Reviewerly/exHarmony huggingface dataset:
Homomorphically Securing AI at the Edge

Edge devices are becoming increasingly pervasive in everyday life. These devices have become more computationally capable allowing for more complex AI reasoning at the edge. Subsequently, there is a need for protecting data to comply with privacy laws and confidentiality regulations. In this paper, we demonstrate the applicability of homomorphic encryption for protecting the data of AI-enabled cameras at the edge by implementing our solution on a commercial edge device. Our solution comprises a local homomorphic key-value database on an edge device populated by an AI camera; permitting the service of homomorphic search to be performed directly on the edge device. We characterize our implementation demonstrating linear behavior with respect to the database size that the edge device can support. Good enough performance is known to be difficult to achieve when employing homomorphic encryption. Our results are encouraging as we achieved solutions considered to be homomorphically fast, for example, linear performance of 1.28 seconds per database entry at over 256 bits of security. This amounts to a query being processed on a database of 200 entries in ~ 5 minutes.
Combining semantic graph and probabilistic topic models for discovering coherent topics

Probabilistic topic models, which frequently represent topics as multinomial distributions over words, have been extensively used for discovering latent topics in text corpora. However, because topic models are entirely unsupervised, they may lead to topics that are not understandable in applicatio ns. Recently, several knowledge-based topic models have been proposed which primarily use word-level domain knowledge in the model to enhance the topic coherence and ignore the rich information carried by entities (e.g, persons, locations, organizations, etc.) associated with the documents. Additionally, there exists a vast amount of prior knowledge (background knowledge) represented as Linked Open Data (LOD) datasets and other ontologies, which can be incorporated into the topic models to produce coherent topics. In this paper, we introduce a novel regularization entity-based topic model (RETM), which integrates an ontology with an entity-based topic model (EntLDA) to increase the coherence of the identified topics through the topic modeling process. Our experimental results demonstrate the effectiveness of the proposed model in improving the coherence of topics.
An experimental scrutiny of visual design modelling: VCL up against UML+OCL

Abstract The graphical nature of prominent modelling notations, such as the standards UML and SysML, enables them to tap into the cognitive benefits of diagrams. However, these notations hardly exploit the cognitive potential of diagrams and are only partially graphical with invariants and operations being expressed textually. The Visual Contract Language (VCL) aims at improving visual modelling; it tries to (a) maximise diagrammatic cognitive effectiveness, (b) increase visual expressivity, and (c) level of rigour and formality. It is an alternative to UML that does largely pictorially what is traditionally done textually. The paper presents the results of a controlled experiment carried out four times in different academic settings and involving 43 participants, which compares VCL against UML and OCL and whose goal is to provide insight on benefits and limitations of visual modelling. The paper’s hypotheses are evaluated using a crossover design with the following tasks: (i) modelling of state space, invariants and operations, (ii) comprehension of modelled problem, (iii) detection of model defects and (iv) comprehension of a given model. Although visual approaches have been used and advocated for decades, this is the first empirical investigation looking into the effects of graphical expression of invariants and operations on modelling and model usage tasks. Results suggest VCL benefits in defect detection, model comprehension, and modelling of operations, providing some empirical evidence on the benefits of graphical software design.
Development of Class Attendance System Using Face Recognition for Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia

The traditional approach of student attendance monitoring system in Universiti Tun Hussein Onn Malaysia is slow and disruptive. As a solution, biometric verification based on face recognition for student attendance monitoring was presented. The face recognition system consisted of five main stages. Firstly, face images under various conditions were acquired. Next, face detection was performed using the Viola Jones algorithm to detect the face in the original image. The original image was minimized and transformed into grayscale for faster computation. Histogram techniques of oriented gradients was applied to extract the features from the grayscale images, followed by the principal component analysis (PCA) in dimension reduction stage. Face recognition, the last stage of the entire system, using support vector machine (SVM) as classifier. The development of a graphical user interface for student attendance monitoring was also involved. The highest face recognition accuracy of 62% was achieved. The obtained results are less promising which warrants further analysis and improvement.
Video Key Frame Detection Using Block Sparse Coding

In the advent of video data explosion, to understand the concept of the video, knowledge of representative data selection and summarization has become essential. In this regard, application of video key frame detection is becoming increasingly critical. Key frame selection of videos is the process of selecting one or more informative frames that depict the essence of the video. In state of the art, researchers have experimented with shot importance measure [], epitome-based methods [], and sparse coding techniques [] to find informative frames of video. We propose block sparse coding formulation, which exploits the temporal correlation of video frames within the sparse coding framework for key frames selection. We solved the block sparse coding formulation using the Alternating Direction Method of Multipliers (ADMM) optimization. We show the comparison of results obtained with the proposed method, state-of-the-art algorithm [] and ground truth on TRECVID 2002 [] dataset. Comparison results show 8x run time and 6% F-score improvement compared to state of the art.
Black-Box Decision based Adversarial Attack with Symmetric α-stable Distribution

