Reviewerly/exHarmony huggingface dataset:
FTG: Score-based black-box watermarking by Fragile Trigger Generation for deep model integrity verification

Deep neural networks (DNN) are widely used in real-world applications, thanks to their exceptional performance in image recognition. However, their vulnerability to attacks, such as Trojan and data poison, can compromise the integrity and stability of DNN applications. Therefore, it is crucial to verify the integrity of DNN models to ensure their security. Previous research on model watermarking for integrity detection has encountered the issue of overexposure of model parameters during embedding and extraction of the watermark. To address this problem, we propose a novel score-based black-box DNN fragile watermarking framework called Fragile Trigger Generation (FTG). The FTG framework only requires the prediction probability distribution of the final output of the classifier during the watermarking process. It generates different fragile samples as the trigger, based on the classification prediction probability of the target classifier and a specified prediction probability mask to watermark it. Different prediction probability masks can promote the generation of fragile samples in corresponding distribution types. The whole watermarking process does not affect the performance of the target classifier. When verifying the watermarking information, the FTG only needs to compare the prediction results of the model on the samples with the previous label. As a result, the required model parameter information is reduced, and the FTG only needs a few samples to detect slight modifications in the model. Experimental results demonstrate the effectiveness of our proposed method and show its superiority over related work. The FTG framework provides a robust solution for verifying the integrity of DNN models, and its effectiveness in detecting slight modifications makes it a valuable tool for ensuring the security and stability of DNN applications.
Wigner quasi-probability distribution of a resonator coherent field interacting with a flux qubit via two-photon coupling

The Wigner quasi-probability distribution function is an important tool for estimating the quantumness of quantum states. The Wigner distribution function (WDF) is related monotonically to the coherent field state density operator and their probability distributions. In this paper, we analyze the time-dependent resonator coherent field states produced by a flux qubit’s interaction with a resonator through a two-photon coupling. The WDF dynamics of the coherent field states is investigated under the effects of the qubit-resonator detuning and the intrinsic decoherence. The WDF non-classicality associated with the mixedness resonator entropy dynamics can be controlled by varying the qubit-resonator interaction, detuning as well as the decoherence. The intrinsic decoherence clearly affects the WDF non-classicality, identified by the negative values of the WDF. Finally, by fixing one or both of the complex phase space components, we are able to explore the dynamics of the Wigner-distribution functions.
Adaptive device sampling and deadline determination for cloud-based heterogeneous federated learning

Abstract As a new approach to machine learning, Federated learning enables distributned traiing on edge devices and aggregates local models into a global model. The edge devices that participate in federated learning are highly heterogeneous in terms of computing power, device state, and data distribution, making it challenging to converge models efficiently. In this paper, we propose FedState, which is an adaptive device sampling and deadline determination technique for cloud-based heterogeneous federated learning. Specifically, we consider the cloud as a central server that orchestrates federated learning on a large pool of edge devices. To improve the efficiency of model convergence in heterogeneous federated learning, our approach adaptively samples devices to join each round of training and determines the deadline for result submission based on device state. We analyze existing device usage traces to build device state models in different scenarios and design a dynamic importance measurement mechanism based on device availability, data utility, and computing power. We also propose a deadline determination module that dynamically sets the deadline according to the availability of all sampled devices, local training time, and communication time, enabling more clients to submit local models more efficiently. Due to the variability of device state, we design an experience-driven algorithm based on Deep Reinforcement Learning (DRL) that can dynamically adjust our sampling and deadline policies according to the current environment state. We demonstrate the effectiveness of our approach through a series of experiments with the FMNIST dataset and show that our method outperforms current state-of-the-art approaches in terms of model accuracy and convergence speed.
SSDLiteX: Enhancing SSDLite for Small Object Detection

Object detection in many real applications requires the capability of detecting small objects in a system with limited resources. Convolutional neural networks (CNNs) show high performance in object detection, but they are not adequate to resource-limited environments. The combination of MobileNet V2 and SSDLite is one of the common choices in such environments, but it has a problem in detecting small objects. This paper analyzes the structure of SSDLite and proposes variations leading to small object detection improvement. The feature maps with the higher resolution are utilized more, and the base CNN is modified to have more layers in the high resolution. Experiments have been performed for the various configurations and the results show the proposed CNN, SSDLiteX, improves the detection accuracy AP of small objects by 1.5 percent points in the MS COCO data set.
General Image Manipulation Detection Using Feature Engineering and a Deep Feed-Forward Neural Network

