Miracle-dz/UniFAR-FTUs huggingface dataset:
A Biometric-Based Encryption Method for Secure Data Sharing in Cloud Environment

When seen from the point of view of information collecting, distributed storage can very easily be converted into a decision-making approach. In a short amount of time, data storage evolves into a technique of preference. Since data is protected remotely rather than locally, consumers from both the home and the technical sphere are inclined to do so. If the dispersed storage is not completely confident, it means that users have "the possibility to incorporate knowledge about the online cloud. " Regardless of whether customers find cloud-based information to be a major cause for concern, the fact remains that it can be a difficult problem, especially because we trade data on cloud servers. To address this issue in a competent and effective manner, the Revocable IBE conspires, and the primary refreshing technique are both already available. In addition, cloud architecture experience needs to be improved to enable the utilization of contemporary safe information structures in distributed computing. In this sense, a figure (or any encoded document) can contain a temporary amount of time. If the characteristics of the figurative content fit the configuration of the key, and if all possible occurrences are permitted to take place, then the figurative content must be decrypted. After a client has specified an end time, the information is permanently removed from the cloud server in a secure manner. The framework proposed is equipped with features such as unfailing viability for both PKG measures and client shared secret key size because of the utilization of the key updating service that is made available by the cloud server, further the time comparison establishes that proposed technique with GSP is more efficient as compared to without GSP. Results show that the proposed system with GSP reduces processing time by approximately 0.001 times.
A Constant-Factor Approximation Algorithm for Reconciliation k-Median

In the reconciliation k -median problem we ask to cluster a set of data points by picking k cluster centers so as to minimize the sum of distances of the data points to their cluster centers plus the sum of pairwise distances between the centers. The problem, which is a variant of classic k -median, aims to ﬁnd a set of cluster centers that are not too far from each other, and it has applications, for example, when selecting a committee to de-liberate on a controversial topic. This problem was introduced recently (Ordozgoiti and Gionis, 2019), and it was shown that a local-search-based algorithm is always within a factor O ( k ) of an optimum solution and performs well in practice. In this paper, we demonstrate a close connection of reconciliation k -median to a variant of the k - facility location problem , in which each potential cluster center has an individual opening cost and we aim at minimizing the sum of client-center distances and the opening costs. This connection enables us to provide a new algorithm for reconciliation k -median that yields a constant-factor approximation (independent of k ). We also provide a sparsiﬁcation scheme that reduces the number of potential cluster centers to O ( k ) in order to substantially speed up approximation algorithms. We empirically compare our new algorithms with the previous local-search approach, showing improved performance and stability. In addition, we show how our sparsiﬁcation approach helps to reduce computation time without signiﬁcantly
Dynamic Forward and Backward Sparse Training (DFBST): Accelerated Deep Learning through Completely Sparse Training Schedule

Neural network sparsification has received a lot of attention in recent years. A number of dynamic sparse training methods have been developed that achieve significant sparsity levels during training, ensuring comparable performance to their dense counterparts. However, most of these methods update all the model parameters using dense gradients. To this end, gradient sparsification is achieved either by non-dynamic (fixed) schedule or computationally expensive dynamic pruning schedule. To alleviate these drawbacks, we propose Dynamic Forward and Backward Sparse Training (DFBST), an algorithm which dynamically sparsifies both the forward and backward passes using trainable masks, leading to a completely sparse training schedule. In contrast to existing sparse training methods, we propose separate learning for forward as well as backward masks. Our approach achieves state of the art performance in terms of both accuracy and sparsity compared to existing dynamic pruning algorithms on benchmark datasets, namely MNIST, CIFAR-10 and CIFAR-100.
Bridging the Great Divide: Investigating the Potent Synergy between Leadership, Zhong-Yong Philosophy, and Green Innovation in China

Zhong-yong thinking, a typical value orientation and mode of thought in traditional Chinese culture, has garnered significant scholarly attention. Various cross-sectional studies have explored the relationship between Zhong-yong thinking and innovation; yet, research specifically examining the impact of Zhong-yong thinking on green innovation is scarce. This study adopts the upper echelons theory and theory of manager cognition to investigate the influence of entrepreneurs’ Zhong-yong thinking on green innovation while simultaneously considering institutional pressure and enterprise ownership types as boundary conditions. By analyzing 302 questionnaire responses, the empirical results demonstrate a direct positive effect of Zhong-yong thinking on green innovation. Moreover, institutional pressure positively moderates the relationship between Zhong-yong thinking and green innovation. State-owned enterprises exhibit a more significant impact of Zhong-yong thinking on green innovation than non-SOEs. Overall, this study contributes to the theoretical research of Zhong-yong in management disciplines, particularly in the green innovation literature. Its findings also hold implications for the practice of green innovation in enterprises.
Performance Evaluation of Fire and Smoke Detection with Object based DNN Algorithm