Developing techniques for adversarial attack and defense is an important research field for establishing reliable machine learning and its applications. Many existing methods employ Gaussian random variables for exploring the data space to find the most adversarial (for attacking) or least adversarial (for defense) point. However, the Gaussian distribution is not necessarily the optimal choice when the exploration is required to follow the complicated structure that most real-world data distributions exhibit. In this paper, we investigate how statistics of random variables affect such random walk exploration. Specifically, we generalize the Boundary Attack, a state-of-the-art blackbox decision based attacking strategy, and propose the Le'vy-Attack, where the random walk is driven by symmetric α-stable random variables. Our experiments on MNIST and CIFAR10 datasets show that the Le'vy-Attack explores the image data space more efficiently, and significantly improves the performance. Our results also give an insight into the recently found fact in the whitebox attacking scenario that the choice of the norm for measuring the amplitude of the adversarial patterns is essential.
Undergraduate Students of Special Education’s Readiness towards the Use of Information and Technology (ICT) in Teaching and Learning the Sign Language

This study intends to identify the perceptions of the undergraduate students of Special Education towards the use of information and technology (ICT) in teaching and learning the sign language.This study involved the perception of 200 undergraduate students of Special Education from 3 particular public universities.All 3 public universities offer both Special Education and the sign language course which are compulsory for the students.This study was conducted by distributing the questionnaire that consists of students' background and demography, as well as the students' readiness towards the use of information and technology (ICT) in teaching and learning the sign language.The data were then analyzed using Packages for Social Sciences (SPSS) version 20.0.The findings have gathered that majority of the students have high readiness and interest towards the use of ICT in teaching and learning the sign language with the overall mean of 4.65 and standard deviation of 0.364.The overall mean has shown that the use of ICT is preferred by majority of the students.In conclusion, it is obvious that students are ready to use technology involved materials in the teaching and learning the sign language.
Using Artificial Neural Networks to Forecast Stock Market Indices

Forecasting of financial time series by neural networks requires special preparation of data, selection of size and architecture of neural network, choice of teaching method. The paper shows how the transformation of time series affects the performance of a neural network model on the example of forecasting stock indices UX and PFTS for the Ukrainian stock market. Neural networks were constructed, UX and PFTS stock indices were forecasted using MLP (multilayer perceptron) and RBF (radial basis function) networks. It is shown that the values predicted using time series with lag of input vector is much more accurate.
Constructing generative logical models for optimisation problems using domain knowledge

In this paper we seek to identify data instances with a low value of some objective (or cost) function. Normally posed as optimisation problems, our interest is in problems that have the following characteristics: (a) optimal, or even near-optimal solutions are very rare; (b) it is expensive to obtain the value of the objective function for large numbers of data instances; and (c) there is domain knowledge in the form of experience, rules-of-thumb, constraints and the like, which is difficult to translate into the usual constraints for numerical optimisation procedures. Here we investigate the use of Inductive Logic Programming (ILP) to construct models within a procedure that progressively attempts to increase the number of near-optimal solutions. Using ILP in this manner requires a change in focus from discriminatory models (the usual staple for ILP) to generative models. Using controlled datasets, we investigate the use of probability-sampling of solutions based on the estimated cost of clauses found using ILP. Specifically, we compare the results obtained against: (a) simple random sampling; and (b) generative deep network models that use a low-level encoding and automatically construct higher-level features. Our results suggest: (1) Against each of the alternatives, probability-sampling from ILP-constructed models contain more near-optimal solutions; (2) The key to the effectiveness of ILP-constructed models is the availability of domain knowledge. We also demonstrate the use of ILP in this manner on two real-world problems from the area of drug-design (predicting solubility and binding affinity), using domain knowledge of chemical ring structures and functional groups. Taken together, our results suggest that generative modelling using ILP can be very effective for optimisation problems where: (a) the number of training instances to be used is restricted, and (b) there is domain knowledge relevant to low-cost solutions.
Cronus: Everlasting Privacy with Audit and Cast

