Reviewerly/exHarmony huggingface dataset:
Clustering SIFT: An Efficient Way to Achieve Flipping Invariance

The design and improvement of feature descriptor is always a prevalent research point. A strong design of feature descriptor can be applied to many industrial fields. However, in many successful designs, there are still have some shortcomings that need to be improved like the flipping operation in image. Although there are many techniques to deal with this problem recently, they still exist some uncertainty factors because they do not compare with different images which have the same but flipped object in their experiments. In this paper, we design an unsupervised learning structure algorithm based on SIFT to achieve flipping invariant and change object to get as many correct matches as possible. Then we will use this algorithm to match in UKBench dataset and indicate that this approach not only solves the problem of flipping operation but also ensures high matching accuracy. Compared with previous other algorithms, our algorithm is extremely simple to implement and does not destroy the structure of SIFT.
Synthesis of TLBO Algorithm Research

Teaching-Learning optimization (TLBO) is simple, easy to implement, requires few parameters to be tuned, and has a higher computational efficiency than traditional methods, which has been widely used in function optimization, neural network optimization, engineering optimization and other fields. However, there is also the phenomenon of precocious convergence. The paper briefly introduces the current state of research, the TLBO algorithm, analyzes the improvements and applications of the algorithm, and makes a prospect for the research.
Research on Optimization of Atmospheric Environmental Quality and Countermeasures

Abstract With the rapid development of economy and society, energy consumption is further accelerated, and the quality of atmospheric environment is also seriously challenged. In order to optimize the quality of the atmospheric environment, this paper preprocesses the relevant raw data of the atmospheric environment quality, analyzes the air composition through the correlation coefficient, uses the whitening function to establish the atmospheric environmental quality analysis standard, and calculates its comprehensive pollution index. The government should formulate corresponding pollution prevention and control countermeasures for industrial pollutants, motor vehicle exhaust and plant greening. The results show that the air pollution index (API) can be drastically reduced if the pollution prevention and control measures are implemented in multiple areas of a certain city.
A Proposed System for the Identification of Modem Arabic Poetry Meters (IMAP)

The following topics are dealt with: learning (artificial intelligence); deep learning (artificial intelligence); data mining; feature extraction; Internet of Things; text analysis; pattern classification; Internet; wireless sensor networks; medical disorders.
Transition Graph Analysis of Sliding Tile Puzzle Heuristics

Sliding tile puzzle or n-puzzle is a standard problem for solving a game by a tree search algorithm such as A*, involving heuristics. A typical example of such a puzzle is 15-puzzle that consists of a square frame containing 4 × 4 numbered square tiles, one of which is missing. The goal is to position the tiles in correct order by sliding moves of the tiles, which use the empty space. The problem is NP-complete and for puzzles involving a greater number of tiles, the search space is too big for standard tree search algorithms. This type of puzzles is therefore quite often used for analysis and testing of heuristics. This paper aims to obtain a better characterization of popular heuristics used for this kind of problems by analysis of the transition graph of admissible moves. Our analysis shows, that both Manhattan distance and Tiles out of place heuristics work properly only near the goal, otherwise the information they provide is next to useless for a single move, IDA* with these heuristics works mainly due to reduction of branching of the search tree, for more consecutive moves.
Measuring Effective Scheduling Interval Against VM Load with Rapid Elasticity in Cloud Computing

Innovative and useful cloud based applications are increasing regularly as its demand also growing. The computing load, either processing or data load, on such application has been increased in multiples during the covid-19 pandemic, because everyone likes to do its jobs through online solutions either for meeting, teaching-learning, presentation, discussion, social data sharing, etc. as these are safe and secure solutions for all. This all increases the computing load proportionally the requirement of resources on the cloud server or Host machine. The host machine is the physical machine that contains the computing resources like storage, processors, bandwidth etc. for cloud based applications. These computing resources are shared and managed between multiple virtual machines in such an efficient manner that if computing load increases very much on some applications, while at the same time on the same server other application's resource utilization in underuse, then these resources will be shared to the applications currently having overloaded with the processing load. As cloud computing provides a pay-per-use model and hence if the resources utilization is managed efficiently and effectively the processing cost incurred to the client or provider will be more cost effective. So, in this paper we will measure the effectiveness of scheduling interval against increasing VM load on rapid elasticity for the existing cloud infrastructure available on the Host Machine.
Constrained multiple model UK filter

Model selection and, more recently, statistical constraint have proved to be applicable for nonlinear multiple model filter. In this correspondence a constrained multiple model UK filter is developed. The following topics are discussed: 1) model switching with feedback from nonlinear filtering, 2) the model-conditioned state nonlinear filtering with prior constraint, and 3) the weight of the fusion estimate. Some related examples are presented. Our theoretical result is shown to conform well to previously published experimental results on bearings-only maneuvering target tracking.
How Effective Is Cyber Security Assurance by Internal Auditors?

