Reviewerly/exHarmony huggingface dataset:
Area–Time Efficient Implementation of NIST Lightweight Hash Functions Targeting IoT Applications

To mitigate cybersecurity breaches, secure communication is crucial for the Internet of Things (IoT) environment. Data integrity is one of the most significant characteristics of security, which can be achieved by employing cryptographic hash functions. In view of the demand from IoT applications, the National Institute of Standards and Technology (NIST) initiated a standardization process for lightweight hash functions. This work presents field-programmable gate array (FPGA) implementations and carefully worked out optimizations of four Round-3 finalists in the NIST standardization process. A novel compact PHOTON-Beetle implementation is proposed wherein the underlying matrix multiplication is executed in serialized fashion to achieve a small hardware footprint. Sparkle implementations are carried out by implementing the ARX-box in serialized, parallelized, and hybrid approaches. For Ascon and Xoodyak, the proposed implementations compute certain permutation rounds in one clock cycle in order to explore the tradeoff between computation time and hardware area. As a result, this work achieves the smallest hardware footprint for PHOTON-Beetle consuming an area <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.4 \times $ </tex-math></inline-formula> smaller than state-of-the-art implementations. Ascon and Xoodyak are implemented in a flexible manner that achieves throughput-to-area (TP/A) ratios <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.8 \times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.9 \times $ </tex-math></inline-formula> higher, respectively, compared to implementations found in the literature. In addition, we propose the first FPGA implementations for the Sparkle hash function. These efficient implementations provide guidelines for choosing a suitable architecture for applications in demand that can be employed in the IoT environment to achieve data integrity for various applications.
Prediction of The Gleason Group of Prostate Cancer from Clinical Biomarkers: Machine and Deep Learning from Tabular Data

Prostate Cancer (PC) has been shown to become an epidemic among men in the world. Early detection of PC is essential for treatment. Biopsies are often done to determine the Gleason score of PC which helps to predict the aggressiveness of PC. As biopsies may cause harm especially for old people, machine learning can be used to predict the Gleason grade of PC from clinical biomarkers that are typically structured in a table. In this paper, we present a comparative study of various machine learning methods to detect the Gleason grade of PC from tabular data. We also investigate the performance of advanced deep learning architectures specialized to deal with tabular data, such as TabNet, for this purpose. Moreover, we propose to build an ensemble of the best performing classifiers to grade PC with a promising performance.
DPXPlain

Differential privacy (DP) is the state-of-the-art and rigorous notion of privacy for answering aggregate database queries while preserving the privacy of sensitive information in the data. In today's era of data analysis, however, it poses new challenges for users to understand the trends and anomalies observed in the query results: Is the unexpected answer due to the data itself, or is it due to the extra noise that must be added to preserve DP? In the second case, even the observation made by the users on query results may be wrong. In the first case, can we still mine interesting explanations from the sensitive data while protecting its privacy? To address these challenges, we present a three-phase framework DPXPlain, which is the first system to the best of our knowledge for explaining group-by aggregate query answers with DP. In its three phases, DPXPlain (a) answers a group-by aggregate query with DP, (b) allows users to compare aggregate values of two groups and with high probability assesses whether this comparison holds or is flipped by the DP noise, and (c) eventually provides an explanation table containing the approximately 'top-k' explanation predicates along with their relative influences and ranks in the form of confidence intervals, while guaranteeing DP in all steps. We perform an extensive experimental analysis of DPXPlain with multiple use-cases on real and synthetic data showing that DPXPlain efficiently provides insightful explanations with good accuracy and utility.
Legal challenges for Blockchain implementation in Colombia

In Colombia, Blockchain is in an incipient state of development, especially in its applications in the industry beyond the financial sector. Different factors influence organizations to compete in the market of technological solutions based on Blockchain, among them, the legal one. The lack of direct regulation generates uncertainty in entrepreneurs wishing to implement this technology, becoming, in the long run, a demotivating factor. This study will analyze the state of Blockchain implementation in Colombia and will determine the main legal risks faced by public and private organizations wishing to venture into this technology.
Remote sensing image segmentation model based on attention mechanism

Remote sensing images are often very large in size, which is difficult to put into GPU for training. Previous work proposed models of global and local branches. On the basis of this model, we add attention mechanism to make feature integration more complete. The results show that our method works well.
Multi-Scaled and Densely Connected Locally Convolutional Layers for Depth Completion

