Reviewerly/exHarmony huggingface dataset:
Towards Generating Adversarial Examples on Combined Systems of Automatic Speaker Verification and Spoofing Countermeasure

The security of unprotected automatic speaker verification (ASV) system is vulnerable to a variety of spoofing attacks where an attacker (adversary) disguises him/herself as a specific targeted user. It is a common practice to use spoofing countermeasure (CM) to improve the security of ASV systems so as to avoid illegal access. However, recent studies have shown that both ASV and CM systems are vulnerable to adversarial attacks. Previous researches mainly focus on adversarial attacks on a single ASV or CM system. But in practical scenarios, ASVs are typically deployed in conjunction with CM. In this paper, we investigate attacking the tandem system of ASV and CM with adversarial examples. The joint objective function is designed to restrict the generating process of adversarial examples. The joint gradient of the ASV and CM system is derived to generate adversarial examples. Fast Gradient Sign Method (FSGM) and Projected Gradient Descent (PGD) are utilized to study the vulnerability of tandem verification systems against white-box adversarial attacks. Through our attack, audio samples whose original labels are spoof or nontarget can be successfully accepted by the tandem system. Experimental results on the ASVSpoof2019 dataset show that the tandem system is vulnerable to our proposed attack.
When We Study the Ability to Attend, What Exactly Are We Trying to Understand?

When we study the human ability to attend, what exactly do we seek to understand? It is not clear what the answer might be to this question. There is still so much to know, while acknowledging the tremendous progress of past decades of research. It is as if each new study adds a tile to the mosaic that, when viewed from a distance, we hope will reveal the big picture of attention. However, there is no map as to how each tile might be placed nor any guide as to what the overall picture might be. It is like digging up bits of mosaic tile at an ancient archeological site with no key as to where to look and then not only having to decide which picture it belongs to but also where exactly in that puzzle it should be placed. I argue that, although the unearthing of puzzle pieces is very important, so is their placement, but this seems much less emphasized. We have mostly unearthed a treasure trove of puzzle pieces but they are all waiting for cleaning and reassembly. It is an activity that is scientifically far riskier, but with great risk comes a greater reward. Here, I will look into two areas of broad agreement, specifically regarding visual attention, and dig deeper into their more nuanced meanings, in the hope of sketching a starting point for the guide to the attention mosaic. The goal is to situate visual attention as a purely computational problem and not as a data explanation task; it may become easier to place the puzzle pieces once you understand why they exist in the first place.
A Real-Time Ship Detector via a Common Camera

Advanced radars and satellites, suitable for remote monitoring, inappropriately reach the economical requirements of short-range detection. Compared with far-sightedness skills, common visible-light sensors offer more ample features conducive to distinguishing the classes. Therefore, ship detection based on visible-light cameras should cooperate with remote detection technologies. However, compared with detectors applied in inland transportation, the lack of fast ship detectors, detecting multiple ship classes, is non-negligible. To fill this gap, we propose a real-time ship detector based on fast U-Net and remapping attention (FRSD) via a common camera. The fast U-Net offered compresses features in the channel dimension to decrease the number of training parameters. The remapping attention introduced boosts the performance in various rain–fog weather conditions while maintaining the real-time speed. The ship dataset proposed contains more than 20,000 samples, alleviating the lack of ship datasets containing various classes. Data augmentation of the cross-background is especially proposed to further promote the diversity of the detecting background. In addition, the rain–fog dataset proposed, containing more than 500 rain–fog images, simulates various marine rain–fog scenarios and soaks the testing image to validate the robustness of ship detectors. Experiments demonstrate that FRSD performs relatively robustly and detects 9 classes with an mAP of more than 83%, reaching a state-of-the-art level.
A Simplistic and Cost-Effective Design for Real-World Development of an Ambient Assisted Living System for Fall Detection and Indoor Localization: Proof-of-Concept

