Reviewerly/exHarmony huggingface dataset:
Improving the Reporting of Threats to Construct Validity

Background: Construct validity concerns the use of indicators to measure a concept that is not directly measurable. Aim: This study intends to identify, categorize, assess and quantify discussions of threats to construct validity in empirical software engineering literature and use the findings to suggest ways to improve the reporting of construct validity issues. Method: We analyzed 83 articles that report human-centric experiments published in five top-tier software engineering journals from 2015 to 2019. The articles' text concerning threats to construct validity was divided into segments (the unit of analysis) based on predefined categories. The segments were then evaluated regarding whether they clearly discussed a threat and a construct. Results: Three-fifths of the segments were associated with topics not related to construct validity. Two-thirds of the articles discussed construct validity without using the definition of construct validity given in the article. The threats were clearly described in more than four-fifths of the segments, but the construct in question was clearly described in only two-thirds of the segments. The construct was unclear when the discussion was not related to construct validity but to other types of validity. Conclusions: The results show potential for improving the understanding of construct validity in software engineering. Recommendations addressing the identified weaknesses are given to improve the awareness and reporting of CV.
Detecting Human Rights Violations on Social Media during Russia-Ukraine War

The present-day Russia-Ukraine military conflict has exposed the pivotal role of social media in enabling the transparent and unbridled sharing of information directly from the frontlines. In conflict zones where freedom of expression is constrained and information warfare is pervasive, social media has emerged as an indispensable lifeline. Anonymous social media platforms, as publicly available sources for disseminating war-related information, have the potential to serve as effective instruments for monitoring and documenting Human Rights Violations (HRV). Our research focuses on the analysis of data from Telegram, the leading social media platform for reading independent news in post-Soviet regions. We gathered a dataset of posts sampled from 95 public Telegram channels that cover politics and war news, which we have utilized to identify potential occurrences of HRV. Employing a mBERT-based text classifier, we have conducted an analysis to detect any mentions of HRV in the Telegram data. Our final approach yielded an $F_2$ score of 0.71 for HRV detection, representing an improvement of 0.38 over the multilingual BERT base model. We release two datasets that contains Telegram posts: (1) large corpus with over 2.3 millions posts and (2) annotated at the sentence-level dataset to indicate HRVs. The Telegram posts are in the context of the Russia-Ukraine war. We posit that our findings hold significant implications for NGOs, governments, and researchers by providing a means to detect and document possible human rights violations.
An Analysis of Islamic Inheritance System Under Object-Oriented Paradigm

The wealth of deceased should be distributed precisely and quickly according to inheritance laws in order to avoid the unrest situation in the society. A professional software solution can serve this purpose. The professional software can be developed through analyzing, designing, building and testing of the manual system. As the analysis is the first and crucial stage of software development life cycle, this paper focuses on the analysis of Islamic Inheritance System (IIS). This analysis can be performed employing either structured or object-oriented approach. The structured analysis depends on the functions of the IIS, while objects are the main part in the object-oriented analysis technique. The one demerit of structured analysis is that the IIS is modeled using Data Flow Diagram (DFD) which is not standard diagram in software engineering community. In addition, the structured analysis phase of IIS does not provide any knowledge of internal modules. In order to address these problems, this paper concentrates on the object-oriented analysis of IIS where standard UMLs are utilized describing the system. The object-oriented analysis is accomplished by generating use case diagram, activity diagram and domain class diagram of IIS which can be easily used in the design and implementation phase of software development process.
Accuracy Assessment on Detail Survey Plan Using iPhone 13 Pro Max LiDAR Sensor

Total stations, global navigation satellite systems (GNSS) instruments, and laser scanners are common tools used in detailed surveys because of the precision they bring to measurements and data collection. While conventional land surveying methods using total stations and GNSS instruments are widely used for their accuracy, they require a team of at least three people and can be costly. In 2021, Apple introduced the iPhone 13 Pro with a built-in LiDAR sensor that can potentially be used for land surveying. However, it is unclear whether the LiDAR data obtained from the iPhone is accurate and reliable enough to replace the conventional surveying methods. Therefore, a comparison study between the conventional method and the iPhone LiDAR sensor needs to be conducted to evaluate the feasibility and potential benefits of using the iPhone LiDAR sensor in land surveying. The purpose of this study is to evaluate the differences between tacheometry method using total station and laser scanning method using iPhone 13 Pro Max in generating detail survey plan. This study was conducted UiTM Shah Alam Stadium, Shah Alam, Selangor. For scanning method, two device poses (distance of sensor to target) are used which are 5 and 10 cm. Based on results and analysis, the difference between the actual elevation value and the scanning data from the device at 5 cm and 10 cm is relatively small. The lowest values for the device's position at 5 cm and 10 cm are -0.025 m and -0.057 m, respectively, and the highest values are 0.023 m and 0.017 m, respectively. The average deviation at the device's position of 5 cm is 0.023 m, while the average deviation at the device's position of 10 cm is 0.017 m. In conclusion, the LiDAR sensor in the iPhone 13 Pro Max has the potential to be a valuable tool for assessing accuracy in detailed survey plans. Its possible applications in different fields are worth further exploration
Land use/land cover (LULC), change detection, and simulation analysis of Manila Bay’s Dolomite mining site in Cebu, Philippines using Sentinel–2 satellite

