Reviewerly/exHarmony huggingface dataset:
OYOLO: An Optimized YOLO Method for Complex Objects in Remote Sensing Image Detection

There have been significant advancements in deep learning based object detection algorithms, which have found widespread applications across various fields, including remote sensing. However, existing algorithms often fall short in detecting complex objects in remote sensing images, resulting in suboptimal overall performance. To address this issue, this letter proposes a novel object detection algorithm called OYOLO, which builds upon the YOLOv4 network and incorporates several optimization techniques. Firstly, a novel feature enhancement network is designed to better learn the contextual information and the object feature. Specifically, this section introduces Adaptive Spatial Feature Fusion and proposes an optimized Spatial Pyramid Pooling method, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., SPPCPC. Furthermore, the Effective Intersection over Union loss function is introduced to refine the bounding box regression, thereby minimizing the interference of non-essential features. Lastly, this letter proposes an improved backbone network, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., DSCDarknet53 to enhance the detection speed of the model. Verified through experiments, OYOLO demonstrates an increase of 2.1% and 1.6% in mean Average Precision (mAP) values on the TGRS-HRRSD and RSOD datasets, respectively, compared to the original YOLOv4 algorithm. The detection speeds of the two datasets are also enhanced by 5.3 Frame Per Second (FPS) and 5.3 FPS, respectively. Specifically, OYOLO exhibits remarkable improvements on a dataset with complex objects, with an mAP gain of 7.9%. Moreover, experimental results demonstrate that OYOLO outperforms other YOLO methods.
Blind Side Channel Analysis on the Elephant LFSR Extended Version

The National Institute of Standards and Technology (NIST) started a competition for lightweight cryptography candidates for authenticated encryption. Elephant is one of the ten finalists. Many physical attacks exist on the different traditional cryptographic algorithms. New standard are a new targets for this domain. In this paper, an improvement of the first theoretical blind side channel attack against the authenticated encryption algorithm Elephant is presented. More precisely, we are targeting the LFSR-based counter used internally. LFSRs are classic functions used in symmetric cryptography. In the case of Elephant, retrieving the initial state of the LFSR is equivalent to recovering the encryption key. This paper is an extension of a previous version. So an optimization of our previous theoretical attack is given. In the previous version, in only half of the cases, the attack succeeds in less than two days. In this extended paper, with optimization, the attack succeeds in three quarters of the cases.
An Empirical Study on the Judicial Regulation of Data Crawling Unfair Competition

In recent years, data crawling, as a new type of unfair competition on the Internet, has become a hot spot of concern in the theoretical and practical circles. This paper composes cases related to digital capture unfair behavior, and through the empirical analysis of typical cases, it is found that such cases present realistic problems in judicial practice such as blurred data ownership boundary, alienation of criteria for determining competitive relationship, non-uniform adjudication basis, and difficulty in determining the amount of damages; in this regard, this paper tries to put forward effective references from specifying the path of data regulation, clarifying the structure for determining competitive relationship, unifying the adjudication of unfair behavior, and clarifying the reference factors for damages. In this regard, this paper tries to put forward effective references from the aspects of specifying the path of data regulation, clarifying the structure of the determination of competition relationship, unifying the basis of improper conduct adjudication, and clarifying the reference factors of damages.
Study on the Effective Methods of Class Management in Online Teaching

The arrival of the new crown outbreak, lead to universities to carry out the "closed not suspended" work task, the teacher at home using network teaching software for students online teaching, the teaching method is very convenient, but the students in the process of class is easy to appear lazy phenomenon, can even to query questions, seriously affected the teaching effect. In order to improve the teaching quality, teachers should focus on the effective methods of managing classes while innovating the teaching means. This paper will analyze the easy problems in the online teaching process, and study the effective methods of management in the online teaching. Hope to provide some reference value for the future online teaching class management.
Comparison of multi-mode Hong-Ou-Mandel interference and multi-slit interference

Hong-Ou-Mandel (HOM) interference of multi-mode frequency entangled states plays a crucial role in quantum metrology. However, as the number of modes increases, the HOM interference pattern becomes increasingly complex, making it challenging to comprehend intuitively. To overcome this problem, we present the theory and simulation of multi-mode-HOM interference (MM-HOMI) and compare it to multi-slit interference (MSI). We find that these two interferences have a strong mapping relationship and are determined by two factors: the envelope factor and the details factor. The envelope factor is contributed by the single-mode HOM interference (single-slit diffraction) for MM-HOMI (MSI). The details factor is given by sin (Nx)/sin (x) ([sin (Nv)/sin (v)]2) for MM-HOMI (MSI), where N is the mode (slit) number and x (v) is the phase spacing of two adjacent spectral modes (slits). As a potential application, we demonstrate that the square root of the maximal Fisher information in MM-HOMI increases linearly with the number of modes, indicating that MM-HOMI is a powerful tool for enhancing precision in time estimation. We also discuss multi-mode Mach-Zehnder interference, multi-mode NOON-state interference, and the extended Wiener-Khinchin theorem. This work may provide an intuitive understanding of MM-HOMI patterns and promote the application of MM-HOMI in quantum metrology.
When Do Pre-Training Biases Propagate to Downstream Tasks? A Case Study in Text Summarization

