Reviewerly/exHarmony huggingface dataset:
Exact algorithms for the repetition-bounded longest common subsequence problem

In this paper, we study exact, exponential-time algorithms for a variant of the classic Longest Common Subsequence problem called the Repetition-Bounded Longest Common Subsequence problem (or RBLCS, for short): Let an alphabet S be a finite set of symbols and an occurrence constraint Cocc be a function Cocc:S→N, assigning an upper bound on the number of occurrences of each symbol in S. Given two sequences X and Y over the alphabet S and an occurrence constraint Cocc, the goal of RBLCS is to find a longest common subsequence of X and Y such that each symbol s∈S appears at most Cocc(s) times in the obtained subsequence. The special case where Cocc(s)=1 for every symbol s∈S is known as the Repetition-Free Longest Common Subsequence problem (RFLCS) and has been studied previously; e.g., in [1], Adi et al. presented a simple (exponential-time) exact algorithm for RFLCS. However, they did not analyze its time complexity in detail, and to the best of our knowledge, there are no previous results on the running times of any exact algorithms for this problem. Without loss of generality, we will assume that |X|≤|Y| and |X|=n. In this paper, we first propose a simpler algorithm for RFLCS based on the strategy used in [1] and show explicitly that its running time is O(1.44225n). Next, we provide a dynamic programming (DP) based algorithm for RBLCS and prove that its running time is O(1.44225n) for any occurrence constraint Cocc, and even less in certain special cases. In particular, for RFLCS, our DP-based algorithm runs in O(1.41422n) time, which is faster than the previous one. Furthermore, we prove NP-hardness and APX-hardness results for RBLCS on restricted instances.
Recognition of Pathological Voices by Human Factor Cepstral Coefficients (HFCC)

Human speech is a means of communication that is very important in our daily lives.It is characterized by its great ability to transmit our ideas, our emotions, our personality etc.So, any alteration of the voice can prevent the person from exercising his professional and daily life naturally.It is for these reasons that it is very necessary to implement systems for detecting and classifying vocal pathologies.These automatic systems can help clinicians customize and detect the existence of any vocal pathology.In this context, several tools have been introduced to achieve early detection of voice disorders.Among these tools are the Human Factor Cepstral Coefficients (HFCC) combined with prosodic parameters, the Noise-Harmonic Ratio (NHR), the Harmonic-Noise Ratio (HNR), analysis of trend Fluctuations (DFA) and Fundamental frequency (F0).These parameters are introduced and calculated in every frame.In this study, we used a variation of HFCC called Equivalent Rectangular Bandwidth (ERB) to study the effects of HFCC on the classification of pathological voices.Using the HTK classifiers, the classification is carried out on two pathological databases, Massachusetts Eye and Ear Infirmary (MEEI) and Saarbruecken Voice Database (SVD).To assess the performance of the system, we used sensitivity and specificity.
Cryptanalysis of Internet of Things (IoT) Wireless Technology

In the given paper, we discuss the lattice-based open source public-key NTRU cryptosystem that can be used in IoT (Internet of Things). IoT spans users' personal sensitive information and many crucial aspects of person's private life and therefore IoT requires a special protection. IoT security involves the network security and the devices security they are connected to. Cryptography technologies are the best solution to withstand communication attacks. NTRU can implement the NTRUEncrypt public key encryption algorithm capable of withstanding the attacks from the quantum computers. Therefore, NTRU is significantly faster than the other public-key cryptosystems such as RSA or ECC, allowing long-term cryptanalysis. NTRU can use one-off keys making possible to change keys in a few seconds what makes difficult for an attacker to crack the encryption trying to obtain more than thousands of keys for doing that.
Short Principal Ideal Problem in multicubic fields

Abstract One family of candidates to build a post-quantum cryptosystem upon relies on euclidean lattices. In order to make such cryptosystems more efficient, one can consider special lattices with an additional algebraic structure such as ideal lattices. Ideal lattices can be seen as ideals in a number field. However recent progress in both quantum and classical computing showed that such cryptosystems can be cryptanalysed efficiently over some number fields. It is therefore important to study the security of such cryptosystems for other number fields in order to have a better understanding of the complexity of the underlying mathematical problems. We study in this paper the case of multicubic fields.
Cancer Informatics in 2019: Deep Learning Takes Center Stage

