Reviewerly/exHarmony huggingface dataset:
Convolutional Neural Networks Considering Local and Global Features for Image Enhancement

In this paper, we propose a novel convolutional neural network (CNN) architecture considering both local and global features for image enhancement. Most conventional image enhancement methods, including Retinex-based methods, cannot restore lost pixel values caused by clipping and quantizing. CNN-based methods have recently been proposed to solve the problem, but they still have a limited performance due to network architectures not handling global features. To handle both local and global features, the proposed architecture consists of three networks: a local encoder, a global encoder, and a decoder. In addition, high dynamic range (HDR) images are used for generating training data for our networks. The use of HDR images makes it possible to train CNNs with better-quality images than images directly captured with cameras. Experimental results show that the proposed method can produce higher-quality images than conventional image enhancement methods including CNN-based methods, in terms of various objective quality metrics: TMQI, entropy, NIQE, and BRISQUE.
On Low-Bitrate Image Compression for Distributed Automotive Perception: Higher Peak SNR Does Not Mean Better Semantic Segmentation

The high amount of sensors required for autonomous driving poses enormous challenges on the capacity of automotive bus systems. There is a need to understand tradeoffs between bitrate and perception performance. In this paper, we compare the image compression standards JPEG, JPEG2000, and WebP to a modern encoder/decoder image compression approach based on generative adversarial networks (GANs). We evaluate both the pure compression performance using typical metrics such as peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and others, but also the performance of a subsequent perception function, namely a semantic segmentation (characterized by the mean intersection over union (mIoU) measure). Not surprisingly, for all investigated compression methods, a higher bitrate means better results in all investigated quality metrics. Interestingly, however, we show that the semantic segmentation mIoU of the GAN autoencoder in the highly relevant low-bitrate regime (at 0.0625 bit/pixel) is better by 3.9 % absolute than JPEG2000, although the latter still is considerably better in terms of PSNR (5.91dB difference). This effect can greatly be enlarged by training the semantic segmentation model with images originating from the decoder, so that the mIoU using the segmentation model trained by GAN reconstructions exceeds the use of the model trained with original images by almost 20 % absolute. We conclude that distributed perception in future autonomous driving will most probably not provide a solution to the automotive bus capacity bottleneck by using standard compression schemes such as JPEG2000, but requires modern coding approaches, with the GAN encoder/decoder method being a promising candidate.
Discovering rare correlated periodic patterns in multiple sequences

Periodic-Frequent Pattern Mining (PFPM) is an emerging problem, which consists of identifying frequent patterns that periodically occur over time in a sequence of events. Though PFPM is useful in many domains, traditional algorithms have two important limitations. First, they are not designed to find rare patterns. But discovering rare patterns is useful in many domains (e.g. to study rare diseases). Second, traditional PFPM algorithms are generally designed to find patterns in a single sequence, but identifying periodic patterns that are common to a set of sequences is often desirable (e.g. to find patterns common to several hospital patients or customers). To address these limitations, this paper proposes to discover a novel type of patterns in multiple sequences called Rare Correlated Periodic Patterns. Properties of the problem are studied, and an efficient algorithm named MRCPPS (Mining Rare Correlated Periodic Patterns common to multiple Sequences) is presented to efficiently find these patterns. It relies on a novel RCPPS-list structure to avoid repeatedly scanning the database. Experiments have been done on several real datasets, and it was observed that the proposed MRCPPS algorithm can efficiently discover all rare correlated periodic patterns common to multiple sequences, and filter many non rare and correlated patterns.
BCH Codes with computational approach and its applications in image encryption

Accelerated adventures in computer Science and technology has made digital technology, a need of the day. Academicians, researchers, and technologists are anxious to share their secret data through the communication channel along with its security. The security of data, transmission rate, and error correction capability are the fundamental questions against the data transmission through any algebraic code dependent communication channel. Though, data security is always questioned due to the synchronized encoding and decoding algorithms. In this paper, a novel approach is developed to ensure the data security issues occurred due to synchronized encoding-decoding of a BCH code and for data transmission, a computational technique is designed by which data can be encoded and transmitted by using Field-Linear BCH code or a Ring-Linear BCH code of the same dimension, designed distance and code length. Although the Ring-Linear BCH code is preferable for encoding, on the other hand decoding of data is adept by Field-Linear BCH code. Accordingly, a computational technique of Barlekamp Massey Algorithm is utilized for the purpose. This scheme provides a quick code selection of the desired level of transmission rate and error correction capability during the communication. Thus, it also addresses the dimension issue of primitive BCH code. In addition, for the data security perspective we utilize a BCH code in round key addition and mixed column matrix steps in AES algorithm and then put on this modified AES algorithm to image encryption. The image encryption quality permits to incorporate this alteration in AES.
An Efficient Random Access Light Field Video Compression Utilizing Diagonal Inter-View Prediction

