Reviewerly/exHarmony huggingface dataset:
Continual Learning for Morphology Control

In complex environment, the evolution of agent forms is pluralistic. How to evolve the ideal agent form in multiple environments is very challenging. In addition, the lifelong reinforcement learning of agents is also a difficulty in the research. The problem of this paper is the strategy of generating the optimal form of agents in multiple environments and maintaining the transfer learning ability of agents while the environment changes. Specifically, we use graph neural network to generate the final form of agents in the process of interaction with complex environment. Secondly, we use Elastic Weight Consolidationn (EWC) algorithm to enhance the lifelong reinforcement learning ability of agents. The experiment proves that the control task in the environment can be completed by generating the agent form in the process of interacting with the environment. The Elastic Weight Consolidation (EWC) can improve the utilization rate of the sample and has a certain anti-forgetting effect on the task.
Unsupervised Multidimensional Time Series Anomaly Detection Based on Federation Learning

Efficient and accurate prediction of electricity data is an important task in electricity data research. Convolutional neural networks have excellent performance in electricity data prediction problems but require large amounts of data to train the models. The major power companies are not willing to share their power data due to privacy and security concerns, making it impossible to train more accurate models. Moreover, the huge amount of data uploading to the central server for training the federated model generates huge network resource overhead. To address these problems, this paper proposes a federation learning algorithm (FedFLA) for unsupervised multidimensional time series anomaly detection based on existing artificial intelligence techniques such as federation learning, time-domain convolutional neural networks, and self-attentive mechanisms, in combination with existing federation learning algorithms. This algorithm uses time-domain convolutional networks and self-attentive mechanisms to fully consider time series local data dependence and global data correlation, and fuses time series model parameters with information on features through cross-stitch units and obtains anomaly scores for each time stamp, so as to determine whether the data is anomalous.
An Improved YOLOv5-Based Small Target Detection Method for UAV Aerial Image

With the continuous development of UAV technology and target detection technology, Using UAVs to detect ground targets has become a current research hotspot. However, due to the altitude of the UAV, the size of the ground target in the aerial image of the UAV is small, and the feature information is not obvious, it is very easy to cause missed detection and false detection. Therefore, algorithms with higher precision are needed for UAVs. For this purpose, based on the YOLOv5 target detection algorithm, this paper improves the detection accuracy of small targets optimizing the prior anchor box, adding a small target detection layer, and adjusting the multi-scale feature fusion structure. Finally, experiments results verifies that the proposed method can effectively increase the detection accuracy for small targets to 10.4%.
An Implementation of Lightweight Cryptographic Algorithm for IOT Healthcare System

IoT connects billions of heterogeneous devices, known as Things, using various communication technologies and protocols to enable end users to access a range of smart applications worldwide. It provides technology and delivers a range of smart applications to end users around the world. Majority of the cryptographic algorithms used today were developed for desktop and server contexts, as a result they are not suitable for small devices. Therefore, lightweight encryption algorithms are considered. In the proposed system, the readings from the real time IoT traffic is taken using sensors and are sent to the Raspberry Pi. Encryption is done by four lightweight cryptographic algorithms. The lightweight cryptographic algorithms considered are SPECK, SIMON, CLEFIA and PRESENT. The four lightweight cryptographic algorithms are used to decrypt the encrypted values after they are retrieved from the hardware module via an MQTT broker thus enabling data security in IoT Healthcare application. A comparative analysis is made on the four lightweight cryptographic algorithms and their performance characteristics is analyzed based on various factors such as time and throughput. The results show that the average time taken for encryption/decryption by SPECK algorithm was the least followed by SIMON, CLEFIA and PRESENT. Based on Throughput analysis, PRESENT algorithm has the least throughput followed by CLEFIA, SIMON, SPECK.
GCN with Pattern Affected Matrix in Human Motion Prediction

Human motion prediction is a challenging and meaningful task in many fields. Recent work has shown that the Graphical Convolutional Network (GCN) based model is very effective for this task. However, a simple GCN cannot perfectly model dynamic information with different inputs. In addition, the blank adjacency matrix in GCN has an omnidirectional search space, making it difficult to converge. Different from existing approaches, this paper proposes a new GCN module, called Graph Convolution Module with Pattern affected Adjacency Matrix (GCN-PAAM), to enhance the adaptability of GCN to different inputs and improve the learning ability of adjacency matrix. We have applied this module to different advanced GCN based models. Experimental results on H3.6m and 3DPW datasets have shown that our module can improve the experimental accuracy of the model by 1% to 9% to varying degrees, achieving state-of-the-art performance.
WSRR: Weighted Rank-Relevance Sampling for Dense Text Retrieval

