Reviewerly/exHarmony huggingface dataset:
Quantized ADAM with Monotonically Increasing Resolution of Quantization

We propose a quantized ADAM learning algorithm that can represent better optimization performance by monotonically reducing the quantization step to time when quantization is composed of integer or fixed-point fractional values applied to an optimization algorithm. According to the White Noise Hypothesis to quantization error with dense and uniform distribution, we can regard the quantization error as an i.i.d. white noise. It leads that we can obtain a stochastic equation about the quantized ADAM learning equation, and we obtain the monotonically decreasing rate of the quantization step that enables the global optimization by the stochastic analysis to the derivation of an objective function. Numerical experiments represent that the proposed algorithm represents a better performance than the conventional ADAM learning schemes on a ResNet for a general image classification test.
Action Item Detection in Meetings Using Pretrained Transformers

Detecting the action items that were agreed upon during a meeting has important practical applications. But, extremely sparse positive labels, noisy annotations, and small datasets limited the improvement in this task via hand-crafted features techniques. Given the breakthrough performance demonstrated by pretrained transformer-based models in a wide variety of NLP tasks, such as BERT [1] and ETC [2], we revisit this task using these modern techniques. We empirically show how these modelling techniques advance the state-of-the-art on action item detection for the ICSI simulated meeting corpus [3] by 75% and establish a baseline for the action item detection from the AMI [4] meeting corpus. We show that these models are competitive on the related ICSI MRDA classification problem. In order to push the performance even further, we re-evaluate the task definition for action item detection, drawing upon the similarities with the span boundary detection realm. We propose the use of Generalized Hamming Distance ghd as an alternative evaluation. We hope to motivate further interest into the action item detection task by the community.
SPMAC: Scalable Prefix Verifiable Message Authentication Code for Internet of Things

Internet of Things is a valuable tool in various application domains, including smart cities, industrial production, healthcare, and so on. Every connected device can share messages with others in an IoT network. However, since such a network is vulnerable to security breaches, authenticating the integrity of these messages is essential. In addition, the message may contain sensitive information, and a portion of the information in the message needs to be authenticated. In the literature, Prefix Verifiable Message Authentication Code has been proposed under such scenario, but it only works if the portion to be authenticated is a prefix of the message. In this paper, we propose a more general scheme called Scalable Prefix Verifiable Message Authentication Code (SPMAC). It inherits the advantage of PMAC while overcoming the drawbacks. For practical SPMAC construction, we propose two provably secure generic constructions, Fibo-SPMAC and ECP-SPMAC. Both of them have practical applications in IoT scenarios. The experiment results demonstrate the performance advantage of SPMAC in terms of efficiency. Moreover, Fibo-SPMAC has a good performance when the bandwidth is limited, whereas ECP-SPMAC performs better when the bandwidth is large.
Quantum Computing

This chapter introduces the foundations of quantum computing, first giving an intuitive idea of how its abstract linear algebra formalism relates to conventional probability theory, and then presenting the apparatus of states, observables and unitary evolutions in more detail. We introduce the building blocks of quantum circuits and walk through some important quantum algorithms.
Program and Methodological Support of the Educational Process in the Magistracy

The article is devoted to the issues of program and methodological support of the educational process in the preparation of undergraduates. In accordance with the requirements of educational standards for magistracy, the author considers a competency-based approach to the educational process in a university. The effectiveness of the developed electronic-methodical complexes according to the programs of educational training of the magistracy is noted. The author also describes the developed electronic courses
Automatically Extract Semantic Map for Semantic Style Transfer

As one of the fields of computer art creation, style transfer has become more and more popular. However, in order to obtain good visual effects, a large number of neural style transfer algorithms use semantic map to guide the style transfer between the correct regions. As an important means to ensure the quality of style transfer, semantic map can meaningfully control the results of style transfer. However, the method of manually generating semantic graph is cumbersome and inefficient. In this paper, we introduce a semantic segmentation network to automatically generate the semantic map required by neural style transfer, and combine it with neural style transfer network, we propose a new neural style transfer algorithm. Experiments show that our algorithm not only avoids cumbersome manual work, but also generates high-quality style transfer results.
Underwater Video Enhancement Using Manta Ray Foraging Lion Optimization-Based Fusion Convolutional Neural Network

