Reviewerly/exHarmony huggingface dataset:
Finding Bit-Based Division Property for Ciphers with Complex Linear Layers

The bit-based division property (BDP) is the most effective technique for finding integral characteristics of symmetric ciphers. Recently, automatic search tools have become one of the most popular approaches to evaluating the security of designs against many attacks. Constraint-aided automatic tools for the BDP have been applied to many ciphers with simple linear layers like bit-permutation. Constructing models of complex linear layers accurately and efficiently remains hard. A straightforward method proposed by Sun et al. (called the S method), decomposes a complex linear layer into basic operations like COPY and XOR, then models them one by one. However, this method can easily insert invalid division trails into the solution pool, which results in a quicker loss of the balanced property than the cipher itself would. In order to solve this problem, Zhang and Rijmen propose the ZR method to link every valid trail with an invertible sub-matrix of the matrix corresponding to the linear layer, and then generate linear inequalities to represent all the invertible sub-matrices. Unfortunately, the ZR method is only applicable to invertible binary matrices (defined in Definition 3).To avoid generating a huge number of inequalities for all the sub-matrices, we build a new model that only includes that the sub-matrix corresponding to a valid trail should be invertible. The computing scale of our model can be tackled by most of SMT/SAT solvers, which makes our method practical. For applications, we improve the previous BDP for LED and MISTY1. We also give the 7-round BDP results for Camellia with FL/FL−1, which is the longest to date.Furthermore, we remove the restriction of the ZR method that the matrix has to be invertible, which provides more choices for future designs. Thanks to this, we also reproduce 5-round key-dependent integral distinguishers proposed at Crypto 2016 which cannot be obtained by either the S or ZR methods.
VisPhoto: Photography for People with Visual Impairment as Post-Production of Omni-Directional Camera Image

It is known that many people with visual impairments desire to take photos. In taking photos, however, they often face difficulty in pointing the camera at the subject. In this paper, we address this problem by proposing a novel photo-taking system named VisPhoto. Unlike existing ones, the proposed system generates a photo as post-production of an omni-directional camera image. That is, when the shutter button is pressed, the proposed system captures an omni-directional camera image. In the post-production, from the objects detected in the image, the user selects the ones that should be included and excluded. Finally, as a "photo," the system outputs a cropped region satisfying the user's preference and looking beautiful from an aesthetic point of view.
Point cloud clustering and outlier detection based on spatial neighbor connected region labeling

Clustering analysis is one of the most important techniques in point cloud processing, such as registration, segmentation, and outlier detection. However, most of the existing clustering algorithms exhibit a low computational efficiency with the high demand for computational resources, especially for large data processing. Sometimes, clusters and outliers are inseparable, especially for those point clouds with outliers. Most of the cluster-based algorithms can well identify cluster outliers but sparse outliers. We develop a novel clustering method, called spatial neighborhood connected region labeling. The method defines spatial connectivity criterion, finds points connections based on the connectivity criterion among the k-nearest neighborhood region and classifies connected points to the same cluster. Our method can accurately and quickly classify datasets using only one parameter k. Comparing with K-means, hierarchical clustering and density-based spatial clustering of applications with noise methods, our method provides better accuracy using less computational time for data clustering. For applications in the outlier detection of the point cloud, our method can identify not only cluster outliers, but also sparse outliers. More accurate detection results are achieved compared to the state-of-art outlier detection methods, such as local outlier factor and density-based spatial clustering of applications with noise.
The Synthesis of Blockchain, Artificial Intelligence and Internet of Things

This paper is an exploration of several theories on the synthesis of the Blockchain(BC), the Internet of Things (IoT), and Artificial Intelligence(AI) through a literature review. Blockchain technology is a decentralized peer-to-peer network that stores records and transactions in immutable blocks secured by cryptography. The decentralization aspect of blockchain eliminates the need for trusted third party interceder. Internet of Things(IoT) is an interrelated computer system that makes a connection between computers and humans to communicate in several areas; smart devices like homes, cars, radio are just a few examples. IoT challenges are security, connectivity, issues with the analysis of big data, centralization, and vulnerability to attacks. Artificial Intelligence(AI) machine learning technology is the advanced decision-making process that influences daily routines such as banking, healthcare, gaming, transportation, and space exploration, among others. AI’s challenges are; security, centralized architecture, and resource limitations. The methodology will be a comprehensive quantitative analysis of the existing research and how these technologies can be a transformative impact on how information is accessed through enterprise and society. The convergence of BC, AI, and IoT will provide scalable, secure high-level intellectual functioning that will be the new paradigm of digital information.
Deep Kernel Learning for Clustering

