Reviewerly/exHarmony huggingface dataset:
Emotion Classification Method of Financial News Based on Artificial Intelligence

With the continuous development of economy, the economic development model is constantly changing. Especially since China’s entry into WTO, the scale of economic development has reached a new height. The continuous development of economy makes the financial news module evolve towards specialization. However, with the emergence of Internet of Things technology, a large number of data appear in the network, which brings some difficulties to the classification and analysis of data economy. Emotion classification refers to the complexity and diversity of people’s emotions. It can be classified from different observation angles. Because the core content of emotion is value, human emotion should be classified mainly according to the different characteristics of the movement and change of value relationship it reflects. This paper is aimed at studying the emotional classification method of financial news based on artificial intelligence and expecting to use artificial intelligence technology and classification method to classify financial news. It allows more people to know the implied information of financial information and promotes economic development. Artificial intelligence is a branch of computer science. It attempts to understand the essence of intelligence and produce a new intelligent machine that can respond in a similar way to human intelligence. This paper mainly summarizes the topic selection characteristics and subdivision topic selection characteristics of financial data news through quantitative and qualitative methods and explores the classification of financial news. In this paper, a simplified classification algorithm based on convolution function is proposed for the classification of traditional financial news networks. The experimental results show that the classification accuracy of artificial intelligence method is improved by 4% compared with the traditional emotion classification method, and the classification accuracy of positive emotion is lower than that of negative emotion by 2%.
Secure Image encryption algorithm based on DNA Encoding and Chaos map for cloud computing

Cloud computing is rapidly expanding in the IT business today, providing a new approach to handling various information systems. The fast advancement of technology necessitates the usage of this technology and reaping its benefits. Understanding and expertise for using algorithmic security in processes and data systems are rising as awareness and concerns about cloud services and information security risks. The major goal of this project is to conduct a security analysis and performance evaluation of a novel lightweight cryptographic method for improving data security in cloud computing. A series of experiments on several test photos were used to verify the efficacy of the suggested technique. Compared to existing schemes, the numerical results show that the suggested algorithm works incredibly well and gives better encryption results despite the larger key sensitivity. Nonetheless, the suggested approach is more resistant to known statistical, differential, and exhaustive attacks.
HCVNet: Binocular Stereo Matching via Hybrid Cost Volume Computation Module With Attention

Binocular stereo matching, a computer vision task typically using cost volume constructed from the left and right feature maps to estimate disparity and depth, is widely applied in 3D reconstruction, autonomous driving and robotics navigation.Though recent study brings an awareness of the convolution neural networks and the attention algorithms used in this field can make great progress, it is still difficult to satisfy the demand of high-precision applications due to many reasons.Study finds that the exist methods usually incline to ignore the intermediate feature map of other scales, pay less attention to the relationship between left and right feature maps and even just tend to use one type of cost volume to train the model.In this article, we mainly focus on solving the three problems mentioned above.Firstly, we present the Multi-scale Feature Extraction and Fusion Module(MFEFM) to get the informational feature maps via fusing all scale feature maps.And then we design the Effective Channel Attention Module(ECAM) applied to better capture and utilize the channel-wise independencies.Finally, we adopt the Hybrid Cost Volume Computation Module(HCVCM) to construct and aggregate cost volume.With these solutions, we build an end-to-end stereo matching network named HCVNet.Comparison with other state-of-the-art models, it can achieve 0.714px EPE on SceneFlow dataset, descending PSMNet(1.09pxEPE) by 37.6%.
Optimum Thresholding for Medical Brain Images Based on Tsallis Entropy and Bayesian Estimation

Thresholding is a popular technique for image segmentation, specifically in the field of medical image processing. The main challenge for image thresholding is to determine the optimum threshold based on intensity distributions of object and background in the image. In this paper, we propose a new image thresholding method by injecting the Bayesian probability estimation into the classical Tsallis entropy framework. The classical algorithm assumes that the intensity distribution of object does not affect the background pixels, and vice versa. However, the intensity distributions of object and background are essentially crossed. It is possible to estimate the probability of a pixel belonging to object or background by Bayes rule, and use it to update the classical form of Tsallis entropy. The optimum threshold is finally determined by optimizing the information measure function defined with the new form of Tsallis entropy. Extensive experiments conducted over two public datasets of medical brain images have verified the significant superiority of the proposed method.
A simple and efficient implementation of strong call by need by an abstract machine

