Reviewerly/exHarmony huggingface dataset:
Experimental Study on Camouflage Effect Evaluation Based on Eye Movement Instrument

Camouflage is a measure to deceive the enemy. Effective camouflage is very important. Therefore, the significance of objective, accurate and rapid analysis of camouflage effect is self-evident. The purpose of this study is to investigate the eye movement characteristics of people in search of camouflaged targets. When the camouflaged target appears randomly in the natural landscape, the performance of each camouflage design is evaluated by target detection hit rate, detection time, number of display fixation points, first eye jump amplitude of the target, number of fixation points of the target, fixation duration of the target and subjective evaluation of the difficulty of search task. The results showed that the camouflage pattern had a significant effect on eye movement, especially on the first saccade and fixation duration, which could improve the sensitivity of camouflage assessment. We assume that the detectability and discrimination of camouflage patterns can be evaluated. All of these can explain the low search efficiency in eye movement. In general, the data obtained from eye movement can significantly enhance the interpretation of different camouflage design effects.
Strands of Memory

Software-intensive systems in difficult environments often have to operate without timely human control. That level of autonomy argues for systems that have highly capable self-and situation-awareness processes, to protect the system, perform its tasks, and also to predict its external environment, so that appropriate plans can be made as far in advance as possible, and modified as conditions change. For this level of adaptability, we have long advocated self-modeling systems, which are systems that have models of their behavior, that they interpret to produce that behavior. That means that changing the models changes the behavior. In this paper, we describe two aspects of the memory in such systems, as reflected in two properties about the management of situation awareness: How to navigate the world and not get surprised (very much). How to recognize situations when they recur (soon enough to do something useful). Our conclusion is that the system must maintain many many “strands” of memory, that is, partially completed stories about what the environment has done, is doing, and is expected to do, and each one of the strands is (usually implicitly) waiting for some kind of extending, supporting, confirming, or refuting observations or other evidence. The details to be determined are about what those observations can be, what levels of abstraction are required, how they are determined, how the stories are managed, and how they relate to each other. This paper is a first step in that direction.
Adithri – (F2) The Farmer’s Friend

In this 21st century of internet world, the adoption of social network and advanced technologies has nurtured a drastic change to automation for communication through messaging applications by using web robots so called chat bots. The stimulation of a real-time conversation between the human and the computer using computer programs are said to be chatbots. For agriculture purposes, it is important to know about the various variables, update rapidly and available easily for the use of farm management by the farmers. In the domain of agriculture using Machine Learning technology chat bot ADITHRI has been prepared. Adithri is developed focusing on the search and query of data by the user deployed on different types of crops by posing query in Facebook which is based on the messenger bot API. The chat bot gives the description of the query posed by the user in the FB messenger bot. Adithri the farmer’s friend is designed in such a way by bringing various individual app services that were developed previously to the farmer such as Government schemes, Weather information, Fertilizers etc., in the shade of one umbrella. ADITHRI provides the services applicable for different types of crops and does not stick to one particular crop. It is expected that with logical capacity over the mass data, it is possible to work towards harmful situations by the farmer.
Enhancement of Cloud Security Feature in OpenStack

OpenStack is an orchestration software that manages resources in the cloud platform. The cloud security is managed by a module named security groups in OpenStack. The OpenStack security groups feature has a few limitations, one of them being a static way for protecting the VM instances. The main objective of this paper is enhancing the OpenStack security groups feature. It provides a dynamic approach to restrict the access to VM instances from malicious hosts on the internet, which are obtained from Cisco Talos. These malicious IP addresses are stored in the binary search trees. Packet sniffing is used to obtain the IP addresses of the incoming packets and IP tables are used as firewalls to filter the incoming traffic and protect the VM instances.
Influences of aberrations on double random phase encoding system

All optical systems are affected by aberrations, and optical encryption systems with lens are no exception. In order to improve the practicality of the optical encryption system, the influences of aberration on the double random phase encoding (DRPE) system are studied. In this paper, Zernike polynomials are used to express the aberration of the lens. Simulation has been carried out. The influences of different types, coefficients, and combinations of aberrations on DRPE system are analyzed. The results of the study indicate that some Zernike polynomials(such as the 4th Zernike polynomial, etc.)will have a greater impact on DRPE system, while some combinations, such as the combination of the 4th and 9th Zernike polynomials, will reduce the influences of aberrations on DRPE system. Besides, the degree to which DRPE system is affected by aberrations is evaluated by the correlation coefficient between the decrypted image and the plaintext. In order to solve the problem of aberration, two solutions are proposed.
A Mobile E-Commerce Recommendation Algorithm Based on Data Analysis

