Reviewerly/exHarmony huggingface dataset:
Multimedia Prayer Application for Education with Rapid Application Development Method

Prayer is a duty for Muslims who must be carried out every day at least 5 times each day from small children to adults without any reason, for small children, especially in primary schools to learn prayers can be done using multimedia media-based learning media to know the order the correct way of praying, the correct verses also know the meaning of each verse spoken, making the application by applying the RAD method can provide convenience for the development of applications from desktop-based to mobile-based or vice versa and expected applications can be used for various platform.
A Tile-based Fused-Layer Approach to Accelerate DCNNs on Low-Density FPGAs

Accelerating Deep Convolutional Neural Networks on FPGAs is achieving a lot of interest across a wide range of applications such as image recognition and classification. Although memory requirement still represents a challenge for fast and efficient inference workloads, fused-accelerated layers have been recently proposed to mitigate memory bandwidth problems. In this scenario, we propose a tiled-based fused-layer approach to exploit temporal and spatial locality in mapping a DCNN to a specialized low-density FPGA. In our tile-based approach, we propose applying a fusing depth of two convolutional layers in order to fit into a low-density FPGA without affecting the performance, while reducing the memory bandwidth. We demonstrated the effectiveness of our two-fused layer approach to accelerate the VGG16 network on a Zynq-7020. We achieved an average speedup of 1.44x times, while saving up to 61% of memory transactions with respect to a baseline represented by tiled-only (not-fused) version. A tiled-based fully-fused version of VGG16 would not have been feasible to be mapped on a low-density Zynq-7020.
Personalized Recommendation Method of POI Based on Deep Neural Network

With the rapid development of Location-based social networks (LBSN), there is a growing demand for location services. How to use the users' historical check-in data for exploring their visit patterns and preference characteristics to realize personalized point-of-interest (POI) recommendation has become an important topic. Finding valid features from the check-in data is the key to POI recommendation. Deep learning is a multi-level representation learning method, which can better explore the relationship between features. Therefore, a new POI recommendation model named DLM based on deep neural network is proposed in this paper. This model incorporates topic features, user preference features and geographical factor features in the LBSN into the POI recommendation tasks, thereby it improves the efficiency of users' personalized POI recommendation. A lot of experiments on public data set Foursquare have proved the advantages and effectiveness of the proposed method.
Multi-Video Temporal Synchronization by Matching Pose Features of Shared Moving Subjects

Collaborative analysis of videos taken by multiple motion cameras from different and time-varying views can help solve many computer vision problems. However, such collaborative analysis usually requires the videos to be temporally synchronized, which can be inaccurate if we solely rely on camera clock. In this paper, we propose to address this problem based on video content. More specifically, if multiple videos cover the same moving persons, these subjects shall exhibit identical pose and pose change at each aligned time point across these videos. Based on this idea, we develop a new Synchronization Network (SynNet) which includes a feature aggregation module, a matching cost volume and several classification layers to infer the time offset between different videos by exploiting view-invariant human pose features. We conduct comprehensive experiments on SYN, SPVideo and MPVideo datasets. The results show that the proposed method can accurately synchronize multiple motion-camera videos collected in real world.
Sparse Wavelet Networks

A wavelet network (WN) is a feed-forward neural network that uses wavelets as activation functions for the neurons in its hidden layer. By predetermining the wavelet positions and dilations, the WN can turn into a linear regression model. The common approach for the construction of these WN families is to use least-squares type algorithms. In this letter, we propose a novel approach by formulating a WN as a sparse linear regression problem, which we call a sparse wavelet network (SWN). In this WN, the problem of calculating the unknown inner parameters of the network becomes that of finding the sparse solution of an under-determined system of linear equations. Our sparse solution algorithm is a non-convex sparse relaxation approach inspired by smoothed L0 (SL0), a distinguished sparse recovery algorithm. The proposed SWN can be applied as a tool for the prediction and identification of dynamical systems.
Multi-path Convolutional Neural Network based on Rectangular Kernel with Path Signature Features for Gesture Recognition

Skeleton based gesture recognition has gained more attention due to its wide application and large-scale databases availability. Recent methods designed for skeleton sequence data mainly pay attention to network architecture but ignore an essential characteristic of skeleton sequences that the temporal dimensionality of skeleton sequences is usually higher than its spatial dimensionality. Directly applying CNNs designed for image classification to skeleton-based data can not capture this unique property. Considering this fact, we propose the rectangular convolution and pooling to skeleton sequence data. Temporal features are crucial for gesture action recognition. Further, we introduce path signature features (PSF) to represent temporal variation characteristics of each joint. Moreover, there only exist a few minor distinctions between some gestures. To classify them more accurately, we add two sub-networks to extract discriminative features from two hands respectively. We evaluate our method on three major benchmark gesture datasets, i.e., ChaLearn 2013, ChaLearn 2016 and MSRC-12, and reach the state-of-the-art performance.
FICAL: Focal Inter-Class Angular Loss for Image Classification

