Reviewerly/exHarmony huggingface dataset:
Text Emotion Classification Based on Students' Evaluation

Student evaluation of teaching is a direct evaluation of the teaching process by students. At the same time, teachers can understand the needs of students through the evaluation information, so as to achieve the purpose of "promoting teaching through evaluation" and improve the teaching level. This paper uses natural language processing technology to classify students' teaching evaluation information, so that the information contained in students' evaluation can be effectively used. Firstly, according to the particularity of teaching evaluation data, the text of teaching evaluation is preprocessed; Secondly, text emotion is classified by SVM model; Finally, the analysis results are visualized in the form of cloud chart to present the students' views intuitively and concisely. Teachers can intuitively and concisely understand students' feedback on the teaching process. At the same time, it is also convenient for educational administrators to carry out training on Teachers' deficiencies in teaching work and help teachers improve their teaching level from the perspective of students.
Being Accountable Never Cheats: An Incentive Protocol for DeFi Oracles

Recently emerging Decentralized Finance (DeFi) transforms traditional financial products into trustless and transparent protocols. However, these protocols often require real-time external price information, and in this respect an entity named oracle is proposed-a third party reporting the prices from real-world sources to the blockchain. Unfortunately, current oracle designs lack transparency, compatibility and accountability (e.g., report deviations and operational failures/anomalies). Further-more, they merely include economic rewards without fine-grained financial penalties. To address these problems, we present BANC, the first transparent incentive protocol that aims at accountability and compatibility for DeFi price oracles. BANC requires oracles to transparently declare a manifest such that their obligations can be provably examined by authorized punishers. With a fair dispute-resolution protocol, BANC enables compliant oracles to invoke self-justifications, which can be resolved by a watchtower consortium securely and efficiently. BANC is designed to enhance operational accountability of DeFi oracles, and can be compatible with most DeFi platforms at low cost. We fully implement BANC to evaluate its efficiency.
Maneuvering Target Tracking in Doppler Blind Zone based on the Fusion of Active and Passive Measurements

In the detection range of Doppler radar, once the relative range rate of the target is less than the minimum detectable velocity (MDV), it means that the target enters the Doppler blind zone (DBZ) of the radar, making the problem of target tracking intractable. A maneuvering target tracking algorithm in Doppler blind zone based on the fusion of active and passive measurements (FAPM) is proposed in this paper. A range rate pseudo measurement under the MDV constraint is constructed based on the unscented transform (UT) firstly. After that, the range rate pseudo measurement and the passive measurement from electronic support measures (ESM) are combined for the filter process in the framework of IMM. Simulation results show that the algorithm has high accuracy and short response time for maneuvering target tracking in the DBZ.
Spatio-temporal Super-resolution Network: Enhance Visual Representations for Video Captioning

Video captioning is a sequence-to-sequence task of automatically generating descriptions for given videos. Due to the diversity of video scenes, learning rich representations is critical for video captioning. However, previous works mainly exploited elaborate features but neglected the loss of information caused by frame sampling and image compression. In this paper, we propose a novel spatio-temporal super-resolution (STSR) network which is jointly trained for the video captioning task and the video super-resolution task in an end-to-end fashion. Specifically, a video super-resolution task consists of two subtasks: spatial super-resolution restores high-resolution image features while temporal super-resolution reconstructs missing frame features between two adjacent sampled frames. By sharing multi-modal encoders across both of these two tasks, STSR encourages encoders to capture salient visual contents and learn context-aware representations. Experiments on two benchmark datasets demonstrate that the proposed STSR boosts video captioning performances significantly and outperforms most state-of-the-art approaches.
How to teach, design and program robotic and IoT systems

This article summarizes the results from more than twenty years of continual quality research related to teaching of construction and programming of robotic systems, construction of systems controlled by microcontrollers, teaching and study materials with internal intelligence and also IoT systems on elementary schools, high schools and universities. The respondents of the research were teachers of elementary schools, high schools, as well as engineers, developers, which develop electronic systems controlled with the microcontrollers in companies. Six engineers and designers from Jihostroj Velešín and Teplárny Brno were involved in the construction and development of systems programmed in assembler. In teaching and construction were involved six teachers from the secondary vocational school of electrical engineering in Uherský Brod. In the teaching of the construction and programming of robots were involved fifteen elementary school teachers from fifteen Moravian elementary schools. Another six lecturers of educational courses for building and programming robots for elementary and high school students. The second group of respondents dealt with the construction and programming of systems programmed in advanced programming languages (such as C, Basic and Python) - teachers from VŠE, eight teachers from elementary schools and gymnasiums, four high school teachers and twelve lecturers of robot construction and programming courses for students of primary and secondary schools.
Terminals all the way down