A more realistic Virtual and Augmented Reality is promised by the emergence of Light Field (LF) technology. Considering LF technology benefits come with an exponential increase in the amount of data required as well as the complexity of view access, the need for efficient compression and random access schemes is essential. In this study, a novel and efficient pseudo-sequence based LF video compression scheme is proposed that offers the best trade-off between coding and access complexity. The philosophy behind our proposed prediction structure is maximally utilizing the low-level frames in hierarchy prediction structure to achieve better random access at a minimal expense of compression efficiency.
Surface Vehicle Detection and Tracking with Deep Learning and Appearance Feature

A real-time vision-based surface vehicle detection and tracking algorithm for the unmanned surface vehicle is proposed in this paper. The algorithm is designed towards the aim of robust close-range vehicle detection and tracking to meet the needs of automatic navigation for the unmanned surface vehicle (USV). We employed a deep convolutional network to obtain high-performance object detection. Then a data association-based multiple target tracking method is implemented by combining the appearance feature and the estimation of the motion state through the Kalman filter. The experimental results show that compared to SORT tracking algorithm, the proposed method can reduce the number of identity switches and better complete the detection and tracking of surface vehicles.
Furiously fast: On the speed of change in formulaic language

Abstract Addressing a topic that has been marginal to discussions within historical linguistics, this study looks at how extent and speed of language change can be quantified meaningfully using corpus data. Looking specifically at formulaic language (understood here as word sequences that instantiate typical phrasings), a solidly data-based assessment of the speed of change within a 100-year time window is offered. This includes both a relative determination of speed (against the speed of change in lexis which is generally thought to be the fastest type of linguistic change, cf. Algeo 1980: 264; Trask and Millar 2010: 7) as well as a new independent measure of speed which is easy to interpret and therefore of high validity, while also robust and potentially applicable to any linguistic feature that can be counted in corpus data. Using data from a diachronic reference corpus of 20th century German, it is shown that change in formulaic language is very notably faster than lexical change, that the extent of change over a century is comparable in extent to contemporary inter-genre variation and that overall, the rate of change does fluctuate somewhat at the level of temporal granularity employed in this study. It is also argued that quantifying the speed of linguistic change can play an important role in building a deeper understanding of language change in general.
Multi-View Video Synopsis via Simultaneous Object-Shifting and View-Switching Optimization

We present a method for synopsizing multiple videos captured by a set of surveillance cameras with some overlapped field-of-views. Currently, object-based approaches that directly shift objects along the time axis are already able to compute compact synopsis results for multiple surveillance videos. The challenge is how to present the multiple synopsis results in a more compact and understandable way. Previous approaches show them side by side on the screen, which however is difficult for user to comprehend. In this paper, we solve the problem by joint object-shifting and camera view-switching. Firstly, we synchronize the input videos, and group the same object in different videos together. Then we shift the groups of objects along the time axis to obtain multiple synopsis videos. Instead of showing them simultaneously, we just show one of them at each time, and allow to switch among the views of different synopsis videos. In this view switching way, we obtain just a single synopsis results consisting of content from all the input videos, which is much easier for user to follow and understand. To obtain the best synopsis result, we construct a simultaneous object-shifting and view-switching optimization framework instead of solving them separately. We also present an alternative optimization strategy composed of graph cuts and dynamic programming to solve the unified optimization. Experiments demonstrate that our single synopsis video generated from multiple input videos is compact, complete, and easy to understand.
Noncontact Detection of Movements of Standing Up From and Sitting Down on a Chair Using Electrostatic Induction

In this paper, a new technique to measure the movements of standing up from and sitting down on a chair, which is an example of daily human activities, without using cameras and video images is presented. The change in the electric potential of the human body caused by everyday tasks induces an electrostatic current in the electrode placed in the proximity of the body. By detecting the motion-induced change in the charge of the human body, it is possible to detect the daily activity being performed. This technique is based on the detection of an electrostatic induction current of the order of sub-picoamperes flowing through an electrode placed 5 m from the subject. Therefore, we measured transferring from chair to chair movement as an example of daily activities and tried to extract the unique features of the individuals included in the movement. The proposed method could further detect the details of an individual by detecting the unique patterns of current flow induced during different motions.
Linn dress

We present the design and prototype of the Linn Dress, a transforming dress that allows its wearer to dynamically reveal and conceal areas of skin, as they feel comfortable and appropriate for different contexts. Electrochromic displays are employed as the elements enabling the garment's dynamic transparency. The Linn Dress is designed for office workers with a lively lifestyle, who shift directly from the work environment to social evening events. The dress presents professional working attire, and when transformed to reveal exciting open cuts and patterns, a cocktail dress.
No One Can Escape: A General Approach to Detect Tampered and Generated Image