Convolutional Neural Networks (CNNs) have been successfully applied in various image analysis tasks and gradually become one of the most powerful machine learning approaches. In order to improve the capability of the model generalization and performance in image classification, a new trend is to learn more discriminative features via CNNs. The main contribution of this paper is to increase the angles between the categories to extract discriminative features and enlarge the inter-class variance. To this end, we propose a loss function named focal inter-class angular loss (FICAL) which introduces the confusion rate-weighted cosine distance as the similarity measurement between categories. This measurement is dynamically evaluated during each iteration to adapt the model. Compared with other loss functions, experimental results demonstrate that the proposed FICAL achieved best performance among the referred loss functions on two image classificaton datasets.
Using a Conditional Generative Adversarial Network (cGAN) for Prostate Segmentation

Prostate cancer is the second most commonly diagnosed cancer among men and currently multi-parametric MRI is a promising imaging technique used for clinical workup of prostate cancer. Accurate detection and localisation of the prostate tissue boundary on various MRI scans can be helpful for obtaining a region of interest for Computer Aided Diagnosis systems. In this paper, we present a fully automated detection and segmentation pipeline using a conditional Generative Adversarial Network (cGAN). We investigated the robustness of the cGAN model against adding Gaussian noise or removing noise from the training data. Based on the detection and segmentation metrics, de-noising did not show a significant improvement. However, by including noisy images in the training data, the detection and segmentation performance was improved in each 3D modality, which resulted in comparable to state-of-the-art results.
A Dataset for the Sentiment Analysis of Indo-Pak Music Industry

The continuous increase in data creates a need, that data be analysed and useful hidden patterns be found and explored. If the data is readily available, it can easily be analysed. But most of the time it needs to be dug. Substantial increase in the use of social media and online services can be witnessed nowadays. People not only buy and sell things online but also give their remarks on those items/services. Many websites provide such services along with a dedicated section for reviews and comments. These items/services can be ranked and analysed based upon these reviews, which shows the sentiments of the reviewer. These reviews are in millions and promptly in billions. The huge increase in reviews there is a need to analyse them through a proper mechanism. This research is targeting the mining of the sentiments from these reviews. Three songs from YouTube are selected and their reviews are scrapped, pre-processed and analysed using Decision Tree (ID3) and Naïve Bayes. Both presented 75%accuracy on test data. This article presents a dataset to perform Sentiment Analysis on Roman Urdu/Hindi reviews. Dataset is a combination of Indo-Pak song reviews.
Reconciliation for CV-QKD using globally-coupled LDPC codes*

Reconciliation is a necessary step in postprocessing of continuous-variable quantum key distribution (CV-QKD) system. We use globally coupled low-density parity-check (GC-LDPC) codes in reconciliation to extract a precise secret key from the raw keys over the authenticated classical public channel between two users. GC-LDPC codes have excellent performance over both the additive Gaussian white noise and binary-erasure channels. The reconciliation based on GC-LDPC codes can improve the reconciliation efficiency to 95.42% and reduce the frame error rate to 3.25 × 10 –3 . Using distillation, the decoding speed can achieve 23.8 Mbits/s and decrease the cost of memory. Given decoding speed and low memory usage, this makes the proposed reconciliation method viable approach for high-speed CV-QKD system.
Physical processes, their life and their history

Here, I lay the foundations of a high-level ontology of particulars whose structuring principles differ radically from the ‘continuant’ vs. ‘occurrent’ distinction traditionally adopted in applied ontology. These principles are derived from a new analysis of the ontology of “occurring” or “happenin g” entities. Firstly, my analysis integrates recent work on the ontology of processes, which brings them closer to objects in their mode of existence and persistence by assimilating them to continuant particulars. Secondly, my analysis distinguishes clearly between processes and events, in order to make the latter abstract objects of thought (alongside propositions). Lastly, I open my ontological inventory to properties and facts, the existence of which is commonly admitted. By giving specific roles to these primitives, the framework allows one to account for static and dynamic aspects of the physical world and for the way that subjects conceive its history: facts account for the life of substances (physical objects and processes), whereas events enable cognitive subjects to account for the life story of substances.
Prostate Segmentation via Fusing the Nested-V-net3d and V-net2d