We propose a deep learning approach for discovering kernels tailored to identifying clusters over sample data.Our neural network produces sample embeddings that are motivated by and are at least as expressive as spectral clustering.Our training objective, based on the Hilbert Schmidt Independence Criterion, can be optimized via gradient adaptations on the Stiefel manifold, leading to significant acceleration over spectral methods relying on eigen-decompositions. Finally, our trained embedding can be directly applied to out-of-sample data.We show experimentally that our approach outperforms several state-of-the-art deep clustering methods, as well as traditional approaches such as k-means and spectral clustering over a broad array of real and synthetic datasets.
Quantum causal correlations and non-Markovianity of quantum evolution

A non-Markovianity measure for quantum channels is introduced based on causality measure - a monotone of causal (temporal) correlations - arising out of the pseudo-density matrix (PDM) formalism which treats quantum correlations in space and time on an equal footing. Using the well known damped Jaynes-Cummings model of a two-level system interacting with a bosonic reservoir at zero temperature as an example, it is shown that breakdown of monotonicity of the causality measure is associated with the revival of temporal (causal) correlations hence with the negativity of the decay rate. Also, a note on the comparison with other well known non-Markovianity measures is given.
VUCA-Managers Training for Smart Systems: Innovative and Organizational Approach

The term “VUCA” represents a four-letter acronym including definitions and contents of volatility, uncertainty, complexity, and ambiguity. These are the features of a modern business environment in which managers have to face and deal with. The area of research is designing and developing the managers’ training according to the qualification requirements revealed in both the educational and professional standards accordingly to social and business demands. Nowadays, modern, effective managers are known as being highly stress-resistant, flexible, creative, and having available hard and soft skills for managing risks in unstable situations. Smart business environment and a university’s activities are based on the ideas of digitalization increasing the social demand for employees who are ready to process large amounts of information, multi-dimensional analysis of data, and identifying intelligent management solutions quickly while meeting the situation of uncertainty and risk. The goal of the research is to review some relevant innovative means and organizational methods of students’ training for being smart and effective managers in conditions of volatility, uncertainty, complexity, and ambiguity. Modern means and methods of personnel training in the digitalization age on the basis of professional standards are considered and revealed. The VUCA-manager training models are designed according to the theoretical framework of the research and modern requirements.
Can Digitalization Help Deter Corruption in Africa?

This paper studies the effect of digitalization on the perception of corruption and trust in tax officials in Africa. Using individual-level data from Afrobarometer surveys and several indices of digitalization, we find that an increase in digital adoption is associated with a reduction in the perception of corruption and an increase in trust in tax officials. Exploiting the exogeneous deployment of submarine cables at the local level, the paper provides evidence of a negative impact of the use of Internet on the perception of corruption. Yet, the paper shows that the dampening effect of digitalization on corruption is hindered in countries where the government has a pattern of intentionally shutting down the Internet, while countries that successfully promote information and communication technology (ICT) enjoy a more amplified effect.
Project Management as a Tool for Smart University Creation and Development

The paper suggests using project management as a tool for smart university for creation and development. The methodological and information infrastructure of smart university project management is considered; it will simplify and speed up the process of management decision making and it more visible to the project team members, the project office, and the university management. Mathematical models of project management support are proposed. They can be applied in the project offices’ activities for managing groups of projects for the development of smart university. It is proposed to use business process management system (BPMS) as a core of the information support infrastructure of smart university and smart technology integration center. The use of an application programming interface (API) in the knowledge management system is also considered. The project management experience for smart university creation and development is studied on the example of Togliatti State University.
Information Seeking Behaviors, Attitudes, and Choices of Academic Mathematicians

Mathematicians in academic institutions utilize a variety of resources and strategies to seek, find, and use scholarly information and news. Using a sample of mathematicians, researchers surveyed 112 students and faculty at four Canadian university institutions to explore self-perceived success rates, resources consulted, databases used, use of social media, and citation management systems. Further, 12 follow-up interviews were completed with mathematicians to better interpret survey results, resulting information-seeking behaviors, choices, strategies, and feelings on keeping up to date with information needs. According to survey results, a minority of mathematicians (12.5 percent) acknowledged that they were successfully keeping up to date. However, a significant number of mathematicians (28.6 percent) indicated that they were unsuccessful and could do better in remaining current with information needs. Co-investigators, using qualitative analyses, identified four emergent themes related to remaining current: (1) The “slower pace of math” pervades all aspects of this discipline;” (2) There are “too many papers – and not enough time” to effectively search, evaluate, and read scholarly papers of interest; (3) Mathematicians collectively acknowledge that they are open to strategies and technologies where they “could do better” keeping up to date; and (4) Mathematicians have divided loyalties using databases when searching for information by means of “MathSciNet in a Google world.” Additional insights document how mathematicians are guided by mathematical peculiarities and discipline-specific practices. This study helps to shed light on opportunities for academic librarians to identify and meet mathematicians’ evolving information needs.
CustNER

