoretical bound. Lastly, our experiments show that MCMR outperforms existing algorithms by up to 4 orders of magnitude in terms of query time.
Driving Instruction and Training of Welfare Vehicle controlled by Virtual Platoon Scheme using Sharing System of AR

Recently, welfare vehicles are widely used among the aged. However, it is not easy for inexperienced beginners to control the welfare vehicle in narrow places such as elevators and corridors. For these problems, the driving assistant system has been proposed in which the virtual vehicle can be projected on Head Mounted Display (HMD) and the virtual vehicle is remotely controlled by a pseudo objective viewpoint so that the user drives easily on the complex courses. However, it is difficult for beginners to drive the virtual vehicles smoothly. The well-suited instruction for beginners is necessary by the instructors, but the virtual vehicle is invisible because it is controlled in Augmented Reality (AR) world. In this paper, the driving instruction and training system using the sharing system of AR is proposed. The experience is shared by the vehicle user and the instructor wearing the HMD. In AR space, the instructor gives the appropriate directions and the vehicle user drives avoiding virtual items. The effectiveness of the proposed system is evaluated by experiments.
Handwritten Changma Numerals Recognition Using Capsule Networks

Handwritten digit recognition is assumed to be a huge part in numerous authentication applications in the state-of-art technologies. As the manually written digits are not always found in similar size, thickness, style and orientation, recognition of handwritten digits is hardly possible in many cases. To address this recognition tasks using handwritten digits, a great deal of work has been conducted with different image processing technologies on a variety of non-Indic's languages. However, this paper represents a handwritten digit recognition system of recognizing Changma Vaj Digit Recognition (CVDR). The digit recognition approach used in this paper is capsule network, an improved version of Convolutional Neural Network (CNN). The proposed system has been trained with more than 3440 sample image and been tested with more than 860 images. The proposed paper also presents a graphical comparison between the recognition accuracy performed by CNN and CapsNet, which in other terms demonstrates the supremacy of CapsNet's accuracy by 5.7%.
User Interface in Virtual Space Using VR Device with Eye Tracking

It is difficult for people with severe disabilities who cannot control their hands to operate the mouse and keyboard. We have engaged in a three-dimensional virtual space user interface based on convergence and divergence using a virtual reality device with a gaze tracking function. In this paper, we propose to add a flick function to the user interface. The flick function is a combination of blinking eye and quick vision shift. By using this flick operation, we have achieved text input to the computer as well as window arrangement. This new feature let the handicapped people operate computers only with eyes as much as healthy people can operate with hands. In order to show the feasibility of our user interface, we have conducted numerical experiments, and obtained favorable results.
Relay protection service support platform architecture and data flow based on big data technology

To achieve "source maintenance, global sharing" of relay protection operation data, improve professional analysis capabilities of relay protection, establish a relay protection equipment label to realize multi-angle and all-round portraits of relay protection equipment, construct an integrated data model of protection operation information based on big data technology, propose the overall modeling principles and the overall architecture of the model. Based on an integrated data model, propose the overall architecture of the relay protection service support platform, the platform is divided into a big data service layer and a logical layer. The big data service layer completes data collection, storage, management, and integration, the logical layer implements 9 categories of applications for protection of life cycle information management, equipment evaluation, fault and motion analysis, intelligent diagnosis, intelligent warning, defect management, maintenance and anti-accident measures, refined management, and intelligent assistance. A data flow architecture based on the service support platform for ledger, defect, maintenance, action, and alarm is proposed.
Exploring logical consistency and viewport sensitivity in compositional VQA models

The most recent architectures for Visual Question Answering (VQA), such as TbD or DDRprog, have already outperformed human-level accuracy on benchmark datasets (e.g. CLEVR). We administered an advanced analysis of their performance based on novel metrics called consistency (sum of all object feature instances in the scene (e.g. shapes) equals the total number of the objects in the scene) and revealed only 56% consistency for the most accurate architecture (TbD). In respect to this finding, we propose a new method of the VQA training, which reaches 98% consistency. Furthermore, testing of the VQA model in real world brings out a problem with precise mimicking of the camera position from the original dataset. We therefore created a virtual environment along with its real-world counterpart with variable camera positions to test the accuracy and consistency from different viewports. Based on these errors, we were able to estimate optimal position of the camera. The proposed method thus allows us to find the optimal camera viewport in the real environment without knowing the geometry and the exact position of the camera in the synthetic training environment.
A Study on Blockchain-Based Lightweight Logging Framework for Service Availability in Resource-Limited Edge Cloud

In the edge cloud, when two types of service providers provide edge nodes, each service provider has different logging areas, making it difficult to access QoS violations and complying with service level agreements (SLAs). Thus, the third-party broker plays a role of confirming QoS violations and compliance between the two providers, thereby securing reliability between the CSP and NP. In addition, by improving the existing logging technique in the edge cloud with limited resources, it reduces the CPU usage to improve the availability of cloud services. Therefore, we propose a broker-based framework that improves the service availability by reducing the CPU usage and secures QoS compliance and violations.
information-Agnostic Traffic Scheduling in Data Center Networks with Asymmetric Topologies

