Reviewerly/exHarmony huggingface dataset:
Timing Synchronization Based on Supervised Learning of Spectrogram for OFDM Systems

This paper proposes a supervised convolutional neural network (CNN) based symbol timing synchronization method using the spectrogram image for preamble-less orthogonal frequency division multiplexing (OFDM) systems. With the development of mobile terminals, OFDM has become an increasingly widespread fundamental technology for wireless communications. While OFDM can achieve high-speed transmission, it is sensitive to synchronization timing for decoding. Thus, the accurate synchronization timing estimation method has become essential for reliable communication. Conventional synchronization timing estimation methods without the preamble lack investigations of estimation accuracy under varying environments, comprehensive performance evaluation, and robustness to Doppler shift. Focusing on the spectrum fluctuations observed when synchronization errors occur, our proposed approach is to train the CNN using spectrogram images to find accurate synchronization points even in noisy and fluctuating environments. The simulation results show that the proposed method achieves better synchronization accuracy than other existing methods. Furthermore, it shows the near-optimal bit error rate (BER) characteristics and superior processing time for BER in an environment close to realistic settings, such as broader synchronization timing and various Doppler shifts.
Using the uniqueness of global identifiers to determine the provenance of Python software source code

We consider the problem of identifying the provenance of free/open source software (FOSS) and specifically the need of identifying where reused source code has been copied from. We propose a lightweight approach to solve the problem based on software identifiers-such as the names of variables, classes, and functions chosen by programmers. The proposed approach is able to efficiently narrow down to a small set of candidate origin products, to be further analyzed with more expensive techniques to make a final provenance determination.By analyzing the PyPI (Python Packaging Index) open source ecosystem we find that globally defined identifiers are very distinct. Across PyPI's 244 K packages we found 11.2 M different global identifiers (classes and method/function names-with only 0.6% of identifiers shared among the two types of entities); 76% of identifiers were used only in one package, and 93% in at most 3. Randomly selecting 3 non-frequent global identifiers from an input product is enough to narrow down its origins to a maximum of 3 products within 89% of the cases.We validate the proposed approach by mapping Debian source packages implemented in Python to the corresponding PyPI packages; this approach uses at most five trials, where each trial uses three randomly chosen global identifiers from a randomly chosen python file of the subject software package, then ranks results using a popularity index and requires to inspect only the top result. In our experiments, this method is effective at finding the true origin of a project with a recall of 0.9 and precision of 0.77.
ActiveGuard: An active intellectual property protection technique for deep neural networks by leveraging adversarial examples as users' fingerprints

The training of Deep Neural Networks (DNN) is costly, thus DNN can be considered as the intellectual properties (IP) of model owners. To date, most of the existing protection works focus on verifying the ownership after the DNN model is stolen, which cannot resist piracy in advance. To this end, we propose an active DNN IP protection method based on adversarial examples against DNN piracy, named ActiveGuard. ActiveGuard aims to achieve authorization control and users' fingerprints management through adversarial examples, and can provide ownership verification. Specifically, ActiveGuard exploits the elaborate adversarial examples as users' fingerprints to distinguish authorized users from unauthorized users. Legitimate users can enter fingerprints into DNN for identity authentication and authorized usage, while unauthorized users will obtain poor model performance due to an additional control layer. In addition, ActiveGuard enables the model owner to embed a watermark into the weights of DNN. When the DNN is illegally pirated, the model owner can extract the embedded watermark and perform ownership verification. Experimental results show that, for authorized users, the test accuracy of LeNet-5 and Wide Residual Network (WRN) models are 99.15% and 91.46%, respectively, while for unauthorized users, the test accuracy of the two DNNs are only 8.92% (LeNet-5) and 10% (WRN), respectively. Besides, each authorized user can pass the fingerprint authentication with a high success rate (up to 100%). For ownership verification, the embedded watermark can be successfully extracted, while the normal performance of the DNN model will not be affected. Further, ActiveGuard is demonstrated to be robust against fingerprint forgery attack, model fine-tuning attack and pruning attack.
Visualizing Quantum Circuit Probability: Estimating Quantum State Complexity for Quantum Program Synthesis

This work applies concepts from algorithmic probability to Boolean and quantum combinatorial logic circuits. The relations among the statistical, algorithmic, computational, and circuit complexities of states are reviewed. Thereafter, the probability of states in the circuit model of computation is defined. Classical and quantum gate sets are compared to select some characteristic sets. The reachability and expressibility in a space-time-bounded setting for these gate sets are enumerated and visualized. These results are studied in terms of computational resources, universality, and quantum behavior. The article suggests how applications like geometric quantum machine learning, novel quantum algorithm synthesis, and quantum artificial general intelligence can benefit by studying circuit probabilities.
Multimodal Stereotactic Brain Tumor Segmentation Using 3D-Znet

