Reviewerly/exHarmony huggingface dataset:
Learning Segmentation Masks with the Independence Prior

An instance with a bad mask might make a composite image that uses it look fake. This encourages us to learn segmentation by generating realistic composite images. To achieve this, we propose a novel framework that exploits a new proposed prior called the independence prior based on Generative Adversarial Networks (GANs). The generator produces an image with multiple category-specific instance providers, a layout module and a composition module. Firstly, each provider independently outputs a category-specific instance image with a soft mask. Then the provided instances’ poses are corrected by the layout module. Lastly, the composition module combines these instances into a final image. Training with adversarial loss and penalty for mask area, each provider learns a mask that is as small as possible but enough to cover a complete category-specific instance. Weakly supervised semantic segmentation methods widely use grouping cues modeling the association between image parts, which are either artificially designed or learned with costly segmentation labels or only modeled on local pairs. Unlike them, our method automatically models the dependence between any parts and learns instance segmentation. We apply our framework in two cases: (1) Foreground segmentation on category-specific images with box-level annotation. (2) Unsupervised learning of instance appearances and masks with only one image of homogeneous object cluster (HOC). We get appealing results in both tasks, which shows the independence prior is useful for instance segmentation and it is possible to unsupervisedly learn instance masks with only one image.
Intersection types and runtime errors in the pi-calculus

We introduce a type system for the π-calculus which is designed to guarantee that typable processes are well-behaved , namely they never produce a run-time error and, even if they may diverge, there is always a chance for them to “finish their work”, i.e., to reduce to an idle process. The introduced type system is based on non-idempotent intersections, and is thus very powerful as for the class of processes it can capture. Indeed, despite the fact that the underlying property is Π 2 0 -complete, there is a way to show that the system is complete , i.e., that any well-behaved process is typable, although for obvious reasons infinitely many derivations need to be considered.
Experimental investigation of white-light interferometry based on sub-dark-field illumination

In this work, a fast imaging method based on sub-dark-field illumination in white-light interferometry is presented. Applying this method, we successfully resolved the surface profile of test structures even for a smooth surface with a roughness of the order of a few angstroms. The true-colour sequence band is presented, which provides the relationship between the surface colours and the optical path difference. Several characteristics (e.g., basic colour, image contrast, thickness variations, and so on) for the white-light interference images are discussed in detail. Pair images with sub-dark-field illumination in the principal interference fringe were also observed and analysed.
Simultaneous Recognition of Horizontal and Vertical Text in Natural Images

Recent state-of-the-art scene text recognition methods have primarily focused on horizontal text in images. However, in several Asian countries, including China, large amounts of text in signs, books, and TV commercials are vertically directed. Because the horizontal and vertical texts exhibit different characteristics, developing an algorithm that can simultaneously recognize both types of text in real environments is necessary. To address this problem, we adopted the direction encoding mask (DEM) and selective attention network (SAN) methods based on supervised learning. DEM contains directional information to compensate in cases that lack text direction; therefore, our network is trained using this information to handle the vertical text. The SAN method is designed to work individually for both types of text. To train the network to recognize both types of text and to evaluate the effectiveness of the designed model, we prepared a new synthetic vertical text dataset and collected an actual vertical text dataset (VTD142) from the Web. Using these datasets, we proved that our proposed model can accurately recognize both vertical and horizontal text and can achieve state-of-the-art results in experiments using benchmark datasets, including the street view test (SVT), IIIT-5k, and ICDAR. Although our model is relatively simple as compared to its predecessors, it maintains the accuracy and is trained in an end-to-end manner.
A Mixed-methods Study of Non-text Social Media Content as a Window into African-American Youth STEM Identities

Abstract A Mixed-methods Study of Non-text Social Media Content as a Window into African American Youth STEM Identities Historically, researchers have observed that some African American youth suppress public expression of their interests in Science, Technology, Engineering and Math (STEM) topics. Prior research has attributed youth denial of STEM identities to their perceived social pressures. When researchers interview participants in STEM education experiences, we receive certain responses that help us to learn about STEM identities. When we look to social media content, which is sometimes shared publicly and voluntarily, we see a possible window into the perspectives and identities of African American youth and young adults that may help us learn about STEM identities in a new way. Non-text paralinguistic elements in social media content, such as emoji, hashtags, and images, provide a potentially rich source of personal identity self-expression and sociocultural insights into emerging African American youth STEM identities. Acknowledging and respecting the modern ways that youth choose to express their identities, we sought to include paralinguistic elements in our analysis of their STEM-related public discourse on Twitter and Instagram social media platforms. Our theoretical framework was that of a sociocultural view of identity development, built upon the concept of communities of practice. We used dimensions of communities of practice as a framework to organize and analyze the artifacts in our datasets, and as a means to understand the contributing roles of paralinguistic elements in those artifacts. When utilizing this framework, we examined youth self-description, looking for trajectories of identification with multiple communities and social spaces, including hybridity and crossing social spaces. In this paper, we describe the mixed-methods study, with results and dataset analysis.
Concept of Automated Support to Problem

