Reviewerly/exHarmony huggingface dataset:
Design of Naturally Distorted Image Database-NDID

The rich content of the electronic images challenge the existing image quality assessment (IQA) models and image aesthetics assessment models. In the meanwhile, it is difficult to get undistorted images, which is important in traditional databases. Therefore, establishing new database containing naturally distorted images is in urgent need. This database contains both image quality factors and image aesthetics factors, which can meet the future research needs of these two types of models. In this paper, we completed the following tasks majorly: (1) We collected 807 naturally distorted images of four semantics in three ways. (2) A subjective evaluation experiment was designed to allow subjective evaluators to rate and label the distorted images comprehensively. (3) We apply NDID to various kinds of image evaluation models and popular deep learning networks. As experimental results showing, NDID have achieved a flat or slightly lower performance compared with other existing databases used in these models. This even or poor performance indicates that NDID has certain practical value for the improvement of images evaluation models.
A Survey on Text Detection from Document Images

Extraction of textual information is a very fundamental step in the domain of information retrieval, searching, editing, documenting, archiving, or recording the appearance of text. Appearance of text means text information is embedded in the image. Textual information can be obtained from various forms like scanners, magazines, newspapers, etc. In this survey paper, an elaborative review on text extraction is given, which includes different classifiers, feature extraction and segmentation techniques.
Autonomous Vehicle and Augmented Reality Usage

With the development of autonomous development technology, the need for additional applications to be used inside and outside the vehicle is increasing.As a result of the literature review, many applications have been developed to display vehicle data directly on the monitor, with reflections on glass, and on hardware devices.These applications have been developed only for a defined problem and for a particular autonomous system.In this study, a basic autonomous vehicle software infrastructure and mobile Augmented Reality application that can work on Android devices have been developed.The Mobile Augmented Reality app serves inside and outside the vehicle.In addition, this application has been shown to support multiple autonomous system infrastructures.
Generative Datalog with Continuous Distributions

Arguing for the need to combine declarative and probabilistic programming, Bárány et al. (TODS 2017) recently introduced a probabilistic extension of Datalog as a “purely declarative probabilistic programming language.” We revisit this language and propose a more principled approach towards defining its semantics based on stochastic kernels and Markov processes—standard notions from probability theory. This allows us to extend the semantics to continuous probability distributions, thereby settling an open problem posed by Bárány et al. We show that our semantics is fairly robust, allowing both parallel execution and arbitrary chase orders when evaluating a program. We cast our semantics in the framework of infinite probabilistic databases (Grohe and Lindner, LMCS 2022) and show that the semantics remains meaningful even when the input of a probabilistic Datalog program is an arbitrary probabilistic database.
Person Re-Identification via Feature Representation Learning Based on Verification Sample Constrain

In person re-identification (ReID) task, the variance between the samples are quite large, and there is no standard sample as a comparison. The current common method is to implement it as a classification task, which can get better results than the classical methods in verification task such as contrastive loss. However, the identification loss used in classification task only seeks the boundary of the classification, the intra-class distance between samples is still large so that it is insufficient for ReID task. In this paper, we consider to overcome these difficulties by proposing a joint loss with an identification loss and constrains of modeling the Euclidean distances between samples and their corresponding eigen data which is constructed by Principal Component Analysis method (PCA), we call it EigenPerson. The entire loss is formed in a linear combination. This work is mainly motivated by the center loss for face recognition problems, of which regularizations are restricted by a simultaneously learned center. We substitute the center with EigenPerson which we constructed offline as an auxiliary training sample. The learned model with our proposed method is evaluated on the benchmark of Market1501 and CUHK03 and achieve comparable results to those methods proposed in the same period.
Indian Currency Recognition Using Radial Basis Function and Support Vector Machine

The currency is one of the important and valuable assets of any country. There are various people who are trying to do malpractices and forgeries with the currency of the country. Therefore, it is very important to make a difference between the original and duplicate currency. It is very difficult for the human to find the difference between original and duplicate notes, therefore, the nation needs the help of technology and intelligent system for recognition of currency. There are various algorithms are implemented for recognition of currency. Here in this research paper, the Indian currencies are used for recognition. The currency consists of various types of features but in this work, the edge and different moment information of Indian currencies are calculated and based on that radial basis function and support vector machine are trained and tested for recognition of Indian currencies. The above algorithms for recognition of Indian currencies are implemented using python programming language and scikit-learn package for machine learning. Here in this paper, the performance of the radial basis function is more than the performance of the support vector machine.
Learner Behavior towards Mobile Learning at the Open University in Thailand

