Reviewerly/exHarmony huggingface dataset:
Improving Image Classification Through Joint Guided Learning

We propose a simple yet effective approach, called Joint Guided Learning, based on knowledge distillation for image classification. Knowledge distillation transfers knowledge from a teacher model to a student model. The teacher model utilizes both correct and incorrect predictive labels to guide the optimization of the student model. However, the incorrect predictive labels have a negative influence on the student model. Moreover, it is not enough to rely solely on predictive labels to transfer knowledge. The student model should make full use of the features of the teacher model. To mitigate these issues, we design a Joint Guided Learning approach, that exploits the joint guidance of features and the predictive labels. Our method involves two novel components: (i) a guided label refinery module that considers the correct predictions and ignores the incorrect predictions. (ii) a channel distillation module that guides the student model to learn the attention map of each channel from feature maps in the teacher model. Experimental results show that our approach can achieve 82.18%, 66.08%, 88.36% and 90.14% accuracy on CIFAR-100, TinyImageNet, CUB-200-2011 and Stanford Dogs, respectively. And, the proposed method consistently outperforms the state-of-the-art approaches on four image classification datasets. Ablation studies further show the contributions of different components in our method.
Parallel Subdomain Synthesis of Cell Mapping for Capturing Global Invariant Sets in High-Dimensional Dynamical Systems

Unaffordable computation cost and memory storage induced by the curse of dimensionality has become the bottleneck of numerical methods in different fields. In the global analysis of nonlinear dynamical systems, the capability of numerical methods, like cell mapping methods, are mostly feasible only to a system dimension under four. Although cell mappings are naturally parallelizable that may be taken used to greatly enhance the computation efficiency, it is still not enough to release the computational burden on a high-dimensional system of eight or more than ten, not mentioning the memory in dealing with millions of billion cells. In this paper, the subdomain synthesis method, which partitions the chosen region in state space into subdomains suitable for dealt with on a computation unit and then synthetizes the so-called virtual invariant sets on them to get the underlying global invariant sets, is promoted to be parallelizable on the subdomains so as to build a two-layer massively parallel architecture in both cell and subdomain levels. The proposed approach can be implemented by GPU Cluster that can maximally give play to the powerful computation capability of hardwares. Examples with global invariant sets in very fine distances of Jerk system and in bifurcations of a twelve-dimensional non-smooth rotor system are presented to demonstrate feasibility of the proposed approach.
Learning from Positive and Negative Examples: Dichotomies and Parameterized Algorithms

We take a closer look on the complexity landscape of one of the most fundamental and well-studied problems in computational learning theory: the problem of learning a finite automaton A consistent with a set $$P$$ of positive examples and with a set $$N$$ of negative examples. By consistency, we mean that A accepts all strings in $$P$$ and rejects all strings in $$N$$ . It is well known that this problem is NP-hard when parameterized only by the number of states of the automaton. Therefore, our analysis takes a more refined parameterization: we consider the number k of states in A, the size $$|\varSigma |$$ of the alphabet, the maximum size l of a string in $$P\cup N$$ , and the number $$c=|P\cup N|$$ of strings in both sets. First, we prove several Pvs. NP-hard dichotomy results for these parameters when the learned automaton is drawn from different classes of finite automata. One of our dichotomy results closes a gap for the general DFA consistency problem, as here, for fixed alphabet size, the NP-hardness proofs in the literature have some issues. Interestingly, our NP-hardness results hold even for severely restricted classes of automata, such as partially-ordered automata and permutation automata. On the other hand, we provide parameterized algorithms for several combinations of parameters and show that most of them are optimal under the exponential time hypothesis.
Iterative Refinement of an AIS Rewards System

Gamification-based reward systems are a key part of the design of modern adaptive instructional systems and can have substantial impacts on learner choices and engagement. In this paper, we discuss our efforts to engineer the rewards system of Kupei AI, an adaptive instructional system used by elementary and middle school students in afterschool programs to study English and Mathematics. Kupei AI's rewards system was iteratively engineered across four versions to improve student engagement and increase progress, involving changes to how many points were awarded for success in different activities. This paper discusses the design changes and their impacts, reviewing the impacts (both positive and negative) of each generation of re-design. The end result of the design was improved learning and more progress for students. We conclude with a discussion of the implications of these findings for the design of gamification for adaptive instructional systems.
Greedy Networking in Cryptocurrency Blockchain