We present an abstract machine for a strong call-by-need strategy in the lambda calculus. The machine has been derived automatically from a higher-order evaluator that uses the technique of memothunks to implement laziness. The derivation has been done with the use of an off-the-shelf transformation tool implementing the "functional correspondence" between higher-order interpreters and abstract machines, and it yields a simple and concise description of the machine. We prove that the resulting machine conservatively extends the lazy version of Krivine machine for the weak call-by-need strategy, and that it simulates the normal-order strategy in bilinear number of steps.
CAEN: A Hierarchically Attentive Evolution Network for Item-Attribute-Change-Aware Recommendation in the Growing E-commerce Environment

Traditional recommendation systems mainly focus on modeling user interests. However, the dynamics of recommended items caused by attribute modifications (e.g. changes in prices) are also of great importance in real systems, especially in the fast-growing e-commerce environment, which may cause the users' demands to emerge, shift and disappear. Recent studies that make efforts on dynamic item representations treat the item attributes as side information but ignore its temporal dependency, or model the item evolution with a sequence of related users but do not consider item attributes. In this paper, we propose Core Attribute Evolution Network (CAEN), which partitions the user sequence according to the attribute value and thus models the item evolution over attribute dynamics with these users. Under this framework, we further devise a hierarchical attention mechanism that applies attribute-aware attention for user aggregation under each attribute, as well as personalized attention for activating similar users in assessing the matching degree between target user and item. Results from the extensive experiments over actual e-commerce datasets show that our approach outperforms the state-of-art methods and achieves significant improvements on the items with rapid changes over attributes, therefore helping the item recommendation to adapt to the growth of the e-commerce platform.
Partition-Tolerant and Byzantine-Tolerant Decision-Making for Distributed Robotic Systems with IOTA and ROS 2

With the increasing ubiquity of autonomous robotic solutions, the interest in their connectivity and in the cooperation within multi-robot systems is rising. Two aspects that are a matter of current research are robot security and secure multi-robot collaboration robust to byzantine agents. Blockchain and other distributed ledger technologies (DLTs) have been proposed to address the challenges in both domains. Nonetheless, some key challenges include scalability and deployment within real-world networks. This paper presents an approach to integrating IOTA and ROS 2 for more scalable DLT-based robotic systems while allowing for network partition tolerance after deployment. This is, to the best of our knowledge, the first implementation of IOTA smart contracts for robotic systems, and the first integrated design with ROS 2. This is in comparison to the vast majority of the literature which relies on Ethereum. We present a general IOTA+ROS 2 architecture leading to partition-tolerant decision-making processes that also inherit byzantine tolerance properties from the embedded blockchain structures. We demonstrate the effectiveness of the proposed framework for a cooperative mapping application in a system with intermittent network connectivity. We show both superior performance with respect to Ethereum in the presence of network partitions, and a low impact in terms of computational resource utilization. These results open the path for wider integration of blockchain solutions in distributed robotic systems with less stringent connectivity and computational requirements.
Research and Practice of Blended Teaching Mode Based on “Mobile Learning Platform and PAD Class”

This paper is to build a blended teaching mode of "Mobile Learning Platform + PAD class", and explain how to combine the PAD class with the blended teaching mode under the learning background of "Internet + Education" and its application in the courses of the second foreign language (Japanese).Through continuous practice, it is found that the PAD class and blended teaching can be well integrated and achieve excellent teaching results.
Secure Collaborative Platform for Health Care Research in an Open Environment: Perspective on Accountability in Access Control