Prostate cancer is caused by uncontrolled growth of cells in the prostate gland. Prostate cancer, unlike benign prostate gland enlargement, is not from the center of the prostate, but from the site of the tumor to the center of the capsule. Therefore, the patient experiences urinary tract complaints at the last stage. Therefore, until the last stage, the patient does not have any finding. For this reason, Magnetic Resonance Imaging (MRI) is used in regular examinations after a certain age or in various diagnostic imaging methods of patients diagnosed with this disease. Proper localization of the prostate is an important step in assisting diagnosis and treatment, such as guiding the biopsy procedure and radiation therapy. However, manual segmentation of the prostate is tedious and time-consuming. It also varies in inter-rater evaluation. The two main challenges for correct MR prostate localization are; nonhomogeneous and inconsistent appearance around the prostate border, wide prostate shape variability in different patients. In this study, Fusing the Nested 3D Dimensional Volumetric Convolutional Neural Network (Nested-Vnet3d) and 2D Volumetric Convolutional Neural Network (V-net2d) models are compared with other V-net based models. In the training conducted on the PROMISE12 dataset, 0.92 validation dice score was achieved. This study showed that the proposed model is a robust deep learning model for prostate segmentation.
Modified Baseline for Light Field Stitching

In traditional 2D image stitching, the baseline method usually means global homography via Direct Linear Transformation (DLT) on inliers. In this paper, a modified baseline method for light field (LF) stitching is proposed to stitch two LFs. The depth map and the center sub-aperture image (SAI) are used to filter the feature points of the entire LF. The global 4D homography is then calculated by DLT to align all SAIs corresponding to the same angular domain coordinates of two LFs. Finally, the improved Markov Random Field (MRF) energy considering the global LF is used to find the seam of 2D SAIs instead of computational 4D graph cut. Experimental results show that the proposed method can effectively stitch the 4D LFs, and preserve the consistency of the angular and spatial domains of the stitched LF compared with implementing 2D image stitching to the corresponding SAIs. Moreover, the method proposed in this paper can easily extend all advanced 2D image stitching methods to 4D LF, so that the acquired LF can have larger field of view and wider applications.
Artificial Intelligence Identification of Multiple Microfossils from the Cambrian Kuanchuanpu Formation in Southern Shaanxi, China

Abstract The Cambrian Kuanchuanpu Formation in southern Shaanxi, China is a critical window for the understanding of the Cambrian explosion, because of abundant and various exceptionally preserved metazoans and embryo fossils yielded. The efficiency of traditional sample manually selecting with microscopes is quite low and hinder the discoveries of new species, thus recognition and classification of microfossils by artificial intelligence (AI) is substantially in the request. In this paper, we develop a procedure for fossil area segmentation in common multi‐typed mixed photos by improved watershed algorithm. And for better fossil recognition, previous histogram of oriented grandient (HOG) algorithm is replaced by scale invariant feature transform (SIFT), which is feasible for the segmented images and increase the accuracy significantly. Thus, the scope of application of AI fossil recognition can be extended form single fossil image to multi‐typed mixed images and the reliability is also secured, as the result of our test presents a high (at least 84%) accuracy of fossil recognition.
A Novel Time and Space Complexity Efficient Variant of Counting-Sort Algorithm

Sorting is a well-known problem, which is most commonly discussed in Algorithms. Although, there exist many algorithms to solve the sorting problem but Counting-sort algorithm has its own importance due to its linear time-complexity, which is O(n+k) with 2n+k space-complexity. Moreover, depending upon the feasibility of input data, It is the most efficient sorting algorithm available in terms of time if holds but a significant value of k-n restricts its usage in practice due to its high memory and computational demands in case of violation. So, in this paper, we have proposed a novel variant of trivial Counting-sort algorithm, which is comparatively not only time but space efficient as well. The time and space complexity of our proposed ARU-Counting-sort algorithm is O(n+√k) and 2n+2√k respectively.
Predicting the visual saliency of the people with VIMS

As is known to us, visually induced motion sickness (VIMS) is often experienced in a virtual environment. Learning the visual attention of people with VIMS contributes to related research in the field of virtual reality (VR) content design and psychology. In this paper, we first construct a saliency prediction for people with VIMS (SPPV) database, which is the first of its kind. The database consists of 80 omnidirectional images and the corresponding eye tracking data collected from 30 individuals. We analyze the performance of five state-of-the-art deep neural networks (DNN)-based saliency prediction algorithms with their original networks and the fine-tuned networks on our database. We predict the atypical visual attention of people with VIMS for the first time and obtain relatively good saliency prediction results for VIMS controls so far.
Two schemes of multiparty quantum direct secret sharing via a six-particle GHZ state

