Reviewerly/exHarmony huggingface dataset:
SPSTracker: Sub-Peak Suppression of Response Map for Robust Object Tracking

Modern visual trackers usually construct online learning models under the assumption that the feature response has a Gaussian distribution with target-centered peak response. Nevertheless, such an assumption is implausible when there is progressive interference from other targets and/or background noise, which produce sub-peaks on the tracking response map and cause model drift. In this paper, we propose a rectified online learning approach for sub-peak response suppression and peak response enforcement and target at handling progressive interference in a systematic way. Our approach, referred to as SPSTracker, applies simple-yet-efficient Peak Response Pooling (PRP) to aggregate and align discriminative features, as well as leveraging a Boundary Response Truncation (BRT) to reduce the variance of feature response. By fusing with multi-scale features, SPSTracker aggregates the response distribution of multiple sub-peaks to a single maximum peak, which enforces the discriminative capability of features for robust object tracking. Experiments on the OTB, NFS and VOT2018 benchmarks demonstrate that SPSTrack outperforms the state-of-the-art real-time trackers with significant margins1
Robust Feature Extraction and Classification Based Automated Human Action Recognition System for Multiple Datasets

Action recognition schemes enable several intelligent machines to recognize human action by using daily life videos.In the last decades, recognizing human actions in the video sequences have been a challenging problem because of its real-world applications.Several action representation techniques have been done to improve action recognition performance.Approaches based on local features and classification are used for action representation, but it failed to capture temporal relationship between actions.In this work, Video Action Recognition (VAR) is assessed by using Weizmann, KTH and own datasets.Initially frames are extracted from input videos and the frames are resized.After the pre-processing, the object detection is done by Blob detection algorithm and tracking the object frame by frame is done using Kalman filter.The features are extracted from the moving object using feature extraction algorithms such as Bi-dimensional Empirical Mode Decomposition (BEMD), Scale Invariant Feature Transform (SIFT) and Discrete Wavelet Transform (DWT).These feature extraction techniques were applied on pre-processed frames to extract the efficient features from multi-scale images.Finally, the features are given to Convolution Neural Network (CNN) classifier for action recognition prediction.The proposed method is called as Hybrid Feature Extraction (HFE) and CNN used for VAR (HFE-CNN-VAR) method.The experimental results showed that the HFE-CNN-VAR method improved the accuracy in action classification.The classification accuracy is 99.01%for KTH dataset, 99.33% for Weizmann dataset and 90% for own dataset.
Experimentally Verified Approach to Nonentanglement-Breaking Channel Certification

Ensuring the nonentanglement-breaking (non-EB) property of quantum channels is crucial for the effective distribution and storage of quantum states. However, a practical method for direct and accurate certification of the non-EB feature is highly desirable. Here, we propose and verify a realistic source based measurement device independent certification of non-EB channels. Our method is resilient to repercussions on the certification from experimental conditions, such as multiphotons and imperfect state preparation, and can be implemented with an information incomplete set. We achieve good agreement between experimental outcomes and theoretical predictions, which is validated by the expected results of the ideal semiquantum signaling game, and accurately certify the non-EB channels. Furthermore, our approach is highly robust to effects from noise. Therefore, the proposed approach can be expected to play a significant role in the design and evaluation of realistic quantum channels.
A Highly Parallelized PIM-Based Accelerator for Transaction-Based Blockchain in IoT Environment

Blockchain has gained a lot of attention from both academia and industry. However, traditional standard blockchains, such as Bitcoin and Ethereum, suffer from low throughput, high computation overhead, and large transaction fee, which is not suitable for Internet of Things (IoT) transactions. Recently, transaction-based approaches, such as Tangle structure which is based on a directed acyclic graph (DAG), have emerged to solve blockchain scalability issues for IoT environment. In transaction-based blockchain, for a transaction, namely, a node, to be attached to the Tangle, it needs to verify two other transactions. However, with the Tangle expanding, this attaching process consumes huge computational resources and energy, which severely limits the performance of the transaction-based blockchain. In this article, we present Re-Tangle, a highly parallelized processing-in-memory (PIM)-based accelerator for transaction-based blockchain. Re-Tangle is composed of a random walking module, a transaction validation module, and a PoW module, to improve the Tangle system performance. These modules transfer Tangle functions, such as fast exponentiation and modular, into ReRAM-based logic analog computation units. In the random walking module, Re-Tangle maintains an exponentiation table to reduce its design complexity and improve its computation efficiency. In the transaction validation module, Re-Tangle further proposes a highly parallel modular unit to accelerate the validation of different tags in a transaction. In the PoW module, we decompose the Curl hash function into basic logic OR, AND, SHIFT, and XOR operations, and map these logic operations to ReRAM crossbars in parallel to accelerate the working process. The experimental results show that Re-Tangle distinguishes itself from other architectures with significant performance improvement and energy saving. The throughput of Re-Tangle is about 22.4× and 2.38× higher compared with CPU and GPU, respectively, and the energy consumption of Re-Tangle is 83.5× and 5.77× less for equal workload.
Video Deblurring via Temporally and Spatially Variant Recurrent Neural Network

