Reviewerly/exHarmony huggingface dataset:
Learning a Neural Solver for Multiple Object Tracking

Graphs offer a natural way to formulate Multiple Object Tracking (MOT) within the tracking-by-detection paradigm. However, they also introduce a major challenge for learning methods, as defining a model that can operate on such \textit{structured domain} is not trivial. As a consequence, most learning-based work has been devoted to learning better features for MOT, and then using these with well-established optimization frameworks. In this work, we exploit the classical network flow formulation of MOT to define a fully differentiable framework based on Message Passing Networks (MPNs). By operating directly on the graph domain, our method can reason globally over an entire set of detections and predict final solutions. Hence, we show that learning in MOT does not need to be restricted to feature extraction, but it can also be applied to the data association step. We show a significant improvement in both MOTA and IDF1 on three publicly available benchmarks. Our code is available at this https URL .
Blockchain Intelligence: When Blockchain Meets Artificial Intelligence

Blockchain is gaining extensive attention due to its provision of secure and decentralized resource sharing manner. However, the incumbent blockchain systems also suffer from a number of challenges in operational maintenance, quality assurance of smart contracts and malicious behaviour detection of blockchain data. The recent advances in artificial intelligence bring the opportunities in overcoming the above challenges. The integration of blockchain with artificial intelligence can be beneficial to enhance current blockchain systems. This article presents an introduction of the convergence of blockchain and artificial intelligence (namely blockchain intelligence). This article also gives a case study to further demonstrate the feasibility of blockchain intelligence and point out the future directions.
Primitive Complete Normal Bases for Regular Extensions: Exceptional Cyclotomic Modules

A primitive completely normal element for an extension $\mathbb{F}_{q^n}/\mathbb{F}_{q}$ of Galois fields is a generator of the multiplicative group of $\mathbb{F}_{q^n}$, which simultaneously is normal over every intermediate field of that extension. We are going to prove that such a generator exists when $\mathbb{F}_{q^n}/\mathbb{F}_{q}$ is an 'exceptional' regular extension. In combination with [6] our investigations altogether settle the existence of primitive completely normal bases for any regular extension. An important feature of the class of regular extensions is that they comprise every extension of prime power degree.
DeePaN: A deep patient graph convolutional network integratingclinico-genomic evidence to stratify lung cancers benefiting from immunotherapy

Abstract Immuno-oncology (IO) therapies have transformed the therapeutic landscape of non-small cell lung cancer (NSCLC). However, patient responses to IO are variable and influenced by a heterogeneous combination of health, immune and tumor factors. There is a pressing need to discover the distinct NSCLC subgroups that influence response. We have developed a dee p pa tient graph convolutional n etwork, we call “DeePaN”, to discover NSCLC complexity across data modalities impacting IO benefit. DeePaN employs high-dimensional data derived from both real world evidence (RWE) based electronic health records (EHRs) and genomics across 1,937 IO treated NSCLC patients. DeePaN demonstrated effectiveness to stratify patients into subgroups with significantly different (p-value of 2.2 × 10 −11 ) overall survival of 20.35 months and 9.42 months post-IO therapy. Significant differences in IO outcome were not seen from multiple non-graph based unsupervised methods. Furthermore, we demonstrate that patient stratification from DeePaN has the potential to augment the emerging IO biomarker of tumor mutation burden (TMB). Characterization of the subgroups discovered by DeePaN indicates potential to inform IO therapeutic insight, including the enrichment of mutated KRAS and high blood monocyte count in the IO beneficial and IO non-beneficial subgroups, respectively. To the best of our knowledge, our work for the first time has proven the concept that graph based AI is feasible and can effectively integrate high-dimensional genomic and EHR data to meaningfully stratify cancer patients on distinct clinical outcomes, with potential to inform precision oncology.
Sustainable KYC through Blockchain Technology in Global Banks

Blockchain has spiked a lot of interest in this decade both from technology perspective and also from the various applications of this potential disruptive technology.This paper performs a systematic review of the research and adoption progress of the much talked about Blockchain technology in the context of Customer Onboarding and KYC for Global Banks.This systematic review embraces the prominence of the peer-to-peer technology and the substantial role it can play in the complete revamp of the KYC processes for Global Banks world-wide.This paper proposes a design for consortium based solution in recent rise of risk events in global markets around reconnaissance and security breaches where the customer data privacy was compromised.The increasing annual budgets for KYC and Annual Refresh for high risk customers has become a norm in the light of terrorist attacks in 9/11 and others.The regulators also have increased the compliance checks for customer onboarding processes and then a plethora of other compliance rules to perform the regular ongoing Trigger Event KYC (change of address, change of source of funds and many more).The need for a check on AML at a macro-economic level aligned to regulations like Patriot Act, Bank Secrecy Act but also at a micro level by performing credit and operational risk management.Strict KYC norms make sure the banks and accountable executives adhere to the fact that every customer has to prove the legitimacy of their existence by submitting Proof of Identity and Proof of Address along with documents supporting Source of Wealth.This paper details on how to determine the need for Blockchain for KYC and then study the benefits of implementing Blockchain for KYC if the technology is indeed needed for the banking organization.
Learning from Synthetic Animals