With the development of the Internet and the change of consumption concept, e-commerce platform has become the primary way of shopping. In this case, personalized recommendation system has become the main way to find favorite businesses, and also become the main means to sell goods on the platform. n the recommendation system of e-commerce platform, collaborative filtering recommendation algorithm is the most widely used. The main idea of the algorithm is to find the similar neighbor set of the target user, and recommend the user preference of the neighbor set to the target user as a recommendation. In this paper, a collaborative filtering recommendation algorithm based on context information is proposed, which focuses on the influence of user context information on the similarity calculation between users in the recommendation algorithm in the mobile Internet environment. The algorithm takes the user location information and user trust relationship as the key factors. First, the algorithm preprocesses the original score data through SVD to alleviate the scarcity of data, then generates a list of merchant candidate recommendations based on the score similarity of users Using collaborative filtering. And then calculates the travel cost of each merchant in the candidate recommendation list to the target user according to the location context, and adjusts the recommendation weight of the merchant in the list, Finally, the final Top-N merchants are generated to recommend to the target users. In the end of this paper, through the experimental simulation of the proposed algorithm, and compared with the traditional collaborative filtering recommendation algorithm, it is proved that the algorithm further improves the effectiveness and accuracy of the recommendation algorithm.
COMMUNICATION IN TIMES OF PANDEMIC, IMPACT IN ECUADORIAN EDUCATION

Currently, the COVID 19 pandemic has changed the world and all systems of society, bringing new demands on education and care in public service entities and the work of millions of people in the world. The new digital technologies have allowed the world to function in some way in this period of time, in all areas of society. In education a constant interaction is needed, between teachers and students, during teaching - learning. However, there are factors that affect this process, including internet accessibility, digital literacy, and the economic capacity of families to acquire an electronic device. In this sense, the Ecuadorian government must take actions to guarantee that students have access to the demands of the 21st century. The objective of this article is to analyze how the population is adapting to the new educational scheme, to visualize the urgent needs and give suggestions to solve them.
An improved device for the 3D optical scanner calibration and related influence factors on calibrating result

Nowadays, in industrial area, many mechanical part manufactures are applying 3D optical scanner in their production shop to do part online inspection, or in their coordinate measurement laboratory to obtain crucial part dimensions. The high demands of the 3D optical scanner make it necessary for researchers to create convenient and stable device for calibrating this kind of contact-less measurement instrument. In a former study, we introduced a plate with standard spheres as a calibration device, which showed a good test result when applied on a 3D optical scanner. However, we decide to do more research on related area. The first purpose of this paper is to present an improved calibration device based on our former study and daily work experience. The new device has a wider test range with less spheres, and it’s structure is more portable, stable and more convenient to provide spatial positions. Results showed that the new device performed well in data stability and is quite easy to practice. The second purpose of this work is to study related influence factors on the 3D optical scanner calibration process. We investigated factors such as temperature, points cloud density, reflection patch density and numbers of images stitching. The results showed that those factors should be limited in proper conditions to ensure an acceptable calibration of 3D optical scanner.
Classification of external signal by spiking neural network of bistable Hodgkin-Huxley neurons

We propose a classifier consisting of Hodgkin-Huxley neurons based on the activation of a different number of neurons depending on the external current amplitude. We consider a network with 2 output neurons and train the classifier for 2 external current pulses with different amplitudes by adaptation of the couplings between neurons of the main network and the output neurons.
Aggregating Spatio-temporal Context for Video Object Segmentation

In this paper, we focus on aggregating spatio-temporal contextual information for video object segmentation. Our approach exploits the spatio-temporal relationship among image regions by modelling the dependencies among the corresponding visual features with a spatio-temporal RNN. Our spatio-temporal RNN is placed on top of a pre-trained CNN network to simultaneously embed spatial and temporal information into the feature maps. Following the spatio-temporal RNN, we further construct an online adaption module to adapt the learned model for segmenting specific objects in given video. We show that our adaption module can be optimized efficiently with closed-form solutions. Our experiments on two public datasets illustrate that the proposed method performs favorably against state-of-the-art methods in terms of efficiency and accuracy.
Research on Advertising Image Recognition Technology Based on Convolutional Neural Network

