Reviewerly/exHarmony huggingface dataset:
Innovative Application of Artificial Intelligence in the Field of Innovation and Entrepreneurship of College Students in Internet Colleges and Universities

With the promotion and development of artificial intelligence, the computer major has become a hot major in innovation and entrepreneurship education. It is increasingly essential to carry out a refined evaluation of creativity and venture education to achieve professional differentiation. The purpose of this paper is to study and explore the innovative use of AI in the field of innovation and entrepreneurship of college students in Internet universities. In the article, AI is first introduced. AI is inherently a novel instrument of human praxis, a tool of productivity that embodies human ability and volition. Then we analyze the innovation and venture education on the ground of the BP neural network, and finally, we conduct an empirical study on the innovation and venture on the ground of the BP neural network. Innovation and entrepreneurship refer to entrepreneurial activities based on one or several innovations in technological innovation, product innovation, brand innovation, service innovation, business model innovation, management innovation, organizational innovation, market innovation, and channel innovation. The report analyzes the influencing factors of creative venture education and designs an evaluation model of creative venture education. The test shows that the output of the BP neural network is the actual value, the real worth is reasonable, and the desired worth is also reasonable. The maximum error between the two is 1.64%. The results tend to be generally satisfactory. The BP neural network model can highly generalize students’ creative venture abilities, increasing the practical application value and research scope of BP neural networks.
Decision-Making Application of the Cloud-Fog Hybrid Model Based on the Improved Convolutional Neural Network in Financial Services in Smart Medical Care

In order to alleviate the "difficulty in seeing a doctor" for the masses, continuously optimize the service process, and explore new financial service processes for admission and discharge, this study proposes a cloud-fog hybrid model UCNN-BN based on an improved convolutional neural network and applies it to financial services in smart medical care. Decision-making applications: this research improves and designs the UCNN network based on AlexNet and introduces small convolution layers to form convolution groups, making the network more adjustable. The network structure is simpler and more flexible, and it is easy to adjust the algorithm. The number of parameters is small, and it can be directly superimposed without having to add new network hidden layers. The experimental results show that the recognition rate of the UCNN network on the FER2013 and CK+ datasets is higher than that of other recognition methods, and the recognition rates on the FER2013 and CK+ datasets are 98% and 68.01%, due to other methods. This shows that the improved convolutional neural network used in this study for financial services in smart medical care has certain applicability, and small convolution kernels help to extract more subtle features, so as to identify more accurately.
Research on the Construction of a Bidirectional Neural Network Machine Translation Model Fused with Attention Mechanism

With the development of deep learning, neural machine translation has also been paid attention and developed by researchers. Especially in the application of encoder-decoder in natural language processing, the translation performance has been significantly improved. In 2014, the attention mechanism was used in neural machine translation, the performance of translation was greatly improved, and the interpretability of the model was increased. This research proposes a research idea of sparsemax combined with AAN machine translation model and conducts multiple ablation experiments for experimental verification. This chapter first studies the problem of insufficient sparse normalization when generating target words in the attention mechanism and studies the neural machine translation model incorporating the sparse normalization calculation method. It solves the problem of inductive bias in the data transfer process of related sub-layers in the model. By combining the strategy of sparse normalization, the similarity value of related word vectors can be obtained more accurately when aligning words, which is more convenient for this chapter. Calculate and analyze the specific principles of the model. In addition, when the model faces a large vocabulary in the decoding stage, too many weights of scattered vocabulary vectors are not conducive to the generation of correct target values. After using the sparse normalization strategy, it can reduce the number of inconveniences. The calculation between related words optimizes the classification accuracy of the target vocabulary. In this chapter, aiming at the waste of the transformer’s decoder calculation in the inference stage, the average attention structure is used to replace the attention calculation layer of the first layer of the decoder part of the original model. Each moment is only related to the previous moment, which alleviates the waste of computing resources.
Transdisciplinary approach to archaeological investigations in a Semantic Web perspective

In recent years, the transdisciplinarity of archaeological studies has greatly increased because of the mature interactions between archaeologists and scientists from different disciplines (called “archaeometers”). A number of diverse scientific disciplines collaborate to get an objective account of the archaeological records. A large amount of digital data support the whole process, and there is a great value in keeping the coherence of information and knowledge, as contributed by each intervening discipline. During the years, a number of representation models have been developed to account for the recording of the archaeological process in data bases. Lately, some semantic models, compliant with the CRMarchaeo reference model, have been developed to account for linking the institutional forms with the formal knowledge concerning the archaeological excavations and the related findings. On the contrary, the archaeometric processes have not been addressed yet in the Semantic Web community and only an upper reference model, called CRMsci, accounts for the representation of the scientific investigations in general. This paper presents a modular computational ontology for the interlinked representation of all the facts related to the archaeological and archaeometric analyses and interpretations, also connected to the recording catalogues. The computational ontology is compliant with CIDOC-CRM reference models CRMarchaeo and CRMsci and introduces a number of novel classes and properties to merge the two worlds in a joint representation. The ontology is in use in “Beyond Archaeology”, a methodological project for the establishing of a transdisciplinary approach to archaeology and archaeometry, interlinked through a semantic model of processes and objects.
Antecedents of e-commerce on intention to use the international trade center: An Exploratory Study in Jordan

