Reviewerly/exHarmony huggingface dataset:
Convergence Analysis of Online Gradient Method for High-Order Neural Networks and Their Sparse Optimization

In this article, we investigate the boundedness and convergence of the online gradient method with the smoothing group <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{1/2}$</tex-math> </inline-formula> regularization for the sigma-pi-sigma neural network (SPSNN). This enhances the sparseness of the network and improves its generalization ability. For the original group <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{1/2}$</tex-math> </inline-formula> regularization, the error function is nonconvex and nonsmooth, which can cause oscillation of the error function. To ameliorate this drawback, we propose a simple and effective smoothing technique, which can effectively eliminate the deficiency of the original group <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{1/2}$</tex-math> </inline-formula> regularization. The group <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{1/2}$</tex-math> </inline-formula> regularization effectively optimizes the network structure from two aspects redundant hidden nodes tending to zero and redundant weights of surviving hidden nodes in the network tending to zero. This article shows the strong and weak convergence results for the proposed method and proves the boundedness of weights. Experiment results clearly demonstrate the capability of the proposed method and the effectiveness of redundancy control. The simulation results are observed to support the theoretical results.
Special Issue on Mechanics, Dynamics and Acoustics of Musical Instruments

The mechanics, dynamics and acoustics of musical instruments concern scientists from different fields, such as physics, mechanics, acoustics, psychology, and music, as well as musical instrument manufacturers, who use these factors to not only understand and explain the phenomena that take place inside and outside of musical instruments as a result of the propagation of sounds, but also those related to the perception of the sounds produced by them [...]
Research on Effective Segment Extraction of Ship Surveillance Videos

Effective segment extraction has broad application prospects in the field of video surveillance. In action recognition or object recognition, a large number of repetitive background segments will reduce the efficiency of computation. This causes the algorithm to consume more time. Effective segments can greatly save computing and storage resources for both target detection and storage. Common algorithms are to process the whole surveillance video, such as frame difference method and optical flow method. However, false detection can occur when the picture is disturbed, such as snowflakes. Will result in low accuracy. The effective segment detection method adopted in this paper. First convert the color space. The surveillance video is then segmented. Features were extracted and repeated segments filtering was carried out to reduce the interference of bad images. Finally, effective segments are extracted. Experimental results show that the accuracy of the effective segment detection method adopted in this paper is increased by more than 50%, and the F-metric value is increased by more than 24%.
Improved Gated Convolutional Neural Network used for Aspect-based Sentiment Analysis

The classical aspect-based sentiment classification combined with Recurrent Neural Network (RNN) and attention mechanism is weak and cannot fully capture long-distance dependencies. These methods only focus on the relative position and ignore sentence order features during sentiment classification. This paper proposed a Gated Convolutional Neural Network denoted as GTRU gating model PosATT-GTRU with improved position attention to achieve aspect-based sentiment classification. First, combine the Laplacian kernel function to define the location information matrix to obtain location features and word order features. Then, filters of different sizes are then used to semantically encode the text position and content. Finally, PosATT-GTRU is used to control the information flow of semantic features, location features, and aspect words while capturing the long-distance dependence of the text. The experimental results on the SemEval2014 data set show that model accuracy has been significantly improved compared with previous models.
DEACT: Hardware Solution to Rowhammer Attacks

Dynamic Random Access Memory (DRAM) is a crucial component in modern computing devices. Improvements in process technology have significantly increased the speed and storage capacity of memory devices. However, as memory cells become smaller and closer to one another, annoying circuit disturbance errors such as the Row-hammer problem have become significant. Studies show that attackers can systematically exploit such errors to induce bit flips and take control of local/remote systems. Even though several hardware and software-based mitigation techniques have been proposed, it is still continuing to be a big threat to system security. In this research, we propose DEACT, a counter-based hardware mitigation to the Rowhammer attack. Contrary to existing countermeasures that refresh victim rows or throttle memory access upon excessive row activation, DEACT uses additional row buffers to keep hot rows and prevent further activation. The size of our counter uses 1.67 times less space than the optimal of existing implementations. DEACT not only eliminates the Rowhammer problem, but it also improves the performance of DRAM. We tested DEACT on the TPC and CPU-2006 benchmarks; the average hit rate has increased by 41% when compared to standard DRAM.
Perancangan Aplikasi Chatting Symmetric Encryption dengan Metode SDLC

