Reviewerly/exHarmony huggingface dataset:
Gradient Privacy-Preserving In Federated Learning via Proxy Re-Encryption

For decentralized computing systems like cloud and edge computing, federated learning (FL) is an effective and safe machine learning technique. Its learning method involves frequent communication as participating local devices submit updates to a central server, which aggregates them and reassigns new weights to the devices. The updates may include gradients or model parameters. Private data is not sent outside of certain local devices in FL, offering a strong privacy protection solution. Since attackers can deduce the user's privacy from the local output, such as gradients, it still has some privacy problems. In order to effectively address this issue, we propose in this paper a proxy re-encryption scheme with a mask that, by incorporating a third-party proxy, effectively protects against server complicity attacks. Experiments demonstrate improved allowing the FL system to increase the acceptable communication cost while achieving more security features during model transmission. Moreover, our scheme is secure to honest-but-curious server setting even if the server colludes with multiple users. Overall, our system offers a safer and more precise system for FL.
Diminishing Empirical Risk Minimization for Unsupervised Anomaly Detection

Unsupervised anomaly detection (AD) is a challenging task in realistic applications. Recently, there is an increasing trend to detect anomalies with deep neural networks (DNN). However, most popular deep AD detectors cannot protect the network from learning contaminated information brought by anomalous data, resulting in unsatisfactory detection performance and overfitting issues. In this work, we identify one reason that hinders most existing DNN-based anomaly detection methods from performing is the wide adoption of the Empirical Risk Minimization (ERM). ERM assumes that the performance of an algorithm on an unknown distribution can be approximated by averaging losses on the known training set. This averaging scheme thus ignores the distinctions between normal and anomalous instances. To break through the limitations of ERM, we propose a novel Diminishing Empirical Risk Minimization (DERM) framework. Specifically, DERM adaptively adjusts the impact of individual losses through a well-devised aggregation strategy. Theoretically, our proposed DERM can directly modify the gradient contribution of each individual loss in the optimization process to suppress the influence of outliers, leading to a robust anomaly detector. Empirically, DERM outperformed the state-of-the-art on the unsupervised AD benchmark consisting of 18 datasets.
Broken Relationship Metaphors Portrayed in Taylor Swift’s Exile (2020): A Cognitive Linguistic Perspective

Exile is a song written by Taylor Swift that describes a relationship that is no longer working on two people. This song describes a broken relationship through metaphors. This study aims to examine broken relationship metaphors in Taylor Swift’s Exile through a cognitive linguistic perspective. The method used for this research is the non-participant observation and identity referential method. The result shows that there are several metaphors that indicate a broken relationship in that song, namely [RELATIONSHIP IS A FILM], [PARTNER IS A HOMELAND], [MOVING ON IS EXILE], [OPINION IS A BREAKING BRANCH], [MOVING ON IS A SIDE DOOR], and [UNCERTAINTY IS A THIN LINE]. In conclusion, this song describes a situation of a broken relationship that is expressed in a metaphorical way to set a scene of breaking up, misunderstanding, and showing different opinions from one to another.Keywords: metaphor; exile; Taylor Swift
APTx: Better Activation Function than MISH, SWISH, and ReLU’s Variants used in Deep Learning

Activation Functions introduce non-linearity in the deep neural networks.This nonlinearity helps the neural networks learn faster and efficiently from the dataset.In deep learning, many activation functions are developed and used based on the type of problem statement.ReLU's variants, SWISH, and MISH are goto activation functions.MISH function is considered having similar or even better performance than SWISH, and much better than ReLU.In this paper, we propose an activation function named APTx which behaves similar to MISH, but requires lesser mathematical operations to compute.The lesser computational requirements of APTx does speed up the model training, and thus also reduces the hardware requirement for the deep learning model.
Tools in Problem-Solving Methodology as Strategic Basis for the Development of Modern Enterprises

