Reviewerly/exHarmony huggingface dataset:
Deep Learning Models for Architectural Façade Detection in Spherical Images

Maintaining a public register by extracting urban objects from photos taken in the city is one of the most important tasks for municipal services. It is of great importance in the field of protection and shaping of the cultural landscape, protection of monuments, and registration of the urban tissue development. The current state of the art shows that deep learning models (DL models) can cope with the problem of extracting urban objects with the same or better performance than non-DL models, and can process video and photos automatically. This paper compares the three main DL models for facade instance detection and facade segmentation: Mask R-CNN, YOLACT, and Mask-Scoring R-CNN. The training and validation datasets used for transfer learning were created on the basis of spherical photos taken in an artificially generated virtual city. The test dataset, on the other hand, included spherical façade photos taken in a real city. The comparative analysis of the DL models was performed using parametric and nonparametric statistical tests for pairwise and multiple comparisons.
Mitigation of Straggler Nodes through Virtualization for Resource Optimization

Abstract Modern computing systems are generally enormous in scale, consisting of hundreds to thou- sands of heterogeneous machine nodes, to meet ris- ing demands for Cloud services. MapReduce and other parallel computing frameworks are frequently used on such cluster architecture to offer consumers de- pendable and timely services. However, Cloud work- loads’ complex features, such as multi-dimensional resource requirements and dynamically changing sys- tem settings, such as dynamic node performance, are posing new difficulties for providers in terms of both customer experience and system efficiency. The strag- gler problem occurs when a small subset of paral- lelized jobs takes an excessively long time to execute in contrast to their siblings, resulting in a delayed job response and the possibility of late-timing failure. Speculative execution is the state-of-the-art method to straggler mitigation. Speculative execution has been used in numerous real-world systems with a variety of implementation improvements, but the results of this thesis’ research demonstrate that it is typicallywasteful. The failure rate of speculative execution might be as high as 71 percent, according to different data center production trace logs. Straggler mitigation is a difficult task in and of itself:1) stragglers may have varying degrees of severity in parallel job execution;2) whether a task should be considered a straggler is highly subjective, depending on various application and system conditions;3) the efficiency of speculative execution would be improved if dynamic node quality could be adequately modeled and predicted;4) Other sorts of stragglers, such as those generated by data skews, are beyond speculative execution’s ca- pabilities.
Discrete Linear Temporal Logic with Knowing-Value Operator

In epistemic logic we are not only interested in the propositional knowledge expressed by “knowing that” operators, but also care about other types of knowledge used in natural language. In [], Plaza proposed the “knowing value” operators and gave the complete axiomatization for the logic of knowledge with nonrigid designators. Moreover, in [] Halpern and colleagues holds that, when analyzing a system in terms of knowledge, not only is the current state of knowledge of the agents in the system relevant, but also how that state of knowledge changes over time. So we introduce temporal logic operators ‘next’ and ‘until’ to extend Plaza’s system. The completeness proof is highly non-trivial and we referred to the work of [, ].
Using PMI to Rank and Filter Edges in Graphs of Words

Text classification is a classic problem in Natural Language Processing. An essential task in text classification is the construction of the representation, which must provide relevant information to the classifier. One of the most effective representation models uses graphs to represent documents. This paper presents an approach that uses this representation model but with weighted graphs. We propose to use a popular word association measure, the Pointwise Mutual Information (PMI), to calculate the weights of graph edges. These weights then serve as a guide to identify and remove edges between words with low association levels. Then, using node2vec, we extract the features of each graph and use a text convolutional neural network for classification. We conducted experiments in order to compare different kinds of graph modeling in terms of classification score and the proportion of edges that were removed. The results obtained indicate that this approach makes it possible to reduce the number of edges in the graphs maintaining classification performance.
Comprehensive Evaluation of Word Embeddings for Highly Inflectional Language

The purpose of this paper is to present the experiments aiming at choosing the best word embeddings for highly inflectional languages. In particular, authors evaluated the word embeddings for Polish language among those available in the literature at the time of writing. The static embeddings like Word2Vec, GloVe, fasttext and their training settings were taken into account. In particular, the evaluation coverted 121 different embedding models provided by IPI PAN, OPI, Kyubyong and Facebook. The experiment phase was divided into two tasks: the first task consisted in examining word analogies and the second verified the similarities and the relatedness of pairs of words. The obtained results showed that in terms of accuracy the Facebook fasttext model learned on the Common Crawl collection should be considered the best model under assumptions of experimental session.
Real-time Implement ORB algorithm in FPGA : oFast with Harris corner and rBrief algorithm