Due to the significance of aquatic robotics and marine engineering, the underwater video enhancement has gained huge attention. Thus, a video enhancement method, namely Manta Ray Foraging Lion Optimization-based fusion Convolutional Neural Network (MRFLO-based fusion CNN) algorithm is developed in this research for enhancing the quality of the underwater videos. The MRFLO is developed by merging the Lion Optimization Algorithm (LOA) and Manta Ray Foraging Optimization (MRFO). The blur in the input video frame is detected and estimated through the Laplacian’s variance method. The fusion CNN classifier is used for deblurring the frame by combining both the input frame and blur matrix. The fusion CNN classifier is tuned by the developed MRFLO algorithm. The pixel of the deblurred frame is enhanced using the Type II Fuzzy system and Cuckoo Search optimization algorithm filter (T2FCS filter). The developed MRFLO-based fusion CNN algorithm uses the metrics, Underwater Image Quality Measure (UIQM), Underwater Color Image Quality Evaluation (UCIQE), Structural Similarity Index Measure (SSIM), Mean Square Error (MSE), and Peak Signal-to-Noise Ratio (PSNR) for the evaluation by varying the blur intensity. The proposed MRFLO-based fusion CNN algorithm acquired a PSNR of 38.9118, SSIM of 0.9593, MSE of 3.2214, UIQM of 3.0041 and UCIQE of 0.7881.
NDPC-Net: A dehazing network in nighttime hazy traffic environments

Nighttime haze seriously affects the objects detection and recognition for autonomous vehicles. Although great progress has been made in daytime dehazing research, it is not suitable for night traffic scenes. Lack of large-scale training datasets of nighttime haze images is one of the main reasons that hinders the neural network model to achieve satisfied dehazing effects. To address it, we propose a novel method to synthesize nighttime traffic dehazing dataset: NTHAZE, and also a dehazing network: NDPC-Net, which includes a Nested DDU-Net block and a Pyramidal convolution block. The former extracts hazy images’ features of different complexity, the latter retains and restores spatial features of different scales. Experiment results show that NDPC-Net can effectively dehaze in low illumination and nighttime traffic environment scenes. The dehazing effect is natural and smooth in color and texture, in NH-HAZE, PSNR and SSIM up to 32.31dB and 0.8822 respectively, 67% higher than SOTA. The precision and recall of objects detection after our proposed dehazing are improved by 22.66% and 4.66% respectively compared with SOTA.
Anomalous Anomaly Detection

Anomaly Detection (AD) is an integral part of AI, with applications ranging widely from health to finance, manufacturing, and computer security. Though AD is popular and various AD algorithm implementations are found in popular toolkits, no attempt has been made to test the reliability of these implementations. More generally, AD verification and validation are lacking. To address this need, we introduce an approach and study on 4 popular AD algorithms as implemented in 3 popular tools, as follows. First, we checked whether implementations can perform their basic task of finding anomalies in datasets with known anomalies. Next, we checked two basic properties, determinism and consistency. Finally, we quantified differences in algorithms’ outcome so users can get a idea of variations that can be expected when using different algorithms on the same dataset. We ran our suite of analyses on 73 datasets that contain anomalies. We found that, for certain implementations, validation can fail on 10–73% of datasets. Our analysis has revealed that five implementations suffer from nondeterminism (19–98% of runs are nondeterministic), and 10 out of 12 implementation pairs are inconsistent.
Digital-age literacy in Indonesia: A systematic literature review using VOSViewer

This research was conducted to determine research development and describe research opportunities on digital-age literacy in Indonesia. Data in journals and proceedings were collected from the Scopus database using search keywords sourced from the main domain of digital-age literacy and other related keywords from 2003-2020. The data collected were 681 articles in the form of 453 journal articles and 228 proceedings. The analysis was carried out using Ms. Excel and VOSViewer. Ms. Excel analyzes the number of publications, author productivity, and the topmost cited articles. In contrast, VOSViewer is used to create and analyze visualizations based on words, known as co-occurrence and co-authorship, to determine the relationship between authors. The results showed that a significant increase in the number of publications occurred from 2018-2020, where the highest publication occurred in 2020, which was 33.63%. Researcher productivity is almost the same, between 1-3 publications. The most cited topics were global awareness, economic literacy, basic literacy, and several articles discussing topics outside digital-age literacy. Network visualization co-occurrence is divided into 10 clusters. The latest topics based on article data in this study include STEM, covid, and higher education published in 2019-2020. The most frequently researched topics are related to students, followed by financial literacy, ICT, media literacy, culture, and higher education. The co-authorship visualization shows no collaboration between researchers.
Efficient Modular Polynomial Multiplier for NTT Accelerator of Crystals-Kyber

