Reviewerly/exHarmony huggingface dataset:
New Integral distinguishers for I - Present™, TANGRAM and CHAM

Integral analysis is one of the most effective analysis methods for lightweight block ciphers. In this paper, the integral property are evaluated to three ciphers, I-Present™, TANGRAM and CHAM. The model of bit-based division property is established and solved by SAT and Gurobi solver. We obtain several new or improved bit-based division distinguishers for I-Present™, TANGRAM and CHAM. Furthermore, we get the 10-round integral distinguisher of Present™, with the improved data complexity about 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">56</sup> . For TANGRAM-128, we find 13-round integral distinguisher, which is the longest distinguisher with more lower data complexity at present. This also shows that the use of SAT solver for integral analysis can attack more block ciphers. At the same time, the integral analysis by using Gurobi solver of CHAM is also carried out, and 19-round and 21-round integral distinguisher of CHAM-64 and CHAM-128 are obtained respectively, which are five rounds and three rounds more than those of the designer.
Automatic Optimization of Hyperparameters Using Associative Self-adapting Structures

In this work, we focus on hyperparameter optimization, which is one of the essential steps in the development of machine learning solutions. This work proposes a novel approach to hyperparameter optimization using associative self-adapting structures, which allows us to efficiently explore the hyperparameter space. The core algorithm introduced in this paper is based on the associative graph data structure (AGDS) merged with the evolutionary approach, inspired by the processes used in drug discovery and human behavior. The proposed algorithm was compared with two state-of-the-art algorithms, a Tree-Structured Parzen Estimator and differential evolution. All experiments were carried out using Penn Machine Learning Benchmarks, repeated ten times for each problem, to provide objective comparisons. We optimized Random Forest and deep neural network models for every dataset of these benchmarks. The results reveal a noticeable improvement compared to both optimization methods in both optimized models, which shows that the presented approach can be successfully used as a good alternative for current state-of-the-art solutions.
Unification and Harmonization of Afenmai Languages: Issues and Challenges

The Edoid languages, to which Afenmai (Niger-Congo) belongs, have the most complex segments in the Kwa group of languages. In some cases, not all the segments co-occur in the same languages, while some are mutually exclusive. Different letters are used in the different languages to represent the same sounds. These challenges and more create a complex situation. Many of the Afenmai languages are endangered due to many reasons, but especially the lack of cultural and linguistic identity. There is, therefore, the need to invent an Afenmai identity. An important aspect of developing such an identity is the unification of the linguistic variations and the harmonization of the alphabet. This chapter presents a proposal for the unification and harmonization of the Afenmai spelling system using linguistic features.
Short Paper: On the Claims of Weak Block Synchronization in Bitcoin

Recent Bitcoin attacks [15, 17, 18] commonly exploit the phenomenon of so-called weak block synchronization in Bitcoin. The attacks use two independently-operated Bitcoin monitors — i.e., Bitnodes and a system of customized supernodes — to confirm that block propagation in Bitcoin is surprisingly slow. In particular, Bitnodes constantly reports that around 30% of nodes are 3 blocks (or more) behind the blockchain tip and the supernodes show that on average more than 60% of nodes do not receive the latest block even after waiting for 10 min. In this paper, we carefully re-evaluate these controversial claims with our own experiments in the live Bitcoin network and show that block propagation in Bitcoin is, in fact, fast enough (e.g., most peers we monitor receive new blocks in about 4 s) for its safety property. We identify several limitations and bugs of the two monitors, which have led to these inaccurate claims about the Bitcoin block synchronization. We finally ask several open-ended questions regarding the technical and ethical issues around monitoring blockchain networks.
Multilevel Domain Adaptive Detection Method for Haze Scenes

Object detection is a focus in the field of computer vision, but in some complex scenes, such as fog, rain, and night applications, it will still face problems of the domain transfer. Therefore, for unsupervised object detection problems under complex scenes, most of the existing methods are adopted in domain adaptive learning technology to solve. The domain adaptive detection method can avoid spending a lot of money to construct labels for training images with fog. It can also avoid the use of synthetic foggy images but directly use real foggy images for training so as to eliminate the error caused by the synthesis of foggy images. However, the existing domain adaptive detection methods ignore the characteristics of haze scenes. The method presented in this paper will propose a new domain adaptive detection network based on the characteristics of the hazing scene itself and has achieved good results in the experiments.
A Survey on Vision-Based Elders Fall Detection Using Deep Learning Models

