Reviewerly/exHarmony huggingface dataset:
Bitcoin Revisited: Formalization, Benchmarking, and Open Security Issues

&lt;p&gt;Thousands of researchers and practitioners around the world neglect foundational understanding of the most trusted cryptocurrency platform, Bitcoin. According to the typical tendency of taking the discussion of Bitcoin's security for granted, it is usually confidently stated that, technically, Bitcoin is one of the most secure platforms for investment. In this paper, we revisit the fundamental calculations and assumptions of this widely adopted platform. We break down the original calculations in order to better understand the underlying assumptions of the Bitcoin proposal. Accordingly, we set open research questions, and we highlight expected scenarios upon the violation of each assumption. Furthermore, we propose a novel formalization of the Bitcoin mining problem using the Birthday Paradox, which we utilize to propose new concepts for benchmarking the security of Bitcoin, including the Critical Difficulty and Critical Difficulty per given portion. Our calculations indicated that it would be profitable to launch Partial Pre-Image attacks on Bitcoin once the mining puzzle difficulty reaches 56 leading zeros. Additionally, we discuss how Quantum Computing can be used to attack Bitcoin, and the implications of Sharding on the security of Bitcoin. The main objective of this work is to highlight, demystify, and justify the confusion between the realistic relative trust versus the common unconditional trust in Bitcoin.&lt;/p&gt;
Using experimental variograms for analyzing survey data during construction

Modern construction begins with the processes of surveying a construction site and identifying the structural features of the upper geological layers, which can affect the parameters of the facility being built. Research is carried out using the most effective and informative technologies, while the amount of information can be excessive and very noisy. To reduce the error and identify certain spatial patterns of the distribution of geological characteristics, the methods of geostatistics are used. This article provides a methodology for analyzing survey data using an experimental variogram. The main methods of calculating such a variogram are substantiated using the classical concept of the construction of this graph. Formulas for calculating the experimental variogram are proposed. Smoothing tools are used to determine the most optimal window. As a result of the construction of an experimental variogram and smoothing with a sliding window, a new approach to the formation of a variogram was created, which can be used when carrying out survey work in construction.
Object Detection in Traffic Videos: A Survey

&lt;p&gt;Traffic video analytics has become one of the core components in the evolution of transportation systems. Artificially intelligent traffic management systems apply computer vision techniques to alleviate the monotony of manually monitoring the video feed from surveillance cameras. Locating the objects of interest is the most crucial step in the pipeline of such video analytics systems. An abundance of research has been conducted to find the location of the targets in traffic scenes. This paper presents a comprehensive review of different algorithms used for object detection in traffic surveillance applications in addition to the recent trends and future directions. Based on the approaches used in the related studies, we categorize the object detection methods into motion-based and appearance-based techniques. We further classify each group of techniques into a number of subcategories and analyze the advantages and disadvantages of each method. The major challenges, limitations, and potential solutions are also discussed along with the future scope and directions.&lt;/p&gt;
DARPA Ground Truth Program Overview.

identify events whose source parameters are accurate enough for use in calibration. LLNL has developed criteria for determining the accuracy of seismic locations and methods to characterize the covariance of calibration datasets. Although the most desirable calibration events are chemical and nuclear explosions with highly accurate locations and origin times, catalogues of naturally occurring earthquakes offer needed geographic coverage that is not provided by man made sources. The issue in using seismically determined locations for calibration is validating the location accuracy. Sweeney (1998) presented a 50/90 teleseismic, network-coverage criterion (50 defining phases and 90{sup o} maximum azimuthal gap) that generally results in 15-km maximum epicenter error. We have also conducted tests of recently proposed local/regional criteria and found that 10-km accuracy can be achieved by applying a 20/90 criteria. We continue to conduct tests that may validate less stringent criteria (which will produce more calibration events) while maintaining desirable location accuracy. Lastly, we examine methods of characterizing the covariance structure of calibration datasets. Each dataset is likely to be effected by distinct error processes that result in a distinct covariance structure. We present covariance models for select data sets and demonstrate how these datasets can be integrated into one calibration-event catalog. LLNL has developed a robust magnitude calibration methodology for sparsely distributed regional stations using narrow band coda envelopes. This technique provides stable magnitudes for small events that makes detection and identification calibration possible at low magnitudes. This approach has most recently been applied to IMS stations located in Israel, Jordan and Egypt for events that span local and near regional distances. Our preliminary results show that a magnitude estimate from one station using the coda is equivalent to a network average of roughly 9 stations when using traditional magnitudes (e.g., m{sub b}(P), M{sub L}, M{sub d}). The stability of the coda comes from measuring a long length of coda using a calibrated synthetic envelope as an empirical metric. We relate the non-dimensional coda amplitudes to an absolute scale by tying them to independent moment estimates from larger waveform-modeled events. Unlike most narrow band magnitudes, this approach yields an azimuthally averaged, moment-rate spectrum that is completely corrected for path and site effects. The resultant magnitudes from the spectra (e.g., M{sub w} and m{sub b}) are fully transportable and do not suffer from regional bias.
Deconstructed Generation-Based Zero-Shot Model

