Reviewerly/exHarmony huggingface dataset:
Identity2Vec: learning mesoscopic structural identity representations via Poisson probability metric

Structural properties in a network have mostly been defined in terms of their microscopic level and macroscopic level. Many existing studies focus mainly on learning representations for vertices in similar local proximity such as the direct neighborhood (microscopic level), while some other studies describe the global network connectivity and characteristics (macroscopic level). It is, however, important to understand the substructures between the vertex and network levels so as to capture the structural identities exhibited by vertices. In this paper, we present a novel and flexible approach for learning representations for structural identities of vertices in a network, such that set of vertices with similar structural identity are mapped closely in the low-latent embedding space. Our approach applies the Poisson distribution-based measure and a divergence estimation score to capture nodes’ structural similarities at k-hop proximity via a guided walk. Then, using the Skipgram model, we learn embeddings for nodes with similar structural identity captured in the walk. Experiments with real-life datasets validate our approach, as well as gave superior performance when compared with existing models on link prediction, node classification, and learning-to-rank tasks.
Sign Language Detection using CNN

Hand signs are a viable type of human-to-human correspondence that has various potential applications. Being a characteristic method for collaboration, they are generally utilized for correspondence purposes by discourse impeded individuals around the world. As a matter of fact, around one percent of the Indian populace has a place with this class. This is the key motivation behind why it would meaningfully affect these people to integrate a structure that would figure out Indian Gesture based communication. In this paper, we present a method that utilizes the Pack of Visual Words model (BOVW) to perceive Indian communication via gestures letter sets (A-Z) and digits (0-9) in a live video transfer and result the anticipated marks as text as well as discourse. Division is done in view of skin tone as well as foundation deduction. In this paper we are going to use convolutional neural network for sign language detection. The convolutional neural network extracts the extraction and classification of sign category. We get 98.45% accuracy for 100 epochs.
FACE EMOTION RECOGNITION USING CNN MODEL

This project involves the design and implementation of face emotion recognition system using convolutional neural network models. Face Emotion Recognition plays an important role in the fields like computer vision and artificial intelligence to its significant academic and commercial potential.The work focuses on studies on facial expression because expressions are one of the important channel in interpersonal communication. KEYWORDS: CNN, Deep Learning, Face expression, Image processing, LBP.
LDIA: Label distribution inference attack against federated learning in edge computing

With the popularity of IoT (Internet of Things) applications, edge computing has received lots of attention. To meet data privacy protection requirements of edge nodes and cope with their unbalanced data distribution, federated learning (FL), a distributed learning framework, is widely used in intelligent edge computing applications. However, recent studies have shown that FL still suffers from privacy leakage problems, including membership inference, data reconstruction, etc. However, these studies mainly focus on the feature information of private data. In this paper, we concern the user-level label privacy in FL. We propose LDIA, a label distribution inference attack against FL in edge computing, exploring the possibility that an honest but curious cloud server can infer the proportions of samples per label in the edge user’s private data. LDIA is inspired by the observation that parameter changes in the output layer of a model can reflect the label distribution of training data. We use a neural network to learn individual features of the output layer updates over different label distributions, and then perform inference from local models uploaded by users. Our comprehensive evaluation shows that LDIA is effective on various datasets in different settings, demonstrating the severe privacy leakage in FL-based edge computing.
On the Methodological Flaws in the Scientific Study of the Industrial Internet of Things

This work is debatable and is devoted to revealing the methodological flaws of scientific research, often observed in Russian-language scientific periodicals. With the conclusion of an effective contract, the teaching staff of universities is artificially stimulated to scientific and publishing activities, which increases the relevance of the study of mental actions performed by young scientists to obtain new scientific knowledge. In this context, the question arises as to whether the researcher obtained new scientific knowledge as a result of committed methodological errors, committed research negligence. Using the example of a single article devoted to the application of a systematic approach to the Industrial Internet of Things, the author highlights a number of methodological flaws and reveals the importance of the depth of the literature review as a key characteristic of the accumulation of basic knowledge. The author also emphasizes the importance of a clear definition of the subject and object of the study, and recommends describing the research methodology not only in a single paragraph, but more specifically in relation to each stage and section of the study. In order to reveal the methodological flaws of scientific research, the author has to return to the definition of scientific methodology, system analysis and the industrial Internet of things. Using the example of a specific work, the author emphasizes the need for a strict and clear application of general scientific research methods, as well as the importance of a depth review of the literature, which should not reflect the subjective ignorance of the scientist. The author also emphasizes the danger of changing the direction of scientific research regarding the specialty of higher education of a young scientist, which can lead to confusion of subjective and objective ignorance due to a superficial review of the literature
Solar Energy Based Object Detection and Automatic Speed Reducing Intelligence Robot Using Sensors