Falls are a leading source of severe wounds in adults over the age of 60, according to WHO. It has the potential to cause significant wounds, and impairments, and can even lead to demise, particularly in older citizens who live alone. As a result, we can improve these people’s quality of life by implementing different automated fall detection systems. Using multiple deep learning architectural models, this study provides a full-scale evaluation of recent fall detection approaches. Its goal is to serve as a resource for academics and industry to research various fall detection methods. We investigated nearly all contemporary along with promising deep learning approaches for fall tracking and detection and divided them into two classes: convolutional neural network-based (CNN) systems and recurrent neural network-based (RNN) systems. The focus of this research is to offer a contrast of several deep learning-based fall detection systems like CNN, LSTM, and RNN as well as illustrated a comprehensive table that gives an overview of the paper published from the year 2017 to 2021 that could be used in better understanding the role and significance of the systems in the fall detection task. At the closure of the report, obstacles and further development have been mentioned. This survey can assist scholars in better understanding of present systems and proposing new techniques by addressing the challenges that have been identified.
DRL-ISP: Multi-Objective Camera ISP with Deep Reinforcement Learning

In this paper, we propose a multi-objective camera ISP framework that utilizes Deep Reinforcement Learning (DRL) and camera ISP toolbox that consist of network-based and conventional ISP tools. The proposed DRL-based camera ISP framework iteratively selects a proper tool from the toolbox and applies it to the image to maximize a given vision task-specific reward function. For this purpose, we implement total 51 ISP tools that include exposure correction, color-and-tone correction, white balance, sharpening, denoising, and the others. We also propose an efficient DRL network architecture that can extract the various aspects of an image and make a rigid mapping relationship between images and a large number of actions. Our proposed DRL-based ISP framework effectively improves the image quality according to each vision task such as RAW-to-RGB image restoration, 2D object detection, and monocular depth estimation.
Construction and Verification of Academic Performance Early Warning Model based on PSO-XGBoost Algorithm

In order to improve the performance of student academic early warning model, this paper improves the problems existing in feature selection and missing value processing, proposes a students' academic early warning model based on PSO-XGBoost algorithm, and verifies the effectiveness of the model by using the mining performance of the algorithm and a single algorithm in the data set of this paper.
Using Cooperative Multi-agent Systems to Support the Generation of Student Learning Profiles in the Deep Dive Land Bridge System

The goal of the project described here was to extend the immersive experience of the user in the Virtual Land Bridge. Such extensions would facilitate the use of the software system to contribute to the development of individual student learning plans over several different age categories. The principal strategy was to modify the multi-agent planning system to allow for the generation of local tactical behavior of the agents in the virtual world. This resulted in the addition of a new layer to the MAS planning system, producing a hybrid system that utilized global knowledge monolithically in the Pathfinder portion and local tactical knowledge in the VR layer. The resultant hybrid system was designed to produce improved caribou agent decision making and movement that could scale up to support large herds on the order of a hundred thousand and more. This new system is shown to increase user immersion that can contribute to the development of student learning profiles.
Towards exploring the code reuse from stack overflow during software development

As one of the most well-known programmer Q&A websites, Stack Overflow (i.e., SO) is serving tens of thousands of developers every day. Previous work has shown that many developers reuse the code snippets on SO when they find an answer (from SO) that functionally matches the programming problem they encounter in their development activities. To study how programmers reuse code on SO during project development, we conduct a comprehensive empirical study. First, to capture the development activities of programmers, we collect 342,148 modified code snippets in commits from 793 open-source Java projects, and these modified code can reflect the programming problems encountered during development. We also collect the code snippets from 1,355,617 posts on SO. Then, we employ CCFinder to detect the code clone between the modified code from commits and the code from SO, and further analyze the code reuse when programmer solves a programming problem during development. We count the code reuse ratios of the modified code snippets in the commits of each project in different years, the results show that the average code reuse ratio is 6.32%, and the maximum is 8.38%. The code reuse ratio in project commits has increased year by year, and the proportion of code reuse in the newly established project is higher than that of old projects. We also find that some projects reuse the code snippets from many years ago. Additionally, we find that experienced developers seem to be more likely to reuse the knowledge on SO. Moreover, we find that the code reuse ratio in bug-related commits (6.67%) is slightly higher than that of in non-bug-related commits (6.59%). Furthermore, we also find that the code reuse ratio (14.44%) in Java class files that have undergone multiple modifications is more than double the overall code reuse ratio (6.32%).
Optimization of multi-objectives for Adaptive Reverse Recommendation method on edge devices