The consequence of forest fire is so great that we need to prevent it from happening with all of our resources. However, the time and human resources required to cover all the forest is very high. In this paper, we use object based Deep Learning detection algorithm, YOLOV5, to address the issue of detecting forest fire and smoke. We collected 13,924 images of forest fire and smoke and manually labeled them. We used YOLOV5n to learn the features. We solved overfitting problem via mosaic data augmentation, non-transfer learning, and hyperparameter evolution. YOLOV5n shows that mAP 0.5 is about 14.1% higher than the official YOLOV5n model.
of Probabilistic Fusion of Neural Networks that Incorporates Global

Permutation invariance of deep FCs We naturally extend Eq. (1) in main text to iteratively define deep FC network: fs(x ) =σ(x(n−1)A(n−1) s W (n) s A s ), (1) where n = 1,2, . , N is the layer index, W (0) s is a identity matrix indicating non-ambiguity in the ordering of input features x = x0 and W (N ) s is a identity matrix for the same purpose in output classes. Conventionally σ(·) is any non-linearity except for xN where it is the identity function (or softmax if we want probabilities instead of logits). To perform matched aggregating of deep FCs acquired from S clients we need to align every layer of every client. Here we consider an iteratively (in layers) matched aggregating formulation which will be detailedly described in later subsections. Permutation invariance of CNNs Before our work, there is seldom study that implement model fusion algorithms on CNNs. Therefore, we introduce permutation invariance of CNNs before applying model fusion methods on CNNs. To understanding permutation invariance of CNNs, the key observation is that instead of neurons, channels define the invariance. To be more specific, define Conv(x,Ws) as the convolutional operation on input data x with weights Ws ∈ RC i n×w×h×C out , where C i n , C out respectively are the numbers of input/output channels and w , h are the width and height of the filters. Applying any permutation to the output channel of the weights and then same permutation to the input channel of the subsequent layer will make the result of corresponding CNN’s forward pass consistent:
The role of game modality in the outcomes of gamification: A research agenda

The interest in gamification is growing every year, as demonstrated by the continuous increase in scientific and commercial outputs. Along with the number of publications, researchers have produced a significant amount of gamification frameworks, with the intent of guiding designers in the creation of gameful systems. Despite the differences between frameworks, and the fact that a holistic framework is still missing, there is one feature common to all the schemes: the consideration for the game modality, intended as different types of social interaction between users (individual, cooperative, competitive, and cooperative-competitive), is none or limited. The current paper represents an attempt to underline the importance of game modality in gamification, presenting evidence about the impact that the modality has on social, behavioral, and psychological outcomes in traditional activities, video games, and gameful systems, along with a research agenda aimed at deepening the knowledge of this connection.
Factual or Contextual? Disentangling Error Types in Entity Description Generation

In the task of entity description generation, given a context and a specified entity, a model must describe that entity correctly and in a contextually-relevant way. In this task, as well as broader language generation tasks, the generation of a nonfactual description (factual error) versus an incongruous description (contextual error) is fundamentally different, yet often conflated. We develop an evaluation paradigm that enables us to disentangle these two types of errors in naturally occurring textual contexts. We find that factuality and congruity are often at odds, and that models specifically struggle with accurate descriptions of entities that are less familiar to people. This shortcoming of language models raises concerns around the trustworthiness of such models, since factual errors on less well-known entities are exactly those that a human reader will not recognize.
Bayesian Change-Point Detection for Bandit Feedback in Non-stationary Environments

The stochastic multi-armed bandit problem has been widely studied under the stationary assumption. However in real world problems and industrial applications, this assumption is often unrealistic because the distributions of rewards may change over time. In this paper, we consider the piece-wise iid non-stationary stochastic multi-armed bandit problem with unknown change-points and we focus on the change of mean setup. To solve the latter, we propose a change-point based framework where we study a class of change-detection based optimal bandit policies that actively detects change-point using the restarted Bayesian online change-point detector and then restarts the bandit indices. Analytically, in the context of regret minimization, our proposal achieves a O ( √ ATK T ) regret upper-bound where K T is the overall number of change-points up to the horizon T and A is the number of arms. The derived bound matches the existing lower bound for abruptly changing environments. Finally, we demonstrate the cumulative regret reduction of the our proposal over synthetic Bernoulli rewards as well as Yahoo! datasets of webpage click-through rates.
Quality Agreement on Learnersourced Multiple Choice Questions

