Reviewerly/exHarmony huggingface dataset:
Offline Handwritten Gurumukhi Character Recognition System Using Deep Learning

Currently, Gurumukhi—a religion-specific language originating from India, ranks as the 14th most spoken language and the 18th most popular writing script language of the entire world. However, while there exists a large body of literature related to recognition of handwritten texts in various languages, number of publications related to recognition of Indian handwritten scripts is considerably smaller. It concerns also the case of Gurumukhi. Hence, in the current contribution, we consider Gurumukhi handwritten character recognition, to fill the existing practical gap. The proposed approach is based on deep convolutional neural networks and has been applied to the 35 core Gurumukhi characters. Obtained results are promising, as accuracy of 98.32% has been achieved for the training dataset, and 74.66%, on the test data. These results are comparable to results reported in earlier research but have been obtained without any feature extraction or post-processing.
Automatic Anonymization of Textual Documents: Detecting Sensitive Information via Word Embeddings

Data sharing is key in a wide range of activities but raises serious privacy concerns when the data contain personal information. Anonymization mechanisms provide ways to transform the data so that identities and/or sensitive data are not disclosed (i.e., data are no longer personal). Even though a variety of methods have been proposed for structured data, automatic anonymization of unstructured text it still far from being solved. Textual data anonymization consists of detecting sensitive pieces of text, which are later removed and/or generalized. The detection process is especially challenging and it is usually based on classifiers pre-trained on large quantities of manually tagged data, which are able to detect a fixed set of (sensitive) entities such as names or locations. However, this approach is severely limited because sensitive information may appear in text in many forms and not all the appearances of a certain entity type may disclose information on the individual to be protected. In this work we propose a more general solution to text anonymization based on the notion of word embedding. The idea is to represent all the entities appearing in the document as word vectors that capture their semantic relationships. Then a particular entity (e.g. an individual or an organization) can automatically be protected by removing the other entities co-occurring in the document whose vectors are similar to the particular entity's vector. Furthermore, our method does not require manually tagged training data and is language-agnostic. We empirically evaluated our proposal on a collection of biographies. Our results show a significant improvement of the detection recall in comparison with classical approaches to text anonymization based on named entity recognition.
Towards a Privacy-Preserving Voting System Through Blockchain Technologies

The voting system is the process to take the opinion of people to run the constitution properly. Fairness, independence, and unbiasedness should be present in the voting system. Hence, it must be a transparent and secured process so that everybody can express their own opinion freely. Worldwide vote manipulation is an intriguing problem in existing voting systems. Since people in different countries are using digital technology in the voting process (e.g., Optical Scan Voting system, Internet Voting system, Electronic Voting system) instead of traditional way (e.g., Ballot Box). Only digitization could not solve the issues completely. Because still there are numerous ways to manipulate or tamper digital technology and hamper the voting process. To build a secure electronic voting environment, we introduce an application of blockchain technology as a service for the distributed electronic voting system. With the use of blockchain, we achieve data integrity which is a necessary attribute of a voting environment. The anonymity of the voters, privacy, and security of the voting environment is the main goal of this work. Through the design of our system and with the help of blockchain we have solved all the security issues in the voting environment.
A Blockchain based Secure IoT Solution for the Dam Surveillance

The use of Cloud and Internet of Things (IoT) in monitoring the dam site will offer many benefits. In fact, it is very requisite to periodically monitor the quality and level of water in the reservoir and the weather conditions to predict the input flow into the reservoir. Moreover, it is crucial to monitor the dam structure to acquire health and safety condition of the dam infrastructure in real time and prevent the occurrence of events. In this paper, we propose a system architecture composed of a sensor cloud and an UAV (unmanned aerial vehicle) cloud designed for the surveillance of a dam site. The sensor cloud provides various sensing data and the UAV cloud collects these data and delivers them to the dam monitoring center (DMC). Our system is based on the Blockchain (BC) technology which provides authentication, storage, data integrity, traceability of the delivery task of the UAV cloud, and payment of the entities that are responsible for the sensing and delivery tasks. A simulation is conducted to evaluate the performance of the solution.
Large-Scale Tag-Based Font Retrieval With Generative Feature Learning