Faisal Ladhak, Esin Durmus, Mirac Suzgun, Tianyi Zhang, Dan Jurafsky, Kathleen McKeown, Tatsunori Hashimoto. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. 2023.
Building 3D Generative Models from Minimal Data

Abstract We propose a method for constructing generative models of 3D objects from a single 3D mesh and improving them through unsupervised low-shot learning from 2D images. Our method produces a 3D morphable model that represents shape and albedo in terms of Gaussian processes. Whereas previous approaches have typically built 3D morphable models from multiple high-quality 3D scans through principal component analysis, we build 3D morphable models from a single scan or template. As we demonstrate in the face domain, these models can be used to infer 3D reconstructions from 2D data (inverse graphics) or 3D data (registration). Specifically, we show that our approach can be used to perform face recognition using only a single 3D template (one scan total, not one per person). We extend our model to a preliminary unsupervised learning framework that enables the learning of the distribution of 3D faces using one 3D template and a small number of 2D images. Our approach is motivated as a potential model for the origins of face perception in human infants, who appear to start with an innate face template and subsequently develop a flexible system for perceiving the 3D structure of any novel face from experience with only 2D images of a relatively small number of familiar faces.
LLM Based Generation of Item-Description for Recommendation System

The description of an item plays a pivotal role in providing concise and informative summaries to captivate potential viewers and is essential for recommendation systems. Traditionally, such descriptions were obtained through manual web scraping techniques, which are time-consuming and susceptible to data inconsistencies. In recent years, Large Language Models (LLMs), such as GPT-3.5, and open source LLMs like Alpaca have emerged as powerful tools for natural language processing tasks. In this paper, we have explored how we can use LLMs to generate detailed descriptions of the items. To conduct the study, we have used the MovieLens 1M dataset comprising movie titles and the Goodreads Dataset consisting of names of books and subsequently, an open-sourced LLM, Alpaca, was prompted with few-shot prompting on this dataset to generate detailed movie descriptions considering multiple features like the names of the cast and directors for the ML dataset and the names of the author and publisher for the Goodreads dataset. The generated description was then compared with the scraped descriptions using a combination of Top Hits, MRR, and NDCG as evaluation metrics. The results demonstrated that LLM-based movie description generation exhibits significant promise, with results comparable to the ones obtained by web-scraped descriptions.
Dividing and Conquering a BlackBox to a Mixture of Interpretable Models: Route, Interpret, Repeat.

ML model design either starts with an interpretable model or a Blackbox and explains it post hoc. Blackbox models are flexible but difficult to explain, while interpretable models are inherently explainable. Yet, interpretable models require extensive ML knowledge and tend to be less flexible and underperforming than their Blackbox variants. This paper aims to blur the distinction between a post hoc explanation of a Blackbox and constructing interpretable models. Beginning with a Blackbox, we iteratively carve out a mixture of interpretable experts (MoIE) and a residual network. Each interpretable model specializes in a subset of samples and explains them using First Order Logic (FOL), providing basic reasoning on concepts from the Blackbox. We route the remaining samples through a flexible residual. We repeat the method on the residual network until all the interpretable models explain the desired proportion of data. Our extensive experiments show that our route, interpret, and repeat approach (1) identifies a diverse set of instance-specific concepts with high concept completeness via MoIE without compromising in performance, (2) identifies the relatively "harder" samples to explain via residuals, (3) outperforms the interpretable by-design models by significant margins during test-time interventions, and (4) fixes the shortcut learned by the original Blackbox. The code for MoIE is publicly available at: https://github.com/batmanlab/ICML-2023-Route-interpret-repeat.
PROJECT TECHNOLOGY AS A MEANS OF PROFESSIONAL COMPETENCE DEVELOPMENT OF FUTURE TEACHERS OF PHYSICS AND MATHEMATICS