Aiming at the problem of advertising image recognition in medium-sized Internet companies, this paper points out the shortcomings of existing technology, analyzes optimization techniques of image recognition using CNN, and proposes a novel convolutional neural network to advertising image recognition. The structure of convolution neural network, the working principles of convolution layer, pooling layer, activation function, loss function and gradient descent are studied. The advantages of convolution neural network in advertising image recognition are pointed out. In the implementation stage, the methods of data expansion are studied, which are image horizontal flip, random matting, scale transformation and rotation, manual data expansion. Three methods are used to obtain models for advertising image recognition: training a convolution neural network from scratch, extracting features with pre-trained network, fine-tuning training network. Various methods, parameters, and results were compared.
An Application and Integration of Machine Learning Approach on a Real IoT Agricultural Scenario

The Internet of Things (IoT) paradigm applied to the agriculture field provides a huge amount of data allowing the employment of Artificial Intelligence for multiple tasks. In this work, solar radiation prediction is considered. To the aim, Multi-Layer Perceptron is adopted considering a complete real complex use case and real-time working conditions. More specifically the forecasting system is integrated considering three different time forecasting horizons and, given different sites, needs and data availability, multiple input features configurations have been considered. The described work allows companies to innovate and optimize their industrial business.
Cryptanalysis on a (k, n)-Threshold Multiplicative Secret Sharing Scheme

Shamir's secret-sharing scheme is an important building block of modern cryptography. However, since multiplication between two variables is not linear, how to confidentially and efficiently multiply two shared secrets remains an open problem. Recently, Taihei et al. presented a feasible (k, n)-threshold secret-sharing protocol which is capable of achieving such product result even if only k servers are available. Nevertheless, we argue their scheme is vulnerable that the threshold property can not withstand collaborative attacks. Thus accordingly, in this paper, we designed a practical cracking method against their scheme. In terms of intensive analysis, it can be see that our scheme is able to efficiently reveal the shared secret with high probability albeit less than k servers are compromised.
The Challenges of Blockchain Technology to Competition Law

Blockchain is a catch-all term for a combination of three technologies: distributed ledger, cryptology and network protocols. The first enables storing the same info in different places, the second allows secure transactions to be recorded and then encrypted on the distributed ledger. The third element governs the network and verifies transactions across the network automatically and independently. Considered by many as “the biggest technological innovation since the Internet”1, blockchain is a decentralized, more secure and transparent model for transactions that operates on an encrypted peer-to-peer basis. This model makes trust between parties superfluous by instead placing trust in the underlying technological platform. This would effectively remove the need for intermediaries whose business has been to make up for the lack of trust; these include banks, brokers, governments, internet platforms, law firms etc. While reducing the costs of contract enforcement and thus facilitating trade, blockchain technology may have significant implications for antitrust law. As decentralized organizations such as blockchain are not recognized as legal persons, this raises questionsabout whether anticompetitive practices and their perpetrators can be identified. For example, can a non-entity hold a dominant position? Can blockchain create a “monopoly without a monopolist”? Finally, if a blockchain is dominant, which users and/or entities hold that dominant position? This article intends to highlight the challenges that blockchain presents to the analyses of unilateral anticompetitive practices.
A Metaheuristic Computation Scheme to Solve Energy Problems

The development of methods to solve optimization problems has increased in recent years. However, many of these methods are not tested in real applications. Energy problems are a topic of high relevance within the scientific community due to its environmental repercussions. Two representative cases involved in the huge energy consumption are the capacitor placement in radial distribution networks and parameter identification in induction motors. Both problems have intrinsically complex characteristics from the optimization perspective, which makes it difficult to solve them by conventional optimization methods. Some of these properties are their high multi-modality, discontinuity and non-linearity. Alternatively, metaheuristic techniques have had shown performance in solving the solution to a wide range of complex engineering problems. A recent metaheuristic based on the intelligent group behavior of crows and their interaction is the Crow Search Algorithm (CSA). Although CSA shows interesting aspects, its performance in exploration mechanism exhibits considerable disadvantages when it confronts high multi-modal conditions. In this chapter, an enhance variant of the CSA approach is introduced to face complex optimization formulations of energy. The improved method is focused on modifying two main features from the original CSA: (I) the random perturbation and (II) the fixed awareness probability (AP). With the modifications, the enhance methodology conserves diversity in global solution and enhances the convergence to difficult high multi-modal optima. The performance evaluation of the presented methodology is addressed in a series of optimization problems related to distribution networks and inductions motors. The results of the evaluation show the robust performance of the presented methodology when it is contrasted with popular approaches in the literature.
Evaluating Non-redundant Rules of Various Sequential Rule Mining Algorithms