Proof of work (PoW) is a widely adopted distributed consensus protocol which enables cryptocurrency transaction processing without a trusted third party. The miners are financially incentivized to participate in the PoW consensus protocol, and PoW relies on the underlying peer-to-peer (P2P) networking for receiving and transmitting the transactions and the up-to-date blocks (which are the inputs for the PoW consensus protocol). We study the rational miner strategy but control an orthogonal parameter from those in the previous blockchain research, which has studied the control of the mining power or the timing of the block submissions (e.g., selfish mining or block withholding). More specifically, we study greedy networking, in which a miner node increases its connectivity beyond the default protocol to expedite the deliveries of blocks and transactions for an unfair mining advantage. While greedy networking has been actively studied in the general P2P networking, it has not been systematically studied in cryptocurrency and blockchain despite the alleged real-world instances by the Bitcoin community. We build an analytical framework for greedy networking and study how the networking control impacts the cryptocurrency application to quantify the benefits and costs of the networking control. To demonstrate the use of our framework, we implement a greedy networking prototype based on an active Bitcoin node connected to the Mainnet while simulating different miner capabilities by varying the computing backend of the miner. In contrast to the previous belief in blockchain and cryptocurrency, we discover that the optimal number of connections is bounded (limiting the greedy behavior) and depends on the hash computing capability of the miner.
Text-Independent Algorithm for Source Printer Identification Based on燛nsemble Learning

Because of the widespread availability of low-cost printers and scanners, document forgery has become extremely popular. Watermarks or signatures are used to protect important papers such as certificates, passports, and identification cards. Identifying the origins of printed documents is helpful for criminal investigations and also for authenticating digital versions of a document in today’s world. Source printer identification (SPI) has become increasingly popular for identifying frauds in printed documents. This paper provides a proposed algorithm for identifying the source printer and categorizing the questioned document into one of the printer classes. A dataset of 1200 papers from 20 distinct (13) laser and (7) inkjet printers achieved significant identification results. A proposed algorithm based on global features such as the Histogram of Oriented Gradient (HOG) and local features such as Local Binary Pattern (LBP) descriptors has been proposed for printer identification. For classification, Decision Trees (DT), k-Nearest Neighbors (k-NN), Random Forests, Aggregate bootstrapping (bagging), Adaptive-boosting (boosting), Support Vector Machine (SVM), and mixtures of these classifiers have been employed. The proposed algorithm can accurately classify the questioned documents into their appropriate printer classes. The adaptive boosting classifier attained a 96% accuracy. The proposed algorithm is compared to four recently published algorithms that used the same dataset and gives better classification accuracy.
Exploring the Impact of Persona Collapse in the Context of the Internet Era in China

Setting personas has become a popular operation among celebrities to building up their personal brands.However, the number of persona collapse events has been rising at the same time.This article mainly focuses on the topic of exploring the impact of persona collapse in the context of the Internet era in China.The article has explained the impacts caused by persona collapse from various perspectives.This article has provided a solid theoretical foundation for future researches which related to the effective methods of alleviating and further eliminating the harmful impacts of persona collapse.This article takes "persona collapse" as the key word, which is used to search articles on the CNKI from 2017 to now, and there are 12 articles which are selected into the classification of this research.The collapse of celebrities' persona will cause various impacts in numerous aspects.It is pathetic that a major of the impacts will do harm to the society, including human beings themselves, the development of economics, the healthy being of mainstream social values, etc.Therefore, the persona collapse thing is harmful and needs to be avoided.
Chainsure: Agent Free Insurance System Using Blockchain for Healthcare 4.0

The health insurance industry requires a secure data management architecture characterized by reliable information processing and fast response. Classical health insurance management system runs on a manually controlled centralized architecture which leverages a vulnerable single point failure and requires frequent human intervention. This makes the model prone to human errors and security attacks. The existing system also lacks the flexibility to choose appropriate health insurance policies for different consumers. To this end, we propose an Ethereum blockchain-based framework, ChainSure, to address this problem. With the power of TOPSIS and smart contracts, ChainSure provides an automated, tamperproof, transparent, scalable system that takes care of all the major functional blocks in a medical insurance environment. We have used the TOPSIS method in this proposed model to help the users to find an insurance policy that best suits their needs. ChainSure works in a decentralized environment and provides an interactive interface for the user. The proposed model is implemented on the Ethereum test network and its performance has been compared empirically with other state-of-art models. ChainSure is found to outperform others in terms of service integrity, latency and cost.
Decoding Crypto and NFT regulations in India