As more and more applications are deployed in data centers, they rely on high performance data center networks (DCNs) to meet users' increasing quality of the experience (QoE) requirements. Hence, minimizing the average flow completion time (FCT) has been one of the most important goals for DCNs. However, existing traffic scheduling methods assume either prior knowledge of flows (i.e., sizes and deadlines) or symmetric topologies (i.e., Fat-Tree or Bcube). In practice, it is difficult to obtain the information of flows. Moreover, even with symmetric topology design, the DCNs will become asymmetric due to the inevitable link failure and congestion. In this case, it is a great challenge to minimize the average FCT in DCNs. In this paper, we propose a flowlet based information-agnostic traffic scheduling mechanism. The key idea of our method is leveraging multiple priority queues in switches to demote the priority of flows dynamically on the flowlet level. More specifically, the priority of a flow will be demoted according to the number of flowlets it has sent, which follows the shortest job first discipline. We formulate the average FCT minimization problem as a nonlinear Sum-of-Ratios problem and design two heuristic methods to derive the sub-optimal demotion thresholds. Experiment results show that our method can reduce the average FCT by up to 15.35% with a realistic workload, as compared to the state-of-the-art traffic scheduling methods.
In Search of a Stochastic Model for the E-News Reader

E-news readers have increasingly at their disposal a broad set of news articles to read. Online newspaper sites use recommender systems to predict and to offer relevant articles to their users. Typically, these recommender systems do not leverage users’ reading behavior. If we know how the topics-reads change in a reading session, we may lead to fine-tuned recommendations, for example, after reading a certain number of sports items, it may be counter-productive to keep recommending other sports news. The motivation for this article is the assumption that understanding user behavior when reading successive online news articles can help in developing better recommender systems. We propose five categories of stochastic models to describe this behavior depending on how the previous reading history affects the future choices of topics. We instantiated these five classes with many different stochastic processes covering short-term memory, revealed-preference, cumulative advantage, and geometric sojourn models. Our empirical study is based on large datasets of E-news from two online newspapers. We collected data from more than 13 million users who generated more than 23 million reading sessions, each one composed by the successive clicks of the users on the posted news. We reduce each user session to the sequence of reading news topics. The models were fitted and compared using the Akaike Information Criterion and the Brier Score. We found that the best models are those in which the user moves through topics influenced only by their most recent readings. Our models were also better to predict the next reading than the recommender systems currently used in these journals showing that our models can improve user satisfaction.
Explanations and sensemaking with AI and HCI

The rapid growth of available data and computational power have brought about the wide adoption of algorithmic solutions, especially using Artificial Intelligence and Machine Learning techniques. Thus, users routinely interact with AI-based systems, even though they might not be aware of it: tasks such as searching, diagnostics, credit analysis, and movie recommendations now employ AI techniques. These techniques analyze large volumes of data, iteratively processing and combining them in multiple ways. This often makes both the techniques and the results hard to understand, despite the (frequently) good results.
Improving Neural Network Classifier Using Gradient-Based Floating Centroid Method

Floating centroid method (FCM) offers an efficient way to solve a fixed-centroid problem for the neural network classifiers. However, evolutionary computation as its optimization method restrains the FCM to achieve satisfactory performance for different neural network structures, because of the high computational complexity and inefficiency. Traditional gradient-based methods have been extensively adopted to optimize the neural network classifiers. In this study, a gradient-based floating centroid (GDFC) method is introduced to address the fixed centroid problem for the neural network classifiers optimized by gradient-based methods. Furthermore, a new loss function for optimizing GDFC is introduced. The experimental results display that GDFC obtains promising classification performance than the comparison methods on the benchmark datasets.
Beyond reasonable doubt: A proposal for undecidedness blocking in abstract argumentation

In Dung’s abstract semantics, the label undecided is always propagated from the attacker to the attacked argument, unless the latter is also attacked by an accepted argument. In this work we propose undecidedness blocking abstract argumentation semantics where the undecided label is confined to th e strong connected component where it was generated and it is not propagated to the other parts of the argumentation graph. We show how undecidedness blocking is a fundamental reasoning pattern absent in abstract argumentation but present in similar fashion in the ambiguity blocking semantics of Defeasible logic, in the beyond reasonable doubt legal principle or when someone gives someone else the benefit of the doubt. The resulting semantics, called SCC-void semantics, are defined using an SCC-recursive schema. The semantics are conflict-free and non-admissible, but they incorporate a more relaxed defence-based notion of admissibility. They allow reinstatement and they credulously accept what the corresponding Dung’s complete semantics accepts at least credulously.