Reviewerly/exHarmony huggingface dataset:
Probabilistic Localization with Gateway Location Errors and Multiple Transmissions

This paper investigates the effect of uncertainty in the positions of a set of spatially separated receivers (gateways) that are used to localize a device (target) that transmits a burst signal intermittently. Such systems have applications in radar, sonar and wireless sensor networks, and have been extensively studied, at least in the context of point estimation. This paper extends the limited work on region estimation to include the effect of uncertainty in the locations of the gateways on the probability that the target is in a specified region. Specifically, this paper extends previous work that found the a posteriori probability density function (pdf) of the target’s coordinates from estimates of the times the transmitted message arrives at the receivers (ToAs) under the assumption the positions of the gateways were known exactly. The expression for the a posteriori pdf is redeveloped to include the uncertainty in the measurement of the receivers’ positions. Three practical scenarios are considered: (i) the target transmits a single message and the gateways measure the ToAs as well as their own positions, (ii) the target transmits multiple messages and the gateways measure the ToAs as well as their own positions upon each reception, and (iii) the target transmits multiple messages and the gateways measure the ToAs upon each reception, but the positions of the gateways are measured once, at the time of installation. Various numerical examples are given to corroborate, provide insights, and illustrate the utility of the obtained results.
Privacy-Preserving Big Data Security for IoT With Federated Learning and Cryptography

In the ever-expanding Internet of Things (IoT) domain, the production of data has reached an unparalleled scale. This massive data is processed to glean invaluable insights, accelerating a myriad of decision-making processes. Nevertheless, the privacy and security of such information present formidable challenges. This study proposes an innovative methodology for resolving these challenges, by augmenting the privacy and efficacy of big data analytics through federated learning in the IoT ecosystem. The proffered approach amalgamates a hierarchical structure, a scalable learning rate, and a rudimentary cryptographic mechanism to foster learning while ensuring robust privacy and security. Additionally, we introduce a novel communication protocol - SEPP-IoT, designed to facilitate efficient, secure, and confidential interactions between IoT devices and a central server. In our pursuit of optimizing communication overhead, we propose an adaptive data compression algorithm, aimed at curbing the volume of data transferred between IoT devices and the central server. To fortify resilience and fault tolerance, our approach incorporates multiple mechanisms such as data replication, error correction codes, and proactive fault detection and recovery. Trust management, a salient feature of our framework, bolsters the security and integrity of federated learning. We recommend a unique technique that gauges the dependability of IoT nodes using four trust parameters. We employ the FedSim simulator to evaluate our method’s effectiveness. The results indicate a notable enhancement in privacy and efficiency of big data analytics within the IoT.
Flexible and Fine-grained Access Control for EHR in Blockchain-assisted E-healthcare Systems

It is of the utmost importance to achieve flexible and fine-grained access control of electronic health records (EHR) in smart elderly healthcare (SEH) for providing high-quality healthcare services for the elderly and protecting their privacy simultaneously. In this paper, a flexible, fine-grained, and elderly-centric access control scheme is presented for EHR data in SEH. In the proposed scheme, Ciphertext Policy Attribute Based Encryption (CP-ABE), permission token, dual-key regression, and blockchain techniques are leveraged to realize multi-dimensional access control of EHR data in terms of data generation time, data user properties, access times, and access period. Moreover, a novel token segmentation algorithm is designed to transfer access rights between doctors efficiently for multi-party diagnosis and treatment. Since the elderly can define the attributes of users accessing his/her EHR data, the access number, the access time, and the access range of data from the time dimension of data generation with the cooperation of the Smart Elderly Healthcare (SEH) institution, the privacy of EHR data of the elderly is well protected. The security analysis demonstrates that our scheme can achieve EHR ciphertext indistinguishability under chosen-plaintext attacks and token unlinkability and unforgeability under data users’ collusion attacks. The experimental results show that our scheme performs well in terms of time cost and computational overhead.
Enrollment-Stage Backdoor Attacks on Speaker Recognition Systems via Adversarial Ultrasound

