Reviewerly/exHarmony huggingface dataset:
Local Differential Privacy Protocol for Making Key–Value Data Robust Against Poisoning Attacks

Local differential privacy is a technique for concealing a user’s information from collectors by randomizing the information within the user’s own device before sending it to unreliable collectors. Ye et al. proposed PrivKV, a local differential privacy protocol for securely collecting key–value data, which comprises two-dimensional data with discrete and continuous values. However, such data is vulnerable to a “poisoning attack,” whereby a fake user sends data to manipulate the key-value dataset. To address this issue, we propose an Expectation-Maximization (EM) based algorithm, in conjunction with a cryptographical protocol for ensuring secure random sampling. Our local differential privacy protocol, called emPrivKV, offers two main advantages. First, it is able to estimate statistical information more accurately from randomized data. Second, it is robust against manipulation attacks such as poisoning attacks, whereby malicious users manipulate a set of analysis results by sending altered information to the aggregator without being detected. In this paper, we report on the improvement in the accuracy of statistical value estimation and the strength of the robustness against poisoning attacks achieved by applying the proposed method to open datasets.
Differentially Private Graph Publishing Through Noise-Graph Addition

Differential privacy is commonly used for graph analysis in the interactive setting, were a query of some graph statistic is answered with additional noise to avoid leaking private information. In such setting, only a statistic can be studied. However, in the non-interactive setting, the data may be protected with differential privacy and then published, allowing for all kinds of privacy preserving analyses. We present a noise-graph addition method to publish graphs with differential privacy guarantees. We show its relation to the probabilities in the randomized response matrix and prove that such probabilities can be chosen in such a way to preserve the sparseness of the original graph in the protected graph. Thus, better preserving the utility for different tasks, such as link prediction. Additionally, we show that the previous models of random perturbation and random sparsification are differentially private, and calculate the $$\epsilon $$ guarantees that they provide depending on their specifications.
Development of Healthcare System Using Soft Computing Methods

Real-world problems associated with healthcare domain are dynamic and non-deterministic in nature. Traditional analytics models are no longer effective for diagnostic processes and frequently require a large amount of computation time for decision-making, which cannot be sustainable in a technology-driven world. Soft computing methods, such as neural networks, machine learning, fuzzy logic, not only deal with the existing environment, but are also highly self-adaptive in terms of predicting and classifying new diseases that appear in the existing environment. As a result, these techniques are extremely beneficial and effective for use in the healthcare system. In this paper, we proposed a weighted averaging ensemble technique to perform supervised multi-level classification of dermatological diseases. Our experimental results show that the proposed method achieved significant better results than individual classifier models.
Healthcare Question–Answering System: Trends and Perspectives

Question–answering (QA) system has been a major breakthrough in the field of medical science. An abundant amount of research work has been done in the field of automated text-based QA agents also called chatbots using different neural networks. There has been a steady rise in the number of QA agents in the medical domain in recent times since the Covid-19 pandemic started in December 2019. People have resorted to online consultation to resolve their health issues due to lockdowns and movement restrictions. In this paper, we discuss contemporary healthcare chatbots that answer user queries related to various health issues. There are two types of such agents based on the response generation—first, the conversational type empathetic chatbots employed for psychological or mental health counseling, and second, the medical QA chatbots having domain-specific medical knowledge. In this paper, we concentrate on the second type of chatbots that are used for healthcare question–answering. We outline the limitations of general chatbots and discuss the shifting trend toward the development of more personalized healthcare chatbots in the near future.
Fast Detection of Specific Fragments Against a Set of Sequences

We design alignment-free techniques for comparing a sequence or word, called a target, against a set of words, called a reference. A target-specific factor of a target T against a reference R is a factor w of a word in T which is not a factor of a word of R and such that any proper factor of w is a factor of a word of R. We first address the computation of the set of target-specific factors of a target T against a reference R, where T and R are finite sets of sequences. The result is the construction of an automaton accepting the set of all considered target-specific factors. The construction algorithm runs in linear time according to the size of $$T\cup R$$ . The second result consists of the design of an algorithm to compute all the occurrences in a single sequence T of its target-specific factors against a reference R. The algorithm runs in real-time on the target sequence, independently of the number of occurrences of target-specific factors.
NLP-Based Sentiment Analysis with Machine Learning Model for Election Campaign—A Survey