With the recent use of IT in health care, a variety of eHealth data are increasingly being collected and stored by national health agencies. As these eHealth data can advance the modern health care system and make it smarter, many researchers want to use these data in their studies. However, using eHealth data brings about privacy and security concerns. The analytical environment that supports health care research must also consider many requirements. For these reasons, countries generally provide research platforms for health care, but some data providers (eg, patients) are still concerned about the security and privacy of their eHealth data. Thus, a more secure platform for health care research that guarantees the utility of eHealth data while focusing on its security and privacy is needed.This study aims to implement a research platform for health care called the health care big data platform (HBDP), which is more secure than previous health care research platforms. The HBDP uses attribute-based encryption to achieve fine-grained access control and encryption of stored eHealth data in an open environment. Moreover, in the HBDP, platform administrators can perform the appropriate follow-up (eg, block illegal users) and monitoring through a private blockchain. In other words, the HBDP supports accountability in access control.We first identified potential security threats in the health care domain. We then defined the security requirements to minimize the identified threats. In particular, the requirements were defined based on the security solutions used in existing health care research platforms. We then proposed the HBDP, which meets defined security requirements (ie, access control, encryption of stored eHealth data, and accountability). Finally, we implemented the HBDP to prove its feasibility.This study carried out case studies for illegal user detection via the implemented HBDP based on specific scenarios related to the threats. As a result, the platform detected illegal users appropriately via the security agent. Furthermore, in the empirical evaluation of massive data encryption (eg, 100,000 rows with 3 sensitive columns within 46 columns) for column-level encryption, full encryption after column-level encryption, and full decryption including column-level decryption, our approach achieved approximately 3 minutes, 1 minute, and 9 minutes, respectively. In the blockchain, average latencies and throughputs in 1Org with 2Peers reached approximately 18 seconds and 49 transactions per second (TPS) in read mode and approximately 4 seconds and 120 TPS in write mode in 300 TPS.The HBDP enables fine-grained access control and secure storage of eHealth data via attribute-based encryption cryptography. It also provides nonrepudiation and accountability through the blockchain. Therefore, we consider that our proposal provides a sufficiently secure environment for the use of eHealth data in health care research.
Photonic multiplexing architectures for optical neuromorphic computation

The simultaneous advances in artificial neural networks and photonic integration technologies have spurred extensive research in optical computing and optical neural networks (ONNs). The potential to simultaneously exploit multiple physi-cal dimensions of time, wavelength and space give ONNs the ability to achieve computing operations with high parallel-ism and large-data throughput. Different photonic multiplexing techniques based on these multiple degrees of freedom have enabled ONNs with large-scale interconnectivity and linear computing functions. Here, we review the recent advanc-es of ONNs based on different approaches to photonic multiplexing, and present our outlook on key technologies needed to further advance these photonic multiplexing/hybrid-multiplexing techniques of ONNs.
Find, Fuse, Fight: Genetic Algorithms to Provide Engaging Content for Multiplayer Augmented Reality Games

In Augmented Reality (AR) mobile games, several technical aspects are still partially under-explored, thus limiting the creativity of game designers and the spectrum of possible uses of AR. As a result, too often AR is used only to superimpose in a static way predefined digital content to real scenarios. In the present work, we have started to tackle this issue by designing a game to overcome the limited interactivity among players and the somewhat static use of AR on resource-limited devices (i.e., cell phones). In particular, we have designed and prototyped FFF: Find, Fuse, Fight, a game that supports multiplayer mode, offers a more creative use of AR, and demonstrates that Procedural Content Generation (PCG) techniques could be effectively exploited for introducing a higher degree of variability both in the content and in the gameplay, even on devices far less performing than a standard PC. In particular, we developed a prototype that exploits Genetic Algorithms (GAs) to create new content and apply meshes deformation to 3D models in real-time. We have used such content to prototype a mobile game that features AR battles among creatures in an online multiplayer environment. The prototypes have undergone a performance test to evaluate the feasibility of AR multiplayer games with generated content, collecting encouraging preliminary outcomes.
A Framework for Flexible ILDA Digital Network (IDN) Audio Streaming

This demo paper presents an update on ILDA (International Laser Display Association) audio stream specification in the context of the ILDA Digital Network (IDN). A framework of software elements is introduced to interface with audio hardware on a computer and other software that is able to handle multi-channel audio data. The demo will showcase the new elements in action and also will point out the interoperability to existing IDN elements.
The Identification of Chinese Herbal Medicine Combination Association Rule Analysis Based on an Improved Apriori Algorithm in Treating Patients with COVID-19 Disease

In this work, an improved Apriori algorithm is proposed. The main goal is to improve the processing efficiency of the algorithm, and the idea and process of the Apriori algorithm are optimized. The proposed method is compared with the classical association rule algorithm to verify its effectiveness. Traditional Chinese medicine plays a certain role in the prevention and treatment of COVID-19. In order to deeply mine the association rules between Chinese herbal medicines for the prevention and treatment of COVID-19, this improved Apriori algorithm is applied from the retrieved published scientific literature and the guidelines for the prevention and treatment of COVID-19 published all over China. Based on the representation of traditional Chinese medicine data in binary form, the potential core traditional Chinese medicine combinations in the treatment of COVID-19 are identified. The results of association rules of Chinese herbal medicine data obtained from the real database provide an important reference for the analysis of COVID-19 combined treatment of Chinese herbal medicine.
Design and Application of English Online Learning Platform

