Reviewerly/exHarmony huggingface dataset:
Certifying quantumness beyond steering and nonlocality and its implications on quantum information processing

Superunsteerability is a particular kind of spatial quantum correlation that can be observed in a steering scenario in the presence of limited shared randomness. In this work, we define an experimentally measurable quantity in a steering scenario to certify superunsteerability. In the context of certification of randomness with this scenario, we demonstrate that such certification of superunsteerability provides a bound on the amount of genuine randomness generation. On the other hand, superlocality is another kind of spatial quantum correlation that can be observed in a Bell scenario in the presence of limited shared randomness. We identify inequalities to certify superlocality in the Bell scenarios that can be adopted to implement $2$-to-$1$ and $3$-to-$1$ random-access codes. We observe that such certification of superlocality acts as resource for the random-access codes in the presence of limited shared randomness. As a by-product of our certification of superunsteerability and superlocality, we identify a new classification of separable states having quantumness.
Blockchain Technology for Better Security by Using Two-Way Authentication Process

In today’s era, blockchain is an emerging and successful technology. Blockchain has an incredible impact on many companies or trades that can change society’s problems in several regions. Due to the invention of blockchain, the cryptocurrency is manufactured and Bitcoin which is the initial digital crypto-forex. It is a document that can visibly give each Bitcoin’s transaction, this is a computer-generated supply of chunks, while the blockchain skills are additionally more consistent and have suitable contributions such as the protection difficulties, and commanding the original or progressive circumstances at the back are also have many dynamic points that need to pay attention. The main problem related to the blockchain is the protection of data with maintaining confidentiality by giving access to the right person; at present, certain studies are there on the blockchain, but there is the absence of a logical assessment on the protection of the data. This research paper delivers a methodical aspect of the protection risks to blockchain and talks about by what means two-way authentication (2-WA) can be established to manage the absence of capacity of individual verification formations. The paper presents the leading fact of blockchain production along with its enhanced confidentiality and the implementation of 2-WA to the block/chunk for an additional level of protection. 2-WA is the primarily valuable element of security that is used to keep data privately and run smoothly without any intervention. In addition, this research paper contains an assessment of the improvement of security solutions for blockchain that can be utilized in the advancement of various blockchain structures and advise some future instructions to do research attempts hooked on this site.
Reward bonuses with gain scheduling inspired by iterative deepening search

This paper introduces a novel method of adding intrinsic bonuses to task-oriented reward function in order to efficiently facilitate reinforcement learning search. While various bonuses have been designed to date, this paper points out that the intrinsic bonuses can be analogous to either of the depth-first or breadth-first search algorithms in graph theory. Since these two have their own strengths and weaknesses, new bonuses that can bring out the respective characteristics are first proposed. The designed bonuses are derived as differences in key indicators that converge in the steady state based on the concepts of value disagreement and self-imitation. Then, a heuristic gain scheduling is applied to the designed bonuses, inspired by the iterative deepening search, which is known to inherit the advantages of the two search algorithms. The proposed method is expected to allow agent to efficiently reach the best solution in deeper states by gradually exploring unknown states. In three locomotion tasks with dense rewards and three simple tasks with sparse rewards, it is shown that the two types of bonus contribute to the performance improvement of the different tasks complementarily. In addition, by combining them with the proposed gain scheduling, all tasks can be accomplished with high performance.
A Novel Phase Measuring Deflectometry Method Using a Curved Screen

Phase measuring deflectometry (PMD) is widely used to measure the surface of specular objects. The planar screen used in traditional PMD systems have the drawbacks of a small field of view of measurement and a large virtual image depth range. This problem can be effectively solved by using the curved screen. As the arc structure of the curved screen is different to the planar structure required in Zhang’s method, the existing methods mainly rely on the stereo vision of two cameras to obtain the screen position. This paper presents a novel PMD based on a curved screen and single camera. A method of calibrating a curved screen by constructing a virtual planar screen is presented. The method only needs one camera to calibrate the curved screen directly with high accuracy. In addition, the change from planar screen to curved screen makes the traditional phase-gradient-height relation invalid. Based on the comprehensive analysis of space optical path models, a novel phase-gradient-height relation is presented. The relation is a general formula. It can be used for any curved screen, including the planar screen. Simulation and quantitative comparison experiments verify the accuracy and reliability of the proposed calibration method. The measurement results of concave mirror show that the proposed method has higher accuracy and a larger field of view in measuring specular objects than traditional methods.
Learning disentangled skills for hierarchical reinforcement learning through trajectory autoencoder with weak labels