Learnersourcing presents an efficient and economical pathway to producing more learning content, whilst engaging students more actively and deeply in their learning. However, it also presents new challenges to solve. Most of all, how best can we manage the variance in quality of content produced. In this work, we focus on the extent to which students and teachers agree on quality of learnersourced multiple choice questions. Students (n=30) were tasked with producing six questions over three weeks of an introductory programming course as part of their assessment. They also had to review 12 questions authored by their peers over the same period using a set of principles for good questions. After this period, four teaching staff involved with the course reviewed the student questions using the same process and principles. Inter-rater reliability statistics found overall positive agreement across principles, however this dropped to weaker agreement for principles aimed at more subjective and higher order concerns of question quality and quality of question feedback.
Demystifying the Scribes behind the Voynich Manuscript using Computational Linguistic Techniques

Earlier work studies the palaeography of the Voynich manuscript and proposes five scribes. In this work, we use computational linguistic models that are based on the character sequences found on the pages, inspired by work on automatic authorship attribution, as opposed to the earlier work focusing on the nature of the writing script. Our rationale is that if these two independent methods should lead to similar results, we have strong evidence for the earlier identification of the scribes in the Voynich manuscript. We carry out a machine learning experiment where the manuscript is divided into an equal number of pages for three of the five scribes. Four machine learning classifiers create models that have been trained on the classification produced in earlier work to predict a scribe for each page using character n-grams as features. The results of each classifier are analysed comparatively with one another and compared with the classification provided based on palaeographic work. The results show when the classifiers produce the same classification as earlier palaeographic work on a held-out dataset.
Applications of Machine Learning Techniques for Fault Diagnosis of UAVs

Due to the heavy usage of Unmanned Aerial Vehicles (UAVs) and the co-evolution of modern technologies, a crucial introduction to fault diagnosis has taken place in recent studies in the avoidance of ravaging consequences. Machine Learning techniques are one of the other major fault-diagnosing approaches in the field of Artificial Intelligence. This review article delivers an elaborated overview of the latest studies concerning UAVs fault diagnosis utilizing Machine Learning and Deep Learning techniques. A summarized comparison of the different methods is distinguishably elaborated where the conclusion highlightsthatresearchonfaultdiagnosissystemsisprogressingandyettoend. Considerationshouldbegiventoagrowing number of research and methodologies.
context and family well-being: the role of psychological capital and the organizational culture

Family-work conflict and enrichment are closely related processes. This study used the Work-Family Resources Model to investigate the direct effects of a demand (perceived work demands) and a contextual job resource (social support at work) on an outcome in the family (flourishing in the family), as well as the mediating role of a personal resource (positive psychological capital) and the moderating role of a macro resource (organizational culture associated with the employees’ values of satisfaction and wellbeing) in these relationships. The sample consisted of 1052 Brazilian workers of both sexes (72.5 % women) from 50 organizations. The results found in Multilevel Structural Equation Modeling provided empirical support for the model by confirming the relationships that were tested. These findings entail important theoretical and practical implications for the family-work relationship, in demonstrating how the different processes imbricated in the work-family interface can occur, and how they can contribute to the quality of life in the family context.
USIM Gate: UpSampling Module for Segmenting Precise Boundaries concerning Entropy

Deep learning (DL) techniques for precise semantic segmentation have remained a challenge because of the vague boundaries of target objects caused by the low resolution of images. Despite the improved segmentation performance using up/downsampling operations in early DL models, conventional operators cannot fully preserve spatial information and thus generate vague boundaries of target objects. Therefore, for the precise segmentation of target objects in many domains, this paper presents two novel operators: (1) upsampling interpolation method (USIM), an operator that upsam-ples input feature maps and combines feature maps into one while preserving the spatial information of both inputs, and (2) USIM gate (UG), an advanced USIM operator with boundary-attention mechanisms. We designed our experiments using aerial images where the boundaries critically influence the results. Furthermore, we verified the feasibility that our approach effectively segments target objects us-ing the Cityscapes dataset. The experimental re-sults demonstrate that using the USIM and UG with state-of-the-art DL models can improve the segmentation performance with clear boundaries of target objects (Intersection over Union: +6.9%; Boundary Jaccard: +10.1%). Furthermore, mathematical proofs verify that the USIM and UG contribute to the handling of spatial information.
Piecewise Stationary Bandits under Risk Criteria

