Reviewerly/exHarmony huggingface dataset:
Research on robustness evaluation and verification for facial deepfake detection

Recently, due to the increasing harm caused by facial deepfake videos, the detection technology of facial deepfakes has attracted wide interest, and designing a scientific evaluation mechanism is conducive to the application of the technology. However, the complex perturbations in the actual scene reduce the detection performance of facial deepfake detection, and the existing evaluation indicators can not fully measure the robustness of the detection technology. To this end, this paper proposes a new robustness evaluation index system, the AUC decrease rate, and AUC stability range under multiple perturbations. Through the verification of multiple detection models with multiple datasets, the experimental results show that the proposed index can simply and intuitively evaluate the robustness of the detection technology, which is valuable for the construction of future evaluation standards.
Lessons Learned from Designing and Implementing Team Project Work in a Software Engineering Course

Team project work is an essential and integral component of software engineering courses for learning practical skills. Agile process models are widely used in the industry for software development. However, setting up project work based on agile process models can be challenging, primarily due to the sequencing of topic coverage in lecture sessions that does not prepare the teams to complete the first sprint early in the semester. This paper presents the design of the project work structure used in a course offering and its implementation. The paper includes the findings from the student feedback and recommendations, which assist faculty members in setting up project work in their course offerings, enable students to gain practical experience in software engineering, and to develop essential skills that will prepare them for the industry.
Great Expectations and EPIC Fails: A Computational Perspective on Irony and Sarcasm

We typically believe that irony is a completely human affair, but there have been interesting attempts to create computational models of irony use and understanding. This chapter presents an overview of some of these models, especially as implemented as conversational agents. One of the beauties, and major challenges, of computer modeling is that it forces researchers to make concrete decisions on how best to implement some linguistic observation or theoretical idea (e.g., how to create a workable model of echoic mention, pretense, or what is meant by incongruity). Veale presents his EPIC model in which an expectation (E) predicts a property (P) of an instance (I) of concept (C) that can get upended by an ironic utterance. This model provides a quantifiable view of what it means for an ironic utterance to achieve its desired effect on an audience. The success of an ironic utterance hinges on its capacity to highlight the failure of a reasonable expectation. The effectiveness of this computational model was partly assessed by obtaining human judgments about the meaning and quality of different ironic utterances in varying contexts that are suggestive of different expectations. In this way, Veale’s work offers insights as to how engineering solutions may be very informative about the way irony functions in human communication.
Inferring Irony Online

The versatility of irony is evident in the variety of ways and places in which it is used. This chapter considers some of the complexities of ironic language on the internet (e.g., social media, messaging apps). Yus views irony as being associated with making a reference to some state of affairs that can be criticized or mocked, and which communicates a speaker’s attitudes toward that situation, including others who adopt a similar point of view. He embraces a “relevance theory” perspective to emphasize the prominence of echoic mention as a key source of pragmatic information when determining an ironic speaker’s dissociative attitude. Yus details the range of contextual information that enables successful irony use, even in situations where individuals do not share the same immediate physical space. This information includes widely held encyclopedic background knowledge, speaker-specific encyclopedic knowledge, previous utterances, particular linguistic cues, and information from the current physical setting. These different sources of contextual information are combined in specific ways to enable ironic meaning interpretation when people are not physical co-present (e.g., when posting messages or writing email on the internet).
Research on Optimization of Multi-Camera Placement Based on Environment Model

Aiming at the model distortion, constraint redundancy, and lack of occlusion detection, an optimization algorithm for multi-camera placement based on environment model is proposed to minimize escape rate and measurement error. First, the measurement error model is established by the reverse modeling method, which reveals the relationship between the pixel quantization error and the measurement error. Second, the environment model is split into camera candidate position model, feature model and obstacle model. Based on the division of field of view and the signedness of tetrahedron volume, a method for judging the relationship between the point and the field of view is proposed by calculating the determinant. Thirdly, the Bresenham algorithm is extended to three-dimensional space for occlusion detection. Finally, a fitness function is built to minimize the escape rate and measurement error. The particle swarm optimization algorithm is used to optimize the multi-camera placement. Extensive experiments demonstrate that the proposed method is effective and robust.
Pengukuran Indeks Pembangunan Literasi Masyarakat (IPLM) Kota Salatiga sebagai Dasar Penyusunan Program Pengembangan dan Pembinaan Perpustakaan