Automatic Speaker Recognition Systems (SRSs) have been widely used in voice applications for personal identification and access control. A typical SRS consists of three stages, i.e., training, enrollment, and recognition. Previous work has revealed that SRSs can be bypassed by backdoor attacks at the training stage or by adversarial example attacks at the recognition stage. In this paper, we propose TUNER, a new type of backdoor attack against the enrollment stage of SRS via adversarial ultrasound modulation, which is inaudible, synchronization-free, content-independent, and black-box. Our key idea is to first inject the backdoor into the SRS with modulated ultrasound when a legitimate user initiates the enrollment, and afterward, the polluted SRS will grant access to both the legitimate user and the adversary with high confidence. Our attack faces a major challenge of unpredictable user articulation at the enrollment stage. To overcome this challenge, we generate the ultrasonic backdoor by augmenting the optimization process with random speech content, vocalizing time, and volume of the user. Furthermore, to achieve real-world robustness, we improve the ultrasonic signal over traditional methods using sparse frequency points, pre-compensation, and single-sideband (SSB) modulation. We extensively evaluate TUNER on two common datasets and seven representative SRS models, as well as its robustness against seven kinds of defenses. Results show that our attack can successfully bypass speaker recognition systems while remaining effective to various speakers, speech content, etc. To mitigate this newly discovered threat, we also provide discussions on potential countermeasures, limitations, and future works of this new threat.
SPLiT: Single Portrait Lighting Estimation Via a Tetrad of Face Intrinsics

This paper proposes a novel pipeline to estimate a non-parametric environment map with high dynamic range from a single human face image. Lighting-independent and -dependent intrinsic images of the face are first estimated separately in a cascaded network. The influence of face geometry on the two lighting-dependent intrinsics, diffuse shading and specular reflection, are further eliminated by distributing the intrinsics pixel-wise onto spherical representations using the surface normal as indices. This results in two representations simulating images of a diffuse sphere and a glossy sphere under the input scene lighting. Taking into account the distinctive nature of light sources and ambient terms, we further introduce a two-stage lighting estimator to predict both accurate and realistic lighting from these two representations. Our model is trained supervisedly on a large-scale and high-quality synthetic face image dataset. We demonstrate that our method allows accurate and detailed lighting estimation and intrinsic decomposition, outperforming state-of-the-art methods both qualitatively and quantitatively on real face images.
BRT: An Efficient and Scalable Blockchain-Based Revocation Transparency System for TLS Connections

Log-based public key infrastructure(PKI) refers to a robust class of CA-attack-resilient PKI that enhance transparency and accountability in the certificate revocation and issuance process by compelling certificate authorities (CAs) to submit revocations to publicly and verifiably accessible logs. However, log-based PKIs suffer from a reliance on centralized and consistent sources of information, rendering them susceptible to split-world attacks, and they regrettably fail to provide adequate incentives for recording or monitoring CA behavior. Blockchain-based PKIs address these limitations by enabling decentralized log audits through automated financial incentives. However, they continue to face challenges in developing a scalable revocation mechanism suited for lightweight clients. In this paper, we introduce BRT, a scalable blockchain-based system for certificate and revocation transparency. It serves to log, audit, and validate the status of certificates within the transport layer security (TLS)/secure sockets layer(SSL) PKI domain. We designed an audit-on-chain framework, coupled with an off-chain storage/computation system, to enhance the efficiency of BRT when operating in a blockchain environment. By implementing a blockchain-based prototype, we demonstrate that BRT achieves storage-efficient log recording with a peak compression rate reaching 8%, cost-effective log updates for large-scale certificates, and near-instantaneous revocation checks for users.
What Is Waiting for Us at the End? Inherent Biases of Game Story Endings in Large Language Models

This study investigates biases present in large language models (LLMs) when utilized for narrative tasks, specifically in game story generation and story ending classification. Our experiment involves using popular LLMs, including GPT-3.5, GPT-4, and Llama 2, to generate game stories and classify their endings into three categories: positive, negative, and neutral. The results of our analysis reveal a notable bias towards positive-ending stories in the LLMs under examination. Moreover, we observe that GPT-4 and Llama 2 tend to classify stories into uninstructed categories, underscoring the critical importance of thoughtfully designing downstream systems that employ LLM-generated outputs. These findings provide a groundwork for the development of systems that incorporate LLMs in game story generation and classification. They also emphasize the necessity of being vigilant in addressing biases and improving system performance. By acknowledging and rectifying these biases, we can create more fair and accurate applications of LLMs in various narrative-based tasks.
Discussion on Talent Cultivation Mode of Wastewater Treatment Technology under the Perspective of Industry-Education Integration

