Reviewerly/exHarmony huggingface dataset:
An Ontological Approach to Knowledge Building by Data Integration

This paper discusses the uncertainty in the automation of knowledge building from heterogeneous raw datasets. Ontologies play a critical role in such a process by providing a well consolidated support to link and semantically integrate datasets via interoperability, as well as semantic enrichment and annotations. By adopting Semantic Web technology, the resulting ecosystem is fully machine consumable. However, while the manual alignment of concepts from different vocabularies is reasonable at a small scale, fully automatic mechanisms are required once the target system scales up, leading to a significant uncertainty.
Freeform surface interferometry with an adaptive ring-cavity compensator

In the existing deformable mirror (DM)-based adaptive interferometers for freeform surfaces test, the departure coverage and the DM surface monitoring load are contradictory to each other. We reported an adaptive ring-cavity compensator (ARCC) based freeform surface interferometer. With the multiple cyclic reflections of the beam in the ARRC, the departure coverage is enlarged to four times of the stroke of one DM. With only one DM, the ARCC achieves the coverage comparable to dual-DMs, without any incremental DM monitoring load and decoupling operation. The interferometer coverage is thus doubled at a relatively low cost. The freeform surface measurement and the DM surface monitoring are performed with a rotatable polaroid for gating, without any other auxiliary devices. Validated experiments are presented.
Residual Learning With Multifactor Extreme Learning Machines for Waveheight Prediction

We explore the use of extreme learning machines (ELMs) for ocean waveheight prediction. Ocean waveheight variations are affected by multiple factors, such as air pressure, wind speed, and wind direction. To investigate the effects of these factors on waveheight prediction from a machine learning perspective, we develop a multifactor ELM (MFELM), which generalizes the vector inputs of existing ELMs into those of matrix inputs. The generalization enables the MFELM to admit the “multiple factor and multiple time” observed data (e.g., the air pressure, wind speed, and wind direction time sequences) for comprehensive waveheight prediction. Furthermore, we define the concept of numerical residuals in terms of the difference between observed data and waveheight numerical predictions, which are generated from equations of ocean dynamics. In contrast to the commonly used training schemes based on observed data, we propose to exploit numerical residuals for training the MFELM. The motivation for the training strategy arises from the observation that the numerical residuals follow an approximate Gaussian distribution and an ELM (or its generalization MFELM) can generate Gaussian random input layer weights and biases that have common distribution with the numerical residuals. This enhances the representational power of the ELM related methodologies in terms of effectively capturing numerical residual variations. The trained MFELM predicts future numerical residuals that are used for correcting numerical predictions, and finally result in more accurate predictions. Experimental results validate the effectiveness of our proposed method.
Iterative Training of Unsupervised Neural and Statistical Machine Translation Systems

Recent work achieved remarkable results in training neural machine translation (NMT) systems in a fully unsupervised way, with new and dedicated architectures that only rely on monolingual corpora. However, previous work also showed that unsupervised statistical machine translation (USMT) performs better than unsupervised NMT (UNMT), especially for distant language pairs. To take advantage of the superiority of USMT over UNMT, and considering that SMT suffers from well-known limitations overcome by NMT, we propose to define UNMT as NMT trained with the supervision of synthetic parallel data generated by USMT. This way we can exploit USMT up to its limits while ultimately relying on full-fledged NMT models to generate translations. We show significant improvements in translation quality over previous work and also that further improvements can be obtained by alternatively and iteratively training USMT and UNMT. Without the need of a dedicated architecture for UNMT, our simple approach can straightforwardly benefit from any recent and future advances in supervised NMT. Our systems achieve a new state-of-the-art for unsupervised machine translation in all of our six translation tasks for five diverse language pairs, surpassing even supervised SMT or NMT in some tasks. Furthermore, our analysis shows how crucial the comparability between the monolingual corpora used for unsupervised training is in improving translation quality.
Additive Ensemble Neural Networks

Deep neural networks (DNNs) have been making progress in many ways. DNNs are typically used to model complex nonlinearity of high-dimensional data in regression or classification problems. As DNNs contain additional hidden layers, they generally improve performance but increase the number of parameters to train, thereby extending the learning time. Many studies, such as those employing Dropout and regularization methods, have undertaken to solve these problems. The method proposed in this paper is an additive ensemble neural networks (AENNs), by which a boosting mechanism of an ensemble methodology is applied to the neural networks instead of regularization techniques. That is, the model by AENNs is obtained by sequentially combining several simple shallow network models. Experiments showed that AENNs yield better results than conventional DNNs and machine learning methods for regression and classification problems, thereby alleviating the troublesome model complexity issue.
Frequency Domain Learning Scheme for Massive MIMO Using Deep Neural Network

