Reviewerly/exHarmony huggingface dataset:
Monocular thermal imaging for pedestrian detection and ranging

To react to the presence of pedestrians, an automated braking system must first find the pedestrian(s) including locations relative to the automobile both in angular position and in distance. In the daytime, cameras and radar can provide the necessary information, but this combination, which requires ambient or active illumination, fails at night. Passive thermal sensors are now being enlisted to dramatically improve imaging at night, whereas substantial effort is underway to assure proper fusing of object information from the thermal sensor with other sensors on the automobile. To simplify the acquisition of information needed to make valid automated braking decisions at night, a camera system with a thermal image sensor was developed that identifies pedestrians and labels each identified pedestrian with location and distance data. The camera utilizes a single uncooled custom microbolometer sensor and a software suite implementing artificial intelligence and machine learning capabilities, running on a combination of convolutional neural networks and fusion processing to provide the data that an automobile host computer needs to implement fast, safe, and accurate automatic braking. We present details of the system construction and operation as well as initial test results showing the potential this technology has to dramatically reduce pedestrian fatalities at night as well as augment safety across all conditions, whether day, night, fog, rain, snow, dust, sun glare, or headlight glare.
A syntactic dependency method for aspect-level sentiment classification by deep learning

Most aspect-level sentiment classification networks include the long short-term memory (LSTM) network, coupled with attention mechanism and memory module, is becoming widely applied in aspect-level sentiment classification. Although it has achieved good results, it cannot extract the global and local information of the context at the same time, and it is only based on the semantic relatedness between an aspect and its corresponding context words to model, while neglecting their syntactic dependencies. This paper proposes the aspect-level sentiment classification by combining convolutional neural network (CNN) and proximity-weighted convolution network (PWCN), as well as a new method to calculate the proximity weight. To obtain contextualized word vectors, corpora has been trained by the model of bidirectional encoder representations from transformers (BERT), which can be taken as text features. The CNN is able to extract sequence features from the text and to take the sequence information from the text into account. In addition, the PWCN can consider the syntactic dependencies inside the sentences. The BERT model also has the ability to model complex features of words, such as their syntactic and semantic changes in a linguistic context. Experiments conducted on the SemEval 2014 benchmark demonstrate compared to the well-established ones, the proposed approach had bigger effectiveness.
A MATURITY CAPABILITY FRAMEWORK FOR SECURITY OPERATION CENTER

Owning a Security Operation Center (SOC) is becoming increasingly common for organizations as part of their cybersecurity strategy to ensure near-real-time detection and adequately respond to cyber-attack engaging the SOC’s humans, technology, and processes. However, SOC investments only sometimes achieve the best possible outcomes and only provide an acceptable protection level in some cases due to the challenges related to the technologies, processes and especially the human factor. This paper proposes a new practical maturity framework for Security Operation Center. This will serve as a roadmap for IT auditors and security experts when they evaluate the maturity of a security operation center in terms of safeguarding the assets of the company, its partners, and its clients.
Circuit connectivity boosts by quantum-classical-quantum interfaces

High-connectivity circuits are a major roadblock for current quantum hardware. We propose a hybrid classical-quantum algorithm to simulate such circuits without swap-gate ladders. As main technical tool, we introduce quantum-classical-quantum interfaces. These replace an experimentally problematic gate (e.g. a long-range one) by single-qubit random measurements followed by state-preparations sampled according to a classical quasi-probability simulation of the noiseless gate. Each interface introduces a multiplicative statistical overhead which is remarkably independent of the on-chip qubit distance. Hence, by applying interfaces to the longest range gates in a target circuit, significant reductions in circuit depth and gate infidelity can be attained. We numerically show the efficacy of our method for a Bell-state circuit for two increasingly distant qubits and a variational ground-state solver for the transverse-field Ising model on a ring. Our findings provide a versatile toolbox for error-mitigation and circuit boosts tailored for noisy, intermediate-scale quantum computation.
Optimal transmission estimation with dark counts

Transmission measurements are essential from fiber optics to spectroscopy. Quantum theory dictates that the ultimate precision in estimating transmission or loss is achieved using probe states with definite photon number and photon-number-resolving detectors (PNRDs). Can the quantum advantage relative to classical probe light still be maintained when the detectors fire due to dark counts and other spurious events? We demonstrate that the answer to this question is affirmative and show in detail how the quantum advantage depends on dark counts and increases with Fock-state-probe strength. These results are especially pertinent as the present capabilities of PNRDs are being dramatically improved.
A sensemaking analysis of API learning using React