The election campaign provides the evaluation and experience of the voters. The analysis of an election campaign comprises the different twists and turns to monitor and evaluate the situation in the elections. India is one of the biggest democratic countries with different languages, races, and policies. Through manual processing in the election, the campaign government can control the situation. The opinion of the voters is a key factor in the determination of the election results. Hence, it is necessary to process the opinion of the voters to get a clear view of the election. To gain knowledge from the opinion of the voter, machine learning (ML)-based techniques are implemented to classify the voter’s opinion about political parties and candidates of the parties. In ML, sentiment analysis is the key factor in the identification of the opinion about parties to estimate the positive and negative opinions of voters. This paper presented a survey about ML and classification techniques in the NLP-based election campaign process. Also, to process the opinion of the people, natural language processing (NLP) is effective for processing. In the NLP process, sentiment analysis is a key factor to identify the opinion of the voters about political parties and candidates. The estimation is based on the evaluation of the election campaign for the computation of the opinion of the voters in the election campaign evaluation. The opinion about candidates and views of the candidates are evaluated in the analysis.
Patch Extraction and Classifier for Abnormality Classification in Mammography Imaging

Breast cancer is a fatal disease that affects millions of women worldwide. The number of cases of breast cancer has risen over time. Although it is difficult to prevent this disease, the survival rate can be improved with early detection and proper treatment planning of the disease. Thanks to breakthroughs in deep learning, the progress of computer-assisted diagnosis (CAD) of breast cancer has seen a lot of improvement. Deep neural networks have advanced to the point where their diagnostic capabilities are approaching those of a human specialist. Mammograms are crucial radiological images used to detect breast cancer at its early stage. Mammogram image scaling at the input layer is required for training deep convolutional neural networks (DCNNs) directly on high-resolution images. This could result in the loss of crucial information for discovering medical abnormalities. Instead of developing an image classifier, the idea is to create a patch classifier. The technique for extracting abnormal patches from mammography images is proposed in this research. Patches are extracted from a benchmark and publicly available MIAS dataset. These patches are then used to train deep learning classifiers such as VGG-16, ResNet-50, and EfficientNet-B7. With the patches already included in the CBIS-DDSM dataset, we contrast the results of the MIAS patches. EfficientNet-B7 on CBIS-DDSM patches produced good results (92% accuracy) when compared to other classifiers such as VGG-16 and ResNet-50. We also discovered that ResNet-50 is demonstrated to be quite robust on both datasets.
Adaptive Modulation Classification with Deep Learning for Various Number of Users and Performance Validation

The automatic modulation classification (AMC) is a key component of modern wireless frameworks. It is commonly used in various military and commercial applications such as electronic surveillance and cognitive radio. There are various regulation types that are considered when it comes to the classification of complex data. This paper presents an overview of the various features-based (FB) AMC techniques. They are mainly focused on the balance between speculation capacity and the constraints of the algorithm. A robust strategy is then presented for handling this test using the convolutional neural network (CNN). This paper presents a method that can be used to characterize the signals received by a system without the need for feature extraction. It can also take advantage of the features of the received signals. In addition, the confusion matrix is drawn and analyzed. The logistic regression algorithm performance is measured in terms of accuracy, sensitivity, specificity and F1_score for various users who are maintaining various distances from the base station.
Machine Learning Techniques for Resource-Constrained Devices in IoT Applications with CP-ABE Scheme

Ciphertext-policy attribute-based encryption (CP-ABE) is one of the promising schemes which provides security and fine-grain access control for outsourced data. The emergence of cloud computing allows many organizations to store their data, even sensitive data, in cloud storage. This raises the concern of security and access control of stored data in a third-party service provider. To solve this problem, CP-ABE can be used. CP-ABE cannot only be used in cloud computing but can also be used in other areas such as machine learning (ML) and the Internet of things (IoT). In this paper, the main focus is discussing the use of the CP-ABE scheme in different areas mainly ML and IoT. In ML, data sets are trained, and they can be used for decision-making in the CP-ABE scheme in several scenarios. IoT devices are mostly resource-constrained and has to process huge amounts of data so these kinds of resource-constrained devices cannot use the CP-ABE scheme. So, some solutions for these problems are discussed in this paper. Two security schemes used in resource-constrained devices are discussed.
Attack on a Code-Based Signature Scheme from QC-LDPC Codes

Meneghetti, Picozzi, and Tognolini proposed a Hamming-metric code-based digital signature scheme from QC-LDPC codes in [10]. Using quasi cyclic codes enables the scheme to have small key sizes, while the structure of LDPC codes helps to achieve good overall performance. In this paper, we discuss the security of this code-based signature scheme. In particular, we present a partial key recovery attack that uses a statistical method to recover an important part of the secret key. Using a proof-of-concept Sagemath implementation, we are able to recover part of the secret key using as few as 25 signatures in less than 2 minutes for all proposed parameter sets. Furthermore, we follow up this partial key recovery attack by a forgery attack.
An Updated Database of $$\mathbb {Z}_4$$ Codes and an Open Problem About Quasi-cyclic Codes

