Reviewerly/exHarmony huggingface dataset:
Recognizing Families through Images with Pretrained Encoder

Kinship verification and kinship retrieval are emerging tasks in computer vision. Kinship verification aims at determining whether two facial images are from related people or not, while kinship retrieval is the task of retrieving possible related facial images to a person from a gallery of images. They introduce unique challenges because of the hidden relations and features that carry inherent characteristics between the facial images. We employ 3 methods, FaceNet, Siamese VGGFace, and a combination of FaceNet and VGG-Face models as feature extractors, to achieve the 9th standing for kinship verification and the 5th standing for kinship retrieval in the Recognizing Family in The Wild 2020 competition. We then further experimented using StyleGAN2 as another encoder, with no improvement in the result.
Research on Optimization of Knowledge Graph Construction Flow Chart

Constructing knowledge graph (KG) is the basic condition to realize the application of knowledge graph such as semantic search and intelligent question and answer. In this paper, the principle of knowledge graph construction is introduced in detail from three aspects: structure, construction method and construction process, and the knowledge graph architecture including data source, knowledge extraction, knowledge fusion, knowledge processing, knowledge representation, knowledge storage and knowledge base is proposed. Then, the current flow chart of knowledge graph construction is analyzed, and three specific problems are put forward. Finally, according to the principle of knowledge graph construction, aiming at the existing problems, the specific optimization scheme is put forward, and the flow chart of knowledge graph construction after optimization is drawn. The flow chart of knowledge graph construction before and after optimization is compared and analyzed from five aspects: accuracy, intuition, complexity, representation adequacy and application scope.
Recent Progress in Quantum Machine Learning

Quantum computing is a new exciting field which can be exploited to great speed and innovation in machine learning and artificial intelligence. Quantum machine learning at crossroads explores the interaction between quantum computing and machine learning, supplementing each other to create models and also to accelerate existing machine learning models predicting better and accurate classifications. The main purpose is to explore methods, concepts, theories, and algorithms that focus and utilize quantum computing features such as superposition and entanglement to enhance the abilities of machine learning computations enormously faster. It is a natural goal to study the present and future quantum technologies with machine learning that can enhance the existing classical algorithms. The objective of this chapter is to facilitate the reader to grasp the key components involved in the field to be able to understand the essentialities of the subject and thus can compare computations of quantum computing with its counterpart classical machine learning algorithms.
Structure Identification and Tracking of Multiple Resolvable Group Targets with Circular Formation

This paper addresses the structure identification and tracking problem for multiple resolvable group targets, which includes multiple subgroups in a big group. Each subgroup moves in a circular formation in the normal case (the formation may change when the task changes suddenly), and we focus on target tracking, group division, structure detection, parameter identification as well as formation constrained estimation. To this end, the labeled multi-Bernoulli (LMB) filter is used to get the individual tracks of the group targets firstly. Then the Kullback-Leibler (KL) divergence is introduced to measure the similarity between the probability distribution functions (pdf) of two targets and the K-mediods clustering method is used to obtain the number and division of the subgroups. Next, the structure detection of each subgroup is performed to confirm whether the subgroup structure accords with the priori circular formation. Then the parameter identification and the formation constrained estimation based on the estimation projection (EP) method are only applied to those subgroups which conform to prior formation. Finally, a simulation study on the group targets tracking with circular formation is provided to demonstrate the proposed algorithm.
Multidimensional and multiscale Higuchi dimension for the analysis of colorectal histological images

Fracta1 techniques are widely explored to quantity and recognize texture patterns in digital images. Among the different types of fractal techniques, one that stands out is the Higuchi fractal dimension. The Higuchi fractal dimension allows determining how much space is filled in a two-dimensional image through the projection of the image in 1D signals. This property provides the possibility to calculate the Higuchi fractal dimension of specific regions of an image. The main drawback of this technique is that it does not allow the analysis of color images. In this work, a new Higuchi fractal dimension model is presented with the inclusion of multidimensional and multiscale strategies to expand the traditional Higuchi dimension for texture analysis in color images. The multidimensional approach was applied considering each pixel of the color image as an n-dimensional vector. The multiscale strategy was defined using different scales of observation. The proposed model was applied to a set of 151 colorectal histological images to test its ability to quantity and separate the benign and malignant groups from colorectal cancer. The performance of the proposed model was compared with that provided by consolidated fractal dimension techniques. The results obtained are promising and indicate that the proposal contributes significantly to the literature focused on the quantification and recognition of texture patterns with fractal techniques.
Mining for Graph-Based Library Usage Patterns in COBOL Systems