The appearance of external “irritants” leads to deterioration in the economic and management activities at the enterprise. This condition is caused by changes that regenerate the business environment and lead to a decrease in the efficiency and effectiveness of the work of modern enterprises. External “irritants” create problems that alter the strategic framework for the development of modern enterprises. The highlighted fact reduces the level of competitiveness of the enterprise, replacing it with the implementation of measures for the survival of economic entities. To consider this condition within the framework of a scientific article, it is proposed to focus on the search for a universal toolkit in the methodology for solving problems. The purpose of this study is to highlight the role of tools in the methodology of problem solving as a strategic basis for the development of modern enterprises. The following aspects are the result of scientific research: the algorithm and the role of problems in the development of a modern enterprise are established, tools for collecting and analyzing information about problems are considered, as well as toolboxes, which allow choosing the best solution to problems from a set of methodologies, are formed. The research tools are the following methods: grouping, algorithmization, comparison, theoretical categorization, information gathering, analysis and search for root causes, standardization, observation, hypothesis building, distribution, and expert assessments.
Cascading Neural Network with Particle Swarm Optimization

This paper presents a system that combines the neural network learning and meta-heuristic techniques sequentially one after another to have better results than individual ones. The main novelty of the approach is to get benefit from heterogeneous learning environment. Initial good single individual is prepared from NN learning and this solution is injected to PSO as a good global position since at starting PSO does not have good knowledge of global space. It is named as NN-PSO. The technique is applied to expressional chaotic time series data and power consumption data. NN-PSO exhibits remarkable results in comparison with other available methods in terms of mean squared error and convergence speed.
Label-based Multiple Object Ensemble Tracking with Randomized Frame Dropping

This paper proposes a novel approach for ensemble tracking of multiple objects. Existing multiple object tracking methods often fail by appearance changes and occlusions of the targets, which lead to misdetection, mismatching, and drift of detections. The proposed method runs multiple "weak" trackers and aggregates the weak tracking results for each frame. Then, the proposed label-based ensemble is performed to track objects by considering a set of "weak" tracking results (instance IDs) for each target in a frame as a feature vector. This paper also proposes the randomized frame dropping that randomly drops input frames for each weak tracker to differentiate the inputs to the trackers. The proposed method can use any tracking algorithm as a weak tracker and pull up its performance. The performance of the proposed method has been confirmed by applying it to the current state-of-the-art method on MOT20, a public benchmark dataset.
Introduction of New Features in Writer Verification Based on Finger-writing of a Simple Symbol

In this study, a method of verifying an individual from their style of drawing simple symbols that everyone is familiar with and never forgets was studied. Individuals were asked to draw symbols using their fingertips on a digital device screen. Various features, such as the finger pressure, the touch area, and the touch direction, which were directly detected by a tablet device, were measured. In addition, the finger volume, force, and amount of work that were derived from the directly detected features were calculated. Subsequently, the verification performance of these features was evaluated.
OnShipVR: Reducing Sea-sickness through Virtual Contents with Synchronized and Amplified Actual Ship Rotation

People experience and suffer from motion sickness the most, when riding on ships, among many transportational means. Sea-sickness is often explained by the sensory mismatch theory: the actual motion (particularly roll) of the ship (on which the user is riding) is not coherent with what is seen - the seemingly not-so-turbulent ocean. In addition, unlike riding on ground transportation, people are usually not familiar with the ship motion and rotation. In this poster, we propose to use VR contents, OnShipVr, in which virtual motion in it is synchronized (by a gyro sensor) with that of the ship/user and even exaggerated and vividly visualized, as a way to lower the extent of the sea-sickness, and in addition, as a by-product provide the basis for reducing the VR sickness at the same time so that people can enjoy VR contents on-board. We have field-tested our approach under several different visualization conditions (e.g. point of view and extent of the motion gain) during an actual ship ride and report the informal results.
Application of Software Engineering in Student Time Management using Prototype Model

Time management is one of the most important things in humankinds' life, especially in student activity. In the new pandemic era, students have already changed and adjust their learning method to an online learning system which does not need them to have activities outside their house. This condition might make them have a lot of time but they lack the ability to manage their own study schedule. To handle this situation, we construct and develop a scheduler application. This application uses Prototyping Model as the development method as it is a user-oriented application. We are using a prototyping model to consider the upcoming details required in the development. It involves communication between user and developer continuously to achieve the goals and things needed by the user. The implementation of software engineering in student time management with a prototype model will upgrade students' skill to the next level of the digital era.
Shop Cheap - A Less Costly and More Convenient Shopping Method with Machine Learning Techniques

