Reviewerly/exHarmony huggingface dataset:
Information and network space as a form of social organization

The focus of the paper is on illuminating the changes in civilization brought about by the computerization of all aspects of modern life and the identification of the underlying principles and particulars of the “network society’s” social structure. In order to achieve this, the key theories of the information society have been clarified, and the socio-cultural changes of the present have been revealed on the basis of these theories. It has been demonstrated that informatization and the subsequent development of information and communication technologies, as well as the Internet network that they have brought about, have changed reality more than the basic tenets of sociality. The principles of information and network space – whose properties dictate the characteristics of social interaction – have emerged as a result of technological advancements that have changed how we think about space. Human communities are still created on a mental (cultural and psychological) basis in the information age, just as they were in earlier eras, but they no longer have a distinct territorial boundary. As a result, the person experiences a noticeably reduced amount of pressure from external instructions and narratives in the information and network space. As a result, the person can communicate with others freely and without any external prerequisites or justifications, and as a result, the process of its self-determination may have a wide range of unique characteristics that call for a separate, in-depth study.
A New Construction Method for Keystream Generators

We introduce a new construction method of diffusion layers for Substitution Permutation Network (SPN) structures along with its security proofs. The new method can be used in block ciphers, stream ciphers, hash functions, and sponge constructions. Moreover, we define a new stream cipher mode of operation through a fixed pseudorandom permutation and provide its security proofs in the indistinguishability model. We refer to a stream cipher as a Small Internal State Stream (SISS) cipher if its internal state size is less than twice its key size. There are not many studies about how to design and analyze SISS ciphers due to the criterion on the internal state sizes, resulting from the classical tradeoff attacks. We utilize our new mode and diffusion layer construction to design an SISS cipher having two versions, which we call DIZY. We further provide security analyses and hardware implementations of DIZY. In terms of area cost, power, and energy consumption, the hardware performance is among the best when compared to some prominent stream ciphers, especially for frame-based encryptions that need frequent initialization. Unlike recent SISS ciphers such as Sprout, Plantlet, LILLE, and Fruit; DIZY does not have a keyed update function, enabling efficient key changing.
iPrivJoin: An ID-Private Data Join Framework for Privacy-Preserving Machine Learning

The world has observed an increasing trend in the development of Privacy-Preserving Machine Learning (PPML) for cross-silo collaborative model training over sensitive data. As the first essential step of cross-silo PPML, it is critical that the parties can align their dataset with privacy assurance, i.e., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">private data join</i> . However, the existing private data join methods typically leak the ID information in the dataset intersection, which often raises privacy concerns. In this work, we propose iPrivJoin: a novel framework of ID-private data join for PPML. Compared with naively using circuit-based Private Set Intersection (circuit-PSI) for data join, the proposed framework has two advantages. (i) data volume reduction. iPrivJoin utilizes oblivious shuffle to securely trim off the redundant data that is outside the intersection, while the entire dataset needs to be carried to further process in the circuit-PSI based approach. (ii) efficiency improvement. iPrivJoin introduces a new private encoding technique to avoid the expensive circuit evaluation that is needed in circuit-PSI. As a result, compared with directly using circuit-PSI, PPML with iPrivJoin enjoys approximately 3× of speedup. Moreover, we propose a new oblivious shuffle protocol, which may be of independent interest. It achieves 1.44× of speedup to the state-of-the-art in the real-world WAN network setting.
Differential Privacy for Class-Based Data: A Practical Gaussian Mechanism

In this paper, we present a notion of differential privacy (DP) for data that comes from different classes. Here, the class-membership is private information that needs to be protected. The proposed method is an output perturbation mechanism that adds noise to the release of query response such that the analyst is unable to infer the underlying class-label. The proposed DP method is capable of not only protecting the privacy of class-based data but also meets quality metrics of accuracy and is computationally efficient and practical. We illustrate the efficacy of the proposed method empirically while outperforming the baseline additive Gaussian noise mechanism.We also examine a real-world application and apply the proposed DP method to the autoregression and moving average (ARMA) forecasting method, protecting the privacy of the underlying data source. Case studies on the real-world advanced metering infrastructure (AMI) measurements of household power consumption validate the excellent performance of the proposed DP method while also satisfying the accuracy of forecasted power consumption measurements.
DeepParking: Deep Learning-Based Planning Method for Autonomous Parking