The depth completion task aims to predict a dense depth map from a sparse LiDAR point cloud and an RGB image. This task is critical because an accurate depth map can be used as prior information to solve many computer vision tasks, such as downstream tasks in autonomous vehicles and robot vision. Previous deep learning methods which focus on the local affinity have achieved impressive results. However, an architecture that is directly designed to extract local affinity has not been proposed yet. In this paper, we propose multi-scaled and densely connected locally convolutional layers to learn the affinity of the neighborhood. We set a different grid factor for each step of this module, and each step consists of several convolutional layers applied only to the local area assigned from the grid factor. In addition, each step is densely connected, sequentially, to take advantage of the multi-scale receptive fields. The proposed module effectively learns the neighbor-hood's affinity in a local area with multiple scales, while keeping the network size small. As a result, our architecture achieves state-of-the-art performance compared to published works on the KITTI depth completion benchmark. On the NYU Depth V2 completion benchmark our method achieves performance comparable to state-of-the-art approaches.
The Use of Continuous Action Representations to Scale Deep Reinforcement Learning: An Application to Inventory Control

We propose a new policy architecture to apply deep reinforcement learning (DRL) with a continuous action representation. This contrasts most current DRL implementations that make use of a discrete action space. The latter are not scalable to large problems. To obtain feasible discrete actions, we add a mapping function to the policy network that maps the continuous outputs of the policy network to a feasible integer solution. We demonstrate our approach on a (capacitated) joint replenishment problem for various settings. We show how the use of a continuous action representation enables solving larger problem instances, while requiring less training time and matching or outperforming state-of-the-art benchmarks. This promising research avenue might pave the way for application of deep reinforcement learning at scale and in practice.
Sentiment Analysis of Movie Reviews in Hindi Language Using Machine Learning

Sentiment analysis has become an important field in the last few years. By performing sentiment analysis of user reviews, news, blogs, etc. we gain a deeper understanding of the general opinion towards a movie, product, etc. Sentiment Analysis is a very valuable tool for organizations to understand the opinion of the public towards their product, service movie, etc. Rather than reading an entire post, blog, or review, Sentiment Analysis allows companies to know the general opinion about their product or movie by deducing the emotion present in the piece of text. Sentiment analysis identifies the keywords in a piece of text and determines the emotion contained in it. Several works have been carried out in the subject area of sentiment analysis on English but hardly any work on Hindi. Hindi is a widely spoken language with a growing number of speakers. The social media platforms too see a large number of reviews, blogs in Hindi. Due to the rising amount of web content in Hindi, there was a requirement to perform sentiment analysis on the Hindi language. We propose a system for sentiment analysis on Hindi by using the Bag of Words model and applying four Machine Learning models.
Occluded Visible-Infrared Person Re-Identification

Visible-infrared person re-identification (VI-ReID) aims to match person images between the visible and near-infrared modalities. Previous VI-ReID methods are based on holistic pedestrian images and achieve excellent performance. However, in real-world scenarios, images captured by visible and near-infrared cameras usually contain occlusions. The performance of these methods degrades significantly due to the loss of information of discriminative features from the occlusion of the images. We define visible-infrared person re-identification in this occlusion scene as Occluded VI-ReID, where only partial content information of pedestrian images can be used to match images of different modalities from different cameras. In this paper, we propose a matching framework for occlusion scenes, which contains a local feature enhance module (LFEM) and a modality information fusion module (MIFM). LFEM adopts Transformer to learn features of each modality, and adjusts the importance of patches to enhance the representation ability of local features of the non-occluded areas. MIFM utilizes a co-attention mechanism to infer the correlation between each image for reducing the difference between modalities. We construct two occluded VI-ReID datasets, namely Occluded-SYSU-MM01 and Occluded-RegDB datasets. Our approach outperforms existing state-of-the-art methods on two occlusion datasets, while remains top performance on two holistic datasets.
A blockchain based approach for Fog infrastructure management leveraging on Non-Fungible Tokens

Fog computing solutions provide a consolidated way to quickly process the huge amount of data generated by IoT devices connected to the Internet. Different to cloud computing, Fog infrastructure achieve efficient data processing without excessive latency on data transfers. The blockchain technology supports distributed and reliable information storage mechanisms and, when combined with smart contracts, it can provide a distributed system where all the nodes independently contribute to a common global state. This provides inherent desirable features, such as immutability and transparency. In this work a blockchain based approach to address fault-tolerance and load balancing in smart environments at Fog level, is proposed. A Non-fungible Tokens based methodology, exploiting tailored smart contracts, is designed and implemented to manage load balancing and failover activities on container-oriented infrastructure. Preliminary results have show the effectiveness of the approach.
Classification of lymphomas images with polynomial strategy: An application with Ridge regularization