Fake or tampered images pose a real problem in today's life.It is easy to unknowingly be drawn to an interesting image that is false.Recently, with the emergence of generative adversarial networks (GANs), it becomes much more easy to generate high-quality fake images in a very realistic way.However, the current digital image forensics algorithms mainly focus on the detection of traditional tampered images or need prior knowledge of the network structure of GANs.Hence, verifying the authenticity of an image is very challenging.In this paper, we propose a general method for simultaneously detecting tampered images, and GANs generated images.First, we use the Scharr operator to extract the edge information of the image.Then, we converted the edge image information matrix into the gray level co-occurrence matrix (GLCM) to scale the image without loss of image information.Finally, GLCM was fed into the deep neural network designed based on depthwise separable convolution for training.Compared with other methods, our model achieves a higher macro average of F1 score of 0.9865.Meanwhile, our method has better performance in detecting tampered images and has strong generalization ability for many GANs models.
KnuckleTouch

While mobile devices have become essential for social communication and have paved the way for work on the go, their interactive capabilities are still limited to simple touch input. A promising enhancement for touch interaction is knuckle input but recognizing knuckle gestures robustly and accurately remains challenging. We present a method to differentiate between 17 finger and knuckle gestures based on a long short-term memory (LSTM) machine learning model. Furthermore, we introduce an open source approach that is ready-to-deploy on commodity touch-based devices. The model was trained on a new dataset that we collected in a mobile interaction study with 18 participants. We show that our method can achieve an accuracy of 86.8% on recognizing one of the 17 gestures and an accuracy of 94.6% to differentiate between finger and knuckle. In our evaluation study, we validate our models and found that the LSTM gesture recognizing archived an accuracy of 88.6%. We show that KnuckleTouch can be used to improve the input expressiveness and to provide shortcuts to frequently used functions.
Travelling Salesman Problem: An Empirical Comparison Between ACO, PSO, ABC, FA and GA

Travelling salesman problem (TSP) is one of the optimization problems which has been studied with a large number of heuristic and metaheuristic algorithms, wherein swarm and evolutionary algorithms have provided effective solutions to TSP even with a large number of cities. In this paper, our objective is to solve some of the benchmark TSPs using ant colony optimization (ACO), particle swarm optimization (PSO), artificial bee colony (ABC), firefly algorithm (FA) and genetic algorithm (GA). The empirical comparisons of the experimental outcomes show that ACO and GA outperform to ABC, PSO and FA for the given TSP.
Generative Model for Zero-Shot Sketch-Based Image Retrieval

We present a probabilistic model for Sketch-Based Image Retrieval (SBIR) where, at retrieval time, we are given sketches from novel classes, that were not present at training time. Existing SBIR methods, most of which rely on learning class-wise correspondences between sketches and images, typically work well only for previously seen sketch classes, and result in poor retrieval performance on novel classes. To address this, we propose a generative model that learns to generate images, conditioned on a given novel class sketch. This enables us to reduce the SBIR problem to a standard image-to-image search problem. Our model is based on an inverse auto-regressive flow based variational autoencoder, with a feedback mechanism to ensure robust image generation. We evaluate our model on two very challenging datasets, Sketchy, and TU Berlin, with novel train-test split. The proposed approach significantly outperforms various baselines on both the datasets.
BCON: Blockchain based access CONtrol across multiple conflict of interest domains

In today's on-demand computing and virtual coalition environment, cross-domain services are acquired and provided. These business domains may belong to either the same or different conflict of interest system. “Transitive access” can cause leakage of information between competitors through some other conflict of interest system's member. Therefore, a secure access control mechanism is required to detect and deny “transitive access” efficiently with minimal trust in externalist. Existing access control mechanisms focused on either single or multiple conflict of interest domains but with no “transitive access”. In addition, these existing mechanisms are centralized with inherited unfair access control and are a single point of failure. Blockchain (BC) is a shared digital ledger encompassing a list of connected blocks stored on a decentralized distributed network that is secured through cryptography. We propose a BC based access control for conflict of interest domains. We have integrated a BC in our architecture to make access control fair, verifiable and decentralized. Users access histories and “transitive accesses” are stored on BC ledger. We propose a novel mechanism called “Transitive Access Checking and Enforcement (TACE)” i.e., “Algorithm.1”. It makes an authorization decision based on BC endorsement that “transitive access” will not occur. “Algorithm.2” verifies and updates users access histories stored at BC before each request approval. Similarly, “Algorithm.3” detects possible future “transitive accesses” and updates Transitive Access Set (TAS) stored at BC after each request approval. The Simple Promela Interpreter (SPIN) model checker is used to verify the proposed mechanisms for “transitive access” detection and prevention. We have identified four conflicting sequences of execution that can cause “transitive access”. Results show that the proposed mechanism is safe against “transitive access” by checking all the four possible conflicting sequences of execution.
A Hybrid PSO-GA Algorithm for Traveling Salesman Problems in Different Environments

