Reviewerly/exHarmony huggingface dataset:
Demodulation of Low SNR QPSK Signal Based on CNN

In order to overcome the problems of high bit error rate of QPSK demodulation under low signal-to-noise ratio in traditional modulation pattern recognition methods, a new QPSK demodulation method under the condition of low signal-to-noise ratio based on convolutional neural network is proposed. This method inputs the IQ signal into the denoise network, and then the convolution neural network is used for demodulation. The loss function is the cross entropy of the demodulated symbol sequence and the original symbol sequence multiplied by the transmitter RF signal and the denoise of the receiver. On the basis of the above, through this method, the bit error rate of QPSK demodulation when the signal-to-noise ratio is in a relatively low state can be effectively reduced. Simulation results show that this method can effectively reduce the bit error rate under the condition of low signal-to-noise ratio. When the SNR is -20dB, the average bit error rate is 6.98%, which is 38.85% lower than traditional coherent demodulation.
Triple M: A Practical Text-to-Speech Synthesis System with Multi-Guidance Attention and Multi-Band Multi-Time LPCNet

In this work, a robust and efficient text-to-speech (TTS) synthesis system named Triple M is proposed for large-scale online application. The key components of Triple M are: 1) A sequence-to-sequence model adopts a novel multi-guidance attention to transfer complementary advantages from guiding attention mechanisms to the basic attention mechanism without in-domain performance loss and online service modification. Compared with single attention mechanism, multi-guidance attention not only brings better naturalness to long sentence synthesis, but also reduces the word error rate by 26.8%. 2) A new efficient multi-band multi-time vocoder framework, which reduces the computational complexity from 2.8 to 1.0 GFLOP and speeds up LPCNet by 2.75x on a single CPU.
Phishing isn't going away anytime soon

Computer Fraud & SecurityVol. 2019, No. 3 FeaturesPhishing isn't going away anytime soonRoger A GrimesRoger A GrimesKnowBe4Search for more papers by this authorRoger A GrimesPublished Online:5 Nov 2021https://doi.org/10.1016/S1361-3723(19)30032-6AboutSectionsView articleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareShare onFacebookTwitterLinked InEmail View article FiguresReferencesRelatedDetails 1 January 2019Volume 2019Issue 3ISSN (print): 1361-3723ISSN (online): 1873-7056 Metrics History Published online 5 November 2021 Published in print 1 January 2019 Information© MA Business Ltd.PDF download
RESTAURANT RECOMMENDER SYSTEM USING ITEM BASED COLLABORATIVE FILTERING AND ADJUSTED COSINE ALGORITHM SIMILARITY

In 2018, the Ministry of Industry (Kemenperin) stated that the food and beverage sector contributed 6.34% of the national gross domestic product (GDP). Currently, culinary information can be easily found, both in print and online. The amount of information available sometimes makes people over-informed, making it difficult to choose a restaurant based on their preferences. To assist consumers in selecting a restaurant, we need a system that can provide several recommendations. This study aims to implement the item-based Collaborative Filtering method using the Adjusted Cosine Similarity algorithm on a restaurant recommendation system. The test was carried out with 40 samples from UIN Syarif Hidayatullah Jakarta using purposive sampling because the sample was selected based on specific criteria, and 40 respondents can be said to be correct because of the minimum number of respondents is 30. The accuracy test uses precision, and the determination of the error value uses MAE. The analysis of the research results used three scenarios, which are 5, 20, and 40 users. The third scenario produces the best precision and MAE values. Precision is better if the precision value is close to 1, and MAE is getting better if the MAE value is getting closer to 0. So it can be concluded that the Item-Based method with the Adjusted Cosine algorithm has the best accuracy and error values when the number of users grows.
Real World Robustness from Systematic Noise

Systematic error, which is not determined by chance, often refers to the inaccuracy (involving either the observation or measurement process) inherent to a system. In this paper, we exhibit some long-neglected but frequent-happening adversarial examples caused by systematic error. More specifically, we find the trained neural network classifier can be fooled by inconsistent implementations of image decoding and resize. This tiny difference between these implementations often causes an accuracy drop from training to deployment. To benchmark these real-world adversarial examples, we propose ImageNet-S dataset, which enables researchers to measure a classifier's robustness to systematic error. For example, we find a normal ResNet-50 trained on ImageNet can have 1%-5% accuracy difference due to the systematic error. Together our evaluation and dataset may aid future work toward real-world robustness and practical generalization.
On Post-Editability of Machine Translated Texts

