Reviewerly/exHarmony huggingface dataset:
Metaheuristic-Based Intelligent Solutions Searching Algorithms of Ant Colony Optimization and Backpropagation in Neural Networks

Computation is concerned with the validation of algorithm, estimation of complexity and optimization. This requires large dataset analysis for the purpose of finding the unknown optimal solution. Aside the intricacies in completing tasks, this process is expensive and time inefficient; attaining solutions with conventional mathematical approaches are unrealizable. Search algorithms were advanced to improve solution approaches for optimization problems by finding the possible sets of solution to a particular problem as contained in search space. However, metaheuristic algorithms suggest three solutions to optimization problems on the basis of the application areas in real-life situations including: near optima, the optimal or the best solution. This paper analyses the decision-making processes of two nature-inspired search algorithms namely: Backpropagation search algorithm and ant colony optimization (ACO). The results revealed that, backpropagation search algorithm without ACO training trailed those trained with ACO for MSE, RMSE, RAE and MAPE. Again, forecasts errors estimated in the neural network set-up were smaller due to directional search mechanism of the ACO as against the approach provided in neuro-fuzzy rules set tuning by Rajab and Sharma (Soft Comput 23:921–936, 2017) [1]. There is need to consider metaheuristic algorithms approaches to obtain better solutions or nearest optimal values to the optimization problems in neural networks.
Improved reinforcement learning with curriculum

Humans tend to learn complex abstract concepts faster if examples are presented in a structured manner. For instance, when learning how to play a board game, usually one of the first concepts learned is how the game ends, i.e. the actions that lead to a terminal state (win, lose or draw). The advantage of learning end-games first is that once the actions leading to a terminal state are understood, it becomes possible to incrementally learn the consequences of actions that are further away from a terminal state – we call this an end-game-first curriculum. The state-of-the-art machine learning player for general board games, AlphaZero by Google DeepMind, does not employ a structured training curriculum. Whilst Deepmind’s approach is effective, their method for generating experiences by self-play is resource intensive, costing literally millions of dollars in computational resources. We have developed a new method called the end-game-first training curriculum, which, when applied to the self-play/experience-generation loop, reduces the required computational resources to achieve the same level of learning. Our approach improves performance by not generating experiences which are expected to be of low training value. The end-game-first curriculum enables significant savings in processing resources and is potentially applicable to other problems that can be framed in terms of a game.
Yi Language Speech Recognition using Deep Learning Methods

Yi language is one of the most representative languages in the Yi branch of the Tibetan-Burmese language family. To strengthen the protection of Yi language, an endangered minority language, this paper covers the continuous speech recognition of Yi language with different deep learning methods to achieve the optimal recognition performance. In the training stage, we first analyzed the Yi language text to realize the language model modeling, and then trained the audio files based on the four acoustic models: hidden Markov model (HMM), deep neural network (DNN), time-delay neural network (TDNN) and end-to-end. In the recognition stage, after matching the language model according to the acoustic model, we obtained the Yi recognized text by combining the dictionary and acoustic feature vector for joint recognition and decoding. Compared with the word error rates of acoustic models, the time-delay neural network is the best in existed Yi corpus, only 16.65%.
Deanonymization of Litecoin Through Transaction-Linkage Attacks

With the development of blockchain technology, the use of cryptocurrencies in online payments has become increasingly prevalent. Litecoin, proposed in 2011, is currently the fifth-largest cryptocurrency in market value. Due to certain characteristics, such as the use of pseudonyms as transaction addresses, user privacy could be protected to some extent. However, there are some problems about its privacy guarantees.In this paper, we aim to reveal the severity of deanonymization attacks on online Litecoin payments. Firstly, we simulate purchases on merchant websites accepting Litecoin and monitor trackers embedding on information and payment pages. Secondly, we conduct transaction-linkage attacks on simulated digital and physical transaction flows, respectively. Our results show that a tracker is more likely to find the target transaction on Litecoin blockchain by collecting the digital transaction flows and implementing transaction-linkage attacks. The number of digital transaction flows with anonymous set size 1 and 2 account for 95% and 5% of all digital transaction flows, respectively. The success rate of the transaction-linkage attack is 0.975. To get the optimal uncertainty parameters of transaction-linkage attacks, we introduce a refined deanonymization attack by making real purchases. Finally, we present two new privacy protection measures against transaction-linkage attacks.
Reply to Loog et al.: Looking beyond the peaking phenomenon