This paper presents a hardware design that efficiently performs the number theoretic transform (NTT) for lattice-based cryptography. First, we propose an efficient modular multiplication method for lattice-based cryptography defined over Proth numbers. The proposed method is based on a K-RED technique specific to Proth numbers. In particular, we divide the intermediate result into the sign bit and the other absolute value bits and handle them separately to significantly reduce implementation costs. Then, we show a butterfly unit datapath of NTT and inverse INTT equipped with the proposed modular multiplier. We apply the proposed NTT accelerator to Crystals-Kyber, which is lattice-based cryptography, and evaluate its performance on Xilinx Artix-7. The results show that the proposed NTT accelerators achieve up-to 3% and 33% higher area-time efficiency in terms of LUTs and FFs, respectively, than conventional best methods. In addition, the low-latency version of the proposed NTT accelerators achieves a 18% lower-latency with an area-time efficiency (in terms of LUTs, FFs, and DSPs) than the existing fastest method.
Twitter Spam Accounts Detection Using Machine Learning Models

The amount of spam accounts on Twitter has recently surged, which has attracted researchers' interest in seeking strategies to mitigate this problem. This paper reviews recent studies in the literature that tackled the Twitter spam accounts problem based on machine learning (ML). It then introduces an empirical study to test several ML models on a publicly access dataset. The model types were individual, ensemble, and majority voting models. It found that the ensemble ML models, and majority voting ML models can improve the prediction accuracy of Twitter spam accounts detection compared to the individual ML models. We concluded that the Random Forest model is the best for Twitter spam accounts detection using an imbalanced dataset.
Smart Contract based Energy Trading-An overview

By having power consumption closer to the source of generation and preventing losses in transmission and distribution, decentralized energy production and consumption significantly helps to save the planet's ecosystem and climate. Emerging intelligent technologies that support energy transactions can expedite the growth and speed up the use of distributed eco-friendly energy sources. Distributed energy technologies are crucial since they are built specifically to generate, store, and distribute green energy. The integration of blockchain technology in the electrical power and energy domain have the capability to promote the adoption of renewable energy sources and offer a solid foundation for tracking real-time data on energy utilization. Additionally, blockchain can facilitate trade between autonomous actors, which could develop more secure energy communities. Smart contracts have become an interesting research area in various fields due to the increased popularity and expanded applications of blockchain technology. In this paper, the advantages of blockchain and smart contracts in energy trading are emphasized. This paper gives an overview of different techniques and areas to be considered for implementing smart contracts for energy transactions.
Cybersecurity and Privacy Aspects of Smart Contracts in the Energy Domain

Smart contracts (SCs) are a set of logical procedures that can be run by individual peers participating within a Distributed Ledger Technology (DLT) network. By design, smart contracts inherit many of the benefits of DLT, including its immutability, scalability and security properties. Nevertheless, they may introduce additional attack vectors, which can lead to cybersecurity explorations that could jeopardize the end-application’s ability to operate as intended or result in data leaks, and privacy violations. In this work an exploration of known problems, and possible attack scenarios will be presented. This is followed by a set of proposed best practices that are intended to assist developers, researchers and other relevant stakeholders to develop secure SC implementations.
Generalised Asynchronous Remote Key Generation for Pairing-Based Cryptosystems

Asynchronous Remote Key Generation (ARKG, introduced in ACM CCS 2020) allows for a party to create public keys for which corresponding private keys may be later computed by another intended party only. ARKG can be composed with standard public-key cryptosystems and has been used to construct a new class of privacy-preserving proxy signatures. The original construction of ARKG, however, generates discrete logarithm key pairs of the form $$(x, g^x)$$ . In this paper we define a generic approach for building ARKG schemes which can be applied to a wide range of pairing-based cryptosystems. This construction is based on a new building block which we introduce and call Asymmetric Key Generation (AKG) along with its extension $$\phi $$ -AKG where $$\phi $$ is a suitable mapping for capturing different key structures and types of pairings. We show that appropriate choice of $$\phi $$ allows us to create a secure ARKG scheme compatible with any key pair that is secure under the Uber assumption (EUROCRYPT 2004). To demonstrate the extensive range of our general approach, we construct ARKG schemes for a number of popular pairing-based primitives: Boneh-Lynn-Shacham (JoC 2004), Camenisch-Lysyanskaya (CRYPTO 2004), Pointcheval-Sanders (CT-RSA 2016), Waters (EUROCRYPT 2005) signatures and structure-preserving signatures on equivalence classes (ASIACRYPT 2014). For each scheme we give an implementation and provide benchmarks that show the feasibility of our techniques.
Salient Object Detection based Aircraft Detection for Optical Remote Sensing Images

