Reviewerly/exHarmony huggingface dataset:
An incremental learning approach for sustainable regional isolation and integration

Humans are capable of acquiring new knowledge on a constant basis, while integrating and optimizing old knowledge without forgetting them. This is mainly attributed to the human brain's ability of partitioned learning and memory replay. In this paper, we simulate this ability and propose an incremental learning network of Sustainable Regional Isolation and Integration (SRII). SRII consists of two phases, regional isolation and regional integration, which are iterated to achieve continuous incremental class learning. Regional isolation isolates new learning processes to avoid interfering with existing knowledge, while regional integration uses knowledge distillation and a margin loss regularization term. The knowledge distillation transfers replay knowledge for alleviating catastrophic forgetting, while the margin loss regularization term is to clarify the boundaries of new and old knowledge for alleviating recency bias. Experimental results on the CIFAR100 and miniImageNet datasets demonstrate that SRII outperforms the state-of-the-arts on avoiding catastrophic forgetting. In all 5-stage and 10-stage incremental settings, SRII outperforms the baseline and achieves at least 5.09%+ average accuracy improvement. Our source code is available at https://github.com/Wuziyi123/SRII.
Security Analysis of Mobile Point-of-Sale Terminals

The increasing prevalence of Card Present (CP) transactions has driven the growth of mobile Point-of-Sale (mPoS) terminals. These compact, wireless, and low-cost terminals allow merchants to process transactions conveniently by utilizing a mobile phone. In this paper, we analyze the security implications of mPoS terminals with a focus to study the merchants’ mobile phones as a key component in the mPoS ecosystem. Our examination covers the security aspects of the mobile phone’s communication with the mPoS terminal and the payment provider server, and also the security risks in the mobile phone application itself. We perform an eavesdropping attack to reveal the cryptographic keys in the BLE (Bluetooth Low Energy) communication between the mPoS terminal and the merchant phone, execute a man-in-the-middle (MITM) attack to tamper with the mPoS terminal messages transmitted between the mPoS terminal and the payment provider server, and reverse engineer the mobile phone application to disable the security features that are controlled by the mobile phone.
TrustedMaaS: Transforming trust and transparency Mobility-as-a-Service with blockchain

Mobility-as-a-Service (MaaS) is an advanced Intelligent Transport System (ITS) that integrates various modes of transportation to meet the demands of travellers. The system relies on frequent communication for data exchange between the MaaS provider and transport service providers. Ideally, such communication would utilize trust technologies between these entities. However, current MaaS systems lack transparency and reliability, and their centralized nature creates a single point of failure for the entire service. To address these issues, this paper proposes a blockchain-based MaaS, which includes an architecture and smart contract functionalities. The solutions are built on permissioned and permissionless Hyperledger Fabric and Ethereum blockchain platforms, respectively, for a realistic deployment of network architecture and proposed smart contracts. Additionally, the paper presents a framework derived from comparing these two blockchain platforms. Finally, the framework is evaluated, and open questions and challenges are analyzed.
Performance Comparison of Statistical vs. Neural-Based Translation System on Low-Resource Languages

Abstract One of the important applications for which natural language processing (NLP) is used is the machine translation (MT) system, which automatically converts one natural language to another. It has witnessed various paradigm shifts since its inception. Statistical machine translation (SMT) has dominated MT research for decades. In the recent past, researchers have focused on developing MT systems based on artificial neural networks (ANN). In this paper, first, some important deep learning models that are mostly exploited in Neural Machine Translation (NMT) design are discussed. A systematic comparison was done between the performances of SMT and NMT concerning the English-to-Bangla and English-to-Hindi translation tasks. Most of the Indian scripts are morphologically rich, and the availability of a sufficient corpus is rare. We have presented and analyzed our work and a survey was conducted on other low-resource languages, and finally some useful conclusions have been drawn.
Spying on the Spy: Security Analysis of Hidden Cameras