Font selection is one of the most important steps in a design workflow. Traditional methods rely on ordered lists which require significant domain knowledge and are often difficult to use even for trained professionals. In this paper, we address the problem of large-scale tag-based font retrieval which aims to bring semantics to the font selection process and enable people without expert knowledge to use fonts effectively. We collect a large-scale font tagging dataset of high-quality professional fonts. The dataset contains nearly 20,000 fonts, 2,000 tags, and hundreds of thousands of font-tag relations. We propose a novel generative feature learning algorithm that leverages the unique characteristics of fonts. The key idea is that font images are synthetic and can therefore be controlled by the learning algorithm. We design an integrated rendering and learning process so that the visual feature from one image can be used to reconstruct another image with different text. The resulting feature captures important font design details while is robust to nuisance factors such as text. We propose a novel attention mechanism to re-weight the visual feature for joint visual-text modeling. We combine the feature and the attention mechanism in a novel recognition-retrieval model. Experimental results show that our method significantly outperforms the state-of-the-art for the important problem of large-scale tag-based font retrieval.
Pricing Efficiency and Arbitrage in the Bitcoin Spot and Futures Markets

This study examines the pricing efficiency for the leading cryptocurrency, Bitcoin using spot prices and all CBOE and CME futures contracts traded from January 2018 to March 2019. We find that the futures basis provide some predictive power for future changes in the spot price and in the risk premium. However, the basis of Bitcoin is a biased predictor of the future spot price changes. Cointegration tests also demonstrate that futures prices are biased predictors of spot prices. Deviations from no-arbitrage between spot and futures markets are persistent and widen significantly with Bitcoin thefts (hacks, frauds) as well as alternative cryptocurrency issuances.
Dynamic Facial Models for Video-Based Dimensional Affect Estimation

Dimensional affect estimation from a face video is a challenging task, mainly due to the large number of possible facial displays made up of a set of behaviour primitives including facial muscle actions. The displays vary not only in composition but also in temporal evolution, with each display composed of behaviour primitives with varying in their short and long-term characteristics. Most existing work models affect relies on complex hierarchical recurrent models unable to capture short-term dynamics well. In this paper, we propose to encode these short-term facial shape and appearance dynamics in an image, where only the semantic meaningful information is encoded into the dynamic face images. We also propose binary dynamic facial masks to remove 'stable pixels' from the dynamic images. This process allows filtering of non-dynamic information, i.e. only pixels that have changed in the sequence are retained. Then, the final proposed Dynamic Facial Model (DFM) encodes both filtered facial appearance and shape dynamics of a image sequence preceding to the given frame into a three-channel raster image. A CNN-RNN architecture is tasked with modelling primarily the long-term changes. Experiments show that our dynamic face images achieved superior performance over the standard RGB face images on dimensional affect prediction task.
Maximum Correntropy Criterion-Based Robust Semisupervised Concept Factorization for Image Representation

Concept factorization (CF) has shown its great advantage for both clustering and data representation and is particularly useful for image representation. Compared with nonnegative matrix factorization (NMF), CF can be applied to data containing negative values. However, the performance of CF method and its extensions will degenerate a lot due to the negative effects of outliers, and CF is an unsupervised method that cannot incorporate label information. In this article, we propose a novel CF method, with a novel model built based on the maximum correntropy criterion (MCC). In order to capture the local geometry information of data, our method integrates the robust adaptive embedding and CF into a unified framework. The label information is utilized in the adaptive learning process. Furthermore, an iterative strategy based on the accelerated block coordinate update is proposed. The convergence property of the proposed method is analyzed to ensure that the algorithm converges to a reliable solution. The experimental results on four real-world image data sets show that the new method can almost always filter out the negative effects of the outliers and outperform several state-of-the-art image representation methods.
Anatomy of Commercial IMSI Catchers and Detectors

IMSI catchers threaten the privacy of mobile phone users by identifying and tracking them. Commercial IMSI catcher products exploit vulnerabilities in cellular network security standards to lure nearby mobile devices. Commercial IMSI catcher's technical capabilities and operational details are still kept as a secret and unclearly presented due to the lack of access to these products from the research perspective.
SMT-COP: Defeating Side-Channel Attacks on Execution Units in SMT Processors

Recent advances in side-channel attacks put intoquestion the viability of Simultaneous Multithreading (SMT) architectures from the security standpoint. To address this problem, we propose SMT-COP-a system that eliminates all known side-channels through shared execution logic, including ports and functional units, on SMT processors. At the core of SMT-COP is a small modification to the instruction scheduling logic that allows support for both spatial and temporal partitioning of the execution units to prevent controllable contention for these resources that causes side-channel attacks. We demonstrate thatthe security benefits of SMT-COP are achieved with modest performance overhead and hardware complexity, and without relying on software support. Further performance improvements can be achieved when SMT-COP is applied selectively, and we consider three forms of such selective application: a) in response to detecting resource contention; b) in response to detecting manipulations with time measurement infrastructure; and c) in response to explicit requests by applications. This study represents a step towards making SMT processors more secure.
Being More Focused and Engaged in Firefighting Training: Applying User-Centered Design to VR System Development