The camera shake and high-speed motion of objects often produce a blurry video. However, it is hard to recover sharp videos using existing single or multiple image deblurring methods, as the blur artifacts in blurry videos are both temporally and spatially varying. In this paper, we propose a temporally and spatially variant recurrent neural network for video deblurring, in which both temporally and spatially variants employ ConvGRU blocks and a weight generator to capture spatio-temporal features. Meanwhile, the proposed model is trained in an end-to-end manner, where the model input and output are set to the same number. Thus, our model does not reduce the number of frames both in training and testing stages, which is important in practical applications. Quantitative and qualitative evaluations on standard benchmark datasets demonstrate that the proposed method outperforms the current state-of-the-art methods.
High Accuracy Perceptual Video Hashing via Low-Rank Decomposition and DWT

In this work, we propose a novel robust video hashing algorithm with High Accuracy. The proposed algorithm generates a fix-up hash via low-rank and sparse decomposition and discrete wavelet transform (DWT). Specifically, input video is converted to randomly normalized video with logistic map, and then content-based feature matrices extract from a randomly normalized video with low-rank and sparse decomposition. Finally, data compression with 2D-DWT of LL sub-band is applied to feature matrices and statistic properties of DWT coefficients are quantized to derive a compact video hash. Experiments with 4760 videos are carried out to validate efficiency of the proposed video hashing. The results show that the proposed video hashing is robust to many digital operations and reaches good discrimination. Receiver operating characteristic (ROC) curve comparisons indicate that the proposed video hashing more desirable performance than some algorithms in classification between robustness and discrimination.
Quadruply Stochastic Gradient Method for Large Scale Nonlinear Semi-Supervised Ordinal Regression AUC Optimization

Semi-supervised ordinal regression (S2OR) problems are ubiquitous in real-world applications, where only a few ordered instances are labeled and massive instances remain unlabeled. Recent researches have shown that directly optimizing concordance index or AUC can impose a better ranking on the data than optimizing the traditional error rate in ordinal regression (OR) problems. In this paper, we propose an unbiased objective function for S2OR AUC optimization based on ordinal binary decomposition approach. Besides, to handle the large-scale kernelized learning problems, we propose a scalable algorithm called QS3ORAO using the doubly stochastic gradients (DSG) framework for functional optimization. Theoretically, we prove that our method can converge to the optimal solution at the rate of O(1/t), where t is the number of iterations for stochastic data sampling. Extensive experimental results on various benchmark and real-world datasets also demonstrate that our method is efficient and effective while retaining similar generalization performance.
Deep Camouflage Images

This paper addresses the problem of creating camouflage images. Such images typically contain one or more hidden objects embedded into a background image, so that viewers are required to consciously focus to discover them. Previous methods basically rely on hand-crafted features and texture synthesis to create camouflage images. However, due to lack of reliable understanding of what essentially makes an object recognizable, they typically result in either complete standout or complete invisible hidden objects. Moreover, they may fail to produce seamless and natural images because of the sensitivity to appearance differences. To overcome these limitations, we present a novel neural style transfer approach that adopts the visual perception mechanism to create camouflage images, which allows us to hide objects more effectively while producing natural-looking results. In particular, we design an attention-aware camouflage loss to adaptively mask out information that make the hidden objects visually standout, and also leave subtle yet enough feature clues for viewers to perceive the hidden objects. To remove the appearance discontinuities between the hidden objects and the background, we formulate a naturalness regularization to constrain the hidden objects to maintain the manifold structure of the covered background. Extensive experiments show the advantages of our approach over existing camouflage methods and state-of-the-art neural style transfer algorithms.
Expressive Public-Key Encryption With Keyword Search: Generic Construction From KP-ABE and an Efficient Scheme Over Prime-Order Groups