Typically, hierarchical reinforcement learning (RL) requires skills that are applicable to various downstream tasks. Although several recent studies have proposed the supervised and unsupervised learning of such skills, the learned skills are often entangled, which hinders their interpretation. To alleviate this, we propose a novel method to use weak labels for learning disentangled skills from the continuous latent representations of trajectories. To this end, we extended a trajectory variational autoencoder (VAE) to impose an inductive bias using weak labels, which explicitly enforces the disentangling of the trajectory representations into factors of interest intended for the model to learn. Using the latent representations as skills, a skill-based policy network is trained to generate trajectories similar to the learned decoder of the trajectory VAE. Furthermore, using the disentangled skill, we propose a skill repetition that can expand the entire trajectories generated by the policy at test time, resulting in an effective planning strategy. Experiments were performed on several challenging navigation tasks in mazes, and the results demonstrate the effectiveness of our method at solving hierarchical RL problems even with a long horizon and sparse rewards.
Review of Literature on Improving the KNN Algorithm

K-Nearest Neighbors (KNN) is a classification algorithm that has been widely used in the world of machine learning. The KNN algorithm classifies objects with learning data that are closest to the object. The special case where the classification is predicted based on the closest learning data is called the Nearest Neighbors algorithm. Classification is an important issue in processing big data, data science, and machine learning. KNN is one of the oldest, simplest, and also accurate algorithms for pattern classification and regression models. Many researchers have also improvised KNNs and the results obtained by these studies have changed a lot in terms of the accuracy of the results. The purpose of this paper is to see the improvement of several types of development of the KNN algorithm. &nbsp
A prototype of a chatbot for evaluating and refining student startup ideas using a large language model

Assessing the soundness of business models is a critical skill for aspiring entrepreneurs and is an essential part of entrepreneurship education. However, evaluating business models can be time-consuming, costly, and subjective. This study describes the design and the prototype of a chatbot as a conversational intelligent tutoring system that assesses and gives feedback on business model soundness using natural language processing techniques and GPT-3.5, a large language model (LLM) trained by OpenAI, to help student co-founders learn and refine their startup ideas. Our method involves indexing articles and rubrics for evaluating technology startup pitches by extracting word embeddings via the OpenAI API. The chatbot accepts descriptions of startup businesses from student co-founders through a Telegram chatbot, and these are formatted as prompts and then fed into GPT-3.5. The responses are formulated by GPT-3 based on another set of prompts instructing the bot to give feedback from three virtual panelists: 1) a harsh judge, 2) a neutral expert, and 3) an optimistic investor.
MalCon: A blockchain-based malware containment framework for Internet of Things

IoT devices have become a primary medium for malware (e.g., botnets) to launch Distributed Denial of Service (DDoS) attacks. Such malware exploit low-security measures in IoT devices to spread in networks and recruit new victims. Thus, there is a need for malware countermeasures that consider both the security and operability of the network. Indeed, some IoT devices might run critical processes that do not tolerate interruptions. This paper proposes MalCon, a blockchain-based malware containment framework for IoT. It aims to stop malware from spreading in a network by a set of containment strategies encoded into smart contracts to be executed by the infected devices. Moreover, MalCon provides a monitoring service that ensures trustworthy behavior in the network and reports to the system administrator any fraudulent activity of the monitored devices. MalCon was tested extensively with real-life malware and use cases. It quickly and drastically reduces the number of infected devices in a network, even in an extreme case of a fully connected network.
Forecasting the PV Power Utilizing a Combined Convolutional Neural Network and Long Short-Term Memory Model

The quantity of photovoltaic systems generated relies on the climate, and to generate significant variations depending on the climatic conditions. Its electricity production processes exhibit unpredictability, instability, and intermittent behavior. The augmented convolutional long-short memory combination network is recommended for PV energy combined forecasting in accordance to overcome the challenge of considerable instability in the high proportion of sustainable energy output. The LSTM and CNN models’ training procedure uses the enhanced artificial bee colony (ABC) to adjust their characteristics. To determine the projected model parameters of PV power, the estimated input parameters of every model are stacked and then reassembled. The studies showed that the forecast precision was significantly higher than the reliability rate of the Convolutional LSTM combinational network as well as the forecasting findings from the BP, CNN, and LSTM independent computational methods. This forecast specificity was predicated on the enhanced ABC to optimize the variables of the CLSTM combinational network for forecasting Photo Voltaic power. For comparative study and assessment, the most recent operational data from a Photovoltaic power plant in northern China have been utilized. The suggested technique resolved more quickly and was higher weather-adaptable than the individual neural network and hybrid forecasting models mentioned prior.
G2IFu: Graph-based implicit function for single-view 3D reconstruction