In order to solve the problems of the existing English online learning platforms, such as the single way of providing learning content and the unclear learning purpose, this article proposes to build a cloud computing online learning platform based on data correlation mining label distribution learning algorithm. With the help of distributed computing and virtualization technology, the software and hardware resource virtualization resource pool is integrated to build an open online learning system, which can change the defects of traditional English online learning system to the maximum extent. At the level of 10000 iterations, the average number of system requests is 2.17/s. The system runs steadily and can meet the English learning needs of student users; it can be popularized and applied in English teaching practice.
AutoWS-Bench-101: Benchmarking Automated Weak Supervision with 100 Labels

Weak supervision (WS) is a powerful method to build labeled datasets for training supervised models in the face of little-to-no labeled data. It replaces hand-labeling data with aggregating multiple noisy-but-cheap label estimates expressed by labeling functions (LFs). While it has been used successfully in many domains, weak supervision's application scope is limited by the difficulty of constructing labeling functions for domains with complex or high-dimensional features. To address this, a handful of methods have proposed automating the LF design process using a small set of ground truth labels. In this work, we introduce AutoWS-Bench-101: a framework for evaluating automated WS (AutoWS) techniques in challenging WS settings -- a set of diverse application domains on which it has been previously difficult or impossible to apply traditional WS techniques. While AutoWS is a promising direction toward expanding the application-scope of WS, the emergence of powerful methods such as zero-shot foundation models reveals the need to understand how AutoWS techniques compare or cooperate with modern zero-shot or few-shot learners. This informs the central question of AutoWS-Bench-101: given an initial set of 100 labels for each task, we ask whether a practitioner should use an AutoWS method to generate additional labels or use some simpler baseline, such as zero-shot predictions from a foundation model or supervised learning. We observe that in many settings, it is necessary for AutoWS methods to incorporate signal from foundation models if they are to outperform simple few-shot baselines, and AutoWS-Bench-101 promotes future research in this direction. We conclude with a thorough ablation study of AutoWS methods.
Expressions Causing Differences in Emotion Recognition in Social Networking Service Documents

It is often difficult to correctly infer a writer's emotion from text exchanged online, and differences in recognition between writers and readers can be problematic. In this paper, we propose a new framework for detecting sentences that create differences in emotion recognition between the writer and the reader and for detecting the kinds of expressions that cause such differences. The proposed framework consists of a bidirectional encoder representations from transformers (BERT)-based detector that detects sentences causing differences in emotion recognition and an analysis that acquires expressions that characteristically appear in such sentences. The detector, based on a Japanese SNS-document dataset with emotion labels annotated by both the writer and three readers of the social networking service (SNS) documents, detected "hidden-anger sentences" with AUC = 0.772; these sentences gave rise to differences in the recognition of anger. Because SNS documents contain many sentences whose meaning is extremely difficult to interpret, by analyzing the sentences detected by this detector, we obtained several expressions that appear characteristically in hidden-anger sentences. The detected sentences and expressions do not convey anger explicitly, and it is difficult to infer the writer's anger, but if the implicit anger is pointed out, it becomes possible to guess why the writer is angry. Put into practical use, this framework would likely have the ability to mitigate problems based on misunderstandings.
Robust Quantum Control: Analysis &amp; Synthesis via Averaging

An approach is presented for robustness analysis and quantum (unitary) control synthesis based on the classic method of averaging. The result is a multicriterion optimization competing the nominal (uncertainty-free) fidelity with a well known robustness measure: the size of an interaction (error) Hamiltonian, essentially the first term in the Magnus expansion of an interaction unitary. Combining this with the fact that the topology of the control landscape at high fidelity is determined by the null space of the nominal fidelity Hessian, we arrive at a new two-stage algorithm. Once the nominal fidelity is sufficiently high, we approximate both the nominal fidelity and robustness measure as quadratics in the control increments. An optimal solution is obtained by solving a convex optimization for the control increments at each iteration to keep the nominal fidelity high and reduce the robustness measure. Additionally, by separating fidelity from the robustness measure, more flexibility is available for uncertainty modeling.