In the current context of environmental protection and sustainable development, there is a growing demand for wastewater treatment technology talents, which mainly stems from the problem of global water shortage and the continuous improvement of environmental protection standards. However, compared with this growth in demand, the supply and cultivation of wastewater treatment technology talents are facing many problems and challenges, such as insufficient practical experience, disconnection between education and industrial demand, and untimely updating of teaching methods and course contents. These problems seriously affect the quality and quantity of wastewater treatment technology talents and hinder the development of environmental protection. This paper discusses the cultivation mode of wastewater treatment technology talents from the perspective of industry-education integration. By analyzing the needs and challenges of wastewater treatment technology talents, it proposes a cultivation mode of wastewater treatment technology talents, which is realized through the strategies of strengthening school-enterprise cooperation, emphasizing hands-on practice and field training, providing flexible learning paths, adaptively updating curricula, and upgrading teachers' quality. This model can help students find a balance between theoretical learning and practical experience, better develop their professional skills, and make positive contributions to the development of the wastewater treatment industry.
PL/pgSQL Essential Extensions

In the previous chapter, we talked about how to return values in PL/pgSQL functions. We explained all the possible ways of returning values with some examples which will help to understand the purpose and use them according to the requirements. In this chapter, we will discuss the extensions that help with PL/pgSQL programming. We will cover a few extensions like plpgsql_check which will help to compile PL/pgSQL functions or procedures in syntactical and symmetrical way and plprofiler which will help to troubleshoot slow-running functions.
Digital Literacy Level and Career Satisfaction of Academics

Technology affects educational institutions like every other field. Higher education institutions, which are the main source of new knowledge creation and dissemination, are now dominated by digital technologies. The acquisition of digital literacy skills, which is among the 21st-century competencies, is possible with the positive attitudes and skills of academics, who are instructors in higher education institutions, towards the use of digital technologies. As role models in strengthening digital literacy, academics should develop themselves in line with strategies that include elements of the digital age and more active learning, and be able to transfer these strategies to educational practices. Otherwise, it will not be possible to increase the quality of education and it is inevitable for academicians to experience career dissatisfaction with the feeling of failure. Despite the importance of the subject, digital literacy of academicians has not been adequately addressed in the literature and the relationship between academics' digital literacy levels and career satisfaction has not been focused. Based on this gap, in this study, the concept of "digital literacy" is handled specifically for academics working in universities operating in Istanbul, Turkey, and it is aimed to contribute to the literature by revealing the relationship between academics' digital literacy and career satisfaction. The cross-sectional survey method, a quantitative research technique, was utilized in the research as a data collection and analysis tool. For this purpose, the data collected through questionnaires from 304 academics with a simple random sampling method were analyzed. As a result of the analysis, it was found that academics' digital literacy explained the career satisfaction variable by 28.1% at a 0.01 significance level. Finally, the significant positive relationship between academics' digital literacy and career satisfaction can be interpreted as that academics who cannot develop digital literacy skills and transfer these skills to teaching environments will not be able to achieve career satisfaction by experiencing a sense of failure because they cannot provide an education in line with the expectations of the digital age. In addition, according to the research findings, it was found that the digital literacy and career satisfaction levels of the academicians differ according to age, working time, academic title, and having a personal web page.
AN ANALYSYIS OF SPEAKING ANXIETY BY THE TENTH GRADE STUDENTS OF SMA NEGERI 1 BAUBAU

The problem statement of this research was what types and factors effecting the english-speaking anxiety of tenth grade students of SMA Negeri 1 Baubau. The objective of this research was to identify what types and factors effecting the speaking anxiety of grade tenth students of SMA Neeri 1 Baubau. This research used the qualitative research design. The subject of this research was the grade tenth students at SMA Negeri 1 Baubau in the odd semester 2022/2023. The data was obtained by observation and interview then was analyzed by the researcher with three steps: data reduction, data display, and draw conclusion or verification. This research found that there were three types and factors that effected the grade tenth students’ speaking anxiety of SMA Negeri 1 Baubau. For types anxiety there were communication apprehension, test anxiety, and fear of negative evaluation. And for factors anxiety, there were afraid of making mistakes, lack of vocabulary, and lack of confident.
Short-Term Electrical Load Forecasting of 150 kV Ternate System Using Optimally Pruned Extreme Learning Machine (OPELM)