Although virtual reality (VR) programs to provide firefighting training continue to be developed and adopted, our investigation with 15 firefighters indicates that a current VR training system tends to convey behavioral tips and does not sufficiently reflect actual firefighters' needs and realities in the field. It often provides somewhat simplified fire simulations and actually lowers the effectiveness of the training. In this paper, we employ Human-Computer Interaction (HCI) methods to examine and identify core elements in firefighting scenarios and develop a VR system that incorporates such elements. We evaluate our system with respect to presence and three design components of the VR simulation (i.e., reality, meaning, play) through a user study with 22 participants. Our study results demonstrate greater user experience and perception toward the four elements in firefighting training with our VR system compared to the existing one. We discuss design implications (e.g., move control, degree of freedom, sight hindrance by smoke, unexpected events) of our study that are expected to help implement and provide an effective VR training system for firefighters.
Recognizing Ping-Pong Motions Using Inertial Data Based on Machine Learning Classification Algorithms

With the development of Internet of Things (IoT) technology and various sensing technologies, some newer ways of perceiving people and the environment have emerged.Commercial wearable sensing devices integrate a variety of sensors that can play a significant role in motion capture and behavioral analysis.This paper proposes a solution for recognizing human motion in ping-pong using a commercial smart watch.We developed a data acquisition system based on the IoT architecture to obtain data relating to areas such as acceleration, angular velocity, and magnetic induction of the watch.Based on the features of the extracted data, experiments were performed using major machine learning classification algorithms including k-nearest neighbor, support vector machine, Naive Bayes, logistic regression, decision tree, and random forest.The results show that the random forest has the best performance, reaching a recognition rate of 97.80%.In addition, we designed a simple convolutional neural network to compare its performance in this problem.The network consists of two convolutional layers, two pooling layers, and two fully connected layers, and it uses data with no extracted features.The results show that it achieves an accuracy of 87.55%.This research can provide training assistance for amateur ping-pong players.
Digital Musical Instruments as Probes: How computation changes the mode-of-being of musical instruments

This article explores how computation opens up possibilities for new musical practices to emerge through technology design. Using the notion of the cultural probe as a lens, we consider the digital musical instrument as an experimental device that yields findings across the fields of music, sociology and acoustics. As part of an artistic-research methodology, the instrumental object as a probe is offered as a means for artists to answer questions that are often formulated outside semantic language. This article considers how computation plays an important role in the authors’ personal performance practices in different ways, which reflect the changed mode-of-being of new musical instruments and our individual and collective relations with them.
Training High-Order Takagi-Sugeno Fuzzy Systems Using Batch Least Squares and Particle Swarm Optimization

Abstract This paper proposes two methods for training Takagi–Sugeno (T-S) fuzzy systems using batch least squares (BLS) and particle swarm optimization (PSO). The T-S system is considered with triangular and Gaussian membership functions in the antecedents and higher-order polynomials in the consequents of fuzzy rules. In the first method, the BLS determines the polynomials in a system in which the fuzzy sets are known. In the second method, the PSO algorithm determines the fuzzy sets, whereas the BLS determines the polynomials. In this paper, the ridge regression is used to stabilize the solution when the problem is close to the singularity. Thanks to this, the proposed methods can be applied when the number of observations is less than the number of predictors. Moreover, the leave-one-out cross-validation is used to avoid overfitting and this way to choose the structure of a fuzzy model. A method of obtaining piecewise linear regression by means of the zero-order T-S system is also presented.
HidingGAN: High Capacity Information Hiding with Generative Adversarial Network

Abstract Image steganography is the technique of hiding secret information within images. It is an important research direction in the security field. Benefitting from the rapid development of deep neural networks, many steganographic algorithms based on deep learning have been proposed. However, two problems remain to be solved in which the most existing methods are limited by small image size and information capacity. In this paper, to address these problems, we propose a high capacity image steganographic model named HidingGAN. The proposed model utilizes a new secret information preprocessing method and Inception‐ResNet block to promote better integration of secret information and image features. Meanwhile, we introduce generative adversarial networks and perceptual loss to maintain the same statistical characteristics of cover images and stego images in the high‐dimensional feature space, thereby improving the undetectability. Through these manners, our model reaches higher imperceptibility, security, and capacity. Experiment results show that our HidingGAN achieves the capacity of 4 bits‐per‐pixel (bpp) at 256 × 256 pixels, improving over the previous best result of 0.4 bpp at 32 × 32 pixels.
MPEG-I Depth Estimation Reference Software

