Reviewerly/exHarmony huggingface dataset:
The Influence of Artificial Intelligence on the Diversity of Plant Design in Landscape Design

Abstract With the progress of the times, artificial intelligence and other high-tech applications in our daily life more and more widely. With the acceleration of urbanization process, in the process of rapid development of the city, people not only work and live, but also have higher requirements for the urban environment, and ecological garden construction has also been rapid development. It is an important problem for landscape designers how to allocate plants effectively and give full play to the ornamental and ecological characteristics of landscape. In order to make the plant design research in landscape design keep up with the pace of the development of this era, and make the plant design research in landscape design more successful, this paper puts forward the development method of applying artificial intelligence technology to plant design in landscape design. The application of artificial intelligence technology in modern landscape design is bound to be the development trend of mainstream design. In the practice process of plant design in landscape design, the design method of artificial intelligence is integrated, and some excellent design methods are selected, so as to formulate a set of diversified development scheme which is most suitable for plant design in the new era. Through the analysis, the research method proposed in this paper provides a new development idea for the study of the impact of artificial intelligence on the diversity of plant design in landscape design.
Blockchain‐based forgery resilient vehicle registration system

Abstract Vehicle registration system is an essential process for recording the transfer of vehicles' ownership. Most of existing systems are administratively centralized, semi‐automated, and rely on a formal proof provision. While these systems were initially proposed to preserve the owner ' s rights, prove their ownerships and legally record used motor‐land transportation means. Unfortunately, these systems also allow the registration of illegally smuggled and stolen vehicles. This is mainly due to inefficient verification methods, long heterogeneous administrative procedures or to corrupt individuals at the motor registration departments. Regardless of the reasons, the fact that the system allows such cases hints on a faulty design. Current systems are centralized and do not perform a double checking until a report is made, which means that this occurs after the fact of registering the forged vehicle. Noting that these reports are not always fruitful, if the vehicle's proofs were properly injected in the system. This paper proposes a system that is transparent where every vehicle registration is done under everyone's watchful eyes. No denying, no alteration, and no unauthorized injection may occur. The proposed system saves the owner's information and the vehicle's descriptions in transactions saved in a public blockchain where all the history of purchases regarding the subject vehicle may be tracked. The use of blockchain technology is motivated by its security, transparency, and traceability as well as its immutability and scalability in terms of users. We have evaluated our proposed solution in terms of its security and resiliency to the injection of forged transactions by the elaboration of the attack tree. The results show our solution's superiority when compared against current registration system.
Automated Decision Making: European (GDPR) and Indian Perspective (Indian Personal Data Protection Bill, 2018)

Collection of the personal data can reveal a lot about an individual, including its behavior or preferences or outlook. For example, the fact or combination of facts that an individual called a criminal lawyer for advice and then, browsed for criminal law books or videos online or bought gardening tools (such as shovel or leveler) online or searched for keywords such as ‘destruction of evidence’ or ‘remove blood stains’ could let anyone to infer that the individual has committed either some felony or at least something incriminating in nature. The collection of such information (including personal data) at a larger scale is called ‘Big Data’, and any form of automated processing which is used to analyze the same for assessing or revealing patterns, sets and trends or human behavior or its attributes is, in simple terms, called ‘profiling’. Once an individual profile is created, then the same can be used to take decisions for the same individual and if this process of taking decision is exclusively done by automated means, then it is called as ‘Automated decision making’. In order to quickly analyze or make cost-effective profiles out of the Big Data, the humans started to deploy machines (based on algorithms and machine-learning systems) and the capacity of these machines to do the same is called ‘artificial-intelligence’ (AI). The combination of the Big Data, AI and Machine Learning is called as ‘Big Data-Analytics’ and profiling or automated decision making is one of the facets of it.There are a lot of advantages of automated decision making and profiling in the modern world viz. customers gets advertisements according to their preferences and tastes or it helps public and private entities to screen out unqualified applicants for the jobs or to detect fraud-activities in advance. But this is not that simple as it looks from the outside. Organizations lay their hands upon loads of personal information of different types of data from different sources such as browsing activities, purchases made or internet searches from social networks, mobile phones or Internet of things and make decisions about the data subjects on their own. That is why, in recent years, a lot of serious concerns are being raised against the profiling or automated decision making system, especially the ones which invite substantial consequences, such as what if the decision making system is designed to be biased and unfair or what if data subjects are being discriminated on basis of sensitive-data or what if the profiling is being undertaken without obtaining explicit consent from the data subjects, or what is the logic behind the machines which are taking a particular decision or what kind of rights a data subject can exercise if it wants to contest the decision taken.This Paper discusses the level of recognition accorded to 'Automated Decision Making' in Europe and in India and the kind of approach taken thereto.
AUTOMATIC TEXT PROCESSING AND LINGUISTIC MODELING AS INSTRUMENTS FOR SOLVING PROBLEMS OF TEXT ATTRIBUTION