Falls, highly common in the constantly increasing global aging population, can have a variety of negative effects on their health, well-being, and quality of life, including restricting their capabilities to conduct activities of daily living (ADLs), which are crucial for one’s sustenance. Timely assistance during falls is highly necessary, which involves tracking the indoor location of the elderly during their diverse navigational patterns associated with different activities to detect the precise location of a fall. With the decreasing caregiver population on a global scale, it is important that the future of intelligent living environments can detect falls during ADLs while being able to track the indoor location of the elderly in the real world. Prior works in these fields have several limitations, such as the lack of functionalities to detect falls and indoor locations in a simultaneous manner, high cost of implementation, complicated design, the requirement of multiple hardware components for deployment, and the necessity to develop new hardware for implementation, which make the wide-scale deployment of such technologies challenging. To address these challenges, this work proposes a cost-effective and simplistic design paradigm for an ambient assisted living system that can capture multimodal components of user behaviors during ADLs that are necessary for performing fall detection and indoor localization in a simultaneous manner in the real-world. Proof-of-concept results from real-world experiments are presented to uphold the effective working of the system. The findings from two comparative studies with prior works in this field are also presented to uphold the novelty of this work. The first comparative study shows how the proposed system outperforms prior works in the areas of indoor localization and fall detection in terms of the effectiveness of its software design and hardware design. The second comparative study shows that the cost of the development of this system is the lowest as compared to prior works in these fields, which involved real-world development of the underlining systems, thereby upholding its cost-effective nature.
Sentiment Analysis of E-commerce Consumer Based on Product Delivery Time Using Machine Learning

In this modern era, e-commerce sites, online selling, and purchasing are at the top of the list. Product quality and delivery time usually divert people's sentiments about e-commerce. We conducted a sentiment analysis of consumer comments on Daraz and Evaly's Facebook pages, and data were gathered from these two pages comments of Facebook. We evaluated the mood of client comments in which they expressed their opinions and experience regarding e-commerce pages services. With diverse models such as logistics regression, decision tree, random forest, multinomial naive Bayes, K-neighbors, and linear support vector machine in n-grams, we employ unigram, bigram, and trigram features. With 90.65 and 89.93% accuracy in unigram and trigram, random forest is the most accurate. With an accuracy of 88.49% in bigram, decision tree is the most accurate. Among the finest fits are the unigram feature and random forest.
Staged query graph generation based on answer type for question answering over knowledge base

Question answering over knowledge base (KBQA) enables users to query over the knowledge base without the need to know the details. A range of existing KBQA approaches treats the entities mentioned in the given question as the starting point to find the answers. While helpful in achieving improvements on the existing benchmarks, they have some limitations on the strategy of query graph generation, which creates too many candidate queries and makes it hard to select the best-matching one to get the answer. In this paper, we propose a staged query graph generation approach based on the answer type, which exploits the correlation between questions and answer types to reduce the size of the candidate set and further improve the performance. Besides, we construct a question/answer-type (QAT) dataset aiming to predict the answer type of a given question. Extensive experiments demonstrate our method outperforms existing methods on both simple questions and complex questions.
Iraqi E-Voting System Based on Smart Contract Using Private Blockchain Technology

In democratic societies including Iraq, electronic voting (e-voting) is an available option as a viable voting system. This system is more economically advisable than face-to-face voting by eliminating the need to pay poll staff. It may also make voting more accessible to the population with impairments and those living overseas. This study will propose an e-voting system based on smart contracts by using a decentralized private Blockchain. The proposed e-voting system will provide transparency, accuracy, fairness, eligibility, anonymity, verifiability, and immutability. This research proposed the most secure way of voting with the help of Blockchain using a mobile application that can be used to implement a large-scale solution and cost-effective. The Blockchain-based system has been designed in conjunction with the existing ECC cryptosystem to assure the dependability, anonymity, and security of votes and voters. The system that has been developed can be used to properly conduct elections, using a quick response code (QR Code), face recognition, and fingerprint for identification before casting a ballot to ensure eligibility. This results in using mobile smartphones in the voting process where voters can observe the transparency of their votes. Furthermore, eliminates the possibility of fraud and manipulation of the votes made due to the used blockchain for secure storage.
Super-optimization of Smart Contracts

Smart contracts are programs deployed on a blockchain. They are executed for a monetary fee paid in gas —a clear optimization target for smart contract compilers. Because smart contracts are a young, fast-moving field without (manually) fine-tuned compilers, they highly benefit from automated and adaptable approaches, especially as smart contracts are effectively immutable, and as such need a high level of assurance. This makes them an ideal domain for applying formal methods. Super-optimization is a technique to find the best translation of a block of instructions by trying all possible sequences of instructions that produce the same result. We present a framework for super-optimizing smart contracts based on Max-SMT with two main ingredients: (1) a stack functional specification extracted from the basic blocks of a smart contract, which is simplified using rules capturing the semantics of arithmetic, bit-wise, and relational operations, and (2) the synthesis of optimized blocks , which finds—by means of an efficient SMT encoding—basic blocks with minimal gas cost whose stack functional specification is equal (modulo commutativity) to the extracted one. We implemented our framework in the tool syrup 2.0 . Through large-scale experiments on real-world smart contracts, we analyze performance improvements for different SMT encodings, as well as tradeoffs between quality of optimizations and required optimization time.
A Fast Quantum Image Component Labeling Algorithm