Current programming practices rely heavily on the use of APIs (Application Programming Interfaces) and frameworks. However, APIs can be challenging to learn and use. Existing research focuses on specific barriers programmers encounter while learning APIs, providing a fragmented understanding of the process. In this paper, we analyze the holistic process of twelve programmers learning the React JS API using sensemaking theory as a guiding framework for qualitative coding of behaviors. We describe how these API learners moved through sensemaking stages and how they interacted with information during each sensemaking stage. Our results highlighted programmers’ tendency to seek understanding when they encountered problems.
Revisiting BoolTest – On Randomness Testing Using Boolean Functions

Pseudo-random number generation is crucial in cryptology and other areas related to information technology. In a broad sense, the security of a protocol relies on the ‘randomness’ provided by the pseudo-random number generators. It is thus important to examine whether a random-looking stream has some kind of non-randomness in it. Here we consider that a binary stream is divided into blocks of a certain length m and we try to identify an m-bit Boolean function in this regard that is optimal to provide the highest Z-score for the output stream generated by the said function. In this regard, we show certain limitations of the BoolTest strategy by Sýs et al (2017) and present combinatorial results related to identifying the most suitable Boolean functions. We show that the existing works related to BoolTest identify the Boolean functions that are sub-optimal, constrained by the low degree in the Algebraic Normal Form. Our results find out the best Boolean function in this regard that will produce the maximum Z-score and the complexity is $$O(N\log N)$$ on the amount of random-looking stream of length N that we read during the evaluation process. We present substantial experimental evidence corresponding to our theoretical ideas. While we solve certain combinatorial problems related to BoolTest, the caveat is, this test is not sufficient to conclude on randomness or non-randomness of a given stream of data.
Finding Three-Subset Division Property for Ciphers with Complex Linear Layers

Conventional bit-based division property (CBDP) and bit-based division property using three subsets (BDPT) introduced by Todo et al. at FSE 2016 are the most effective techniques for finding integral characteristics of symmetric ciphers. At ASIACRYPT 2019, Wang et al. proposed the idea of modeling the propagation of BDPT, and recently Liu et al. described a model set method that characterized the BDPT propagation. However, the linear layers of the block ciphers which are analyzed using the above two methods of BDPT propagation are restricted to simple bit permutation. Thus the feasibility of the MILP method of BDPT propagation to analyze ciphers with complex linear layers is not settled. In this paper, we focus on constructing an automatic search algorithm that can accurately characterize BDPT propagation for ciphers with complex linear layers. We first introduce BDPT propagation rule for the binary diffusion layer and model that propagation in MILP efficiently. The solutions to these inequalities are exact BDPT trails of the binary diffusion layer. Next, we propose a new algorithm that models Key-Xor operation in BDPT based on MILP technique. Based on these ideas, we construct an automatic search algorithm that accurately characterizes the BDPT propagation and we prove the correctness of our search algorithm. We demonstrate our model for the block ciphers with non-binary diffusion layers by decomposing the non-binary linear layer trivially by the COPY and XOR operations. Therefore, we apply our method to search integral distinguishers based on BDPT of SIMON, SIMON(102), PRINCE, MANTIS, PRIDE, and KLEIN block ciphers. For PRINCE and MANTIS, we find $$(2+2)$$ and $$(3+3)$$ round integral distinguishers respectively which are longest to date. We also improve the previous best integral distinguishers of PRIDE and KLEIN. For SIMON, SIMON(102), the integral distinguishers found by our method are consistent with the existing longest distinguishers.
Arithmetic Circuits, Structured Matrices and (not so) Deep Learning

This survey presents a necessarily incomplete (and biased) overview of results at the intersection of arithmetic circuit complexity, structured matrices and deep learning. Recently there has been some research activity in replacing unstructured weight matrices in neural networks by structured ones (with the aim of reducing the size of the corresponding deep learning models). Most of this work has been experimental and in this survey, we formalize the research question and show how a recent work that combines arithmetic circuit complexity, structured matrices and deep learning essentially answers this question. This survey is targeted at complexity theorists who might enjoy reading about how tools developed in arithmetic circuit complexity helped design (to the best of our knowledge) a new family of structured matrices, which in turn seem well-suited for applications in deep learning. However, we hope that folks primarily interested in deep learning would also appreciate the connections to complexity theory.
A MULTIPLE AND ROBUST IMAGE WATERMARKING APPROACH BASED ON LWT AND FWHT FOR MEDICAL DATA SECURITY