The chapter deals with the concept of training for technicians, able to adjust to ongoing changes in the manufacturing environment. It is proposed to apply the systematic approach with a targeted use of collected methods and acmeological planning in a learning process when extra time is found owing to interdisciplinary integration and increased self-learning. Such planning is done with crosscutting design and problem-modular training with cross-rated instruction materials and learning outcomes. Education information systems are recommended as tools (like those based on modular object-oriented dynamic learning environment according to shareable content object reference model standard) together with automated information systems for self-learning, business and role plays with methods to rate career guidance and multimedia aids as learning tools.
A New Attack Scheme on the Bitcoin Reward System

The reward of the Bitcoin system is designed to be proportional to miner's computational power. However, rogue miners can increase their rewards by using the block withholding attacks. For raising awareness on the Bitcoin reward system, a new attack scheme is proposed, where the attackers infiltrate into an open pool and launch the selfish mining as well as the block withholding attack. The simulation results demonstrate that the proposed attack outperforms the conventional block withholding attacks.
Optimizing Online Permutation-Based AE Schemes for Lightweight Applications

We explore ways to optimize online, permutation-based authenticated encryption (AE) schemes for lightweight applications. The lightweight applications demand that AE schemes operate in resource-constrained environments, which raise two issues: 1) implementation costs must be low, and 2) ensuring proper use of a nonce is difficult due to its small size and lack of randomness. Regarding the implementation costs, recently it has been recognized that permutation-based (rather than block-cipher-based) schemes frequently show advantages. However, regarding the security under nonce misuse, the standard permutation-based duplex construction cannot ensure confidentiality. There exists one permutation-based scheme named APE which offers certain robustness against nonce misuse. Unfortunately, the APE construction has several drawbacks such as ciphertext expansion and bidirectional permutation circuits. The ciphertext expansion would require more bandwidth, and the bidirectional circuits would require a larger hardware footprint. In this paper, we propose new constructions of online permutation-based AE that require less bandwidth, a smaller hardware footprint and lower computational costs. We provide security proofs for the new constructions, demonstrating that they are as secure as the APE construction.
The Effect of Software Warranties on Cybersecurity

This workshop focused on bringing software developers and legal professionals together to understand the shared challenges they face in promoting the development of secure software on the one hand, and software at all, on the other hand. This report summarizes current scientific research on the topics and challenges discussed in the workshop breakout sessions. The insights from the workshop highlight a number of interesting directions for further research on the interplay between software warranties and cybersecurity.
Resilience Approach to Critical Information Infrastructures

This chapter discusses new societal risk due to the fast information and communication as well as operational technology changes which are not yet fully taken into account by governmental policymakers and regulators. Internet-of-things, cloud computing, mass consumer markets and embedded operational technologies are some of the areas outlined in this chapter which may be the cause for serious disruptions of critical infrastructures, critical information infrastructures, essential services, and the undisturbed functioning of the society. Current national protection approaches mainly focus on the classical telecommunication sector and the stove-piped critical sectors such as energy, health, transport, etcetera. This chapter argues that a change of mind and actions are needed to properly govern the new cyber risk before serious incidents occur and that such a new approach is urgently needed to make the societies at large more resilient.
Scaling deep learning without increasing batchsize

Summary Deep learning has proven itself to be a difficult problem in the HPC space. Although the algorithm can scale very efficiently with a sufficiently large batchsize, the efficacy of training tends to decrease as the batchsize grows. Scaling the training of a single model may be effective in narrow fields such as image classification, but more generalizable options can be achieved when considering alternate methods of parallelism and the larger workflow surrounding neural network training. Hyperparameter optimization, data set segmentation, hierarchical fine tuning, and model parallelism can all provide significant scaling capacity without increasing batchsize and can be paired with a traditional, single‐model scaling approach for a multiplicative scaling improvement. This paper intends to further define and examine these scaling techniques in how they perform individually and how combining them can provide significant improvements in overall training times.
Research of crypto-code designs construction for using in post quantum cryptography

