Reviewerly/exHarmony huggingface dataset:
Comparison Four Kernels of SVR to Predict Consumer Price Index

Abstract The economy of a region is affected by the stability of food supplies. If the market price of the food supply is stable, the purchasing power level will increase. The price stability of food supplies can be anticipated by using the Support Vector Regression Method, to predict the Consumer Price Index, known as CPI. In the Consumer Price Index assessment, using data based on recording, measurement and calculation of the goods and services average price which consumed by households in a certain period of time. Goods and services that are deemed to represent household expenses are then averaged. The CPI in this study is a type of food supply issued by the Indonesian Central Statistics, and the input variable is taken from the prices of staple commodities in the city of Surabaya, Malang and Kediri based on data from the Siskaperbapo website. To get the supported vector data, the hyperplane maximized by the SVR concept. This concept is able to overcome the overfitting, in order to obtain more accurate prediction results. In predicting the Consumer Price Index, reference data is divided as training data 2016-2019 and testing data 2017-2020. All four kernels were used in the test, namely Spline kernel, Gaussian-RBF kernel, Linear kernel and Polynomial kernel. All four kernels are compared to see their MAPE, this can be shown by the Mean Absolute Percentage Error (MAPE) of less than three, if by using Gaussian RBF kernel. The smallest MAPE value showed by Malang CPI value, which is 1.8242 with C = 50, followed by Kediri with the MAPE value of 2.251 with C = 50 and MAPE value of Surabaya which is 2.5279 with C = 50.
I-SCRAM: A Framework for IoT Supply Chain Risk Analysis and Mitigation Decisions

Supply chain security is becoming an important factor in security risk analysis for modern information and communication technology (ICT) systems. As Internet of Things (IoT) devices proliferate and get adopted into critical infrastructure, the role of suppliers in risk assessment becomes all the more significant. IoT security risks are affected by supplier trust since suppliers possess the capacity to modify black box systems without detection. The risks posed by potentially malicious or compromised suppliers are compounded by interdependence among suppliers. In this paper, we propose I-SCRAM, a framework to analyze supply chain risks in IoT systems and to support risk mitigating decisions. After defining an expanded system model that consists of interconnected components and a hierarchy of component vendors, we develop and propose metrics to quantify systemic risks. Finally, we present a decision framework that helps in selection of vendors to mitigate supply chain risk. Through a case study and simulation, we show that I-SCRAM successfully minimizes system risk as higher budget and more reliable component sources become available, while allowing flexibility in prioritizing sources of risk.
Dual-Determination of Modulation Types and Signal-to-Noise Ratios Using 2D-ASIQH Features for Next Generation of Wireless Communication Systems

In order to pursue rapid development of the new generation of wireless communication systems and elevate their security and efficiency, this paper proposes a novel scheme for automatic dual determination of modulation types and signal to noise ratios (SNR) for next generations of wireless communication systems, fifth-generation (5G) and beyond.The proposed scheme adopts unique signatures depicted in two-dimensional asynchronously sampled in-phase-quadrature amplitudes' histograms (2D-ASIQHs)-based images and applies the support vector machines (SVMs) tool.Along with the estimation of the instantaneous SNR values over 0-35 dB range, the determination of nine modulation types that belong to different modulation categories i.e., phase-shift keying (Binary-PSK, Quadrature-PSK, and 8-PSK), amplitude-shift keying (2-ASK and 4-ASK) and quadrature-amplitude modulation (4-QAM, 16-QAM, 32-QAM, and 64-QAM) could be achieved by this scheme.The application of this scheme has been simulated using a channel model that is impaired by additive white Gaussian noise (AWGN) and Rayleigh fading, covering a broad range of SNRs of 0-35 dB.The performance of this dual-determination scheme shows high modulation recognition accuracy and low mean SNR estimation error.Therefore, it can be a better alternative for designers of next generation wireless communication systems.
Comparison of Deep and Traditional Learning Methods for Email Spam Filtering