Dolomite mining in Barangay Pugalo – Pasol, Alcoy, Cebu, and the Manila Bay beach project along Roxas Boulevard, Metro Manila, Philippines has long been controversial due to the ecological, environmental, and economic implications. From the threat of rising sea levels to questionable management solutions, this sand project cannot be fully measured because of the absence of an environmental impact study. The primary objective of this research is to develop a geospatial study of the Cebu mining site using Sentinel–2 satellite data utilizing remote sensing techniques and Quantum GIS where 2018, 2020, 2030, and 2050 classification and simulation analysis shows an increase in Soil and a decrease in Vegetation and Built-up classes. Dolomite sand sustainability and longevity in Manila Bay's Dolomite Beach, are also assessed using related literature. With the help of this analysis, it’s possible to identify specific changes and predict which land will be impacted by upcoming years when current practices in the area do not change. It can also be utilized as resource management, environmental policy, and regulation support tools in identifying ecological problems while serving as a source of historical information for future land management and support for the long-term use of natural resources and expanding populations.
Identification of relevant features influencing movie reviews using sentiment analysis

Sentiment analysis is a systematic text mining research that examines individuals' behaviour, approach, and viewpoint. This paper analyses viewers' sentiments towards the movies released during the pandemic. This study employs the sentiment analysis techniques on movie reviews' accessed in real-time from internet movie database (IMDb). The paper's main objective is to identify the potential words that contribute to the biases of the reviews and influence overall viewers. The proposed methodology has employed valence aware dictionary for sentiment reasoning based on sentiment analysis of overall reviews, followed by application to various movie genres. Finally, we have applied Pearson's correlation analysis to find the association between the words among the genres. The paper also calculates the sentiment scores of reviews using different sentiment analysis models. Our results showed a minimum of 17% features common genre-wise. It reveals sets of most distinct influential words, which may be vital for understanding the nature of the language used for a particular kind of movie.
A Study of College English Reading Education Methods Using the Videoconferencing System, Zoom.

The purpose of this study is to compare a teaching method that combines both synchronous and asynchronous online classes with a teaching method that uses only synchronous online classes in college English reading classes. The study aims to determine the level of interest, participation, and satisfaction of learners in classes and verify the effectiveness of the best non-face-to-face online learning. For the purpose, this study conducted an experimental study on a total of 61college students for 12 weeks. Thirty of them took the English reading classes using mixed learning(non real time + real time) as an experimental group A. The other thirty-one took the classes using only real time learning with Zoom as an experimental group B. For the study, data were collected from each group and their responses were analyzed through statistics. The experimental group A showed that class interest, class participation, and class satisfaction were statistically significant. Also, the experimental group A showed that learners themselves increase their willingness to study while self-directed continuos repetitive learning. These results show that mixed teaching method that combines real-time and non-real-time online class in college English reading classes has a significant influence on learners' learning interest and satisfaction. These findings can be considered helpful in improving teacher’s teaching styles by effectively applying mixed online learning to English Learning. (Hanbat National University)
Multi-spectral LWIR Measurements and Analysis for Improved Target Ranging in Passive Systems

Multi-spectral LWIR Measurements and Analysis for ImprovedTarget Ranging in Passive Systems
Geological Mapping of Grib Kimberlite Pipe Using 3D Magnetization Vector Inversion From Airborne Data

