Reviewerly/exHarmony huggingface dataset:
An Extensive Study of Digital Image Steganography Techniques

The rise of digital technology raises the importance of data security when transmitting data across a wireless communication medium. Steganography is a method of ensuring information security on a digital media. The goal of this work is to look at information hiding techniques in images which are in the spatial domain. This paper will also distinguish between watermarking and steganography, the two most popular domains of information concealment. The paper examines the benefits and drawbacks of existing algorithms, tools, and strategies to safeguard data in the digital image steganography domain.
Self-attention Based Cross-Level Fusion Network for Camouflaged Object Detection

Although CNN-based camouflaged object detection(COD) has made great progress in recent years, their prediction maps usually contain incomplete detail information due to the similarity between the camouflaged object and the background. To alleviate this, a CNN-based framework named SACF-Net is designed for COD via cross-level fusion that facilitates the detection of camouflaged object detail. On the one hand, the low-level features contain abundant edge detail information to distinguish the camouflaged object from the background. On the other hand, the Polarized Self-Attention(PSA) mechanism is introduced to refine high-level features that contain extensive semantic information to enhance inner details and performance. Finally, cross-level complementarity fusion is performed progressively to generate prediction maps in a top-down manner. Extensive experiments on four COD datasets exhibit that the proposed method is better than the state-of-the-art methods.
Research on Lightweight Blockchain Technology Based on Edge Computing

With the continuous development of Internet of Things technology, It faces many security and trustworthiness issues. Blockchain technology has the characteristics of secure communication, tamper-proof, multi-party consensus and so on. Much research focused on the integration of blockchain and IoT. However, the traditional blockchain for IoT is based on cloud computing architecture, which has shortcomings such as prolonged communication time, slow blockchain processing speed, and difficulty in real-time uploading of massive IoT data to the blockchain. On the other hand, the traditional edge computing-based blockchain for IoT system only collects IoT data on the edge side, and the actual blockchain interaction work is still implemented in the cloud. This causes problems of data loss and high pressure on the central server. Against these problems, this paper proposes a lightweight and high-performance blockchain dedicated to IoT scenarios: Fast and Lightweight Blockchain for IoT. The proposed lightweight blockchain improves the speed of storage of IoT data. Experiments indicate the Transaction Per Second of FLBC-IoT far exceeds that of traditional blockchains such as Ethereum and Hyperledger Fabric. The proposed lightweight blockchain is suitable for IoT system based on edge computing.
Prevalence of Malaria among Febrile Patients Attending Government Hospitals in Ondo State, South-West Nigeria

Background: The use of malaria infection prevalence among febrile patients is a valuable epidemiological surveillance tool. Methods: In this study, a cross sectional study was conducted among febrile patients in selected government Hospitals in Ondo State for malaria prevalence. Results: Plasmodium falciparum is the only encountered malaria parasite with prevalence values of 82.72% (426/515) and 80.19% (413/515) obtained using microscopy and RDTs respectively. The prevalence of malaria among the males (86.59%) was higher than the females (80.65%), all age groups in this study were vulnerable with highest infection rate of 89.66% among age group 11-20 years. The parasites densities ranged between 209 and 22310 parasites/μL with a mean parasitaemia of 5522.17±183.30 parasites/μL. The prevalence of malaria among the febrile participants that have used antimalarial drug before visiting the hospital is 82.94% (389/469) with the mean parasitaemia of 4615.21±188.14 parasites/μL while those that have used herbs before visiting the hospital had malaria prevalence of 85.03% (142/167) with the mean parasitaemia of 4913.81±330.20 parasites/μL. Conclusion: There was high prevalence of malaria among febrile patients. This finding will help improve the diagnosis and treatment of other febrile (non-malaria) infections, limit antimalarial usage to only malaria parasite-based test true positives and serve as a guide to combat malaria drug resistance in the study area.
Application of multi adaptive particle swarm optimization in robot path planning

Standard particle swarm optimization is easy to fall into local optimization, premature and poor search effect in complex environment, which limits its development in robot path planning. A multi adaptive particle swarm optimization is proposed. The concept of particle evolution degree is proposed. The particle adaptively selects the parameters that meet its needs according to its own evolution state. The adaptive strategy of population number and the concept of population aggregation degree are integrated, and the population number is dynamically adjusted according to the aggregation degree in the process of algorithm optimization. Cubic spline interpolation and improved algorithm are combined to solve the path planning problem to get a smoother robot moving path. Compared with the standard particle swarm optimization and adaptive particle swarm optimization, the multi adaptive particle swarm optimization in the same environment is obviously better than other algorithms in robot path planning.
BxTB: cross-chain exchanges of bitcoins for all Bitcoin wrapped tokens

