Reviewerly/exHarmony huggingface dataset:
About the applicability of Apache2 web server memory forensics

With the increasing use of the Internet for criminal activities, web servers have become more and more important during forensic investigations. In many cases, web servers are used to host leaked data, as a management interface for Command and Control servers, or as a platform for illicit content. As a result, extracting information from web servers has become a critical aspect of digital forensics. By default, a lot of information can already be extracted by performing traditional storage forensics including the analysis of logs. However this approach quickly reaches its limits as soon as anti-forensic techniques such as the deletion of configuration files or the deactivation of logging capabilities are implemented. This paper evaluates the feasibility of memory forensics as a complement to traditional storage forensics for cases involving web servers. For this purpose, we present a methodology for extracting forensically relevant artefacts from the memory of Apache web servers, which are among the most commonly used on the Internet. Through various experiments, we evaluate the applicability of our approach in different scenarios. In the process, we also take a closer look at the overall existence of digital traces, which cannot easily be found by following a structured approach. Our findings demonstrate that certain Apache web server structures contain important information that can be retrieved from memory even after the originating event has passed. Additionally, traces such as IP addresses were still found in memory even after complete structures were already overwritten by further interaction. These results highlight the benefits and the potential of memory analysis for web servers in digital investigations.
Robust Extraction of Vectorized Buildings via Bidirectional Tracing of Keypoints From Remotely Sensed Imagery

Automatic extraction of vector polygons of buildings from remotely sensed images is an important but difficult task. Recent existing methods based on deep learning usually adopt a multi-stage solution of semantic segmentation, contour detection, and polygon simplification. Such a long processing chain may lead to unreliable results as the boundary regularization and optimization processes are ultimately completed by utilizing low-level features, which ignores the potential of deep features in polygon generation. In this paper, we present an algorithm for directly extracting simplified polygons of buildings in remotely sensed images. The key of this task is the encoding of the polygon structure. PolyMapper [1] utilizes a recurrent neural network (RNN) to produce vertices of a polygon sequentially. Due to the limitation of RNN, this approach is unstable and difficult to deal with objects with complex shapes. In this work, we encode the polygon into a tensor representation and utilize a non-recurrent manner to recover the polygon structure. In our algorithm, two types of points are utilized, i.e., the corner point and the connecting point. Corner points are utilized to delineate the building outlines and form the vertices of the final polygon. Meanwhile, connecting points are sampled from the edges of the buildings for the assistance of the connection of the corner points. Furthermore, we predict the forward and backward directions of each keypoint in a polygon and propose a bidirectional tracing strategy for the polygon structure recovery. Our approach is simple, effective and robust. Experiments on public datasets demonstrate the superiority of the proposed algorithm. The code is made publicly available at https://github.com/sz94/bldvec.
A Trajectory-oriented Locality-sensitive Hashing Method for User Identification

User identification across social sites, which benefits many applications, has recently been attracting considerable attention. Most existing methods focused more on the effectiveness of user identification, rather than on efficiency. Matching as many crosssite user accounts as possible, which causes very high computation overhead posed by the full-scale pairwise comparisons, remains unsolved, especially when the number of users reaches tens of millions or more. To address this issue, we present a novel locality sensitive hashing method for user identification (UI-LSH), which consists of four components. (1) It involves embedding stay points into vectors, (2) and constructing locality-sensitive hashing families suitable for stay points. (3) It presents a method for projecting stay points into hash buckets that ensures the close stay points are placed in the same bucket with high probability. (4) It constructs the candidate user pairs based on the projection results. The experiments on three ground-truth datasets show that our method reduces the number of user pairs to be compared by as much as 81.87%, 67.68%, and 63.15%, respectively. Overall, UI-LSH holds great promise for significantly improving the efficiency of user identification.
Parallelized Particle Swarm Optimization on FPGA for Realtime Ballistic Target Tracking

This paper addresses the problem of tracking a high-speed ballistic target in real time. Particle swarm optimization (PSO) can be a solution to overcome the motion of the ballistic target and the nonlinearity of the measurement model. However, in general, particle swarm optimization requires a great deal of computation time, so it is difficult to apply to realtime systems. In this paper, we propose a parallelized particle swarm optimization technique using field-programmable gate array (FPGA) to be accelerated for realtime ballistic target tracking. The realtime performance of the proposed method has been tested and analyzed on a well-known heterogeneous processing system with a field-programmable gate array. The proposed parallelized particle swarm optimization was successfully conducted on the heterogeneous processing system and produced similar tracking results. Also, compared to conventional particle swarm optimization, which is based on the only central processing unit, the computation time is significantly reduced by up to 3.89×.
Stand-Up Comedy as Media to Improve Students' Speaking Skills in The Second Academic Year of SMA Negeri 1 Sumbul