Despite great success in human parsing, progress for parsing other deformable articulated objects, like animals, is still limited by the lack of labeled data. In this paper, we use synthetic images and ground truth generated from CAD animal models to address this challenge. To bridge the domain gap between real and synthetic images, we propose a novel consistency-constrained semi-supervised learning method (CC-SSL). Our method leverages both spatial and temporal consistencies, to bootstrap weak models trained on synthetic data with unlabeled real images. We demonstrate the effectiveness of our method on highly deformable animals, such as horses and tigers. Without using any real image label, our method allows for accurate keypoint prediction on real images. Moreover, we quantitatively show that models using synthetic data achieve better generalization performance than models trained on real images across different domains in the Visual Domain Adaptation Challenge dataset. Our synthetic dataset contains 10+ animals with diverse poses and rich ground truth, which enables us to use the multi-task learning strategy to further boost models' performance.
Deep Learning-Based Decoding of Block Markov Superposition Transmission

In this paper, we present deep learning-based decoding of block Markov superposition transmission (BMST). For the basic code, a deep neural network (DNN) is trained, providing with good decoding performance. The complexity of the trained DNN can be reduced by removing those negligibly small coefficients. For the BMST code, we introduce a sliding window decoding algorithm, which integrates the DNN decoder of the basic code in an iterative manner. Numerical results show that the performance of our proposed scheme is close to that of the BMST system with conventional decoders and matches well with the corresponding lower bound of BMST. The above results verify that the extra gain and lower bound of BMST are effective for neural network decoder and thus demonstrate the universality of BMST.
Cognitive Modeling of Lingual Cultural Type “Woman” (Graph analysis)

This paper observes an analysis of lingual cultural type of woman by using graphical modeling approach. The study has been carried out in terms of applied linguistics and illustrates the integration of natural language processing technologies. The main contribution is an attempt to implement Text Mining tools to analyze poetic discourse of the late 16th - early 17th centuries and provide cognitive model of lingual cultural type. The topicality of cognitive modeling application in linguistic studies is the ability to structure and systematize the existing information, identify the scenarios for information system, and predict the relationship dynamics between the components of this system. The paper also examines the possibility of using graph theory to study lexical variables that reflect the objects of human life. Overall, the research findings indicate the most frequent connections between the lingual cultural type of woman and the lexical variables used to depict physical, social or spiritual objects of the reality in English culture.
Scale-wise Convolution for Image Restoration

While scale-invariant modeling has substantially boosted the performance of visual recognition tasks, it remains largely under-explored in deep networks based image restoration. Naively applying those scale-invariant techniques (e.g. multi-scale testing, random-scale data augmentation) to image restoration tasks usually leads to inferior performance. In this paper, we show that properly modeling scale-invariance into neural networks can bring significant benefits to image restoration performance. Inspired from spatial-wise convolution for shift-invariance, "scale-wise convolution" is proposed to convolve across multiple scales for scale-invariance. In our scale-wise convolutional network (SCN), we first map the input image to the feature space and then build a feature pyramid representation via bi-linear down-scaling progressively. The feature pyramid is then passed to a residual network with scale-wise convolutions. The proposed scale-wise convolution learns to dynamically activate and aggregate features from different input scales in each residual building block, in order to exploit contextual information on multiple scales. In experiments, we compare the restoration accuracy and parameter efficiency among our model and many different variants of multi-scale neural networks. The proposed network with scale-wise convolution achieves superior performance in multiple image restoration tasks including image super-resolution, image denoising and image compression artifacts removal. Code and models are available at: https://github.com/ychfan/scn_sr
Stochastic Conditional Generative Networks with Basis Decomposition