The aim of this paper is to analyze how effective internal audit of cybersecurity is. We developed a Cybersecurity Audit Index composed of three dimensions (planning, performing and reporting) to address this question. We hypothesize that CSA effectiveness is positively related to cyber risk management maturity and negatively to the probability of a successful cyber attack. We tested our hypotheses in a survey with auditors and Chief Audit Executives from various countries and industries. We found that CSA Index scores significantly vary, with a mean of 58 on a scale from 0 to 100. While planning and performing CSA are strongly and positively correlated, they are less strongly related to reporting about CS risk management effectiveness to the Board of Directors. In line with our hypothesis, the CSA Index is positively associated with CS risk maturity, but contrary to our hypothesis, it is not related to the probability of a cyber attack. This is the first paper that comprehensively measures the effectiveness of cybersecurity audit and its effects on CS risk management.
Dense short connection network for efficient image classification

Abstract With the continuous development of convolutional neural networks (CNNs), image classification technology has entered a new stage in solving visual cognition tasks. Recent advances have shown that if containing short connections, convolutional networks can be more accurate and efficient to train. However, these short connections almost only exist between convolutional layers, which potentially make the feature information flow insufficiently. In addition, the multi‐scale representation ability of CNNs using short connections can be further explored. Thus, the dense short connection network (DSCNet) for efficient image classification is designed in this paper. In DSCNet, we propose a simple yet effective architectural unit, namely dense short connection (DSC) module, which allows hierarchical dense short connections for multi‐scale context information across different feature channels within a single convolutional layer. DSCNets have several noteworthy advantages: they improve the multi‐scale representation ability, enhance feature propagation, and reduce the number of parameters. To validate our DSC, we conduct comprehensive experiments on CIFAR‐100 and Tiny ImageNet datasets. Experimental results show that DSCNets achieve very competitive results with previous baseline models, whilst requiring less computation to achieve higher recognition accuracy. Further ablation studies show that our proposed method can achieve consistent performance gains in image classification tasks.
Differential Photometric Consistency

A key bottleneck in the use of Multiview Stereo (MVS) to produce high quality reconstructions is the gaps arising from textureless, shaded areas and lack of fine-scale detail. Shape-from-Shading (SfS) has been used in conjunction with MVS to obtain fine-scale detail and veridical reconstruction in the gap areas. The similarity metric that gauges candidate correspondences is critical to this process, typically a combination of photometric consistency and brightness gradient constancy. Two observations motivate this paper. First, brightness gradient constancy can be erroneous due to foreshortening. Second, the standard ZSSD/NCC patchwise photometric consistency measures when applied to shaded areas is, to a first-order approximation, a calculation of brightness gradient differences, which can be subject to foreshortening. The paper proposes a novel trinocular differential photometric consistency that constrains the brightness gradients in three views so that the image gradient in one view is completely determined by the image gradients at corresponding points in the the other two views. The theoretical developments here advocate the integration of this new measure, whose viability in practice has been demonstrated in a set of illustrative numerical experiments.
Secure two-party input-size reduction: Challenges, solutions and applications

The computation and communication costs of many secure multiparty protocols would benefit from a preprocessing that replaces large inputs with much smaller values without changing the outputs. This preprocessing is especially advantageous when its cost can be amortized over subsequent computations that all benefit from smaller inputs. The above holds for protocols based on garbled circuits, homomorphic encryption, or other techniques. Problems benefiting from such preprocessing include pattern matching, information retrieval, and sequence comparisons that depend on (in)equality of comparands. Motivated by this (in)equality-preservation requirement, we define the problem as follows: Alice’s and Bob’s inputs are their respective private sets SA and SB of large integers, and their private outputs are images of their sets under a function ρ that injectively maps SA∪SB into {0,1,…,N-1} for a small N⩾|SA|+|SB|. Alice’s (Bob’s) knowledge of this mapping on SA (SB) must reveal nothing about SB (SA). Thus, neither party should be able to learn ρ(x) for any x that is not in its private set; otherwise, s/he could exploit the small codomain of ρ to learn about the other party’s set. We formalize the problem, propose efficient and secure (semi-honest model) solutions to it, and discuss its use cases.
COVID-19 Pandemic and Virtual Information Services