Public key encryption with keyword search (PEKS) allows a cloud server to retrieve particular ciphertexts without leaking the contents of the searched ciphertexts. This kind of cryptographic primitive gives users a special way to retrieve the encrypted documents they need while preserving privacy. Nevertheless, most existing PEKS schemes only offer single-keyword search or conjunctive-keyword searcha. The poorly expressive ability and constantly inaccurate search results make them hard to meet users' requirements. Although several expressive PEKS (EPEKS) schemes were proposed, they entail high computation and communication costs. An ideal EPEKS scheme should enable fast and accurate ciphertext retrieval, while lowering the storage server's load and reducing the amount of communication data. Drawing on the strongly expressive ability of key-policy attribute-based encryption (KP-ABE), we propose a generic construction of EPEKS from KP-ABE. We demonstrate that the derived EPEKS scheme is secure under the chosen keyword attack if the implicit KP-ABE scheme fulfills the anonymity under the chosen plaintext attack. Furthermore, we present a concrete EPEKS scheme over the prime-order groups. The comparison and experimental results indicate that our scheme is more efficient than the existing EPEKS schemes.
Texture classification using block intensity and gradient difference (BIGD) descriptor

In this paper, we present an efficient and distinctive local descriptor, namely block intensity and gradient difference (BIGD). In an image patch, we randomly sample multi-scale block pairs and utilize the intensity and gradient differences of pairwise blocks to construct the local BIGD descriptor. The random sampling strategy and the multi-scale framework help BIGD descriptors capture the distinctive patterns of patches at different orientations and spatial granularity levels. We use vectors of locally aggregated descriptors (VLAD) or improved Fisher vector (IFV) to encode local BIGD descriptors into a full image descriptor, which is then fed into a linear support vector machine (SVM) classifier for texture classification. We compare the proposed descriptor with typical and state-of-the-art ones by evaluating their classification performance on five public texture data sets including Brodatz, CUReT, KTH-TIPS, and KTH-TIPS-2a and -2b. Experimental results show that the proposed BIGD descriptor with stronger discriminative power yields 0.12%∼6.43% higher classification accuracy than the state-of-the-art texture descriptor, dense microblock difference (DMD).
Integrated Decision Making using the Blockchain

This article describes an Integrated Decision Making Environment modeled after the human anatomy and various naturally occurring phenomena and processes, like swarming, and the human neural decision making process. Observing the natural fractals related to decision making, which can be clearly seen in bee swarms, ant colonies and human neurons, together with the organic capabilities of storing and distributing information using the DNA and the overall anatomy of the human body, we want to define a coherent organic Decision Making Environment custom-tailored for the human society by nature itself. In order to properly replicate the organic operating system used by the human body to govern itself we must first start looking at how data is distributed between cells and stored inside the DNA. In order to reproduce this kind of distributed and decentralized database we decided to use Blockchain technology as it shares a lot of the key properties with human DNA.
PENGGUNAAN MEDIA GAME KAHOOT UNTUK MENINGKATKAN HASIL DAN MINAT BELAJAR MATEMATIKA

This research is motivated by the world of education which requires innovative learning media so that students do not feel bored during the teaching and learning process.The purposes of this study are: a) find out the improvement of student learning outcomes; b) know the student's process skills; c) know your interest in learning.This type of research is Classroom Action Research (CAR).Data collection used the test, observation, and questionnaire methods.The results of this study are: a) there is an increase in the average learning outcomes 87 in cycle I and cycle II of 92; b) there was an increase in students' process skills by 83 in cycle I and cycle II by 92; c) there was an increase in interest in learning by 82% in cycle I and 93% in cycle II.
Tracking Video Target via Particle Filtering on Manifold

Most of existing particle filtering-based video target tracking algorithms are in Euclidean space, when object posture and scale size changes, and to track high dimensional system, it is difficult to guarantee the tracking effect. This paper describes the covariance descriptor to represent the object image region, the geometric deformation of the object image region can be realized by an affine transformation, and the affine transformation matrix is one element of the Lie group. Then particle filter algorithm based on lie group of manifold is proposed , the video tracking system state lies directly on a low dimensional manifold, state samples are drawn moving on the manifold geodesics, thus state space of intrinsic geometrical characteristic can be in full use, which provides a new idea for improving the tracking efficiency and robustness. Simulation results show that object in the case of geometric deformation including scale size changes, rotating, etc. The proposed manifold particle filtering algorithm can still realize target tracking well and improve the real-time performance.
Weakly Supervised POS Taggers Perform Poorly on &lt;em&gt;Truly&lt;/em&gt; Low-Resource Languages

Part-of-speech (POS) taggers for low-resource languages which are exclusively based on various forms of weak supervision – e.g., cross-lingual transfer, type-level supervision, or a combination thereof – have been reported to perform almost as well as supervised ones. However, weakly supervised POS taggers are commonly only evaluated on languages that are very different from truly low-resource languages, and the taggers use sources of information, like high-coverage and almost error-free dictionaries, which are likely not available for resource-poor languages. We train and evaluate state-of-the-art weakly supervised POS taggers for a typologically diverse set of 15 truly low-resource languages. On these languages, given a realistic amount of resources, even our best model gets only less than half of the words right. Our results highlight the need for new and different approaches to POS tagging for truly low-resource languages.
Impact of strategic capabilities on organizational ambidexterity in the commercial banks in Jordan: The mediating role of knowledge management