&lt;div style="text-align: justify;"&gt;The development of public literacy in Indonesia is very dependent on several aspects of people’s reading skills. In determining program development policies and indicators of success, periodic reviews and benchmarks are required. The formulation of the Community Literacy Development Index measurement needs to be done in Salatiga City. The CLDI research aims to determine the value of CLDI in Salatiga City, such as the condition of all types of libraries, both from the aspect of library distribution, collections, library staff, to users in Salatiga City. CLDI measurements were carried out using a census approach. From the results of the research, the score for the Salatiga City CLDI in 2023 was 81.21%. It can be concluded that the score of 81.21% is in the interval between 80 and 90 so the CLDI of Salatiga City in 2023 is included in the high level.&lt;/div&gt;
An Road Boundary Detection Algorithm Based on Radar that Can Improve Multiple-Target Tracking Performance for Autonomous Vehicles on Highway Condition

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Radar is playing more and important role in multiple object detection and tracking system due to the fact that Radar can not only determine the velocity instantly but also it is less influenced by environment conditions. However, Radar faces the problem that it has many detection clutter，false alarms and detection results are easily affected by the reflected echoes of road boundary in traffic scenes. Besides this, With the increase of the number of targets and the number of effective echoes, the number of interconnection matrices increases exponentially in joint probability data association, which will seriously affect the real-time and accuracy of high-speed scene algorithms.in the tracking system. So, A method of using millimeter wave radar to detect and fit the boundary guardrail of high-speed road is proposed, and the fitting results are applied to the vehicle detection and tracking system to improve the tracking accuracy. Through the comparison and verification of ablation experiments, this method can screen out the impact of the guardrail reflection echo on the vehicle detection and tracking in real time, and improve the stability of the detection and tracking system.&lt;/div&gt;&lt;/div&gt;
Humanising robot-assisted navigation

Abstract Robot-assisted navigation is a perfect example of a class of applications requiring flexible control approaches. When the human is reliable, the robot should concede space to their initiative. When the human makes inappropriate choices the robot controller should kick-in guiding them towards safer paths. Shared authority control is a way to achieve this behaviour by deciding online how much of the authority should be given to the human and how much should be retained by the robot. An open problem is how to evaluate the appropriateness of the human’s choices. One possible way is to consider the deviation from an ideal path computed by the robot. This choice is certainly safe and efficient, but it emphasises the importance of the robot’s decision and relegates the human to a secondary role. In this paper, we propose a different paradigm: a human’s behaviour is correct if, at every time, it bears a close resemblance to what other humans do in similar situations. This idea is implemented through the combination of machine learning and adaptive control. The map of the environment is decomposed into a grid. In each cell, we classify the possible motions that the human executes. We use a neural network classifier to classify the current motion, and the probability score is used as a hyperparameter in the control to vary the amount of intervention. The experiments collected for the paper show the feasibility of the idea. A qualitative evaluation, done by surveying the users after they have tested the robot, shows that the participants preferred our control method over a state-of-the-art visco-elastic control.
A Study on Toponymic Entity Recognition Based on Pre-Trained Models Fused with Local Features for Genglubu in the South China Sea

Toponymic entity recognition is currently a critical research hotspot in knowledge graphs. Under the guidance of the national ancient book protection policy and the promotion of the wave of digital humanities research, this paper proposes a toponymic entity recognition model (ALBERT-Conv1D-BiLSTM-CRF) based on the fusion of a pre-trained language model and local features to address the problems of toponymic ambiguity and the differences in ancient and modern grammatical structures in the field of the Genglubu. This model extracts global features with the ALBERT module, fuses global and local features with the Conv1D module, performs sequence modeling with the BiLSTM module to capture deep semantics and long-distance dependency information, and finally, completes sequence annotation with the CRF module. The experiments show that while taking into account the computational resources and cost, this improved model is significantly improved compared with the benchmark model (ALBERT-BiLSTM-CRF), and the precision, recall, and F1 are increased by 0.74%, 1.28%, and 1.01% to 98.08%, 96.67%, and 97.37%, respectively. The model achieved good results in the field of Genglubu.
A Data-Driven Artificial Neural Network Approach to Software Project Risk Assessment