Histological image analysis through systems to aid diagnosis plays an important role in medicine with supplementary reading for the specialist’s diagnosis. This work proposes a method based on the association of extracted features by fractal techniques, regularization and polynomial classifier. The feature vectors were classified by applying the cross-validation technique with 10 folds. The evaluation of the results occurred through metrics such as accuracy (ACC) and imbalance accuracy metric (IAM). The proposed approach achieved significant results for all metrics with non-Hodgkin lymphoma lesion sets. The proposed approach provided values around 0.97 of IAM and 99% of ACC for investigated groups. These results are considered relevant to studies in the literature and the association of Hermite polynomial and regularization can contribute to the detection of the lesions by supporting specialists in clinical practices.
A Dynamical System Approach to Decentralized Collision-free Autonomous Coordination of a Mobile Assistive Furniture Swarm

In order to facilitate and assist the indoor mobility of people with special needs, the classically static objects in the environment, such as furniture, can be rendered mobile. The need for efficient and safe autonomous coordination of a mobile furniture swarm arises. We present a closed-form approach for mobile furniture obstacle avoidance and navigation within an indoor environment. The approach shows that each mobile furniture agent, defined by a polygonal surface, does not collide with any static or mobile obstacle (e.g., a person is moving around). All controllable mobile furniture converges towards a defined goal position and orientation. We showcase the application of this algorithm in simulation on mobile furniture for smart environments. Results demonstrate that the proposed method can coordinate a swarm of mobile furniture to get out of the way of a mobile agent representing a person with limited mobility passing through the room while avoiding obstacles and converging towards a predefined target pose.
BayesCap: Bayesian Identity Cap for Calibrated Uncertainty in Frozen Neural Networks

High-quality calibrated uncertainty estimates are crucial for numerous real-world applications, especially for deep learning-based deployed ML systems. While Bayesian deep learning techniques allow uncertainty estimation, training them with large-scale datasets is an expensive process that does not always yield models competitive with non-Bayesian counterparts. Moreover, many of the high-performing deep learning models that are already trained and deployed are non-Bayesian in nature and do not provide uncertainty estimates. To address these issues, we propose BayesCap that learns a Bayesian identity mapping for the frozen model, allowing uncertainty estimation. BayesCap is a memory-efficient method that can be trained on a small fraction of the original dataset, enhancing pretrained non-Bayesian computer vision models by providing calibrated uncertainty estimates for the predictions without (i) hampering the performance of the model and (ii) the need for expensive retraining the model from scratch. The proposed method is agnostic to various architectures and tasks. We show the efficacy of our method on a wide variety of tasks with a diverse set of architectures, including image super-resolution, deblurring, inpainting, and crucial application such as medical image translation. Moreover, we apply the derived uncertainty estimates to detect out-of-distribution samples in critical scenarios like depth estimation in autonomous driving. Code is available at https://github.com/ExplainableML/BayesCap .
The Effects of Device and Spatial Layout on Social Presence During a Dynamic Remote Collaboration Task in Mixed Reality

This paper evaluates factors of social presence during a dynamic remote collaboration task in a technologically asymmetric Mixed Reality (MR) setting for two spatial layouts. While active movement during MR remote collaboration is afforded by how the shared 3D space is mediated and configured, studies investigating the impact of these conditions on user experience have been scarce. In a between-group study $(\mathrm{n}=48)$, a host user in Augmented Reality (AR) and a remote user in Virtual Reality (VR), both wearing Head Mounted Displays (HMDs), simultaneously moved around the shared space to find and assemble parts of a Mars exploration rover together, one group in a Peripheral layout and the other in a Scattered layout with disparate levels of spatial affordance. Results show that while VR facilitates higher co-presence and spatial presence than AR through HMDs, the Peripheral layout enables users to pay more attention to one another than the Scattered. We analyze the results and derive implications aimed at bridging the AR-VR gap in social presence for dynamic MR remote collaboration through the adaptive placement of virtual content in shared spaces.
Message Classification for Breast Cancer Chatbot using Bidirectional LSTM