We present a new online voting scheme with everlasting privacy and cast-as-intended verifiability. We follow the so called “audit-and-cast” paradigm where the voter audits the ballot before casting it. To mitigate the ability of this information to harm the voter’s privacy, we provide measures for avoiding coercion by allowing any party to create fake proofs for the content of any vote. We propose an efficient implementation and formally verify its security properties.
Widget detection on screenshots using computer vision and machine learning algorithms

In this paper we consider the problem of detecting and recognizing widgets in screenshots of computer programs’ graphical user interface (GUI). This problem is fundamental in business process automation. The solution we propose here is based on detecting GUI elements with Canny edge operator, and recognizing already detected GUI elements with classifiers: neural networks, random forests, XGBoost, and others.
A Generalized Cubemap for Encoding 360<sup>°</sup> VR Videos using Polynomial Approximation

Abstract 360 ° VR videos provide users with an immersive visual experience. To encode 360 ° VR videos, spherical pixels must be mapped onto a two‐dimensional domain to take advantage of the existing video encoding and storage standards. In VR industry, standard cubemap projection is the most widely used projection method for encoding 360 ° VR videos. However, it exhibits pixel density variation at different regions due to projection distortion. We present a generalized algorithm to improve the efficiency of cubemap projection using polynomial approximation. In our algorithm, standard cubemap projection can be regarded as a special form with 1st‐order polynomial. Our experiments show that the generalized cubemap projection can significantly reduce the projection distortion using higher order polynomials. As a result, pixel distribution can be well balanced in the resulting 360 ° VR videos. We use PSNR, S‐PSNR and CPP‐PSNR to evaluate the visual quality and the experimental results demonstrate promising performance improvement against standard cubemap projection and Google's equi‐angular cubemap.
A Novel Design Method for Digital FIR/IIR Filters Based on the Shuffle Frog-Leaping Algorithm

The design of both FIR and IIR digital filters is a multi-variable optimization problem, where traditional algorithms fail to obtain optimal solutions. A modified Shuffled Frog Leaping Algorithm (SFLA) is here proposed for the design of FIR and IIR discrete-time filters as close as possible to the desired filter frequency response. This algorithm can be considered a type of memetic algorithm. In this paper, simulations prove the obtained filters outperform those designed using the traditional bilinear Z transform (BZT) method with elliptic approximation. Besides, results are close to, and even slightly better, than those reported in recent bio-inspired approaches using algorithms such as particle swarm optimization (PSO), differential evolution (DE) and regularized global optimization (RGA).
Threats to Central Bank Independence: High-Frequency Evidence with Twitter

This paper presents market-based evidence that President Trump influences expectations about monetary policy. The main estimates use tick-by-tick fed funds futures data and a large collection of Trump tweets criticizing the conduct of monetary policy. These collected tweets consistently advocate that the Fed lowers interest rates. Identification in our high-frequency event study exploits a small time window around the precise time stamp for each tweet. The average effect of these tweets on the expected fed funds rate is strongly statistically significant and negative, with a cumulative effect of around negative 10 bps. Therefore, we provide evidence that market participants believe that the Fed will succumb to the political pressure, which poses a significant threat to central bank independence.
Digital Image Watermarking Using Optimization and Encryption

Abstract In this paper, with the technique of chaotic encryption-based blind digital image watermarking, we introduce a new grey wolf optimization. Discrete cosine transform (DCT) is employed before embedding the watermark within the host image. The image at host is split into 8x8 which is a non-overlapping blocks before employing DCT application, and the watermark bit is included through modifying the differences between the neighboring blocks DCT coefficients. Then, grey wolf optimization algorithm (GWO) is employed. Arnold transform is used along with the chaotic encryption to provide additional watermark security. The presented model is applied to a collection of benchmark images and the results validated the performance in terms of PSNR. The presented method attains maximum PSNR value on entirely given test images.
Bayesian Neural Networks and GLM

The learning of large neural networks is an ill-posed problem and there is generally a continuum of possible set of admissible weights. In this case, we cannot rely anymore on asymptotic properties of maximum likelihood estimators to approximate confidence intervals. Applying the Bayesian learning paradigm to neural networks or to generalized linear models results in a powerful framework that can be used for estimating the density of predictors. Within this approach, the uncertainty about parameters is expressed and measured by probabilities. This formulation allows for a probabilistic treatment of our a priori knowledge about parameters based on Markov Chain Monte Carlo methods. In order to explain those methods that are based on simulations, we need to review the main features of Markov chains.
Embossed Characters Enhancement Based on Convolutional Neural Network