While it is possible to exchange tokens whose smart contracts are on the same blockchain, cross-exchanging bitcoins for a Bitcoin wrapped token is still cumbersome. In particular, current methods of exchange are still custodial and perform privacy-threatening controls on the users in order to operate. To solve this problem we present BxTB: cross-chain exchanges of bitcoins for any Bitcoin wrapped tokens. BxTB lets users achieve that by bypassing the mint-and-burn paradigm of current wrapped tokens and cross-exchanging already minted tokens in a P2P way. Instead of relaying on HTLCs and the overhead of communication and slowness due to time-locks, we leverage Stateless SPVs, i.e. proof-of-inclusion of transactions in the Bitcoin chain validated through a smart contract deployed on the other blockchain. Furthermore, since this primitive has not been introduced in the academic literature yet, we formally introduce it and we prove its security.
Potentials of reinforcement learning in contemporary scenarios

This paper reviews the present applications of reinforcement learning in five major spheres including mobile autonomy, industrial autonomy, finance and trading, and gaming. The application of reinforcement learning in real time cannot be overstated, it encompasses areas far beyond the scope of this paper, including but not limited to medicine, health care, natural language processing, robotics and e-commerce. Contemporary reinforcement learning research teams have made remarkable progress in games and comparatively less in the medical field. Most recent implementations of reinforcement learning are focused on model-free learning algorithms as they are relatively easier to implement. This paper seeks to present model-based reinforcement learning notions, and articulate how model-based learning can be efficient in contemporary scenarios. Model based reinforcement learning is a fundamental approach to sequential decision making, it refers to learning optimal behavior indirectly by learning a model of the environment, from taking actions and observing the outcomes that include the subsequent sate and the instant reward. Many other spheres of reinforcement learning have a connection to model-based reinforcement learning. The findings of this paper could have both academic and industrial ramifications, enabling individual.
Divergenze parallele. Valentino Dell’Antonio e Luigi Canori, pionieri della moderna letteratura ladina

Based on recently published works, this paper draws a comparison between the human and intellectual life of Valentino Dell’ANTONIO Monech and that of Er-manno ZANONER Gabana, beginning with the years of the poetic-literary com-munity that emerged in the post-war period and ending with the divergences that arose in the early 1960s around the “spelling question”. The analysis of the texts available today, supported by a series of still unpublished documents, provides an insight into the dynamics that distinguished the cultural development of the Ladin community in the second half of the 20th century. The result is an inter-pretation that allows us to fully understand and appreciate the importance of the contributions of two authors who both shared the commitment to elevate their mother tongue to the status of “literary language”, and who – despite their differences – contributed decisively to the rebirth of the Ladin consciousness in Val di Fassa.
Development of the CPM Device for Ankle Joints

Living in a bed and a wheelchair on daily basis reduces muscle strength, causing decreases in various mental and physical functions. Such functional deteriorations are collectively called the disuse syndrome. In order to overcome the disuse syndrome, to move joints and muscles is very important. Ankle contracture is one of the disuse syndromes of the lower limbs. CPM devices can often prevent/improve the symptom. But the distributed devices cannot dorsiflex ankles alternately with strong power. We then develop the simple CPM device which has small size, high power, and ankles' interlocking system. The structure and evaluation are described in this paper.
Behavioural Intervention Technology in UX Design: Conceptual Review, Synthesis, and Research Direction

The notion of Behavioural Intervention Technologies (BITs) has received increasing attention from researchers and practitioners creating design and technology to support users’ behavioural change processes. However, the value of consolidating previous findings to connect insightful research and practical design works and generating new knowledge on BITs has been under-recognized in the design community. To support researchers and practitioners who struggle with creating human-centred BITs to effectively induce changes in human behaviour, this paper investigates existing literature on BITs through a systematic literature review with 28 selected publications. From this literature review process, this paper proposes a structured synthesis of extant perspectives and approaches, offering an overarching perspective for effective applications of the BIT design approach. At the end of the paper, these findings are extended to discuss further research and implementations of design practices for changes in human behaviours.
Accurate and Time-saving Deepfake Detection in Multi-face Scenarios Using Combined Features