This article describes CustNER: a system for named-entity recognition (NER) of person, location, and organization. Realizing the incorrect annotations of existing NER, four categories of false negatives have been identified. The NEs not annotated contain nationalities, have corresponding resource in DBpedia, are acronyms of other NEs. A rule-based system, CustNER, has been proposed that utilizes existing NERs and DBpedia knowledge base. CustNER has been trained on the open knowledge extraction (OKE) challenge 2017 dataset and evaluated on OKE and CoNLL03 (Conference on Natural Language Learning) datasets. The OKE dataset has also been annotated with the three types. Evaluation results show that CustNER outperforms existing NERs with F score 12.4% better than Stanford NER and 3.1% better than Illinois NER. On another standard evaluation dataset for which the system is not trained, the CoNLL03 dataset, CustNER gives results comparable to existing systems with F score 3.9% better than Stanford NER, though Illinois NER F score is 1.3% better than CustNER.
Action Research in Hadith Literacy: A Reflection of Hadith Learning in the Digital Age

When the internet has become the main reference source for academics and students in completing their assignments, critical attitudes towards information accessed via the internet, especially about hadith are often ignored. Critical attitude is needed to ensure the authenticity of a hadith because its discourse has a level of quality, be it sahih hadith (authentic, accepted) or da'if (rejected). Along with the development of internet technology, many false hadith s are circulating on the internet. Therefore, adequate literacy skills in reading and tracing the authenticity of a hadith are needed. This research provides examples of the results of collaborative team work to improve hadith literacy. Action Research Design was chosen to find solutions to problems through critical reflection in the process of learning Al-Qurâ€™an. The Action Research procedures were combined with the Kernâ€™s 3R model namely Responding, Revising, and Reflecting. This study involved 398 students as research participants at one of the higher education institutions in West Java, Indonesia. This research resulted in several findings: 1) Six steps were taken to improve hadith literacy, namely the formulation of learning objectives, selection of media, update of the knowledge of hadith , understanding the learning media, application of learning media, and evaluation. 2) Two applications were used as learning media in improving hadith literacy, namely the science of hadith and the Ensiklopedi Hadits Kitab 9 Imam (EHK9I, Encyclopedia of Hadith in the book of 9 Imams). 3) The reflective actions and the integration of the two applications showed an increase in three aspects of literacy namely data literacy (54.33%), technological literacy (63.07%), and human literacy related to narration of the hadith (93.59%). It can be concluded that the steps taken by utilizing the two applications as learning media can improve the participantsâ€™ ability to trace and determine the authenticity of a hadith . https://doi.org/10.26803/ijlter.19.5.6
Privately Learning Markov Random Fields

We consider the problem of learning Markov Random Fields (including the prototypical example, the Ising model) under the constraint of differential privacy. Our learning goals include both structure learning, where we try to estimate the underlying graph structure of the model, as well as the harder goal of parameter learning, in which we additionally estimate the parameter on each edge. We provide algorithms and lower bounds for both problems under a variety of privacy constraints -- namely pure, concentrated, and approximate differential privacy. While non-privately, both learning goals enjoy roughly the same complexity, we show that this is not the case under differential privacy. In particular, only structure learning under approximate differential privacy maintains the non-private logarithmic dependence on the dimensionality of the data, while a change in either the learning goal or the privacy notion would necessitate a polynomial dependence. As a result, we show that the privacy constraint imposes a strong separation between these two learning problems in the high-dimensional data regime.
Evidence-Aware Inferential Text Generation with Vector Quantised Variational AutoEncoder

Generating inferential texts about an event in different perspectives requires reasoning over different contexts that the event occurs.Existing works usually ignore the context that is not explicitly provided, resulting in a context-independent semantic representation that struggles to support the generation.To address this, we propose an approach that automatically finds evidence for an event from a large text corpus, and leverages the evidence to guide the generation of inferential texts.Our approach works in an encoderdecoder manner and is equipped with a Vector Quantised-Variational Autoencoder, where the encoder outputs representations from a distribution over discrete variables.Such discrete representations enable automatically selecting relevant evidence, which not only facilitates evidence-aware generation, but also provides a natural way to uncover rationales behind the generation.Our approach provides state-ofthe-art performance on both Event2Mind and ATOMIC datasets.More importantly, we find that with discrete representations, our model selectively uses evidence to generate different inferential texts.
Forging An Optimized Bayesian Network Model With Selected Parameters For Detection of The Coronavirus In Delta State of Nigeria