Hidden cameras, also called spy cameras, are surveillance tools commonly used to spy on people without their knowledge. Whilst previous studies largely focused on investigating the detection of such a camera and the privacy implications, the security of the camera itself has received limited attention. Compared with ordinary IP cameras, spy cameras are normally sold in bulk at cheap prices and are ubiquitously deployed in hidden places within homes and workplaces. A security compromise of these cameras can have severe consequences. In this paper, we analyse a generic IP camera module, which has been packaged and re-branded for sale by several spy camera vendors. The module is controlled by mobile phone apps available on iOS and Android. By analysing the Android app and the traffic data, we reverse-engineered the security design of the whole system, including the module’s Linux OS environment, the file structure, the authentication mechanism, the session management, and the communication with a remote server. Serious vulnerabilities have been identified in every component. Combined together, these vulnerabilities allow an adversary to take complete control of a spy camera from anywhere over the Internet, enabling arbitrary code execution. This is possible even if the camera is behind a firewall. All that an adversary needs to launch an attack is the camera’s serial number, which users sometimes unknowingly share in online reviews. We responsibly disclosed our findings to the manufacturer. Whilst the manufacturer acknowledged our work, they showed no intention to fix the problems. Patching or recalling the affected cameras is infeasible due to complexities in the supply chain. However, it is prudent to assume that bad actors have already been exploiting these flaws. We provide details of the identified vulnerabilities in order to raise public awareness, especially on the grave danger of disclosing a spy camera’s serial number.
Campus Hate Crimes During the Trump Era: The Rhetoric of Conflict During the 2017 Unite the Right Rally at the University of Virginia in Charlottesville

This chapter situates the U.S. Federal Bureau of Investigation’s definition of hate crimes and the Council of Europe’s Committee of Ministers’ definition of hate speech within the context of Academia. The crisis studied is cultural polarization in the U.S., specifically the 2017 Unite the Right Rally of White supremacists at the University of Virginia. This momentous event “served as a wake-up call and exposed many higher education institutions to a threat they had not previously considered: Clashes between extremists and the protestors who oppose them” (Thelen and Robinson 2019, 219, 445). The authors trace how rising tensions about race across the U.S. became manifest during the Rally and how university officials responded to the crisis. The authors show that the university also engaged in transformational crisis communication, leading to a reassessment of its culture and engaging more fully with its historical relationship with slavery.
A novel multi-modal neural network approach for dynamic and generic sports video summarization

Video Summarization is a video compression/compaction technique to create a shorter yet informative version of original video. Video summarization has offered solutions to plenty of media, user and engineering applications. Though sports video summarization has been an active research topic for some time; there still exists a void for multi-modal, dynamic, generic and domain knowledge based approach for Cricket Sport video summarization. This paper presents a multi-modal video summarization approach to summarize Cricket sport videos. This work captures the domain knowledge acquired from multi-modal (audio-visual) cues. A dual neural network architecture pipeline is proposed to dynamically segment and dynamically summarize Cricket videos for generic target audience. The former Neural Network is grounded on Cricket bowling activity (visual feature) for dynamic video segmentation of Cricket videos. The segments are then forwarded to the latter Neural Network for identification of key segments. The key segment detection module relies on Audio analysis of Cricket video stream to identify exciting, content representative and informative segments as per Cricket domain. Experimental analysis on two novel proposed benchmark datasets, i.e. DPCS (Delivery Play Cricket Sport) image dataset and EXINP (Excited Interval Normal Play) Cricket Dataset (audio based) shows promising results. The results indicate that the proposed multi-modal approach generates exciting, content representative, informative, generic and dynamic summary incorporating domain knowledge of the sport.
RRML: Privacy Preserving Machine Learning Based on Random Response Technology

Machine learning algorithms are proven to be vulnerable to model inversion and membership inference attacks, which raises much privacy concerns for its applications in sensitive scenarios. Typically, the state-of-the-art privacy preserving machine learning methods tend to provide privacy protection guarantee at the cost of losing data utility. This inevitably causes degradation of the model performance, especially for those tasks who are trained using small data sets. Therefore, optimizations on the trade-offs between individual privacy and data utility become a critical issue in machine learning. In this work, we proposed a privacy preserving machine learning algorithm RRML (Random Response Machine Learning) by combining the random response mechanism with the semi-supervised teachers-student learning way, and give the privacy analysis. Extensive experiments have been conducted to validate the effectiveness of RRML in addressing the above mentioned problem. The experimental results confirmed its superiority in balancing data utility and privacy against the state-of-the-art privacy preserving machine learning algorithms, especially in small data scenarios.
PyPO: a Python package for Physical Optics

PyPO is a Python interface for end-to-end design, simulation, and analysis of (quasi-)optical reflector systems.It can model the forward and backward propagation of electromagnetic field distributions between multiple planar and (off-axis) quadric surfaces, as well as far-field propagation.Simulations are performed using either geometrical optics (GO) or the equivalent surface current approach, belonging to the field of physical optics (PO) (Balanis, 1989).The GO and PO calculations are performed using libraries written in C++ and CUDA, allowing for multi-threading and GPU acceleration.Common figures of merit, such as aperture efficiency and half-power beamwidth, can be calculated and used for quantitative analysis of the designed system.Input beam patterns can be selected from a range of models, such as Gaussian beams, point sources, and uniform current distributions.Custom beam patterns can also be imported to, for example, model the propagation of measured beam patterns through simulated optical systems.PyPO can be used through either a scripting-based approach, where simulations are defined in Python scripts, or through the graphical user interface (GUI).It only carries core dependencies on NumPy (Harris et al., 2020), Matplotlib (Hunter, 2007), and SciPy (Virtanen et al., 2020).The unittesting framework carries a dependency on nose2.The GUI carries dependencies on PySide6, pyqtdarktheme, and attrs.
Poseidon-AI, Where Aquatic Intelligence Meets Artificial Intelligence

