Reviewerly/exHarmony huggingface dataset:
Low delay AES S-box designs based on matrix merging method

In this paper, a low delay circuit structure is proposed for composite field S-box circuit. In the low delay structure, the multiplications over GF((22)2) are constructed by XOR-AND-XOR-networks, and the multiplicative inverse over GF((22)2) are constructed by AND-XOR-networks. As XOR-networks are linear operations, they can be further expressed as constant matrix multiplications. In this paper, the adjacent matrix multiplications in S-box are merged to reduce the delay. Compared with previous works, our S-box implementations have lower delay.
Informative Path Planning for Active Learning in Aerial Semantic Mapping

Semantic segmentation of aerial imagery is an important tool for mapping and earth observation. However, supervised deep learning models for segmentation rely on large amounts of high-quality labelled data, which is labour-intensive and time-consuming to generate. To address this, we propose a new approach for using unmanned aerial vehicles (UAVs) to autonomously collect useful data for model training. We exploit a Bayesian approach to estimate model uncertainty in semantic segmentation. During a mission, the semantic predictions and model uncertainty are used as input for terrain mapping. A key aspect of our pipeline is to link the mapped model uncertainty to a robotic planning objective based on active learning. This enables us to adaptively guide a UAV to gather the most informative terrain images to be labelled by a human for model training. Our experimental evaluation on real-world data shows the benefit of using our informative planning approach in comparison to static coverage paths in terms of maximising model performance and reducing labelling efforts.
Maximum Likelihood Uncertainty Estimation: Robustness to Outliers

We benchmark the robustness of maximum likelihood based uncertainty estimation methods to outliers in training data for regression tasks. Outliers or noisy labels in training data results in degraded performances as well as incorrect estimation of uncertainty. We propose the use of a heavy-tailed distribution (Laplace distribution) to improve the robustness to outliers. This property is evaluated using standard regression benchmarks and on a high-dimensional regression task of monocular depth estimation, both containing outliers. In particular, heavy-tailed distribution based maximum likelihood provides better uncertainty estimates, better separation in uncertainty for out-of-distribution data, as well as better detection of adversarial attacks in the presence of outliers.
Towards Universal Backward-Compatible Representation Learning

Conventional model upgrades for visual search systems require offline refresh of gallery features by feeding gallery images into new models (dubbed as "backfill"), which is time-consuming and expensive, especially in large-scale applications. The task of backward-compatible representation learning is therefore introduced to support backfill-free model upgrades, where the new query features are interoperable with the old gallery features. Despite the success, previous works only investigated a close-set training scenario (i.e., the new training set shares the same classes as the old one), and are limited by more realistic and challenging open-set scenarios. To this end, we first introduce a new problem of universal backward-compatible representation learning, covering all possible data split in model upgrades. We further propose a simple yet effective method, dubbed as Universal Backward-Compatible Training (UniBCT) with a novel structural prototype refinement algorithm, to learn compatible representations in all kinds of model upgrading benchmarks in a unified manner. Comprehensive experiments on the large-scale face recognition datasets MS1Mv3 and IJB-C fully demonstrate the effectiveness of our method.
A Survey on Patients Privacy Protection with Stganography and Visual Encryption

In this survey, thirty models for steganography and visual encryption methods have been discussed to provide patients privacy protection.
Image Compression and Actionable Intelligence With Deep Neural Networks

If a unit cannot receive intelligence from a source due to external factors, we consider them disadvantaged users. We categorize this as a preoccupied unit working on a low connectivity device on the edge. This case requires that we use a different approach to deliver intelligence, particularly satellite imagery information, than normally employed. To address this, we propose a survey of information reduction techniques to deliver the information from a satellite image in a smaller package. We investigate four techniques to aid in the reduction of delivered information: traditional image compression, neural network image compression, object detection image cutout, and image to caption. Each of these mechanisms have their benefits and tradeoffs when considered for a disadvantaged user.
Synthetic Target Domain Supervision for Open Retrieval QA

Neural passage retrieval is a new and promising approach in open retrieval question answering. In this work, we stress-test the Dense Passage Retriever (DPR) -- a state-of-the-art (SOTA) open domain neural retrieval model -- on closed and specialized target domains such as COVID-19, and find that it lags behind standard BM25 in this important real-world setting. To make DPR more robust under domain shift, we explore its fine-tuning with synthetic training examples, which we generate from unlabeled target domain text using a text-to-text generator. In our experiments, this noisy but fully automated target domain supervision gives DPR a sizable advantage over BM25 in out-of-domain settings, making it a more viable model in practice. Finally, an ensemble of BM25 and our improved DPR model yields the best results, further pushing the SOTA for open retrieval QA on multiple out-of-domain test sets.
TempTracker: A Service Oriented Temporal Natural Language Processing Based Tool for Document Data Characterization and Social Network Analysis