Feature point detection is a popular algorithm for techniques such as object detection and tracking. To detect objects using features in a vision system, feature points and descriptors are required. In this paper, we implemented a real-time vision system that outputs specific points and descriptors from input images in FPGA based on ORB algorithm. In the proposed hardware architecture, the feature detector module uses a total of 314,908 clocks to process the entire 640x480 image. The entire system including the descriptor generation module takes 525,230 Clock. In this paper, we describe the equations of the algorithm used in the proposed hardware structure, and then Describes the implemented hardware structure. Finally, it concludes by comparing it with SW. The proposed hardware structure can be used in various systems such as object detection and tracking in the future.
Multi-task Learning for User Engagement and Adoption in Live Video Streaming Events

Nowadays, live video streaming events have become a mainstay in viewer's communication in large international enterprises. Provided that viewers are distributed worldwide, the main challenge resides on how to schedule the optimal event's time so as to improve both the viewer's engagement and adoption. In this paper we present a multi-task deep reinforcement learning model to select the time of a live video streaming event, aiming to optimize the viewer's engagement and adoption at the same time. We consider the engagement and adoption of the viewers as independent tasks and formulate a unified loss function to learn a common policy. In addition, we account for the fact that each task might have different contribution to the training strategy of the agent. Therefore, to determine the contribution of each task to the agent's training, we design a Transformer's architecture for the state-action transitions of each task. We evaluate our proposed model on four real-world datasets, generated by the live video streaming events of four large enterprises spanning from January 2019 until March 2021. Our experiments demonstrate the effectiveness of the proposed model when compared with several state-of-the-art strategies. For reproduction purposes, our evaluation datasets and implementation are publicly available at https://github.com/stefanosantaris/merlin.
An Image Importance Partition-Based Compression Method for COVID-19 Computed Tomography Scan

As the coronavirus disease 2019 (COVID-19) spreads around the world, industrial automated medical diagnosis systems have been developed, which complete a large amount of medical diagnosis work through computed tomography (CT) images. In these systems, how to quickly store and transmit such a large amount of CT image information has important research significance. In this article, a more targeted COVID-19 chest CT image codec is proposed to make image data not only occupy less space but also have higher image quality. First, the bilateral lung contours are extracted to calculate the position information of the region of interest (ROI). Then, a CT image is classified into four types of nonuniform image blocks according to the characteristics of COVID-19 chest CT images and ROI position information. Next, a series of new transformations are proposed for more efficient transform coding. Finally, a flexible quantization strategy is proposed for the adaptive quantization part. In the experiments, the proposed method is superior to some of the existing methods with similar computational complexity. At the same bit rate, it significantly improves the image quality. This means that chest CT images can still be used for disease diagnosis while taking up less space. In addition, because of the low computational complexity of the proposed method, it can be more easily embedded into the CT equipment with low computational power.
Efficient Resolution Enhancement of JPEG2000 Compressed Multispectral Images Using Deep Super-resolution Methods

Multispectral imaging is one of the most important Earth observation techniques in remote sensing. Although their advantages, multispectral imaging systems continuously capture images resulting in enormous data volumes. To this end, some of the multispectral satellites use JPEG2000 based compression methods. However, the images that are compressed at low bit-rates contain compression artifacts and may present low-performances in remote sensing applications such as target detection and classification. In this paper, we propose the usage of three single image super-resolution methods, SRResNet, EDSR, and WDSR, for the resolution enhancement of JPEG2000 compressed images. First, the multispectral image is subsampled at factor of 4 along both spatial axes, and then the resulting image is compressed with JPEG2000. Finally, super-resolution methods are performed to improve the resolution and reduce the compression artifacts. Experiments were carried out on the Onera dataset shared by IEEE GRSS. The results are compared in terms of quality metrics such as signal-to-noise ratios, mean spectral angle, maximum spectral angle, and maximum absolute difference. Experimental results demonstrate that the proposed approaches provide higher quality metrics and better visual performance compared to bicubic upsampling.
Assessing Obsolescence of Software Engineers

Unlike other professions, where knowledge and skills become obsolete more slowly, it is estimated that an IT professional will become obsolete in two years. Thus, the accurate assessment of technical skills can help software engineers to take the necessary actions to mitigate the risk of professional obsolescence. This work proposes a method to assess the degree of professional obsolescence, based on the concept of Participatory Sensing Network. To evaluate the proposed method, we collected data from 50 software engineers, and presented it to 9 professional recruiters to reach a measure of their actual employability. Then, we compared these data against the calculations of our algorithm. The results showed no significant difference between the traditional recruitment process and the proposed algorithm, which means that the algorithm can be an accurate surrogate for such employability assessment.
Targeting the Most Important Words Across the Entire Corpus in NLP Adversarial Attacks