Machine Translated texts are often far from perfect and postediting is essential to get publishable quality. Post-editing may not always be a pleasant task. However, modern machine translation (MT) approaches like Statistical MT (SMT) and Neural MT (NMT) seem to hold greater promise. In this work, we present a quantitative method for scoring translations and computing the post-editability of MT system outputs.We show that the scores we get correlate well with MT evaluation metrics as also with the actual time and effort required for post-editing. We compare the outputs of three modern MT systems namely phrase-based SMT (PBMT), NMT, and Google translate for their Post-Editability for English to Hindi translation. Further, we explore the effect of various kinds of errors in MT outputs on postediting time and effort. Including an Indian language in this kind of post-editability study and analyzing the influence oferrors on postediting time and effort for NMT are highlights of this work.
Student Performance Prediction Using A Cascaded Bi-level Feature Selection Approach

Features in educational data are ambiguous which leads to noisy features and curse of dimensionality problems. These problems are solved via feature selection. There are existing models for features selection. These models were created using either a single-level embedded, wrapperbased or filter-based methods. However single-level filter-based methods ignore feature dependencies and ignore the interaction with the classifier. The embedded and wrapper based feature selection methods interact with the classifier, but they can only select the optimal subset for a particular classifier. So their selected features may be worse for other classifiers. Hence this research proposes a robust Cascade Bi-Level (CBL) feature selection technique for student performance prediction that will minimize the limitations of using a single-level technique. The proposed CBL feature selection technique consists of the Relief technique at first-level and the Particle Swarm Optimization (PSO) at the second-level. The proposed technique was evaluated using the UCI student performance dataset. In comparison with the performance of the single-level feature selection technique the proposed technique achieved an accuracy of 94.94% which was better than the values achieved by the single-level PSO with an accuracy of 93.67% for the binary classification task. These results show that CBL can effectively predict student performance.
Automatic Comprehension of Geometry Problems using AMR Parser (S)

Automatic comprehension of geometry problems described in natural language is a crucial and challenging stage of numerous automatic geometry problem solvers.These systems should comprehend the existing information in natural language geometry problems with the purpose to extract the geometric relationships between elements and to accomplish automatic solutions using intelligent methods.Abstract Meaning Representation is a popular framework for annotating whole sentence meaning.This paper proposes the addition of a new feature to an existing transition-based AMR parser that constructs AMR graphs from statement of geometry problems described in English language.The new feature consists in explicit embedding of the coreference detection into the parser.Integrating the automatic comprehension method with different geometry systems will greatly enhance the efficiency and intelligence in automatic solving.This approach shows improvements over the best previously published systems for extract information from statement of geometry problems described in English language.
Exercise Perceptions: Experience Report from a Secure Software Development Course

The ubiquitous use of software in critical systems necessitates integrating cybersecurity concepts into the software engineering curriculum so that students studying software engineering have adequate knowledge to securely develop software projects, which could potentially secure critical systems. An experience report of developing and conducting a course can help educators to gain an understanding of student preferences on topics related to secure software development. We provide an experience report related to the ‘Secure Software Development’ course conducted at Tennessee Technological University. We discuss student motivations, as well as positive and negative perceptions of students towards exercises. Based on our findings, we recommend educators to integrate real-world exercises into a secure software development course with careful consideration of tool documentation, balance in exercise diversity, and student background.
Implementasi Media Pembelajaran Interaktif Mata Kuliah Pengantar Multimedia Menggunakan App Inventor Berbasis Android

App Inventor 2 was used to construct interactive media for multimedia introductory classes. However, the study was conducted by employing the Multimedia Development Life Cycle (MDLC) technique. The Concept, design, material collection, assembling, testing, and distribution are the six stages that run up the whole method. The results showed that the validation of media experts was in range 79% and categorized as feasible. While the validation results of material experts are 73% and categorized as feasible. The results of the test on users obtained 84% and stated that the interactive media is acceptable and feasible to be applied as teaching and learning facilities in terms of the introductory multimedia courses.
Rethinking Deep Image Prior for Denoising

Deep image prior (DIP) serves as a good inductive bias for diverse inverse problems. Among them, denoising is known to be particularly challenging for the DIP due to noise fitting with the requirement of an early stopping. To address the issue, we first analyze the DIP by the notion of effective degrees of freedom (DF) to monitor the optimization progress and propose a principled stopping criterion before fitting to noise without access of a paired ground truth image for Gaussian noise. We also propose the `stochastic temporal ensemble (STE)' method for incorporating techniques to further improve DIP's performance for denoising. We additionally extend our method to Poisson noise. Our empirical validations show that given a single noisy image, our method denoises the image while preserving rich textual details. Further, our approach outperforms prior arts in LPIPS by large margins with comparable PSNR and SSIM on seven different datasets.
Towards Learning Outcome Prediction via Modeling Question Explanations and Student Responses