The letter “A brief prehistory of double descent” (1) written in response to our article “Reconciling modern machine-learning practice and the classical bias–variance trade-off” (2) brings a number of interesting points and important references. We agree that the … [↵][1]1To whom correspondence may be addressed. Email: mbelkin{at}cse.ohio-state.edu. [1]: #xref-corresp-1-1
Semi-infinite programming for trajectory optimization with non-convex obstacles

This article presents a novel optimization method that handles collision constraints with complex, non-convex 3D geometries. The optimization problem is cast as a semi-infinite program in which each collision constraint is implicitly treated as an infinite number of numeric constraints. The approach progressively generates some of these constraints for inclusion in a finite nonlinear program. Constraint generation uses an oracle to detect points of deepest penetration, and this oracle is implemented efficiently via signed distance field (SDF) versus point cloud collision detection. This approach is applied to pose optimization and trajectory optimization for both free-flying rigid bodies and articulated robots. Experiments demonstrate performance improvements compared with optimizers that handle only convex polyhedra, and demonstrate efficient collision avoidance between non-convex CAD models and point clouds in a variety of pose and trajectory optimization settings.
Noise Removal in Embedded Image With Bit Approximation

Stego-images are often contaminated by interchannel noise or active noise attack when communicating on the Web.And it is challenging to restore embedded image from corrupted stego-image.This paper studies a kNN-bit approximation algorithm to remove noises in embedded image.The proposed algorithm distinguishes reliable bits from extracted bits, and estimates pixel values by keeping reliable bits unchanged and correcting unreliable bits.Specifically, the 8th (highest) unreliable bit of a pixel can be approximated with its nearest neighbor pixels.And then, if an unreliable bit locates at any one of the 5 th $ 7 th bits of a pixel, it is adjusted with two nearest neighbors of the pixel, where the pixel is in-between these two nearest neighbors.Finally, for other unreliable bits, each one is approximated by the maximum and minimum possible values of nearest neighbors of its pixel.We conduct experiments for illustrating the efficiency, and demonstrate that the proposed algorithm can recover the embedded images with good visual quality from corrupted stego-images.
High sensitivity to conceptual cues in Turkish heritage speakers with dominant German L2

This study investigates semantics-morphosyntax and pragmatics-morphosyntax interfaces in separate experiments, yet examining the same structure, namely the optional verb number marking in Turkish. We tested a group of bilingual heritage speakers of Turkish, whose dominant language is German. Optional use of the overt verb number marking in Turkish interacts with semantic and pragmatic properties of the plural subject of the sentence. The interaction of optional verb number marking with these properties is tested separately in two different experiments, using the Magnitude Estimation technique. The results showed that the bilingual speakers treat both interface types differently from the monolingual speakers. More precisely, the bilingual speakers make finer distinctions regarding the semantic and pragmatic notions that were put into test. This sensitivity results in a semantically and pragmatically constrained pattern, which is in line with both language-specific descriptions and cross-linguistic tendencies. This outcome is taken to suggest that the nonconvergence in the bilingual data stems from a high sensitivity to the semantic and pragmatic properties that constrain the use of the morphosyntactic structure under investigation. The research results are further evaluated from a processing based linguistic framework, namely Modular Online Growth and Use of Language (MOGUL).
Quantitative Schlieren Image-Noise Reduction Using Inverse Process and Multi-Path Integration

This report deals with introducing two new techniques based on a novel concept of complex brightness gradient in quantitative schlieren images, "inverse process" and "multi-path integration" for image-noise reduction.Noise in schlieren images affects the projections (density thickness) images of computerized tomography (CT).One spot noise in the schlieren image appears in a line shape in the density thickness image.Noise effect like an infectious disease spreads from a noisy pixel to the next pixel in the direction of single-path integration.On the one hand, the noise in the schlieren image reduces the quality of the image and quantitative analysis and is undesirable; on the other it is unavoidable.Therefore, the importance of proper noise reduction techniques seems essential and tangible.In the present report, a novel technique "multi-path integration" is proposed for noise reduction in projections images of CT.Multi-path integration is required the schlieren brightness gradient in two orthogonal directions.The 20-directional quantitative schlieren optical system presents only images of schlieren brightness in the horizontal gradient and another 20-directional optical system seems necessary to obtain vertical schlieren brightness gradient, simultaneously.Using the "inverse process", a new technique enables us to obtain vertical schlieren brightness gradient from horizontal experimental data without the necessity of a new optical system and can be used for obtaining any optional directions of schlieren brightness gradient.
Dropping Classes for Deep Speaker Representation Learning

