Miracle-dz/UniFAR-FTUs huggingface dataset:
Identity-Disentangled Adversarial Augmentation for Self-supervised Learning

Data augmentation is critical to contrastive self-supervised learning, whose goal is to distinguish a sample’s augmentations (positives) from other samples (negatives). However, strong augmen-tations may change the sample-identity of the positives, while weak augmentation produces easy positives/negatives leading to nearly-zero loss and ineffective learning. In this paper, we study a simple adversarial augmentation method that can modify training data to be hard positives/negatives without distorting the key information about their original identities. In particular, we decompose a sample x to be its variational auto-encoder (VAE) reconstruction G ( x ) plus the residual R ( x ) = x − G ( x ) , where R ( x ) retains most identity-distinctive information due to an information-theoretic interpretation of the VAE objective. We then adversarially perturb G ( x ) in the VAE’s bottleneck space and adds it back to the original R ( x ) as an augmentation, which is therefore sufﬁciently challenging for contrastive learning and meanwhile preserves the sample identity intact. We apply this “identity-disentangled adversarial augmentation (IDAA)” to different self-supervised learning methods. On multiple benchmark datasets, IDAA consistently improves both their efﬁciency and generalization performance. We further show that IDAA learned on a dataset can be transferred to other datasets. Code is available at https://github.com/kai-wen-yang/IDAA.
Consistent Polyhedral Surrogates for Top-k Classification and Variants

Top-$k$ classification is a generalization of multiclass classification used widely in information retrieval, image classification, and other extreme classification settings. Several hinge-like (piecewise-linear) surrogates have been proposed for the problem, yet all are either non-convex or inconsistent. For the proposed hinge-like surrogates that are convex (i.e., polyhedral), we apply the recent embedding framework of Finocchiaro et al. (2019; 2022) to determine the prediction problem for which the surrogate is consistent. These problems can all be interpreted as variants of top-$k$ classification, which may be better aligned with some applications. We leverage this analysis to derive constraints on the conditional label distributions under which these proposed surrogates become consistent for top-$k$. It has been further suggested that every convex hinge-like surrogate must be inconsistent for top-$k$. Yet, we use the same embedding framework to give the first consistent polyhedral surrogate for this problem.
Improving Adversarial Robustness via Mutual Information Estimation

Deep neural networks (DNNs) are found to be vulnerable to adversarial noise. They are typically misled by adversarial samples to make wrong predictions. To alleviate this negative effect, in this paper, we investigate the dependence between outputs of the target model and input adversarial samples from the perspective of information theory, and propose an adversarial defense method. Specifically, we first measure the dependence by estimating the mutual information (MI) between outputs and the natural patterns of inputs (called natural MI) and MI between outputs and the adversarial patterns of inputs (called adversarial MI), respectively. We find that adversarial samples usually have larger adversarial MI and smaller natural MI compared with those w.r. t. natural samples. Motivated by this observation, we propose to enhance the adversarial robustness by maximizing the natural MI and minimizing the adversarial MI during the training process. In this way, the target model is expected to pay more attention to the natural pattern that contains objective semantics. Empirical evaluations demonstrate that our method could effectively improve the adversarial accuracy against multiple attacks.
Podrška odlučivanju u upravljanju prometnim projektima u urbanim sredinama

Faza planiranja unutar upravljanja urbanim prometnim projektom složen je proces kako sa stajalista menadžmenta tako i s tehnicko-ekonomskih stajalista. Ovo istraživanje usmjereno je na procese donosenja odluka vezanih za fazu planiranja prilikom upravljanja urbanim cestovnim infrastrukturnim projektima. Predloženi koncept se temelji na visekriterijskim metodama te na umjetnim neuronskim mrežama. U ovom radu predstavljeni koncept za podrsku odlucivanju testiran je na cestovnoj infrastrukturi grada Splita i prikazuje nacin na koji se može unaprijediti planiranje urbane cestovne infrastrukture.
DISTANCE CONTAINED SKELETON FOR VOLUME RECONSTRUCTION

Skeleton is an effective and compact shape descriptor for volume data in many applications, such as path planning, image compression, and motion tracking. However, volume singularity maintenance and volume reconstruction are difficult problems in skeletonization. Combining distance transformation and topological thinning, Distance Contained Skeleton (DCS) algorithm is presented to extract a line-shaped skeleton and to compute the distance from the skeleton to each step of the object boundaries. Spurious branches of the skeleton are adaptively removed through topological reorganization and an iterative region-growing process is adopted to reconstruct the object. Experiments show that the reconstructed volume obtained through DCS represent the complex shape of the original volume fairly well.
Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Biomolecular Structures and Interaction Networks