Predicting the current learning outcome of a student based on his/her responses to previous questions is a vital task for personalized education.Deep learning based models have achieved satisfactory performance on this task as they can automatically extract meaningful signals related to student learning outcome predictions.However, these models are unable to leverage the following critical pieces of information.First, the correct choice to a question is often accompanied by a textual explanation on why this choice is correct.
A Hybrid Cloud Deployment Architecture for Privacy-Preserving Collaborative Genome-Wide Association Studies

The increasing availability of sequenced human genomes is enabling health professionals and genomics researchers to well understand the implication of genetic variants in the development of common diseases, notably by means of genome-wide association studies (GWAS) which are very promising for personalized medicine and diagnostic testing. However, the ever present need to handle genetic data from different sources to conduct large studies entails multiple privacy and security issues. Actually, classical methods of anonymization are inapplicable for genetic data that are now known to be identifying per se. In this paper, we propose a novel framework for privacy-preserving collaborative GWAS performed in the cloud. Indeed, our proposal is the first framework which combines a hybrid cloud deployment with a set of four security mechanisms that are digital watermarking, homomorphic encryption, meta-data de-identification and the Intel Software Guard Extensions technology in order to ensure confidentiality of genetic data as well as their integrity. Furthermore, our approach describes meta-data management which has rarely been considered in state-of-the-art propositions despite their importance to genetic analyses; in addition, the new deployment model we suggest fits with existing infrastructures which makes its integration straightforward. Experimental results of a prototypical implementation on typical data sizes demonstrate that our solution protocol is feasible and that the framework is practical for real-world scenarios.
A Query-Efficient Quantum Algorithm for Maximum Matching on General Graphs

We design quantum algorithms for maximum matching. Working in the query model, in both adjacency matrix and adjacency list settings, we improve on the best known algorithms for general graphs, matching previously obtained results for bipartite graphs. In particular, for a graph with n vertices and m edges, our algorithm makes $$O(n^{7/4})$$ queries in the matrix model and $$O(n^{3/4}(m+n)^{1/2})$$ queries in the list model. Our approach combines Gabow’s classical maximum matching algorithm [Gabow, Fundamenta Informaticae, ’17] with the guessing tree method of Beigi and Taghavi [Beigi and Taghavi, Quantum, ’20].
Raw Waveform Encoder with Multi-Scale Globally Attentive Locally Recurrent Networks for End-to-End Speech Recognition

End-to-end speech recognition generally uses hand-engineered acoustic features as input and excludes the feature extraction module from its joint optimization. To extract learnable and adaptive features and mitigate information loss, we propose a new encoder that adopts globally attentive locally recurrent (GALR) networks and directly takes raw waveform as input. We observe improved ASR performance and robustness by applying GALR on different window lengths to aggregate fine-grain temporal information into multi-scale acoustic features. Experiments are conducted on a benchmark dataset AISHELL-2 and two large-scale Mandarin speech corpus of 5,000 hours and 21,000 hours. With faster speed and comparable model size, our proposed multi-scale GALR waveform encoder achieved consistent character error rate reductions (CERRs) from 7.9% to 28.1% relative over strong baselines, including Conformer and TDNN-Conformer. In particular, our approach demonstrated notable robustness than the traditional handcrafted features and outperformed the baseline MFCC-based TDNN-Conformer model by a 15.2% CERR on a music-mixed real-world speech test set.
Threat Analysis using N-median Outlier Detection Method with Deviation Score

Any organization can only operate optimally if all employees fulfil their roles and responsibilities.For the majority of tasks and activities, each employee must collaborate with other employees.Every employee must log their activities related with their roles, responsibilities, and access permissions.Some users may deviate from their work or abuse their access rights in order to gain a benefit, such as money, or to harm an organization's reputation.Insider threats are caused by these types of users/employees, and those users are known as insiders.Detecting insiders after they have caused damage is more difficult than preventing them from posing a threat.We proposed a method for determining the amount of deviation a user has from other users in the same role group in terms of log activities.This deviation score can be used by role managers to double-check before sharing sensitive information or granting access rights to the entire role group.We first identified the abnormal users in each individual role, and then used distance measures to calculate their deviation score.In a large data space, we considered the problem of identifying abnormal users as outlier detection.The user log activities were first converted using statistics, and the data was then normalized using Min-Max scalar standardization, using PCA to transform the normalized data to a two-dimensional plane to reduce dimensionality.The results of N-Median Outlier Detection (NMOD) are then compared to those of Neighbour-based and Cluster-based outlier detection algorithms.
Enhanced Framework for Big Data Requirement Elicitation

