Reviewerly/exHarmony huggingface dataset:
Invited Speech: Data Analytics and (Interpretable) Machine Learning for Social Good

In recent years, in all contexts of our lives, we have seen a real explosion of data. From a research standpoint, data processing needs have increasingly become common in an ever growing number of applications, with potential benefits not only in our work but also in our lives: the need not just to acquire, store and perform modest operational tasks but also to analyze and properly interpret data. In this talk, we consider some of the hottest and most demanding scenarios in our daily lives, which include: medical analytics to improve the quality of life of the elderly and reduce health care expenses; social network analytics for enhancing cultural heritage dissemination; exploration of work datafication potential in improving the management of human resources (HRM); game analytics to foster Computational Thinking in education. We describe the recent findings we have obtained in our research in these contexts using the latest technology for data analytics, including interpretable machine learning, and discuss the consequences and directions for the future.
Anti-Replay: A Fast and Lightweight Voice Replay Attack Detection System

Due to the open nature of voice and voice interface, attackers can easily record the user's voice commands and spoof the voice recognition systems by replaying them. Existing voice replay attack detection methods mainly rely on extra hardware to determine the sound source or require excessively computing resources for training the classifier with a large number of acoustic features. Hence, we propose Anti-Replay, a fast and lightweight detection system for voice replay attacks. To overcome the challenge of redundant classification feature vectors and complex calculation, we first investigate the spectrum difference between live-human voice and the replayed audio caused by the non-linear distortion of the attacker's microphones and speakers and then extract 72-dimensional feature vectors. Then we employ a single deep convolutional neural network classifier (SE-ResNet50) to enhance the robustness of our classification model. Finally, we evaluate the performance of Anti-Replay on the datasets of ASVspoof2017 and ASVspoof2019. Results show that Anti-Replay can achieve an equal error rate (EER) of 2.38% and 0.82% on two datasets, respectively. Meanwhile, the training time and the model size of Anti-Replay have decreased by 56% and 84% compared with the baseline model (i.e., CQCC-GMM).
Intelligent Question Answering System Based on Knowledge Graph

In order to build a smart city and pursue more efficient city management, various industries have introduced intelligent question answering into process management. The intelligent question answering system based on the knowledge graph is dedicated to feeding back the results of natural language questions to users, which greatly simplifies the urban management process. From the four methods of template-based, semantic parsing, sorting and retrieval, and deep learning, the intelligent question answer system based on the knowledge graph is summarized, and combined with the current application status, it summarizes and introduces the question answering system based on the knowledge graph that helps various industries in the construction of smart cities. It helps to provide new ideas for more researchers who are committed to urban management and knowledge graph question answering.
Shifting the paradigms from teaching project management to teaching software project management at Jordan university of science and technology based on the IEEE software engineering management knowledge area

software engineers usually find themselves in project/Administrative/Operational management positions. Therefore, it is very important to include a senior software project management course in any software engineering curriculum. software engineers mostly experience planning Challenges when it comes to budgeting, scheduling, identifying and assessing potential risks, and communicating with their customers and team members. transforming the traditional project management education to a software project management education is crucial to equipping software engineers with adequate practical experience in the software Domain.This paper describes a senior software project management course that is based on the project management Body of knowledge (PMBOK Guide), and the IEEE software engineering Body of knowledge (SWEBOK-V3.0). It also attempts to answer several research questions that involves the main differences between the traditional project management approaches and the software project management approach, and the course compliance with the accreditation Criteria of the institute of the engineering and technology (IET) and the accreditation board for engineering and technology (ABET).
Hybrid Quantum Variational Autoencoders for Representation Learning

Representation learning is a standard area that has seen many improvements based on machine learning advances. Quantum machine learning advances are now spreading across different application areas such as representation learning. This paper introduces a novel hybrid quantum machine learning approach to representation learning by using a quantum variational circuit that is trainable with traditional gradient descent techniques. We use marketing data to showcase the learning potential of our model.
An Improved RRT*-Based Real-Time Path Planning Algorithm for UAV

This work studies the real-time path planning for UAV in the dynamic environment. We propose a real-time path planning algorithm to get an optimized path suitable for UAV. The improved algorithm is based on the multi-optimized RRT&#x002A;. We redefine the cost function by considering both Euclidean distance and the turning angle. Then sampling method is modified for reducing the sampling space and fastening the convergence. Due to the branches connected to the executed waypoints no longer affect the next path, we introduce the node deletion method to reduce the memory consumption. Experimental results show that the path generated by the MO-RRT&#x002A;Replan algorithm has better quality. Also, our algorithm ensures real-time capability from these experiments.
Optimal deployment design of repeaters and memories in quantum networks