Geometric deep learning has broad applications in biology, a domain where relational structure in data is often intrinsic to modelling the underlying phenomena. Currently, efforts in both geometric deep learning and, more broadly, deep learning applied to biomolecular tasks have been hampered by a scarcity of appropriate datasets accessible to domain specialists and machine learning researchers alike. To address this, we introduce Graphein as a turn-key tool for transforming raw data from widely-used bioinformatics databases into machine learning-ready datasets in a high-throughput and ﬂexible manner. Graphein is a Python library for constructing graph and surface-mesh representations of biomolecular structures, such as proteins, nucleic acids and small molecules, and biological interaction networks for computational analysis and machine learning. Graphein provides utilities for data retrieval from widely-used bioinformatics databases for structural data, including the Protein Data Bank, the AlphaFold Structure Database, chemical data from ZINC and ChEMBL, and for biomolecular interaction networks from STRINGdb, BioGrid, TRRUST and RegNetwork. The library interfaces with popular geometric deep learning libraries: DGL, Jraph, PyTorch Geometric and PyTorch3D though remains framework agnostic as it is built on top of the PyData ecosystem to enable inter-operability with scientiﬁc computing tools and libraries. Graphein is designed to be highly ﬂexible, allowing the user to specify each step of the data preparation, scalable to facilitate working with large protein complexes and interaction graphs, and contains useful pre-processing tools
A Review: Fabrication of Multipurpose Chair with Aids of Joystick

: There are different chairs and ladders available in market for the utility purpose but we are identify that there is need to create such a multipurpose chair that can be used for various purposes, hence supervisor gives a task and a title for this project. To design and fabricate this ladder cum chair with aids of joystick. it must compare with the other product that available in market. It will perform the work of ladder, study table, writing work, safety buzzer as usual joystick to move from person one place to another place. Information and idea to design and fabricate a multi-purpose operation can be created. Multipurpose chair easier for individuals with disabilities and elders persons. This type of multipurpose chair can be used in library and home. control. We also discuss our research on novel hardware and software approaches that are revolutionizing joystick interface technology and that allow more customizability for individual users with special needs and abilities. [1] B. Fehr L, Langbein W, Skaar S. : The extreme difficulty with which persons with severe disabilities have been taught to maneuver a power wheelchair has been described in case studies, and anecdotal evidence suggests the existence of a patient population for whom mobility is severely limited if not impossible given currently available power wheelchair control interfaces. Since our review of the literature provided little evidence either in support or refutation of the adequacy of existing power wheelchair control interfaces, we surveyed 200 practicing clinicians, asking them to provide information about their patients and to give their impressions of the potential usefulness of a new power wheelchair navigation technology. [2] C. Jones DK, Albright S, Cooper RA, PhD : People with disabilities such as multiple sclerosis and Parkinson's disease have difficulty operating conventional movement-sensing joysticks (MSJs) because of varying levels of tremor. We developed an isometric joystick (IJ) that has performed as well as a conventional MSJ when used by persons with upper-limb impairments in real and virtual wheelchair driving tasks. The Weighted-Frequency Fourier Linear Combiner (WFLC) filter has been used to cancel tremor effectively in microsurgery. In this study, we compared an MSJ, IJ, and IJ with the WFLC filter in individuals performing a virtual driving task. Although the WFLC filter did not improve driving performance in this study, the IJ without a filter yielded better results than the conventional MSJ and thus may be a potential alternative to the MSJ in minimizing the effects of tremor. [4] D. Mohan kumar R. and et al. :Found design to multipurpose chair for physically challenged and elder person. The design of wheel chair started by means of literature review to know its evaluation from earlier
AIR SCRIPTING USING OPENCV

: In recent years, one of the most fascinating and challenging research areas in image processing and pattern recognition has been writing in the air. It provides a major contribution to the advancement of an automated process and can improve the human-machine interface in a variety of applications. Several studies have developed innovative methods and practices that would cut processing time while increasing recognition accuracy. Within the discipline of Computer Vision, object tracking is regarded as a critical problem. Deaf and partially disabled people can use Air Scripting to effectively communicate.
Incremental learning for RNN-Transducer based speech recognition models

This paper investigates an incremental learning framework for a real-world voice assistant employing RNN-Transducer based automatic speech recognition (ASR) model. Such a model needs to be regularly updated to keep up with changing distribution of customer requests. We demonstrate that a simple ﬁne-tuning approach with a combination of old and new training data can be used to incrementally update the model spending only several hours of training time and without any degradation on old data. This paper explores multiple rounds of incremental updates on the ASR model with monthly training data. Results show that the proposed approach achieves 5-6% relative WER improvement over the models trained from scratch on the monthly evaluation datasets. In addition, we explore if it is pos-sible to improve recognition of speciﬁc new words. We simulate multiple rounds of incremental updates with handful of training utterances per word (both real and synthetic) and show that the recognition of the new words improves dramatically but with a minor degradation on general data. Finally, we demonstrate that the observed degradation on general data can be mitigated by interleaving monthly updates with updates targeting speciﬁc words.
Linked Fault Analysis