Electronic mail, or email, is a method for communicating using the internet which is inexpensive, effective, and fast.Spam is a type of email where unwanted messages, usually unwanted commercial messages, are distributed in large quantities by a spammer.The objective of such behavior is to harm email users; these messages need to be detected and prevented from being sent to users in the first place.In order to filter these emails, the developers have used machine learning methods.This paper discusses different methods which are used deep learning methods such as a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) models with(out) a GloVe model in order to classify spam and non-spam messages.These models are only based on email data, and the extraction set of features is automatic.In addition, our work provides a comparison between traditional machine learning and deep learning algorithms on spam datasets to find out the best way to intrusion detection.The results indicate that deep learning offers improved performance of precision, recall, and accuracy.As far as we are aware, deep learning methods show great promise in being able to filter email spam, therefore we have performed a comparison of various deep learning methods with traditional machine learning methods.Using a benchmark dataset consisting of 5,243 spam and 16,872 not-spam and SMS messages, the highest achieved accuracy score is 96.52% using CNN with the GloVe model.
Target attention deep neural network for infrared image enhancement

The inherent high background radiation and low contrast of infrared images severely cripple the precision of target detection and recognition. However, existing infrared image enhancement methods still struggle to balance the target enhancement and background suppression. To further address this issue, we present an innovative target attention deep neural network (TADNN) to realize a discriminative enhancement in an end-to-end manner. In this framework, a joint convolution unit (JCU) is constructed to comprehensively excavate the complementary multi-scale spatial features, followed that, a target attention unit (TAU) is designed to further refine the features from JCU for particularly enhancing the targets. Besides, an improved S-curve response model is put forward to generate the task-oriented training set for pursuing a superior fitting solution. Extensive experiments validate that the proposed method outperforms the competitive approaches on both of subjective visual effect and quantitative assessments.
IoT root union: A decentralized name resolving system for IoT based on blockchain

A name resolving system services as the entrance to the IoT data, thus protecting the name resolving process and making it straight and simple are critical for the widespread application of IoT services. However, due to the centralized name management structure of the existing IoT name resolving systems, it is hard to secure the name resolving process from being tampered by the root owner of the name resolving system; due to the diversity and heterogeneous of the existing name resolving systems, it is hard to provide an unified name resolving service because the existing name resolving systems can hardly be compatible with each other. Herein, it is necessary to construct a general entrance to the existing IoT name resolving systems, and ensure that no IoT name could be malicious tampered. In this paper, we propose the IoT-RU, a decentralized name resolving system for IoT. To persuade the IoT data service providers to participate in our IoT-RU, we construct its name resolving database on the blockchain ledger. To enable the adoption of the existing IoT naming schemes, IoT-RU integrates various name resolving schemes to resolve the IoT service providers in our system, and leave the job of locating individual IoT devices to the IoT service providers. We further investigate the feasibility of name management and resolving based on the current blockchain architectures. Based on the Ethereum platform, we construct a prototype of our name resolving system, and the corresponding evaluation indicates that our name resolving system manages to provide acceptable and scalable name resolution services.
Meta-Transfer Learning for Low-Resource Abstractive Summarization

Neural abstractive summarization has been studied in many pieces of literature and achieves great success with the aid of large corpora. However, when encountering novel tasks, one may not always benefit from transfer learning due to the domain shifting problem, and overfitting could happen without adequate labeled examples. Furthermore, the annotations of abstractive summarization are costly, which often demand domain knowledge to ensure the ground-truth quality. Thus, there are growing appeals for Low-Resource Abstractive Summarization, which aims to leverage past experience to improve the performance with limited labeled examples of target corpus. In this paper, we propose to utilize two knowledge-rich sources to tackle this problem, which are large pre-trained models and diverse existing corpora. The former can provide the primary ability to tackle summarization tasks; the latter can help discover common syntactic or semantic information to improve the generalization ability. We conduct extensive experiments on various summarization corpora with different writing styles and forms. The results demonstrate that our approach achieves the state-of-the-art on 6 corpora in low-resource scenarios, with only 0.7% of trainable parameters compared to previous work.
Improving blood cells classification in peripheral blood smears using enhanced incremental training

Peripheral Blood Smear (PBS) analysis is a vital routine test carried out by medical specialists to assess some health aspects of individuals. The automation of blood analysis has attracted the attention of researchers in recent years, as it will not only save time, money and reduce errors, but also protect and save lives of front-line workers, especially during pandemics. In this work, deep neural networks are trained on a synthetic blood smears dataset to classify fifteen different white blood cell and platelet subtypes and morphological abnormalities. For classifying platelets, a hybrid approach of deep learning and image processing techniques is proposed. This approach improved the platelet classification accuracy and macro-average precision from 82.6% to 98.6% and 76.6%–97.6% respectively. Moreover, for white blood cell classification, a novel scheme for training deep networks is proposed, namely, Enhanced Incremental Training, that automatically recognises and handles classes that confuse and negatively affect neural network predictions. To handle the confusable classes, we also propose a procedure called “training revert”. Application of the proposed method has improved the classification accuracy and macro-average precision from 61.5% to 95% and 76.6%–94.27% respectively.
Application of Improved Asynchronous Advantage Actor Critic Reinforcement Learning Model on Anomaly Detection