A study was conducted to determine the behavior of learners at Sukhothai Thammathirat Open University (STOU) in Thailand before developing mobile learning. It attempted to determine the needs and conditions of mobile technology usage according to the perceptions about mobile learning. The sample consisted of 39 male and 112 female undergraduate students (totaling 151 students) studying at the School of Liberal Arts from 3 programs -- Thai Studies (42 percent), Information Science (34 percent), and English (24 percent). Most of the respondents were between 31 and 35 years old and were largely working in government organizations. About 48 percent of learners spent time using their mobile phones between 5-11 pm. The findings indicated positive learner behaviors toward using mobile technology. Each of the following scores are ranked in ascending order: behavior in business for finance and online shopping (33 percent), behavior in entertainment for photo and video taking (42 percent), behavior in society for social networking (57 percent), and behavior in education for searching information (58 percent). Furthermore, all learners agreed on the benefits of mobile learning, including online learning anywhere and anytime, more understanding of lessons, self-learning assessment, easy user interface, and interaction among students and between students and teachers.
Context-Based Synthetic Data for Logo Recognition

In order to solve the problem of sparse training samples in logo recognition task, a multi-type context-based logo data synthesis algorithm is proposed. The algorithm comprehensively utilizes the local and full context of the logo object and the scene image to guide the synthesis of the logo image. The experimental results on the FlickrLogos-32 show that the proposed algorithm can greatly improve the performance of the logo recognition algorithm without relying on additional manual annotation, verify the validity of the synthesis algorithm, and further prove that multi-type context can improve the performance of the object recognition algorithm.
Enhancing Security in Medical Image Informatics with Various Attacks

The objective of the work is to provide security to the medical images by embedding medical data (EPR-Electronic Patient Record) along with the image to reduce the bandwidth during communication. Reversible watermarking and Digital Signature itself will provide high security. This application mainly used in tele-surgery (Medical Expert to Medical Expert Communication). Only the authorized medical experts can explore the patients' image because of Kerberos. The proposed work is mainly to restrict the unauthorized access to get the patients'data. So medical image authentication may be achieved without biometric recognition such as finger prints and eye stamps etc. The EPR itself contains the patients' entire history, so after the extraction process Medical expert can able to identify the patient and also the disease information. In future we can embed the EPR inside the medical image after it got encrypted to achieve more security. To increase the authentication, Medical Expert biometric information can be embedded inside the image in the future. Experiments were conducted using more than 500 (512 × 512) image archives in various modalities from the NIH (National Institute of Health) and Aycan sample digital images downloaded from the internet and tests are conducted. Almost in all images with greater than 15000 bits embedding size and got PSNR of 60.4 dB to 78.9 dB with low distortion in received image because of compression, not because of watermarking and average NPCR (Number of Pixels Change Rate) is 98.9 %.
A Framework to Impede Phishing Activities Using Neural Net and Website Features

Internet has metamorphosed into an unreliable platform due to the deceiving acts of phishing. With the emerging social networking platforms and growing online businesses, attackers find an enormous number of heedless victims. Envisaging this current scenario, the number of threats will keep on rising unless a gratifying solution is discovered. In this paper, we designed an anti-phishing framework to analyze a website using its features. When a website needs to be verified, its URL is submitted to the framework. The website is classified as either Phishing, Legitimate, or Suspicious. The model is compared to other existing Machine learning classifiers for evaluation. Proposed Neural net achieved the highest accuracy of 90.03% with True Positive rate of 0.933 and False positive rate of 0.099.
Binary Outer Product Expansion of Convolutional Kernels

Convolutional neural networks (CNNs) have found great success in many artificial intelligence applications. At the heart of a CNN is the operation of convolution between multi-channel data and learned kernels. It is usually implemented as a floating-point large matrix multiplication, a major bottleneck of computational speed and memory usage. In this paper, we propose a binary outer product expansion (BOPE) method to represent a kernel matrix or tensor as a weighted sum of the outer products between binary vectors of values +1 or -1. This allows for network compression and simplified computation at the same time. Our theoretical analysis shows such a decomposition converges to the original matrix given a sufficient number of binary vectors. We present computational methods to estimate outer product weights using either optimized or random binary base vectors. Significant data compression can be achieved for a highly redundant matrix, since weights and binary vectors require less storage than array elements. In addition, most floating-point multiplication in matrix convolution can be replaced by addition and binary XOR, lessening computation and memory requirements. We propose a compact convolutional layer in which highly redundant convolutional kernels are projected onto binary vectors, and represented as a weighted sum of outer products. It is shown that the number of weights in AlexNet, VGG-19 and ResNet-50 can be reduced 3.45, 6.87 and 2.95 times respectively with less than a 1% loss in the top-1 and top-5 classification accuracy on ImageNet, and MobileNetV2 can be reduced 2.31 times with a 2% loss. Compared to the standard CNN, this compact convolutional network has fewer trainable weights, is better regularized, and is easier to train from fewer training samples. Therefore, it is particularly suited for devices with limited computation, memory, and battery power.
Learn to Gesture