Planning methods for parking are an important topic in the realm of autonomous driving. To achieve successful parking, complicated maneuvers are required to be reasonably performed in a very limited space. Moreover, since unstructured parking scenarios are lack of significant common features, creating useful heuristics manually to adapt to changing conditions is a non-trivial task. Therefore, we propose a two-stage scheme, Deep Neural Networks based path prediction for the first stage and sampling-based optimization for the second stage. Specifically, a customized network is used for predicting a feasible path with a high successful rate and the prediction error is modeled by Gaussian in the second stage. With the modeled error, a variant Bi-RRT $$^*$$ method is specially-designed to correct the possible prediction error and further improve the path quality. We carry out experiments to validate the performance of the proposed scheme and compare it to existing methods. Experimental results demonstrated that our planning scheme can infer a path by neural networks within 25 ms and plan a final path around 75 ms.
DLSC: Distributed Multi-Agent Trajectory Planning in Maze-Like Dynamic Environments Using Linear Safe Corridor

This article presents an online distributed trajectory planning algorithm for a quadrotor swarm in a maze-like dynamic environment. We utilize a dynamic linear safe corridor to construct the feasible collision constraints that can ensure interagent collision avoidance and consider the uncertainty of moving obstacles. We introduce mode-based subgoal planning to resolve deadlock faster in a complex environment using only previously shared information. For dynamic obstacle avoidance, we adopt heuristic methods such as collision alert propagation and escape point planning to deal with the situation where dynamic obstacles approach the agents clustered in a narrow corridor. We prove that the proposed algorithm guarantees the feasibility of the optimization problem for every replanning step. In an obstacle-free space, the proposed method can compute the trajectories for 60 agents on average 7.66 ms per agent with an Intel i7 laptop and shows the perfect success rate. Also, our method shows 64.5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> shorter flight time than buffered Voronoi cell and 34.6 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> shorter than with our previous work. We conduct the simulation in a random forest and maze with four dynamic obstacles, and the proposed algorithm shows the highest success rate and shortest flight time compared to state-of-the-art baseline algorithms. In particular, the proposed algorithm shows over 97 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> success rate when the velocity of moving obstacles is below the agent's maximum speed. We validate the safety and robustness of the proposed algorithm through a hardware demonstration with ten quadrotors and two pedestrians in a maze-like environment.
Asynchronous Remote Usability Tests Using Web-Based Tools Versus Laboratory Usability Tests: An Experimental Study

Remote usability testing is performed by evaluators who are in different physical locations from the participants (synchronous remote testing) and possibly operating at different times (asynchronous remote testing). The tools developed in recent years to support remote tests exploit web technology based on HTML5 and JavaScript ES6 and thus enable previously unexplored scenarios. However, studies providing evidence on the benefits or drawbacks of utilizing recent web-based tools have not yet been reported in the literature. This article sheds some light on the impact of such tools on asynchronous remote usability testing of websites by reporting an experimental study with 100 participants and 15 evaluators to compare real-time laboratory tests with asynchronous remote tests. The study investigates: 1) how the metrics results of asynchronous remote usability tests performed through a web-based tool differ from those of usability tests conducted in real-time laboratory settings; and 2) how the experience of participants differs in the two types of tests. The lessons learned in the study are instrumental in informing the design of future tools. Some results of particular interest indicate that the web technology used by the tool for asynchronous remote testing affects task execution times and participants’ satisfaction. Another indication is that slow internet connections must be managed in asynchronous remote testing; slow connections introduce delays when transferring large amounts of collected data, which, together with the lack of human support, make participants of asynchronous remote tests more prone to feel negative emotions.
S&amp;GDA: An Unsupervised Domain Adaptive Semantic Segmentation Framework Considering Both Imaging Scene and Geometric Domain Shifts

Unsupervised domain adaptation uses labeled data from a source domain to help learn a target domain without any labeled data. Previous studies have not systematically analyzed the causes of remote sensing (RS) domain shifts, making it difficult to effectively model domain shifts caused by differences in geographic scene and platform imaging positions and attitudes. Therefore, this study conducts detailed analysis of the causes of domain shifts in RS images, and an unsupervised domain adaptive semantic segmentation framework, called S&GDA, that considers both imaging scene and geometric domain shifts is proposed. S&GDA comprised two modules: imaging scene simulation and imaging geometric simulation modules. The imaging scene simulation module is instrumental in mitigating domain shifts in geographical scenes due to variations in natural and human factors, thereby achieving cross-domain imaging scene consistency. Meanwhile, the imaging geometric simulation module allows for accurate simulation of domain shifts caused by changes in the position and attitude of a platform, ensuring cross-domain imaging geometry consistency. Note that none of these modules add additional parameters or computational complexity to the model as they only work on the input side of the data. Comprehensive experiments are conducted on the LoveDA and ISPRS datasets to evaluate S&GDA. Results indicate that S&GDA outperforms the state-of-the-art (SOTA) unsupervised domain adaptive semantic segmentation method by 3.12% of mIoU and can achieve 85% of the performance of the fully supervised method.
LOACR: A Cache Replacement Method Based on Loop Assist