This paper focuses on the approbation of an integrative method of attribution text analysis in Russian, based on a combination of the results of an interpretive study of the material and objectification of these results through mathematical statistics. The study has been conducted according to the following algorithm: 1) automatic extraction of text parameters describing the idiostyle from the point of view of the author’s pragmaticon, thesaurus, and lexicon; 2) automatic search for traditional stylometric text data (length of sentences, words, etc.); 3) weight assignment to each parameter; 4) creation of mathematical models of compared texts; 5) comparison of mathematical models in order to identify the level of their correlation with each other. The search for parameters describing the authors’ individual style is carried out on the basis of the approach to the text as a product of a specific language personality. The author’s language personality is described according Yu. N. Karaulov’s approach. Automatic extraction of predefined parameters is performed using the algorithms designed in Python. To test the algorithm, texts of non - genre fiction of different themes and obviously known authorship were used: «Nashi» by S.D. Dovlatov and «The Overtone» by V.P. Astaf’iev. The study proves the efficiency of the methodology developed.
A quantified approach of dataset selection for training ML models on hard-to-classify patterns

In the semiconductor fabrication process, yield is negatively impacted by defects that appear systematically within specific patterns of the physical layout design. Those defective patterns are popularly known as hotspots, and they can arise due to various causes. There are several known approaches of hotspot detection. One approach for hotspot detection is Machine Learning (ML), where known hotspot and non-hotspot patterns are used for training the model to be used afterwards in prediction of new hotspots. The objective in ML approaches is to maximize the hit rate (i.e. finding all potential hotspots) and to minimize the false alarm rate (i.e. reduce the overhead of false positives). The model’s ability to correctly classify between hotspots and non-hotspots depends on the coverage of the training data set. The real-world challenge in training a ML system to classify hotspots/non-hotspots is the imbalanced nature of the problem, where the known hotspot patterns are always in the minority class. Another challenge specific to the problem of hotspot classification is the difficulty to correctly classify non-hotspots that are similar to hotspots. These “hard-to-classify” patterns are ones with high mask error enhancement factor (MEEF), as small variations in the pattern can make it change between hotspot and non-hotspot. These two challenges cause conventional methods of handling imbalanced training datasets to be inadequate to the problem of hotspot detection. This paper will present a flow for quantified training dataset selection approach and put extra focus on the patterns that are hard to classify due to close similarity with known hotspots. Improved model accuracy is illustrated when adopting the quantified sampling approach compared to conventional sampling approaches.
Distance ordinal regression loss for an improved nuclei segmentation

In digital pathology, nuclei segmentation still remains a challenging task due to the high heterogeneity and variability in the characteristics of nuclei, in particular, the clustered and overlapping nuclei. We propose a distance ordinal regression loss for an improved nuclei instance segmentation in digitized tissue specimen images. A convolutional neural network with two decoder branches is built. The first decoder branch conducts the nuclear pixel prediction and the second branch predicts the distance to the nuclear center, which is utilized to identify the nuclear boundary and to separate out overlapping nuclei. Adopting a distance-decreasing discretization strategy, we recast the problem of the distance prediction as an ordinal regression problem. To evaluate the proposed method, we conduct experiments on multiple independent multitissue histology image datasets. The experimental results on the multi-tissue datasets demonstrate the effectiveness of the proposed model.
Samples Classification Analysis Across DNN Layers with Fractal Curves