The impact of climate change on the one hand and global population growth on the other highlight the need for innovative technologies to ensure the world’s future. Scarcity of resources, rising protein demand, and depleted ocean stocks all add to the value of the aquaculture sector. As a result, there are numerous companies that address the issues that this valuable sector faces. From disease control to feed optimization, innovative deep tech companies can be found in the countries with the highest aquaculture production in Asia, Europe, and Latin America.
Lexical complexity in exemplar EFL texts: towards text adaptation for 12 grades of basic English curriculum in China

Abstract Lexical complexity has been a key consideration of teaching preparation in determining grade appropriateness of teaching materials. However, the lack of quantified and defined standards for benchmarking lexical complexity has made it difficult for teachers when adapting source texts to target learners. This study has assessed quantitative differences in lexical complexity of exemplar texts at different points of schooling using a range of lexical diversity and sophistication features. The data consists of 2,372 texts from popular curriculum packages adopted from 1 to 12 grades of the English curriculum in China. One-way ANOVAs revealed significant differences in 16 out of 17 lexical complexity indices among different grades. Subsequent post hoc tests identified three lexical diversity features and four sophistication features that helped to differentiate exemplar texts across these 12 grades. These findings on the nature and role of lexical complexity have yielded new insights into the establishment of grade-level benchmarks for material preparation.
Explaining Black-Box Models through Counterfactuals

We present CounterfactualExplanations.jl: a package for generating Counterfactual Explanations (CE) and Algorithmic Recourse (AR) for black-box models in Julia. CE explain how inputs into a model need to change to yield specific model predictions. Explanations that involve realistic and actionable changes can be used to provide AR: a set of proposed actions for individuals to change an undesirable outcome for the better. In this article, we discuss the usefulness of CE for Explainable Artificial Intelligence and demonstrate the functionality of our package. The package is straightforward to use and designed with a focus on customization and extensibility. We envision it to one day be the go-to place for explaining arbitrary predictive models in Julia through a diverse suite of counterfactual generators.
Deep learning in physical layer communications: Evolution and prospects in 5G and 6G networks

Abstract With the rapid development of the communication industry in the fifth generation and the advance towards the intelligent society of the sixth generation wireless networks, traditional methods are unable to meet the ever‐growing demands for higher data rates and improved quality of service. Deep learning (DL) has achieved unprecedented success in various fields such as computer vision, large language model processing, and speech recognition due to its powerful representation capabilities and computational convenience. It has also made significant progress in the communication field in meeting stringent demands and overcoming deficiencies in existing technologies. The main purpose of this article is to uncover the latest advancements in the field of DL‐based algorithm methods in the physical layer of wireless communication, introduce their potential applications in the next generation of communication mechanisms, and finally summarize the open research questions.
Phonology Analysis Theory of Phonetics

Phonetic features such as phonemes, minimal pairs, suspicious pairs, and procedures are applied in this study. Identifies the analyzed phoneme, interview methods, native speaker involment and methods. Literature studies are for data collection, data analysis, and especially during analysis. Phonemes are completed through multiple processes including preprocessing (pre-process), separation process, merger process (separation procedure).
Dynamical Markov decision-making model based on mass function to quantitatively predict interference effects

Experimental results demonstrate that the law of total probability which is used to manage probabilities of a number of decision stages, is violated when interference effects occur in the decision process. Although some attempts have been made to predict interference effects, these studies have only been able to do so for certain data while failing to do so for others in the same experiment. With the help of C-D experiment and D experiment, this paper develops a dynamical Markov decision-making model based on mass function to quantitatively predict the interference effects. This model employs both the mass function and discount coefficient to generate distribution of initial state. A transition matrix based on the characteristics of unitary matrix is then generated, which is capable of realizing both transition between adjacent states as well as constraining variation interval of the discount coefficient. Next, this model quantifies the difference between the two experimental results obtained through a probability transformation to predict interference effects. Finally, our proposed model is applied to existing dataset with the results indicating that our model can process all existing data associated with the experiments, as compared to other models.
Entangled kernel of coded information using quantum string