The Neural Architecture Search (NAS) enables Deep Learning engineers to automatically explore the wide variety of all possible neural architectures to design the solution for a given problem. Though the NAS methods automatically generate the best possible architecture, it is time-consuming, and resource constraint since the search space consists of the best architectures(good accuracy) and includes non performing architectures as well. Researchers have proposed various NAS methods especially based on evolutionary algorithms which consists of diverse search space due to the presence of genetic operations leading to expensive search time cost. These NAS methods also deal with multiple objectives like FLOPS, latency and power usage, and model accuracy, for image classification tasks. Though the generated architectures from NAS have multi-objective, they require significant computing resources and are not suitable for running on edge devices with limited computational resources. The proposed method includes a novel model optimization method designed for the neural architecture generated from NAS methods to make it smaller in size, memory efficient, and faster at inference with minimal loss of accuracy. We optimized the models designed for standard datasets and decreased the parameters by 7.82% for CIFAR-10, 2.95% for CIFAR-100, 6.36% for STL-10, and 3.57% for Food-101 datasets. The resulting models have achieved an accuracy of 97.4% for CIFAR-10, 86% for CIFAR-100, 96.7% for STL-10, and 87.4% for Food-101 datasets.
Frames for Graph Signals on the Symmetric Group: A Representation Theoretic Approach

An important problem in the field of graph signal processing is developing appropriate overcomplete dictionaries for signals defined on different families of graphs. The Cayley graph of the symmetric group has natural applications in ranked data analysis, as its vertices represent permutations, while the generating set formalizes a notion of distance between rankings. Taking advantage of the rich theory of representations of the symmetric group, we study a particular class of frames, called Frobenius-Schur frames, where every atom belongs to the coefficient space of only one irreducible representation of the symmetric group. We provide a characterization for all Frobenius-Schur frames on the group algebra of the symmetric group which are “compatible” with respect to the generating set. Such frames have been previously studied for the permutahedron, the Cayley graph of the symmetric group with the generating set of adjacent transpositions, and have proved to be capable of pro-ducing meaningful interpretation of the ranked data set via the analysis coefficients. Our results generalize frame constructions for the permutahedron to any inverse-closed generating set.
Fuzzy Cognitive Maps as a Toll for Structuring New Research Problems

Fuzzy Cognitive Maps (FCM) is a research tool used to study cause-and-effect links between factors determining the development (or lack thereof) of the studied phenomenon. The main effect of its application is a graphical presentation of the relationships between the factors identified as the key ones. This solution usually applies as an auxiliary at the stage of structuring the research problem. It provides answers to questions about how different kinds of factors connect and how many connections there are between them. In the subject literature, there are numerous examples of the use of this tool to diagnose various phenomena. Two approaches are most commonly used to elaborate cognitive maps: Econometric, which models mathematical relationships, and expert procedure, which mainly uses the Delphi method, as well as relatively simple calculation procedures. The second approach is especially dedicated to studying less recognised problems such as the structurisation of the new research problems, little known in economic sciences. In the paper, FCM was used to study the relations between the areas of National Smart Specialisation (KIS). The term smart specialisation denotes areas of concentration of economic activity of enterprises with the potential to develop research and development and technological activities. Currently, 13 KISs have been identified in Poland, with numerous relations with each other. Since in official statistics no data shows the structure of enterprises according to the Polish classification (KIS), research in this area can be supported with methods allowing for the better recognition of this problem.
A Joint Knowledge Graph Reasoning Method

Facing the massive data generated by edge intelligent interconnection applications in the mobile edge computing (MEC) environment, timely and efficient data mining has become an urgent technical problem to be solved. Knowledge graph reasoning is a promising solution to the above challenges. However, the traditional knowledge graph reasoning method can not meet the requirements of MEC for low latency and fewer resources. This paper presents a MEC-oriented knowledge graph reasoning method gate recursive unit for logic reasoning (GRULR). Specifically, the technology regards logical rules as variables and trains two models in an iterative manner under the MEC architecture, namely, Rule Miner and Reasoning Evaluator. The two models are deployed in the central cloud and the edge cloud respectively, jointly trained and mutually enhanced. Rule Miner generates rule sequences based on gate recurrent unit (GRU) network, and optimizes network parameters by using high-quality rules generated by Reasoning Evaluator. Experiments show that this method has a good edge reasoning effect, and can generate high-quality logic rules and send them to the central cloud server for sharing.
A Cost-Variant Renewable Energy-Based Scheduling Algorithm for Cloud Computing