Presentation is one of the most important and vivid methods to deliver information to audience. Apart from the content of presentation, how the speaker behaves during presentation makes a big difference. In other words, gestures, as part of the visual perception and synchronized with verbal information, express some subtle information that the voice or words alone cannot deliver. One of the most effective ways to improve presentation is to practice through feedback/suggestions by an expert. However, hiring human experts is expensive thus impractical most of the time. Towards this end, we propose a speech to gesture network (POSE) to generate exemplary body language given a vocal behavior speech as input. Specifically, we build an "expert" Speech-Gesture database based on the featured TED talk videos, and design a two-layer attentive recurrent encoder-decoder network to learn the translation from speech to gesture, as well as the hierarchical structure within gestures. Lastly, given a speech audio sequence, the appropriate gesture will be generated and visualized for a more effective communication. Both objective and subjective validation show the effectiveness of our proposed method.
Time-Domain Coding for Resource-Efficient Deep Neural Networks

The field of machine learning has an urgent demand of on-edge solutions that implement neural network inference at low-cost in terms of hardware resources. This paper presents a novel architecture of neural network that targets extreme area reduction. The key proposal is the use of a time-domain coding that just needs a single-wire connection between the elements of the network. This coding allows us to implement all the multiply-accumulate operations of a neuron with a single adder and a register. Also, the non-linear activation function needs to be implemented just once for the whole architecture, supposing an important reduction of area. Compared to previous works that employ the same word-length representation, the proposal achieves a significant area saving: 35.2% reduction in the number of LUTs and a 72.5% reduction in the number of FFs without employing any BRAM or DSP slices, albeit with a throughput reduction of 184.2 times. The proposal opens a windows of opportunity for the exploration of architectures that employ alternative coding styles.
Heuristics for the Specific Substring Problem with Hamming Distance

An important problem in Computational Biology is to determine genetic markers, substrings of a set of sequences that do not occur on sequences of other sets. Applications for this problem include finding small specific regions for primer design and to find specific organisms or sequences in metagenomes. Genetic markers can be addressed by the Specific Substring Problem - SSP which consists of finding all minimal substrings in a given set of sequences with at least k differences among all the substrings in another sequence set. Since this problem spend quadratic time when Hamming distance is considered and we have, in general, a large volume of data to be processed, this solution becomes impractical. With this in mind, the main focus of this work is to propose and investigate the use of heuristic and parallel approaches for the SSP whose effectiveness were verified with artificial and real data experiments.
Multi-View Collaborative Representation Classification

With the increase popularity of multi-view data, multi-view learning has attracted vital attentions in pattern recognition as well as machine learning. Most of existing methods apply in traditional single view learning. However, these methods neglect the complementary information among the views. The aim of multi-view is to discover complementary information and enhance the single view learning result. Multi-view is capable of capture incomplete and different types of information from multiple sources. However, multi-views may contain redundant information. Many multi-view methods assume that multi-views are generated from various view-specific generation matrices. This paper proposes the multi-view collaborative representation classification (MVCRC) algorithm which contains the information of different views and the connection of view-to-view. Experimental results conducted on five practical databases are used to confirm the effectiveness of the proposed approach.
Minimum-Impact First

Virtual Machine (VM) migration is an important feature for ensuring smooth operations during maintenance and disaster recovery scenarios. The migration might be inter-site and in such a case the inter-site bandwidth which is typically Wide Area Network (WAN) might be a bottleneck. In such a case, the bandwidth is affected by the amount of inter-VM traffic that becomes separated during the migration process. The amount of separated traffic might not only cause degradation of the of the Quality of Service (QoS) of inter-communicating VMs but can also delay the migration process due to the congestion of the migration link. The state-of-the-art algorithm due to Yazidi et al. is an affinity aware algorithm that does not consider the completion time of the migration. The first stage of our algorithm is identical to Yazidi et al. where we resort to graph partitioning theory in order to partition the VMs into groups with high intra-group communication. In the second stage, we devise a greedy algorithm for controlling the order of the migration groups by considering their inter-group traffic that greedily selects groups with the lowest impact in terms of volume of separated traffic which we denominate Minimum-Impact First (MIF). We also design a latency-aware algorithm that only schedules the quickest migration first. The latter simple heuristic interestingly outperforms legacy works in the case of migration over a non-dedicated link. We find that our MIF algorithm consistently outperforms the state-of-the-art algorithms by a clear margin using real-traffic traces by a margin larger than 40%. We show that the MIF algorithm ensures the lowest amount of separated traffic in both dedicated-link and non-dedicated-link scenarios.
A CPU-GPU Cooperative Sorting Approach