As the demand for 3D models increases, there is growing interest in reconstructing 3D objects from images using AI. In this paper, we propose G2IFu, a graph-based implicit function that successfully reconstructs a highly detailed 3D object mesh from a single image. Unlike previous methods that learn implicit functions with points, G2IFu aims to map graphs to implicit values. We make the following contributions: (1) Compared to independent 3D points, graphs have a larger perception space and contain specific spatial structure information. Therefore, we extend a 3D point p to a graph Gp by generating hypothesis points and establishing edges between them. We then predict the corresponding implicit value using a graph convolution network. Our experiments show that this method can effectively improve the prediction accuracy of implicit functions. (2) We introduce a prior boundary loss based on Gp to make the network pay more attention to the “key” points near the shape surface. To the best of our knowledge, G2IFu is the first model that introduces a graph into neural implicit representation. (3) Inspired by previous methods, we utilize the image’s global and local features to initialize Gp. We also introduce a self-attention module into G2IFu for better performance. We conduct experiments on the ShapeNet dataset and demonstrate that G2IFu can generate higher-quality 3D object shapes than previous single-view reconstruction methods. Additionally, we extend G2IFu to multi-view 3D reconstruction and achieve good performance.
Building User Stories and Behavior Driven Development Scenarios with a Strict Set of Concepts: Ontology, Benefits and Primary Validation

Behavior Driven Development (BDD) offers a way to express scenarios, written in structured natural language, on how the system should act to fulfill a requirement. Such a test scenario is written together with the requirement; this way these two can be conceived in unison, nested into each other. Lots of templates have been written to construct BDD scenarios and various practices were born out of usage. We fail to find documentation on templates. A strict set of templates with a clear definition of the used keywords aligned with the intends of BDD has been proposed recently in the form of an ontology. The present paper (i) evaluates the ontology on existing BDD scenarios found in the GitHub repository (exogenous validation) and (ii) merges an ontology for user story elements' representation with one for expressing BDD scenarios to evaluate its ability to guide the writing of BDD scenarios (endogenous validation). By linking both through strictly-identified concepts, we (i) provide guidance to the practitioner in the agile requirements engineering phase and (ii) with the adequate tagging of elements during the requirements engineering process we get meta-data allowing to suggest treatments to the BDD scenario (e.g. test automation or forward engineering into a software architecture or even code).
Influence of Blockchain Technology Application in Education on Online Teaching Resources Sharing

With the continuous expansion of educational view, blockchain technology develops into the cutting-edge applied science and technology of educational information construction. Blockchain technology can effectively solve such problems as information security and trust crisis in the current application of big data in online education by virtue of its characteristics of decentralization, traceability, multi-party consensus mechanism, and high trust. Blockchain technology is conducive to protecting online educational resources, strengthening the transparency of online teaching resources, simplifying the copyright transaction process, and thus enhancing the scientific and technological innovation ability of teachers. Studies regarding the blockchain technology application in education were analyzed, and the influences of the four aspects of the blockchain technology application in education on online teaching resource sharing were explored. Results showed that the Cronbach α coefficient of the questionnaire designed in this research was 0.856, and the KMO value was 0.859, indicating its good reliability and validity. The cross-institutional learning record storage space, learning certificate management mechanism, and collaboratively developed educational ecosystem of the blockchain technology application in education obviously promoted online teaching resource sharing at a significance level of 5%. The results are important reference values for strengthening the integration of educational resources under blockchain technology, improving the application mechanism of online digital educational resources, and improving the accurate service quality of online educational resources.
Private Data Sharing in EMU Maintenance: A Method Study Based on Federated Learning

In the maintenance of (Electric Multiple Unit) EMU, the operation of intelligent operation and maintenance model needs the support of data sharing from multiple subjects. However, in the context of strict safety supervision in the railway industry, the data security and privacy protection requirements of each subject have formed a huge challenge to data sharing. Therefore, this paper proposes a new method of EMU maintenance data sharing based on federated learning, which solves the problem that some data subjects cannot share data due to the private data. In this paper, a case analysis of the method is carried out to realize the multi-subject joint training of a decision tree prediction model under the premise of protecting the privacy data in the real maintenance scenarios.
Improving Image Encoders for General-Purpose Nearest Neighbor Search and Classification