Stereotactic brain tumor segmentation based on 3D neuroimaging data is a challenging task due to the complexity of the brain architecture, extreme heterogeneity of tumor malformations, and the extreme variability of intensity signal and noise distributions. Early tumor diagnosis can help medical professionals to select optimal medical treatment plans that can potentially save lives. Artificial intelligence (AI) has previously been used for automated tumor diagnostics and segmentation models. However, the model development, validation, and reproducibility processes are challenging. Often, cumulative efforts are required to produce a fully automated and reliable computer-aided diagnostic system for tumor segmentation. This study proposes an enhanced deep neural network approach, the 3D-Znet model, based on the variational autoencoder-autodecoder Znet method, for segmenting 3D MR (magnetic resonance) volumes. The 3D-Znet artificial neural network architecture relies on fully dense connections to enable the reuse of features on multiple levels to improve model performance. It consists of four encoders and four decoders along with the initial input and the final output blocks. Encoder-decoder blocks in the network include double convolutional 3D layers, 3D batch normalization, and an activation function. These are followed by size normalization between inputs and outputs and network concatenation across the encoding and decoding branches. The proposed deep convolutional neural network model was trained and validated using a multimodal stereotactic neuroimaging dataset (BraTS2020) that includes multimodal tumor masks. Evaluation of the pretrained model resulted in the following dice coefficient scores: Whole Tumor (WT) = 0.91, Tumor Core (TC) = 0.85, and Enhanced Tumor (ET) = 0.86. The performance of the proposed 3D-Znet method is comparable to other state-of-the-art methods. Our protocol demonstrates the importance of data augmentation to avoid overfitting and enhance model performance.
Design of continuous-time recurrent neural networks with piecewise-linear activation function for generation of prescribed sequences of bipolar vectors

A recurrent neural network (RNN) can generate a sequence of patterns as the temporal evolution of the output vector. This paper focuses on a continuous-time RNN model with a piecewise-linear activation function that has neither external inputs nor hidden neurons, and studies the problem of finding the parameters of the model so that it generates a given sequence of bipolar vectors. First, a sufficient condition for the model to generate the desired sequence is derived, which is expressed as a system of linear inequalities in the parameters. Next, three approaches to finding solutions of the system of linear inequalities are proposed: One is formulated as a convex quadratic programming problem and others are linear programming problems. Then, two types of sequences of bipolar vectors that can be generated by the model are presented. Finally, the case where the model generates a periodic sequence of bipolar vectors is considered, and a sufficient condition for the trajectory of the state vector to converge to a limit cycle is provided.
HOTS Based Scientific Reasoning: Cognitive Model Assessment in Work and Energy Material

Higher Order Thinking Skill (HOTS) is one of the higher thinking skills that must be possessed in 21st-century education.HOTS is a higher cognitive level of humans thinking in explaining concepts, thinking logically, and solving problems.The purpose of this study is to describe HOTS students based on several indicators of scientific reasoning as one of the models of evaluation of innovative learning at the college level, specifically in the field of science.The research method uses quantitative and qualitative descriptive research using a design explanatory where an explanation of the qualitative data will follow the data description of the quantitative data.This research involved 32 students majoring in Physics education at Musamus University Merauke who had or were programming introductory physics courses.The data collection technique uses in the form of a test essay followed and evaluated extensively using the scientific reasoning indicator.The results of the study indicated that students have an entry-level HOTS category based on criteria at the scientific reasoning level.This is evidenced by the acquisition of a percentage 62.5% of students' HOTS abilities at the beginner level.As many as 25% of students' HOTS abilities are at a competent level.As many as 12.5% of students' HOTS abilities are at the professional level.Interview data reinforced the gain to several students that they did not yet have a good page in evaluating the HOTS level integrated scientific reasoning problem.
The Interactive Students E-Worksheet Based on Discovery Learning and Representations on Hydrolyzing Salt Topic