The global growth of cloud computing services is rising abruptly due to a large variety of services like computing, storage, network, etc. It expresses cloud service providers (CSPs) for better usage of existing datacenter resources, increasing agility, and reducing the need for unanticipated datacenter growth. These datacenters use a lot of energy generated from fossil fuels (i.e., non-renewable energy (NRE) sources) and omit a lot of nitrous oxide and carbon dioxide, which cause the greenhouse effect and are harmful to the environment. Moreover, NRE sources are limited in supply and cannot be sustained over a long period. As a circumstance, CSPs are moving towards renewable energy (RE) sources, such as solar, wind, hydro, and biomass, to decarbonize datacenters even though these resources are not available round the clock. Therefore, recent studies focus on using both RE and NRE sources to avoid any interruption of the datacenter services. However, these studies consider the equal cost for all the RE sources and do not consider the categorization among user requests (URs). This paper considers the different costs for RE sources and two categories of URs, namely critical and non-critical, and introduces a cost-variant RE-based scheduling (CRES) algorithm for cloud computing. Here, the critical UR does not depend on the RE resources due to the unpredictability of RE sources. On the other hand, the non-critical UR can be accommodated by both RE and NRE resources. We simulate the proposed algorithm by considering 20 to 100 URs and 5 to 25 datacenters and compare the performance with the future-aware best fit (FABEF) and highest available renewable first (HAREF) algorithms in terms of cost and usage count of RE resources to show its usefulness.
A Cross-Mutation Global Harmony Search Algorithm Based on Lévy Flight

A cross-mutation global Harmony Search algorithm based on Lévy Flight (CGHSLF) was proposed to improve the efficacy and computational efficiency of the traditional Harmony Search (HS) algorithm and overcome its shortcomings. The algorithm includes a new improvisation scheme that generates new harmony using the best and worst harmony cross-mutation to improve local and global search capabilities, as well as the Lévy flight approach to boost population variety and balance global and local search. By dynamically adjusting the parameters, the algorithm may balance development and exploration in global and local search processes, thus expanding the scope of the search. Two test experiments are set up as follows: Five existing harmony search algorithms are compared with the proposed algorithm. According to the experimental data, the approach in this study has a faster convergence time and is better at solving high-dimensional optimization problems. The algorithm is utilized to solve the PID parameter optimization problem, and the results show that it is capable of achieving well.
Short-and-Long-Term Impact of Initialization Functions in NeuroEvolution

Neural evolutionary computation has risen as a promising approach to propose neural network architectures without human interference. However, the often high computational cost of these approaches is a serious challenge for their application and research. In this work, we empirically analyse standard practices with Coevolution of Deep NeuroEvolution of Augmenting Topologies (CoDeepNEAT) and the effect that different initialization functions have when experiments are tuned for quick evolving networks on a small number of generations and small populations. We compare networks initialized with the He, Glorot, and Random initializations on different settings of population size, number of generations, training epochs, etc. Our results suggest that properly setting hyperparameters for short training sessions in each generation may be sufficient to produce competitive neural networks. We also observed that the He initialization, when associated with neural evolution, has a tendency to create architectures with multiple residual connections, while the Glorot initializer has the opposite effect.
Label-Enhanced Graph Neural Network for Semi-Supervised Node Classification

Graph Neural Networks (GNNs) have been widely applied in the semi-supervised node classification task, where a key point lies in how to sufficiently leverage the limited but valuable label information. Most of the classical GNNs solely use the known labels for computing the classification loss at the output. In recent years, several methods have been designed to additionally utilize the labels at the input. One part of the methods augment the node features via concatenating or adding them with the one-hot encodings of labels, while other methods optimize the graph structure by assuming neighboring nodes tend to have the same label. To bring into full play the rich information of labels, in this article we present a label-enhanced learning framework for GNNs, which first models each label as a virtual center for intra-class nodes and then jointly learns the representations of both nodes and labels. Our approach could not only smooth the representations of nodes belonging to the same class, but also explicitly encode the label semantics into the learning process of GNNs. Moreover, a training node selection technique is provided to eliminate the potential label leakage issue and guarantee the model generalization ability. Finally, an adaptive self-training strategy is proposed to iteratively enlarge the training set with more reliable pseudo labels and distinguish the importance of each pseudo-labeled node during the model training process. Experimental results on both real-world and synthetic datasets demonstrate our approach can not only consistently outperform the state-of-the-arts, but also effectively smooth the representations of intra-class nodes.