Cache is used to reduce performance differences between storage layers. It is widely used in databases, operating systems, network systems, and applications. Loop reference pattern where blocks are referenced repeatedly with regular intervals is a common phenomenon during data referencing. Good management of looping reference blocks can effectively help improve the performance of cache management. In this work, we propose a loop assistant cache replacement (LOACR) policy. We divide the cache into two parts, one part is used to store looping reference data, and the rest of the cache uses an ML-based algorithm to manage. We regularly identify the looping reference pattern and the specific information of the loop at the end of every window. At the same time, we will place the looping reference data that may appear in the next window into the cache in advance to improve the hit rate of the cache. The remaining space in the cache drives the LRU and LFU specialists to cache replacement through a parameter-free machine learning approach. Finally, we evaluated LOACR across a broad range of experiments using multiple sets of cache configurations across multiple data sets.
Continuous Sub-prevalent Co-location Pattern Mining

Spatial co-location pattern represents a subset of spatial features whose instances are frequently located together in space. Sub-prevalent co-location pattern mining discovers patterns with richer spatial relationships based on star instance model instead of clique instance model. Further, discovering spatiotemporal sub-prevalent co-location pattern is important to reveal the spatiotemporal interaction between spatial features and promote the application of patterns. However, the methods for mining spatiotemporal sub-prevalent co-location pattern measure the interestingness of patterns by the frequency of patterns in time slice set, and ignore the duration of patterns which is an important spatiotemproal information in patterns. Thus, this paper presents mining spatiotemporal sub-prevalent co-location pattern by considering the duration and the frequency of patterns. Specifically, a novel pattern, is proposed by defining the continuous sub-prevalent index. Then, an efficient algorithm is designed to mine the proposed patterns by utilizing the anti-monotonicity of continuous sub-prevalent index to prune unpromising patterns. Extensive experiments on synthetic and real datasets verify the practicability of the proposed patterns and the effectiveness of the proposed algorithm.
Automating Malaria Diagnosis with XAI: Using Deep-Learning Technologies for More Accurate, Efficient, and Transparent Results

Malaria is a deadly infectious disease that claims numerous lives worldwide each year, primarily due to delayed or incorrect diagnosis using the manual microscope. This article proposes the automation of the diagnosis process through deep-learning technologies, specifically convolutional neural networks (CNNs), based on the intensity characteristics of Plasmodium parasites and erythrocytes. The approach involves feeding images into CNN models such as ResNet50, CNN, and MobileNet, with the MobileNet model achieving the best overall performance. The first novelty of this paper is that we update the pre-trained models which give us better results. To further enhance the system, the article advocates for the use of explainable artificial intelligence (XAI) techniques, including feature attribution and counterfactual explanations, to improve the accuracy, efficiency, and transparency of the malaria diagnosis system. The proposed system integrates deep learning and XAI, which can provide clear and interpretable explanations for decision-making processes, guide the development of more effective diagnostic tools and save lives. For instance, we use Grad-CAM and Grad-CAM++ which counter the affected areas on the images and that could be a noble contribution to this paper. It is shown via extensive performance study that auto-mating the process can accurately and efficiently detect the malaria parasite in blood samples with a sensitivity of over 95% and less complexity than prior methods reported in the literature.
Pixel Value Prediction Task: Performance Comparison of Multi-Layer Perceptron and Radial Basis Function Neural Network

Pixel value prediction plays an important role in areas such as image compression and reversible data hiding. Many traditional pixel value predictors are available in literature. These predictors predict image pixel value using neighboring pixel values. These predictors establish a mathematical mapping from the neighboring pixel values to the predicted pixel value. Such heuristics may not handle the varied neighborhood that can occur in an image. Finding such kind of mapping is not a trivial task. On the contrary, a neural network can be trained for pixel value prediction. In this approach, a neural network takes neighborhood pixel values to produce the predicted value of the pixel as output. In this paper, the performances of two shallow neural networks - multi-layer perceptron (MLP) and radial basis function (RBF) neural network - are investigated for pixel value prediction. Mean squared error (MSE) measure between original and predicted pixel values is used to compare the performances of pixel value predictors. Experiments reveal that the pixel predictors using these two shallow neural networks perform better than comparing state-of-the-art predictors. Moreover, MLP-based pixel predictor performs better than the RBF neural network based pixel predictor.
A Hyper-Network Based End-to-End Visual Servoing With Arbitrary Desired Poses