This study develops interactive electronic worksheets for students based on discovery learning and chemical representations of salt hydrolysis, and the relevance of describing teacher and student answers in relation to the developed electronic worksheets.The purpose was to Research and development by Borg & Gall was used as the study design, extending to Level 5, the modified level of test results.The instruments used were expert validation, teacher and student response questionnaires.The data obtained were analyzed using descriptive statistical analysis.The results of our expert review of the content suitability and readability aspects were of a high standard, and the structural aspects were of a very high standard.Based on the results, thematic hydrolysis salt discovery learning and interactive student electronic worksheets based on chemical representations are valid.In addition, teacher responses on suitability, organization and readability, and student responses on readability and attractiveness, are each assessed to very high standards.
The Application of Android-Based Business English Learning Module

this world requires all elements of society to adapt through the use of digital technology, especially in the education aspect.The application of android mobile-based learning module becomes one of the solutions within solving problems in learning, so that education goals can be reached.This research focuses on the development of Basic English teaching materials in universities, which can be accessed by students through smartphones.Research and development in developing learning innovation products is used as an alternative method in developing the module.Data collection would be done through relevant previous research studies, questionnaires, and interviews.The subjects of the study are the students in the Digital Business Study Program who have been studying online.Validation of this product is done through construct validation and content validity to experts in the field of android development and digital education.Through comprehensive teaching guidance, this android's module could be used as a tool of learning in improving student understanding and learning motivation.
Investigating Students’ Perception Toward the Use of Instagram in EFL Learning

Educational context should take the benefits of innovative pedagogy and digital devices to go to the deeper content exploration, classroom management, engagement, and motivation of students in EFL teaching learning process.Moreover, it should be used to meet the students' need by reforming the old learning to the newest one.In doing this, social media can be used as a learning tool to apply a digital content learning, mainly in EFL teaching learning process.Since instagram has become one of the most popular social media in Indonesia, it has chosen by many English teachers as a learning media in their teaching.This paper is intended to observe students' perception towards the use of instagram in EFL learning.The subjects are 90 XII grade students of SMAN 1 Jatiagung, South Lampung, Lampung Province.The data were gained using questionnaire.Then, it was analyzed using descriptive qualitative method.The results indicated that most students show a positive perception towards the use of instagram in their EFL learning.In doing this, students also face some obstacles in their learning process such as internet connection and internet speed.Thus, it is suggested to the teacher to also consider these facts in the future.
Analysis of Self Responsibility Level for Elementary School Students

The purpose of this study was to (1) describe the personal responsibility of elementary school students and ( 2) analyze the level of responsibility of elementary school students.Researchers used a qualitative approach.The survey method is a survey method.Data collection techniques using observations and questionnaires.The research location is SD 5 Bae in Kudus province.Respondents to this study were SD 5 Bae students of the Kuds Regency.In this study, researchers used tests to validate the data.Data validation is done through triangulation and the use of reference materials.The triangulation used is technique and source triangulation.Technical triangulation is performed by checking data from the same source using different techniques such as observations and questionnaires.Source triangulation is used to test data by examining data obtained from multiple sources.Qualitative descriptive data analysis techniques are used to organize data, classify it into manageable units, integrate it, spot and spot patterns, discover what is important and what can be learned from it, and how it can be learned from others.It is the activity that determines what can be shared.This study uses the data model analysis technique of Miles and Huberman.Data analysis activities i.e. data reduction, data presentation and inference/validation.The results showed that (1) students needed improvement regardless of SD 5 Bae and (2) students were in the lower category regardless of SD 5 Bae.
A lightweight YOLOX-based model for detection of metallic gaskets on High-speed EMU wheelset

Abstract In order to solve the requirements of real-time detection on metallic gaskets of wheelset assembly under the condition of limited hardware resources, here we propose an improved lightweight YOLOX-like network that can be deployed on mobile devices with low computing resource consumption. Firstly, the basic components of ShuffleNetV2 network structure are trimmed to reduce the computation intensity and the number of training parameters. Thus the lightweight ShuffleNetV2 is used as the backbone structure of YOLOX. The prediction layer for $$20\times 20$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>20</mml:mn> <mml:mo>×</mml:mo> <mml:mn>20</mml:mn> </mml:mrow> </mml:math> size large targets is also removed from the head of this structure to further reduce the calculation time and improve the inference speed. Besides, the channel-attention enhancement module efficient channel attention is added into the network to improve the capability of feature extraction and the accuracy of target detection. Finally, the verification of inference for the proposed model is carried out on mobile terminal devices.The results show that the improved lightweight algorithm proposed in this paper not only ensures the detection accuracy, but also greatly reduces training parameters and computing resources, and it particularly can be run rapidly on mobile terminal devices with low-cost of computation.
Technoculture as a “Culture Revolution” in Network Society