There has been an increasing interest in Deepfake detection because of the hidden risks that Deepfake technology poses for social privacy and security. Nowadays, many models achieve impressive performance on existing public benchmarks. However, the majority of existing methods are restricted to single-face scenarios. In this paper, we propose a model that can perform accurate and time-saving Deepfake detection in multi-face scenarios. We fuse different levels of features to improve the performance of the model and use single-face data to aid the training of the multi-face data. Our apporach achieves the state-of-the-art performance in multi-face scenarios and comprehensible experiments have been conducted to demonstrate the soundness and validity of our model.
Solving the problem of reading culture on the examples of different countries

The article deals with the problem of reading culture in the information society. Examples of solving this problem in different countries are given. Recommendations are given to improve the level of reading culture. The essence of the project is that the younger generation would have the opportunity to choose the desired works, then follow the questions on the Antolin system for a specific work. A student who reads additional literature has a culture of reading, has analytical thinking, is able to find ways to solve various problems, develops harmoniously. Thus, we can conclude that the problem of reading culture occupies an important place not only in our country, but also in other, more developed and developing ones.
Designing emotions for healthcare chatbots: A text-based or icon-based approach? (Preprint)

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Full Head Performance Capture Using Multi-scale Mesh Propagation

We present a template fitting based method for efficient markerless full head performance capture. Our method starts with high-resolution multi-view videos and efficiently outputs high-fidelity full head mesh sequences with the same topology of a common template model. A GPU accelerated stereo reconstruction firstly computes a high-quality point cloud at each frame. Then the template model is warped and fitted to the reconstructed geometries using a combination of detected landmarks constraint, nearest neighbor constraint, and volumetric regularization. Additionally, we reconstruct the detailed ear structures at the initialization frame and track the movement with a global rigid transformation assumption in the following frames. To solve the error accumulation problem when dealing with long sequences, the method updates the positions of mesh vertices using priors from the previous and initial frames in a coarse to fine manner. Summing up the above technical innovations, our method can significantly reduce the whole processing time for reconstructing topology consistent full head meshes with fine details. We conduct several experiments and demonstrate the efficiency and out-performance of the proposed method compared to previous methods.
Human Motion Segmentation via Velocity-Sensitive Dual-Side Auto-Encoder

Human motion segmentation (HMS) aims to segment a long human action video into a bunch of short and meaningful action clips. Existing supervised learning approaches need a large amount of training data which may be costly in real-world scenario, while most unsupervised clustering methods cannot fully explore the temporal correlations among human motions and hard to achieve promising performances. In our paper, we design a novel unsupervised framework, called Velocity-Sensitive Dual-Side Auto-Encoder (VSDA), for HMS task. Specifically, a multi-neighbor auto-encoder (MNA) is proposed to extract informative temporal features, which fully explores the local temporal patterns of human motions. In addition, a long-short distance encoding (LSE) strategy is designed. It constrains the encoded representations of close (short-distance) frames becoming similar while the representations of far-away (long-distance) frames becoming distinctive. Similarly, this strategy is also deployed on the decoded outputs as the long-short distance decoding (LSD) module. The LSE/LSD guides the learning process explicitly and implicitly to achieve the dual-side structure. Moreover, we consider the energy variations during the human motion to propose the velocity-sensitive (VS) guidance mechanism for further model improvement. VSDA leverages the temporal characteristics of human motion and derives promising HMS performance. Comprehensive experiments on six real-world human motion datasets illustrate the effectiveness of our proposed model.
Document Modeling

Modeling is the critical process at the heart of every application development. We could easily dedicate a whole book to this topic, so devoting just one chapter to this topic inevitably leads to many omissions. However, our goal here is to give you a brief introduction to the process of modeling and factors affecting it and to explain a document-based approach.
Construction of Educational Management Software Design Pattern Based on Deep Neural Network

With the popularization of information technology and network in schools and education management departments, the whole school management model based on campus network has become the mainstream of the development of education management information system. Relying on the theoretical basis of the information management system MS, the system application platform designed and developed for the teaching management of colleges and universities can assist the staff to efficiently process various kinds of information in the school and provide information services for the teachers and students of the whole school. Deep learning under steam education means that students, under the guidance of teachers, actively, joyfully and interactively participate in teaching activities around challenging, integrated and ideological topics, realize the understanding, connection and transfer of knowledge on the basis of original experience, and apply it to problem solving in real situations, so as to finally form a critical and creative high-level thinking ability. In view of the shortcomings of the traditional course push model algorithm, this paper combines the multi-layer neuron model algorithm with the personalized recommendation algorithm, and verifies the course data.
Automated subject indexing using word embeddings and controlled vocabularies: a comparative study