Recently, several works achieve end-to-end visual servoing (VS) for robotic manipulation by replacing traditional controller with differentiable neural networks, but lose the ability to servo arbitrary desired poses. This letter proposes a differentiable architecture for arbitrary pose servoing: a hyper-network based neural controller (HPN-NC). To achieve this, HPN-NC consists of a hyper net and a low-level controller, where the hyper net learns to generate the parameters of the low-level controller and the controller uses the 2D keypoints error for control like traditional image-based visual servoing (IBVS). HPN-NC can complete 6 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula> of freedom visual servoing with large initial offset. Taking advantage of the fully differentiable nature of HPN-NC, we provide a three-stage training procedure to servo real world objects. With self-supervised end-to-end training, the performance of the integrated model can be further improved in unseen scenes and the amount of manual annotations can be significantly reduced.
Re-examining Class Selectivity in Deep Convolutional Networks

While Deep Neural Networks have become ubiquitous, some of their properties have remained elusive. Among these is class selectivity, which measures the association of a unit of a model to its inputs and outputs. Current literature is split on the effect of class selectivity of CNNs as some have determined it to be harmful for generalization, while others have found it to be beneficial. The results of such analyses can vary widely with the definition of selectivity used as there is no consensus on it. In this work, we provide a new flexible definition of class selectivity towards rectifying this discrepancy, which can better describe the network’s association with a class at various layers. We compare with the standard class selectivity metric and associated regularizer. We show experimentally that our proposed metric quantifies selectivity in a more consistent manner. We also dispel the notion that selectivity is harmful for generalization by showing that evaluation results do not change by increasing selectivity. We also analyze the association between selectivity, feature disentanglement, and decomposability and show that selectivity and filter disentanglement are complementary. Finally, we also provide the source code for all our experiments.
On Some Properties of a New PoisN Wavelet Family

This paper presents a family of new real valued continuous wavelet functions derived from Poisson kernel. Properties like Admissibility condition, vanishing properties, frequency response and time-frequency bandwidth are studied. A comparison is made between new wavelet family “PoisN” with Gaussian wavelet family. Finally, the de-noising capability of the proposed new wavelet is demonstrated with a noisy version of Gaussian function with different values of SNR.
Incremental Classifier in the Semi Supervised Learning Environment

Due to rapid growth in information technologies, millions of data are being generated at every time span. Data storage and processing are more expensive in terms of memory, resources, and time. The traditional machine learning classifier performs well when all the data is available along with the label information at the time of training, which may not be possible in every circumstances. Semi-supervised techniques overcome this limitation. With limited labeled data, semi-supervised learning makes use of both classification and clustering techniques for constructing efficient classifiers, and there is a demand to update the classifier periodically. Unlike traditional approaches, which use the entire data set for updating the classifier, our method updates in an incremental fashion. This paper proposes a framework for incremental classifiers in a semi-supervised environment. Experiments are conducted on benchmark datasets with the proposed approach and compared to the conventional method.
How Good are Transformers in Reordering?

Translation requires transfer of lexical items (words/phrases) from Source Language to Target Language and also reordering of the transferred lexical items as appropriate for the target language. Whatever be the approach used, quality of translation depends on both the quality of lexical transfer and quality of reordering. In this paper, we explore how good the state-of-the-art sequence-to-sequence Transformer model is in reordering. Reordering models are tested for sequence to sequence mapping from an Intermediate Language (which uses the words of the target language arranged in the source language order) to target language. We build models using the samanantar English-Kannada parallel corpus. BLEU, TER and RIBES scores show significant improvement after reordering. We have also tested our models on the Machine Translation task as a whole. Compared to the default lexicalized reordering models used in Statistical Machine Translation, our transformer based reordering models have shown better performance.
Python for Deep Learning: A General Introduction

PythonPython is a high-level programming languageProgramming Language that has become increasingly popular over the years, thanks to its simplicity, ease of use, and the wide range of libraries and tools available for developers. In recent years, PythonPython has also emerged as a dominant programming languageProgramming Language for Deep LearningDeep Learning, a subfield of artificial intelligence that involves building and training neural networks to learn from data.