Requirement engineering is one of the software development life cycle phases; it has been recognized as an important phase for collecting and analyzing a system's goals.However, despite its importance, requirement engineering has several limitations such as incomplete requirements, vague requirements, lack of prioritization, and less user involvement, all of which affect requirement quality.With the emergence of big data technology, the complexity of big data, which is defined by large data volume, high velocity, and large data variety, has gradually increased, affecting the quality of big data software requirements.This study proposes a framework with four sequential phases to improve requirement engineering quality through big data software development.By integrating the proposed framework's phases in which user requirements are collected in a complete vision using traditional requirement elicitation techniques with agile methodology and mind mapping, the collected requirements are displayed via a graphical representation using mind maps to achieve high requirement accuracy with connectivity and modifiability, enabling the accurate prioritization of requirements implemented using agile SCRUM methodology.The proposed framework improves requirement quality in big data software development, which is represented by accuracy, completeness, connectivity, and modifiability to understand the value of the collected requirements and effectively affect the quality of the implementation phase.
Vibration-Based Pattern Password Approach for Visually Impaired People

The pattern password method is amongst the most attractive authentication methods and involves drawing a pattern; this is seen as easier than typing a password.However, since people with visual impairments have been increasing their usage of smart devices, this method is inaccessible for them as it requires them to select points on the touch screen.Therefore, this paper exploits the haptic technology by introducing a vibration-based pattern password approach in which the vibration feedback plays an important role.This approach allows visually impaired people to use a pattern password through two developed vibration feedback: pulses, which are counted by the user, and duration, which has to be estimated by the user.In order to make the proposed approach capable to prevent shoulder-surfing attacks, a camouflage pattern approach is applied.An experimental study is conducted to evaluate the proposed approach, the results of which show that the vibration pulses feedback is usable and resistant to shoulder-surfing attacks.
Multi-View Attention-based Late Fusion (MVALF) CADx system for breast cancer using deep learning

Breast cancer is a leading cause of death among women. Early detection can significantly reduce the mortality rate among women and improve their prognosis. Mammography is the first line procedure for early diagnosis. In the early era, conventional Computer-Aided Diagnosis (CADx) systems for breast lesion diagnosis were based on just single view information. The last decade evidence the use of two views mammogram: Medio-Lateral Oblique (MLO) and Cranio-Caudal (CC) view for the CADx systems. Most recent studies show the effectiveness of four views of mammogram to train CADx system with feature fusion strategy for classification task. In this paper, we proposed an end-to-end Multi-View Attention-based Late Fusion (MVALF) CADx system that fused the obtained predictions of four view models, which is trained for each view separately. These separate models have different predictive ability for each class. The appropriate fusion of multi-view models can achieve better diagnosis performance. So, it is necessary to assign the proper weights to the multi-view classification models. To resolve this issue, attention-based weighting mechanism is adopted to assign the proper weights to trained models for fusion strategy. The proposed methodology is used for the classification of mammogram into normal, mass, calcification, malignant masses and benign masses. The publicly available datasets CBIS-DDSM and mini-MIAS are used for the experimentation. The results show that our proposed system achieved 0.996 AUC for normal vs. abnormal, 0.922 for mass vs. calcification and 0.896 for malignant vs. benign masses. Superior results are seen for the classification of malignant vs benign masses with our proposed approach, which is higher than the results using single view, two views and four views early fusion-based systems. The overall results of each level show the potential of multi-view late fusion with transfer learning in the diagnosis of breast cancer.
Journalistic Guidelines Aware News Image Captioning

The task of news article image captioning aims to generate descriptive and informative captions for news article images. Unlike conventional image captions that simply describe the content of the image in general terms, news image captions follow journalistic guidelines and rely heavily on named entities to describe the image content, often drawing context from the whole article they are associated with. In this work, we propose a new approach to this task, motivated by caption guidelines that journalists follow. Our approach, Journalistic Guidelines Aware News Image Captioning (JoGANIC), leverages the structure of captions to improve the generation quality and guide our representation design. Experimental results, including detailed ablation studies, on two large-scale publicly available datasets show that JoGANIC substantially outperforms state-of-the-art methods both on caption generation and named entity related metrics.
Some Notes on a Formal Algebraic Structure of Cryptology

Cryptology, since its advent as an art, art of secret writing, has slowly evolved and changed, above all since the middle of the last century. It has gone on to obtain a more solid rank as an applied mathematical science. We want to propose some annotations in this regard in this paper. To do this, and after reviewing the broad spectrum of methods and systems throughout history, and from the traditional classification, we offer a reordering in a more compact and complete way by placing the cryptographic diversity from the algebraic binary relations. This foundation of cryptological operations from the principles of algebra is enriched by adding what we call pre-cryptological operations which we show as a necessary complement to the entire structure of cryptology. From this framework, we believe that it is improved the diversity of questions related to the meaning, the fundamentals, the statute itself, and the possibilities of cryptological science.