The world is now seeing a cosmic scale of online buying and mobile shopping, both of which have offered enormous opportunity for buyers and sellers to do business through a variety of channels. Because there are so many different shopping platforms available, it may be time-consuming to search through all of them for a certain item, get the cheapest price, look for discount coupons, and so on. This issue is exacerbated by the proliferation of shopping platforms. The Model "Shop Cheap" is an application that will reduce the efforts and time spent by a user on various platforms by providing them with the best deals, offers, and coupons related to any product from all of the platforms collectively. This will allow the user to spend less time and effort on the various platforms they use. The users of the recommendation system will have products suggested to them based on their shopping habits, budget marks, and discount rates from which they regularly buy, and the users will also be suggested new deals and offer and will be notified of the latest discount rates on their favourite items based on their shopping history. The recommendation system will be able to do this because their shopping history will be analysed.
Analysis of Dynamic Resource Allocation in Digital Education Ecosystems

The use of technology enhanced learning in education institutions has been developing at a rapid pace. Higher education administration affirms the value of digitization in learning platforms, especially in view of the response to the COVID-19 pandemic. The conventional education system is not consistent with the changing demands of modern education concepts, which are the driving factors towards formation and development of the digital educational ecosystem. To digitally transform education systems within a cross organizational environment, educational institutions should be interconnected, and the learners are able to access the learning modules from anywhere in the world. The effective allocation of learning modules to the students is of crucial importance due to the scarceness of such resources. The main objective of this paper is to identify the methods for assigning digital learning modules from a set of existing solutions. Facilitated by optimal resource allocation, the digital ecosystem can provision learning instances via virtual machines or containers and allocate it based on demand. Through the optimal resource allocation, the overall cost and power consumption shall be decreased and the availability of the learning service shall be increased. In this paper, literature research of different scheduling and allocation policies are discussed under varying statistical processes, priority, and performance metrics to increase efficiency and reduce operating cost of servers with no allocated task.
RPCA-Induced Graph Tensor Learning for Incomplete Multi-view Inferring and Clustering

The existing incomplete multi-view graph clustering (IMGC) methods mainly focus on leveraging available samples among different views to explore the weak local structure information, resulting in inexact or unreliable affinity graphs for clustering. More importantly, they fail to exploit the spatial structure information among graphs. To graciously address both, in this paper, we propose a novel IMGC method, claimed as robust principle component analysis (RPCA)-induced graph tensor learning (RPCA-IGTL) for incomplete multi-view inferring and clustering. This model can synchronously perform missing part filling, complete data inferring, and diagonalized graph tensor learning to obtain the more exact and reliable affinity graphs for clustering. Especially, the proposed method first designs a RPCA-induced local manifold learning framework, which bridges complete data inferring in feature space and the diagonalized graph learning in graph semantic space. Both objects can boost each other to fully exploit the underlying local structure information among incomplete view data. Besides, a t-SVD based tensor low-rank constraint introduces to exploit the spatial structure of graph tensor and complementary information of diagonalized affinity graphs. Extensive experiments have demonstrated the effectiveness of our method compared to the previous state-of-the-art methods.
The usability of eye tracking in the design of digital training materials

The use of appropriate teaching materials is one of the basic elements of effective learning. The wide range of available materials makes it necessary for both the learner and the teacher to search for an appropriate training method in order to achieve high learning effectiveness. It is assumed that properly designed and prepared course material will be effective in terms of its reception as well as the learning process. Testing the effectiveness of the training material perception is an important argument that helps to determine their usefulness. The problem addressed in this article is how to build digital training materials to make them as useful and effective as possible for learning. For the purposes of the experiment, training materials for future drivers were developed in line with existing solutions. Visual perception was assessed using eye tracking analyses supplemented by a diagnostic survey. The results showed that digital materials containing elements such as voice commentary had a positive effect on visual perception. It was also found that an excess of text leads to a low level of effectiveness of its reception and the occurrence of the phenomenon of cognitive load.
Facing Up Fare War: Generating Competitive Price Models With Gene Expression Programming

In the airline industry, the Revenue and Pricing teams generally spend a considerable amount of time analysing and interpreting the actions of their competitors.Most of the time the analysts have to use their analytical skills to create ad-hoc methods to interpret or find patterns in the fares.In this field, it is key to automate the process, avoid human errors, and add new features that provide accurate fares.Considering this, a gene expression programming algorithm is proposed to carry out this process, returning an interpretable rule set which acts as a recommender system to ease the daunting process done by the pricing teams manually.The proposal was applied to a real scenario with the information provided by the Air Canada airline for five months in Canadian markets.The experimental analysis revealed, by means of non-parametric statistical tests, that the proposed gene expression programming algorithm was key to getting the appropriate features and, hence, accurate and highly interpretable results.The proposal obtained extremely accurate results (around 96% in both accuracy and F1 measure) with a reduction of around 50% in the rule set in many cases.
Management of scientific and educational systems of intellectual integration "digital region-digital university"