The terminal is a remarkably resilient interface for many programming activities. From its humble beginnings as a teletypewriter more than half a century ago, through video terminals like the VT100, to present-day windowed terminal emulators, it has remained a relevant and productive, albeit very gaunt, interface. This is in stark contrast with feature-rich integrated development environments (IDEs), which on top of their innate complexity allow for the creation of custom extensions. Indeed, researchers have been prolific in proposing innumerable, but often ignored, plug-ins.
Incorporating Uncertainty of Entities and Relations into Few-Shot Uncertain Knowledge Graph Embedding

In this paper, we study the problem of embedding few-shot uncertain knowledge graphs. Observing the existing embedding methods may discard the uncertainty information, or require sufficient training data for each relation, we propose a novel method by incorporating the inherent uncertainty of entities and relations (i.e. element-level uncertainty) into uncertain knowledge graph embedding. We introduce different metrics to quantify the uncertainty of different entities and relations. By employing a metric-based framework, our method can effectively capture both semantic and uncertainty information of entities and relations in the few-shot scenario. Experimental results show that our proposed method can learn better embeddings in terms of the higher accuracy in both confidence score prediction and tail entity prediction.
A Deep Neural Network Optimization Strategy based on Roofline Model

It is obvious that the inference time of the deep neural network (DNN) may be various when DNN is deployed on different types of devices. Relying on the device features, DNN may be further optimized significantly. This paper proposes a DNN’s optimization strategy based on device’s Roofline Model, which can be carried out as follows: Firstly, the device’s Roofline Model is modeled, and computing characteristics of the device are evaluated correspondingly. Secondly, some convolutional layers are picked out as optimal objects, from the original DNN specified by users, according to their floating point operations and memory access cost. Thirdly, based on the Roofline Model, the working regions of the layers with different convolution structure are classified as computing bound region or memory bound region. Fourthly, the inference time is evaluated according to their working regions, and all time-optimal convolution structures of the layers are reached. Finally, these obtained convolution structures are used to reconstruct the original DNN. As an example, YOLO v3 is optimized for Jetson NANO by applying above strategy, namely, YOLO v3-R. The results show that the inference time of YOLO v3-R is 26% lower than YOLO v3, while keeping mAP almost constant.
Classifying Hierarchical Data Streams using Global Classifiers and Summarization Techniques

The hierarchical classification of data streams requires models capable of handling a class hierarchy and updating themselves whenever a new example arrives, within restrained processing time and memory consumption. Current state-of-the-art models store raw instances and handle the hierarchy locally, performing a high number of computations at every hierarchy level and with all, eventually redundant, data. This paper introduces Global k-Nearest Centroids (kNC) and Global Dribble, two novel methods for the hierarchical classification of data streams. Both methods use summarization techniques to represent data with constant computational resources usage and a global classification approach to process instances in less time when compared to local strategies. We compare both methods with a state-of-the-art local classifier, and the proposed methods achieved a higher number of correct predictions and process instances nearly twice as fast.
Visualized Document Similarity Framework with the aid of Knowledge Graph

Document processing has its foundation laid over the precise and efficient computation of document similarity. With the exponential growth of information resources, the document quantity explodes digitally and there’s always a tendency to equip with tools and frameworks which would assist in capturing the relevant and useful patterns from this free flow of contents. This paper illustrates a text refinement framework to compute the similarity of documents and visualize the similarity analysis. The method proposed in the paper employs knowledge graph technique to aid in visualizing the similarity scores of documents. The visualization is built on top of an information rich corpus extracted from the input documents in the form of triplets. The triplet information corpus then facilitates the computation of similarity score and aids in visualizing the analysis. Prior to triplet generation the input documents are pre-processed to eliminate noise, reduce randomness and lemmatized. The pre-processing and the triplet corpus aid in handling long documents by enhancing the process of similarity computation and visual analysis.
Incremental Schema Generation for Large and Evolving RDF Sources