Recent advances in computer vision research led to large vision foundation models that generalize to a broad range of image domains and perform exceptionally well in various image based tasks. However, content-based image-to-image retrieval is often overlooked in this context. This paper investigates the effectiveness of different vision foundation models on two challenging nearest neighbor search-based tasks: zero-shot retrieval and k-NN classification. A benchmark for evaluating the performance of various vision encoders and their pre-training methods is established, where significant differences in the performance of these models are observed. Additionally, we propose a fine-tuning regime that improves zero-shot retrieval and k-NN classification through training with a combination of large publicly available datasets without specializing in any data domain. Our results show that the retrained vision encoders have a higher degree of generalization across different search-based tasks and can be used as general-purpose embedding models for image retrieval.
The impact of personally relevant consumption contexts during product evaluations in virtual reality

Using immersive technology to restore relevant context in consumer sensory testing facilitates evaluations reflective of real-world experiences, enabling data collection with improved discriminability and reliability. Prior research has applied the same contextual environment to all participants without accounting for varying individual consumption habits. This study investigated how personal relevance (usage frequency) of the consumption environment influenced perception and acceptance. Sixty-two consumers evaluated four granola bars on overall liking, attribute intensity, and appropriateness of attribute level via Just-About-Right (JAR) questions under two 360° virtual environments individually selected as each subject’s most and least frequently used scenarios. To assess repeatability, subjects replicated the test three weeks later. Across participants, results indicated the products were rated similarly between environments. However, an individual’s responses were more reliable when made in the more relevant consumption scenario as demonstrated by higher correlation coefficients obtained between replicates. Additionally, penalty analyses revealed products tended to be penalized more for being not-JAR in the more relevant environment. Participants reported higher presence in the less relevant environment (p < 0.001) whose 360° videos tended to be livelier. Nonetheless, subject engagement was equally high in both environments (p > 0.05). These findings demonstrate that providing a personally relevant scenario for consumer testing generated more repeatable data to obtain reliable consumer insights. Differences in liking penalty between environments suggests evaluating products in a more familiar setting may allow consumers to draw more stable references for comparisons based on past experiences, resulting in a more severe decrease in acceptance when products fall below expectations.
THE TRENDS IN THE DIGITALIZATION OF TEACHER PROFESSIONAL TRAINING IN HUNGARY

In today's conditions, education in general has undergone a fundamental digital transformation and is to keep pace with the global trends of digital development. The trends in the digitalization of teacher professional training in Hungary are being discussed and identified in the article.The digitalization of teacher professional training has become a significant necessity and demand over the last three years. Remote communication in all areas of human activity, particularly in the education system, makes digital teaching technology competence an essential component of a teacher's professional development. Theoretical method is used to achieve the research goal. In the context of our research, we analyzed trends in the digitalization of teacher professional training, which involves developing a system of requirements for regulatory and technically-oriented support that promotes the development of digital competence; educational programs of different cycles that encourage the development of digital competencies of professionals at various stages of their professional development. The official documents, statistical data, and materials on higher education, vocational education, and training in EU countries and Hungary are being analyzed in the article. We also analyzed the educational programs of universities in Hungary that provide teacher professional training. The theoretical and practical aspects of digitalization of teacher professional training are considered and based on the works of domestic and foreign scientists. Further research of the issue is relevant. It will contribute to the borrowing of all modern positive trends in the educational space of Ukraine, since the process of digitalization of teachers professional training and similar trends in Hungary have not been studied enough.
Decentralized and secure deduplication with dynamic ownership in MLaaS

Machine Learning as a Service (MLaaS) these days has been an integral offering of many Internet giants, shaping people’s lives in an intelligent manner. Both academic and industrial communities are dedicated to exploring a variety of functional MLaaS platforms, where massive data is typically required for model training to achieve better capability. As the widely used training data can be repeatedly stored, data deduplication has been deemed essential. Meanwhile, the proliferation of ML-aimed attacks has raised security awareness. In light of these, we propose a secure and robust deduplication scheme over encrypted data for decentralized MLaaS platforms. We utilize message-locked encryption for privacy protection on blockchain over decentralized cloud storage. To balance the overhead from blockchain, we offload the computation off-chain and only maintain the system state on-chain. We remedy the potential leakages from the transparency of blockchain, through carefully tailored cross-user deduplication workflow. Our proposed scheme is also robust against short-information and brute-force attacks. Furthermore, we apply binary tree based key distribution to support dynamic ownership updates. We implement a prototype on Ethereum, and comprehensive experiments show that our design can function as intended with modest on-chain update gas cost (i.e., 1.67×10−4 ETH), and the blockchain-related operations run less than 6 s.