Embossed characters are widely used in manufacturing to record product information. It is of great value to recognize embossed characters both effectively and efficiently. Different from conventional Optical Character Recognition (OCR), image enhancement is an essential step in identifying embossed characters. Most of the published algorithms use handcrafted enhancement scheme to segment embossed characters from a background which does not robust to variations. In this paper, we proposed a Convolutional Neural Network (CNN) based enhancement algorithm to improve the contrast between embossed characters and background. After the step, conventional OCR algorithms can be directly applied to the enhanced image. An experiment is carried out on soles with embossed characters. The results show that the algorithm can effectively improve the contrast between embossed characters and background in real-time.
Handling Conditional Queries on Hyperledger Fabric Efficiently

As a popular consortium blockchain platform, Hyperledger Fabric has received increasing attention recently. When conducting quer-ies that meet some specific conditions on such platform, we need to search ledger data which usually has multiple attributes. Although efficiently handling conditional queries can be leveraged to support various use-cases, it presents significant challenges as data on Hyperledger Fabric is organized on file-system and exposed via limited API. To tackle the problem, we propose the following novel methods in this paper. In the first one, we use all conditions of the query to create composite keys before executing it. To further improve the performance of conditional queries on Fabric, we build an index called AUP in the second method, where we also study the update of AUP during transactions. The extensive experiments conducted on the real-world dataset demonstrate that the proposed methods can achieve high performance in terms of efficiency and memory cost.
Visual Saliency Based Ship Extraction Using Improved Bing

In this paper, a novel ship extraction algorithm is proposed to acquire the precise segmentation. First, Binarized Normed Gradients (BING) is improved to locate the potential ship regions according to the remote sensing application. Second, the visual saliency computation by combining Hypercomplex Frequency Domain Transform (HFT) and Phase Quaternion Fourier Transform (PQFT) presented to analyze the located regions. Third, segmentation on the computed saliency map is conducted to extract precise blobs of the ship candidates. Finally, the prior knowledge about ship is utilized to remove the false alarms in the discrimination stage. The experimental results of typical remote sensing images show that the algorithm is very effective.
Domain-General Versus Domain-Specific Named Entity Recognition: A Case Study Using TEXT

Named entity recognition (NER) seeks to identify and classify named entities within bodies of text into language categories such as nouns, that are reflective of locations, organizations, and people. As it is language dependent, the approach taken for most NER systems are domain-general, meaning that they are designed based on a language and not on a specific targeted domain. With current usage of non-formal languages on social media, this instigates the need to compare the performance of domain-general and domain specific NERs. A domain specific NER (vehicle traffic domain), TEXT, is described and the performance of domain-general NER versus TEXT is compared. The results of the evaluation show that the performance of domain-specific NER significantly outperforms domain-general NER. The domain-general NER could only perform adequately for common scenarios.
Dynamic path planning based on variable step size rolling window derivation and obstacle prediction

Abstract Aiming at the problem of dynamic path planning for UAVs in complex environment, a path planning algorithm combining dynamic obstacle trajectory estimation and variable step rolling window deduction is proposed. When the dynamic obstacle distance is close to the UAV, the Kalman filter is used to estimate the dynamic obstacle trajectory. Then the window deduction is performed to judge the track area of the deduction window, so that the algorithm has certain "forward-looking". In turn, the window step size, the flight speed of the drone and the flight direction are changed, and the avoidance of the dynamic obstacle is completed, and the final goal is reached by the rolling plan. The experimental analysis shows that the algorithm can complete the dynamic path planning of UAVs in complex environment in real time.
Gold metallogeny of the northern Capricorn Orogen

SummaryThe timing and distribution of gold mineralization in Proterozoic orogens is influenced by crustal architecture which is often established long before gold mineralization occurs. Gold occurrences in such settings are commonly associated with crustal-scale faults formed at cratonic margins. Once established, these faults provide critical pathways for hydrothermal and mineralizing fluids which during repeated fault reactivations can result in remobilization or introduction of new auriferous fluids resulting in overprinting gold events. Recently published geochronological data for the northern part of the Proterozoic Capricorn Orogen in Western Australia show it has experienced at least three episodes of gold mineralization occurring at c. 2400, 1770 and 1680 Ma. Many of the gold deposits are associated with intracratonic reworking during the 1820–1770 Ma Capricorn Orogeny and 1680–1620 Ma Mangaroon Orogeny. Intracratonic settings are not normally considered prospective for gold mineralization due to a lack of input of juvenile material. However, it appears that repeated hydrothermal fluid flow during intracratonic events, has the potential to upgrade gold mineralization or increase gold endowment throughout the orogen, either through gold remobilization or through introduction of new gold, increasing the potential for economic gold deposits.