The average human life span has decreased since ancient times. Because the number of automobiles on the road is growing every day, the death rate from accidents has dramatically grown. Because brake failure causes so many accidents, we can lessen their impact when the brake is controlled automatically. A receiver and an emitter make up an ultrasonic setup that is put up in front of the vehicle. When an obstacle is recognized, the ultrasonic waves are always emitted by the ultrasonic emitter and are reflected back to the receiver, which then picks up the signal. The Arduino controller receives the signal from the reflected wave, and depending on the object's distance, it activates the buzzer and stops the car with LED indicators. motor used to move the vehicle.
Industrial Disaster Control Systems in Unmanned Aerial Vehicles

The frequency of catastrophes is rising. Whether a disaster is caused by anything man-made or something natural, the effects are always serious and will take a long time to resolve with human strength alone. To cut down on time and human exposure, robotic technologies are being used more and more in search and rescue operations. It has suffered from a lack of environmental adaptability.Every robot is built differently in order to carry out the tasks assigned to it more effectively and efficiently. For continuous industry monitoring without human interaction, our concept suggests a hanging robot. With the use of forward and backward motors, a hung robot may travel along a rope path from one location to another.
Development of Higher Education Institutions and Online Communication with Students in Modern Conditions

The conditions that have developed in the educational sphere since the beginning of the Russian military aggression in Ukraine have significantly complicated the implementation of the educational process, the establishment of professional communication – from contacts with the immediate environment to global and intercultural ties. The stress burden experienced by both teachers and students includes the forced relocation of participants of the educational process to other regions, the participation of teachers and students in hostilities, territorial defence units and volunteer movements, the loss of relatives and friends, etc. In such conditions, teachers in higher educational institutions should react quickly and take corrective measures in order to restore professional and pedagogical communication. The purpose of the academic paper is to characterize, clarify the advantages, disadvantages and determine the features of certain types of online communication of higher educational institutions with students in modern conditions. In the course of the present research, a number of general and special methods of analysis of scientific sources were applied, as well as an online survey was used to study the standpoint of participants of the educational process, namely, students and teachers of higher educational institutions, regarding the features of online communication of higher education institutions with students in current conditions. Based on the results of the research, the main characteristics of the communication processes taking place in the online mode in higher education were studied, and the standpoint of the participants of the educational process regarding the priority components of the organization of the educational online system was clarified.
Literatur im Unterricht. Zur Vereinfachung von französischen Originaltexten in der Reihe Easy Readers

This paper analyses the strategies of simplification applied to eight original texts of French short prose in the Easy Readers series. The aim is to clarify whether the simplifications are limited to the obvious complexity on the linguistic surface or whether they can also reduce the three essential factors of semantic and discourse-traditional complexity according to Meier (2020), namely deviations of norms and traditions, context-dependent implicity and demands on the readers’ knowledge. Furthermore, an evaluation of the simplified versions is made on the basis of the analysis. This considers whether the adaptations are sufficiently effective to offer L2 learners a more fluent and motivating reading of French literature. On the other hand, it is also revealed that the supposed simplifications can have problematic effects on the meaning. From this, it can be deduced which genres such a form of simplification is suitable for and which it is not suitable for.
Zur Komplexität von Texten. Von der Lesbarkeitsformel zur textlinguistischen Komplexität

This article depicts the evolution of text complexity research from the first readability formulas and their modern computational versions, which include surficial text characteristics such as word and sentence length, to models of text complexity which also consider discourse features and psycholinguistic aspects.In this article, text complexity is defined as a property of a text that emerges from the interaction of different text features and levels that can be measured and observed objectively.From a procedural point of view, complexity can also be defined as the behaviour of a text which is seen as a dynamic system.Here text complexity and text difficulty are regarded as different concepts.Text difficulty is considered a consequence of the interaction of text complexity and extralinguistic parameters, such as the reader or task characteristics (e.g.motivation, cognitive capabilities or type of task).First, readability studies and some readability formulas for English and Spanish will be presented.Then different approaches from text linguistics and psycholinguistics will be discussed.Finally, the Common Core State Standards model of text complexity will be depicted as an example of a model which includes quantitative and qualitative text features as well as reader and task characteristics.
Intellectualized Collection and Analysis of Multimedia Computer Teaching Based on the Fusion of Audio-visual Information