The article analyzes construction of crypto-code designs (CCDs) on the basis of asymmetric Mac-Alice and Niederreiter crypto-code systems on elliptical (EC) and modified elliptic codes (MEC), which, in the conditions of post-quantum cryptography, allow to provide a guaranteed level of crypto stability, to counteract the modern Attacks and attack by V. Sidelnikov on the theoretical code schemes of McAlias and Niederreiter. Schemes of hybrid crypto-code designs constraction the lossy codes are addressed. Methods of constructing mechanisms of confidentiality and integrity of banking information resources under hybrid threats to security components (information security, cybersecurity, information security) are proposed. Using of a lossy code is suggested to this end. Lossy Code allow you to increase the speed of code changes by reducing the power of the field when causing damage to open text and reducing the amount of data transferred by causing harm to the cipher text. The methods of constructing unprofitable codes and approaches for use in hybrid KKK of McAlias and Niederreiter on modified elliptic codes are considered. Practical algorithms for the use of the MV2 mechanism in McAlias’ CCD and Niederreiter’s modified elliptic codes are proposed, which allows the implementation of the CCD hybrid scheme. The comparative results of the study of stability and power capacity with respect to their practical use in automated banking systems are presented.
A stochastic coordinate descent for bound constrained global optimization

This paper presents a stochastic coordinate descent algorithm for solving bound constrained global optimization problems. The algorithm borrows ideas from some stochastic optimization methods available for the minimization of expected and empirical risks that arise in large-scale machine learning. Initially, the algorithm generates a population of points although only a small subpopulation of points is randomly selected and moved at each iteration towards the global optimal solution. Each point of the subpopulation is moved along one component only of the negative gradient direction. Preliminary experiments show that the algorithm is effective in reaching the required solution.
Spatial Crowdsourcing Quality Control Model Based on K-Anonymity Location Privacy Protection and ELM Spammer Detection

The spatial crowdsourcing task places workers at a risk of privacy leakage. If positional information is not required to submit, it will result in an increased error rate and number of spammers, which together affects the quality of spatial crowdsourcing. In this paper, a spatial crowdsourcing quality control model is proposed, called SCQCM. In the model, the spatial k-anonymity algorithm is used to protect the position privacy of the general spatial crowdsourcing workers. Next, an ELM (extreme learning machine) algorithm is used to detect spammers, while an EM (expectation maximization) algorithm is used to estimate the error rate. Finally, different parameters are selected, and the efficiency of the model is simulated. The results showed that the spatial crowdsourcing model proposed in this paper guaranteed the quality of crowdsourcing projects on the premise of protecting the privacy of workers.
RETRACTED ARTICLE: Noise tailoring for quantum circuits via unitary 2t-design

Because of environmental variations and imperfect operations, real-world quantum computers produce different coherent errors that are difficult to estimate. Here, we propose a method whereby the twirled noise over a unitary 2t-design (a set of unitary matrices that approximate the entire unitary group) for quantum circuits can be tailored into stochastic noise. Then, we prove that local random circuits for twirling separable noisy channel over the Clifford group can be used to construct a unitary 2t-design, which is easy to implement in experiments. Moreover, we prove that our method is robust to gate-dependent and gate-independent noise. The stochastic noise can be both estimated by average fidelity and directly obtained by randomized benchmarking via unitary 2t-designs. Obtaining such tailored noise is an important guarantee for achieving fault-tolerant quantum computation.
A Study on Application Method for Automation Solution Using Blockchain dApp Platform

The GOB Chain is a universal Blockchain platform designed to play the role of a perfect Blockchain hub for every Blockchain. First, it connects every Blockchain in order to facilitate the exchange of data between Blockchains based on W3C standard meta data, thus guaranteeing inter-operability and use, as well as searches of transaction information. In addition, it links the Blockchain technology to various existing legacy systems so that it can be immediately applied to the business in service. It allows the GOB chain to apply the Blockchain technology to existing businesses at the sites of the Fourth Industrial Revolution, thus enabling new productivity and competitiveness and improving profitability to a remarkable extent. The GOB Chain cooperates with all Blockchain technologies, companies, and researchers around the world to overcome the limitations of the existing Blockchain technology, and implements the Blockchain-platform ecology chain and the eco system, making our lives more convenient while revolutionizing every industry. In addition, it automates all block-chain systems and suggests a system that anyone can easily provide, operate, and maintain block-chain services.
Searching and Sorting