Objective: To summarize significant research contributions on cancer informatics published in 2019. Methods: An extensive search using PubMed/Medline and manual review was conducted to identify the scientific contributions published in 2019 that address topics in cancer informatics. The selection process comprised three steps: (i) 15 candidate best papers were first selected by the two section editors, (ii) external reviewers from internationally renowned research teams reviewed each candidate best paper, and (iii) the final selection of two best papers was conducted by the editorial committee of the Yearbook. Results: The two selected best papers demonstrate the clinical utility of deep learning in two important cancer domains: radiology and pathology. Conclusion: Cancer informatics is a broad and vigorous subfield of biomedical informatics. Applications of new and emerging computational technologies are especially notable in 2019.
Crowd Counting with Semantic Scene Segmentation in Helicopter Footage

Continually improving crowd counting neural networks have been developed in recent years. The accuracy of these networks has reached such high levels that further improvement is becoming very difficult. However, this high accuracy lacks deeper semantic information, such as social roles (e.g., student, company worker, or police officer) or location-based roles (e.g., pedestrian, tenant, or construction worker). Some of these can be learned from the same set of features as the human nature of an entity, whereas others require wider contextual information from the human surroundings. The primary end-goal of developing recognition software is to involve them in autonomous decision-making systems. Therefore, it must be foolproof, which is, it must have good semantic understanding of the input. In this study, we focus on counting pedestrians in helicopter footage and introduce a dataset created from helicopter videos for this purpose. We use semantic segmentation to extract the required additional contextual information from the surroundings of an entity. We demonstrate that it is possible to increase the pedestrian counting accuracy in this manner. Furthermore, we show that crowd counting and semantic segmentation can be simultaneously achieved, with comparable or even improved accuracy, by using the same crowd counting neural network for both tasks through hard parameter sharing. The presented method is generic and it can be applied to arbitrary crowd density estimation methods. A link to the dataset is available at the end of the paper.
Online Scammers and Their Mules in Malaysia

Scammers have adopted various scam techniques in defrauding their victims. One of the most common ways is by engaging mules to assist them. This article discusses relevant legal provisions in Malaysia which are enforced against these illegal activities. The objectives are to highlight the significance of section 415 and 420 in dealing with online scammers, and section 414 and 424 in dealing with mules who assist them. Relevant cases are also cited to enable readers to understand the real scenario behind these scammers and their mules. The article ends with a scenario to be pondered in between human conscience and government intervention in order to prevent, if not eradicate, these illegal activities.
Electronic Health Records Exploitation Using Artificial Intelligence Techniques

The exploitation of electronic health records (EHRs) has multiple utilities, from predictive tasks and clinical decision support to pattern recognition. Artificial Intelligence (AI) allows to extract knowledge from EHR data in a practical way. In this study, we aim to construct a Machine Learning model from EHR data to make predictions about patients. Specifically, we will focus our analysis on patients suffering from respiratory problems. Then, we will try to predict whether those patients will have a relapse in less than 6, 12 or 18 months. The main objective is to identify the characteristics that seem to increase the relapse risk. At the same time, we propose an exploratory analysis in search of hidden patterns among data. These patterns will help us to classify patients according to their specific conditions for some clinical variables.
Feature Drift Detection in Evolving Data Streams

Most current stream mining techniques can adapt to data distribution changes, known as concept drift. Common concept drift detectors focus on detecting and signaling drift when a model's prediction accuracy deteriorates. To allow us to evaluate a model's accuracy we need data with ground truth. We focus on feature drift that shifts the model's boundaries, and present a framework to detect feature drift without labels. The framework detects abrupt and gradual feature drift by two distance functions, Wasserstein and Energy, and discuss feature changes in the data stream. A less explored area is describing the changes in the data stream. Crucially, the ability to describe changes in the data stream would enable a better understanding of the changing dynamics in the relationships that take place over time. In particular, we seek to answer the following question: Whether the distribution changes of important features will also cause concept drift. Experimental results show that the proposed framework detects and describes the feature drift.
We’re Here for You: The Unsolicited Covid-19 Email

Although companies have long used email to correspond directly with consumers in times of crisis (George &amp; Pratt, 2012), the Covid-19 pandemic has incited an unprecedented flood of emails to our inboxes from companies reassuring us that “we’re all in this together.” As composition scholars begin to investigate how organizations have responded to this pandemic, this article explores the rise of the “we’re here for you” email, a rapidly developing genre that reveals an unsettling relationship with the voice behind our consumer products and also a paradigm shift in how organizations connect with consumers during times of crisis.
PodRecs: Workshop on Podcast Recommendations