With the advent of Web 2.0 based technology, news sites and micro-blog sites have become popular and have attracted the attention of people around the world. Existing textual data captured by these sites is highly beneficial for extracting (a) new information to analyze, and (b) temporal course of change in entities, topics and sentiment for differing granularities. This has been demonstrated by the study described in this paper. After collecting the data, several directions have been investigated in order to demonstrate its effectiveness under the umbrella of entity extraction, topic and sentiment analysis using Natural Language Processing (NLP) tools, temporal social media analysis, and time varying trend results of entity and sentiment aspect of entities. A service-based architecture has been proposed to process text data with NLP tools and to enrich the data. Text data is collected and processed via NLP tools. It is retrieved upon request for data analysis. The reported results illustrate the applicability and effectiveness of the conducted study.
Video-Text Pre-training with Learned Regions

Video-Text pre-training aims at learning transferable representations from large-scale video-text pairs via aligning the semantics between visual and textual information. State-of-the-art approaches extract visual features from raw pixels in an end-to-end fashion. However, these methods operate at frame-level directly and thus overlook the spatio-temporal structure of objects in video, which yet has a strong synergy with nouns in textual descriptions. In this work, we propose a simple yet effective module for video-text representation learning, namely RegionLearner, which can take into account the structure of objects during pre-training on large-scale video-text pairs. Given a video, our module (1) first quantizes visual features into semantic clusters, then (2) generates learnable masks and uses them to aggregate the features belonging to the same semantic region, and finally (3) models the interactions between different aggregated regions. In contrast to using off-the-shelf object detectors, our proposed module does not require explicit supervision and is much more computationally efficient. We pre-train the proposed approach on the public WebVid2M and CC3M datasets. Extensive evaluations on four downstream video-text retrieval benchmarks clearly demonstrate the effectiveness of our RegionLearner. The code will be available at https://github.com/ruiyan1995/Region_Learner.
Tackling critical slowing down using global correction steps with equivariant flows: the case of the Schwinger model

We propose a new method for simulating lattice gauge theories in the presence of fermions. The method combines flow-based generative models for local gauge field updates and hierarchical updates of the factorized fermion determinant. The flow-based generative models are restricted to proposing updates to gauge-fields within subdomains, thus keeping training times moderate while increasing the global volume. We apply our method performs to the 2-dimensional (2D) Schwinger model with $N_f=2$ Wilson Dirac fermions and show that no critical slowing down is observed in the sampling of topological sectors up to $\beta=8.45$. Furthermore, we show that fluctuations can be suppressed exponentially with the distance between active subdomains, allowing us to achieve acceptance rates of up to $99\%$ for the outer-most accept/reject step on lattices volumes of up to $V=128\times128$.
Asymptotic Experiments with Data Structures: Bipartite Graph Matchings and Covers

We consider instances of bipartite graphs and a number of asymptotic performance experiments in three projects: (1) top movie lists, given databases of movies and viewers, (2) maximum matchings, and (3) minimum set covers. Experiments are designed to measure the asymptotic runtime performance of abstract data types (ADTs) in three programming languages: Java, R, and C++. The outcomes of these experiments may be surprising. In project (1), the best ADT in R consistently outperforms all ADTs in public domain Java libraries, including the library from Google. The largest movie list has $2^{20}$ titles. In project (2), the Ford-Fulkerson algorithm implementation in R significantly outperforms Java. The hardest instance has 88452 rows and 729 columns. In project (3), a stochastic version of a greedy algorithm in R can significantly outperform a state-of-the-art stochastic solver in C++ on instances with $num\_rows \ge 300$ and $num\_columns \ge 3000$.
Personalised Combination of Multi-Source Data for User Profiling

Human interaction with intelligent systems, services, and devices generates large volumes of user-related data. This multi-source information can be used to build richer user profiles and improve personalization. Our goal is to combine multi-source data to create user profiles by assigning dynamic individual weights. This paper describes a multi-source user profiling methodology and illustrates its application with a film recommendation system. The contemplated data sources include (i) personal history, (ii) explicit preferences (ratings), and (iii) social activities (likes, comments, or shares). The MovieLens dataset was selected and adapted to assess our approach by comparing the standard and the proposed methodologies. In the standard approach, we calculate the best global weights to apply to the different profile sources and generate all user profiles accordingly. In the proposed approach, we determine, for each user, individual weights for the different profile sources. The approach proved to be an efficient solution to a complex problem by continuously updating the individual data source weights and improving the accuracy of the generated personalised multimedia recommendations.
Reducing Catastrophic Forgetting in Self Organizing Maps with  Internally-Induced Generative Replay