Numerous fault models with distinct characteristics and effects have been developed. The costs, repeatability, and practicability of these models should be assessed. Moreover, there must be effective ways to use the injected fault to retrieve the secret key, particularly if the implementation includes any countermeasures. In this paper, we introduce a new fault analysis called “linked fault analysis” (LFA), a more powerful technique than other well-known fault attacks against implementations of symmetric primitives, especially in software implementations. For known fault analysis, the basis for the fault model is either the bias over the faulty value or the relationship between the correct value and the faulty one. In the LFA, however, a single fault involves two intermediate values. The faulty target variable, <inline-formula> <tex-math notation="LaTeX">$u'$ </tex-math></inline-formula>, is linked to a second variable, <inline-formula> <tex-math notation="LaTeX">$v$ </tex-math></inline-formula>, such that a particular relation holds: <inline-formula> <tex-math notation="LaTeX">$u'=l(v)$ </tex-math></inline-formula>. LFA lets the attacker perform fault attacks without the input control, using far fewer data than previously introduced fault attacks in the same class. We show the utilization of LFA in the presence or absence of typical redundancy-based countermeasures by introducing “Linked Differential Fault Analysis” (LDFA) and “Linked Ineffective Fault Analysis” (LIFA). We also demonstrate that, under specific circumstances, LFA is still effective even when masking protections are in place. We have performed our attacks against the public implementation of AES and PRESENT in ATMEGA328p to show the feasibility of LFA in the real world. The practical results and simulations validate our theoretical models as well.
Matchmaking Problems in MOBA Games

MOBA is a popular genre that requires teamwork to achieve victory. A close and tight match is what make MOBA fun to play and increase its user satisfaction, but some factor may ruin the matchmaking and create unbalanced match between the two team. Those problems factors are high latency, players with bad attitude, and players doing unfamiliar role. We use DOTA 2 as our case study. Then we compare the DOTA 2 matchmaking system in other sector to make comparison. Lastly, we discuss about solution to solve MOBA matchmaking problem such as displaying live information about online players, players searching for games, servers online and ETA for gaming to start. In addition, we proposed new variable to be considered in the matchmaking system, which are Preferences Role, player’s chosen preferences role will be considered while the system set up the game to minimize the number of unbalanced games in MOBA.
Simulation tools, environments and frameworks for UAVs and multi-UAV-based systems performance analysis (version 2.0)

ABSTRACT During the last decade, Unmanned Aerial Vehicles (UAVs) gained wide attention and are integrated into diverse systems and deployed into many contexts. The need to assess the UAVs’ performances, impacts of applications, routing protocols, mobility as well as other features in a network is necessary. However, conducting real experiments within UAV-based systems and particularly with multi-UAV-based systems can be extremely costly and complex. To fill this gap simulators, emulators, and frameworks are used to evaluate the performance and viability of UAV-based systems with lower cost and in a short period. Accordingly, researchers and software engineers developed several simulators and co-simulators dedicated to the performance evaluations of UAV-based systems. In this regard, this investigation highlights and identifies the most suitable simulators, co-simulators, emulators, and frameworks for UAV flights and management. Both the tools dedicated to mono-UAV-based systems and the tools dedicated to multi-UAV-based systems are pinpointed. In this paper, the goals, the requirements, the strengths, and the limitations of each studied tool are detailed. Besides that, a UAV simulators catalog is provided. It includes guidelines that will be useful for researchers and help them to identify and to select the adequate tools for UAVs performance analysis that meet their needs.
The Impact of Mobility and Interventions on the Spread of Diseases

Human behavior plays a vital role in spreading contagious diseases, and understanding its influence on the outbreak could be the key to devising better mitigatory strategies. The varied individual behaviors give rise to different patterns in the evolution of the disease. Such trends in the disease outbreak that arise due to microscopic behaviors of heterogeneous agents in a populace are typical of agent-based models. Therefore, we propose an agent-based framework equipped with a population mixing algorithm, and stochastic disease transmission and evolutionary dynamics. The mixing algorithm incorporates different mobility trends in a synthetic populace to generate contact patterns among them. On the other hand, the disease model characterizes the health condition of agents using three crucial traits of the disease: 1) infection status; 2) severity; and 3) awareness, endowed with age-dependent probabilities for transmission, progress, and recovery of the disease. Together with different mobility patterns, we demonstrate the impact of mitigatory measures, such as social distancing, vaccination, testing, isolation, and containment zones, on the spread of disease. The proposed model is used to demonstrate trends in the COVID-19 outbreak rather than predict its course of evolution. The results indicate that enhanced mobility of agents facilitates contagion, and a combination of mitigation strategies needs to be adopted to combat the spread of the disease effectively.
Optimization of Grover's Search Algorithm