Anomaly detection research was conducted traditionally using mathematical and statistical methods. This topic has been widely applied in many fields. Recently reinforcement learning has achieved exceptional successes in many areas such as the AlphaGo chess playing and video gaming etc. However, there were scarce researches applying reinforcement learning to the field of anomaly detection. This paper therefore aimed at proposing an adaptable asynchronous advantage actor-critic model of reinforcement learning to this field. The performances were evaluated and compared among classical machine learning and the generative adversarial model with variants. Basic principles of the related models were introduced firstly. Then problem definitions, modelling processes and testing were detailed. The proposed model differentiated the sequence and image from other anomalies by proposing appropriate neural networks of attention mechanism and convolutional network for the two kinds of anomalies, respectively. Finally, performances with classical models using public benchmark datasets (NSL-KDD, AWID and CICIDS-2017, DoHBrw-2020) were evaluated and compared. Experiments confirmed the effectiveness of the proposed model with the results indicating higher rewards and lower loss rates on the datasets during training and testing. The metrics of precision, recall rate and F1 score were higher than or at least comparable to the state-of-the-art models. We concluded the proposed model could outperform or at least achieve comparable results with the existing anomaly detection models.
Face-Off: Adversarial Face Obfuscation

Abstract Advances in deep learning have made face recognition technologies pervasive. While useful to social media platforms and users, this technology carries significant privacy threats. Coupled with the abundant information they have about users, service providers can associate users with social interactions, visited places, activities, and preferences–some of which the user may not want to share. Additionally, facial recognition models used by various agencies are trained by data scraped from social media platforms. Existing approaches to mitigate associated privacy risks result in an imbalanced trade-off between privacy and utility. In this paper, we address this trade-off by proposing Face-Off, a privacy-preserving framework that introduces strategic perturbations to images of the user’s face to prevent it from being correctly recognized. To realize Face-Off, we overcome a set of challenges related to the black-box nature of commercial face recognition services, and the scarcity of literature for adversarial attacks on metric networks. We implement and evaluate Face-Off to find that it deceives three commercial face recognition services from Microsoft, Amazon, and Face++. Our user study with 423 participants further shows that the perturbations come at an acceptable cost for the users.
Miss Yoga: A Yoga Assistant Mobile Application Based on Keypoint Detection

This paper demonstrates a Yoga assistant mobile application based on human-keypoints detection models, which imitates the scene that real Yoga tutors guide and supervise their students to do Yoga via the video chat. In order to provide humanize, safe and convenient service, the core function is designed as hands-free using voice service, and embedding fast and accurate models to detect keypoints and calculate the scores. In addition, we propose an improved algorithm to calculate scores that can be applied to all poses. Our application is evaluated on different Yoga poses under different scenes, and its robustness is guaranteed.
Rnn-transducer With Language Bias For End-to-end Mandarin-English Code-switching Speech Recognition

Recently, language identity information has been utilized to improve the performance of end-to-end code-switching (CS) speech recognition task. However, previous work use an additional language identification (LID) model as an auxiliary module, which increases computation cost. In this work, we propose an improved recurrent neural network transducer (RNN-T) model with language bias to alleviate the problem. We use the language identities to bias the model to predict the CS points. This promotes the model to learn the language identity information directly from transcriptions, and no additional LID model is needed. We evaluate the approach on a Mandarin-English CS corpus SEAME. Compared to our RNN-T baseline, the RNN-T with language bias can achieve 16.2% and 12.9% relative mixed error reduction on two test sets, respectively.
Performance-Based Analysis of Blockchain Scalability Metric

Cryptocurrencies like Bitcoin and Ethereum, are widely known applications of blockchain technology, have drawn much attention and are largely recognized in recent years. Initially Bitcoin and Ethereum processed 7 and 15 Transactions Per Second (TPS) respectively, whereas VISA and Paypal process 1700 and 193 TPS respectively. The biggest challenge to blockchain adoption is scalability, defined as the capacity to change the block size to handle the growing amount of load. This paper attempts to present the existing scalability solutions which are broadly classified into three layers: Layer 0 solutions focus on optimization of propagation protocol for transactions and blocks, Layer 1 solutions are based on the consensus algorithms and data structure, and Layer 2 solutions aims to decrease the load of the primary chain by implementing solutions outside the chain. We present a classification and comparison of existing blockchain scalability solutions based on performance along with their pros and cons
Digitalization and Innovation in Nigerian Firms