A lifelong learning agent is able to continually learn from potentially infinite streams of pattern sensory data. One major historic difficulty in building agents that adapt in this way is that neural systems struggle to retain previously-acquired knowledge when learning from new samples. This problem is known as catastrophic forgetting (interference) and remains an unsolved problem in the domain of machine learning to this day. While forgetting in the context of feedforward networks has been examined extensively over the decades, far less has been done in the context of alternative architectures such as the venerable self-organizing map (SOM), an unsupervised neural model that is often used in tasks such as clustering and dimensionality reduction. Although the competition among its internal neurons might carry the potential to improve memory retention, we observe that a fixed-sized SOM trained on task incremental data, i.e., it receives data points related to specific classes at certain temporal increments, experiences significant forgetting. In this study, we propose the continual SOM (c-SOM), a model that is capable of reducing its own forgetting when processing information.
BoxeR: Box-Attention for 2D and 3D Transformers

In this paper, we propose a simple attention mechanism, we call box-attention. It enables spatial interaction between grid features, as sampled from boxes of interest, and improves the learning capability of transformers for several vision tasks. Specifically, we present BoxeR, short for Box Transformer, which attends to a set of boxes by predicting their transformation from a reference window on an input feature map. The BoxeR computes attention weights on these boxes by considering its grid structure. Notably, BoxeR-2D naturally reasons about box information within its attention module, making it suitable for end-to-end instance detection and segmentation tasks. By learning invariance to rotation in the box-attention module, BoxeR-3D is capable of generating discriminative information from a bird's-eye view plane for 3D end-to-end object detection. Our experiments demonstrate that the proposed BoxeR-2D achieves state-of-the-art results on COCO detection and instance segmentation. Besides, BoxeR-3D improves over the end-to-end 3D object detection baseline and already obtains a compelling performance for the vehicle category of Waymo Open, without any class-specific optimization. Code is available at https://github.com/kienduynguyen/BoxeR.
Delaunay Component Analysis for Evaluation of Data Representations

Advanced representation learning techniques require reliable and general evaluation methods. Recently, several algorithms based on the common idea of geometric and topological analysis of a manifold approximated from the learned data representations have been proposed. In this work, we introduce Delaunay Component Analysis (DCA) - an evaluation algorithm which approximates the data manifold using a more suitable neighbourhood graph called Delaunay graph. This provides a reliable manifold estimation even for challenging geometric arrangements of representations such as clusters with varying shape and density as well as outliers, which is where existing methods often fail. Furthermore, we exploit the nature of Delaunay graphs and introduce a framework for assessing the quality of individual novel data representations. We experimentally validate the proposed DCA method on representations obtained from neural networks trained with contrastive objective, supervised and generative models, and demonstrate various use cases of our extended single point evaluation framework.
Robocup@Home 2021 Champion Team Paper: SSPL Winner Uchile Peppers

This paper summarizes the development and strategies used by the Uchile Peppers Team to become champions of the Robocup@Home Social Standard Platform League. First both vision and manipulation skills of the robot are analyzed. Next, the implementation of these modules to create the state machines that solve the tasks given is explained. Finally, a general conclusion about our development experience is given.
Perception, Awareness, Presentation: Using Meditation Techniques to Teach Data Visualisation Literacy – A Case Study

Inspired by the ideas of Gestalt and the practice of meditation, this paper describes an approach to bring together the corresponding techniques to teach data visualisation, a subfield of information literacy whose importance will considerably grow within the years to come. The course can be subsumed under the motto that data, (i.e., everything that is given), is the basis of meditation and that data visualisation in its essence is a meditative process. The paper should be seen as a return-of-experience based on a case study that shows how both techniques intertwine almost naturally and can mutually benefit from each other.
Width is Less Important than Depth in ReLU Neural Networks

We solve an open question from Lu et al. (2017), by showing that any target network with inputs in $\mathbb{R}^d$ can be approximated by a width $O(d)$ network (independent of the target network's architecture), whose number of parameters is essentially larger only by a linear factor. In light of previous depth separation theorems, which imply that a similar result cannot hold when the roles of width and depth are interchanged, it follows that depth plays a more significant role than width in the expressive power of neural networks. We extend our results to constructing networks with bounded weights, and to constructing networks with width at most $d+2$, which is close to the minimal possible width due to previous lower bounds. Both of these constructions cause an extra polynomial factor in the number of parameters over the target network. We also show an exact representation of wide and shallow networks using deep and narrow networks which, in certain cases, does not increase the number of parameters over the target network.
READ-CSRL: Relation Entities Augmented Datasets for Conversational Semantic Role Labeling