The growth of crypto is a revolutionary development in the world of internet. Increasing popularity of these blockchain based technologies has raised many questions on their regularity and legal status. Currently neither there is a ban nor any regulations on crypto The Present paper is going to discuss growth of blockchain based technologies in India and their regulatory impacts. At present, a separate legal framework does not exist for Crypto as well as NFT’s in India. These are mostly governed by the general principles of contract. It’s wait and watch situation until the government introduces a crypto-specific bill in parliament and it is passed and becomes law, crypto will be seen as being legal. So the paper will study all the perspective related to that aspect.
Coping with Service Failure Via Rumination and Ewom: A Fuzzy Clustering Perspective

This study aims to segment strategies consumers use to ‘cope’ with service failure using a novel clustering algorithm. It evaluates whether identified customer segments exhibit different anger prototypes, alongside variations in felt intensity therein. It also investigates whether different segments employ diverse rumination behaviours in response to service failure, with this used to predict manifestations of eWOM. To do so, customers with experience of hotel industry service failure within the Iranian context were surveyed. Findings suggest that four forms of coping behaviour activate different rumination behaviours in response to anger prototypes and their felt intensity. Further, logit models demonstrate that the three eWOM archetypes are driven by different predictors; suggesting that consumers’ online behaviours following service failure are not homogeneous, but instead shaped by the coping strategy employed. Implications for theory and practice are offered by way of conclusion.
Financial Management Early Warning Model Based on Two-Layer Kriging Meta-model Algorithm

Financial management is the core issue of an enterprise and plays a vital role in economic operation. Due to the lack of effective risk assessment tools to make scientific predictions, it is of practical value to study the improvement of early warning capabilities. To this end, this paper proposes a financial management early warning model based on the two-layer Kriging meta-model algorithm, with the purpose of accelerating the establishment of a financial management early warning model. This article mainly uses the experimental method and the comparative method to compare and analyze the financial management early warning model. Experimental data shows that the accuracy of the two-layer Kriging meta-model algorithm can reach 95%. Therefore, the Kriging method has good predictive ability and is superior to other models in terms of computational efficiency and accuracy.
Effect of Native Language on Innovation and Economic Growth

Language plays an important role in the economies of both developed and developing countries. We realized through our research that countries that pay enough attention to investing in their native language seem to have a sustainable economic performance but if the development of the native language is taken for granted, its effect on learning is impeding. This research is to investigate the effect of native language on economic growth and Innovation. We stipulate that use of native language increases economic growth and innovation. To achieve the above mentioned objective, the study utilizes panel dataset with the fixed and random effect techniques of estimation. The results show a positive effect of using native language on innovation and GDP per capita growth. The interaction term also shows a positive significant impact on GDP per capita growth. The connection between human development and economic growth is a multilevel one. there is a lopsided effect. We realized the lack of better language policy is linked with poor leadership.
Effort Estimation in Agile Software Development: The State of the Practice in Colombia

Software effort estimation is fundamental for planning and controlling software projects and critical for their success. The practice has shown that realistic goals setting, accurate estimates, and good planning and control are essential activities for project success, even for agile endeavors. This paper focuses on how Colombian developers perform effort estimates within agile projects. For this purpose, we conducted an exploratory survey study that involved 60 respondents. The main findings are the following: (1) agile practitioners prefer non-algorithmic estimation techniques, mainly those based on Expert Judgement. (2) Most of the respondents perceive that their estimates have a medium accuracy level; however, in 70\% of the cases no formal analysis of the error magnitude is carried out. (3) The determining effort predictors/cost drivers are characteristics of the project team (size, experience, and skills) and attributes of the software to be built (complexity, type, and domain).Â (4) The use of datasets for estimation is not common, the proprietary ones predominate, and are used for productivity comparisons within the company. (5) Most of the results of related studies are comparable with ours; however, there are significant differences in terms of the roles involved in the estimation process and the methods used.
Software Architecture Recovery Using Integrated Dependencies Based on Structural, Semantic, and Directory Information

Architecture recovery techniques study dependencies in source code and reconstruct architecture. Most techniques either use structural or semantic dependencies and it is observed that the use of directory information helps in improving architecture recovery. The research carried out to date has focused on using the semantic information in a very limited manner, and directory information in a trivial manner without considering directory hierarchy. Further, all three (structural, semantic, and directory-structure) are reported to be very useful in architecture recovery but have not been used in a combined manner at all. So, this paper proposes a new scheme for architecture recovery using a weighted combination of all three dependencies. A new approach is designed to effectively mine semantic dependencies and extract directory dependencies. Finally, different dependency schemes are evaluated with four clustering algorithms on three open-source projects. The obtained results show that the proposed scheme performs better than the existing approaches in architecture recovery.
Identifying the Leak Sources of Hard Copy Documents