Recent research on Generalized Zero-Shot Learning (GZSL) has focused primarily on generation-based methods. However, current literature has overlooked the fundamental principles of these methods and has made limited progress in a complex manner. In this paper, we aim to deconstruct the generator-classifier framework and provide guidance for its improvement and extension. We begin by breaking down the generator-learned unseen class distribution into class-level and instance-level distributions. Through our analysis of the role of these two types of distributions in solving the GZSL problem, we generalize the focus of the generation-based approach, emphasizing the importance of (i) attribute generalization in generator learning and (ii) independent classifier learning with partially biased data. We present a simple method based on this analysis that outperforms SotAs on four public GZSL datasets, demonstrating the validity of our deconstruction. Furthermore, our proposed method remains effective even without a generative model, representing a step towards simplifying the generator-classifier structure. Our code is available at \url{https://github.com/cdb342/DGZ}.
Perform Like an Engine: A Closed-Loop Neural-Symbolic Learning Framework for Knowledge Graph Inference

Knowledge graph (KG) inference aims to address the natural incompleteness of KGs, including rule learning-based and KG embedding (KGE) models. However, the rule learning-based models suffer from low efficiency and generalization while KGE models lack interpretability. To address these challenges, we propose a novel and effective closed-loop neural-symbolic learning framework EngineKG via incorporating our developed KGE and rule learning modules. KGE module exploits symbolic rules and paths to enhance the semantic association between entities and relations for improving KG embeddings and interpretability. A novel rule pruning mechanism is proposed in the rule learning module by leveraging paths as initial candidate rules and employing KG embeddings together with concepts for extracting more high-quality rules. Experimental results on four real-world datasets show that our model outperforms the relevant baselines on link prediction tasks, demonstrating the superiority of our KG inference model in a neural-symbolic learning fashion.
Quantum Cramér-Rao bound for quantum statistical models with parameter-dependent rank

Recently, a widely-used computation expression for quantum Fisher information was shown to be discontinuous at the parameter points where the rank of the parametric density operator changes. The quantum Cramér-Rao bound can be violated on such singular parameter points if one uses this computation expression for quantum Fisher information. We point out that the discontinuity of the computation expression of quantum Fisher information is accompanied with the unboundedness of the symmetric logarithmic derivation operators, based on which the quantum Fisher information is formally defined and the quantum Cramér-Rao bound is originally proved. We argue that the limiting version of quantum Cramér-Rao bound still holds when the parametric density operator changes its rank by closing the potential loophole of involving an unbounded SLD operator in the proof of the bound. Moreover, we analyze a typical example of the quantum statistical models with parameter-dependent rank.
Robust Graph Meta-learning for Weakly-supervised Few-shot Node Classification

Graphs are widely used to model the relational structure of data, and the research of graph machine learning (ML) has a wide spectrum of applications ranging from drug design in molecular graphs to friendship recommendation in social networks. Prevailing approaches for graph ML typically require abundant labeled instances in achieving satisfactory results, which is commonly infeasible in real-world scenarios since labeled data for newly emerged concepts (e.g., new categorizations of nodes) on graphs is limited. Though meta-learning has been applied to different few-shot graph learning problems, most existing efforts predominately assume that all the data from those seen classes is gold-labeled, while those methods may lose their efficacy when the seen data is weakly-labeled with severe label noise. As such, we aim to investigate a novel problem of weakly-supervised graph meta-learning for improving the model robustness in terms of knowledge transfer. To achieve this goal, we propose a new graph meta-learning framework -- Graph Hallucination Networks (Meta-GHN) in this paper. Based on a new robustness-enhanced episodic training, Meta-GHN is meta-learned to hallucinate clean node representations from weakly-labeled data and extracts highly transferable meta-knowledge, which enables the model to quickly adapt to unseen tasks with few labeled instances. Extensive experiments demonstrate the superiority of Meta-GHN over existing graph meta-learning studies on the task of weakly-supervised few-shot node classification.
Repairing DoS Vulnerability of Real-World Regexes