When migrating legacy systems to more contemporary platforms, it is not always feasible to migrate their external library dependencies as well. A workaround can be to partially reimplement the library dependencies on the new platform, limiting oneself to the features that are used by the migrated system. For contemporary programming languages, several approaches to mining for library usage patterns have been proposed. For legacy programming languages, in contrast, such tools are lacking. This encumbers establishing what parts of a library need to be rewritten during a migration project, especially when considering large-scale systems.In this industry track paper, we present an approach to mining library usage patterns in COBOL code. We describe a library usage extractor for COBOL, which produces graphs that capture the control and data flow involved in library calls. The extractor supports legacy control flow features, such as GO TO. We use these graphs as input to two state-of-the-art frequent subgraph mining algorithms, and report on the scalability of their use for mining common library usage patterns in two industrial COBOL systems. The mined library usage patterns can help assess and subsequently steer the migration effort.
Community Attention Network for Semi-Supervised Node Classification

Graph neural networks (GNNs) have achieved great success for semi-supervised node classification by embedding node representation into a low-dimensional space. However, existing approaches usually ignore one intrinsic property of graphs: community structure, where the formation of distinct communities in graphs is often driven by different subset of attributes. In this paper, we introduce a new method, called Community Attention Network (CAT), aiming to extract community-specific features and then enhance node embeddings for classification. To learn such community-specific information, we design a new loss function to ensure the nodes in the same community should share similar attributes (i.e., low covariance), and any unlabelled node should belong to only one class with high probability (i.e., low community distribution entropy) in a community attention network. Extensive experimental results demonstrate the effectiveness of CAT and its advantages over many state-of-the-art approaches. To further illustrate the benefits of CAT to capture the community information, a case study is given and discussed.
Cloud Computing Adoption: Influencing Factors on Online Firms

In recent years, cloud computing is remaining as a contemprory technology that adds feather to the ınformtion science paradigm. Cloud technology has several benefits that need to be considered while adopting it to overcome the existing challenges. This proposed research paper identifies significant factors for online firms to consider this technology and analyze their effects on online business firms by collecting data from various firms to examine and intrepret the rsesult.
Attentional Dilated Convolution Neural Network for Nuclei Segmentation in Histopathology Images

The foundational task of pathological diagnosis is to accurately segment the nuclei in pathology images. Deep learning has become a well-validated method of nuclei segmentation, but the touching or overlapping nuclei are difficult to segment. In this research, we bring forward a new strategy for nuclei segmentation. The channel attention module is utilized for reweighting channel-wise features and further improving segmentation performance. Moreover, dilated convolution is used for collecting multi-scale contextual information and preserving original resolution. We name our method as Attentional Dilated Convolution Neural Network (ACDNN). Experiments on a large tissue image dataset show that our strategy is superior to other advanced methods.
Optimal Probabilistic Classification in Active Class Selection

The goal of active class selection (ACS) is to optimize the class proportions in newly acquired data; a classifier trained from that data should exhibit maximum performance during its deployment. This paper provides an information-theoretic examination of the problem, resulting in an upper bound of the classifier's error. This upper bound shows that the more data is acquired, the better is the performance of the class proportions that occur during deployment; other class proportions can outperform these natural proportions in the beginning of data acquisition, but natural proportions certainly yield optimal probabilistic classifiers in the limit. Put differently-and perhaps surprisingly-the more data is acquired, the less beneficial are ACS strategies. Our bound further reveals that the degree to which non-natural class proportions are eligible depends on the correlation between the features and the class label. Experiments on standard ACS data sets quantify these effects and also show that the conclusions drawn from our analysis take over to non-probabilistic classifiers.
The Influence of the Implementation of Android-Assisted Experiential Learning Model-Based Science Learning on Students’ Scientific Literacy

Abstract This study aims to determine the effect of implementing an android application which was based on Experiential Learning Model (ELM) on students’ scientific literacy in terms of knowledge, competencies, and attitude. This research was a quasi-experiment with pre-post research of non-equivalent control group design. The population of the study were students of grade VII Junior High Schools in Kotawaringin Timur Regency. The sample was selected by stratified random sampling technique based on school stratification, which included high, medium, and low category schools. Research data of scientific literacy knowledge and competencies were measured using test instrument, while scientific research literacy attitude data was measured using non-test instrument (self-assessment questionnaire). The data analysis technique used was one-way manova with significance level of .05. The result of the data analysis showed that science learning which was based on Experiential Learning Model (ELM) with android support has significant influence on the scientific literacy, covering some aspects: knowledge, competencies, and attitude of students of grade VII Junior High Schools in Kotawaringin Timur Regency.
Empirical Validation of Requirements Traceability Metrics for Requirements Model of Data Warehouse using SVM