In recent years, deep learning has revolutionized many tasks, from machine vision to natural language processing. Deep neural networks have reached extremely high accuracy levels in many fields. However, they still encounter many challenges. In particular, the models are not explainable or easy to trust, especially in life and death scenarios. They may reach correct predictions through inappropriate reasoning and have biases or other limitations. In addition, they are vulnerable to adversarial attacks. An attacker can subtly manipulate data and affect a model’s prediction. In this paper, we demonstrate a brand new adversarial attack method in textual data. We use activation maximization to create an importance rating for each unique word in the corpus and attack the most important words in each sentence. The rating is global to the whole corpus and not to each specific data point. This method performs equal or better when compared to previous attack methods, and its running time is around 39 times faster than previous models.
Approaches to Educational Activities for Research Ethics at Tohoku University

In this report for Case Study, we introduce the educational activities for research ethics conducted as part of the promotion of responsible conduct of research at Tohoku University. In particular, we describe in detail the Career Stage-Based Learning Reference Standards and the Research Integrity Advisor System as distinctive systems and initiatives.
Research on Efficient Distributed Mining Algorithm of Large Scale Graph Data Based on Multi Utility Threshold

In graph data mining, setting a single threshold on the data set is easy to cause the loss of rare item sets, resulting in excessive memory consumption of the algorithm. This paper proposes an efficient distributed mining algorithm for large-scale graph data based on multi utility threshold. In the form of undirected label, the frequent item sets of large-scale graph data are constructed, and the graph data item sets are sorted according to the minimum utility average value, and the useless items are deleted. Design a graph data utility list to store the necessary item set information. The pruning strategy is set based on multi utility threshold, and the upper utility limit of transaction is adjusted to reduce the search space. Design the distributed mining algorithm of graph data, traverse the search space, and achieve the balance of computing load. The experimental results show that in the same data set test, the memory peak of the proposed algorithm is lower than FHM algorithm, HAUIM-MMAU algorithm and FP-Storm algorithm. Therefore, the algorithm designed in this paper can reduce the occupation of memory, reduce memory consumption, and has a certain application value.
Is Bitcoin a Hedge? How Extreme Volatility Can Destroy the Hedge Property

A large literature tests whether Bitcoin can hedge portfolio risk, i.e. reduce the risk if added to a portfolio. Intuitively, given the extreme volatility and thus risk of Bitcoin and cryptocurrencies, the idea that Bitcoin is a hedge may be puzzling. Indeed, we show that for extreme levels of volatility, Bitcoin does not reduce the risk if added to a benchmark equity portfolio. This is not only true on average but also holds for sub-samples, including the COVID-19 crisis period. We conclude that a focus on correlations is not sufficient for extreme levels of volatility.
Securing Smart Connected World: Key Challenges and Possible Solutions

To build fully functional smart applications, critical challenges need to be properly addressed for user privacy, system availability, and cyber resilience. In this chapter, we identify the challenges in privacy, security, and trust aspects and review existing solutions adopted to protect IoT systems from malicious attacks, unauthorized access, and privacy breaches. Due to a high volume of interconnected devices raising scalability, heterogeneity, and interoperability issues, we propose semantic-enabled solutions to protect sensitive data shared for clinical collaboration, such as record linkage. As semantic reasoning allows effective management of user privileges and anonymity schemes, both privacy and utility can be ensured in the release of record linkage.
Evaluating Tooling and Methodology when Analysing Bitcoin Mixing Services After Forensic Seizure

Little or no research has been directed to analysis and researching forensic analysis of the Bitcoin mixing or &#x2018;tumbling&#x2019; service themselves. This work is intended to examine effective tooling and methodology for recovering forensic artifacts from two privacy focused mixing services namely Obscuro which uses the secure enclave on intel chips to provide enhanced confidentiality and Wasabi wallet which uses CoinJoin to mix and obfuscate crypto currencies. These wallets were set up on VMs and then several forensic tools used to examine these VM images for relevant forensic artifacts. These forensic tools were able to recover a broad range of forensic artifacts and found both network forensics and logging files to be a useful source of artifacts to deanonymize these mixing services.
Ensemble of AES - RSA Cryptographic Model for Securing Sensitive Laptop Data