In the last years, the internet and multimedia technologies are widely used to exchange medical images and practicing telemedicine; which exposes this data to various illegitimate attacks due to their sensitivity. For this reason, many researchers work to afford a proper security and an efficient protection during the transmission. In this context, we propose a blind multi-watermarking approach for medical images using Lifting Wavelet Transform and Fast Walsh–Hadamard Transform. This approach proves the ability to embed a binary image ([Formula: see text]), used for integrity purposes, in addition to the patient information (EPR) associated with the medical cover image. Moreover, an error correcting code was used to improve the security of the EPR. We tested the proposed approach on different imaging modalities, and the obtained results show a good imperceptibility and robustness against several common attacks.
Key Reuse Attacks on Post-quantum Cryptosystems, Revisited

Abstract The National Institute of Standards and Technology (NIST) has been working on standardization of post-quantum cryptography and is approaching the end of round-3 evaluation of algorithms. Key reuse security evaluation is an important part of algorithm evaluation. In order to evaluate the key reuse security of candidate IND-CPA PKEs, at Eurocrypt’19, B$\breve{\text{a}}$etu et al. proposed a classical key recovery under plaintext checking attack (KR-PCA) which can recover the reused secret keys by querying an oracle thousands of times. However, the method does not work for cryptosystems which shorten ciphertexts by rounding off the low bits, such as round-3 finalists Kyber and Saber. Subsequently, Dumittan and Vaudenay (ACNS’20) and Qin et al. (ASIACRYPT’21) came up with new effective methods, which require carefully constructed queries. In this paper, we propose an automatic method to recover the reused secret keys of IND-CPA PKEs in Kyber and Saber. Instead of constructing queries carefully, our method uses automated search combined with an optimized bruteforce. The effect and cost of the method depend on the specific parameters. In particular, we can recover the secret keys after thousands of queries in all parameter sets, which is comparable with the current best result.
Prototipe Mesin Penghitung Potongan Botol Plastik Kemasan 1,5 Liter

Especially ornamental fish farmers. This bottle a function as a medium for freshwater ornamental fish containers. hardware bottle-cutting tool to be controlled using programming software using an Arduino microcontroller with the help of an infrared sensor. This Arduino microcontroller will make it easier for users to calculate the bottles that have been cut and the cost of cutting bottles that will be displayed on the 20 x 4 LCD without having to calculate manually which will take more time. In addition to the many features obtained by the owner/user, they can also benefit from selling bottles that will be traded to freshwater ornamental fish farmers. With this innovation, besides simplifying human tasks, can also educate the user community so that they can innovate for the development of the ornamental fishery sector.
Time series prediction for thermal management in server applied with deep learning

The portion of cooling power reaches up to one third of total power budget in the operation of data centers. Recently researchers pay more attention on thermal management for improving energy efficiency in the data centers as well as the servers. To the best of existing literatures, the complex interactions of cooling and heating resources is the main problem encountered in the design of server fan control. Moreover, to achieve better control performance, the thermal information of time series should be taken into account as well. Therefore, this work proposes time series prediction of server temperatures by utilizing two multivariate time series prediction models, named MTSMFF and MsA. An automatic data acquisition system has been designed for collecting massive scale dataset. To ensure the robustness of trained results, the dataset is divided into training data and validation data. These two models are trained with the training data, and MsA provides the best performance in accuracy with the validation data. The experimental results show that the time series prediction of MsA performs splendidly for the trend of the server temperatures.
Exploration of the Vienna City Library Poster Collection using Computer Vision Approaches

Parts of collections of libraries, archives, and e.g. museums can still be uncatalogued. Even if metadata is provided, only standardized information of the described resources (dependent on the collection) is available, i.e., creator names, titles, and subject terms, limiting the search options for experts and typical users. In the case of image-based collections, the information of the image itself can be used as an additional feature to extend the search capabilities of the user. This paper analyzes the use of standard computer vision methods to explore the Vienna City Library poster collection using additional image-based properties. The proposed exploration tool allows a search based on the provided metadata and features based on face detection and retrieval, image retrieval, object detection, text recognition, and main color similarity. The OpenSearch engine is used to index the metadata and visual features, allowing for a real-time search of extensive collections. The qualitative and quantitative analysis shows the potential of visual features within a search tool.
Efficient Solution to Develop an Exercising Algorithm using Model-Centric and Data-Centric Artificially Intelligent Approaches

Data is an integral part of any Artificial Intelligence (AI) based application, and improving the data iteratively can provide more precise outcomes, as opposed to iteratively improving the models. A paradigm shift is underway where the go-to approach for developing AI incorporated products is gradually moving from model-centric to data-centric. The objective of this research work is to provide an efficient solution based on dataset parameters/features by exercising model-centric and/or data-centric AI approaches. In this novel method, data is subjected to both the approaches, either individually or to their hybridizations, and the results are compared quantitatively against a common ground. The CIFAR-10 dataset has been processed by the proposed method and concluded that the model-centric approach gives better performance than the data-centric approach with the same hyperparameters. The solution proposed in this work can be employed to develop an automated framework for image classification. Similarly, such frameworks can be developed for other computer vision-based tasks such as object detection, object localization, and so on.
Reliability of Saliency Methods Used in Graph Neural Network Models