Short-term electrical loads forecasting is one of the most important factors in the design and operation of electrical systems. The purpose of electric load forecasting is to balance electricity demand and electricity supply. The load characteristics of Ternate City vary, so this study uses the Optimally Pruned Extreme Learning Machine (OPELM) method to predict electrical loads. The advantages of OPELM are the fast-learning speed and the selection of the right model, even though the data has a non-linear pattern. The accuracy of the OPELM method can be determined using a comparison method, namely the ELM method. Mean Absolute Percentage Error (MAPE) is used as the accuracy criterion. The results of the comparison of accuracy criteria show that the predictive performance of OPELM is better than that of ELM. The minimum error average of the OPELM forecast test results shows a MAPE of 5,2557%, for Saturday's forecast, while the ELM method gives a MAPE of 6.4278% on the same day.
Physical Properties Of Particle-Reinforced Particle Board And Gaba Fiber Using Pvac (Polyvinyl Acetate) Filler

Traditional materials are often selected and used in the construction of houses and buildings based on local knowledge that has been tested for centuries. The use of sago leaf stalks as a building material has several advantages. They are generally easy to find in tropical areas and have natural properties that allow good air circulation, thereby helping to keep the indoor temperature cool. In addition, this material is also environmentally friendly because it can decompose properly and can be renewed naturally. In an effort to preserve these cultural and traditional values, we can take several steps. First, public education and awareness about the advantages of traditional building materials need to be improved. Organizing workshops or seminars to share knowledge about the use of traditional materials and their benefits can help increase people's appreciation of these materials. The use of sago stalks as building materials can be part of cultural conservation and environmental preservation efforts. By combining traditional knowledge with modern innovation, we can create building solutions that are environmentally friendly, sustainable and maintain our cultural heritage. By using PVAc filler, this study aims to study its effect on the physical properties of the resulting composite material. Variations in the composition of the mix will enable the identification of changes in the characteristics of the composite board and efforts to obtain optimal physical properties.
Entropy-Based Sampling Strategy for Long-Tail Target Detection of SAR Images

SAR images reflect the scattering characteristics of targets, whereas the quantity and the characteristics vary greatly from categories of targets, which causes the extreme inter-class imbalance problem. Most target detection methods for SAR images pay more attention to the algorithms but neglect the imbalance problem caused by the dataset itself. Therefore, an entropy-based sampling strategy with logarithmic smoothing is proposed to solve the extreme long-tail class imbalance problem for SAR detection tasks. Local image entropy is introduced to evaluate the information quantity or difficulty of a SAR image, and the relationship between them has been discussed elaborately. Logarithmic smoothing is utilized to avoid excessive scores due to ignoring the marginal effect. The results on the MSAR dataset also illustrate the best performance of the proposed method, which proves that the proposed method considered the quantity and the entropy simultaneously will get better performance.
Optimized Feature Selection Approach Based on Entropy for Multi-Class Data Classification

Efficient machine learning heavily relies on solving the critical issue of feature selection, which serves as a valuable pre-processing method for improving data quality. Classification models face significant challenges, particularly when confronted with data containing irrelevant and redundant features. Unfortunately, only a few techniques specifically address feature selection in datasets with multi-class attributes. This research paper introduces a novel approach for feature selection called TSEFS, which utilizes entropy-based techniques such as fuzzy entropy and mutual information. TSEFS employs a two-stage process to select relevant features. The experimental results on three datasets demonstrate that the proposed method outperformed the existing feature selection methods.
Microsoft Azure Lab

The chapter instructs how to develop and manage cloud resources using the Microsoft Azure cloud platform. You will follow step-by-step instructions along with snapshots to complete the lab. The lab begins with activating a free tier Azure account, followed by the creation of a lab plan and the creation of a virtual network using Azure cloud services, including a resource group, virtual network (VNet), VNet security rules, NAT gateway, compute virtual machines, web server instances, load balancer, instance group with uutoscaling, Microsoft SQL Server database instance, and a Cloud Storage bucket. Your infrastructure will be fully operational on the AWS global infrastructure. By completing this lab, you will have experienced the application of the Azure theoretical content previously presented.