In quantum communication networks, the transmission success rate of entangled photons decreases exponentially with the transmission distance in fiber channel. With quantum repeaters, quantum networks can realize arbitrary long-distance links based on quantum entanglement swapping, quantum storage, and quantum teleportation. Thanks to the development of quantum memory technology, the throughput of quantum relay networks can be improved by increasing the number of quantum memories. In this paper, we study the routing metric, the goal of which is to achieve a higher throughput in a long-distance quantum network by reasonably allocating repeater positions with a given memory count between source and destination. A routing metric <tex>$E$</tex> was proposed, where the number and distance range of repeater deployments can be obtained by numerical analysis to support an optimal network throughput. According to the above principle, various repeaters were deployed on a linear quantum network to test the effectiveness of the proposed allocation mechanism with the given transmission loss, Bell-State Measurement (BSM) success rate, memory quantity, and distance. The results of evaluation show that increasing the number of repeaters blindly can lead to a waste of resources and a throughput decreasing, which implies that repeaters need to be placed at an appropriate deployment distance to achieve the optimal performance.
Enhancing Textual Knowledge Discovery using a Context-Awareness Approach

Semantic relation extraction has been a known branch in Natural language processing allowing the identification of relations between words. Knowledge-based approaches including wordnet and pattern matching approaches such as Hearst for hypernyms are used among many others. The search for relations is limited to sentences which avert the rest of the text narrowing the semantic relation extraction. In this paper, we introduce the use of context for improving the extraction using global information from the text and domain ontology.
A Comparative Study of NLP based Semantic Web Standard model using SPARQL database

The work offers evidence of a philosophical idea that many sectors will draw considerable interest. A remote natural language interface (NLI) is the term for the issue of knowledge bases (KBs). The framework uses software from CoreNLP for natural language technology and allows KBs to use the SPARQL query language. Natural Language Processing (NLP) uses the semantics of a natural language query to interpret and produce related information. The query can be asked about KBs containing linked information with properly defined relationships. The related data fits the RDF model in terms of semantic threefold: subject-predicate-objet relationships. The RDF model. The data are connected to the RDF model. Any KB can be understood semantically with our NLI. AI will learn how to grasp the semantics of the RDF data stored in the KB by having the correct training data. Similar information derives from KB questions thanks to its capacity to understand RDF data. Questions can be translated into SPARQL and asked by relational expertise on the KB.
Nonnegative Matrix Factorization Approach for Image Reconstruction

Nonnegative Matrix Factorization (NMF) has been proved to be a powerful method in data processing and has also shown success in applications such as feature extraction and image representation. In this paper, we propose two symmetric matrix-based methods, Symcom and Symize, to achieve square strategy (SQR) in SQR-NMF. This integration process allows the matrix to preserve symmetry property associated with images to enhance image reconstruction. Simulation results show that Symcom performs better on super wide or super long data matrices and Symize achieves better results on symmetrical data matrices.
A convolutional generative adversarial framework for data augmentation based on a robust optimal transport metric

Enhancement of the vanilla generative adversarial network (GAN) to preserve data variability in the presence of real world noise is of paramount significance in deep learning. In this study, we proposed a new distance metric of cosine distance in the framework of optimal transport (OT), and presented and validated a convolutional neural network (CNN) based GAN framework. In comparison with state-of-the-art methods based on Graphics Processing Units (GPU), the proposed framework could maintain the data diversity and quality best in terms of inception score (IS), Fr&#x00E9;chet inception distance (FID) and enhancing the classification network of bone age, and is robust to noise degradation. The proposed framework is independent of hardware and thus could also be extended to more advanced hardware such as specialized Tensor Processing Units (TPU), and could be a potential built-in component of a general deep learning networks for such applications as image classification, segmentation, registration, and object detection.
Ethiopic Base Characters Image Recognition using LSTM