Nowadays the use of Optical remote sensing images (RSIs) for detecting any particular object is being increased.Salient object detection is a fascinating aspect of optical RSIs (SOD). Regarding optical RSIs, there are a plethora of problems, like crowded backdrops, diverse object orientations, different object scales, etc. As a result, the execution of the current salient object detection models frequently suffers greatly. The relevance of information about edges, which is essential for producing correct saliency maps, is frequently overlooked by existing SOD models. To overcome this issue, this model uses Spatial Channel Attention U-Net (SCAU-Net) for detecting the edge maps. This model pop-out aircraft in optical RSIs using SOD. First, the input is sent into Encoder and SCAUNet simultaneously. The SCAU-Net provides salient edge cues and the encoders are used to give a good feature representation for salient objects. Then the output of the encoder is sent to the decoders. The decoders of the feature-merge module gives position attention to salient objects. The efficient edge map provided by SCAU-Net is used for improving the position attention cues. The final step is to combine all position attention cues to obtain the final output. The final output contains the aircraft detected in it. By seeing the obtained results we can say that our model can precisely and accurately detect the aircrafts present in the given input.
A General and NLP-based Architecture to perform Recommendation: A Use Case for Online Job Search and Skills Acquisition

Natural Language Processing (NLP) is crucial to perform recommendations of items that can be only described by natural language. However, NLP usage within recommendation modules is difficult and usually requires a relevant initial effort, thus limiting its widespread adoption. To overcome this limitation, we introduce FORESEE, a novel architecture that can be instantiated with NLP and Machine Learning (ML) modules to perform recommendations of items that are described by natural language features. Furthermore, we describe an instantiation of such architecture to provide a service for the job market where applicants can verify whether their curriculum vitae (CV) is eligible for a given job position, can receive suggestions about which skills and abilities they should obtain, and finally, can obtain recommendations about online resources which might strengthen their CVs.
KINERJA OPERASI DAN PEMELIHARAAN BENDUNGAN KEULILING KABUPATEN ACEH BESAR

The Keuliling Dam in Kuta Cot Glie District, Aceh Besar Regency has an important role in meeting irrigation needs, supplying raw water and controlling floods. Based on field observations, there is a lot of garbage and material deposits in the body of the dam. Since operating for more than 10 years, it is necessary to evaluate the performance of operations and maintenance. Evaluation and operation and maintenance of dams is a very important activity to assess the dam is working normally, thus providing benefits to the surrounding community. The purpose of the study was to identify the operation and maintenance performance of the Keuliling Dam by using the Balanced Scorecard method to measure the performance of operations and maintenance activities. The stages in conducting the Balanced Scorecard begin with distributing questionnaires based on assessment points that have been adjusted to the critical performance area and processing using the results of the questionnaire, then conducting a performance assessment of the dam using the Balanced Scorecard method. Measurement with the Balanced Scorecard refers to four categories of conditions: score 1 is bad condition, score 2 is moderate condition, score 3 is good condition and score 4 is very good. The results obtained using the Balanced Scorecard obtained a value of 3 included in the good category. The values obtained provide an overview of the value of the condition of the Keuliling Dam as a basis for determining the handling of the required maintenance actions.
Anomaly Detection in Sleep Habits Using Deep Learning

In the last decade many researchers have shown interest in the sleep analysis field. The reason behind it relies on the fact that it could lead to the early discovery of some health issues, especially for elders. In this article, a new method was presented to detect anomalies in patients' sleep habits. This method is based on creating a new database of 2500 patients divided into 5 different habits. Then we used this data set to test the categorization of the patient by using the mean-shift clustering method. Finally, a presentation of the auto-encoder method to detect the anomalies was made. Using an auto-encoder based on Long Short Term Memory (LSTM) network, we were able to reach satisfying results.
Bivariate Multifractal Analysis for Non-Homogenous Point Processes, with Application to Geospatial Data

Multifractal analysis has become an important procedure for estimating local regularities in experimental data. However, while univariate multifractal analysis is well-established, how cross-dependencies of regularities fluctuate amongst signal components is less often addressed. Further, multifractal analysis is now thoroughly outlined for fields defined everywhere on raster-like data structures. Still, it is less well suited for analyzing processes that exist only on restricted and partial supports, possibly with heterogeneous densities. These two issues, critical for a relevant application of multifractal analysis to geospatial data, are addressed here by proposing an original point process-oriented bivariate multifractal analysis. The relevance of the proposed procedure is illustrated at work on synthetic data constructed by combining homogenous and non-homogeneous point processes with multifractal textures. Illustration on real-world geospatial data is also provided.
A maximal correlation embedding method for multilabel human context recognition

Real-time human context recognition is one of the most exciting emerging technologies in sensing nowadays. Compared with most recognition problems in machine learning, the challenge lies in the complexity and incompleteness of labels, in other words, each sample can have several label concepts simultaneously but some of them could be missing. This poster proposes an effective approach for multilabel human context recognition with signals from sensors embedded in the wearable devices. The proposed algorithm demonstrates to be very robust to incomplete labels.