Massive MIMO is one of the cornerstones of 5G technology. MIMO scaled up to hundreds or even thousands of antenna terminals can result in an extensive increase in the capacity at reduced computational complexity. Channel State Information (CSI) estimation has an indispensable role in the deployment of massive MIMO. Since the spatial information is important for the massive MIMO phase component to have higher significance as compared to the magnitude component in CSI. If the phase estimation of the channel can be made accurate, we can ensure efficient estimation of channel gains as well. Thereby ensuring the error-free transmission of massive data. The proposed multi-layer perceptron model for massive MIMO takes the beamformed signal with higher directivity as its input and learns the features of different channel conditions and predict the direction of arrival (DoA) or Angle of Arrival (AoA) of the received signal. This accurate prediction of DoA helps in the estimation of channel conditions much better than the time domain counterpart especially with a reduced number of iterations. The proposed system has better metrics about the accuracy, mean squared error (MSE) performance, and bit error rate (BER) performance. The number of epochs required for training is less implies computational complexity is less, which is a significant improvement comparing with other data-driven techniques. Such a scheme that can make predictions on the channel at a very lesser time helps to adapt the transmission parameters according to the channel thereby ensuring in building a communication network that can handle the transmission of the huge volume of data that are free from any transmission errors or distortions.
Dynamic mode decomposition via dictionary learning for foreground modeling in videos

Accurate extraction of foregrounds in videos is one of the challenging problems in computer vision. In this study, we propose dynamic mode decomposition via dictionary learning (dl-DMD), which is applied to extract moving objects by separating the sequence of video frames into foreground and background information with a dictionary learned using block patches on the video frames. Dynamic mode decomposition (DMD) decomposes spatiotemporal data into spatial modes, each of whose temporal behavior is characterized by a single frequency and growth/decay rate and is applicable to split a video into foregrounds and the background when applying it to a video. And, in dl-DMD, DMD is applied on coefficient matrices estimated over a learned dictionary, which enables accurate estimation of dynamical information in videos. Due to this scheme, dl-DMD can analyze the dynamics of respective regions in a video based on estimated amplitudes and temporal evolution over patches. The results on synthetic data exhibit that dl-DMD outperforms the standard DMD and compressed DMD (cDMD) based methods. Also, the results of an empirical performance evaluation in the case of foreground extraction from videos using publicly available dataset demonstrates the effectiveness of the proposed dl-DMD algorithm and achieves a performance that is comparable to that of the state-of-the-art techniques in foreground extraction tasks.
Mobile based MCQ Answer Sheet Analysis and Evaluation Application

Multiple Choice Question (MCQ) script sheets are very popular in Bangladesh as it is an easy way to take exams that include Multiple choice questions. It is also a great way of collecting a huge amount of data in a short period of time. This research proposes a novel approach to analyzing and evaluating MCQ scripts without using any OMR machine or OMR paper. The first step is to scan the MCQ script as an image via an android smartphone. Secondly, the specific answer area from the image is cropped. After that, each answer choice is extracted and Black pixels are calculated in each choice to obtain the given answer. In the final step, a text file containing given answers to all questions is generated.
Adversarial Examples Detection and Analysis with Layer-wise Autoencoders

This paper presents a mechanism for detecting adversarial examples based on data representations taken from the hidden layers of the target network. Individual autoencoders at intermediate layers of the target network are trained for this purpose. This describes the manifold of true data and, in consequence, can be used to classify whether a given example has the same characteristics as true data. It also gives insight into the behavior of adversarial examples and their flow through the layers of a deep neural network. Experimental results show that our method outperforms the state of the art in supervised and unsupervised settings.
A Comparative Approach to Predictive Analytics with Machine Learning for Fraud Detection of Realtime Financial Data