In this paper, two new efficient multiparty quantum direct secret sharing schemes are proposed via a six-particle GHZ state and Bell measurements. In the first scheme, based on the theory of security cryptanalysis, the secret message of the sender is directly encoded into the transmitted particles, and all the agents can obtain their information by performing bell measurement on the received particles, and then cooperate to recover the information of the sender. In the second scheme, we define a new secret shared coding method by performing local unitary operations on the transmitted particles, then agents perform Bell measurements on their own particles respectively, and feedback the measurement to the dealer. If the agent's results are matched with the previous coding method, the protocol will work out. In addition, the proposed two schemes have the following common advantages: the sender can send all prepared particles to the receiver, and can send an arbitrary key to the receiver, rather than a random secret key; the proposed schemes do not need to insert any detection sets to detect eavesdropping and can resist both existing attacks and spoofing attacks by dishonest agents. The sender need not to retain any photons, so the sender's quantum memory could be omitted here.
A Survey on Blockchain Anomaly Detection Using Data Mining Techniques

With the more and more extensive application of blockchain, blockchain security has been widely concerned by the society and deeply studied by scholars, of which anomaly detection is an important problem. Data mining techniques, including conventional machine learning, deep learning and graph learning, have been concentrated for anomaly detection in the last few years. This paper presents a systematic survey of the blockchain anomaly detection results using data mining techniques. The anomaly detection methods are classified into 2 main categories, namely universal detection methods and specific detection methods, which contain 8 subclasses. For each subclass, the corresponding research are listed and compared, presenting a systematic and categorized overview of the current perspectives for blockchain anomaly detection. In addition, this paper contributes in discussing the advantages and disadvantages for the data mining techniques employed, and suggesting future directions for anomaly detection methods. This survey helps researchers to have a general comprehension of the anomaly detection field and its application in blockchain data.
Coq à la carte: a practical approach to modular syntax with binders

The mechanisation of the meta-theory of programming languages is still considered hard and requires considerable effort. When formalising properties of the extension of a language, one hence wants to reuse definitions and proofs. But type-theoretic proof assistants use inductive types and predicates to formalise syntax and type systems, and these definitions are closed to extensions. Available approaches for modular syntax are either inapplicable to type theory or add a layer of indirectness by requiring complicated encodings of types.
Comparative Study on the Performance of Heuristic Optimization Techniques in Robotic Path Planning

This work presents a comparative study between the three popular optimization techniques namely the Genetic Algorithm (GA), Particle Swarm Optimization technique (PSO) and the Artificial Bee Colony (ABC) in the robotic path planning field. Path planning techniques suffer from a well-known trade-off between path quality and swiftness. Researchers had to always trade-off between shorter paths and higher computational execution time. However, with the invention of heuristic optimization techniques such as GA, PSO and ABC, there is an expect that these optimization techniques can overcome this trade-off. Thus, this paper investigates the performance of the three optimization techniques in robotic path planning problems.
Idols with Feet of Clay: On the Security of Bootloaders and Firmware Updaters for the IoT

IoT devices are generally implemented with low-cost embedded solutions, and connectivity and communication capabilities are the raison d'être of such devices. But this is a double-edged sword, since connectivity also implies (1) to open the door to more attack possibilities, and (2) the targeted system, once breached, can be the support for attacks at a larger scale, possibly involving many connected systems. Our observation is that such devices lack proper hardware and software security protections. Bootloader and Firmware Update (BFU) mechanisms are critical components in the software stack of IoT devices. BFUs are a target of choice since they use the highest privileges and are executed before the system's security policy is set up. An attacker able to compromise the BFU can gain full control over the target system. Moreover, the update mechanism often supported by the BFU is essential to ensure devices can be upgraded and maintained for a long time. If not properly secured, the BFU allows an attacker to gain control over a system throughout its whole lifetime, including future upgrades. In this paper, we provide an overview of the threats targeting BFUs, and existing protections. We cover the hardware and software attacks that are known to the scientific literature. Also, we argue that vulnerabilities to physical attacks, in particular to fault injection attacks, are mostly left un-attended.
WITHDRAWN: Multi-model feature fusion for human action recognition toward sport sceneries

This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/our-business/policies/article-withdrawal.
Visual Cryptography and Image Processing Based Approach for Bank Security Applications

Authentication is a critical step in initializing a bank transaction. Core banking and net banking systems use ID cards, passwords/PIN, OTP, etc. for authentication. However, these methods are still prone to forgery and hacking, due to which unauthorized person could gain access to user’s account. We propose a methodology which uses an image share as authentication key. This has been created using visual cryptography and other image processing techniques to encrypt authentication parameters. It is encrypted such that the share becomes incomprehensible to human eyes and non-decryptable by hacker, thus protecting the data from forgery or hacking. This paper proposes a double authentication system where user is identified by decrypted image and PSNR value. Hence this system provides a very high level of security.