Chat Application is an application that allow people to communicate with each other without face-to-face meetings or time issue. To design a well-functioning chat application, this study was conducted using the Software Development Life Cycle (SDLC) method for designing chat application. Apart from that, this study will be using the Python programming language, the PyMySQL module used to connect python to the database, and the Tkinter module is used as a tool in this study to create a Graphical User Interface (GUI) for chat application, and not to mention this study will be using Symmetry Encryption Cryptography to perform encryption and decryption of user chat data.
Perancangan Prototype Brankas Menggunakan Sistem Pengenalan Wajah Dengan Metode Convolutional Neural Network (CNN)

A safe-deposit box is a box used for keeping precious items. Safe-deposit boxes are designed to be difficult for people to open by force. There are various security systems that may be used in it, such as mechanical key, combinational lock, PIN, etc. However, a safe-deposit box is still prone to unpermitted access because anyone who knows the PIN or possesses the key is still able to open it. This research aims to create a safety-box prototype which has a face recognition system implemented on it to ensure no unauthorized person may access this box. Experiment is performed on three different classes, which are “erwin”, “unknown”, and “willy”. Class of “erwin” and “willy” are defined as safe owners, while “unknown” is defined as anyone who is not both owners. Classification on safe owners is considered success if the percentage output in corresponding classes is at least 90 %. Classification on “unknown” class is considered success if the result is at least 90 % or percentage on each class is lower than 90 %. Accuracy for each class is 0 %, 71.43 %, dan 100 %.
Analysis of Oral Exams With Speaker Diarization and Speech Emotion Recognition: A Case Study

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Contribution:</i> During the lockdown period of the COVID-19 pandemic, online oral exams have become a necessary reality. Considering the possibility of carrying out and recording the oral exam sessions, analyses were conducted to assess how the speaking time ratio and emotional states are affected during the exams. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>Background:</i> Although many studies have indicated that oral exams are an effective tool to evaluate students’ conceptual understanding, their use has been limited or disregarded due to concerns about students’ anxiety/emotional state, technical failures, and scalability. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>Research Questions:</i> Two hypotheses were formulated. First, better grades are obtained when students maintain the flow of response, with few pauses/interruptions and hesitations during the oral exam. Second, the prevalence of more positive (i.e., happiness) and neutral emotions points out better grades whereas the prevalence of negative (i.e., sadness and anger) emotions implies worse students’ performance. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>Methodology:</i> Speaker diarization algorithms have been employed to extract speaking ratios and students’ speech segments. A transfer learning method for speech emotion recognition (SER) has been used to distinguish students’ emotional state in four emotional classes by using a pretrained HuBERT model and an InceptionTime network. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>Findings:</i> Correlation tests demonstrate that students’ speaking ratios are correlated to their grades, that is, the higher the students’ speaking ratios are, the better the students’ grades tend to be. The SER model indicates a neutral emotional state in most periods of the oral test and sadness in some hesitation moments. However, correlation tests point out that emotional states are not correlated with the students’ grades.
A Cloud-Native-Oriented AI Epidemic Four-Color Situation Decision Support System

Aiming at the prevention and control of epidemic diseases in the community, a four-color situation decision support system for cloud native is proposed, Through a single situation prediction algorithm and comprehensive situation prediction, the cloud native AI epidemic prevention and control four-color situation decision support system is realized. Taking cloud native as the technical base, based on the environment of microservice+Kubernetes+container, the system first adopts a two category learner to solve the multi category problem model, Finally, the performance evaluation of the trained model on the test set has achieved an ideal result; At the same time, based on the stack generalization model, the integration of four prediction models is realized, including the aggregation degree of high-risk personnel, regional mobility, the aggregation degree of regional key sites, and the epidemic prevention and control support capability. In order to control the spread of the epidemic, we have predicted how the virus will spread based on environmental conditions in a variety of ways, and optimized the allocation of medical resources.
Sentinel data for critical raw materials (CRM) exploration: first results of the S34I project