Many recent works on deep speaker embeddings train their feature extraction networks on large classification tasks, distinguishing between all speakers in a training set. Empirically, this has been shown to produce speaker-discriminative embeddings, even for unseen speakers. However, it is not clear that this is the optimal means of training embeddings that generalize well. This work proposes two approaches to learning embeddings, based on the notion of dropping classes during training. We demonstrate that both approaches can yield performance gains in speaker verification tasks. The first proposed method, DropClass, works via periodically dropping a random subset of classes from the training data and the output layer throughout training, resulting in a feature extractor trained on many different classification tasks. Combined with an additive angular margin loss, this method can yield a 7.9% relative improvement in equal error rate (EER) over a strong baseline on VoxCeleb. The second proposed method, DropAdapt, is a means of adapting a trained model to a set of enrolment speakers in an unsupervised manner. This is performed by fine-tuning a model on only those classes which produce high probability predictions when the enrolment speakers are used as input, again also dropping the relevant rows from the output layer. This method yields a large 13.2% relative improvement in EER on VoxCeleb. The code for this paper has been made publicly available.
Influence of tweets and diversification on serendipitous research paper recommender systems

In recent years, a large body of literature has accumulated around the topic of research paper recommender systems. However, since most studies have focused on the variable of accuracy, they have overlooked the serendipity of recommendations, which is an important determinant of user satisfaction. Serendipity is concerned with the relevance and unexpectedness of recommendations, and so serendipitous items are considered those which positively surprise users. The purpose of this article was to examine two key research questions: firstly, whether a user’s Tweets can assist in generating more serendipitous recommendations; and secondly, whether the diversification of a list of recommended items further improves serendipity. To investigate these issues, an online experiment was conducted in the domain of computer science with 22 subjects. As an evaluation metric, we use the serendipity score (SRDP), in which the unexpectedness of recommendations is inferred by using a primitive recommendation strategy. The results indicate that a user’s Tweets do not improve serendipity, but they can reflect recent research interests and are typically heterogeneous. Contrastingly, diversification was found to lead to a greater number of serendipitous research paper recommendations.
Satellite image-based generation of high frequency solar radiation time series for the assessment of solar energy systems

Solar energy is envisaged as a major pillar of the global transition to a climate-friendly energy system.Variability of solar radiation requires additional balancing measures to ensure a stable and secure energy supply.In order to analyze this issue in detail, solar radiation time series data of appropriate temporal and spatial resolution is necessary.Common weather models and satellites are only delivering solar surface irradiance with temporal resolutions of up to 15 min.Significant short-term variability in irradiances within seconds to minutes however is induced by clouds.Ground-based measurements typically used to capture this variability are costly and only sparsely available.Hence, a method to synthetically generate time series from currently available satellite imagery is of value for researchers, grid operators, and project developers.There are efforts to increase satellite resolution to 1 min, but this is not planned everywhere and will not change the spatial resolution.Therefore, the fundamental question remains if there are alternative strategies to obtain high temporal resolution observations at a pinpoint.This paper presents a method to generate 1 min resolved synthetic time series of global and direct normal irradiances for arbitrary locations.A neural network based on satellite image derived cloud structure parameters enables to classify high-frequency solar radiation variability.Combined with clear-sky radiation data, 1 min time series which reflect the typical variability characteristics of a site are reproduced.Testing and validation against ground observations (BSRN) show that the method can accurately reproduce characteristics such as frequency and ramp distributions.An application case demonstrates the usage in low-voltage grid studies.
Optimization Scheme for Text Classification Using Machine Learning Naïve Bayes Classifier

Text classification is an essential advance in characteristic dialect processing. It very well may be performed utilizing different classification algorithms. Hadoop Map Reduce is widely utilized in text classification to perform classification on colossal measure of text data. However, Map Reduce required a ton of time to perform the tasks thereby increasing latency and since the data is distributed over the cluster it builds time and thus reducing processing speed. Also, Hadoop utilizes long queue of code. Motivated by this, we propose a basic yet compelling machine learning method which uses Naïve Bayes classifier for text data. In Machine Learning approach, the classifier is built automatically by learning the properties of categories from a set of predefined training data. Hence, it can process complex furthermore, multi assortmentinformation in dynamic situations. Here we propose a naïve bayes classifier which scales directly with number of indicators and data points which can be used for both binary and multi-class classification problems. We implemented the presented schemes using Machine Learning tool. The experimental results demonstrate the performance improvement in the classification technique.
Smart Surveillance and Real-Time Human Action Recognition Using OpenPose