Within digital forensics, a notable emphasis is placed on the detection of the application of fundamental image-editing operators, including but not limited to median filters, average filters, contrast enhancement, resampling, and various other operations closely associated with these techniques. When conducting a historical analysis of an image that has potentially undergone various modifications in the past, it is a logical initial approach to search for alterations made by fundamental operators. This paper presents the development of a deep-learning-based system designed for the purpose of detecting fundamental manipulation operations. The research involved training a multilayer perceptron using a feature set of 36 dimensions derived from the gray-level co-occurrence matrix, gray-level run-length matrix, and normalized streak area. The system detected median filtering, mean filtering, the introduction of additive white Gaussian noise, and the application of JPEG compression in digital Images. Our system, which utilizes a multilayer perceptron trained with a 36-feature set, achieved an accuracy of 99.46% and outperformed state-of-the-art deep-learning-based solutions, which achieved an accuracy of 97.89%.
The Pre-Toda Verb A Reconstruction

Toda is a Dravidian language that is well known for its aberrant phonology, namely due to ubiquitous vowel dropping, simplification of consonant clusters, and phonemic diversification. Although efforts have succeeded to some extent in mapping these phonemes to those of related languages, the origins and implications of Toda morphology have not yet been explored in detail. This work aims to reconstruct key aspects of the Pre-Toda verb in order to provide a dataset to be faithfully used in comparative Dravidian linguistics. Here, the formation of the secondary stem and the nonpast suffixes are demonstrated to show more affinity towards Old Kannada rather than Old Tamil. At the same time, the tenseless and dubitative conjugations, along with personal terminations, are found to retain archaic Dravidian suffixes.
Wavelet-based temporal models of human activity for anomaly detection in smart robot-assisted environments1

Detecting anomalies in patterns of sensor data is important in many practical applications, including domestic activity monitoring for Active Assisted Living (AAL). How to represent and analyse these patterns, however, remains a challenging task, especially when data is relatively scarce and an explicit model is required to be fine-tuned for specific scenarios. This paper, therefore, presents a new approach for temporal modelling of long-term human activities with smart-home sensors, which is used to detect anomalous situations in a robot-assisted environment. The model is based on wavelet transforms and used to forecast smart sensor data, providing a temporal prior to detect unexpected events in human environments. To this end, a new extension of Hybrid Markov Logic Networks has been developed that merges different anomaly indicators, including activities detected by binary sensors, expert logic rules, and wavelet-based temporal models. The latter in particular allows the inference system to discover deviations from long-term activity patterns, which cannot be detected by simpler frequency-based models. Two new publicly available datasets were collected using several smart-sensors to evaluate the approach in office and domestic scenarios. The experimental results demonstrate the effectiveness of the proposed solutions and their successful deployment in complex human environments, showing their potential for future smart-home and robot integrated services.
Evaluation of cloud-type impact on performance of the GL model version 1.2 for estimation of solar irradiance

This work evaluates the quality of the satellite-based GLobal radiation model version 1.2 (GL1.2) estimates for four cloud classes. The GL1.2 calculates the global solar irradiance at ground level using images from a single visible band channel (VIS) of the Geostationary Operational Environmental Satellite – GOES (GOES-East). The model´s performance was assessed by comparing hourly mean GL1.2 values with ground-based hourly measurements from the Brazilian National Institute of Meteorology (INMET, 354 automatic weather stations), for the entire year 2017. A satellite-based cloud classifier was adopted to discriminate the datasets according to prevailing cloud conditions (cumulus, stratus, cirrus and multilayer), but the clear sky behaviour was presented. The results were analysed for the five Brazilian regions. In the first analysis, we selected days with a predominance for five stations. It was found that the diurnal cycle was well reproduced. Then the regional investigation for the cloud types reveals that the best results are found for the Center West region over multilayer cloud (mean bias error, MBEannual = -2 ± 91 Wm−2 and root mean squared error, RMSE = 91 Wm−2), while the worst ones are in the North over cumulus fields (MBEannual = 101.5 ± 136.4 Wm−2). When considering all cloud types, the MBEannual is lower than 5 Wm−2 for the Northeast, Southeast and South regions, but it reaches 101 Wm−2 in the North. It is noteworthy that winter has the highest MBE in all classes analysed in the North, as well as cirrus situations in other regions. Although the inhomogeneities of cumulus and the semitransparent cirrus clouds tend to propagate errors to the model, the quality of GL1.2 data has a high degree of agreement with the observations. Improvements including an updated monthly minimum reflectance (Rmin) and water vapour column (H2Ovapour), and a better spatial resolution of the Advanced Baseline Imager (ABI) of GOES-16 (ABI/GOES-16) VIS imagery will allow refinement, especially for cumulus clouds.
Semantic computing with IEML