Research on codes over finite rings has intensified after the discovery that some of the best binary nonlinear codes can be obtained as images of $$\mathbb {Z}_4$$ -linear codes. Codes over various finite rings have been a subject of much research in coding theory after this discovery. Many of these rings are extensions of $$\mathbb {Z}_4$$ and numerous new linear codes over $$\mathbb {Z}_4$$ have been found in the last decade. Due to the special place of $$\mathbb {Z}_4$$ , an online database of $$\mathbb {Z}_4$$ codes was created in 2007. The original database on $$\mathbb {Z}_4$$ codes recently became unavailable. The purpose of this paper is to introduce a new and updated database of $$\mathbb {Z}_4$$ codes. We have made major updates to the original database by adding 8699 new linear codes over $$\mathbb {Z}_4$$ . These codes have been found through exhaustive computer searches on cyclic codes and by an implementation of the ASR search algorithm that has been remarkably fruitful to obtain new linear codes from the class of quasi-cyclic (QC) and quasi-twisted (QT) codes over finite fields. We made modifications to the ASR algorithm to make it work over $$\mathbb {Z}_4$$ . The initial database contained few codes that were not free. We have now added a large number of non-free codes. In fact, of the 8699 codes we have added, 7631 of them are non-free. Our database will be further updated by incorporating data from [16]. We also state an open problem of great theoretical interest that arose from computational observations.
Intersubjective Dynamics in Cooperative Robots

The present research regards the world of cooperative robots which cooperate with other robots toward a common and shared goal. After explaining the meaning the term ‘cooperation’ acquires within multi-agent robot systems, their nature, with special reference to internal architecture, decision-making and communication, will be analyzed. We will then examine the crucial characteristics of the so-called holonic organization, which proves the most functional to understand the intersubjective dynamics that “regulate” cooperative multi-robot interaction. To better understand this, we will also turn to philosophical enquiry and especially to the philosophical logic and categories typical of Aristotle’s and Plato’s reflections. Besides focusing on cooperative robots rather than on collaborative robots, which constitute the favorite aim of the study of expert researchers, the main goal of the present work is to consider whether and how philosophical analysis may support the understanding of the structural and essential mechanisms underlying robotic intersubjectivity, with special reference to cooperative robots.
Machine-Learning Based Energy Estimation on a High-Speed Transportation System

Reducing the energy absorption of automatic machines used in industry is one of the main goals towards the reduction of the carbon footprint, as well of the economic cost, of mass-produced goods. Incorporating energy improvements to existing machines and established technological processes can however be challenging, due to the complexity of estimating with a sufficient level of detail the actual energy consumption of a machine and even more by the difficulty of guessing the required modifications that allow to reduce such energy consumption. This work explores the possibility of using machine learning as a tool that allows estimating the energy consumption of a transportation system from a reduced set of numerical data that represent the main feature of the motion profile, in order to develop a model to be used for planning energy-efficient motion profiles. The investigation is based on experimental data gathered for a high-speed transportation device.
Multi-User Entanglement Routing for Quantum Mesh Networks

Multipath routing for sharing entanglement between multiple devices was simulated on real network topologies and compared against single-path routing. Results show improved entanglement rates, especially for topologies with a higher average nodal degree.
Is Intelligence the Answer to Deal with the 5 V’s of Telemetry Data?

Telemetry data and big data share volume, velocity, variety, veracity and value characteristics. We propose a distributed telemetry architecture and show how intelligence can help dealing with the 5 V’s of optical networks telemetry data.
Research to Determine the Potential Use of Humanoid (Anthropomorphic) Robots in Accommodation Facilities

Study participants evaluated the use of robots in general, and specifically the use of humanoid robots for 36 different job positions in accommodation establishments in Turkey. This exploratory study aimed to determine the positions in which it will be easier to adopt the use of robots in accommodation businesses. It also examined the role of the participant's gender and age regarding the potential use of robots. An online survey was used to collect data, and the data was obtained from 407 participants. Contrary to the theory of anthropomorphism, but consistent with the Uncanny Valley and social comparison theories, the results of the study showed that the participants were adamant that it was not appropriate to use robots for 25 of the job positions out of 36. Humanoid robots were considered appropriate for positions that provide cleaning services, perform takeaway and delivery services, or where customers do not interact one-on-one during their stay. It was concluded that young people evaluated the use of robots in the sector more positively than older people. Similarly, women tended to make more positive evaluations than men. The original value of this research is based on the lack of studies evaluating the potential of using robots for positions in accommodation establishments.
Utilization of ICT in Japanese Language Learning During the Covid 19 Pandemic: Case Study of Japanese in Use I Courses