Data warehouse stores historical information, which is used by managers to take organizational decisions. Thus, information quality of data warehouse becomes important and assessed by the quality of its data model. Various authors proposed different metrics for assessment of the data model quality at physical, logical and conceptual level. However, very less research proposals are seen in the literature to assess the quality of requirements data model. Requirements completeness and traceability metrics for requirements model are proposed and formally validated, but no empirical validation is witnessed in the past. In this paper, empirical validation of requirements traceability metrics is performed for predicting the understandability of data warehouse requirements schemas using support vector machine. The results showed high accuracy and precision for predicting understandability of requirements schemas. In this way, quality of requirements model may be improved and subsequently used for obtaining good quality of conceptual data model for data warehouse.
Analyzing the Case for Adopting Distributed Ledger Technology in the Bank of Canada

This paper examines Distributed Ledger Technology (DLT), a type of blockchain technology poised to transform central banks. The potential impact of disruptive Blockchain/DLT technologies on central banks worldwide is unimaginably large and would have significant implications for financial and monetary transactions and economic stability. Strong public interest in cryptocurrencies such as Bitcoin has popularized the term blockchain/DLT. The goal of DLT is to remove the costly and time-consuming back-office processes and the need for third-party ‘middlemen’ in many transactions. The core question addressed in this paper asks, what is the potential impact of DLT on improving central banks’ performance? To address this question the paper presents as a case study the major findings from the Bank of Canada’s Project Jasper which consists of four phases. The first two phases explore and compare two distinct DLT Platforms; an Ethereum platform and the R3 Corda platform. The four-main areas of focus for guiding hypotheses in Phase I is cost, resilience, accessibility, and control, while Phase 2 focuses on improvement in regard to privacy. Phase 3 explored the potential role for DLT in the Canadian financial market and determine if the new business value in terms of greater speed and efficiency can be achieved through the DLT-based automation of the securities settlement process. Phase 4 focuses on managing issues in the cross-border payment and settlement space because of the lack of standardization between jurisdictions in terms of regulatory requirements, data standards and operating hours. The overall analysis of Bank of Canada innovations into DLT noted challenges of security and privacy but the key challenge is moving from highly complex centralized payments systems to new near-real-time payment platforms.
Teaching Requirement Engineering Using Industrial-Infused Project-Based Learning

This paper reports the result of an exploratory study conducted to assess (1) the achievement of learning outcomes (LOs) and (2) the perceived effectiveness of students' learning from Industrial-Infused Project-Based Learning (II-PrBL). The experiment and evaluation were made on the Software Engineering (SE) students in second year who enrolled in the 977-271 Requirement Engineering and System Modeling (RESM) course, semester 1/2019. The result shows that the students were able to think, analyse, integrate and apply the knowledge and skills achieved from the lecture to the real practice in problem-solving based on the industrial case study. They were able to achieve the necessary LOs for the RE course in both specific (hard) and generic (soft) skills. Furthermore, this II-PrBL resulted in the high perceived effectiveness of students' learning and the strong level of satisfactory teaching.
(Quantum) Cryptanalysis of Misty Schemes

In this paper, we review the best known cryptanalysis results on the variants of Misty schemes and we provide new (quantum) cryptanalysis results. First, we describe a non-adaptive quantum chosen plaintext attack (QCPA) against 4-round Misty L and Misty LKF schemes, and a QCPA against 3-round Misty R and Misty RKF schemes. We extend the QCPA attack against 3-round Misty RKF schemes to recover the keys of d-round Misty RKF schemes with complexity $$\tilde{\mathcal {O}}(2^{(d-3)n/2})$$ . We then provide a security proof for Misty R schemes with 3 rounds against chosen plaintext attacks using the H coefficients technique. This shows that the best known non-quantum attack against Misty R schemes with 3 rounds is optimal.
FAVR - Accelerating Direct Volume Rendering for Virtual RealitySystems