With digital strategies coping up with banks and financial institutions, enormous data passed to these sectors, business transactions are becoming more prone to frauds and threats resulting in data leakage and personal details exposed to fraudsters leading to huge loss to organizations as well as to customers. This makes organizations adapt to high-level security and data handling technology solutions like machine learning, deep learning and predictive analytics which are efficient enough to deal with highly sensitive data, predict frauds and unwanted behavioural patterns in this data. This paper reviews the different advance technologies commonly used to deal with this type of data forms a comparison among them and suggests the most efficient and informative method to use in this sector. Through the end of the review, feature engineering and its selection of parameters for achieving better performance are discussed.
Survey of Preprocessing Techniques and Classification Approaches in Online Signature Verification

This paper reviews the latest results in the field of online signature verification and summarizes the previously published major surveys and also over 30 papers from the last decade. We examine the steps of the verification process and show the most popular approaches used. Our results show that alignment and scaling are the most common methods used in preprocessing. Position, velocity, and pressure are the most commonly used measures for feature extraction while dynamic time warping is the most commonly used approach for verification. A comparison between these methods using different databases concludes this work. The error rate varies between 0.77% to 7.13%, with an average of 2.94%. The results and comparisons published in this paper may help researchers choose the most promising approaches for their systems.
Stability of quantum linear logic circuits against perturbations

Abstract Here we study transformation of waveshapes of photons under the action of the linear logic circuits and other related architectures involving only linear optical networks and measurements. We show that the gates are working well not only in the case when all photons are separable and located in the same mode, but in some more general cases. For instance, the photonic waveshapes are allowed to be slightly different in different channels; in this case, Zeno effect prevents the photons from decoherence after the measurement, and the gate thus remains neutral to the small waveshape perturbations.
Supervised Fractional-Order Embedding Geometrical Multi-View CCA (SFGMCCA) for Multiple Feature Integration

Techniques for integrating different types of multiple features effectively have been actively studied in recent years. Multiset canonical correlation analysis (MCCA), which maximizes the sum of pairwise correlations of inter-view (i.e., between different features), is one of the powerful methods for integrating different types of multiple features, and various MCCA-based methods have been proposed. This work focuses on a supervised MCCA variant in order to construct a novel effective feature integration framework. In this paper, we newly propose supervised fractional-order embedding geometrical multi-view CCA (SFGMCCA). This method constructs not only the correlation structure but also two types of geometrical structures of intra-view (i.e., within each feature) and inter-view simultaneously, thereby realizing more precise feature integration. This method also supports the integration of small sample and high-dimensional data by using the fractional-order technique. We conducted experiments using four types of image datasets, i.e., MNIST, COIL-20, ETH-80 and CIFAR-10. Furthermore, we also performed an fMRI dataset containing brain signals to verify the robustness. As a result, it was confirmed that accuracy improvements using SFGMCCA were statistically significant at the significance level of 0.05 compared to those using conventional representative MCCA-based methods.
DG3: Exploiting Gesture Declarative Models for Sample Generation and Online Recognition

In this paper, we introduce DG3, an end-to-end method for exploiting gesture interaction in user interfaces. The method allows to declaratively model stroke gestures and their sub-parts, generating the training samples for the recognition algorithm. In addition, we extend the algorithms of the $-family for supporting the online (i.e., real-time ) stroke recognition and their parts, as declared in the models. Finally, we show that the method outperforms existing approaches for online recognition and has comparable accuracy with offline methods after a few gesture segments.
Multi-Staged Training of Deep Neural Networks for Micro-Expression Recognition

Micro-expressions are fast involuntary movements of the face that convey the emotions of people. They are hard to simulate or hide, so their recognition (when spotted) can be used as an indicative of true emotions. We propose in this paper a method for recognizing micro-expressions from high-speed video sequences. We use a deep neural network which we trained using a multi-stage approach. Our model is composed of a convolutional neural network which extracts representative features from individual frames of the sequence and a recurrent neural network which captures the evolution of the face during the video sequence. We use convolutional autoencoders for learning the most expressive facial features. We present our results on recognizing the emotion conveyed by the micro-expressions and the impact our multi-staged approach has on the performance of the network.
Classifying Software Vulnerabilities by Using the Bugs Framework