Studying during the covid 19 pandemic in Bina Nusantara University Institution, which previously was carried out with a face-to-face system, became online distance learning using zoom. This is a challenge in itself since it is related to communicative competence, where the students’ speaking skills are required to advance in the final result. This study uses a qualitative method by sharing questionnaires with 46 respondents taking up Japanese in Use I courses to find out their responses on the utilization of ICT. This study is an introductory study on how ICT helps respondents enhance their speaking skills to the basic level 1. Every ICT media used in the studies has its own advantages and disadvantages. While audio focuses more on listening, visual serves to clarify the material, while audio-visual is a combination of sound and image to make understanding objects or materials easier, print media is a reference source. Most of the respondents choose to use audio-visual ICT media because it has complete features using audio and visual. In addition, the influence of the rapid development of technology makes it easier for respondents to access media according to their learning interests.
Measuring and Analyzing the Factors Affecting User’s Satisfaction with the PeduliLindungi Application

Many countries in the world use Digital Contact Tracing as an effort to prevent the spread of the Covid-19 virus. On one hand, this application is quite effective in controlling the virus, but on the other hand, issues arise related to the use of personal information collected through the application. This paper attempts to reveal the role of privacy concern, trust belief and risk belief on the satisfaction of users of the PeduliLindungi application, a DCT application developed by the Indonesian government. Apart from these three variables, this paper tries to reveal the role of several variables proposed by DeLone and McLean and Davis related to the adoption of information technology. The method used in this research is quantitative by collecting respondents using a questionnaire sent through online social media platforms and instant messaging platforms. The data collected is processed using Smart PLS 3 software. After analyzing the data from 242 respondents, this study confirmed the role of the variables privacy concern, risk belief, system quality, perceived usefulness, service quality, perceived ease of use on application user satisfaction and it was also found that information quality and trust did not have a significant relationship with user satisfaction. In addition, this study reveals the role of the Risk belief in mediating privacy concerns towards user satisfaction.
Preserving Candi Kidal’s Relief to Sustain a Cultural Heritage Site using ATUMICS Approach

Candi Kidal represents a cultural heritage structure that should be preserved. This temple structure is similar to the other temples in that it contains a foot section (Bhurloka), a body part (Bhuvarloka), and a summit portion (Swarloka). Some reliefs are read on the body using the Prasawya technique, and a story about the loyalty of a child named Garudeya to his mother, Winata, will appear. The story of a son freeing his mother from slavery is analogous to the Indonesian people's battle for freedom from Dutch colonialism. This study intends to introduce Garudeya's intangible reliefs at Candi Kidal and explain the moral message they embody. The Garudeya relief found in the Candi Kidal will be re-displayed on a sitting lamp as part of this research. The author did a site survey, interviews, questionnaires, and a literature review in this study. The author used the ATUMICS approach to create a prototype in the form of a sitting lamp, which aids in transforming traditions into contemporary contexts. The main focus of this study is on the concept of form, which is the transformation of Candi Kidal's silhouette into the shape of a sitting lamp. Furthermore, the lithophane technique, which converts a photograph into a 3D relief, is used on the body of the sitting lamp. As a result, the relief seen on Candi Kidal's body can be reproduced on the sitting lamp's body.
Element quality indicator: A quality assessment and defect detection method for software requirement specification

A software requirements specification (SRS) provides a detailed description of the requirements of a software system that is to be developed. The Element Quality Indicator (EQI) is a novel method to detect defects and assess the quality of an SRS. It does not hinge on review guidelines and is based on the SRS element questions extraction method (EQEM). In this study, we optimized the EQI and carried out a systematic and comprehensive experiment to validate and evaluate its effectiveness. The controlled experiment, which included 60 software engineering students, found that 100% of the subjects identified defects in the SRS using EQI. Moreover, the results indicated that the average number of defects detected with EQI was greater than that of the classical review technique - perspective-based reading. Furthermore, the controlled experiment demonstrated that EQI provides a comparatively objective and accurate evaluation of the quality of the SRS and markedly diminishes the bias in understanding software requirements due to the ambiguity of natural language.