In recent years, virtual reality (VR) has become readily available using robust and affordable head-mounted display (HMD) systems. Several VR-based scientific visualization solutions were proposed recently, but direct volume rendering (DVR) was considered only in a handful of VR-applications. We attribute this to the high computational demand of DVR and the limited rendering budget available for VR systems. For a heavily fragment-bound method such as DVR, it is challenging to achieve the very high update rates essential for VR. We propose an acceleration technique designed to take advantage of the specific characteristics of HMD systems. We utilize an adaptive rendering approach based on the lens distortion of HMDs and the visual perception of the human eye. Our implementation reduces the rendering cost of DVR while providing an experience indistinguishable to standard rendering techniques.
Meta-DRN: Meta-Learning for 1-Shot Image Segmentation

Modern deep learning models have revolutionized the field of computer vision. But, a significant drawback of most of these models is that they require a large number of labelled examples to generalize properly. Recent developments in few-shot learning aim to alleviate this requirement. In this paper, we propose a novel lightweight CNN architecture for 1-shot image segmentation. The proposed model is created by taking inspiration from well-performing architectures for semantic segmentation and adapting it to the 1-shot domain. We train our model using 4 meta-learning algorithms that have worked well for image classification and compare the results. For the chosen dataset, our proposed model has a 70% lower parameter count than the benchmark, while having better or comparable mean IoU scores using all 4 of the meta-learning algorithms.
Image Encryption Algorithm of Hyper-chaotic System Based on Spiral Scrambling

In view of the shortcomings of existing chaotic image encryption algorithms, such as low encryption efficiency and inability to resist chosen plaintext attack, this paper proposes an image encryption algorithm based on hyper chaotic system with spiral scrambling. Firstly, the initial scrambling point is selected according to the plaintext image, and then the pseudo random sequence is generated by hyper chaotic system, and then the plaintext associated pixel diffusion is carried out. Experimental results and simulation analysis show that the algorithm has high efficiency and good security, and thus has good application scenarios.
Let’s Tessellate: Tiling for Security Against Advanced Probe and Fault Adversaries

The wire probe-and-fault models are currently the most used models to provide arguments for side-channel and fault security. However, several practical attacks are not yet covered by these models. This work extends the wire fault model to include more advanced faults such as area faults and permanent faults. Moreover, we show the tile probe-and-fault adversary model from CRYPTO 2018's CAPA envelops the extended wire fault model along with known extensions to the probing model such as glitches, transitions, and couplings. In other words, tiled (tessellated ) designs offer security guarantees even against advanced probe and fault adversaries. As tiled models use multi-party computation techniques, countermeasures are typically expensive for software/hardware. This work investigates a tiled countermeasure based on the ISW methodology which is shown to perform significantly better than CAPA for practical parameters.
Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data

Algorithm selection refers to the task of automatically selecting the most suitable algorithm for solving an instance of a computational problem from a set of candidate algorithms. Here, suitability is typically measured in terms of the algorithms' runtimes. To allow the selection of algorithms on new problem instances, machine learning models are trained on previously observed performance data and then used to predict the algorithms' performances. Due to the computational effort, the execution of such algorithms is often prematurely terminated, which leads to right-censored observations representing a lower bound on the actual runtime. While simply neglecting these censored samples leads to overly optimistic models, imputing them with precise though hypothetical values, such as the commonly used penalized average runtime, is a rather arbitrary and biased approach. In this paper, we propose a simple regression method based on so-called superset learning, in which right-censored runtime data are explicitly incorporated in terms of interval-valued observations, offering an intuitive and efficient approach to handling censored data. Benchmarking on publicly available algorithm performance data, we demonstrate that it outperforms the aforementioned naïve ways of dealing with censored samples and is competitive to established methods for censored regression in the field of algorithm selection.
Dynamic Window Approach with path-following for Unmanned Surface Vehicle based on Reinforcement Learning

Recently, autonomous navigation technology is actively being developed due to the increasing demand of an unmanned surface vehicle(USV).Local planning is essential for the USV to safely reach its destination along paths.the dynamic window approach(DWA) algorithm is a well-known navigation scheme as a local path planning.However, the existing DWA algorithm does not consider path line tracking, and the fixed weight coefficient of the evaluation function, which is a core part, cannot provide flexible path planning for all situations.Therefore, in this paper, we propose a new DWA algorithm that can follow path lines in all situations.Fixed weight coefficients were trained using reinforcement learning(RL) which has been actively studied recently.We implemented the simulation and compared the existing DWA algorithm with the DWA algorithm proposed in this paper.As a result, we confirmed the effectiveness of the proposed algorithm.