This study is describing airborne geophysical survey, carried out in Northeastern Russia (Archangelsk region) in order to aimed at geological mapping of Grib kimberlite pipe, which is known to have strong presence of remanent magnetization. Airborne data were inverted using two techniques, the magnetization vector inversion (MVI) and a QDIK inversion, a proprietary algorithm developed at the Irkutsk State University (ISTU), which allows extracting four parameters from airborne magnetic data (magnetic susceptibility, inclination, declination and Koeniksberger ratio). The inversion results were further analyzed and verified against the known geology of the Grib kimberlite. The study allowed to better map geology of Grib kimberlite using only uninvasive (remote sensing) technology
Exploiting Cyber Threats And Protecting Cryptocurrencies Toward Bitcoin Exchanges

The concept of Bitcoin first came into creation in 2008, as a response to the Great Financial Crisis, and the world of finances relies upon banks for all kinds of financial transactions as intermediaries. Bitcoin is more than a cryptocurrency used as payment for investors to hold and hope for value; it is considered a digital asset these days. While Bitcoin transactions take place in the cyber network, possibilities are there for cyber-attacks. To protect Bitcoins from illegal miners of Bitcoin data, it is required to find all possible intrusions, called vulnerabilities. If more vulnerabilities exist in the network that makes the transactions vulnerable. As of now, Bitcoin uses centralized Blockchain technology, where the transaction made by the user are managed by various blockchain methods like Merkelroot, Mining, Halving, Keys, and Wallets. These methods provide security to the cryptocurrencies online. Among all the four methods, Wallets are somewhat vulnerable due to their dual services i.e., custodians and non-custodians. Non-custodian Wallet type doesn’t cause vulnerability due to not having any storage for keys. Whereas the Custodian Wallet type provides storage of keys for the customers which makes it as vulnerable. The techniques that lead to vulnerability in the custodian type are hot storage, cold storage, and deep cold storage. The exploitation of all these individual techniques is the main task of this work. When it comes to transactions of bitcoin along the network where the receiver needs to wait around 30 minutes due to mempool. This is also a loophole that may trigger the threat to the bitcoin exchange. These vulnerabilities can be reduced by regulatory oversight and reducing the mempool waiting time or it is possible by maintaining more legal crypto miners for every block of transaction. To verify the whole process of bitcoin transaction exchanges, few cloud services use blockchain technology for processing transactions. The proposed work is to implement the above methods, so that protection is provided to cryptocurrencies toward bitcoin exchanges.
Distributed Multi-target Tracking by Multi-AUV Based on PMB Filter in Underwater

This paper focuses on the problem of multi-target tracking (MTT) by multiple Autonomous underwater vehicles (AUVs). It presents a distributed information fusion method to improve the robustness and performance of AUVs in MTT. In this method, each AUV locally obtains the posterior probability density function (pdf) of multi-target using local measurements based on the Poisson multi-Bernoulli (PMB) filter. In addition, a target-wise fusion rule based on generalized covariance intersection is proposed, allowing the locally obtained multi-target pdf to be shared between AUVs and fused effectively. The simulation results show that the proposed method performs well for underwater MTT by multi-AUV.
Quadratic filtering for non-Gaussian stochastic parameter systems with buffer-aided strategy

This paper is concerned with the quadratic filtering problem for a class of linear discrete time-varying systems with non-Gaussian stochastic parameters and intermittent measurements. In order to improve the filtering performance, a buffer-aided strategy is used to store the historical measurement data which is sent to the filter via a shared communication channel at each transmission instant. The main objective of this paper is to design a quadratic filter to estimate the states of the considered non-Gaussian stochastic systems by using the buffer-aided strategy. Specifically, an augmented system is constructed by combining the original system states and their second-order Kronecker powers. As such, the considered quadratic filtering problem amounts to the Kalman filtering problem for the augmented system. The filtering error covariance (FEC) conditioned on the transmission intervals is firstly derived recursively and then is minimized by choosing the suitable filtering gains. Moreover, sufficient conditions are deduced to guarantee the boundedness of both the FEC and the conditional FEC. Finally, two illustrative examples are provided to demonstrate the effectiveness of the proposed filtering method.
A full 6 degree-of-freedom visual estimation with laser assisted

Efficient and accurate measurement in unstructured environment is a challenging issue and also research interest in the industrial field. The accuracy of vision is very high in the close-range scene and the stability of laser measurement is better, with a large achievable range. A 6-DOF pose measurement method based on vision and assisted by laser is proposed in this paper. By combining their respective advantages, the method can be immune to ambient light. The optimization objective is established by using the projection coordinates of laser spots in different coordinate systems and solved by Newton–Raphson iteration. The initial value is estimated by Kronecker product. The Monte Carlo method is further used to simulate. The causes of measurement errors and the influence factors affecting the accuracy are analyzed. On this basis, an experimental prototype is constructed. The accuracy and repeatability accuracy experiments are conducted to verify the correctness of the proposed method.
State Space Estimation: from Kalman Filter Back to Least Squares