The data mining techniques help discover hidden knowledge from a huge database. In the pattern mining field, the main goal is to discover interesting patterns in large databases. The sequential pattern mining technique is specialized for discovering sequential patterns with only one measure called support. It is not sufficient and misleading for the user. Sequential rule mining is a good solution that takes another measure into an account called confidence. This paper presents a comparative analysis between two sequential rule mining algorithms, namely non-redundant with dynamic bit vector (NRD-DBV), and TRuleGrowth algorithm. The study clarifies the execution time, the number of rules, and the memory usage for each algorithm. In addition, exposure to the most proper field for each algorithm to achieve elevated efficiency.
Teaching Adversarial Machine Learning: Educating the Next Generation of Technical and Security Professionals

The growth in machine learning has created an opportunity to expand education to include the study of "adversarial" machine learning, specifically in undergraduate and graduate courses for cybersecurity professionals and machine learning experts. This paper presents tools available in teaching these concepts. This information also helps system designers reduce design flaws, as well as design against malicious attacks. This paper recommends using these tools to improve offensive cyber security practices that may harden machine learning systems. These tools include newly developed machine learning libraries that make this approach a practical alternative.
Blind Quality Evaluation for Screen Content Images Based on Regionalized Structural Features

Currently, screen content images (SCIs) are widely used in our modern society. However, since SCIs have distinctly different properties compared to natural images, traditional quality assessment methods of natural images cannot precisely evaluate the quality of SCIs. Thus, we propose a blind quality evaluation method for SCIs based on regionalized structural features that are closely relevant to the intrinsic quality of SCIs. Firstly, the features of textual and pictorial regions of SCIs are extracted separately. For textual regions, since they contain noticeable structural information, we propose improved histograms of oriented gradients extracted from multi-order derivatives as structural features. For pictorial regions, since human vision is sensitive to texture information and luminance variation, we adopt texture as the structural feature; meanwhile, luminance is used as the auxiliary feature. The local derivative pattern and the shearlet local binary pattern are used to extract texture in the spatial and shearlet domains, respectively. Secondly, to derive the quality of textual and pictorial regions, two mapping functions are respectively trained from their features to subjective values. Finally, an activity weighting strategy is proposed to combine the quality of textual and pictorial regions. Experimental results show that the proposed method achieves better performance than the state-of-the-art methods.
A Data-Driven Method for Intrinsic Decomposition of 3D City Reconstruction Scene

Nowadays, 3D reconstruction pipelines based on structure-from motion and multi-view stereo techniques can reconstruct meticulous, large-scale geometry models and high-resolution textures. However, due to the shadow parts of the acquired images, the reconstructed texture map has dark areas, which is not convenient for relighting, which limits the use of the reconstruction model in VR/AR programs or games. In this paper, we introduce a large-scale synthetic city intrinsic images dataset for evaluating intrinsic decomposition of city scenes. Given our database, we developed a data-driven method for obtaining the diffuse albedo of reconstructed scenes, our method achieves good results in our intrinsic images dataset and real images.
Joint Probabilistic Data Association Filter Using Adaptive Gibbs Sampling

This paper proposes a novel adaptive Gibbs sampling algorithm to implement joint probabilistic data association filter for multiple targets tracking. Instead of uniformly visiting and sampling each single element in one joint association hypothesis, the proposed algorithm selects an optimal element visiting sequence that tends to keep the most probable single association hypothesis. Compared to the random Gibbs sampling, it has been demonstrated that the proposed adaptive Gibbs sampling provides faster convergence speed, thus improving the tracking accuracy when the number of samples is limited, and improved robustness against the variation of the number of burnin samples. Extensive empirical simulations are undertaken to validate the performance of the proposed approach.
A Blob Detector Images-Based Method for Counterfeit Coin Detection by Fuzzy Association Rules Mining

Image processing techniques using the knowledge obtained from known historical data has become recently one of the most intensively studied topics in decision science and computer science. This paper presents an automatic system for fake coins detection based on image content. In this study, a blob detector image-based method by fuzzy association rules mining is proposed to detect counterfeit coins. This method consists of two-stages. In the first stage, the original image dataset is preprocessed by a blob detector. This provides all frequent features that must be mined in the next stage. In the second stage, fuzzy association rules mining extracts the effective fuzzy rules and classifies automatically the coin image data. The performance of the proposed method has been compared with some other methods and we demonstrate that our framework surpasses in terms of classification accuracy, which is a desirable level when compared with recent studies in this field. This research demonstrates the proposed framework is a reliable intelligent detection system and can be utilized for other applications based on image content.