Ethiopic scripts are widely used in Ethiopia by acting as a writing script in many languages. In the scripts, there is a shape similarity of characters and an abundant number of letters in the alphabet. The visual or shape similarity and a large number of alphabets are a problem for any optical character recognition system. To address this issue, in this study, we developed the Recurrent Neural Network-based character recognition model for 26 base Ethiopic characters. In the proposed model, we used our own collected 28 by 28 size image datasets, which has 8912 samples to train and test the recognition model and the Long Short-Term Memory networks used as the recognition algorithm. In the recognition model, a promising testing accuracy reported as a performance measure.
Fast Distributed Stochastic Gradient Descent for Big Data Classification

Large-scale classification is an important task of machine learning, especially in the smart city field, which is a big data environment. In recent years, single-threaded optimization algorithms can no longer meet the needs of big data applications. When a single machine is trained, it will face the problem of insufficient memory and limited computing capacity. Therefore, distributed algorithms have been proposed to solve big data classification, and have received much attention. Particularly, distributed gradient descent (GD) is a widely-used algorithm for solving big data problem. However, distributed algorithms based on GD always converge slowly and suffer from expensive time in the training process. To alleviate this problem, we propose a novel fast distributed stochastic gradient descent algorithm for big data classification in this paper. By formulating the original optimization problem as a series of subproblems for classification in each machine. In particular, we exploit a fast stochastic method combined with the Nesterov acceleration method for sub-problem optimization in parallel to improve the convergence speed, and thus reduce the training time. The experimental results on some public and benchmark data sets show that our proposed distributed algorithm can converge faster with the competitive accuracy performance compared with distributed GD-based methods, which is suitable to big data classification.
ZombieCoin3.0: On the Looming of a Novel Botnet Fortified by Distributed Ledger Technology and Internet of Things

P2P Botnet is famous for the resilience against termination. However, its dependence on Neighbor List (NL) makes it susceptible to infiltration and poison, also leading to a dearth of adequate protection of Botmaster&#x0027;s identity. So as to overcome these weaknesses, the novel botnet infrastructure combined with blockchain emerges. But due to some flaws such like high latency and expensive transactions fees on blockchain, these new schemes can&#x0027;t satisfy the need of constructing hyper large-scale botnet in practice. We propose ZombieCoin3.0, a Botnet underlying overlay leveraging IoT devices to be maintainers and integrated with Distributed Ledger Technology (DLT), which is able to deploy bots fully on blockchain. And it is versatile for file transfer, data feedback and other command and control scenarios that many contemporaries are impotent to cope with. Our proposal eliminates the P2P botnets&#x0027; dependence on NL and the bottlenecks that other blockchain combined botnets have. Experiments&#x0027; results authenticated that the effectiveness of our proposal as well as low latency of communication and nearly zero overhead of cost other comparatives unable to attain are achieved.
A Comprehensive Framework for Measuring and Improving API Usability

In this paper, we develop a comprehensive framework for measuring and improving API usability, consisting of both a comprehensive measurement framework and an analysis/modeling framework. Our measurement framework consists of both a set of direct measurements of API usability and a set of indirect measurements of factors that may influence API usability. Our analysis/modeling framework validates the measurement framework empirically and establishes predictive relations between its two sets of measurements. Such predictive relations can help us improve API usability through recommended actions applied on its influence factors. Examples from a preliminary case study are included to demonstrate the viability and potential effectiveness of our approach.
Pairwise Learning for Imbalanced Data Classification

Imbalanced data classification problems appear quite commonly in real-world applications and impose great challenges to traditional classification approaches which work well only on balanced data but usually perform poorly on the minority class when the data is imbalanced. Resampling preprocessing by oversampling the minority class or downsampling the majority class helps improve the performance but may suffer from overfitting or loss of information. In this paper we propose a novel method called pairwise robust support vector machine (PRSVM) to overcome the difficulty of imbalanced data classification. It adapts the non-convex robust support vector classification loss to the pairwise learning setting. In the training process, samples from the minority class and the majority class always appear as pairs. This automatically balances the impact of two classes. Simulations and real-world applications show that PRSVM is highly effective.
Developing an API for Block-Cipher Encryption powered by Supervised Learning

In cryptography, one of the standard classes of algorithms used today is called Advanced Encryption Standard (AES). Although these algorithms provide sufficient strength, they are not unbreakable, either by quantum computing or adversaries trying to decipher data. This study shows the potential of using machine learning (ML) by building an application Programming Interface (API) driven framework for encryption and decryption that utilizes ML to add abstracted layers of security over encrypted data. This abstracted data can only be interpreted by the developed framework. The utilization of this API will ultimately provide a cohesive system that can limit many vulnerabilities and prove how ML can be utilized as a tool against cyber-attacks.
RF Transmitter Identification and Classification Based on Deep Residual Shrinkage Network