Deep Neural Networks are becoming the prominent solution when using machine learning models. However, they suffer from a black-box effect that renders complicated their inner workings interpretation and thus the understanding of their successes and failures. Information visualization is one way among others to help in their interpretability and hypothesis deduction. This paper presents a novel way to visualize a trained DNN to depict at the same time its architecture and its way of treating the classes of a test dataset at the layer level. In this way, it is possible to visually detect where the DNN starts to be able to discriminate the classes or where it could decrease its separation ability (and thus detect an oversized network). We have implemented the approach and validated it using several well-known datasets and networks. Results show the approach is promising and deserves further studies.
Sentiment Analysis Applied to Analyze Society's Emotion in Two Different Context in Social Media

In the last few decades, the growth in the use of the Internet has generated a substantial increase in the circulation of information on social media. Due to the high interest of several areas of society in the analysis of these data, a study of better techniques for the manipulation and understanding of this type of data is of great importance so that this enormous volume of information can be interpreted quickly and accurately. Based on this context, this study shows two approaches of sentiment analysis to verify the emotion of the population in different context. The first approach analyses the 2018 presidential elections in Brazil considering data from the Twitter social network. The second approach performs analysis of data from social media to identify threats level of armed conflicts considering data off the conflict between Syria and the USA in 2017. To achieve this goal, machine learning techniques such as auto-encoder and deep learning will be considered in conjunction with NLP text analysis techniques. The results obtained show the effectiveness of the approaches used in the classification of feelings within the domains used according to the methodology developed for this work.
Automated Discovery of Adaptive Attacks on Adversarial Defenses

Reliable evaluation of adversarial defenses is a challenging task, currently limited to an expert who manually crafts attacks that exploit the defense's inner workings or approaches based on an ensemble of fixed attacks, none of which may be effective for the specific defense at hand. Our key observation is that adaptive attacks are composed of reusable building blocks that can be formalized in a search space and used to automatically discover attacks for unknown defenses. We evaluated our approach on 24 adversarial defenses and show that it outperforms AutoAttack, the current state-of-the-art tool for reliable evaluation of adversarial defenses: our tool discovered significantly stronger attacks by producing 3.0\%-50.8\% additional adversarial examples for 10 models, while obtaining attacks with slightly stronger or similar strength for the remaining models.
Factors Affecting Youth Entrepreneurial Intention and Suggestions for Policymaking: The Case of Vinh Long Province

As a part of the Mekong Delta’s development plan, Vinh Long province implements various policies and programs to promote entrepreneurial intentions, especially for young people to generate income for themselves and jobs for locals, thereby contributing to developing the provincial well-being. However, the number of startup enterprises in the province does not fulfill the expectation and economic growth target of Vinh Long, resulting in the province being ranked in the least developed group of all Vietnam’s provinces (standing at 55/64 provinces and cities). The aim of this study is to identify the factors affecting the entrepreneurial intentions of young people, which are necessary to propose policymaking implications. Using an exploratory factor analysis (EFA) method with 359 samples, the results show that there are five factors affecting the entrepreneurial intentions of youth, including: Perceived legitimacy of law and policy; Motivation to become an entrepreneur; Individual ability; Personality characteristics; and Family background. In addition, the study also indicates one of the major constraints of starting a business in Vinh Long province is that the level of science, technology, and innovation applied in projects is underdeveloped, resulting in low quality business plans and low level of attention from supporting funds, investors, and businesses.
GDPR Compliant Recruitment Platform using Smart Contracts and Executable Choreographies

This paper presents a recruiting application that is based on blockchain technology and uses PrivateSky platform, all ecosystem being developed in accordance with General Data Protection Regulation (GDPR). The presented application can be easily adapted to any industry recruitment methodology. It uses new privacy principles applied with help of blockchain technologies and the authors will present in the final paper some functionalities obtained with help of Machine Learning (ML) and Natural Language Processing (NLP) techniques.
Understanding the Blockchain Technology Beyond Bitcoin