A Thesis, English Departement Teacher’s Training Faculty of HKBP Nommensen University This study aims to determine the results of using a communicative approach to improve students’ speaking skills. The researcher conducted the research at SMA Negeri 1 Sumbul. This study was carried out by Experimental quantitative research. The sample of this study was class XI-3 (the experimental class) and class XI-4 (the control class), with a total of 20 students for each group. The research conducted experimental teaching and speaking tests with stand-up comedy personal experience to collect data. The result from this research can be seen as follows: t-coun t&gt; t-table (p = 0.1) with the degree of freedom (df) = 38. This means 6,665 &gt; 1.21 (p=0.1) with df 38. This means that the use of a communicative approach has a significant effect on students’ speaking skills. Using the communicative approach as a teaching method through stand-up comedy in class activities improved students’ speaking skills.
Assessing Power in Punching Sounds From Mainstream Films and Video Games

A preliminary study was conducted with the aim of contributing to a scientifically based sound design for interactive movement sonification, drawing on tacit sound design knowledge. Specifically, the perceived power in punching sounds from mainstream films and video games was assessed through subjective ratings. The same sound clips were analyzed to extract audio features, and correlations with power ratings were examined. Results showed that perceived power in punching sounds is especially influenced by decay time, spectral slope, and features related to Mel-frequency cepstral coefficients (MFCCs). These findings can inform sound design for interactive movement sonification, particularly in the context of action-oriented applications.
Two-Stage Feature Generator for Handwritten Digit Classification

In this paper, a novel feature generator framework is proposed for handwritten digit classification. The proposed framework includes a two-stage cascaded feature generator. The first stage is based on principal component analysis (PCA), which generates projected data on principal components as features. The second one is constructed by a partially trained neural network (PTNN), which uses projected data as inputs and generates hidden layer outputs as features. The features obtained from the PCA and PTNN-based feature generator are tested on the MNIST and USPS datasets designed for handwritten digit sets. Minimum distance classifier (MDC) and support vector machine (SVM) methods are exploited as classifiers for the obtained features in association with this framework. The performance evaluation results show that the proposed framework outperforms the state-of-the-art techniques and achieves accuracies of 99.9815% and 99.9863% on the MNIST and USPS datasets, respectively. The results also show that the proposed framework achieves almost perfect accuracies, even with significantly small training data sizes.
Becoming Mobile Readers-Narratives of Zimbabwean Teenagers’ Mobile Reading Experiences

This article critically reviews the mobile reading experiences of a group of twenty-three Zimbabwean urban adolescent learners. This paper contends that while mobile reading is a growing mode of literacy development across the world, little is known about the different strategies Zimbabwean adolescent learners have had to employ as they reconfigure and rethink their literacy practices around new reading media like cell phones and tablets. This paper adopts a constructivist approach to trace, document, and analyse the personal narratives of the participants’ mobile reading experiences during their transformational journeys towards becoming active mobile readers. The participants’ voices were captured using task-based evaluative focus group interviews. Through this cooperative collaborative approach, the researcher managed to engage the participants on critical issues surrounding their digital literacy practices, their digital identities, their digital inclusion, and digital exclusion thereof. It was established that the adolescent participants were a digital generation living in a digital age and their reading needs and expectations were ever-growing and ever-changing. The adolescent participants expected their digital reading to be like the rest of their digital lives: quick, uninterrupted, personalised, and smart. Educational practitioners ought not to assume that adolescent learners as digital natives will automatically become successful mobile readers, but rather they should make accommodations for, and teach adolescents the art of digital learning and mobile reading. This paper recommends the scaling up of mobile reading initiatives and research in Zimbabwe so that the mobile reading phenomenon is further demystified.
Research on intelligent prediction of T-stage of rectal cancer based on multi-instance learning