Piecewise stationary stochastic multi-armed ban-dits have been extensively explored in the risk-neutral and sub-Gaussian setting. In this work, we consider a multi-armed bandit framework in which the reward distributions are heavy-tailed and non-stationary, and evaluate the performance of algorithms using general risk criteria. Specifically, we make the following contributions: (i) We first propose a non-parametric change detection algorithm that can detect general distributional changes in heavy-tailed distributions. (ii) We then propose a truncation-based UCB-type bandit al-gorithm integrating the above regime change detection algorithm to minimize the regret of the non-stationary learning problem. (iii) Finally, we establish the regret bounds for the proposed bandit algorithm by characterizing the statistical properties of the general change detection algorithm, along with a novel regret analysis.
Fuzzy gradual rules model for assessing emotions through physiological signals

Affective computing agenda is to enhance the quality of human computer interaction by making it more enjoyable by automatically recognizing and adapting to the user’s affective states. More especially, it has a particular interest in the field of health such as providing emotional empathy for people living with autism. Therefore, there is need to develop methodologies for assessing user’s emotional experiences. In this context, among a vast range of possible ways to access a user’s emotional responses, physiological measures have a key advantage as they grant an access to non-conscious and non-reportable processes. However, to map physiological patterns from sensors to user emotional states remains a difficult task. To begin with, physiological signals tend to vary from participant to participant and even within the same participant physiological signals vary from time to time. The current methods tend to rely on some forms of normalization using some baseline yet, the correlation between the baseline and the various emotions also vary from person to person and at different occasions for the same person. In this study, we propose a model based on gradual rules to characterize affective states of the form: the more or less of A, the more or less of B . Specifically, we consider the physiological signals variation with time during a particular affective state, such as: Heart Rate increases with time during Joy more than 60% of the time or Heart Rate increases with time during Disgust less than 40% of the time . Secondly, emotions are conceptual quantities with indeterminate fuzzy boundaries. Besides, the physiological data from sensors is itself imperfect, such that it is difficult to express the results in crisp terms. Therefore, it is more natural to formulate a fuzzy set theory based model to represent these continuous transitions, uncertainties and imperfections. In this study, we consider a fuzzy approach to map physiological patterns to affective states. After we extract the support for each gradual item set, we define fuzzy rules to characterize the various emotions.
Mode-constrained Model-based Reinforcement Learning via Gaussian Processes

Model-based reinforcement learning (RL) algorithms do not typically consider environments with multiple dynamic modes, where it is beneficial to avoid inoperable or undesirable modes. We present a model-based RL algorithm that constrains training to a single dynamic mode with high probability. This is a difficult problem because the mode constraint is a hidden variable associated with the environment’s dynamics. As such, it is 1) unknown a priori and 2) we do not observe its output from the environment, so cannot learn it with supervised learning. We present a nonparametric dynamic model which learns the mode constraint alongside the dynamic modes. Importantly, it learns latent structure that our planning scheme leverages to 1) enforce the mode constraint with high probability, and 2) escape local optima induced by the mode constraint. We validate our method by showing that it can solve a simulated quadcopter navigation task whilst providing a level of constraint satisfaction both during and after training.
Tight Regret and Complexity Bounds for Thompson Sampling via Langevin Monte Carlo

In this paper, we consider high dimensional contextual bandit problems. Within this setting, Thompson Sampling and its variants have been proposed and successfully applied to multiple machine learning problems. Existing theory on Thompson Sampling shows that it has suboptimal dimension dependency in contrast to upper confidence bound (UCB) algorithms. To circumvent this issue and obtain optimal regret bounds, (Zhang 2021) recently proposed to modify Thompson Sampling by enforcing more exploration and hence is able to attain optimal regret bounds. Nonetheless, this analysis does not permit tractable implementation in high dimensions. The main challenge therein is the simulation of the posterior samples at each step given the available observations. To overcome this, we propose and analyze the use of Markov Chain Monte Carlo methods. As a corollary, we show that for contextual linear bandits, using Langevin Monte Carlo (LMC) or Metropolis Adjusted Langevin Algorithm (MALA), our algorithm attains optimal regret bounds of ˜ O ( d √ T ) . Furthermore, we show that this is obtained with ˜ O ( dT 4 ) , ˜ O ( dT 2 ) data evaluations respectively for LMC and MALA. Finally, we validate our findings through numerical simulations and show that we outperform vanilla Thompson sampling in high dimensions.