A blockchain is a decentralized distributed public and digital ledger consisting of blocks that record transactions across many systems, ensuring security as these blocks cannot be altered retroactively. This technology enables individuals and companies to participate with trust and transparency. A well-known application of blockchain is the cryptographic currencies Bitcoin, an emerging technology like Ethereum and many more applications are possible. Blockchain, with great potential, allows us to create, value, trust, truthfully by clever use of distributed ledger, cryptography, and computation. It emphasizes the potential of a decentralized world, where users of technology can be empowered without being bound by a third-party power broker and many other benefits this technology presents to us. Blockchain technology is the driving force behind the next basic change in information technology. Many uses of blockchain technology are widely available today, each with the potential of a specific application domain. A blockchain is a diary that is almost impossible to forge. Blockchain technology is an interesting and a safe alternative for people, using this does not demand you to have a technical background, it will somehow have a positive effect on citizens. It will prove to be a better financial system and excites us to make blockchain more mainstream. Here, we have provided a brief survey on blockchain technology and how it is establishing itself to make an impact on the future of technology.
Conditional Score Matching for Image to Image Regression

Image to image regression is an essential machine learning problem that is commonly encountered in computer vision tasks. Previous research works have been concentrating on task-dependent end-to-end regression models. Although such a discriminative approach has shown its proficiency in producing precise predictions, its architecture is usually overly complicated for interpretation and unable to provide uncertainty quantification for prediction. We propose a general image to image regression framework named CSMNet which is based on conditional score matching and Langevin dynamics. The proposed framework is generative and emphasize on the probabilistic perspective of general image to image regression. Discriminative models are also involved as sufficient feature extractors to complement the generative modeling of the distribution of the target image conditioned an input image. The proposed model is trained by multi-level denoising score matching. Subsequently, the learned model is used for sampling from the conditional distribution via annealed Langevin dynamics. We introduce a grid selection procedure of Langevin sampling which can produce robust prediction. Comprehensive experiments are conducted on diverse image to image regression problems. The proposed model surpasses other purely generative models and achieves comparable performance to some end-to-end regression methods. Our method also demonstrates remarkable generalization ability which further demonstrates the advantage of the proposed conditional score matching framework.
WITHDRAWN: Enhanced block based copy paste image forgery detection

With the easy availability of wide variety of image editing software, digital images can be easily edited and manipulated. The need of the hour is to expose such image forgeries and manipulations. Copy move attack is one such forgery method where a portion of the image is copied from one place and pasted into a different region on the same image and made to appear like an original part of the image. In Adobe photoshop imaging software, a part of the image is copied and pasted onto the same image by using Rectangle Marquee tool, such duplicated portion of the image can be detected with block-based copy paste forgery detection method. The Polygonal Lasso Tool available in Adobe photoshop can also be used for copy paste operation. This tool creates selection with straight edges making it easy to select objects with different shapes. Because of its straight edges and corners created in copy paste region we are not able to determine the complete region with block-based copy paste detection method (Fridrich et al., 2003). In this paper we investigate the method to detect the complete pasted region in an image using Adobe’s Polygonal Lasso Tool. The proposed method detects the pasted image region by block matching. It also detects additional pixels around the boundaries of the pasted region, additional pixels that are not part of the interested region are excluded by using the morphological open operation. This proposed method has been experimentally verified, and it detects more pixels in the pasted region as compared to the traditional block-based.
Forecasting Using Reservoir Computing: The Role of Generalized Synchronization.

Reservoir computers (RC) are a form of recurrent neural network (RNN) used for forecasting time series data. As with all RNNs, selecting the hyperparameters presents a challenge when training on new inputs. We present a method based on generalized synchronization (GS) that gives direction in designing and evaluating the architecture and hyperparameters of a RC. The 'auxiliary method' for detecting GS provides a pre-training test that guides hyperparameter selection. Furthermore, we provide a metric for a well trained RC using the reproduction of the input system's Lyapunov exponents.
The State of Finance in Fragile States

This chapter describes the state of financial development in fragile states. Our analysis primarily relies on indicators from the World Bank Global Financial Development Database, which have been used extensively in the literature to capture the degree to which financial services and activities are present in an economy (depth) and the extent to which they are disseminated and made available to the population (inclusion). We find that financial depth in fragile states is underdeveloped and financial inclusion is low, but with significant heterogeneity among fragile states. We conduct empirical exercises which suggest that fragility is negatively related to financial development, both in terms of depth and especially in terms of inclusion, and exercises that also point to certain aspects of fragility most associated with financial underperformance. Finally, we use a benchmarking exercise to estimate how much financial underdevelopment in fragile states is costing them, in terms of economic growth.