In this study particle swarm optimization (PSO) is modified and hybridised with genetic algorithm (GA) using one’s output as the other's input to solve Traveling Salesman Problem(TSP). Here multiple velocity update rules are introduced to modify the PSO and at the time of the movement of a solution, one rule is selected depending on its performances using roulette wheel selection process. Each velocity update rule and the corresponding solution update rule are defined using swap sequence (SS) and swap operation (SO). K-Opt operation is applied in a regular interval of iterations for the movement of any stagnant solution. GA is applied on the final output swarm of the PSO to search the optimal path of the large size TSPs. Roulette wheel selection process, multi-point cyclic crossover and the K-opt operation for the mutation are used in the GA phase. The algorithm is tested in crisp environment using different size benchmark test problems available in the TSPLIB. In the crisp environment the algorithm gives approximately 100% success rate for the test problems up to considerably large sizes. Efficiency of the algorithm is tested with some other existing algorithms in the literature using Friedman test. Some approaches are incorporated with this algorithm for finding solutions of the TSPs in imprecise (fuzzy/rough) environment. Imprecise problems are generated from the crisp problems randomly, solved and obtained results are discussed. It is observed that the performance of the proposed algorithm is better compared to the some other algorithms in the existing literature with respect to the accuracy and the consistency for the symmetric TSPs as well as the Asymmetric TSPs.
A Study on Catastrophic Forgetting in Deep LSTM Networks

We present a systematic study of Catastrophic Forgetting (CF), i.e., the abrupt loss of previously acquired knowledge, when retraining deep recurrent LSTM networks with new samples. CF has recently received renewed attention in the case of feed-forward DNNs, and this article is the first work that aims to rigorously establish whether deep LSTM networks are afflicted by CF as well, and to what degree. In order to test this fully, training is conducted using a wide variety of high-dimensional image-based sequence classification tasks derived from established visual classification benchmarks (MNIST, Devanagari, FashionMNIST and EMNIST). We find that the CF effect occurs universally, without exception, for deep LSTM-based sequence classifiers, regardless of the construction and provenance of sequences. This leads us to conclude that LSTMs, just like DNNs, are fully affected by CF, and that further research work needs to be conducted in order to determine how to avoid this effect (which is not a goal of this study).
Consistent Online Gaussian Process Regression Without the Sample Complexity Bottleneck

Gaussian process regression provides a framework for nonlinear nonparametric Bayesian inference applicable across machine learning, robotics, chemical engineering, and other settings. Unfortunately, the computational burden of the posterior mean and covariance scales cubically with the training sample size. Even worse, in the online setting where samples perpetually arrive, this complexity approaches infinity. Thus, popular perception is that Gaussian processes cannot be used with streaming data, and that approximations are required. Motivated by this necessity, we develop the first compression sub-routine for online Gaussian processes that preserves their convergence to the population posterior, i.e., asymptotic posterior consistency, while ameliorating their intractable complexity growth with the sample size. We do so by after each sequential Bayesian update, fixing an error neighborhood with respect to the Hellinger metric centered at the current empirical probability measure, and greedily tossing out past kernel dictionary elements until we hit the boundary of this neighborhood. We call the resulting method Parsimonious Online Gaussian Processes (POG). When we set the error radius, or compression budget, go to null with the sample size, then exact asymptotic consistency is preserved (Theorem li) at the cost of unbounded memory in the limit. On the other hand, for constant compression budget, POG converges to a neighborhood of the population posterior distribution (Theorem 1ii) but with finite memory that is at-worst determined by the metric entropy of the feature space (Theorem 2). Experiments on benchmark data demonstrates that POG exhibits favorable performance in practice.
Classification of liver tumors with CEUS based on 3D-CNN

Liver cancer has the third highest mortality rate in the world. Effective treatment depends on the accurate identification of benign and malignant tumors. This paper proposed a computer-aided system for distinguishing liver lesions based on CEUS, a widely accepted inspection technique. Video sequences are handled by the 3D convolutional neural network (3D-CNN) to extract spatial and temporal features. Meanwhile, the framework is trained by a specific dataset to yield a classification network. According to the results, our system obtained higher performance than the previous system. The average accuracy rate reached 93.1% with ten-fold cross-validation. It is noteworthy that the system is potential and easy to expand to other applications.