Laptops have become the workstation for a majority of people during the modern days. Many IT firms have migrated to laptops so that the employees have the necessary flexibility to work even from home due to unprecedented havocs like natural disasters and pandemics. Hence it is vital to safe guard the sensitive client related data present inside the laptop during theft. File system encryption is the widely adopted method to achieve the intended data security. But having the presence of decryption key within the stolen laptop is a loop hole for hardware experts to retrieve the sensitive contents. Hence our work proposes an ensemble of AES and RSA cryptographic model which ensures that the file encryption key is distributed to a remote server and is only accessible via an android phone request from an authorized user.
Meta-Adversarial Training of Neural Networks for Binary Classification

We propose a novel framework for classification using neural networks via an adversarial training procedure, in which we simultaneously train a main classifier—a neural network that solves the original classification task, i.e classifying instances into two main categories—and two meta-classifiers which act as discriminators and aim to detect false positives and negatives predicted by the original classifier. Our framework operates in two stages: In a first stage, both main and meta classifiers are pre-trained using the cross-entropy loss. The second stage consists of an adversarial training stage in which both main and meta classifiers are placed in a min-max game. Therefore, we switch to our new loss function so that the goal for the main classifier becomes to maximize the probability of failure of the adversarial meta-classifiers. Our training procedure can be explained by the fact that the meta-classifiers are more accurate when the main classifier is weak i.e., instances misclassified by the main classifier are naturally easy to separate and assign to the correct class membership. Opposingly, if the main classifier is robust enough, then the meta-classifiers are supposed to distinguish between instances that are naturally hard to classify, making thus more mistakes. In this work, both main and meta-classifiers are defined by Multi-Layer Perceptrons (MLP) and the entire training system is performed using backpropagation with gradient descent optimization. Experiments demonstrate the potential of our framework in outperforming the traditional learning scheme in improving the classification accuracy.
Towards waveshape-insensitive flying qubit gates

Temporal/spectral waveshapes of photons involved in gate operation are absolutely crucial for the gate operation. In particular, if we consider linear logic approaches [1] the photons in different channels should be un-correlated and indistinguishable [2] , [3] . The waveshape is also important for so called coherent photon conversion (CPC) proposed in [4] . It uses four wave mixing (FWM) between single-photons in presence of a strong coherent field. For instance, two photons in the distinct modes which we call here signal s and idler i (see Fig. 1a ) can be converted into a "pump" p photon and then back, demonstrating Rabi-like oscillations; after each oscillation, the phase of the wavepacket is shifted by π , leading to a nonlinear sign (NS) gate functionality. However, since only time-coincident ( t i = t s ) parts of the two-photon wavepacket interact, almost any waveshape leads to a non-ideal gate operation [5] . This problem was elegantly solved in [6] where the signal and idler are assumed to propagate through each other with nonzero velocity (see Fig. 1a ), and interaction of independent photons was demonstrated.
Deep Learning - An Emerging Paradigm

In the past few years, deep learning has emerged as a vital paradigm in various fields such as vision, semantic understanding, natural language processing and speech recognition. A number of architectures and algorithms have been proposed and used for the extraction of multiple levels of feature abstractions. Deep learning is a great research interest nowadays due to its commendable properties in contrast to conventional neural network which was not so successful to generate favorable outcomes in many applications because of having shallow architecture and handcrafted features. This paper focuses on providing an understanding of different deep learning architectures and emerging application areas of deep learning such as internet of things, big data and blockchain.
The potential effects of the COVID-19 pandemic on learning

In this paper, we use a new database for Mexico to model the possible long-run effects of the pandemic on learning. First, based on the framework of Neidhoffer, Lustig, and Tommasi (2021), we estimate the loss of schooling due to the transition from in-person to remote learning using data from the National Survey on Social Mobility (ESRU-EMOVI-2017), census data, and national statistics of COVID-19 incidence. In this estimation, we account for the attenuation capacity of households by considering the parental educational attainment and the economic resources available to the household in the calculation of the short-run cost. Secondly, we estimate the potential long-run consequences of this shock through a calibrated learning profile for five Mexican regions following Kaffenberger and Pritchett (2020). Assuming the distance learning policy adopted by the Mexican government is entirely effective, our results indicate that a learning loss equivalent to the learning during a third of a school year in the short run translates into a learning loss equivalent to an entire school year further up the educational career of students. On the other hand, if the policy was ineffective, the short-run loss increases to an entire school year and becomes a loss of two years of learning in the long run. Our results suggest substantial variation at the regional level, with the most affected region, the South experiencing a loss thrice as large as that of the least affected region, the Centre region.