As in many other domains based in the contrastive learning paradigm, negative sampling is seen as a particular sensitive problem for appropriately training dense text retrieval models. For most cases, it is accepted that the existing techniques often suffer from the problem of uninformative or false negatives, which reduces the computational effectiveness of the learning phase and even reduces the probability of convergence of the whole process. Upon these limitations, in this paper we present a new approach for dense text retrieval (termed WRRS: Weighted Rank-Relevance Sampling) that addresses the limitations of current negative sampling strategies. WRRS assigns probabilities to negative samples based on their relevance scores and ranks, which consistently leads to improvements in retrieval performance. Under this perspective, WRRS offers a solution to uninformative or false negatives in traditional negative sampling techniques, which is seen as a valuable contribution to the field. Our empirical evaluation was carried out against the AR2 baseline on two well known datasets (NQ and MS Doc), pointing for consistent improvements over the SOTA performance.
Strategies of Indefiniteness Marking in Central Sicilian—Evidence from the Dialect of Delia

This paper is meant as a contribution to the research project on variation and optionality in the determiner system in Italo-Romance, with novel data from the Sicilian dialect of Delia. The study is based on fieldwork interviews and the construction of a small corpus of 850 observations by 24 native speakers of Deliano (mean age: 36.37; age range: 19–72). The participants were asked to (i) describe in Deliano a 3 min videotape of an Italian speaking woman during her shopping session at a supermarket and (ii) talk about their own shopping routines in Deliano. These activities were designed mainly to detect the following strategies to express indefiniteness: (i) ART, i.e., a definite article with an indefinite interpretation; (ii) ZERO, a zero determiner for bare nouns; (iii) DI+ART, the so-called “partitive article”; (iv) pseudo-partitives such as ‘a bit of’; (v) the grammaticalised cardinal ‘two’; and (vi) the grammaticalised cardinal ‘four’. The data confirm (i) the preference for ZERO in negative episodic sentences in the past; (ii) the general lack of bare DI and DI+ART, and of certo ‘certain’ used as an indefinite; (iii) the use of different specialised forms of pseudo-partitive ‘a bit of’ in older speakers of Deliano; (iv) the neutralisation of this pseudo-partitive specialisation and the consequent emergence of some true optionality in younger speakers.
An Exponential Autoregressive Time Series Model for Complex Data

In this paper, an exponential autoregressive model for complex time series data is presented. As for estimating the parameters of this nonlinear model, a three-step procedure based on quantile methods is proposed. This quantile-based estimation technique has the benefit of being more robust compared to least/absolute squares. The performance of the introduced exponential autoregressive model is evaluated by means of four established goodness-of-fit criteria. The practical utility of the novel time series model is showcased through a comparative analysis involving simulation studies and real-world data illustrations.
Structured matrix recovery from matrix‐vector products

Abstract Can one recover a matrix efficiently from only matrix‐vector products? If so, how many are needed? This article describes algorithms to recover matrices with known structures, such as tridiagonal, Toeplitz, Toeplitz‐like, and hierarchical low‐rank, from matrix‐vector products. In particular, we derive a randomized algorithm for recovering an unknown hierarchical low‐rank matrix from only matrix‐vector products with high probability, where is the rank of the off‐diagonal blocks, and is a small oversampling parameter. We do this by carefully constructing randomized input vectors for our matrix‐vector products that exploit the hierarchical structure of the matrix. While existing algorithms for hierarchical matrix recovery use a recursive “peeling” procedure based on elimination, our approach uses a recursive projection procedure.
Secure Genotype Imputation Using the Hidden Markov Model with Homomorphic Encryption

Genotype imputation is a statistical technique used to infer missing genotypes using a haplotype reference panel. This is typically computationally intensive so it is outsourced to a cloud. However, there is a privacy risk in outsourcing genomic data to the cloud. We solved this problem by using homomorphic encryption in developing a genotype imputation application using the hidden Markov model (HMM). Our solution performs the training of the HMM parameters locally using the Baum-Welch algorithm and then outsourcing the inference step to the cloud using the Viterbi algorithm. It is the Viterbi algorithm that we implemented homomorphically. We have implemented our solution using EVA, which compiles our Python program into a format that can be compiled with the SEAL homomorphic encryption library.
Computer-Aided Development of Adaptive Learning Games