This study seeks to explain the impact of strategic capabilities on organizational ambidexterity, in the presence of knowledge management.The study population consists of managers of commercial banks in Jordan.The sample included (200) respondents by distributing the study questionnaires to them, where the returned questionnaires and valid to statistical analysis were (168).Structural equation modelling (SEM) was used as an inferential statistical analysis technique to test the hypotheses of the study.The result of this study referred that knowledge management mediates the relationship between strategic capabilities and organizational ambidexterity.Hence, all dimensions of strategic capabilities (marketing capability, market-linking capability, technology capability, and management capability) have a statistical impact on both knowledge management dimensions (knowledge acquisition, knowledge creation, knowledge application, and knowledge storage) and the dimensions of organizational ambidexterity (exploitation and exploration).Therefore, the study recommends managers and decision-makers of commercial banks in Jordan to focus on strategic capabilities that enhance their abilities to identify and acquire opportunities from the business environment by building good relationships with both customers and suppliers.Besides, improving their employees' capacity to deal with new knowledge and apply it to create novel products and services.
SURVEY OF SUPER RESOLUTION TECHNIQUES

The key objective of super-resolution imaging is the process of reconstructing a high resolution image from one or a set of low resolution images, to overcome the ill-posed conditions of the image capturing process to get better visualization. It has found practical applications in many real world problems such as surveillance, medical imaging, text image analysis, biometrics, satellite imaging, to name a few. This has encouraged many researchers to develop a new super resolution algorithm for a particular purpose. The objective of the review paper is to explore the different image super resolution algorithms used to enhance the low resolution images to high resolution images. This survey on super resolution algorithms will help the researchers to comprehend the effectiveness of the super resolution process and will make easy to develop advanced super resolution methods.
Multilayer feature selection method for polyp classification via computed tomographic colonography

Polyp classification is a feature selection and clustering process. Picking the most effective features from multiple polyp descriptors without redundant information is a great challenge in this procedure. We propose a multilayer feature selection method to construct an optimized descriptor for polyp classification with a feature-grouping strategy in a hierarchical framework. First, the proposed method makes good use of image metrics, such as intensity, gradient, and curvature, to divide their corresponding polyp descriptors into several feature groups, which are the preliminary units of this method. Then each preliminary unit generates two ranked descriptors, i.e., their optimized variable groups (OVGs) and preliminary classification measurements. Next, a feature dividing-merging (FDM) algorithm is designed to perform feature merging operation hierarchically and iteratively. Unlike traditional feature selection methods, the proposed FDM algorithm includes two steps for feature dividing and feature merging. At each layer, feature dividing selects the OVG with the highest area under the receiver operating characteristic curve (AUC) as the baseline while other descriptors are treated as its complements. In the fusion step, the FDM merges some variables with gains into the baseline from the complementary descriptors iteratively on every layer until the final descriptor is obtained. This proposed model (including the forward step algorithm and the FDM algorithm) is a greedy method that guarantees clustering monotonicity of all OVGs from the bottom to the top layer. In our experiments, all the selected results from each layer are reported by both graphical illustration and data analysis. Performance of the proposed method is compared to five existing classification methods by a polyp database of 63 samples with pathological reports. The experimental results show that our proposed method outperforms other methods by 4% to 23% gains in terms of AUC scores.
The rise of internet of things (IoT) and its applications in finance and accounting

Purpose-The purpose of this study is to analyze the usage of internet of things (IoT) on finance and accounting.Internet of things refers the interconnection between devices via internet including mechanical and digital objects.The advancements including internet, wireless communication, microelectromechanical systems (MEMS) and radio frequency identification (RFID) created a world of interconnected devices, with IoT this interconnection covers everyday objects.In this study it is examined the areas where IoT can be used to facilitate finance and accounting.Methodology-In this study a descriptive research approach has been followed.In this research, it has been summarized the relevant sources of literature from a critical point of view; it has been reviewed main concepts and aimed to explain the concept with a finance and accounting perspective.Findings-Research findings has been listed in two parts. 1 st findings on the relationship between internet of things and finance and 2nd findings on the relationship between internet of things and accounting.In both business branch, internet of things has a wide area of research. Conclusion-As a result of the study, it was evaluated that the concept of Internet of Things, which symbolizes the communication of objects used in daily life via internet, can have a wide range of usage in the field of finance, primarily in banking, and will also have an effect on increasing the level of automation in accounting activities through the data transferred directly from the objects.