Traditional specific emitter identification (SEI) methods need to artificially design extraction methods of the signal fingerprint features, which require a large amount of calculation and low classification accuracy. A supervised SEI framework based on deep residual shrinkage Network (DRSN) is proposed in this paper, which is used to adaptively learn the deep features of transmitted signals and classify them. In our framework, soft thresholding is introduced to reduce the noise of the original I/Q signal which contains a lot of noise, so as to reduce the influence of noise on the inaccuracy of RF fingerprint feature extraction. The I/Q signal of the receiver is sliced and spliced (pre-processing step) before inputting to the DRSN model to reduce the computational complexity. The experimental results show that the proposed scheme can correctly distinguish 16 credible transmitters with 98&#x0025; accuracy respectively. Compared with the existing SEI methods, our scheme has the advantages of excellent identification accuracy and strong anti-interference ability.
Blind Image Quality Assessment Based On Multi-scale Spatial Pyramid Pooling

The current state-of-the-art image quality assessment (IQA) methods are based on convolutional neural networks (CNNs). However, due to the constraint of the feature size by the fully connected layer, the original image must be cropped or stretched into a fixed-size (e.g., 224 x 224) before input into the CNN, which destroys the original structure and content of the image, thus affects the prediction accuracy of the quality score. In this paper, we propose a blind image quality evaluation method based on multi-scale spatial pyramid pooling (named CSPP-IQA). CSPP structure is proposed to fix the semantic feature size, the model can get its quality score by directly inputting the entire image avoid cropping or stretching. Moreover, with the help of introducing the CBAM attention mechanism and adaptive content understanding module, the proposed model improves the prediction accuracy, generalization ability and speed compared with state-of-the-art IQA methods. Experimental results show that the proposed algorithm can accurately and quickly predict the image quality of real scene images.
Decentralized Service Discovery and Composition in Dynamic IoT Systems

Internet of Things (IoT) interconnects billions of smart sensors, devices, actuators, as well as people, over a distributed environment. To make further use of these things towards a better and smarter physical world, it is necessary to dynamically compose the IoT services to handle complex, dynamically arising tasks. In this paper, we consider how to efficiently discover and compose IoT services to achieve some given goals. When a dynamic task arises, a distributed planning is taken place over the IoT network to discover the needed available IoT services and how to compose them. To ensure planning efficiency, a Logic-based Overlay Network of Services (LONS) is maintained and updated periodically. We then develop a decentralized Graphplan based algorithm to traverse through the LONS to derive the desired service composition. An intelligent mutual exclusion consideration model in our algorithm results in an efficient backward search procedure for solution extraction. We consider different distributed planning variations and compare their tradeoffs in terms of network traffic and composition efficiency experimentally.
Punchcard: A Practical Red-Zone Based Scheme for Low-Overhead Heap Protection

Memory-unsafe programming languages, such as C and C++, remain indispensable due to their performance and memory management advantages. Several studies addressed the memory safety issues by proposing hardware-assisted techniques. However, we observe the proposed techniques fail to enforce the security policies due to the naive assumption - the memory can be only accessed through the processor caches. In this paper, we demonstrate that different memory access techniques can compromise the state-of-the-art blacklisting technique. Then, we propose Punchcard, a light-weight and full coverage technique. Punchcard introduces a micro-unit inside the MMU, which ensures the enforcement of the security policies to all memory accesses. Additionally, Punchcard minimizes the performance overhead by re-purposing the unused physical address bits to store the metadata. Punchcard achieves this comprehensive memory protection by incurring an average of 11&#x0025; performance overhead, and an average of 9&#x0025; memory overhead.
Cascaded based Adaptive R-CNN Network for Insulator Defect Detection

Target detection is a popular direction in computer vision and digital image processing. It is widely used in robot navigation, intelligent video surveillance, industrial detection, aerospace, and many other fields. It has important practical significance to reduce the consumption of human capital through computer vision. Due to the widespread use of deep learning, target detection algorithms have developed relatively rapidly. In this paper, we propose a method for detecting insulator defects Based on Cascaded Fast R-CNN. We use RoIAlign instead of RoIPooling to get a better positioning effect and add a mask branch on the basis of Faster R-CNN to complete the pixel-level segmentation task, which can detect insulator defects in real-time with high detection accuracy.