We discuss the modern trends in educational software, technologies and tools for its development. The features and problems of developing adaptive learning games as a new category of educational software, models, methods and technologies used for its development are considered. An approach to modeling learning process as the dynamics of the learner’s state in the knowledge space, and the calculus of state in this space is described. An adaptation model is proposed based on dynamic content matching of non-linear learning and game scenarios, preserving the logic of the learning process in a game context. The design and technological procedures for the automated development of adaptive learning games are determined. A portable adaptation module embeddable in the learning game project at the stage of its implementation is designed and implemented, which makes it possible to reduce the complexity of developing adaptive learning games and improve their quality in terms of the effectiveness of learning in a game context. The development of adaptive learning game using proposed model and tools is shown and the laboriousness of the development process is estimated. The aim of the work is to reduce the complexity of developing adaptive learning games by automating the design process and using ready-made design and software solutions.
Design Thinking (DT) and User Experience (UX) as Springboard to Teacher-Made Mobile Applications

This paper describes the experience of nine in-service basic education teachers in learning how to design a mobile app using concepts of DT and UX as springboards. The teachers were enrolled in a 36-session training program that is sectioned in three parts: introduction, active, and application phases. Based on their accounts, the teachers were able to design a reading practice mobile app that is interactive and intuitive by incorporating the elements such as arrows, buttons, audio translation, and easy to find icons with specific function. Using DT and UX as springboards, they were able to solve designing problems, collaborate with evaluators and possible targets, and to use creativity in making user friendly human-computer interactions. The research recommends conducting continuing education programs on mobile apps development to empower local developers on customizing learning apps based on their educational-technological landscape.
Optimizing Image and Signal Processing Through the Application of Various Filtering Techniques: A Comparative Study

This study aimed to explore different filtering techniques in order to showcase the performance of the filters and improve image and signal detection accuracy. The filters were evaluated using performance metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). These performance metrics were supported by a confidence interval assessment to statistically validate the results. The results demonstrated that the median filter for smoothing, Butterworth filter for sharpening, and Inverse filters for restoration showed promising results. In the case of EEG signals, the Least Mean Square (LMS) filter for adaptive filtering was found to be the best choice, while the Notch filter for Infinite Impulse Response (IIR) and the Coiflets3 for Wavelet Transform outperformed other filters. Therefore, these results serve as a reference guide for determining which filters should be applied for denoising image and signal datasets. Additionally, it is worth noting that when selecting a filter, practitioners should carefully consider the characteristics of the filter in relation to the type of noise, resolution, and lighting conditions.
Social Media to Develop Students’ Creative Writing Performance

After a two-year suspension, the Philippine Department of Education resumed limited face-to-face (ftf) classes in May 2022, replacing modular distance learning. Nonetheless, the threat of COVID-19 transmission and the infrastructure damage caused by Typhoon Rai compelled us to transition to a blended learning mode of instruction. This mode of instruction paved the way for the incorporation of social media into education and displayed encouraging results for fostering student engagement. According to the results of the pre-test, students performed poorly in creative writing. To address the issue, we used Instagram as an intervention to improve students’ writing performance. During the implementation of blended learning, the teacher taught the concepts in ftf modality for three days while students wrote their works on Instagram online. This study examined the impact of using Instagram as an e-portfolio on the creative writing performance of students. The t-test results demonstrated a significant mean gain in their pretest-posttest performance after using Instagram. Social media enhanced their creative writing skills, grammar, vocabulary, self-awareness, and motivation, according to qualitative research. Students indicated that Instagram’s self-editing capabilities enhance metacognition and self-development via real-time comments and feedback.
Blockchain-Enhanced Labor Protection: An Innovative Complaint Platform for Transparent Workplace Compliance and Fair Competition

Information systems for labor protection play a crucial role in managing and maintaining workplace safety and health. These digital platforms aim to minimize risks, promote safe work practices, and ensure compliance with relevant laws and regulations. Despite the implementation of such systems, countries still face issues related to unfair competition due to incomplete recording of employee work data. Employers may underreport work hours, wages, or workforce size, evading taxes, social security contributions, or compliance with labor regulations, creating an uneven competitive landscape. In this paper, we propose a blockchain-based complaint platform that leverages real-time data and operates in conjunction with existing labor protection information systems. This innovative solution aims to address the problem of undeclared or underreported work by allowing for anonymous and transparent complaint submissions. By integrating blockchain technology with existing labor protection systems, the proposed platform ensures the validity of complaints in real time while protecting employee interests and promoting fair competition among businesses.
Ontological Model of Knowledge Representation for Assessing the City Visual Environment Quality