The lack of a descriptive schema for an RDF dataset has motivated several research works addressing the problem of automatic schema discovery. The goal of these approaches is to provide the underlying structural schema of a given RDF dataset, either from the existing instances, or using some schema-related declarations if provided. However, as the instances in the RDF dataset evolve, the generated schema may become inconsistent with the dataset. It is therefore necessary to incrementally update the existing schema according to the changes occurring in the dataset over time. In this paper, we propose a schema discovery approach for massive RDF datasets which incrementally deals with both the insertion and the deletion of entities. It is based on a scalable and incremental density-based clustering algorithm which propagates the changes occurring in the dataset into the clusters corresponding to the classes of the schema. Our approach is implemented using big data technologies to scale-up to massive data, while providing a high quality clustering result. We present some experiments which demonstrate the efficiency of our proposal on both synthetic and real datasets.
Storyboards in VR Narratives Planning: How to Create and Evaluate Them

In a linear narrative, in the planning process, are used illustrations, normally to study the worlds landscapes, villages, houses, tools as also the characters, and the Storyboards, that explore the action and movement, exploring visually how events are depicted in time and movement, and seen from where (camera behavior). In Games or Gamification, as the player can act and move in the tale’s world it is difficult to place the camera and plan the composition from its movement and positioning. Illustrations are here even more important because the fictional world and characters don’t change with the player’s action. In Virtual Reality Technology, the way to depict the plot and its several steps are unknown territory, but their value is felt as being keystone. Without knowing how to represent what are the VR narrative plans, and how the storytelling develops, one fundamental tool is missing: proper use and methodology of the Storytelling drawings. The goal of this article is to explore and assemble approaches and methods that can enable efficient and versatile VR tale and telling development through Storyboards, Layout, and Concept draw planning. By the initial approach, we believe that progress can be made in this study, but, certainly, the layout drawn planning must have multiple approaches not like in movie planning, but certain aspects in those can be used otherwise, to develop the intended R.V. plot, space, and interaction planning.
Positional Multi-Cross-Attention for Bone Age Estimation Using Deep Multiple Instance Learning

The bone age estimation is clinically important as it can be used by physicians to read and report medial images such as bone scintigraphy considering age-related bone changes. Some recent research has applied deep learning techniques to assess bone age and have achieved positive results. Whole-body bone scintigraphy usually contains millions of pixels, which is computationally infeasible. This problem can be solved effectively with multiple instance learning by treating a whole image as a bag of instances. However, many approaches usually assume that there are no dependencies or ordering among instances. In the present study, we propose a multiple instance learning based method for bone age prediction using whole-body bone scan images. Our network architecture combines attention-based multiple instances learning with a multi-cross attention module to handle large input images and dependencies among instances. The experiments conducted on the Chonnam National University Hwasun Hospital data set have shown the potential performance of our model. The proposed framework can be used as a robust support tool for clinicians to analyze and prognosticate bone aging and age-related diseases.
Optimal Generic Attack Against Basic Boneh-Boyen Signatures

The Boneh-Boyen digital signature scheme is a pairing-based signature scheme that features short signatures consisting of one group element, the minimum possible size. In contrast to earlier short signature schemes such as Boneh-Lynn-Shacham, the Boneh-Boyen scheme achieves security without the use of random oracles, but at the cost of a non-standard mathematical assumption, the q-Strong Diffie-Hellman (or q-SDH) assumption, which is known to be less secure than discrete logarithms against generic attacks. However, unlike discrete logarithms, in which the fastest known generic attacks match the known provable lower bounds for solving generic discrete logarithms, the fastest known generic attacks against Boneh-Boyen prior to this work did not match the provable lower bounds for generically solving q-SDH instances. In this work, we demonstrate that when $$p-1$$ has suitably sized divisors (where p is the order of the underlying group used in the scheme), which in particular almost always occurs for cryptographic pairings instantiated from elliptic curves, Boneh-Boyen can indeed be broken in the sense of weak existential forgery under chosen-message attack (the same security definition as what was used in the original Boneh-Boyen paper) in $$O(p^{\frac{1}{3}+\varepsilon })$$ time using generic algorithms, matching the provable lower bound for generically solving q-SDH instances.
Saliency Can Be All You Need in Contrastive Self-supervised Learning

We propose an augmentation policy for Contrastive Self-Supervised Learning (SSL) in the form of an already established Salient Image Segmentation technique entitled Global Contrast based Salient Region Detection. This detection technique, which had been devised for unrelated Computer Vision tasks, was empirically observed to play the role of an augmentation facilitator within the SSL protocol. This observation is rooted in our practical attempts to learn, by SSL-fashion, aerial imagery of solar panels, which exhibit challenging boundary patterns. Upon the successful integration of this technique on our problem domain, we formulated a generalized procedure and conducted a comprehensive, systematic performance assessment with various Contrastive SSL algorithms subject to standard augmentation techniques. This evaluation, which was conducted across multiple datasets, indicated that the proposed technique indeed contributes to SSL. We hypothesize whether salient image segmentation may suffice as the only augmentation policy in Contrastive SSL when treating downstream segmentation tasks.
Low-Poisoning Rate Invisible Backdoor Attack Based on Important Neurons