There has been much work on synthesizing and repairing regular expressions (regexes for short) from examples. These programming-by-example (PBE) methods help the users write regexes by letting them reflect their intention by examples. However, the existing methods may generate regexes whose matching may take super-linear time and are vulnerable to regex denial of service (ReDoS) attacks. This paper presents the first PBE repair method that is guaranteed to generate only invulnerable regexes. Importantly, our method can handle real-world regexes containing lookarounds and backreferences. Due to the extensions, the existing formal definitions of ReDoS vulnerabilities that only consider pure regexes are insufficient. Therefore, we first give a novel formal semantics and complexity of backtracking matching algorithms for real-world regexes, and with them, give the first formal definition of ReDoS vulnerability for real-world regexes. Next, we present a novel condition called real-world strong 1-unambiguity that is sufficient for guaranteeing the invulnerability of real-world regexes, and formalize the corresponding PBE repair problem. Finally, we present an algorithm that solves the repair problem. The algorithm builds on and extends the previous PBE methods to handle the real-world extensions and with constraints to enforce the real-world strong 1-unambiguity condition.
Improving Students’ Writing Ability through Small Group Discussion Technique

This research aimed to improve students’ writing ability by using Small Group Discussion Technique. The subject of this research was the 8th B Grade of SMP Negeri 9 Bandar Lampung. It consisted of 30 students. This research used Classroom Action Research (CAR) method which was consisted of 2 cycles. In this research, the writer used test and non-test in collecting data. The test used in the research was formative and summative test. Meanwhile in non-test, the writer used field note and observation sheet. In analyzing data, this research used quantitative and qualitative data. Based on the result of the research, it found that there was an improvement in students’ writing ability after implementing Small Group Discussion. Refers to the result of the test, it showed that a number of students who get the score ≥75 is about 17 students with the percentage 56.70% in the first cycle. Meanwhile, it was better in the second cycle. In this cycle, a number of students who get the score ≥75 is about 22 students with percentage 73.30%. in the other hand, referring to the students’ attitude or behaviour to the lesson. It showed that there were some positive progression that supported them in mastering the lesson. Therefore, it can be concluded that the implementation of Small Group Discussion is successful to improve students’ writing ability at the Eighth grade of SMP Negeri 9 Bandar Lampung.
An Approach for a Fog-centric Secure Cloud Storage Scheme

Currently, cloud computing is being considered as a possible replacement for providing storage services. Security concerns with cloud storage might prevent its mainstream adoption. New security hazards to cloud storage include data breaches, malicious alterations, and data loss. For safe storage employing different clouds, a three-tier architecture built on Fog servers has recently been described. Hash Solomon's code and a specially created hash algorithm are the fundamental methods employed to accomplish the task. This does not, however, provide improved change detection and data recovery and results in the loss of a tiny amount of data to cloud servers. This research suggests a unique secure cloud storage system focused on fog to guard against illegal access, alteration, and destruction of data. The suggested strategy employs a novel method of data obscuration to prevent unwanted access. Additionally, the outcomes of Block - Management to stop unauthorised access and provide greater recovery in case of data loss. In addition, we provide a method based on a hash algorithm to make it simpler to find high-probability changes. Through security analysis, we show that the suggested approach is reliable. Experimental findings support the suggested scheme's performance advantage over modern alternatives in terms of data processing speed.
PENGGUNAAN MEDIA KOMUNIKASI DI SLB NEGERI SALAKAN

The purpose of this study was to determine the communicate media used at SLB Negeri Salakan in teaching and learning activities for mentally retarded students during the Covid-19 pandemic. This type of research is a case study using purposive sampling technique in determining informants. In this study, there were five people who were appointed as informants, and the data collection techniques used were observation and in depth interviews, which used interview guidelines as data collection media.The results showed that teaching and learning activities in SLB Negeri Salakan for children with mental retardation, there were four types of media used, the first media used images printed on paper, the second media used the Secil application, the third media used YouTube videos, and the fourth media used music/ song. Of the four media used, two of them are internet based, the media used mean is Secil application learning and YouTube videos. Referring to the media richness theory which looks at the four criteria according to the results of research in the field that image media, Secil application learning, YouTube video, and music media have the ability to convey cues, promptness in giving feedback, diversity of language used, and ability to focus.
ODIP: Towards Automatic Adaptation for Object Detection by Interactive Perception