Component Labeling, as a fundamental preprocessing task in image understanding and pattern recognition, is an indispensable task in digital image processing. It has been proved that it is one of the most time-consuming tasks within pattern recognition. In this paper, a fast quantum image component labeling algorithm is proposed, which is the quantum counterpart of classical local-operator technique. A binary image is represented by the modified novel enhanced quantum image representation (NEQR) and a quantum parallel-shrink operator and quantum propagate operator are executed in succession, to finally obtain the component label. The time complexity of the proposed quantum image component labeling algorithm is O(n2), and the spatial complexity of the quantum circuits designed is O(cn). Simulation verifies the correctness of results.
Scalable Color Quantization for Task-centric Image Compression

Conventional image compression techniques targeted for the perceptual quality are not generally optimized for classification tasks using deep neural networks (DNNs). To compress images for DNN inference tasks, recent studies have proposed task-centric image compression methods with quantization techniques optimized for DNN inference. Among them, color quantization was proposed to reduce the amount of data per pixel by limiting the number of distinct colors (color space) in an image. However, quantizing images into various color space sizes requires training and inference of multiple DNNs, each of which is dedicated to each color space. To overcome this limitation, we propose a scalable color quantization method, where images with variable color space sizes can be extracted from a master image generated by a single DNN model. This scalability is enabled by weighted color grouping that constructs a color palette using critical color components for the classification task. We also propose an adaptive training method that can jointly optimize images with various color-space sizes. The results show that the proposed method supports dynamic changes of the color space size between 1–6 bit color space per pixel, while even increasing the inference accuracy at a low bit precision up to 20.2% and 46.6% compared to other task- and human-centric color quantizations, respectively.
Electron Probe Microanalysis measurement of <scp>F‐Cl‐Nb‐P</scp> for Geological Glasses

Quantitative in situ electron probe microanalysis (EPMA) was applied to determine the F, Cl, Nb and P contents of glass materials. Using reference glasses with low FeO T (&lt; 10% m/m ) mass fractions to construct a F calibration line to correct the Fe interference on the F peaks, is unsuitable for analysing natural Fe‐rich melt inclusions with up to 21% m/m FeO T , which commonly occur in picritic, nephelinitic and basanitic samples. For this reason, this study presents a series of seven F‐free glasses, which show a wider range of FeO T (1–20% m/m ) that were synthesised to improve the overlap correction of Fe on F. Using this improved method, reference glasses of USGS and glasses synthesised from powders of other rock RMs were analysed to evaluate well‐known reference materials (RMs) as quality control materials for EPMA measurement of F, Cl, Nb and P mass fractions. The results show that amongst the tested RMs, USGS BHVO‐2G is the most appropriate RM for F‐Cl determination because different pieces of this glass RM analysed by different laboratories yielded results that agree within uncertainty.
A Novel Distributed Ledger Technology Structure for Wireless Sensor Networks Based on IOTA Tangle

Wireless Sensor Networks (WSNs) consist of many wireless sensor nodes for collecting and sensing information. Distributed Ledger Technologies (DLTs) such as Blockchain allow organizations to store and share data in a decentralized, immutable, and secure way through a network of distributed peer-to-peer users or computers. The application of DLT to the Internet of Things (IoT) can improve the efficiency of information transmission and network security. IOTA Tangle is a DLT developed for IoT to process transactions. WSN is a core technology for IoT, and the two have a lot in common in terms of applications. Many solutions for IoT applications can be implemented with WSNs. However, the sensor nodes in WSNs have limited processing speed, storage capacity, communication bandwidth, and energy consumption capabilities. Therefore, a lightweight solution needs to be designed according to the characteristics of WSNs, rather than directly applying Tangle. The similarities between IoT and WSNs determine that the Tangle can be an essential reference for designing new solutions. In this paper, we propose a new DLT structure based on Tangle named Fishing Net Topology (FNT). The aim is to meet the lightweight requirements of sensor nodes in WSNs. We compared FNT with Tangle in terms of the packet network structure and algorithm and also experimentally analyzed the waste rate in the FNT network. It is concluded that FNT can be used at a reasonable Rate based on the requirement of the WSN applications, and it can significantly reduce the computation while enhancing the security of WSNs. Due to its structural stability and algorithmic simplicity, FNT outperforms Tangle in WSNs.
Future Event Prediction Based on Temporal Knowledge Graph Embedding