While generative adversarial networks (GANs) have revolutionized machine learning, a number of open questions remain to fully understand them and exploit their power. One of these questions is how to efficiently achieve proper diversity and sampling of the multi-mode data space. To address this, we introduce BasisGAN, a stochastic conditional multi-mode image generator. By exploiting the observation that a convolutional filter can be well approximated as a linear combination of a small set of basis elements, we learn a plug-and-played basis generator to stochastically generate basis elements, with just a few hundred of parameters, to fully embed stochasticity into convolutional filters. By sampling basis elements instead of filters, we dramatically reduce the cost of modeling the parameter space with no sacrifice on either image diversity or fidelity. To illustrate this proposed plug-and-play framework, we construct variants of BasisGAN based on state-of-the-art conditional image generation networks, and train the networks by simply plugging in a basis generator, without additional auxiliary components, hyperparameters, or training objectives. The experimental success is complemented with theoretical results indicating how the perturbations introduced by the proposed sampling of basis elements can propagate to the appearance of generated images.
Simple Notification Service

The Simple Notification Service or simply SNS is a notification service that allows fast and time-sensitive message delivery, in bulk, to multiple subscribers in a single instance. This service operates on a push mechanism which allows the sender to broadcast messages via several endpoints like email, SMS, HTTP, and so on. The sender usually creates a topic to which the recipients can subscribe. Let us first understand what a topic is. A topic is nothing but a channel of communication to which the sender or a publisher can publish their messages. This will automatically send the published message to all the receivers who have subscribed to that specific topic.
PENGGUNAAN EMOJI UNTUK MENINGKATKAN PERILAKU BAIK (WELL-BEING) DAN KEMAMPUAN BAHASA ANAK USIA DINI

This study aims to improve children’s well-being and communication skills using emoji as a learning media. Emoji images used consist of facial emojis, objects and activities that are easily understood and close to children’s daily experiences. The techniques of collecting data were observation sheet and direct observation by researchers. This study used a qualitative descriptive method and the samples were 25 children, collected through purposive sampling, aged between 5-6 years in one PAUD in Konawe Selatan. The results of this study found that emoji media can positively improve children’s well-being and their language abilities. Children always showed huge interest when imitating, affirming, explaining and connecting emojis with objects and their feelings. Children told their daily experiences associated with emojis, hence they become the center of learning where their opinion and ideas are truly heard and understood, which is an important indicator to improve children’s well-being. The open-ended nature of emojis makes children have the opportunity to discuss emoji symbols that are correct according to them and also help researchers interpret how children see their own world. Meanwhile, language ability aspects increased during this activity were vocabulary, the children ability to provide definition, solutions and examples of certain problems, and self-confidence.&#x0D; Keywords: children well-being; emoji; communication skill; preschools
Designing and Building Client-Server Based Student Admission Applications

Admission of New Students is a routine activity organized by the Educational Institution. Early Childhood Education (PAUD) is included in out-of-school education in the age range of two to five years, the goal is to help improve physical and spiritual growth and development so that children have the readiness to enter further education. Admission of New Students to Al-Qudwah is still done using prospective students visiting Al-Qudwah and filling out the registration form with paper. New Student Admission Application at Al Quwah aims to promote the school to the community at large and can also be registered without parents of prospective students visiting educational institutions. This application makes it easy for parents of prospective students to find out the facilities and infrastructure and the information available on Al Quwah. We built this application using HTML and PHP programming languages with the PHP MySQL database and using the waterfall method.
Bayesian Optimization Under Uncertainty for Chance Constrained Problems

Chance constraint is an important tool for modeling the reliability on decision making in thepresence of uncertainties. Indeed, the chance constraint enforces that the constraint is satisfiedwith probability1−α(0< α <1) at least. In addition, we consider that the objective func-tion is affected by uncertainties. This problem is challenging since modeling a complex systemunder uncertainty can be expensive and for most real-world stochastic optimization will not becomputationally viable.In this talk, we propose a Bayesian methodology to efficiently solve such class of problems.The central idea is to use Gaussian Process (GP) models [1] together with appropriate acquisi-tion functions to guide the search for an optimal solution. We first show that by specifying aGP prior to the objective function, the loss function becomes tractable [2]. Similarly, using GPmodels for the constraints, the probability satisfaction can be efficiently approximated. Sub-sequently, we introduce new acquisition functions to iteratively select the points to query theexpensive objective and constraint functions. Finally, we present numerical examples to validateour approach compared to benchmark results.
Threat Assessment of Enterprise Applications via Graphical Modelling