Object detection plays a deep role in visual systems by identifying instances for downstream algorithms. In industrial scenarios, however, a slight change in manufacturing systems would lead to costly data re-collection and human annotation processes to re-train models. Existing solutions such as semi-supervised and few-shot methods either rely on numerous human annotations or suffer low performance. In this work, we explore a novel object detector based on interactive perception (ODIP), which can be adapted to novel domains in an automated manner. By interacting with a grasping system, ODIP accumulates visual observations of novel objects, learning to identify previously unseen instances without human-annotated data. Extensive experiments show ODIP outperforms both the generic object detector and state-of-the-art few-shot object detector fine-tuned in traditional manners. A demo video is provided to further illustrate the idea.
Validation of Sentiment Markers Extracted by Using Machine Learning: Twitter Mining of COVID-19

Although sentiment analysis for COVID-19 tweets is becoming popular, no study has mined a sentiment other than polarity. This study aims to extract ‘disaster’ sentiment by using various machine learning models, statistically validate sentiment markers extracted by deep learning, and discuss the potentials of ‘disaster’ as valid sentiment marker.&#x0D; A total of 7,613 disaster tweets from Kaggle site were utilized to train nine machine learning models. A total of 15,619 tweets in English sent from USA were downloaded using streaming API with keywords of Covid, and Omicron, respectively and were classified into disaster/non-disaster categories using the four best performing models: MNB, deep learning, USE and BERT. Principal component analysis, correlation analysis and regression analysis were performed to determine the psychometric properties.&#x0D; Cronbach Alpha for 13 sentiment markers was 0.71. All 4 machine learning markers were loaded in a factor. A higher level of unfavorable emotions (e.g., fear), a lower level of favorable emotions (e.g., joy), and a higher level of negative polarity were found during surging Omicron variants than early onset of COVID-19. The higher frequency of disaster tweets was found during surging Omicron variants than the early onset of COVID-19.&#x0D; Our study revealed that disaster tweets were characterized to be a higher level of unfavorable emotions and negative polarity, and a lower level of favorable emotions. Since disaster as a sentiment marker was evidently reliable and valid, it should be a part of the sentiment analysis in describing the global health issues.
Part-Aware Data Augmentation for 3D Object Detection in Point Cloud

Data augmentation has greatly contributed to improving the performance in image recognition tasks, and a lot of related studies have been conducted. However, data augmentation on 3D point cloud data has not been much explored. 3D label has more sophisticated and rich structural information than the 2D label, so it enables more diverse and effective data augmentation. In this paper, we propose part-aware data augmentation (PA-AUG) that can better utilize rich information of 3D label to enhance the performance of 3D object detectors. PA-AUG divides objects into partitions and stochastically applies five augmentation methods to each local region. It is compatible with existing point cloud data augmentation methods and can be used universally regardless of the detector's architecture. PA-AUG has improved the performance of state-of-the-art 3D object detector for all classes of the KITTI dataset and has the equivalent effect of increasing the train data by about 2.5$\times$. We also show that PA-AUG not only increases performance for a given dataset but also is robust to corrupted data. The code is available at https://github.com/sky77764/pa-aug.pytorch
Supervised learning using support vector machine applied to sentiment analysis of teacher performance satisfaction

Satisfaction with teaching performance is an important measurement process in higher education institutions, for this reason, applying sentiment analysis to the opinions of university students through the support vector machine (SVM) Fine Gaussian supervised learning algorithm represents an important contribution to the academic literature. This article identifies the best classification algorithm according to performance parameters for predicting student satisfaction with teaching performance through sentiment analysis; the subsequent implementation of the research has the purpose of strengthening teaching practices, in addition to allowing continuous training of teaching for the benefit of student learning. This article has provided a compact predictive model, with literature review based on SVM and sentiment analysis techniques. Through the machine learning classification learner technique, it is identified that the SVM algorithm: Fine Gaussian SVM is the one with the best accuracy equal to 98.3%. Likewise, the performance metrics for the four classes of the model were identified, which have a sensitivity equal to 88.89%, a specificity of 98.04%, a precision of 99.21% and an accuracy of 98.85%.
Development of data encryption standard algorithm based on magic square

Data encryption standard is one of the famous algorithms that used in many fields for security purpose but it s susceptible for many attacks. This paper+ proposes a new modification of data encryption standard (DES) algorithm called magic square data encryption standard (MSDES) algorithm in order to include a high-level security by increase the mixing between the plaintext (96 bit) and the key (48 bit). This modification is done by using magic square 3×3 and an additional key is created using linear first shift register (LFSR) in each round of the Feistel of DES. A colour image encryption is simulated and presented as the comparison between the original DES and MSDES algorithm. The proposed algorithm gets the best results in complexity, histogram, entropy, peak-signal-to-noise ratio-(PSNR) and coefficient-correlation.