The new incidence rate of rectal cancer accounts for 12.2% of all cancers, which seriously affects the health of the people. In addition, the early clinical diagnosis of T staging of rectal cancer will strongly support the formulation of effective treatment plans. However, it is difficult for clinicians to accurately determine the T stage of rectal cancer before operation because the site of rectal tumor invasion is not obvious. Therefore, this paper first proposes a bilinear feature fusion mechanism, which effectively avoids the problem of information loss in the process of convolution neural network training of rectal cancer MRI images; Secondly, the new weighted loss function designed can solve the problem of multi-example imbalance; Finally, the experiment proves that the constructed intelligent prediction strategy for T-stage of rectal cancer has a good accuracy, which provides a good auxiliary result for the clinical treatment of rectal cancer.
A Novel Approach to Grover's Quantum Algorithm Simulation: Cloud-Based Parallel Computing Enhancements

A truly universal quantum computer is still on the small scale at the present. There has been no transition from the laboratory use of quantum computers to more widespread use of the technology. As a result, quantum simulation has emerged as the accepted norm for supporting the validation of quantum algorithms. Many conventional algorithms benefit from grover's quantum search method because it accelerates their usage of search heuristics. For better multi-core load balancing, memory utilization, and simulation efficiency, this research presented a high-performance grover method simulation that combines the benefits of grover's technique with the parallel of cloud computing. This study also suggests and experimentally compares five distinct cloud-based computing architectures that are well-suited to quantum simulation. Through research and testing, this study also verifies the optimization's efficiency. Depending on the size of the current configuration, the experimental results reveal that the simulation can reach 31 bits.
PENGEMBANGAN MEDIA PEMBELAJARAN PERMAINAN EDUKASI BERBASIS ANDROID PADA MATERI SISTEM PENCERNAAN MANUSIA KELAS XI SMA/MA

This study aimed to develop an Android-based educational game media on the human digestive system material that was suitable for use in the learning process at school. This study used research and development using the four-D development model. The stages carried out were define, design development, and disseminate The results of the study indicated that the development of this media was feasible to be used as teaching materials in schools. The developed product had been validated by a material expert who got an average score of 4.6 and a percentage score of 91. The results of the material expert validation show that the product is very well used for learning. The product developed had been validated by a design expert who got an average score of 4.6 and a percentage score of 91. The results of the validation of the design expert stated that the product was very well used for learning. The trial was conducted on 15 students of class XI at SMA Negeri 5 Metro who were randomly selected. The overall average of the small group trials was 4.6 and the percentage value was 92. The results showed that the product is very good and students were very happy to learn to use teaching materials for Android-based educational game media
Study on Adoption of 3D Computer Vision and Speech Recognition in Intelligent Manufacturing

This work aims to adopt state-of-art, artificial intelligence (AI) based technology in intelligent manufacturing applications, which in involve using 3D computer vison and speech recognition (voice commands) for control of typical intelligent manufacturing systems such as robotic workstations. As a first attempt, we consider a relatively simple scenario in manufacturing, i.e. object grasping, with a 3-axis Cartesian robot arm. On this workstation, we have developed a 3D vision system using the Intel RealSense D455 depth camera for object detection and capture, which is processed by SSD algorithm. We have also developed a speech (audio or voice) recognition system with our proposed algorithm, combining both LSTM and GMM-HMM audio recognition models. Good performance is achieved in terms of both efficiency and accuracy in controlling normal operation, as well as emergency stop functionality for our robotic workstation. Possible future improvements to our system and relevant methodology are discussed.
Research on the Regulatory System of Digital Currency under the Background of Digital Finance

From the perspective of the composition of the world financial system, the emergence and development of digital finance are closely related to information technology, especially artificial intelligence, big data, blockchain, Internet of Things and other technologies, which have transformed the traditional financial system from a "credit" framework to a "data"-based digital financial business model. Digital currency lacks unified industry standards and mature policy supervision, and its advantages of no geographical restrictions and anonymity of transactions are easily seized by criminals and become an important way to escape investigation. Therefore, it is necessary to recognize the practical difficulties in the supply-side reform of the financial system, analyze its potential risks around the encrypted digital currency, and put forward regulatory suggestions from the perspective of improving the regulatory mechanism. In this regard, this paper analyzes the operating characteristics of various digital currency and the contingent risk factors in the development of digital currency. Combined with the operational characteristics of digital currency, the central bank of China, this paper constructs the supervision system of digital currency, and puts forward some supervision thoughts on digital financial products and blockchain risk management.
Tuning DNN Model Compression to Resource and Data Availability in Cooperative Training