The material of this article concerns the use of project technology by future physics teachers in their professional activities as an effective means of increasing the educational and cognitive activity of students in the process of learning physics. The new physics curricula state that the main goal of physics education is the development of personality, the formation of a scientific worldview and the corresponding style of thinking, the formation of subject, scientific-natural and key competences of students by means of physics as an educational subject. Significant potential for the development of individual abilities and independence of students is laid in research competence. Educational projects are an effective means of forming research competence in physics lessons.
STEM-EDUCATION IN THE INNOVATIVE PROCESSES OF FORMING A MODERN PHYSICAL AND TECHNOLOGICAL SPECIALIST

Humanity needs constant improvement. Education is also dynamic, as it must adequately respond to the new challenges of civilization, taking into account the trends and perspectives of the development of society. One of its main tasks is the introduction of educational innovations and information technologies. Based on this, the task of training highly qualified personnel for the IT industry, where dynamic changes in educational technologies are particularly striking, becomes particularly important and difficult. STEM education contributes to the successful formation of the competences of such specialists. The work shows how cooperation with stakeholders helps to solve interdisciplinary integration, which allows modernization of methodological principles, content, volume of educational material of subjects of the natural-mathematical-technological cycle, technologicalization of the learning process and the formation of educational competencies of a qualitatively new level of the physical-technological profile.
Unveiling elemental fingerprints: A comparative study of clustering methods for multi-element nanoparticle data

Single particle-inductively coupled plasma-time of flight-mass spectrometers (SP-ICP-TOF-MS) generates large datasets of the multi-elemental composition of nanoparticles. However, extracting useful information from such datasets is challenging. Hierarchical clustering (HC) has been successfully applied to extract elemental fingerprints from multi-element nanoparticle data obtained by SP-ICP-TOF-MS. However, many other clustering approaches can be applied to analyze SP-ICP-TOF-MS data that have not yet been evaluated. This study fills this knowledge gap by comparing the performance of three clustering approaches: HC, spectral clustering, and t-distributed Stochastic Neighbor Embedding coupled with Density-Based Spatial Clustering of Applications with Noise (tSNE-DBSCAN) for analyzing SP-ICP-TOF-MS data. The performance of these clustering techniques was evaluated by comparing the size of the extracted clusters and the similarity of the elemental composition of nanoparticles within each cluster. Hierarchical clustering often failed to achieve an optimal clustering solution for SP-ICP-TOF-MS data because HC is sensitive to the presence of outliers. Spectral clustering and tSNE-DBSCAN extracted clusters that were not identified by HC. This is because spectral clustering, a method developed based on graph theory, reveals the global and local structure in the data. tSNE reduces and maps the data into a lower-dimensional space, enabling clustering algorithms such as DBSCAN to identify subclusters with subtle differences in their elemental composition. However, tSNE-DBSCAN can lead to unsatisfactory clustering solutions because tuning the perplexity hyperparameter of tSNE is a difficult and a time-consuming task, and the relative distance between datapoints is not maintained. Although the three clustering approaches successfully extract useful information from SP-ICP-TOF-MS data, spectral clustering outperforms HC and tSNE-DBSCAN by generating clusters of a large number of nanoparticles with similar elemental compositions.
Simplified Modeling of MITM Attacks for Block Ciphers: New (Quantum) Attacks

The meet-in-the-middle (MITM) technique has led to many key-recovery attacks on block ciphers and preimage attacks on hash functions. Nowadays, cryptographers use automatic tools that reduce the search of MITM attacks to an optimization problem. Bao et al. (EUROCRYPT 2021) introduced a low-level modeling based on Mixed Integer Linear Programming (MILP) for MITM attacks on hash functions, which was extended to key-recovery attacks by Dong et al. (CRYPTO 2021). However, the modeling only covers AES-like designs. Schrottenloher and Stevens (CRYPTO 2022) proposed a different approach aiming at higher-level simplified models. However, this modeling was limited to cryptographic permutations.In this paper, we extend the latter simplified modeling to also cover block ciphers with simple key schedules. The resulting modeling enables us to target a large array of primitives, typically lightweight SPN ciphers where the key schedule has a slow diffusion, or none at all. We give several applications such as full breaks of the PIPO-256 and FUTURE block ciphers, and reduced-round classical and quantum attacks on SATURNIN-Hash.
Improving model transferability for clinical note section classification models using continued pretraining