Quantum mechanical computers unlike classical computers can be in a superposition of states and carry out multiple operations at the same time. Grover has shown that a quantum computer can do a random database search in O( p N) steps. He has fur- ther proved that using the superposition of the states in quantum computing, a quantum computer should be able to do a search in a single step. He has stated that the query generated to do this search will require ›(NlogN) steps. In this paper we aim at optimizing the Grover's search algorithm. In our algorithm, we have repeated the inversion step a number of times instead of stopping after a single step. Measurement after a single step required a larger number of subsystems which im- paired the efiectiveness of the algorithm leading to ›(NlogN) steps in the preparation and processing of the query. Repetition of the inversion step brought the the number of subsystems down drastically by in- creasing the amplitude of the desired state.We have proved that by doing so the overall efiectiveness of Grover's algorithm can be brought down to ›( 3 p N).
Gender differences in character strengths among Chinese pre-adolescents and adolescents: a multidimensional scaling profile analysis

Background: The present study explores gender differences through a characterological lens, inspired by the positive-psychological VIA-Youth measure. Existing research on gender differences in character development has established a certain stereotype of girls outperforming boys in western culture. Methods: Based on a sample of 695 students in elementary, middle, and high school students, we analyzed gender differences in character development in the Chinese cultural context using multidimensional scaling (MDS) profile analysis, providing a cultural account of gender differences via profile patterns. Results: Our study identified specific items where pre-adolescent and adolescent boys outperformed girls (while acknowledging there are also specific items where girls outperformed boys) based on data sourced from a Chinese context. We found gender differences in the same character strengths due to situational factors, such as intensive and sustained educational interventions and established gender traditions/expectations, which can shape the exhibition of particular character strengths differently among girls and boys. Conclusion: Findings can offer an antidote to a Western-dominated research perspective on character or moral education. It enriches the literature on moral education in a different cultural context and provides evidence of gender differences in character strengths.
Distance- and Momentum-Based Symbolic Aggregate Approximation for Highly Imbalanced Classification

Time-series representation is the most important task in time-series analysis. One of the most widely employed time-series representation method is symbolic aggregate approximation (SAX), which converts the results from piecewise aggregate approximation to a symbol sequence. SAX is a simple and effective method; however, it only focuses on the mean value of each segment in the time-series. Here, we propose a novel time-series representation method—distance- and momentum-based symbolic aggregate approximation (DM-SAX)—that can secure time-series distributions by calculating the perpendicular distance from the time-axis to each data point and consider the time-series trend by adding a momentum factor reflecting the direction of previous data points. Experimental results for 29 highly imbalanced classification problems on the UCR datasets revealed that DM-SAX affords the optimal area under the curve (AUC) among competing time-series representation methods (SAX, extreme-SAX, overlap-SAX, and distance-based SAX). We statistically verified that performance improvements resulted in significant differences in the rankings. In addition, DM-SAX yielded the optimal AUC for real-world wire cutting and crimping process dataset. Meaningful data points such as outliers could be identified in a time-series outlier detection framework via the proposed method.
Inspections, reviews, inquiries and recommendations pertaining to youth justice centres in New South Wales between 2015 and 2021

ABSTRACT It is widely recognised that there is a lack of analysis of criminal justice policy-making (and youth justice policy-making specifically) within the wider criminological project. To partially address the gap, this article focuses on one part of the dynamic and complex policy-making environment—reviews, inquiries and findings of oversight bodies between 2015 and 2021 relevant to youth detention in New South Wales (NSW). We estimate that the reports arising from these inquiries and reviews have generated approximately 1040 recommendations (approximately 590 relevant to Youth Justice NSW (YJNSW) broadly and 284 relevant to youth justice centres more specifically). The sheer volume of recommendations, some of which are very broad, raises questions about the ability of an agency, such as YJNSW, to implement them; about which recommendations might be prioritised; and how YJNSW might resolve conflicts between recommendations arising from different and often parallel processes. We suggest that, for the purposes of achieving the best outcomes for young people in custody, there might be greater merit in having fewer inquiries that deal with parts of youth detention and a coordinated and prioritised response to recommendations to address issues of youth detention.