Cyber resiliency has been a very challenging engineering research. There have been several case studies done to assess cyber resiliency of enterprise business application through application of attack graphs. The challenge of automation lies in extracting from a general business enterprise system, the distinct layers like asset layer, service layer, business process task layer etc., so that the task dependencies together with formal vulnerability specification can be integrated to arrive at attack graphs. In this paper, we develop a model for threat analysis of an enterprise from a set of given vulnerabilities in various layers of the business process. Starting from the business process model (BPMN) of the given enterprise, we first obtain its’ task dependency graph, we obtain the hierarchical dependency graph consisting of asset-, service- and business process-layer. From the graphical dependency graph and the vulnerability specifications we obtain a logical specification of vulnerability/threat propagation for deriving multi step multi stage attacks using MulVAL (MulVAL: http://people.cs.ksu.edu/xou/argus/software/mulval .). The attack graph generated from MulVAL, is imported into the graphical DB, Neo4J so that an online/real-time flexible analysis of vulnerability/threat propagation can be done. We further demonstrate how with additional inputs, it is possible to realize risk analysis of the system. Thus, our integrated model has made threat analysis both re-configurable and scalable. We illustrate the application of our approach to enterprise systems and the power of graphical modeling for the analysis of threat assessments of business enterprise applications. This in turn allows the use of various mitigation techniques for controlling the propagation of threats/vulnerabilities.
Utility Software Design to Comprehend The Cryptography Cast-128 Method

Abstract At present, data security techniques known as cryptography are influential and widely used. There are lots of cryptographic algorithms. The main problem faced by a student taking this course is they difficult to understand the procedures of a cryptographic algorithm due to its complexity and involves many calculations. CAST is a block ciphers belonging to the family of DES (Data Encryption System) that uses substitutions and permutations (known as Substitution Permutation Network or SPN) in key calculations and encryption and decryption processes. The CAST algorithm has two versions, namely CAST-128 and CAST-256 where both are distinguished by the length of the used key. The maximum Key length allowed in CAST is 128 bits or 16 characters. In addition CAST-128 allows key sizes to vary from 40 bits to 128 bits with the addition of 8-bits. While the length of plain text that can be encrypted and decrypted is 64 bits (8 characters) and supports all types of plain text. This study describes the procedures of CAST-128, the design of encryption procedures from CAST-128, calculations from the Key Schedule using Substitution Boxes (S-Boxes), how the CAST-128 encryption and decryption algorithms work, the results of the implementation of the CAST-128 algorithm created a program that also functions as a learning program to understand the CAST- 128 algorithm with the process of key formation, CAST-128 algorithm encryption and decryption.
Highlighting the Container Memory Consolidation Problems in Linux

The container mechanism supports server consolidation; to ensure memory performance isolation, Linux relies on static memory limits. However, this results in poor performance, because an application needs are dynamic. In this article we will show current problems with memory consolidation for containers in Linux.
Weighted posets: Learning surface order from dependency trees

This paper presents a novel algorithm for generating a surface word order for a sentence given its dependency tree using a two-stage process.Using dependency-based word embeddings and a Graph Neural Network, the algorithm first learns how to rewrite a dependency tree as a partially ordered set (poset) with edge-weights representing dependency distance.The subsequent topological sort of this poset reflects a surface word order.The algorithm is evaluated against a naive baseline of average dependency distances across 14 languages, performing well in terms of rank correlation and resulting rate of projectivity based on Universal Dependencies corpora.
Electronic Financial Fraud: Abstract, Definitions, Vulnerabilities, Issues and Causes

Definitions, Vulnerabilities, Issues and CausesIn financial systems, data under goes many phases of processing such as retrieving phase, data analysis and management phase, decision making and reporting phase making the data flow very comprehensive and complicated.Financial decisions are directly affected by offer and demand information which is relayed through gateways from numerous intangible sources or sensors distributed all over the financial universe.The communication of this information is generally defined as follows: unicast (one to one), multicast (many to many) and broadcast (one to many) generating massive amounts of heterogeneous data dumped in huge databases, the security of which is defined by 3 basic categories: data itself, storage of data and the access of data.
Measuring Dataset Granularity

Despite the increasing visibility of fine-grained recognition in our field, "fine-grained'' has thus far lacked a precise definition. In this work, building upon clustering theory, we pursue a framework for measuring dataset granularity. We argue that dataset granularity should depend not only on the data samples and their labels, but also on the distance function we choose. We propose an axiomatic framework to capture desired properties for a dataset granularity measure and provide examples of measures that satisfy these properties. We assess each measure via experiments on datasets with hierarchical labels of varying granularity. When measuring granularity in commonly used datasets with our measure, we find that certain datasets that are widely considered fine-grained in fact contain subsets of considerable size that are substantially more coarse-grained than datasets generally regarded as coarse-grained. We also investigate the interplay between dataset granularity with a variety of factors and find that fine-grained datasets are more difficult to learn from, more difficult to transfer to, more difficult to perform few-shot learning with, and more vulnerable to adversarial attacks.