Machine learning algorithm have become veritable tools for effective decision support towards the construction of systems that assist experts (individuals) in their field of exploits and endeavor with regards to problematic tasks.. They are best suited for tasks where data is explored and exploited; and cases where the dataset contains noise, partial truth, ambiguities and in cases where there is shortage of datasets. For this study, we employ the Bayesian network to construct a model trained towards a target system that can help predict best parameters used for classification of the novel coronavirus (covid-19). Data was collected from Federal Medical Center Epidemiology laboratory (a centralized databank for all cases of the covid-19 in Delta State). Data was split into training and investigation (test) dataset for the target system. Results show high predictive capability.
Synthesizing Aspect-Driven Recommendation Explanations from Reviews

Explanations help users make sense of recommendations, increasing the likelihood of adoption. Existing approaches to explainable recommendations tend to rely on rigidly standardized templates, only allowing fill-in-the-blank aspect-level sentiments. For more flexible, literate, and varied explanations that cover various aspects of interest, we propose to synthesize an explanation by selecting snippets from reviews to optimize representativeness and coherence. To fit the target user's aspect preferences, we contextualize the opinions based on a compatible explainable recommendation model. Experiments on datasets of varying product categories showcase the efficacies of our method as compared to baselines based on templates, review summarization, selection, and text generation.
Bottom-up and Top-down: Bidirectional Additive Net for Edge Detection

Image edge detection is considered as a cornerstone task in computer vision. Due to the nature of hierarchical representations learned in CNN, it is intuitive to design side networks utilizing the richer convolutional features to improve the edge detection. However, there is no consensus way to integrate the hierarchical information. In this paper, we propose an effective and end-to-end framework, named Bidirectional Additive Net (BAN), for image edge detection. In the proposed framework, we focus on two main problems: 1) how to design a universal network for incorporating hierarchical information sufficiently; and 2) how to achieve effective information flow between different stages and gradually improve the edge map stage by stage. To tackle these problems, we design a consecutive bottom-up and top-down architecture, where a bottom-up branch can gradually remove detailed or sharp boundaries to enable accurate edge detection and a top-down branch offers a chance of error-correcting by revisiting the low-level features that contain rich textual and spatial information. And attended additive module (AAM) is designed to cumulatively refine edges by selecting pivotal features in each stage. Experimental results show that our proposed methods can improve the edge detection performance to new records and achieve state-of-the-art results on two public benchmarks: BSDS500 and NYUDv2.
Dress like an Internet Celebrity: Fashion Retrieval in Videos

Nowadays, both online shopping and video sharing have grown exponentially. Although internet celebrities in videos are ideal exhibition for fashion corporations to sell their products, audiences do not always know where to buy fashion products in videos, which is a cross-domain problem called video-to-shop. In this paper, we propose a novel deep neural network, called Detect, Pick, and Retrieval Network (DPRNet), to break the gap between fashion products from videos and audiences. For the video side, we have modified the traditional object detector, which automatically picks out the best object proposals for every commodity in videos without duplication, to promote the performance of the video-to-shop task. For the fashion retrieval side, a simple but effective multi-task loss network obtains new state-of-the-art results on DeepFashion. Extensive experiments conducted on a new large-scale cross-domain video-to-shop dataset shows that DPRNet is efficient and outperforms the state-of-the-art methods on video-to-shop task.
The Graph-based Mutual Attentive Network for Automatic Diagnosis

The automatic diagnosis has been suffering from the problem of inadequate reliable corpus to train a trustworthy predictive model. Besides, most of the previous deep learning based diagnosis models adopt the sequence learning techniques (CNN or RNN), which is difficult to extract the complex structural information, e.g. graph structure, between the critical medical entities. In this paper, we propose to build the diagnosis model based on the high-standard EMR documents from real hospitals to improve the accuracy and the credibility of the resulting model. Meanwhile, we introduce the Graph Convolutional Network into the model that alleviates the sparse feature problem and facilitates the extraction of structural information for diagnosis. Moreover, we propose the mutual attentive network to enhance the representation of inputs towards the better model performance. The evaluation conducted on the real EMR documents demonstrates that the proposed model is more accurate compared to the previous sequence learning based diagnosis models. The proposed model has been integrated into the information systems in over hundreds of primary health care facilities in China to assist physicians in the diagnostic process.