Some intelligent applications such as intelligent customer service, in-depth Q&A, chat bot, etc. need multiple conversation rounds, and there is a strong correlation between conversational sentences. Traditional Semantic Role Labeling (SRL) can only perform well on single-sentence tasks, however, the performances on tasks with multiple conversational sentences are unsatisfactory. Conversational Semantic Role Labeling (CSRL) was proposed for conversational tasks. Then knowledge enhanced CSRL was proposed, commonsense was introduced as external knowledge which is usually represented as a triple of request entity, relation and relation entity. Experiments show that after knowledge enhanced by full triples may bring some noise into the original input. So, in this paper, we propose a novel method to reduce the knowledge noise, and generate more augmented datasets enhanced with only relation entities. Experiments evidently illustrate that the proposed method outperforms the CSRL benchmarks. This provides a promising new way to fuse external knowledge in conversational SRL task.
Upaya Memperlancar Distribusi Air Bersih Ke Instalasi dalam Bangunan di Program Studi Pendidkan Teknik Bangunan

Building Engineering Education Study Program is part of the Faculty of Teacher Training and Education (FKIP) of Palangka Raya University which has facilities and infrastructure to support teaching and learning activities. There are several problems faced by partners, especially clean water installations. The clean water installation consists of a toren (water reservoir) and a pipe network. The water pressure that comes out through the faucet is relatively small. This problem is very important to be resolved because the availability and smooth running of water is an absolute necessity during the COVID-19 pandemic. To prevent and break the chain of the spread of COVID-19, it is necessary to apply health protocols properly, one way is to wash hands frequently, which of course requires the availability of water. The solution offered to overcome this problem is to add an air pipe (ventilation). The addition of an air pipe can increase the flow of water. The increase in water discharge at point 1 is 25.93%, at point 2 is 1.98%.
Invariant Representation Driven Neural Classifier for Anti-QCD Jet Tagging

We leverage representation learning and the inductive bias in neural-net-based Standard Model jet classification tasks, to detect non-QCD signal jets. In establishing the framework for classification-based anomaly detection in jet physics, we demonstrate that, with a \emph{well-calibrated} and \emph{powerful enough feature extractor}, a well-trained \emph{mass-decorrelated} supervised Standard Model neural jet classifier can serve as a strong generic anti-QCD jet tagger for effectively reducing the QCD background. Imposing \emph{data-augmented} mass-invariance (and thus decoupling the dominant factor) not only facilitates background estimation, but also induces more substructure-aware representation learning. We are able to reach excellent tagging efficiencies for all the test signals considered. In the best case, we reach a background rejection rate of 51 and a significance improvement factor of 3.6 at 50 \% signal acceptance, with the jet mass decorrelated. This study indicates that supervised Standard Model jet classifiers have great potential in general new physics searches.
Human Centred Software Development Approaches

Software development engineering is widely used in various organizations to provide functionalities to the end users. The main reason behind the failure of software projects is their incapability to meet the current needs of business and end users. The recent agile software development practices help development teams towards optimal and quality outcomes of the software product. These practices use accretion approach with the main focus on greater communication and relationship between the user level. In our research paper, we have used "Agile based engineering practices and design-thinking technique for develop a human centred software development (HCSD)". Design thinking is a practical and creative way to solve a problem quickly. In this paper we give an upper level of identification and understanding of real time end-users that lead to a human-centred iterative design for developing higher quality software products used by agile and design thinking software approaches.
Adversarial Examples in Random Neural Networks with General Activations

A substantial body of empirical work documents the lack of robustness in deep learning models to adversarial examples. Recent theoretical work proved that adversarial examples are ubiquitous in two-layers networks with sub-exponential width and ReLU or smooth activations, and multi-layer ReLU networks with sub-exponential width. We present a result of the same type, with no restriction on width and for general locally Lipschitz continuous activations. More precisely, given a neural network $f(\,\cdot\,;{\boldsymbol \theta})$ with random weights ${\boldsymbol \theta}$, and feature vector ${\boldsymbol x}$, we show that an adversarial example ${\boldsymbol x}'$ can be found with high probability along the direction of the gradient $\nabla_{{\boldsymbol x}}f({\boldsymbol x};{\boldsymbol \theta})$. Our proof is based on a Gaussian conditioning technique. Instead of proving that $f$ is approximately linear in a neighborhood of ${\boldsymbol x}$, we characterize the joint distribution of $f({\boldsymbol x};{\boldsymbol \theta})$ and $f({\boldsymbol x}';{\boldsymbol \theta})$ for ${\boldsymbol x}' = {\boldsymbol x}-s({\boldsymbol x})\nabla_{{\boldsymbol x}}f({\boldsymbol x};{\boldsymbol \theta})$.