To achieve sustainable development goals, it is crucial to ensure the best practices through the whole mining cycle of Critical Raw Materials (CRM) used in high-tech, low-carbon goods (batteries, wind turbines, electronics). This is the aim of the Secure and Sustainable Supply of Raw Materials for EU Industry (S34I) Horizon Europe (HE) project that began on January 1st, 2023. S34I is investigating new data-driven methods to analyze Earth Observation (EO) data, supporting systematic mineral exploration (and other) activities to increase European autonomy regarding CRM resources. The S34I project is based on satellite data, airborne, Unmanned Aerial Vehicle (UAV), ground-based conventional in-situ techniques/methods, and fieldwork. This study presents the first preliminary results based on Sentinel-1 and Sentinel-2 data using classical image processing techniques (RGB combinations, band ratio, Principal Component Analysis – PCA, and unsupervised classification) for mineral exploration in Spain, either: (i) on land (Aramo area), to gain knowledge on cobalt (Co) deposits (and associate CRMs) by hydrothermal alteration mapping; and (ii) offshore/shallow waters (Ria de Vigo area), to update the knowledge on coastal metallic placers including CRMs such as Ti, Sn, Li, Rare Earth Elements (REEs) and Au. Among the techniques, PCA proved most effective, followed by band ratios, and self-proposed algorithms for detecting placer minerals showed promise, warranting further investigation. In the future, S34I will exploit Copernicus Contributing Missions (CCM) and other EU hyperspectral satellite sensors such as PRISMA and EnMAP. Different Artificial Intelligence (AI) techniques will, in the future, promote advances and innovative methods that mining stakeholders can use to address the challenges faced in different phases of the mining life cycle.
Super-resolution algorithm of lunar panchromatic image based on random degradation model

In recent years, lunar exploration has become a hot spot in the world again. High-resolution lunar surface images are of great significance to lunar research, and at the same time are crucial to the safe landing of lunar probes. Due to the limitation of the orbital height and hardware, the resolution of the lunar remote sensing images is restricted, so it is particularly important to carry out super-resolution reconstruction of the lunar surface image. At present, most image super-resolution algorithms use a single fixed degradation model, such as using only bicubic interpolation algorithm for down-sampling, or adding specified blur, noise, etc. However, the real image degradation model is extremely complex and difficult to express with specific formulas, so this paper introduces a more complex degradation model when super-resolving the lunar image and simulates the complex degradation process in reality by adding more randomness. Secondly, this paper uses a deep learning network that combines a CNN network with residual structure and a transformer architecture for image super-resolution reconstruction, where the transformer architecture is used for deep feature extraction. The proposed method is experimented on Chang'e-2 7-meter resolution lunar surface remote sensing images, which verifies the effectiveness of the super-resolution algorithm proposed in this paper and outperforms the current popular methods in terms of visual effects and commonly used evaluation metrics. This work aims to improve the image clarity of the lunar surface in order to enhance the environment-awareness capability of the lunar probe and further improve its autonomous capability on the lunar surface.
The rotating wave approximation

Subject Astronomy and Astrophysics Computational Physics Collection: Oxford Scholarship Online
diachrony of the new political terrorism. Neologisms as discursive framing in Swedish parliamentary data 1971–2018

&#x0D; &#x0D; &#x0D; This paper begins to unpack the framing of terrorism in the Swedish Parliament through distant reading and by chronologically extracting neologisms in a comprehensive corpus of transcripts of parliamentary debates. Combining language technology and historical contextualization, we find support for the argument that the term ‘terrorism’ gained much of its modern meaning around 1970. Specifically, our study points to a legislative framing of the issue of terrorism in Swedish parliamentary debate from the early 1970s and onwards. We also find a proliferation in the production of neologisms and compounds after 9/11 2001, reflecting, among other things, the rise of a more distinct counter-terrorism discourse and more ‘specialized’ roles and functions related to terrorism and counter-terrorism activities. The paper concludes by emphasizing the analytical benefits of tracing parliamentary discourse through neologisms as an explorative approach to identify significant patterns for further investigation.&#x0D; &#x0D; &#x0D;
Oblivious Transfer from Rerandomizable PKE

The relationship between oblivious transfer (OT) and public-key encryption (PKE) has been studied by Gertner et al. (FOCS 2000). They showed that OT can be constructed from special types of PKE, i.e., PKE with oblivious sampleability of public keys or ciphertexts. In this work, we give new black-box constructions of OT from PKE without any oblivious sampleability. Instead, we require that the PKE scheme is rerandomizable, meaning that one can use the public key to rerandomize a ciphertext into a fresh ciphertext. We give two different OT protocols with different efficiency features based on rerandomizable PKE. For 1-out-of-n OT, in our first OT protocol, the sender has sublinear (in n) cost, and in our second OT protocol, the cost of the receiver is independent of n. As a comparison, in the PKE-based OT protocols of Gertner et al., both the sender and receiver have linear cost.
Determination of Geographic Origin of Turquoise by Combining Laser Ablation Inductively Coupled Plasma Mass Spectrometry Analysis and Chemometrics