Accurate prediction of future events brings great benefits and reduces losses for society in many domains, such as civil unrest, pandemics, and crimes.Knowledge graph is a general language for describing and modeling complex systems.Different types of events continually occur, which are often related to historical and concurrent events.In this paper, we formalize the future event prediction as a temporal knowledge graph reasoning problem.Most existing studies either conduct reasoning on static knowledge graphs or assume knowledges graphs of all timestamps are available during the training process.As a result, they cannot effectively reason over temporal knowledge graphs and predict events happening in the future.To address this problem, some recent works learn to infer future events based on historical event-based temporal knowledge graphs.However, these methods do not comprehensively consider the latent patterns and influences behind historical events and concurrent events simultaneously.This paper proposes a new graph representation learning model, namely Recurrent Event Graph ATtention Network (RE-GAT), based on a novel historical and concurrent events attention-aware mechanism by modeling the event knowledge graph sequence recurrently.More specifically, our RE-GAT uses an attention-based historical events embedding module to encode past events, and employs an attentionbased concurrent events embedding module to model the associations of events at the same timestamp.A translation-based decoder module and a learning objective are developed to optimize the embeddings of entities and relations.We evaluate our proposed method on four benchmark datasets.Extensive experimental results demonstrate the superiority of our RE-GAT model comparing to various baselines, which proves that our method can more accurately predict what events are going to happen.
The Kimchi premium and bitcoin-cashing outlets

The Kimchi premium – the gap in cryptocurrency prices in Korean exchanges compared to US counterparts – soared up to 20.8% on 19 May 2021. However, due to capital controls and financial regulations on virtual currencies in South Korea, it seemed that most profits from the Kimchi premium were exploited by foreign arbitrageurs. Using confidential administrative data for 1,211 foreign exchange business institutions in South Korea, this study reveals that the Kimchi premium is positively related to the upsurge of overseas remittances to China, which used to be the world's largest cryptocurrency mining hub until all cryptocurrency transactions were banned in 2017. Our findings imply that Chinese arbitrageurs use Korean financial institutions as bitcoin-cashing outlets, converting virtual currencies into fiat ones while the Kimchi premium was persistently high.
Robust discriminative projection with dynamic graph regularization for feature extraction and classification

Learning an efficient discriminative projection to extract meaningful features from high-dimensional data is a challenging task in machine learning. However, most of the existing discriminative projection literatures have the following two issues: (1) the performance of projection would largely degenerate when handling the data corrupted by complex outlier, e.g., noisy and outlier mixed data; and (2) fixed graph in graph regularization can be negatively affected by outliers or noisy features. To deal with above issues, we design a new feature extraction method called robust discriminative projection with dynamic graph regularization (RDPDG) to handle above problems. Specifically, RDPDG simulates complex outlier features, i.e., low rank and sparse structured outlier features, using two matrices constrained by the nuclear and adaptive loss norm. RDPDG is able to boost the performance of classification, which primarily due to two key factors: (1) learns reliable projection through a unified framework that integrates discriminant analysis, reconstituted data and manifold learning; and (2) can exploit the local intrinsic structure among data effectively by dynamic graph regularization. Furthermore, an alternating iteration algorithm is designed to solve the proposed nonconvex model, which seamlessly connects the update process of all components in the RDPDG framework, offering a powerful discriminative capability for projection. Comprehensive experiments on a synthetic and several real-world data sets demonstrate that the RDPDG outperforms the precious related methods for classification.
Applying a reproducible research approach to distance education

Objectives . Emerging as a response to the global threats presented by the COVID-19 pandemic, the changing nature of problem-solving in the field of information technology associated with economic globalization, including possibilities of remote working, imposes new requirements on the competencies and skills of future professionals. This, in turn, requires adjustments to the higher education process. Agile project management methodologies such as Scrum, along with Infrastructure-as-Code approaches in information and telecommunication infrastructure management, and Documentation-as-Code approaches in documentation development, aim to present design, development, testing, and documentation as short cycle iterative processes to permit the rapid and transparent addition of new product value in discrete portions. Applied to the education sphere, this approach implies new knowledge and practical skills of students that can be easily and transparently measured in the process of mastering a discipline. The present paper aims to develop methods of applying modern software development techniques to training students of technical specialties. Methods . The use of reproducible research methods and agile design practices while organizing and managing practical tasks for students is proposed. Results . Contemporary tools used in software development based on Git hosting services (GitLab and GitHub) are presented alongside reproducible research paradigms in distance education using the R Markdown format by RStudio. Conclusions . In addition to increasing the involvement of students in the process of practical tasks, the proposed approach can be used to reduce the workload of teachers when checking and evaluating student working results.