Technological advancements have made it possible to use relatively inexpensive hardware and software to replicate and leak sensitive documents. This chapter proposes a novel canary trap method for determining the source of a leaked hard copy document. The method generates self-identifying documents that secretly encode unique information about the individuals who receive them by modifying the inter-word spacing in the original reference document. The encoded information is robust to changes introduced during printing, scanning and copying, rendering the method useful for hard copy as well as digital documents. Due to the lack of publicly-available datasets, a custom hard copy document leakage dataset comprising 100 scanned self-identifying documents encoded at four levels of robustness was created. The hard copy document leakage dataset was subsequently employed to evaluate the performance of the canary trap leak detection method.
Can Interactive Online Training Make High School Students More Entrepreneurial? Experimental Evidence from Rwanda

We study the short-run effects of a gamified online entrepreneurship training offered to high school students in Rwanda during the COVID-19 pandemic. Using a randomized controlled trial, we estimate sizeable effects of the 6-week training on entrepreneurial activity. One month after the training, participants in schools offered the training were much more likely to own a business than participants in control schools. The training induced students to participate more actively in their school's business club, to undertake more business-oriented actions, to improve their business practices, and to interact more with other youth and family members about their business ideas. We hypothesize that the training might have motivated treated students to sustain their business activities during the COVID-19 crisis.
Markov Decision Processes with Discounted Reward: Suboptimal Actions

A large number of practical problems from different fields can be seen as Markov decision problems (MDPs) that they give a natural framework for modeling sequential decision making under uncertainty. These problems can, in principle, be solved via tools of dynamic programming. For this goal, it is necessary to develop efficient computational procedures for finding an optimal policy and the corresponding supreme value of the objective function. Next the Bellman optimality equation is the basic entity in MDP theory. When the discounted reward criterion is considered to solve such problems, we are interested to determine lower and upper bounds of the optimal return in order to reduce the computational complexity if it is possible. Thus, we propose a novel test to identify suboptimal actions mainly in the infinite planning horizon case and at discrete times points. Next, we compare our previous test with those provided by MacQueen and Porteus to highlight the degree of its novelty and to show its strong point. Our action elimination procedure can be applied in a wide range of subject areas including financial management, economics, bioinformatics, social networking, queuing, inventory control, and artificial intelligence.
Design of a Virtual Reality based Pedagogical Framework

In this study, we designed and implemented a virtual reality-based pedagogical framework. The framework can be altered to be used for any laboratory-based class such as biology, chemistry, and physics. The framework has four main modules: Interface, Companion, Virtual Reality Scene, and Online Dashboard. The "Interface" module allows for the human computer interaction. The "Companion" module is based on voice recognition and replaces a laboratory assistant. The Online Dashboard acts as a user interface for teachers to create virtual laboratory scenes and upload them to the Virtual Reality Scene. We carried out a usability study and asked five faculty members with different backgrounds to carry out five tasks. The results showed that all the subjects had a positive experience with the virtual reality based pedagogical framework. The subjects mentioned that they expect virtual reality to be part of education in the near feature, especially for laboratory classes and that the pandemic has proved that VR is the future.
Mineral potential re-evaluation of the Seis Lagos Carbonatite Complex. Amazon. Brazil

The Seis Lagos Carbonatite Complex hosts a lateritic deposit developed over a siderite carbonatite. In the 1980’s. it was estimated to contain 2.89 Bt @ 2.81 wt% Nb2O5. Recent studies have produced new data allowing re-evaluation of the area’s mineral potential. In this work. a geological model has been constructed and new estimates of grades and tonnages are presented. The volume of cavities that occur in the lateritic crust is still unknown. and it was then segmented in possible scenarios ranging from 0 to 63%. impacting estimates of tonnage. The estimations were divided into two domains that vary according to the available data. In Domain 1. it has been estimated between 10.5-28.3 Mt @ 2.97 wt% Nb2O5 and 0.71 wt% REE2O3. and in Domain 2. between 1.40-3.78 Bt @ 1.17 wt% Nb2O5 and 0.46 wt% REE2O3. High grades of Ti. Sn and W were also detected for the lateritic crust. It is estimated that sediments in the Esperança Basin contain 11.8 Mt @ 1.76 wt% REE2O3 and 2.36 wt% P2O5. whereas the siderite carbonatite shows high grades of Nb. REE. Sc and Zn. further increasing the area’s mineral potential. Soil grids on neighboring hills do not show similarly high Nb grades. but do present high grades of REE and P.