For enabling virtual reality on natural content, Depth Image-Based Rendering (DIBR) techniques have been steadily developed over the past decade, but their quality highly depends on that of the depth estimation. This paper is an attempt to deliver good-quality Depth Estimation Reference Software (DERS) that is well-structured for further use in the worldwide MPEG standardization committee.The existing DERS has been refactored, debugged and extended to any number of input views for generating accurate depth maps. Their quality has been validated by synthesizing DIBR virtual views with the Reference View Synthesizer (RVS) and the Versatile View Synthesizer (VVS), using the available MPEG test sequences. Resulting images and runtimes are reported.
Discrete Matrix Factorization and Extension for Fast Item Recommendation

Binary representation of users and items can dramatically improve efficiency of recommendation and reduce size of recommendation models. However, learning optimal binary codes for them is challenging due to binary constraints, even if squared loss is optimized. In this article, we propose a general framework for discrete matrix factorization based on discrete optimization, which can 1) optimize multiple loss functions; 2) handle both explicit and implicit feedback datasets; and 3) take auxiliary information into account without any hyperparameters. To tackle the challenging discrete optimization problem, we propose block coordinate descent based on semidefinite relaxation of binary quadratic programming. We theoretically show that it is equivalent to discrete coordinate descent when only one coordinate is in each block. We extensively evaluate the proposed algorithms on eight real-world datasets. The results of evaluation show that they outperform the state-of-the-art baselines significantly and that auxiliary information of items improves recommendation performance. For better showing the advantages of binary representation, we further propose a two-stage recommender system, consisting of an item-recalling stage and a subsequent fine-ranking stage. Its extensive evaluation shows hashing can dramatically accelerate item recommendation with little degradation of accuracy.
Improvement and Application of Hybrid Firefly Algorithm

Aiming at the problem of poor global search ability and slow convergence speed when solving optimization problems, this paper proposes improved hybrid firefly algorithm (HFA). HFA improves the position updating method, mutation strategy, chaotic search method and evolution strategy of the population. Specifically, the improved position update formula considers both the effect of high-brightness fireflies on position-updated fireflies, and the effects of the optimal firefly on position-updated fireflies. At the same time, a method of adaptive adjustment parameters in the position update formula is presented, which makes the position update method exhibit strong global search ability and local search ability in the initial stage and the later stage of iteration, respectively. In addition, a combined mutation operator is introduced into HFA, which effectively takes the local search and global search ability of the algorithm into account. Since chaotic search exhibits good ergodicity, an operation of randomly moving all fireflies in the population according to chaotic search is given, which enhances the ability of the algorithm to traverse the whole search space, and further improves the global search ability of the algorithm. To verify the effectiveness of HFA, 28 CEC2017 test problems are selected. The calculation results of 28 CEC2017 test problems show that compared with other algorithms, the accuracy of HFA is obviously better than that of other algorithms. Finally, HFA and other intelligent optimization methods in the literatures are used to optimize the structural parameters of cantilever beams. The optimization results show that the weight of the cantilever beam obtained by HFA is obviously smaller than other algorithms. The calculation results of CEC2017 test problems and practical problem show that the solving quality of HFA is obviously better than other algorithms.
New insights into the structural model of the Makran subduction zone by fusion of 3D inverted geophysical models

Geological modeling by geophysical data in the Makran subduction zone is a challenging task which comes from its extreme structural and geological complexity. Different geological models were presented for this zone in previous studies by the use of various sources of information. Among these studies, numerous geophysical models were introduced to illustrate depth, geometry and location of the subduction zone and definition boundaries with adjacent zones. However, structural complexity of the media and high degree of variation in geological units reduce accuracy of geophysical model derivation. In this study, an integrated strategy via model fusion for geological model building of the Makran subduction zone was presented using gravity and magnetic data. In the presented strategy, gravity and magnetic models were integrated through fusing procedure. Herewith airborne magnetic and gravity data were inverted by fine gridding and solving large scale problems for each set of potential field data. While magnetic model inherently was able to reveal near surface structures, gravity data modeled large depth anomalies. Afterwards, a discrete wavelet transform rule was used for fusion magnetic and gravity models to derive a unique model containing advantage of both sets of data. The fused model revealed more detail of the Makran zone structure, location of the subduction zone and steep dip faults. The final models proposed depth of 40 km for the top of the oceanic crust with variation in thickness from 5 to 8 km, basin ward. The subduction zone located 200 km in the onshore and it shows a very low dip.