Graph neural networks (GNN) have recently been successfully used to combine data stored in electronic health records to identify individuals with similar phenotypes and model them to predict, for example, the discharge of patients from intensive care units or the best possible medical therapies based on similar patients' diagnoses. However, the explainability of the model is essential for a transparent and reliable application in medicine. This can be done using gradient-based methods such as saliency methods. While saliency methods in medicine are generally used in radiography to better understand what a model currently "observes" in regions of an image to find a prediction or make a classification, very little has been done in other areas also relevant in medicine such as GNNs applied in medical decision-making. To this end, we have tested gradient methods on GNNs and calculated their saliency scores with three different methods: i.) gradient, ii.) integrated gradients, and iii.) DeepLIFT. It is expected that the scores obtained with each method will be convergent. However, we found that less than 50% of the calculated scores converge. In addition, the quality of this convergence depends on whether the analysis is performed on the entire graph or on subgraphs. This result is relevant as it demonstrates that these methods are probably not fully reliable when applied to GNNs. Because of this, we propose an alternative strategy for using these scores: instead of using unique saliency scores, we suggest sampling the scores computed with different methods. In this way, it is not only possible to understand which input information influences the computation of the prediction, but it is also possible to compute a level of confidence of the predictions depending on the distribution of the scores, which makes it a human-centered method informing medical customers about the reliability of the model.
Enhancement Techniques for Improving Facial Recognition Performance in Convolutional Neural Networks

The advent of convolutional neural networks (CNNs) to the development of face recognition system has been a game changer in the field of computer vision and pattern recognition.This research work uses a pre trained MobileNet-V1 model to develop an effective CNN model capable of high performance.We also tackle several common facial recognition challenges which include occlusions, illumination variations, make-ups, pose variation and ageing through the use of several improvement techniques.The techniques include adopting a less computationally costly approach, transfer learning and hyper-parameter finetuning.The Top-1 accuracy 70.6% and Top-5 accuracy 89.5% of the base MobileNet-V1 model has been improved using these techniques to achieve training accuracy of 95% and accuracies of 96.4%, 98.0% and 99.1% on the Pins face recognition dataset, FaceScrub data-set and LFW data-set, respectively.The work done so far illustrates the need for further research into improvement techniques for convolutional neural networks.
Dimension Reduction Techniques for Image Classification

The world is surrounded by a huge amount of data increasing day by day. The increase of data leads to the presence of high-dimensional data causing a challenge in data mining as it becomes costly in computation time and memory space. Moreover, high-dimensional data can affect the classification accuracy of machine learning algorithms because of the existence of redundant and irrelevant features, and that can cause the Curse of Dimensionality problem. Therefore, Dimensionality Reduction has been introduced to solve the Curse of Dimensionality problem by feature extraction techniques. Since there is a lack in the performance of some machine learning models because of high-dimensional data, this paper introduces two common dimensionality reduction methods, and conducts an empirical comparison between , Principal Component Analysis (PCA) and Auto-Encoders (AE) to study their effect in improving the performance of classification in high-dimensional data. The study uses three classification models, K-Nearest Neighbour(KNN),Support Vector Machine (SVM), and Random Forest (RF) to perform the classification in the MNIST, and Fashion-MNIST datasets. The results have been compared , analyzed, and show that AE has a better effect on improving performance of KNN, and SVM classifiers on MNIST dataset, as the SVM accuracy is improved from 94% to 97% when the AE used to reduce dimension with a percentage of 95%,90%, and 50%.Moreover, KNN accuracy has been improved by AE from 91% to 95% , when dimension is reduced with a percentage of 95%,90%, and 50%.On the other hand, the accuracy of the same classifier has been improved in Fashion-MNIST dataset from 81% to 83%, when dimension is reduced by AE with a percentage of 90% and 50%.
The Effects of Fully Connected Layers Adjustment for Lightweight Convolutional Neural Networks

Reducing the size and the computational need of a Convolutional Neural Network (CNN) is a crucial task that can be extremely important for the deployment phase of such models. In this work, we are quoting the most efficient techniques in order to reduce the size of CNNs while maintaining prediction accuracy. Through a case study, we investigate the effects, especially of the dense layers and how the number of nodes in each layer can change the weight, performance and training process of a CNN. Although there are lots of works around this subject, in our paper we are proving how and why the number of nodes in fully connected (dense) layers is the most important factor for reducing the size of a CNN.