This paper presents IEML, Information Economy MetaLanguage, a constructed language with the same expressive power as a natural language and with computable semantics. Distinguished from pragmatic and referential semantics, linguistic semantics have not yet been completely formalized. Only its syntagmatic dimension has been mathematized in the form of regular languages. Its paradigmatic dimension remained to be formalized. In order to complete the mathematizing of language, including its paradigmatic dimension, I have coded linguistic semantics with IEML. This article introduces its 3000-word dictionary, its formal grammar, and its integrated tools for building semantic graphs. For the future, IEML could become a vector for a fluid calculation and communication of meaning—semantic interoperability—capable of de-compartmentalizing the digital memory, and of advancing the progress of collective intelligence, artificial intelligence, and digital humanities. I conclude by indicating some research directions.
4th International Conference on Sustainable Futures: Environmental, Technological, Social and Economic Matters

Abstract This paper presents the preface of the proceedings for the 4th International Conference on Sustainable Futures: Environmental, Technological, Social, and Economic Matters (ICSF 2023), a multidisciplinary event that explores the challenges and opportunities of sustainability in various domains. The preface outlines the conference’s objectives, themes, workshops, and topics, as well as its contribution to advancing sustainable development and global dialogue. It also acknowledges the efforts and inputs of various stakeholders who have made the conference possible, especially in light of the pandemic situation. Furthermore, it thanks IOP Publishing for its support and flexibility in facilitating open access publishing. The paper concludes by looking forward to future editions of ICSF and the ongoing quest for a more sustainable and interconnected world. The paper invites readers to delve into the rich and diverse content that shapes this influential conference.
ECM: Enhanced confidentiality method to ensure the secure migration of data in VM to cloud environment

Cloud is the technology behind all modern IT paradigms today. All kinds of applications run in the cloud environment and host their data. Enterprises show interest in migrating their data and servers to the cloud to benefit the cloud. The cloud is an open distributed network environment; hence, it is vulnerable to data security attacks. Migration in cloud computing permits the handover of resources, for example, virtual machine (VM) and data from off-premises to on-premises. During the migration, the data needs to be secured. This paper proposes an enhanced confidentiality technique (ECM) to ensure data security when migrated to the cloud. The proposed enhancement is on the advanced encryption standard (AES) to strengthen the protection level of the AES. The standard AES runs for rounds with four unique stages of data processing: Substitute bytes, add round key, shift rows and mix columns. The proposed ECM enhances the substitute bytes stage with dynamic substitution boxes for each round of the AES. The proposed ECM is evaluated for its efficiency by the avalanche effect. The result of the ECM improves data protection when the data is migrated to the cloud.
The Role of Digital Finance in Economic Development: A Cross Country Analysis

Nations having strong infrastructure on digital platform are likely to have more acceleration in digital finance sector and reap the benefits of this sector. The term digital finance means to provide financial services through personal computers, mobile phones or any reliable digital payment system. Digital finance has the capacity to give convenient, inexpensive and firm banking services. Financial inclusion is a win-win situation that is fulfill by digital finance. Sufficient evidences confirms that internet penetration and mobile phones built up sturdy infrastructure for digital finance and thus support in boosting economic development. Therefore, the objective of this research is to examine the impact of digital finance on World’s economic development. By pursuing the fulfillment of objective, this study collected the data of 77 countries from during the period from 2011 to 2021 and adopted two step system Generalized method of moments (GMM) to inspect the effects of mobile cellular subscriptions and internet usage on economic development parameters. Our findings indicate that mobile cellular services and internet uses have better impact on economic growth and per capita income. These findings also proposes some major policy implementations especially for developing countries that they must adopt such policies which make better ground to promote digital financial system and thus move ahead to cashless economies in order to get immense benefits from it.
Data science and machine learning methods for detecting credit card fraud

Credit card fraud remains a persistent challenge in the realm of financial security, necessitating innovative approaches for detection. This paper presents a comprehensive investigation into credit card fraud detection, focusing on integrating rule-based systems and machine learning methods to enhance accuracy and efficiency. The methodology encompasses data collection from a reputable source, thorough preprocessing, model development, and online execution. Performance evaluation employs a diverse array of metrics, including precision, recall, F1 score, accuracy, confusion matrix, false positive rate, learning curve, precision-recall curve, cumulative gains curve, and ROC curve. Results demonstrate a balanced trade-off between precision and recall, essential for effective fraud detection. Detailed discussions interpret these findings, offering valuable insights and avenues for future research. This research contributes to advancing fraud detection methodologies and holds promise for enhancing financial transaction security
Understanding the Experience of Administrative Staffs from the School Context Perspective