Sorting is a fundamental operation in computer science. Sorting is normally done in CPU. Graphics processing Units (GPU) are basically used to render graphical objects. Nowadays GPUs are also used for high-performance general-purpose computation. Due to the availability of GPUs for general purpose computation sorting can also be done on GPUs. The CPU has fewer cores, but it operates at higher frequency than GPUs. GPUs possess large number of cores and hence provide high throughput. The drawback of CPU-GPU execution model is that when the GPU is executing, the CPU remains idle. Due to this model of execution enormous computation power of CPU cores is wasted. By cooperatively utilizing both CPU and GPU for performing a task, we can reduce the computation time of a task. In this paper we perform merge sort using both CPU and GPU cooperatively. With this method we obtained a speedup of around 3 compared to the GPU-only execution.
Twitter Sentimental Analytics Using Hive and Flume

With the use of web 2.0 technology, people often share their reviews, opinions, news, and information with others. They express their experiences regarding vacations, complain about movies, or rave about restaurants, and discuss various latest sports rumors. The speed and ease of communication have increased due to social media platforms. Consumer shares their opinions about products and services on consumer opinion portals such as Facebook, Instagram, Twitter, blogs, WhatsApp, Snapchat, LinkedIn, etc. Thousands of blogs, millions of tweets, and billions of emails are written each day. Among these social media platforms, Twitter is one of them that is gaining high popularity nowadays. It provides fast and efficient way to analyze customers’ perspectives toward a product or service. Developing a program for sentimental analysis is the method to be used to mark customer’s perceptions or his/her reviews toward a product. Twitter is a microblogging site that enables users to send updates in the form of reviews or messages to a group of followers. Based on the opinion reflected, a tweet can be classified as positive, negative, or neutral. In this paper, we are basically investigating the sentiment of Twitter messages.
An Improved Security Analysis on an Indeterminate Equation Public Key Cryptosystem by Evaluation Attacks

Akiyama, Goto, Okumura, Takagi, Nuida and Hanaoka introduced an indeterminate equation analogue of learning with errors (IE-LWE) problem as a new computationally hard problem and constructed a candidate of post-quantum cryptosystem, called “Giophantus”. Giophantus satisfies the indistinguishability under chosen plaintext attack (IND-CPA) if IE-LWE problem is computationally infeasible. Akiyama et al., Shimizu and Ikematsu proposed improved Giophantus to the post-quantum standardization project. Beullens, Castryck and Vercauteren proposed an evaluation at one attack against IND-CPA security of Giophantus. However, Akiyama et al. assert that recommended parameters can resist Vercauteren et al.’s attack. Therefore, the security analysis on Giophantus is still needed. In this paper, we propose a new kind of evaluation attack against IND-CPA security of Giophantus. Our attack solves IE-LWE problem by combining a part of Vercauteren et al.’s attack with a lattice attack on low rank lattices, e.g., 6-rank lattices for recommended parameters. Moreover, we investigate a way to avoid our attack and some variants of our attack. We give some remarks on modification of the IE-LWE problem. Our experimental analysis shows that our attack can solve IE-LWE problem efficiently, and that Giophantus does not satisfy IND-CPA security unless IE-LWE problem is modified appropriately.
Realtime Water-Hazard Detection and Visualisation for Autonomous Navigation and Advanced Driving Assistance

Water on road is a potential hazard for road vehicles. In complex traffic, it is difficult for drivers and autonomous navigation systems to safely share the road with other vehicles and pedestrians while detecting and avoiding water puddles and pot holes. Detection of water puddles needs to be both accurate and fast enough for realtime assistance or planning. We present a new fast deep-learning based water detection network that exploits temporal information to achieve an accuracy comparable to the state-of-the-art accuracy while requiring less than half memory and CUDA cores. We also show how the detection results are visualised in 3D for AR/VR applications.