The possibility of encoding classical string of information into a quantum string via CNOT operation and Hadamard gate is discussed. It has been shown that maximizing/minimizing the quantum correlation of the generated quantum string depends on the values of the initial classical strings. The kernel of each string, as well as, the total string had been examined, where by applying the Hadamard gate on the classical string, one can decrease its similarity as well as the similarity of the total kernel. However, in the absence of the Hadamard gate, the large similarity of the final quantum string has been observed. The kernel of the final quantum string may be used as an indictor of the security degree of the encoded information, where the large degree of security has depicted at small kernel. The high degree of security can be implemented by applying the Hadamard gate on only one classical string.
Evolutionary learning of selection hyper-heuristics for text classification

This paper introduces an evolutionary model in the scope of automated machine learning. This model is in charge of learning hyper-heuristics that represent selection rules of the form if-then, such that given a dataset for a text classification problem, the hyper-heuristics select the best classification method to use with it, based on the data distribution of the dataset. The evolutionary model starts by building a set of hyper-heuristics using a series of meta-features extracted from a training group of datasets that represent their data distribution. Hyper-heuristics are then evolved using adapted crossover and mutation operators. During the evolution, each hyper-heuristic is evaluated on its performance to classify each dataset in the training group. When the evolutionary process is done, the best hyper-heuristic is selected and evaluated for its generality with an independent test group of datasets. The results show that the best learned hyper-heuristic obtains an average classification performance close to the general optimum, and has a similar performance to the two most popular state-of-the-art automated machine learning systems, but with less computational cost. The approach used by the present model is relevant for automated machine learning in three aspects, the generality of the hyper-heuristics so they could be applied to groups of datasets; the interpretability of the representations that facilitate the understanding of the method selection by non-expert users; and the reduction of computational time and resources to reach a decision. Furthermore, the model extends the applicability of evolutionary computation methods, with their problem-independent properties and their ability to explore search spaces, to tackle new complex problems, such as the decision of the best classifier for a text classification dataset.
Physics-informed deep learning to forecast $${\widehat{{\varvec{M}}}}_{{\varvec{m}}{\varvec{a}}{\varvec{x}}}$$ during hydraulic fracturing

Short-term forecasting of estimated maximum magnitude ([Formula: see text]) is crucial to mitigate risks of induced seismicity during fluid stimulation. Most previous methods require real-time injection data, which are not always available. This study proposes two deep learning (DL) approaches, along with two data-partitioning methods, that rely solely on preceding patterns of seismicity. The first approach forecasts [Formula: see text] directly using DL; the second incorporates physical constraints by using DL to forecast seismicity rate, which is then used to estimate [Formula: see text]. These approaches are tested using a hydraulic-fracture monitoring dataset from western Canada. We find that direct DL learns from previous seismicity patterns to provide an accurate forecast, albeit with a time lag that limits its practical utility. The physics-informed approach accurately forecasts changes in seismicity rate, but sometimes under- (or over-) estimates [Formula: see text]. We propose that significant exceedance of [Formula: see text] may herald the onset of runaway fault rupture.
C-RSA: Byzantine-robust and communication-efficient distributed learning in the non-convex and non-IID regime

The emerging federated learning applications raise challenges of Byzantine-robustness and communication efficiency in distributed non-convex learning over non-IID data. To address these issues, in this paper we propose a compressed Byzantine-robust stochastic model aggregation method, abbreviated as C-RSA. C-RSA utilizes robust stochastic model aggregation to obtain Byzantine-robustness over non-IID data, and compresses the transmitted messages for achieving high communication efficiency. Theoretically, we exploit Moreau envelope and proximal point projection as technical tools to analyze the convergence of C-RSA for distributed non-convex learning. Extensive numerical experiments are conducted on neural network training tasks to demonstrate the superior performance of C-RSA.
Integration test order generation based on reinforcement learning considering class importance

The task of ordering classes reasonably in the context of integration testing has been discussed by many researchers. Existing methods regard the class integration test order with the minimum stubbing cost as the optimal result. However, they ignore that class importance can also affect the class integration test order. This paper presents a design of a new algorithm, which considered the class importance, and its evaluation using computational experiments. Specifically, two novel reinforcement learning-based methods to generate class integration test orders are proposed, which aim to consider the class importance and minimize the stubbing cost. First, we advance the concept of class importance and optimize its measurement method. Then, we refine the calculation of stubbing complexity, which is the evaluation indicator of stubbing cost. After that, we combine both class importance and the stubbing complexity into the reinforcement learning algorithm to guide the agent to explore. Finally, we evaluate the proposed methods using computational experiments on five benchmark programs and four open-source programs. The experimental results show that our proposed methods can significantly reduce the stubbing cost while prioritizing the classes of high importance.