Model compression is a fundamental tool to execute machine learning (ML) tasks on the diverse set of devices populating current-and next-generation networks, thereby exploiting their resources and data. At the same time, how much and when to compress ML models are very complex decisions, as they have to jointly account for such aspects as the model being used, the resources (e.g., computational) and local datasets available at each node, as well as network latencies. In this work, we address the multi-dimensional problem of adapting the model compression, data selection, and node allocation decisions to each other: our objective is to perform the DNN training at the minimum energy cost, subject to learning quality and time constraints. To this end, we propose an algorithmic framework called PACT, combining a time-expanded graph representation of the training process, a dynamic programming solution strategy, and a data-driven approach to the estimation of the loss evolution. We prove that PACT’s complexity is polynomial, and its decisions can get arbitrarily close to the optimum. Through our numerical evaluation, we further show how PACT can consistently outperform state-of-the-art alternatives and closely matches the optimal energy consumption.
TCGIS: Text and Contour Guided Controllable Image Synthesis

Recently, text-to-image synthesis (T2I) has received extensive attention with encouraging results. However, the research still has the following challenges: 1) the quality of the synthesized images cannot be effectively guaranteed; 2) the human participation in the synthesis process is still insufficient. Facing the above challenges, we propose a text- and contour-guided artificial controllable image synthesis method. The method can synthesize corresponding image results based on the manual input text and simple contour, wherein the text determines the basic content and the contour determines the shape position. Based on the above idea, we propose a reasonable and efficient network structure using the attention mechanism and achieve amazing synthetic results. We validate the effectiveness of our proposed method on three widely used datasets, and both qualitative and quantitative experimental results show promising performance. In addition, we design a lightweight structure to further improve the practicability of the model.
Semi-supervised Learning with Easy Labeled Data via Impartial Labeled Set Extension

Traditional Semi-supervised Learning (SSL) methods usually assume that the labeled data is independent and identically distributed (i.i.d.) from the underlying distribution. However, several relevant researches have revealed that i.i.d. assumption may not always hold. Influenced by the human preference or automatic labeling, in some cases, the labels (or trusted labels) would be concentrated in the easy samples which have distinctive characteristics. Such a biased labeled set will lead to grave misestimating for the decision boundaries in learning process. In this paper, we proposed a novel evolutionary SSL framework, Solar Eclipse (SE), to address the problem. This framework is based on the concept of progressively enlarging the labeled set with the closest unlabeled samples. Specifically, a novel relative distance measurement Regional Label Propagation (R-LP) is designed. In R-LP, the sample space is divided into several regions according to the class similarities, and the distance is calculated independently in each region with label propagation. Such segregation strategy efficiently reduces the complicity of distance measurement in the feature space. Moreover, R-LP also facilitates the ensemble of different feature views. In our practice, an unbiased self-supervised feature view is introduced to assist the measurement. Experiments show that such dual-view scheme can help us find more reliable extending samples. The evaluation on the popular SSL benchmarks shows that the proposed SE framework achieves the most advanced performance with the easy labeled data. Except that, it also shows advantages when only a few i.i.d. labeled samples is provided, given that they may also have sampling bias.
Cancer Detection in Digital Image Using Machine Learning Algorithms

Every day, a large amount of medical data is being produced in hospitals and medical centers. Using this data to analyze the results can save treatment costs for both patients and the government. Therefore, it is very important to collect medical data about various diseases as well as their appropriate and correct analysis. In recent years, the use of machine learning algorithms to extract and identify patterns from various diseases, including in cancer research, which is a significant challenge for humans with high morbidity and mortality, has attracted much attention. In this article, several machine learning algorithms are employed for cancer detection on UCI standard data. The obtained results show the appropriate accuracy of the proposed method.
Intelligent System to Transformer Slang Words into Formal Words

Understanding and utilizing informal words not recognized in standard dictionaries poses challenges for users. These words are specific to certain communities, hindering comprehension for those outside. Natural language processing tasks, like translation and summarization, struggle with informal vocabulary and local dialects. Although existing models can translate informal words, comprehensive solutions are elusive due to regional and contextual variations. Developing natural language processing models that consider informal words and local dialects is crucial for future research. This paper presents an updated dataset of informal English words tailored to current usage. Multiple models from the Transformer core library on the Hugging Face platform were trained and evaluated, with the facebook/bart-base model demonstrating high accuracy (training data loss: 0.05299). Continued research and innovation in this field are imperative for effective cross-cultural and intercommunity communication