In the post-epidemic era, online classrooms and multimedia computers are increasingly connected. In order to efficiently conduct online teaching and answer questions after class, this paper intelligently collects and analyzes current multimedia computer teaching materials based on the latest audio-visual information fusion algorithm. First, the spatial frequency SF of the online classroom is selected as the input excitation of audio-visual information, and the direction information at the pixel is fixed as the link strength slice relationship of PCNN. Compared with traditional computer teaching, this method has many advantages. Multimedia electronic classrooms are very suitable for computer teaching, and the use of multimedia electronic classrooms can diversify the forms of computer teaching. Then, the source image is decomposed in a two-dimensional experience mode, and the obtained residual images are fused to complete the intelligent collection and analysis of the characteristics of teaching information; the function design is carried out under the framework of learning activities, and the functional upgrade of technical products requires the teaching and learning.
Body Pose Estimation using Deep Learning

Abstract: Healthcare, sports analysis, gaming, and entertain- ment are just some of the many fields that could benefit from solving the challenging issue of real-time human pose detection and recognition in computer vision. Capturing human motion, analysing physical exercise, and giving feedback on performance can all benefit from reliable detection and recognition of body poses. The recent progress in deep learning has made it possible to create real-time systems that can accurately and quickly recognise and identify human poses.
Attribute-Based Storage Supporting Secure Deduplication of Encrypted Data in Cloud

Abstract: At present data duplication will extend in the dispersed stockpiling locales and in view of this duplicate substance the appropriated extra room may diminished. To additionally foster the additional room of the cloud, we need to play out the deduplication on dispersed extra room. In this paper, we are doing Trademark based Encryption (ABE) plot used to help the safeguarded deduplication. Not simply secure deduplication, in this paper we additionally doing get to systems to share the data furtively to the cloud clients. In our proposed structure the deduplication processes done by the private dislike ordinary deduplication plans. From the preliminary outcomes we can show the way that the proposed system can generally chip away at the strong deduplication execution close by secretly data sharing.
Cryptanalysis of a Multiround Image Encryption Algorithm Based on 6D Self-Synchronizing Chaotic Stream Cipher

Recently, a chaotic secure video communication system based on FPGA is proposed, which essentially encrypts the original image data of each frame in the video in multiple rounds by using the 6D self-synchronizing chaotic stream cipher. In this paper, the security performance of the multiround encryption algorithm is analyzed based on divide-and-conquer attack. In the case of one round encryption, the keys [Formula: see text] [Formula: see text] can be deciphered by setting 30 appropriate initial conditions according to known-plaintext attack. After that, according to chosen-ciphertext attack, the chaotic iterative equation of the decryption end is degenerated into a linear one if ciphertexts are all set to zero. Under this condition, the equations between the unknown keys and the plaintext-ciphertext pairs can be obtained by setting 24 appropriate initial conditions, and the keys [Formula: see text] [Formula: see text] can be deciphered by solving the equations in Mathematica. Secondly, in order to reduce the multiplication times of the unknown keys in the case of two-round encryption, the number of pixels in an image can be selected as little as possible. According to chosen-ciphertext attack, when the number of the pixels in an image is only one, the keys [Formula: see text] [Formula: see text] can be deciphered by setting 30 appropriate initial conditions. Then, when the number of pixels in an image is two, the keys [Formula: see text] [Formula: see text] can be deciphered by setting 24 appropriate initial conditions. However, in the case that the encryption round is three or more, the keys cannot be deciphered by the attack method proposed in this paper. The analysis results show that the multiround image encryption algorithm is not secure when the number of encryption rounds is one or two.
Research on the Reform and Innovation of College Students' Career Planning and Employment Guidance Courses in the New Era

The career planning and employment guidance courses in colleges and universities play a crucial role in helping students prepare for their future careers. However, with the rapid changes in the economy and job market, these courses need to be reformed and innovated to better meet the evolving needs of students. This thesis explores the current status of career planning and employment guidance courses in colleges and universities in the new era, identifies the challenges faced by these courses, and proposes strategies for reform and innovation.
A Weakly Supervised Semantic Segmentation Method based on Local Superpixel Transformation

Abstract Weakly supervised semantic segmentation (WSSS) can obtain pseudo-semantic masks through a weaker level of supervised labels, reducing the need for costly pixel-level annotations. However, the general class activation map (CAM)-based pseudo-mask acquisition method suffers from sparse coverage, leading to false positive and false negative regions that reduce accuracy. We propose a WSSS method based on local superpixel transformation that combines superpixel theory and image local information. Our method uses a superpixel local consistency weighted cross-entropy loss to correct erroneous regions and a post-processing method based on the adjacent superpixel affinity matrix (ASAM) to expand false negatives, suppress false positives, and optimize semantic boundaries. Our method achieves 73.4% mIoU on the PASCAL VOC 2012 validation set and 73.9% on the test set, and the ASAM post-processing method is validated on several state-of-the-art methods. If our paper is accepted, our code will be published.