In the realm of software project management, predicting and mitigating risks are pivotal for successful project execution. Traditional risk assessment methods have limitations in handling complex and dynamic software projects. This study presents a novel approach that leverages artificial neural networks (ANNs) to enhance risk prediction accuracy. We utilize historical project data, encompassing project complexity, financial factors, performance metrics, schedule adherence, and user-related variables, to train the ANN model. Our approach involves optimizing the ANN architecture, with various configurations tested to identify the most effective setup. We compare the performance of mean squared error (MSE) and mean absolute error (MAE) as error functions and find that MAE yields superior results. Furthermore, we demonstrate the effectiveness of our model through comprehensive risk assessment. We predict both the overall project risk and individual risk factors, providing project managers with a valuable tool for risk mitigation. Validation results confirm the robustness of our approach when applied to previously unseen data. The achieved accuracy of 97.12% (or 99.12% with uncertainty consideration) underscores the potential of ANNs in risk management. This research contributes to the software project management field by offering an innovative and highly accurate risk assessment model. It empowers project managers to make informed decisions and proactively address potential risks, ultimately enhancing project success.
The blockchain conundrum: An in‐depth examination of challenges, contributing technologies, and alternatives

Summary The accelerated development of information and communication technologies has generated a demand for data storage that is effective, transparent, immutable, and secure. Distributed ledger technology and encryption techniques such as hashing and blockchain technology revolutionised the landscape by meeting these requirements. However, blockchain must overcome obstacles such as low latency, throughput, and scalability for its full potential. Investigating blockchain's structure, types, challenges, promises, and variants is necessary to understand blockchain and its capabilities comprehensively. This paper overviews various aspects, such as emergent blockchain protocols, models, concepts, and trends. We classify blockchain variants into five essential categories, DAG, TDAG, Sharding, Consensus, and Combining methods, based on the structure each follows, and conduct a comparative analysis. In addition, we explore current research tendencies. As technology progresses, it is essential to comprehend the fundamental requirements for blockchain development.
Evaluating the Potential Impact of Transition Finance on Economic Growth and Development in India

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PREPARATION OF STUDENTS IN SHORT-DISTANCE RUNNING TECHNICAL UNIVERSITY

In the proposed article, the issues of training second-year students of a technical university in short-distance running are considered. It was revealed that both in the preparatory and compet-itive periods it is possible to successfully apply speed-strength abilities in various forms and methods of training
Hardware Development for Jogger-Specific Vital Signs Monitoring with Alarm Mechanism using E-Textile Technology

This hardware development is designed for a monitoring system for joggers, specifically a wearable, with the use of e-textile technology that checks the basic vital signs of the body. Main vital signs that are being measured by this system are body temperature and the pulse rate. This e-textile system will serve as a warning mechanism for the jogger while running to keep track on the body temperature and pulse rate prior to reaching the critical point. The purpose of this study is to be able to come up with a monitoring system that would aid the joggers on the evaluation of the response of the body while performing the said activity.
A Detailed Sentiment Analysis Survey Based on Machine Learning Techniques

Sentiment analysis is a rapidly growing topic of research as a result of the tremendous growth of digital information. In the modern era of artificial intelligence, one of the most crucial technologies for obtaining sentiment data from the vast amounts of data is sentiment analysis. It refers to a procedure of finding and categorising the opinions expressed in a source text. Reaching a consensus regarding business decisions is made much easier by conducting a sentiment analysis on consumer data. Machine learning offers an efficient and trustworthy technique for sentiment categorization and opinion mining. State-of-art machine learning techniques and methodologies have evolved and expanded. In addition to summarising research articles based on movie reviews, product reviews, and Twitter reviews, this survey article covers sentiment analysis notations, needs, levels, methodologies, sources, and machine learning approaches and tools. This research aims to determine the significance of sentiment analysis and to generate interest in the subject.
Dementia Detection from Speech: What If Language Models Are Not the Answer?

A growing focus among scientists has been on researching the techniques of automatic detection of dementia that can be applied to the speech samples of individuals with dementia. Leveraging the rapid advancements in Deep Learning (DL) and Natural Language Processing (NLP), these techniques have shown great potential in dementia detection. In this context, this paper proposes a method for dementia detection from the transcribed speech of subjects. Unlike conventional methods that rely on advanced language models to address the ability of the subject to make coherent and meaningful sentences, our approach relies on the center of focus of the subjects and how it changes over time as the subject describes the content of the cookie theft image, a commonly used image for evaluating one’s cognitive abilities. To do so, we divide the cookie theft image into regions of interest, and identify, in each sentence spoken by the subject, which regions are being talked about. We employed a Long Short-Term Memory (LSTM) neural network to learn different patterns of dementia subjects and control ones and used it to perform a 10-fold cross validation-based classification. Our experimental results on the Pitt corpus from the DementiaBank resulted in a 82.9% accuracy at the subject level and 81.0% at the sample level. By employing data-augmentation techniques, the accuracy at both levels was increased to 83.6% and 82.1%, respectively. The performance of our proposed method outperforms most of the conventional methods, which reach, at best, an accuracy equal to 81.5% at the subject level.
Karatsubha Multiplier by using Verilog