The study was carried out to explore the experiences of frontline library staff and users during the COVID-19 pandemic. With the unprecedented nature of the pandemic, institutions, including universities, have found means of staying afloat through the use of technology to offer services virtually. Makerere University rolled out an emergency program for online, distance, and e-learning, and the library has been supporting learning and research, since March 2020. The chapter sought to find out the experiences of library staff at the forefront and users in order to understand how online information services can be enhanced and retooled to support eLearning. Structured telephone interviews were conducted, and findings show virtual services that have been rendered to staff, students, and researchers as well as challenges. Challenges included equipment, poor infrastructure, and data costs. The university has signed up with major service providers to alleviate some of the handicaps to learners' virtual experiences.
Tropical Cyclones Tracking Based on Satellite Cloud Images: Database and Comprehensive Study

The tropical cyclone is one of disaster weather that cause serious damages for human community. It is necessary to forecast the tropical cyclone efficiently and accurately for reducing the loss caused by tropical cyclones. With the development of computer vision and satellite technology, high quality meteorological data can be got and advanced technologies have been proposed in visual tracking domain. This makes it possible to develop algorithms to do the automatic tropical cyclone tracking which plays a critical role in tropical cyclone forecast. In this paper, we present a novel database for Typical Cyclone Tracking based on Satellite Cloud Image, called TCTSCI. To the best of our knowledge, TCTSCI is the first satellite cloud image database of tropical cyclone tracking. It consists of 28 video sequences and totally 3,432 frames with $$6001\times 6001$$ pixels. It includes tropical cyclones of five different intensities distributing in 2019. Each frame is scientifically inspected and labeled with the authoritative tropical cyclone data. Besides, to encourage and facilitate research of multimodal methods for tropical cyclone tracking, TCTSCI provides not only visual bounding box annotations but multimodal meteorological data of tropical cyclones. We evaluate 11 state-of-the-art and widely used trackers by using OPE and EAO metrics and analyze the challenges on TCTSCI for these trackers.
BENEFIT OR HARM FROM PHILOSOPHY: REFLECTIONS OF 19TH CENTURY PHILOSOPHERS (I. M. Skvortsov, V. N. Karpov, P. I. Linitsky)

The author reveals the views of I. M. Skvortsov, V. N. Karpov, P. I. Linitsky – Russian religious philoso-phers of the XIX century on the role of philosophy in the knowledge of the world around him in its integri-ty and comprehension of the "eternal law", on the connection of philosophy with private sciences and determination of the place of philosophy in the system of education, its influence on the formation of the thought culture of the student's personality, on the role of philosophy in the self-knowledge and upbring-ing of the moral personality.
Application of Convexified Convolutional Neural Network in Text Classification

This paper introduces a type of convexified convolutional neural network (CCNN), introduced by Zhang, Liang and Wainwright. We applied this model on the classification of text-based online shopping reviews. This work makes an estimate on the error term brought by the low rank approximation. We also build our codes on the work done by Schaik. We make adjustments on the choices of kernel functions and further extend the application of the algorithm to multilayer CCNNs. The results show that Zhang's model is practical on learning shallow CCNNs. However, there is no big improvement in multilayer CCNN. Analysis of likely causes are mentioned by the end of this section. Strengths, weaknesses as well as future work are discussed in the end.
Hand Gesture Recognition with Gaussian Scaling and Kirsch Edge Rotation

Hand gesture recognition is a vital aspect of robotic vision models. This paper presents a fusion based approach for hand gesture recognition. In this approach, we first extract the Gaussian scale space of an image and compute features on different scales. Kirsch's convolution mask is then applied on the feature map. The aim of the proposed approach is to remove unwanted information extract scale, rotation, and illumination invariant patterns from hand gestures. The final feature vector is aggregated through the concatenation of multiscale histograms. The Support Vector Machine classifier is demonstrated using extracted features. Moreover, we calculate the progress efficiency of proposed methods on three distinct databases by conducting experiments viz, Thomson, Bochum, and HGRI. The proposed method achieves classification accuracies of 94.25%, 92.77%, and 95.78% respectively on the investigated databases that outperform the existing approaches for hand gesture recognition.
Advanced Active Learning Strategies for Object Detection.