Searching data and sorting through data are fundamental algorithms. Searching refers to iterating over the data structure’s elements to retrieve some data. Sorting refers to putting the data structure’s elements in order. The searching and sorting algorithms are different for every data structure. This chapter focuses on searching and sorting for arrays. By the end of this chapter, you will understand how to use common sorting and searching algorithms for arrays.
An argument for reporting data standardization procedures in multi-site predictive modeling: case study on the impact of LOINC standardization on model performance

We aimed to gain a better understanding of how standardization of laboratory data can impact predictive model performance in multi-site datasets. We hypothesized that standardizing local laboratory codes to logical observation identifiers names and codes (LOINC) would produce predictive models that significantly outperform those learned utilizing local laboratory codes.We predicted 30-day hospital readmission for a set of heart failure-specific visits to 13 hospitals from 2008 to 2012. Laboratory test results were extracted and then manually cleaned and mapped to LOINC. We extracted features to summarize laboratory data for each patient and used a training dataset (2008-2011) to learn models using a variety of feature selection techniques and classifiers. We evaluated our hypothesis by comparing model performance on an independent test dataset (2012).Models that utilized LOINC performed significantly better than models that utilized local laboratory test codes, regardless of the feature selection technique and classifier approach used.We quantitatively demonstrated the positive impact of standardizing multi-site laboratory data to LOINC prior to use in predictive models. We used our findings to argue for the need for detailed reporting of data standardization procedures in predictive modeling, especially in studies leveraging multi-site datasets extracted from electronic health records.
Madukaku as the Basis of Being Human in (Igbo) African Worldview; A Critical Reflection

Critical understanding of Western idea of human personality reveals a lot of weaknesses. It reveals individualistic and anthropocentric tendency which is the reason for unpleasant relationship between man and fellow man and man and the environment. This kind of thinking has led Western environmental ethicist to now propound theories towards communitarian stand, for instance; deep ecology, land ethic, eco-feminism to mention a few. Enyimba, Maduka holds a radical point of view from many African communal philosophers. Maduka holds that a person is human because he/she is worth more in quality and essence than other beings and things. Such thinking has been the underlying rationale behind man’s overexploitation of nature. The thrust of the essay is to look at the basic tenets of Madukakism as the philosophy of being human in Africa. This paper agrees with the idea of Madukaku that humanity is at the centre of the universe, but differs from its individualist assumptions. In African ontology, the hierarchy of beings, God, lesser deities and ancestors are above human, If this is the case then man is not supreme and cannot be ‘the measure of all things’. Hence this paper rejects Maduka’s postulation of human being as supreme, because even within African environment there are some trees, rivers, mountains etc that are considered sacred and its therefore considered a taboo to toy with them. To this, no matter how highly placed man is, he is not allowed to touch nor exploit them, without dare consequence, which clearly shows that man is not the measure of all things but is in a complimentary state with other beings in African belief system.
The importance of evaluating findings given activity level propositions in order to avoid misleading evidence

The advancement of DNA technology comes with the increased sensitivity of amplification systems, where DNA traces are routinely detected without a known biological source. These systems also have increased discriminating capacity, providing larger likelihood ratios (LRs) when a corresponding DNA profile is observed. Questions in court are shifting from identity to transfer mechanism, where the presence of an individual’s DNA is conceded by both parties, but the activities that led to its deposition is in dispute. One way of handling propositions developed at the activity level is with the use of graphical structures known as Bayesian Networks (BNs). The following is an evaluation of a case, given activity level propositions, through the application of BNs. Alternative case findings will be explored for the given scenario to show the potential value of the DNA evidence for different outcomes within the broader case context.
Image Compression in Wireless Sensor Networks Using Autoencoder and RBM Method

Wireless sensor networks are used in various day-to-day applications. With advanced technology, wireless sensor networks (WSNs) play a key role in networking technologies since it can be expanded without communication infrastructure. There is so much demand for wireless sensor networks that are useful in day-to-day applications. Various techniques like multilayer restricted Boltzmann machine (RBM) network and variational autoencoder (VAE) methods are designed for transmission of images in wireless sensor networks and tested for both gray (2D) and color (3D) images in this paper. The proposed variational autoencoder method is compared with multilayer RBM when the layers are 2, 4, and 8, and the peak signal-to-noise ratio (PSNR) and signal-to-noise ratio (SNR) are compared.