Microsampling elemental analysis is widely used for gemstone and mineralogy traceability. Using laser ablation inductively coupled plasma mass spectrometry combined with chemometrics, the contents of 56 elements in turquoise samples from 5 distinct producing areas in 3 nations were measured. An origin identification model for turquoise samples from various producing areas was established through random forest importance analysis, principal component analysis, and linear discriminant analysis. When combined with random forest importance screening, the traceability efficiency of principal component analysis is significantly improved. Moreover, by taking 48 elements as characteristic variables and introducing them into the discriminant model, a Fisher discriminant model for identifying the origin of turquoise was successfully established. The effective element fingerprint information of turquoise species is closely related to the species origin, and the accuracy of cross-validation reaches 99.5%, demonstrating the feasibility of the proposed model for the identification of the origin of turquoise samples.
SMiShing Attack Vector: Surveying End-User Behavior, Experience, and Knowledge

Phishing attacks steal sensitive and valuable information from end-users. Human Factors researchers have extensively studied Phishing over decades, revealing numerous psychological vulnerabilities. A greater understanding of the interaction between attackers and end-users has produced more effective: policies, models, training, and defensive mechanisms. The Human Factors literature lacks information about non-technical users’ knowledge, experience, and behaviors in response to SMiShing. The current survey collected pilot data from college students with average cyber hygiene scores. Our results suggest that while users do not clearly understand the term SMiShing, they report knowing the danger. While all participants reported that using links within text messages from unknown senders can lead to security vulnerabilities, most reported using the links anyway. Users vet messages based on the sender’s phone number and typos, rather than delivery time and other message recipients. These descriptive findings can be used to support future work attempting to defend against SMiShing attacks.
Multi-camera trajectory matching based on hierarchical clustering and constraints

Abstract The fast improvement of deep learning methods resulted in breakthroughs in image classification, object detection, and object tracking. Autonomous driving and traffic monitoring systems, especially the on-premise installed fixed position multi-camera configurations, benefit greatly from recent advances. In this paper, we propose a Multi-Camera Multi-Target (MCMT) vehicle tracking system using a constrained hierarchical clustering solution, which improves trajectory matching, and thus provides a more robust tracking of objects transitioning between cameras. YOLOv5, ByteTrack, and ResNet50-IBN ReID networks are used for vehicle detection and tracking. Static attributes such as vehicle type and vehicle color are determined from ReID features with SVM. The proposed ReID feature-based attribute categorization shows better performance, than its pure CNN counterpart. Single-camera trajectories (SCTs) are combined into multi-camera trajectories (MCTs) using hierarchical agglomerative clustering (HAC) with time and space constraints (our proposed algorithm is denoted by MCT#MAC). Similarities between SCTs are measured by comparing the mean ReID features cumulated on the trajectory. The system was evaluated on more datasets, and our experiments demonstrate that constraining HAC by manipulating the proximity matrix greatly improves the multi-camera IDF1 score.
Efficient Private Multiset ID Protocols

Private-ID (PID) protocol enables two parties, each holding a private set of items, to privately compute a set of random universal identifiers (UID) corresponding to the records in the union of their sets, where each party additionally learns which UIDs correspond to which items in its set but not if they belong to the intersection or not. PID is very useful in the privacy computation of databases query, e.g. inner join and join for compute. Known PID protocols all assume the input of both parties is a set. In the case of join, a more common scenario is that one party’s primary key (unique) needs to join the other party’s foreign key (duplicate). How to construct an efficient Private Multiset ID (PMID) protocol to support the above key-foreign key join remains open. We resolve this problem by constructing efficient PMID protocols from Oblivious PRF, Private Set Union, and a newly introduced primitive called Deterministic-Value Oblivious Programmable PRF (dv-OPPRF). We also propose some PMID applications, including Private Inner Join, Private Full Join, and Private Join for Compute. We implement our PMID protocols and state-of-the-art PID protocols as performance baselines. The experiments show that the performances of our PMID are almost the same as the state-of-the-art PIDs when we set the multiplicity $$U_x = U_y = 1$$ . Our PMID protocols scale well when either $$U_x > 1$$ or $$U_y > 1$$ . The performances also correctly reflect excessive data expansion when both $$U_x, U_y > 1$$ for the more general cross join case.
The Application of "Output Oriented Method" in Writing Teaching for English Majors in Universities

The output oriented approach is a teaching method that is student-centered and oriented towards students' language abilities and learning needs, aiming to improve their language output ability. The application of this method in writing teaching for English majors in universities can help teachers adjust teaching strategies, stimulate students' interest in learning, help students master writing skills, and improve language output ability. This article analyzes the basic concepts and application considerations of the output oriented approach, and proposes the application strategies of the output oriented approach in writing teaching for English majors in universities. Through research, it has been found that the output oriented approach can help improve the efficiency of writing teaching for English majors in universities and help students improve their language output ability.