Text mining methods contribute significantly to the understanding and the management of digital content, increasing the potential of entry links. This paper introduces a method for subject analysis combining topic modelling and automated labelling of the generated topics exploiting terms from existing knowledge organisation systems. A testbed was developed in which the Latent Dirichlet Allocation (LDA) algorithm was deployed for modelling the topics of a corpus of papers related to the Digital Library Evaluation domain. The generated topics were represented in the form of bags-of-words word embeddings and were utilised for retrieving terms from the EuroVoc Thesaurus and the Computer Science Ontology (CSO). The results of this study show that the domain of DL can be described with different vocabularies, but during the process of automatic labelling the context needs to be taken into account.
Combining Montgomery Multiplication with Tag Tracing for the Pollard Rho Algorithm in Prime Order Fields

In this short paper we show how to apply Montgomery multiplication to the tag tracing variant of the Pollard rho algorithm applied to prime order fields. This combines the advantages of tag tracing with those of Montgomery multiplication. In particular, compared to the previous version of tag tracing, the use of Montgomery multiplication entirely eliminates costly modular reductions and replaces these with much more efficient divisions by a suitable power of two.
Title: Gentle Protest – Contemplations on an Interactive Tableau

Gentle Protest Is an Environmentally Sensitive Interactive Textile Design Created With the Rationales of Critical Design Practice. Addressing the Conference Theme [CRITICAL] DESIGN MODES, It Attempts to Explore the Co-Creatorship Between the Designer and Audiences Afforded By Interactive Textiles With the Hybridised Mode of Designing and Creating. Through the Encouraged Human-Artefact Engagement and Collective Meaning Signification, Gentle Protest Is Expected to Stimulate Introspection and Unfold Discussion On the Context Behind the Created Piece. The design practice intersects the ideas of interactivity, metaphoric connotation expression, aesthetics of use, engagement uncertainty, potential ambiguity, and textile craftsmanship to create a dynamic interactive tableau. Embedded with thermochromism and photoluminescence, the designed object responses to external stimuli and performs temporary visual transformation. Equipped with the corresponding engagement aids, audiences are offered the role of co-creator with autonomous control of the tableau surface appearance to respond to the meaning perceived from the work and the interaction.
A Knowledge Graph Based Approach to Operational Coordination Recognition in Wargame

Recognizing military coordination relationships among adversarial operations is essential in task planning and decision making. Due to the complexity of modern informationized war, it is challenging to analyze the diverse relations between entities in the war situation. In this study, we propose a novel framework based on knowledge graph to predict operational coordinations. We first construct a novel large scale knowledge graph that consits of 29313 nodes and 191542 edges from Wargame Competition dataset. The embedding method jointly considers information from node attributes, local situations and global structure, and then combine the three parts with a self-attention mechanism. Experiments compared with baselines demonstrate that the proposed model is more accurate and robust than existing methods.
Fine-grained sentiment classification based on semantic extension of target word

The current existing fine-grained sentiment analysis method usually extract the context of the sentence while ignoring the semantic representation of the target words. We extent the target words in comments as the additional input parameters to the deep learning model in this paper. And the influence of the number of extended words on the model's performance is also discussed thoroughly during the experimenting process. Main procedures of our proposed fine-grained sentiment classification method can be described as: 1) firstly, target words are expanded by using the semantic distance of the word embedding, which used as the key information; 2) bidirectional LSTM neural network is used to extract the semantic information afterwards; 3) additionally, the attention mechanism is employed to learn the sentiment weight distribution of the target words among the text automatically. The experiments conducted on the SemEval 2014 Task 4 corpus showed that the proposed method outperforms the other LSTM model.
EXTRACURRICULAR EDUCATIONAL ACTIVITIES: WORK EXPERIENCE IN REGIONAL UNIVERSITIES

The processes taking place in the socio-cultural, political and economic spheres of society required the implementation of transformations in the educational sphere. The higher education system pays attention not only to educational, but also to educational work. There are various types and forms of educational work aimed at the development of self-organization of the student, self-improvement of personal qualities. Conducting extracurricular activities contributes to the actualization of the acquired knowledge, the expansion of ideas about the culture of your region.