The present research on label-consistent invisible backdoor attacks mainly faces the problem of needing a high poisoning rate to achieve a high attack success rate. To address the problem, this paper proposes a low-poisoning rate invisible backdoor attack based on important neurons (INIB) by enhancing the connection between triggers and target labels with the help of the neural gradient ranking algorithm. The method first identifies the neurons with the most significant influence on the target label with the help of the neural gradient ranking algorithm, secondly establishes a strong link between the important neurons and the trigger using the gradient descent algorithm, and then generates a trigger based on the established strong link by minimizing the difference between the current activation value and the expected activation value of the important neurons, thus causing the important neurons to be strongly activated when images have the trigger, which in turn causes the model to misidentify them as the target label. Finally, detailed experimental results show that INIB is able to achieve a very high attack success rate with a very low poisoning rate. Specifically, INIB achieves a 98.7% backdoor attack success rate with the poisoning rate of only 1.64% on the MNIST dataset.
The history of scientific and philosophical thought as the integration basis of STEM education

This article draws attention not only to the advantages of “Science, Technology, Engineering and Mathematics” education, but also to the shortcomings linked to the loss of fundamental knowledge. In this regard, the history of science and philosophy is suggested as a conceptual basis for deep interdisciplinary integration and cultural synthesis to be achieved. The results of the study conducted in 2011-2021 during History and Philosophy of Science course for the Krasnoyarsk Science Center graduate students demonstrated a strong relationship between acquaintance with the development of scientific and philosophical thought and a positive change in the professional portrait of a future specialist. Non-directive interview, participated observation, comparative analysis, phenomenological description and other methods were used. In general, the study was qualitative and informal. The results obtained during the approbation of the course for graduate students are planned to be used in the process of designing “Science, Technology, Engineering and Mathematics” curricula for students of schools or other educational institutions.
A Graph-based Approach for Adaptive Serious Games

In higher education, researchers have recently focused on exploring automated, personalized instructional systems to enhance students’ learning experiences. Motivated by this, we propose a personalized instructional system using a straightforward graph system to offer an educational game that is effective for students and intuitive for developers. Our system uses a directional graph, called an action graph, for representing solutions to in-game problems based on possible player actions. Through our proposed algorithm, a serious game integrated with our system would both detect player errors and provide personalized assistance to direct a player in the direction of a correct solution. To verify system performance, we present comparison testing on a group of students engaging in the game both with and without AI. Students who played the AI-assisted game showed an average 20% decrease in time needed and average 58% decrease in actions taken to complete the game.
Algorithm to build quantum circuit from classical description of DFSM

There are several papers that define quantum finite automaton analogous to classical finite automaton. Some papers describe the power of quantum automaton. In general, in quantum automata unitary matrices are associated to input symbols. Even though quantum circuits (or unitary matrices) are shown in specific cases, there is no general method given to construct a circuit or unitary matrix is found in the literature. This research focuses on construction of unitary matrices for each input symbol from the classical definition of a Deterministic Finite State Machine (DFSM). An algorithm is developed to associate a quantum circuit to an input symbol which considered as a mapping of classical DFSM states. IBM quantum composer is used to design circuits and build examples.
Cyber Harassment In Transgender Community: Status And Legal Regulation In India

The internet is a medium for people to communicate freely. Communication over the medium is not at all restrictive, and people can communicate with one another irrespective of gender, caste, religion, or colour. The advantages of the internet are numerous, but all things today coexist with a dark side. Some so many people misuse the internet and do not face repercussions as they do it anonymously. Although anonymity may be a myth and everyone can be traced down in some way or another, this is very difficult in cases where the crime itself has not been reported. Cyber Harassment is no new concept; the first case for the same was reported in 2001 and is a major concern even today. In the recent past, a new trend has been observed; persons belonging to the transgender community are more likely to become targets of such digital harassment merely owing to the gender they associate with. The oppressed community may or may not even be able to do anything to stop such incidents from occurring. This paper enumerates why we should discuss this growing issue, along with the existing laws to govern it, and covers how they are not enough.