This study examined the determinants of digitalization and its impact on innovation in Nigeria. The study applied the logit regression and propensity score matching (PSM) on data sourced from the World Bank 2014/2015 enterprise survey. The result from the logit regression shows that size of the firm, educational qualification of the top manager of the firm, business age, employment growth and sector of operation are the major significant determinants of the extent to which firms digitalized in Nigeria. On the other hand, the result from the propensity score matching shows that digitization is positive and significant in explaining the level of firms' innovation in Nigeria. This means that an increased level of ICT will synonymously increase the level of firms' ability to innovate. Based on the results, the study concludes by recommending that managers of various firms should employ a tactical approach to improve on the rate of digitization and innovation to achieve the desired level of productivity.
Contactless Fall Detection for the Elderly

Early fall detection imposes a challenge for preventing life-threatening conditions to the health in geriatrics. As per the World Health Organization, around that half of total elderly individuals fall every year, which is considered a major cause of death. Fall-down detection and prediction is an active research topic to track seniors who reside at home alone or under treatment in a hospital. With the advancement of pervasive sensing technology and artificial intelligence techniques, automatic and contactless fall detection is now possible via passive monitoring systems. However, such systems’ accuracy is not up to an admissible limit due to the context and ambient conditions. Among the two main categories of fall detection systems, such as wearable sensor-based and contactless sensor-based, security issues of wearable sensor-based detection systems make it unacceptable to researchers. Moreover, wearable sensors are required to be worn by the user for a longer period, making it undesirable by the users. As a result, throughout recent years, ambient and wireless sensor-based systems have been commonly used for fall detection. To define existing approaches for potential research directions, it is important to analyze the elderly fall detection system’s ongoing practices. This book chapter aims to provide a detailed overview of different existing methods/systems of fall detection based on contactless sensors.
Unsupervised Deep Quadruplet Hashing with Isometric Quantization for image retrieval

Numerous studies have shown deep hashing can facilitate large-scale image retrieval since it employs neural networks to learn feature representations and binary codes simultaneously. Despite supervised deep hashing has made great achievements under the guidance of label information, it is hardly applicable to a real-world image retrieval application because of its reliance on extensive human-annotated data. Furthermore, the pair-wise or triplet-wise unsupervised hashing can hardly achieve satisfactory performance due to the absence of local similarity of image pairs. To solve those problems, we propose a novel unsupervised deep hashing framework to learn compact binary codes, which takes the quadruplet forms as input units, called Unsupervised Deep Quadruplet Hashing with Isometric Quantization (UDQH-IQ). Specifically, by introducing the rotation invariance of images, the novel quadruplet-based loss is designed to explore the underlying semantic similarity of image pairs, which could preserve local similarity with its neighbors in Hamming space. To decrease the quantization errors, Hamming-isometric quantization is exploited to maximize the consistency of semantic similarity between binary-like embedding and corresponding binary codes. To alleviate redundancy in different bits, an orthogonality constraint is developed to decorrelate different bits in binary codes. Experimental results on three benchmark datasets indicate that our UDQH-IQ achieves promising performance.
A case study of batch and incremental recommender systems in supermarket data under concept drifts and cold start

Recommender systems uncover relationships between users and items, thus allowing personalized recommendations. Nonetheless, users’ preferences may change over time, the so-called concept drifts; or new users and items may appear, making the recommender system unable to accurately map the relationship between users and items due to the cold start problem. Consequently, concept drift and cold start are challenges that downgrade the recommender system’s predictive performance. This paper assesses existing approaches for collaborative-filtering recommender systems over a real supermarket dataset that exhibits both of the issues mentioned above. For this purpose, our comparative analysis encompasses batch and streaming learning approaches. As a result, we can observe that streaming-based models achieve better recommendation rates since these are tailored to fit the concept drift. More specifically, the predictive performance of streaming-based recommendations increases by up to 21% over those provided by batch methods. The supermarket dataset used in experimentation is also made publicly available for future studies and recommender systems comparisons.