Visual ecology is one of the new interdisciplinary directions, the development of which is necessary for the formation of the principles of modern urban planning. This is a section of sensory ecology that is responsible for the quality of the human environment, perhaps no less than clean air. The article examines the current state of issues related to the analysis of the quality of the visual environment of the city. The factors influencing visual ecology are considered: the monotony of the surrounding space, increased visual noise level, color harmony, etc. And a system of criteria for the formation of a knowledge model for this subject area is proposed. Ontology is chosen as a formal model of knowledge representation, which provides integration of heterogeneous information and the possibility of automating its processing. The “Protege” editor was used to implement the ontology. A technology based on SWRL rules was used to formalize the rules.
Augmented Reality for Enhancing Linguistic Skills of International Students in Preschool Education

The utilization of new technologies in education is essential for providing an effective and of high quality learning environment. The preschool education is considered quite demanding, especially with the increasing number of multilingual and multi-ethnic families in European countries. This fact leads to constantly find new teaching methods and ways of learning that deviate from traditional ones, in order to promote the active learning and innovative teaching. As such, this study presents the development of an Augmented Reality (AR) application, using the Quiver tool, to enrich vocabulary acquisition of international students who attend Greek Kindergartens and are taught Greek as a second / foreign language. For the needs of this study, qualitative research method – and specifically, case study and observation – was used to investigate the impact of the developed AR application in oral communication of foreign preschool students. The results show that the use of AR has a positive impact in vocabulary and linguistic skills acquisition, fluency in oral communication, motivation, commitment to the learning process and satisfaction.
Kripto Para Merkezli Kara Para Aklama Senaryolarının Adli Bilişim Açısından İncelenmesi

One of the biggest innovations brought by the digitalized world is undoubtedly the invention of crypto money, which is decentralized, anonymous and complex, and connected to blockchain technology. This relatively new technology has attracted the attention of many people with its revolutionary changes in payment systems and great price movements in the market, as well as the economic balances it has changed around the world. In addition to this interest, it also attracted the attention of crime and crime organizations in a short time, and over time it turned into a tool used by illegal organizations such as laundering the proceeds of crime. Although this structure was initially exposed to the reaction of some states at the level of nation states, on the other hand, it managed to get the support of many states. However, although the spread of blockchain-based money laundering methods is an undeniable problem for all states, a significant cooperation has not been achieved by international collaborations and organizations to prevent this situation. On the other hand, it is of great importance for law enforcement and forensic analysts to clarify this situation and to fight against these structures in order to protect national interests. In this study; In this study, an approach that will detect money laundering is tried to be presented through sample scenarios by bringing a broad perspective to crypto money-based money laundering methods, which are very difficult to trace due to their nature. In addition, it is expected that the difficulties in implementation of the proposed approach will be clearly addressed and will shed light and inspire further study.
Development of a Module for Generating Function Header Tasks Through the Analysis of Textual Comments

The relevance of this research topic stems from the efficacy of utilizing simulators to create function headers during programming training, as well as the labor-intensive nature of generating tasks for these simulators when dealing with extensive volumes of available commented program code. Analyzing commented program code enables the extraction of valuable information and data, which serve as the foundation for automating the generation of such tasks. This necessitates the development of suitable approaches, methods, and algorithms for program code analysis and the subsequent generation of tasks based on the acquired data. The development of a generator of function headers creation tasks aims to address the challenges of complexity and labor intensity associated with manual task creation.
E-Health Cloud Based Systems: A Survey on Security Challenges and Solutions

Cloud based healthcare systems have transformed the healthcare sector in many ways. It has several advantages like scalability of the required service, flexibility, and resource sharing. However, the centralization of data on the cloud increases many security and privacy issues. Several surveys exist in the field; however, they do not do not deal with decentralized and hybrid eHealth cloud. This paper presents survey in the field of cloud-based e-Health security systems, including a comparison between the various approches (i.e., centralized, decentralized and hybrid). For this purpose, we start by defining the main concepts and common security issues in and summarizing the existing surveys that deal with e-Health cloud-based security. Then, we provide a state-of-the-art toward secure and privacy-preserving for cloud-based e-Health systems. Finally, we highlight open research challenges and possible future research directions.