The viability and efficiency of regional scientific and educational systems largely depend on the capabilities of the digital environment in the use of technological management tools. To what extent are modern managers of education and science able to use the best experience of world digital technologies in everyday life? How are educational systems using the experience of the European project “learning region-learning community”? The authors of the article propose a discussion about modern trends and technologies of digital transformation in scientific and educational systems of the intellectual integration of specialists and scientific/educational organizations. The systemic interaction of regions and universities forms new practices of intellectual integration and is capable of shaping qualitative transformations in the digital environment. The value of the “digital university-digital region” system reinforces the importance of social practices in digital transformation and the quality of education. New digital projects, practices and data mining began to define new socio-technological and digital communications between regions and region-forming universities. The speed of the "flight of time" into the digital space of education and communication in the Covid-pandemic influenced both the motivation for improving the skills and tools for using digital technologies. It also had impact on the awareness of the contradictions associated with trends and technologies of digital transformation in science, industry, education, and culture. Will these speeds determine the ability of organizations to compete in attracting skilled personnel? Are qualified personnel capable of full-fledged, long-term intellectual integration outside the formal economic market?
Flipping of Computer Pages Using Eye Blinks

As computer vision technology has advanced quickly in recent years, 3D eyeball tracking and its movement have drawn the greatest attention from researchers because of its demand in screen handling and running the applications by eye blinks or moving the eye lids. The Physically Challenged People Suffering with NO limbs or Diplegia Paralysis of Symmetrical Body Parts like Hands and for one step towards a more comfortable life of Human civilization. In this research paper flipping and scrolling of pages on computer screen using eye blinks has been proposed and implemented successfully. The extraction of the face structure points that refers to the eyes using the Haar-Cascade faces dataset points. These extracted parts are matched up with the Eye-aspect-ratio for measuring the threshold values affirmation of the voluntary eye blinks. The “Divyang” disadvantageous people with no hands can use this tool for scrolling and flipping the pages of the screen. The OpenCV tools have used with Python for implementation.
Convolutional Neural Network Accelerator for Compression Based on Simon k-means

Convolutional Neural Networks (CNN) are popular models widely used in image classification, target recognition, and other fields. FPGA-based accelerators for CNN are a standard method in recent years to reduce CNN's inference time and energy efficiency. However, the limitations of on-chip storage space and computing resources introduce deep compression. Contrary to most compression algorithms that pay no attention to the underlying hardware acceleration strategy and hardware-only accelerators, this paper introduces a novel model compression scheme with software and hardware collaboration for accelerating inference. First, we propose a pre-processing algorithm named Simon k-means based on clustering to quantify trained weight to speed up inference. Next, we propose a new encoding method for the quantized weight, significantly reducing the model's storage size. Finally, we give the architecture design of the accelerator using the quantized weight to accelerate the convolution. We have evaluated many popular CNNs in image classification tasks on various data sets. Experiments show that the number of multiply-accumulate operations on the convolutional layer can be reduced 66.6% with a slight loss of precision.
Generative Adversarial Network-assisted Graph Convolutional Network (GANA-GCN)

Generative adversarial networks (GANs) have attracted considerable attention due to the generator having fewer restrictions compared to existing techniques like Boltzmann machines. However, in the field of graph convolutional networks (GCNs), most efforts to improve the embedding quality have focused on making node representations contain more structural information, and a method to optimize the node embedding using the adversary concept has thus far been lacking. In the present work, we discover that the adjacency matrix generated from node labels is helpful to learn better node embeddings and improve semi-supervised node classification accuracy. We thus propose GANA-GCN, which consists of a generator <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$G$</tex> that learns node embeddings and labels to make a classification and a discriminator <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$D$</tex> that encourages node embeddings include label information when the discriminability is weakened. Experimental results demonstrate that GANA-GCN is effective to improve the classification accuracy on a non-Euclidean dataset. Sensitivity analysis further illustrates the effect of key parameters on the overall performance.