This study proposed a chatbot application for breast cancer patients in Thailand. This is to increase the communication channels and address the problem related to a shortage of medical staffs. The first step in the chatbot system is message classification. Natural language processing techniques such as word segmentation, stop words removal, word indexing, and word vectorization are used for the text pre-processing steps. After that, a deep learning technique with Bi-LSTM is used for text classification. There are 1,139 sample messages for learning which can be classified into 60 classes. The results from the experiment showed that the accuracy rate of the proposed technique is 0.869.
XAI Analysis of Online Activism to Capture Integration in Irish Society Through Twitter

Online activism over Twitter has assumed a multidimensional nature, especially in societies with abundant multicultural identities. In this paper, we pursue a case study of Ireland’s Twitter landscape and specifically migrant and native activists on this platform. We aim to capture the level to which immigrants are integrated into Irish society and study the similarities and differences between their characteristic patterns by delving into the features that play a significant role in classifying a Twitterer as a migrant or a native. A study such as ours can provide a window into the level of integration and harmony in society.
SwitchTx

Online-transaction-processing (OLTP) applications require the underlying storage system to guarantee consistency and serializability for distributed transactions involving large numbers of servers, which tends to introduce high coordination cost and cause low system performance. In-network coordination is a promising approach to alleviate this problem, which leverages programmable switches to move a piece of coordination functionality into the network. This paper presents a fast and scalable transaction processing system called SwitchTx. At the core of SwitchTx is a decentralized multi-switch in-network coordination mechanism, which leverages modern switches' programmability to reduce coordination cost while avoiding the central-switch-caused problems in the state-of-the-art Eris transaction processing system. SwitchTx abstracts various coordination tasks (e.g., locking, validating, and replicating) as in-switch gather-and-scatter (GaS) operations, and offloads coordination to a tree of switches for each transaction (instead of to a central switch for all transactions) where the client and the participants connect to the leaves. Moreover, to control the transaction traffic intelligently, SwitchTx reorders the coordination messages according to their semantics and redesigns the congestion control combined with admission control. Evaluation shows that SwitchTx outperforms current transaction processing systems in various workloads by up to 2.16X in throughput, 40.4% in latency, and 41.5% in lock time.
IS-MVSNet:Importance Sampling-Based MVSNet

This paper presents a novel coarse-to-fine multi-view stereo (MVS) algorithm called importance-sampling-based MVSNet (IS-MVSNet) to address a crucial problem of limited depth resolution adopted by current learning-based MVS methods. We proposed an importance-sampling module for sampling candidate depth, effectively achieving higher depth resolution and yielding better point-cloud results while introducing no additional cost. Furthermore, we proposed an unsupervised error distribution estimation method for adjusting the density variation of the importance-sampling module. Notably, the proposed sampling module does not require any additional training and works reasonably well with the pre-trained weights of the baseline model. Our proposed method leads to up to $$20\times $$ promotion on the most refined depth resolution, thus significantly benefiting most scenarios and excellently superior on fine details. As a result, IS-MVSNet outperforms all the published papers on TNT’s intermediate benchmark with an F-score of 62.82%. Code is available at github.com/NoOneUST/IS-MVSNet.
Solar Energy Forecasting using Artificial Neural Network

To meet the targets of the Paris agreement and to offset the depleting reserves of conventional sources of energy, there has been a significant push for renewables, particularly Solar and Wind. Since the energy output from a solar unit is highly unpredictable and depends on the weather parameters such as temperature, cloud cover, humidity, angle of incidence, etc. the power output varies with changing conditions. Therefore, forecasting the available energy in advance is important to maintain a balance between the power supply and demand. This paper uses the artificial neural network (ANN) model for forecasting the solar energy availability, and it is more accurate as compared to existing models of linear regression (LR) and support vector machine (SVM) used for forecasting.
Deep Convolution Neural Network in classification of liver tumor as benign or Malignant from Abdominal Computed Tomography

The most shared reason of cancer demise is liver cancer, which is detected by analysing changes in the grey level liver tissue in computer Tomography images. A computer-assisted diagnostic approach for characterization of liver tumours from abdominal Computed Tomography (CT) is proposed in this research to assist doctors in reducing misdiagnosis. The objective of this study is to generate a computer-aided diagnostic model that uses a deep Convolutional Neural Network (CNN) to differentiate between benevolent and malevolent liver tumours. With the help of a clinician, ROIs belonging to focal nodular hyperplasia, hemangioma, hepatoma, and cholangiocarcinoma are segmented. The features are extracted and decision model is built using CNN. The developed CNN model can able to differentiate liver diseases such as focal nodular hyperplasia and hemangioma as benign, hepatoma and cholangiocarcinoma as malignant. The result shows that the developed CNN model gives very good accuracy of 91%.