The last year has been a breakout year for podcasts. There are now over 1 million podcast shows and over 64 million podcast episodes available through public RSS feeds. In the United States, 32% of all people listened to a podcast every month, and forecasts point to global podcast listenership to reach 2.2 billion monthly listeners by 2024. The workshop on Podcast Recommendations (PodRecs), collocated with RecSys 2020, introduces researchers in other domains of recommender systems to the special characteristics and challenges of podcast recommendations: how podcasts are as created, consumed, and how we might see algorithms being designed specifically for podcast content. We hope this workshop will help grow a community of researchers and foster an active research and innovation in the field.
FST Morphology for the Endangered Skolt Sami Language

We present advances in the development of a FST-based morphological analyzer and generator for Skolt Sami. Like other minority Uralic languages, Skolt Sami exhibits a rich morphology, on the one hand, and there is little golden standard material for it, on the other. This makes NLP approaches for its study difficult without a solid morphological analysis. The language is severely endangered and the work presented in this paper forms a part of a greater whole in its revitalization efforts. Furthermore, we intersperse our description with facilitation and description practices not well documented in the infrastructure. Currently, the analyzer covers over 30,000 Skolt Sami words in 148 inflectional paradigms and over 12 derivational forms.
Guided Cascaded Super-Resolution Network for Face Image

The image super-resolution algorithm can overcome the imaging system's hardware limitation and obtain higher resolution and clearer images. Existing super-resolution methods based on convolutional neural networks(CNN) can learn the mapping relationship between high-resolution(HR) and low-resolution(LR) images. However, when the reconstruction target is a face image, the reconstruction results often have problems that the face area is too smooth and lacks details. We propose a guided cascaded face super-resolution network, called guided cascaded super-resolution network (GCFSRnet). GCFSRnet takes the LR image and a high-quality guided image as inputs, and it consists of a pose deformation module and a super-resolution network. Firstly, the pose deformation module converts the guide image's posture into the same as the low-resolution face image based on 3D fitting and 3D morphable model (3DMM). Then, the LR image and the deformed guide image are used as input of the super-resolution network. The super-resolution networks are formed by a cascade of two layers of networks, which extract different features. During the reconstruction process, the guide image can provide real facial details and help generate subtle facial textures. The cascade structure of a super-resolution network can gradually extract features and restore different levels of image details. The experimental results on the CASIA Web Face and CelebA datasets show that the proposed method can generate facial images with clear outlines and rich details, which are superior to other state-of-the-art methods such as SRResNet, SRGAN, VDSR, DBPN, etc.
Research on Object Detection Algorithm Based on Multilayer Information Fusion

At present, object detectors based on convolution neural networks generally rely on the last layer of features extracted by the feature extraction network. In the process of continuous convolution and pooling of deep features, the position information cannot be completely transferred backward. This paper proposes a multiscale feature reuse detection model, which includes the basic feature extraction network DenseNet, feature fusion network, multiscale anchor region proposal network, and classification and regression network. The fusion of high-dimensional features and low-dimensional features not only strengthens the model's sensitivity to objects of different sizes but also strengthens the transmission of information, so that the feature map has rich deep semantic information and shallow location information at the same time, which significantly improves the robustness and detection accuracy of the model. The algorithm is trained and tested in Pascal VOC2007 dataset. The experimental results show that the mean average precision of the objects in the dataset is 73.87%. At the same time, compared with the mainstream faster RCNN and SSD detection models, the mean average precision of object detection algorithm based on DenseNet is improved by 5.63% and 3.86%, respectively.
Key Frame Extraction Algorithm of Motion Video Based on Priori

Key frame extraction technology is one of the core technologies of content-based video retrieval.For video types with complex content, various scenes, and rich actions, the performance of existing key frame extraction methods is not ideal.Based on the Visual Geometry Group (VGG), this article proposes an image saliency extraction model assisted by deep prior information, and uses a large-scale data set for training on the server to obtain a trained model, and then integrates multiple features.The saliency extraction algorithm is combined with the image saliency extraction model assisted by deep prior information, and a saliency extraction algorithm based on multi-feature fusion and deep prior information is proposed.A new method for extracting key frames of motion video is introduced in detail.Taking into account that sports videos in real applications are susceptible to interference from various factors, resulting in poor picture quality, this article constructs a new visual attention model for moving targets in sports videos, which integrates images.The combination of multiple features of the bottom-level features and the skin color confidence map of the moving target overcomes the problem that a single feature cannot fully express the moving target.Since the processing object in this article is for the moving target in the video of the sports room, the extracted moving target can provide samples for video post-processing.The experimental results show that the proposed key frame extraction algorithm can quickly grasp the pedestrian information in the motion video and provide effective processing samples for the motion target for video post-processing.
An Experimental Comparison for Compression of PLA Specimens Printed in In-Plane and Out-of-Plane Directions