This study is a qualitative case study that comprehensively understands the experiences of administrative staffs at A high school through four dimensions: situated, professional, material, and external context. While complaints in situational context such as school location and external context such as work support from higher institutions tended to be offset in relation to professional collusion, professional context related to school management such as consultative communication culture is a factor that allows members to work with psychological stability in warm relationships. The material context such as budget, facilities, and human resources is a factor directly related to the work of administrative employees and has the greatest impact on their experiences. Based on the understanding of the experiences of administrative staffs, the followings are suggested to enhance administrative staffs’ experience. First, administrative staffs should be able to actively participate in various communication structures in the school. Second, for tasks in which they commonly complain of difficulties, the transfer to higher institutions or professional supports should be provided. Third, in order to develop expertise to handle various school field cases, the formation of out-of-school networks that can be consulted on business should be institutionalized.
PRAXEOLOGICAL APPROACH IN WORKING WITH THE ORCHESTRA COLLECTIVE AND ENSEMBLE IN THE CONDITIONS CONTINUOUS EDUCATION

The article substantiates the methodology of working with orchestral teams and ensembles, proves the role of the conductor in working with the team, the importance of rehearsal work and interpretation of musical works. Key words: orchestra group, ensemble, rehearsal, work on the score
Utilizing the Vector Autoregression Model (VAR) for Short-Term Solar Irradiance Forecasting

Forecasting solar irradiance is a critical task in the renewable energy sector, as it provides essential information regarding the potential energy production from solar panels. This study aims to utilize the Vector Autoregression (VAR) model to forecast solar irradiance levels and weather characteristics in the San Francisco Bay Area. The results demonstrate a correlation between predicted and actual solar irradiance, indicating the effectiveness of the VAR model for this task. However, the model may not be sufficient for this region due to the requirement of additional weather features to reduce disparities between predictions and actual observations. Additionally, the current lag order in the model is relatively low, limiting its ability to capture all relevant information from past observations. As a result, the model's forecasting capability is limited to short-term horizons, with a maximum horizon of four hours.
A semantic similarity-based method to support the conversion from EXPRESS to OWL

Abstract Product data sharing is fundamental for collaborative product design and development. Although the STandard for Exchange of Product model data (STEP) enables this by providing a unified data definition and description, it lacks the ability to provide a more semantically enriched product data model. Many researchers suggest converting STEP models to ontology models and propose rules for mapping EXPRESS, the descriptive language of STEP, to Web Ontology Language (OWL). In most research, this mapping is a manual process which is time-consuming and prone to misunderstandings. To support this conversion, this research proposes an automatic method based on natural language processing techniques (NLP). The similarities of language elements in the reference manuals of EXPRESS and OWL have been analyzed in terms of three aspects: heading semantics, text semantics, and heading hierarchy. The paper focusses on translating between language elements, but the same approach has also been applied to the definition of the data models. Two forms of the semantic analysis with NLP are proposed: a Combination of Random Walks (RW) and Global Vectors for Word Representation (GloVe) for heading semantic similarity; and a Decoding-enhanced BERT with disentangled attention (DeBERTa) ensemble model for text semantic similarity. The evaluation shows the feasibility of the proposed method. The results not only cover most language elements mapped by current research, but also identify the mappings of the elements that have not been included. It also indicates the potential to identify the OWL segments for the EXPRESS declarations.
An Interactive Collaborative Robotic System to Play Italian Checkers

An interactive collaborative robotic system to play Italian checkers is presented in this paper. The system detects the state of the game using a camera, calculates the optimal move using a developed decision-making algorithm, and executes pick-and-place tasks to physically move the pieces on the board. The developed system is implemented in a real-world setup using a Franka Emika arm as pieces manipulator. The experimental results show the feasibility of proposed approach.
Universal Configurable Navigation and Control System for Industrial Unmanned Ground Vehicles with Differential Chassis

We describe a novel navigation and control system designed for industrial unmanned ground vehicles, utilizing the Robot Operating System (ROS). Our primary objective was to develop a versatile solution that could adapt to various chassis and robot configurations. The system is characterized by its modularity, reconfigurability, flexibility, and partial hardware/system environment agnosticism. It combines modern SLAM (Simultaneous Localization and Mapping) techniques, advanced trajectory planning algorithms, and robust AI image processing capabilities, all accessible through a user-friendly web frontend. The paper delves into the system's architecture and its seamless integration with the mechanical components. Additionally, it describes some challenges encountered during the implementation of this advanced software, its architecture and interaction with the mechanical part of the system.