Abstract Objective The classification of clinical note sections is a critical step before doing more fine-grained natural language processing tasks such as social determinants of health extraction and temporal information extraction. Often, clinical note section classification models that achieve high accuracy for 1 institution experience a large drop of accuracy when transferred to another institution. The objective of this study is to develop methods that classify clinical note sections under the SOAP (“Subjective,” “Object,” “Assessment,” and “Plan”) framework with improved transferability. Materials and methods We trained the baseline models by fine-tuning BERT-based models, and enhanced their transferability with continued pretraining, including domain-adaptive pretraining and task-adaptive pretraining. We added in-domain annotated samples during fine-tuning and observed model performance over a varying number of annotated sample size. Finally, we quantified the impact of continued pretraining in equivalence of the number of in-domain annotated samples added. Results We found continued pretraining improved models only when combined with in-domain annotated samples, improving the F1 score from 0.756 to 0.808, averaged across 3 datasets. This improvement was equivalent to adding 35 in-domain annotated samples. Discussion Although considered a straightforward task when performing in-domain, section classification is still a considerably difficult task when performing cross-domain, even using highly sophisticated neural network-based methods. Conclusion Continued pretraining improved model transferability for cross-domain clinical note section classification in the presence of a small amount of in-domain labeled samples.
Computer-oriented management of students' educational activity in informatics practicum

The paper explores various types of managing students’ educational activity, focusing on the educational discipline “Practicum of problem solving in informatics” for third-year students aspiring to become informatics teachers. The study aims to realize pedagogical conditions for computer-oriented management of students’ educational activity. The courseware design revolves around a progressive shift from direct management to self-management, with co-management and subsidiary management as intermediate stages. The information and communication educational environment is built on the Moodle learning management system, with the Workshop elements playing a central role in managing students’ educational activity. The suggested approach has been evaluated through pedagogical observation and assessment, demonstrating its efficiency. Furthermore, the study highlights students’ lack of competency in time planning and introspection based on experimental data.
Generating full-stack 5G security datasets: IP-layer and core network persistent PDU session attacks

With the increasing number of users accessing the Internet over 5G systems, security concerns have become a major challenge that needs to be addressed. This paper proposes a solution to this challenge by proposing a system to train defenders to handle cyber attacks and develop intrusion detection systems that can timely notify of security events, also within the 5G core itself. This paper builds on our previous contributions on a containerized 5G testbed and proposes a novel set of full-stack attacks targeting networked hosts and 5G Network Functions (NFs) alike. Importantly, we identify the potential to generate persistent Packet Forwarding and Control Protocol (PFCP) Denial of Service (DoS) attacks to deprive end users of connectivity to the data network, even in the event of a handover to another gNodeB. This framework is envisaged to facilitate the generation of highly diverse and realistic datasets, containing malicious GPRS Tunneling Protocol (GTP) and PFCP traffic captured over 5G interfaces, thereby enhancing the security of next-generation networks.
Deep Learning Technology to Recognize American Sign Language Alphabet

Historically, individuals with hearing impairments have faced neglect, lacking the necessary tools to facilitate effective communication. However, advancements in modern technology have paved the way for the development of various tools and software aimed at improving the quality of life for hearing-disabled individuals. This research paper presents a comprehensive study employing five distinct deep learning models to recognize hand gestures for the American Sign Language (ASL) alphabet. The primary objective of this study was to leverage contemporary technology to bridge the communication gap between hearing-impaired individuals and individuals with no hearing impairment. The models utilized in this research include AlexNet, ConvNeXt, EfficientNet, ResNet-50, and VisionTransformer were trained and tested using an extensive dataset comprising over 87,000 images of the ASL alphabet hand gestures. Numerous experiments were conducted, involving modifications to the architectural design parameters of the models to obtain maximum recognition accuracy. The experimental results of our study revealed that ResNet-50 achieved an exceptional accuracy rate of 99.98%, the highest among all models. EfficientNet attained an accuracy rate of 99.95%, ConvNeXt achieved 99.51% accuracy, AlexNet attained 99.50% accuracy, while VisionTransformer yielded the lowest accuracy of 88.59%.
Modeling multi-aspect preferences and intents for multi-behavioral sequential recommendation

Multi-behavioral sequential recommendation has recently attracted increasing attention. However, existing methods suffer from two major limitations. Firstly, user preferences and intents can be described in fine-grained detail from multiple perspectives; yet, these methods fail to capture their multi-aspect nature. Secondly, user behaviors may contain noises, and most existing methods could not effectively deal with noises. In this paper, we present an attentive recurrent model with multiple projections to capture Multi-Aspect preferences and INTents (MAINT in short). To extract multi-aspect preferences from target behaviors, we propose a multi-aspect projection mechanism for generating multiple preference representations from multiple aspects. To extract multi-aspect intents from multi-typed behaviors, we propose a behavior-enhanced LSTM and a multi-aspect refinement attention mechanism. The attention mechanism can filter out noises and generate multiple intent representations from different aspects. To adaptively fuse user preferences and intents, we propose a multi-aspect gated fusion mechanism. Extensive experiments conducted on real-world datasets have demonstrated the effectiveness of our model.