Software vulnerabilities, specific type of software bugs, are defined as occurrences of a software weakness, which can be exploited by an agent to cause various consequences such as modifying or accessing unintended data. Identifying and fixing software vulnerabilities thus plays an important role in software security and software engineering. A reliable body of knowledge on categories of vulnerabilities is critical to identify software vulnerabilities. In this paper, we use data-mining techniques to identify software vulnerabilities, classify them into different categories by using the Bugs Framework proposed by the National Institute of Standards and Technology (NIST), and design a model to predict the weakness of future vulnerabilities. Knowledge about vulnerability types helps software engineers save time and energy, develop programs by avoiding security vulnerabilities, and program with precaution.
A real-time multi-constraints obstacle avoidance method using LiDAR

Obstacle avoidance is one of the essential and indispensable functions for autonomous mobile robots. Most of the existing solutions are typically based on single condition constraint and cannot incorporate sensor data in a real-time manner, which often fail to respond to unexpected moving obstacles in dynamic unknown environments. In this paper, a novel real-time multi-constraints obstacle avoidance method using Light Detection and Ranging(LiDAR) is proposed, which is able to, based on the latest estimation of the robot pose and environment, find the sub-goal defined by a multi-constraints function within the explored region and plan a corresponding optimal trajectory at each time step iteratively, so that the robot approaches the goal over time. Meanwhile, at each time step, the improved Ant Colony Optimization(ACO) algorithm is also used to re-plan optimal paths from the latest robot pose to the latest defined sub-goal position. While ensuring convergence, planning in this method is done by repeated local optimizations, so that the latest sensor data from LiDAR and derived environment information can be fully utilized at each step until the robot reaches the desired position. This method facilitates real-time performance, also has little requirement on memory space or computational power due to its nature, thus our method has huge potentials to benefit small low-cost autonomous platforms. The method is evaluated against several existing technologies in both simulation and real-world experiments.
QFR: A QoE-driven Fine-grained Routing Scheme for Virtual Reality Video Streaming over SDN

In order to meet the Quality of Experience (QoE) requirements of Virtual Reality (VR) video users under limited resources, efficient and adaptive routing scheme is required. The next generation mobile networks 5G can match network and computing resources according to service requirements, which will be the communication technology for the VR industry. In 5G architecture, the introduction of Software Defined Networking (SDN) decouples the control plane and the forwarding plane, and provides the ability of more granular network resource management. It can actively allocate resources for VR video to optimize transmission performance. In this paper, a QoE-driven Fine-grained routing (QFR) scheme based on SDN has been proposed. The core of QFR is the route calculation algorithm and the route allocation strategy. The route calculation algorithm is a two-stage adaptive routing algorithm. In the first stage, by means of an improved Dijkstra algorithm, the algorithm calculates k paths with the shortest delay. In the second stage, the k paths with the shortest delay are ranked according to the predicted QoE of each path. In addition, tile-based VR video provides a prerequisite for fine-grained routing scheduling. Through differentiated routing of Field of View (FoV) video streaming and Non-FoV video streaming, we develope a fine-grained route allocation strategy. The route allocation strategy determines how to allocate the sorted k paths with the shortest delay according to the residual bandwidth. Comparative evaluation of QFR is conducted to show its preponderance over several existing routing schemes, in terms of download bitrate and QoE of VR video.
Guest Column

Randomness is a valuable resource in computation. Randomness is used to run various Monte Carlo simulations of complex systems such as the stock market or weather prediction systems. Various randomized algorithms have been discovered that often vastly outperform known deterministic counterparts (see [MR10] for examples). Cryptography is another area that crucially relies on access to random bits, and it is known that various basic cryptographic primitives fail to be secure if the quality of the randomness used is poor [DOPS04]. However natural sources of randomness are typically defective. This leads to the following basic question: \Can we efficiently produce truly random bits given access to defective sources of randomness?"
A Reinforcement Learning Agent based on Genetic Programming and Universal Search

Universal search can serve as an asymptotically optimal agent for machine inversion and time-limited optimization problems. The optimality is independent of problem size, but search space has an exponential dependency on solution size. Reinforcement learning with gradient ascent can dampen this search space. However, in many scenarios in large state spaces, the gradient information becomes nonexistent for a long time which slows down learning. Genetic programming merged with universal search is proposed and build a reinforcement learning agent to alleviate this problem. The universal search is implemented using a functional dataflow graph-based programming model with equivalent program pruning and gradient ascent based incremental learning. The genetic programming naturally fits into the universal search with implicit crossover and mutation operators and without any need of problem-specific population initialization. The agent is experimented on two problem environments and outperformed state of the art method.