The Karatsuba multiplier is a widely-used algorithm that aims to efficiently multiply large numbers. Traditional multiplication algorithms have a time complexity of O(n^2), where n represents the number of digits in the input numbers. In contrast, the Karatsuba multiplier achieves a faster multiplication process by recursively splitting the numbers into smaller parts and performing intermediate multiplications using only three multiplications instead of four. This Modified architecture saves the 14.9% computation time and it consumes 45.5% less slices than existing Karatsuba multiplier. The proposed architecture has been simulated and synthesized by Xilinx vivado design suite for Spartan &amp; Vertex device family. The new architecture is simple &amp; easy. It emphasizes the significance of the Karatsuba multiplier in improving computational efficiency and highlights its implementation in different domains to accelerate large-scale multiplication tasks. Proposed hardware was implemented on different FPGA devices for various operand sizes, and performance parameters were determined. Comparing to state-of-theart works, the proposed method resulted in a lower combinational delay and area-delay product indicating the efficiency of design. In this we use two 32 bit inputs and produced the 64 bit as the output. In Spartan3E FPGA device family, Modified Karatsuba Algorithm (MKA) is 26.5% faster than Karatsuba Algorithm (KA).It consumes 61.7% less slices than existing KA based Convolution
Semantic Modelling Approach for Safety-Related Traffic Information Using DATEX II

The significance of Linked Open Data datasets for traffic information extends beyond just including open traffic data. It incorporates links to other relevant thematic datasets available on the web. This enables federated queries across different data platforms from various countries and sectors, such as transport, geospatial, environmental, weather, and more. Businesses, researchers, national operators, administrators, and citizens at large can benefit from having dynamic traffic open data connected to heterogeneous datasets across Member States. This paper focuses on the development of a semantic model that enhances the basic service to access open traffic data through a LOD-enhanced Traffic Information System in alignment with the ITS Directive (2010/40/EU). The objective is not limited to just viewing or downloading data but also to improve the extraction of meaningful information and enable other types of services that are only achievable through LOD. By structuring the information using the RDF format meant for machines and employing SPARQL for querying, LOD allows for comprehensive and unified access to all datasets. Considering that the European standard DATEX II is widely used in many priority areas and services mentioned in the ITS Directive, LOD DATEX II was developed as a complementary approach to DATEX II XML. This facilitates the accessibility and comprehensibility of European traffic data and services. As part of this development, an ontological model called dtx_srti, based on the DATEX II Ontology, was created to support these efforts.
Personal and Social Skills’ Impact on Marketing Effectiveness

ABSTRACT This study examines the importance of emotional intelligence (EI) in marketing among professionals in the Algerian telecommunications industry. EI plays a crucial role in building strong relationships, effective communication, and connectionbuilding, especially as technology and AI advance. However, there is limited research on EI in marketing professionals within telecommunications. Using a quantitative survey and regression analysis, the study finds a positive impact of EI dimensions on marketing effectiveness. These findings have implications for improving marketing team performance by implementing training programs that enhance EI skills. The study contributes to the scientific community by emphasising the significance of EI in marketing and encouraging further research.
S <scp>pacelord</scp> : Private and Secure Smart Space Sharing

Space sharing services like vacation rentals and meeting rooms are being equipped with smart devices such as cameras, door locks and many other sensors. However, the sharing of such devices poses privacy and security problems, as there is typically no clear control transfer between owners and users. In this paper, we propose S pacelord , a system to time-share smart devices contained in a shared space privately and securely while allowing users to configure them. When a user stays at a space, S pacelord ensures that the smart devices contained in it run code and configurations the user trusts while removing pre-installed code and configurations. When the user leaves the space, S pacelord reverts any changes the user has introduced to the smart devices, deletes any remaining private data and lets the owner take back control over the devices. We evaluate S pacelord for two realistic space-sharing cases—smart home and coworking meeting room—and observe smart space provisioning delays of ∼ 82 s across three different platforms. Moreover, the average runtime overhead of our system varies from 7.8% to 11.8% across different hub hardware, running native applications.