Future self-driving cars must be able to perceive and understand their surroundings. Deep learning based approaches promise to solve the perception problem but require a large amount of manually labeled training data. Active learning is a training procedure in which the model itself selects interesting samples for labeling based on their uncertainty, with substantially less data required for training. Recent research in active learning has mostly focused on the simple image classification task. In this paper, we propose novel methods to estimate sample uncertainties for 2D and 3D object detection using Ensembles. We moreover evaluate different training strategies including Continuous Training to alleviate increasing training times introduced by the active learning cycle. Finally, we investigate the effects of active learning on imbalanced datasets and possible interactions with class weighting. Experiment results show both increased time saving around 55% and data saving rates of around 30%. For the 3D object detection task, we show that our proposed uncertainty estimation method is valid, saving 35% of labeling efforts and thus is ready for application for automotive object detection use cases.
GUIDELINES FOR THE IMPLEMENTATION OF POSYANDU IN THE PANDEMIC TIME COVID-19 AT THE CADRE IN RW 05 AND 06 KELURAHAN SUKAJAYA KECAMATAN TAROGONG KIDUL KAB. GARUT

The COVID-19 pandemic has limited space for activities due to the speed of distribution and not yet available drugs, on the other hand, implementing Posyandu is a must even in limited circumstances. Posyandu implementation must use the correct health protocol procedures, however, the knowledge and skills of cadres hamper the implementation of Posyandu. The aim of this PENGMAS is to train cadres in carrying out safe Posyandu activities in accordance with standard health protocols. The PENGMAS method is carried out in three activities, namely socialization, seminars, and Posyandu protocol simulations by measuring cadres' knowledge before and after the activity is carried out to measure the success of this activity. After the activity was carried out, it could be concluded that there was a significant increase in knowledge and understanding from the participants about the implementation of health protocols in the implementation of the Posyandu.
Research of UAV track planning and dynamic obstacle avoidance algorithm

Track planning and dynamic obstacle avoidance algorithm of UAV are proposed in this paper. In global path planning, an improved genetic algorithm is put forward. There are four kinds of operators: selection operator, crossover operator, mutation operator and deletion operator, in which the deletion operator is added. Meanwhile, the roulette method is improved to the directional selection method in the new algorithm. The total cost function is established according to length, smoothness and security. The optimal path is obtained and smoothed. In local path planning, candidate paths are generated by a cubic spline method. The optimal candidate path is chosen by the total cost function. Simulation results show that the new algorithm of this paper is superior to genetic algorithm and ant colony algorithm and improved ant colony algorithm in path length, running time and iteration times. Dynamic obstacle avoidance algorithm can avoid sudden threats.
ACCNet: Attention-based Contextual Convolutional Network for Crowd Counting

In recent years, crowd counting has been widely used in computer vision. But on many issues like large scale variations, perspective distortions and complex backgrounds, it remains a challenging task. Previous methods usually adopted dilation convolution to enlarge the receptive field, these methods can generate poor density maps. To solve this problem, in this paper, we proposed a novel structure called Attention-based Contextual Convolution Network for crowd counting. ACCNet contains two components. The Contextual Convolution Network first gets the relative influence of different scale-aware feature and attention module to further enlarge the range of scales while getting the final crowd density map in the network. The key section of our network is the dense dilated module, this method can make the front and back dilation layers tightly connected to preserve information from changing scales. Other than that, attention mechanism also plays an important role in learning density maps from input images. To verify the feasibility of our method, we compare it against the state-of-the-art structure on four crowd counting datasets (ShanghaiTech Part_A, ShanghaiTech Part_B, WorldEXPO'10 and UCF_CC_50), which demonstrates that ACCNet is effective and robust for complex scenes.
Dynamic Symmetric Encryption Over E-mail in Cloud Server

With the precinct of e-mail message discharge events, for example, the Hillary Clinton’s electronic mail argument, certification besides safety of subtle e-mail info consume changed into customer’s chief concern. Mixed e-mail is unmistakably a sensible response for giving security; regardless, it will outright tie their exercise. Open encryption with catchphrase search plan is a standard headway to solidify safekeeping shield in addition inconceivable feasibility works organized that can recognize a huge development in inspecting mixed electronic message in a cloud server. Within the paper, we recommend a applied PEKS plan termed by means of open key multi-catchphrase available encryption through covered edifices (PMSEHS). That may perhaps attract e-mail recipients toward the multi-watchword also Boolean requesting from the monstrous mixed electronic post database as savvy would be reasonable, deprived of skimpy extra data to the cloud server. In same manner, we provide relative tests, whichever show that our strategy takes a sophisticated capability in multi-catchphrase break down used for mixed messages.