This note reviews a direct least squares estimation of a state space model and highlights its advantages over the standard Kalman filter in some applications. Although there is a close relationship between these two concepts, dual understanding of the estimation problem seems to be little appreciated by the mainstream econometric literature as well as applied researchers. Due to computational and theoretical advancements, the least squares estimation of a state space model has become a viable alternative in many fields, showing great potential in solving otherwise difficult problems. This note gathers and discusses some possible applications to illustrate the point and contribute to their wider use in practice.
Quantifying the robustness of deep multispectral segmentation models against natural perturbations and data poisoning

In overhead image segmentation tasks, including additional spectral bands beyond the traditional RGB channels can improve model performance. However, it is still unclear how incorporating this additional data impacts model robustness to adversarial attacks and natural perturbations. For adversarial robustness, the additional information could improve the model’s ability to distinguish malicious inputs, or simply provide new attack avenues and vulnerabilities. For natural perturbations, the additional information could better inform model decisions and weaken perturbation effects or have no significant influence at all. In this work, we seek to characterize the performance and robustness of a multispectral (RGB and near infrared) image segmentation model subjected to adversarial attacks and natural perturbations. While existing adversarial and natural robustness research has focused primarily on digital perturbations, we prioritize on creating realistic perturbations designed with physical world conditions in mind. For adversarial robustness, we focus on data poisoning attacks whereas for natural robustness, we focus on extending ImageNet-C common corruptions for fog and snow that coherently and self-consistently perturbs the input data. Overall, we find both RGB and multispectral models are vulnerable to data poisoning attacks regardless of input or fusion architectures and that while physically realizable natural perturbations still degrade model performance, the impact differs based on fusion architecture and input data.
Learning disentangled representation of video for pallet decomposition in industrial warehouses

Over the past decade, several approaches have been proposed to learn disentangled representations for video prediction. However, reported experiments are mostly based on standard benchmark datasets such as Moving MNIST and Bouncing Balls. In this work, we address the problem of learning disentangled representation for video prediction in an industrial environment. To this end, we use decompositional disentangled variational auto-encoder, a deep generative model that aims to decompose and recognize overlapped boxes on a pallet. Specifically, this approach disentangles each frame into a dynamic component (box appearance) and a temporally variant component (box location). We evaluate this approach on a new dataset, which contains 40000 video sequences. The experimental results demonstrate the ability to learn both the decomposition of the bounding boxes and their reconstruction without explicit supervision.
Creating a calibrated scene for objective measurements of multi-band camera systems

Many modern electro-optical systems incorporate multiple electro-optical sensors, each having unique wavebands, alignment, and distortion. Traditional laboratory testing requires multiple measurement setups for metrics like inter-channel sensor alignment, near/far focus performance, color accuracy, etc. In this study, a calibrated scene is developed for objective measurements of multiple electro-optical cameras from a single mounting position. This scene uses multiple targets (size and shape), multiple flat fields (blackbodies and spectralon panels), and temporal sources. Some targets work well in both the emissive and reflective bands, allowing for accuracy relative distortion to be measured. Specific attention was given to testing in the presence of scene-based algorithms such as auto-gain/level/exposure, where bright and dark objects are used to drive dynamic range. This approach allows for various measurements to be taken simultaneously and efficiently.
How to Address This Problem

Connected to, and contained within, imperfect technology are assets organizations deem as critical… and attackers find valuable. The ability to clearly identify and describe this observation in meaningful ways is a critical step to addressing the cybersecurity problem—so that it may be managed as a risk. With the risk actually understood, organizational functions may become clearer, and appropriate measures of effectiveness become more insightful.
Understand the Problem

Tying risk to critical assets is the core component of any cybersecurity risk management program. Understanding what attackers find valuable and what imperfections exist in the organization will help communicate risk and drive what safeguards the business is willing to resource.
Why Is It Complicated?

Technology’s imperfections are where the cybersecurity problem begins, exposing attractive perforations in its intended functionality. Deploy imperfect technology imperfectly, and these unintended perforations multiply in innumerable ways. Layer imperfect technologies into existing technologies, each with perpetual pursuits to communicate and interact with interoperable counterparts, and these perforations spread in orthogonally interconnected and largely unmanageable ways, introducing vulnerabilities either visible or perfectly hidden for the malicious individual, or group, to exploit. This is the complication of the cybersecurity problem.