Security is one of the major components of the Smart Cities Mission of the Indian Government. This paper presents an efficient way of improving the existing security system and making the threat prevention and protection process more automated. We introduce a dynamic smart security camera system which has a trained image classifier model fed into it that observes and recognizes the movements and gestures of the human or object in its video range and detects any illegal or suspicious activity.
Glanceable, legible typography over complex backgrounds

Modern digital interfaces display typeface in ways new to the 500 year old art of typography, driving a shift in reading from primarily long-form to increasingly short-form. In safety-critical settings, such at-a-glance reading competes with the need to understand the environment. To keep both type and the environment legible, a variety of ‘middle layer’ approaches are employed. But what is the best approach to presenting type over complex backgrounds so as to preserve legibility? This work tests and ranks middle layers in three studies. In the first study, Gaussian blur and semi-transparent ‘scrim’ middle layer techniques best maximise legibility. In the second, an optimal combination of the two is identified. In the third, letter-localised middle layers are tested, with results favouring drop-shadows. These results, discussed in mixed reality (MR) including overlays, virtual reality (VR), and augmented reality (AR), considers a future in which glanceable reading amidst complex backgrounds is common. Practitioner summary: Typography over complex backgrounds, meant to be read and understood at a glance, was once niche but today is a growing design challenge for graphical user interface HCI. We provide a technique, evidence-based strategies, and illuminating results for maximising legibility of glanceable typography over complex backgrounds. Abbreviations: AR: augmented reality; VR: virtual reality; HUD: head-up display; OLED: organic light-emitting diode; UX: user experience; MS: millisecond; CM: centimeter
Global and Local Multi-scale Feature Fusion Enhancement for Brain Tumor Segmentation and Pancreas Segmentation

The fully convolutional networks (FCNs) have been widely applied in numerous medical image segmentation tasks. However, tissue regions usually have large variations of shape and scale, so the ability of neural networks to learn multi-scale features is important to the segmentation performance. In this paper, we improve the network for multi-scale feature fusion, in the medical image segmentation by introducing two feature fusion modules: i) global attention multi-scale feature fusion module (GMF); ii) local dense multi-scale feature fusion module (LMF). GMF aims to use global context information to guide the recalibration of low-level features from both spatial and channel aspects, so as to enhance the utilization of effective multi-scale features and suppress the noise of low-level features. LMF adopts bottom-up top-down structure to capture context information, to generate semantic features, and to fuse feature information at different scales. LMF can integrate local dense multi-scale context features layer by layer in the network, thus improving the ability of network to encode interdependent relationships among boundary pixels. Based on the above two modules, we propose a novel medical image segmentation framework (GLF-Net). We evaluated the proposed network and modules on challenging brain tumor segmentation and pancreas segmentation datasets, and very competitive performance has been achieved.
Powered Wheelchair for Mobility with Features to Address Physical Strength, Cognitive Response, and Motor Action Development Issues

For the mobility of the disabled a wheelchair is an important aid. Particularly for the physically impaired and the aged adults. The physical disability may lead to several other disabilities such as cognitive deterioration that takes a person towards forgetness i.e., mild memory loss to dementia and later lead to Alzheimer’s, Parkinson’s disease. This deteriorates the motor action of a person and makes them dependable on others. The present work addresses the hypothesis between the physical development and physiological development of aged group suffering with physical and cognitive disability. The proposed project is the development towards the previous finding carried out and extended with further added features. The ARM processor is incorporated in the module using MEMS for navigation purpose. Stimulation and exercise of body-hands, legs, back is carried out using motors. The cognitive operations are carried out using a Display and associated Wi-Fi connectivity.
Continuous-Discrete Reinforcement Learning for Hybrid Control in Robotics.

Many real-world control problems involve both discrete decision variables - such as the choice of control modes, gear switching or digital outputs - as well as continuous decision variables - such as velocity setpoints, control gains or analogue outputs. However, when defining the corresponding optimal control or reinforcement learning problem, it is commonly approximated with fully continuous or fully discrete action spaces. These simplifications aim at tailoring the problem to a particular algorithm or solver which may only support one type of action space. Alternatively, expert heuristics are used to remove discrete actions from an otherwise continuous space. In contrast, we propose to treat hybrid problems in their 'native' form by solving them with hybrid reinforcement learning, which optimizes for discrete and continuous actions simultaneously. In our experiments, we first demonstrate that the proposed approach efficiently solves such natively hybrid reinforcement learning problems. We then show, both in simulation and on robotic hardware, the benefits of removing possibly imperfect expert-designed heuristics. Lastly, hybrid reinforcement learning encourages us to rethink problem definitions. We propose reformulating control problems, e.g. by adding meta actions, to improve exploration or reduce mechanical wear and tear.