The present work proposes an experimental comparison between the in-plane and out-of-plane compressive behaviors of 3D printed PolyLactic Acid (PLA) specimens.These specimens are produced via the Fused Deposition Modeling (FDM) technique by using two different printing directions: the first printing direction in the xz plane allows experimental compression tests in the out-of-plane direction; the second printing direction in the xy plane allows experimental compression tests in the in-plane direction.The specimens are more rigid in the out-of-plane direction than in the in-plane direction.In the first case, a bigger linear compressive Young modulus has been found and both maximum stress and proportional limit stress were determined.In the second case, the linear compressive Young modulus is smaller and the specimens are less rigid but at the same time more resistant: in fact, no maximum stress and proportional limit stress have been obtained because the test machine has reached the maximum applicable load.This research demonstrates as the compressive behavior of 3D FDM printed polymeric elements depends on the printing and test directions and this feature is fundamental when such elements will be used for structural applications.The experimental tests performed in the present paper are accompanied by statistical and capability analyses for the dimensional parameters and for the mechanical properties in order to also evaluate the stability of the production process and the validity ranges for the mechanical characteristics.
The Effect of Class Definitions on the Transferability of Adversarial Attacks Against Forensic CNNs

In recent years, convolutional neural networks (CNNs) have been widely used by researchers to perform forensic tasks such as image tampering detection. At the same time, adversarial attacks have been developed that are capable of fooling CNN-based classifiers. Understanding the transferability of adversarial attacks, i.e. an attacks ability to attack a different CNN than the one it was trained against, has important implications for designing CNNs that are resistant to attacks. While attacks on object recognition CNNs are believed to be transferrable, recent work by Barni et al. has shown that attacks on forensic CNNs have difficulty transferring to other CNN architectures or CNNs trained using different datasets. In this paper, we demonstrate that adversarial attacks on forensic CNNs are even less transferrable than previously thought even between virtually identical CNN architectures! We show that several common adversarial attacks against CNNs trained to identify image manipulation fail to transfer to CNNs whose only difference is in the class definitions (i.e. the same CNN architectures trained using the same data). We note that all formulations of class definitions contain the unaltered class. This has important implications for the future design of forensic CNNs that are robust to adversarial and anti-forensic attacks.
Active Model Estimation in Markov Decision Processes.

We study the problem of efficient exploration in order to learn an accurate model of an environment, modeled as a Markov decision process (MDP). Efficient exploration in this problem requires the agent to identify the regions in which estimating the model is more difficult and then exploit this knowledge to collect more samples there. In this paper, we formalize this problem, introduce the first algorithm to learn an $\epsilon$-accurate estimate of the dynamics, and provide its sample complexity analysis. While this algorithm enjoys strong guarantees in the large-sample regime, it tends to have a poor performance in early stages of exploration. To address this issue, we propose an algorithm that is based on maximum weighted entropy, a heuristic that stems from common sense and our theoretical analysis. The main idea here is to cover the entire state-action space with the weight proportional to the noise in the transitions. Using a number of simple domains with heterogeneous noise in their transitions, we show that our heuristic-based algorithm outperforms both our original algorithm and the maximum entropy algorithm in the small sample regime, while achieving similar asymptotic performance as that of the original algorithm.
A Blockchain Simulator for Evaluating Consensus Algorithms in Diverse Networking Environments

The massive scale, heterogeneity and distributed nature of Internet-of-Things (IoT) presents challenges in realizing a practical and effective security solution. Blockchain empowered platforms and technologies have been proposed to address aspects of this challenge. In order to realize a practical Blockchain deployment for IoT, there is a need for a testing and evaluation platform to evaluate performance and security of Blockchain applications and systems. In this paper, we present a Blockchain simulator that evaluates the consensus algorithms in a realistic and configurable network environment. Though, there are several Blockchain evaluation platforms, they are either wedded to a specific consensus protocol and do not allow evaluation in a configurable and realistic network environment. In our proposed simulator, we provide the ability to evaluate the impact of the consensus and network layer that will inform practitioners on the appropriate choice of consensus algorithms and the impact of network layer events in congested or contested scenarios in IoT. To accomplish this a generalized representation for consensus methods is proposed. The Blockchain simulator uses a discrete event simulation engine for fidelity and increased scalability. We evaluate the performance of the simulator by varying the number of peer nodes and number of messages required to find consensus.