Manuel Castells proposes a thesis on technoculture, a form of culture that is determined by technology.The revolution of technology determines the emergence of a new form of culture, a network society culture, which is characterized by its informational, networked, and global scale attribute.Castells' sociological observation holds a philosophical dimension in relation to the cultural ontology of society, agent position, and form of communication in new technoculture.This paper intends to philosophically elaborate Castells' thinking on the issue of the revolution of technology and its implication on culture, identity, society, capitalism, ideology, and agency.This paper argues that technoculture as a current epoche is a culture revolution in the age of network society.This kind of society is distinct from the past society which is possible by the progress of informational technology.The reflective thinking of this society is needed to perform analysis of the role of agent and disposition of the information age.
Students’ Perception on Blog Based Liveworksheet in English Language Learning at Senior High School of Sport Lampung

The present research was aimed at investigating how students of grade X at SMA Negeri Olahraga (Senior High School of Sport) Lampung perceive Blog Based Liveworksheet in English Language Learning and exploring its challenges.Two classes of grade X students participated in this research.The students were requested to fill online questionnaire and write guided reflections regarding their English Language learning through Blog Based Liveworksheet and the challenges they encountered.To validate the data, the students were interviewed.Data coding was done and appropriate extracts were informed in results section.The findings showed that the students perceived English Language Learning through Blog Based Liveworksheet meaningful and enjoyful.However, problems emerged from some points such as understanding worksheet instruction, Limitation of data package, internet signal, and broken mobile phone.As a result, the English Language learning through Blog Based Liveworksheet did not run well as expected.The overall problems stemmed from the infrastructure development of internet access.Implications for better online learning are discussed.Future prospective researches are directed and encouraged.
Education Towards Society 5.0: Humanizing Digitalized Learning Through Multicultural Literature and Emphatic Technology

This article was inspired by the research we conducted towards English and Non-English Department students in Indonesia and Taiwan regarding the role of multicultural literature in cultivating empathy and Cultural Competence (CQ) to successfully navigate diversity in today's network society.The goal of this article is to reflect and elaborate the role of Multicultural Literature (ML) in today's smart cyber physical system and super smart human-centered society.Textual analysis of two poems on diversity were done and questionnaires related to the poems were also distributed to both Indonesian and Taiwanese students to be analyzed.The result of the research shows that 96.66% of Indonesian students approve that ML helps them develop their CQ, while 86.66% of Taiwanese students are certain of the same, meaning that multicultural literature has its potential in cultivating empathy and cultural competence among students that will make digital learning more humanized and still give the students agency to utilize the advanced yet emphatic technology for their benefit in their future profession.
Secure and Efficient Code-Based Cryptography for Multi-Party Computation and Digital Signatures

Code-based cryptography is a promising candidate for post-quantum cryptography due to its strong security guarantees and efficient implementations. In this paper, we explore the use of code-based cryptography for multi-party computation and digital signatures, two important cryptographic applications. We present several efficient and secure code-based protocols for these applications, based on the McEliece cryptosystem and its variants. Our protocols offer strong security guarantees against both classical and quantum attacks, and have competitive performance compared to other post-quantum cryptographic schemes. We also compare code-based cryptography with other post-quantum schemes, including lattice-based and hash-based cryptography, and discuss the advantages and disadvantages of each approach.
A Robust Pedestrian Re-Identification and Out-Of-Distribution Detection Framework

Pedestrian re-identification is an important field due to its applications in security and safety. Most current solutions for this problem use CNN-based feature extraction and assume that only the identities that are in the training data can be recognized. On the one hand, the pedestrians in the training data are called In-Distribution (ID). On the other hand, in real-world scenarios, new pedestrians and objects can appear in the scene, and the model should detect them as Out-Of-Distribution (OOD). In our previous study, we proposed a pedestrian re-identification based on von Mises–Fisher (vMF) distribution. Each identity is embedded in the unit sphere as a compact vMF distribution far from other identity distributions. Recently, a framework called Virtual Outlier Synthetic (VOS) was proposed, which detects OOD based on synthesizing virtual outliers in the embedding space in an online manner. Their approach assumes that the samples from the same object map to a compact space, which aligns with the vMF-based approach. Therefore, in this paper, we revisited the vMF approach and merged it with VOS to detect OOD data points. Experiment results showed that our framework was able to detect new pedestrians that do not exist in the training data in the inference phase. Furthermore, this framework improved the re-identification performance and holds a significant potential in real-world scenarios.