Digital divide, trust towards e-commerce system, and facilitating conditions as the factors impacting the use intention of the international trade center were examined in this study, through a survey carried out on 500 Jordanian students. The proposed conceptual model was empirically tested, and significant impact of trust and facilitating conditions on intention to use the international trade center was affirmed. Meanwhile, the digital divide did not impact the intention to use the international trade center. Being limited to the Jordanian context, the study findings may not be generalizable to other countries.
On-Chip Quantum Communication Devices

We present here results of the Quantum Technology Flagship project UNIQORN in the area of integrated photonics for quantum communication applications.Three distinct integration platforms, namely indium phosphide based monolithic integration, polymer-based hybrid integration and the CMOScompatible silicon platform, have been employed to manufacture components and sub-systems on chip for quantum communication devices.The choice of different platforms was made to exploit the best characteristics of each platform for the intended quantum communication device.The indium phosphide platform was employed to manufacture a transmitter chip for quantum key distribution featuring laser, modulators, and attenuators.The transmitter chip was evaluated in a QKD experiment achieving a secure rate of 1 kbit/s.The polymer platform was investigated for engineering non-classical light sources.Entangled and heralded single-photon sources, based on non-linear optics, were assembled on the polymer in a hybrid fashion together with waveguides and other passive micro-optical elements.A quantum random number generator, featuring a 70% randomness extraction efficiency, was also fabricated using the polymer integration technique.An array of 32 individual single-photon avalanche diodes, operating at room temperature and featuring an onboard coincidence logic, was coupled to the chip to demonstrate direct detection of photons on the polymer.Finally, a transimpedance amplifier based on gallium arsenide high electron mobility transistors was produced with an exceptional large electrical noise clearance of 28 dB at 100 MHz.
Research on the Bolt Positioning System Based on Multieye Vision Industrial Robots

In order to improve the accuracy of bolt positioning for industrial robots, this paper studies the bolt positioning system of industrial robots combined with multieye vision technology. This paper introduces three reconstruction algorithms ART, SIRT, and SART from the perspective of theory and implementation. Moreover, the projection matrix calculated with 0, 1 weighting, length weighting, and linear interpolation weighting is used for three reconstruction algorithms to carry out reconstruction experiments. In addition, this paper combines the actual working conditions of bolt positioning to construct the system of this paper and conducts system simulation research combined with the working conditions of the robot. The research shows that the bolt positioning system based on the multieye vision industrial robot proposed in this paper has a good performance in bolt positioning.
Automatic oracle generation in microsoft's quantum development kit using QIR and LLVM passes

Automatic oracle generation techniques can find optimized quantum circuits for classical components in quantum algorithms. However, most implementations of oracle generation techniques require that the classical component is expressed in terms of a conventional logic representation such as logic networks, truth tables, or decision diagrams. We implemented LLVM passes that can automatically generate QIR functions representing classical Q# functions into QIR code implementing such functions quantumly. We are using state-of-the-art logic optimization and oracle generation techniques based on XOR-AND graphs for this purpose. This enables not only a more natural description of the quantum algorithm on a higher level of abstraction, but also enables technology-dependent or application-specific generation of the oracles.
Motion Based Indian Sign Language Recognition using Deep Learning

Despite a large section of people being hearing impaired, there is still less awareness about sign language. These people use sign language to communicate with others, which includes various gestures using hands, facial expressions and body pose. There are many variants of sign language which vary from country to country. This paper proposes a model for Motion-Based Indian Sign Language (ISL) recognition using deep learning. The gesture is captured using OpenCV, key points are detected using MediaPipe and sign prediction is done by a trained LSTM (Long Short-Term Memory) model. The dataset used for this was created by the research team members. An average accuracy of 92% was obtained. The proposed system can be used for real-time Indian Sign Language recognition and can be integrated with video-conferencing applications.
An Improved Bot Identification with Imbalanced Data using GG-XGBoost

In online social networks (OSNs), bots usually disseminate fraudulent information. In OSNs, when compared to the benign users, the bots are much less in number. However, the bot can create more negative impact in OSNs. One of the main research topics is to improve the accuracy of bot identification. Traditional bot identification approaches rely on supervised learning models. However, most supervised learning models are trained with a dataset which contains highly unbalanced distribution of data with benign users and bots. In this work, a generative adversarial network (GAN) with gated recurrent unit (GRU) is considered to overcome the unbalanced distribution of bots in OSNs. Further, we propose a novel algorithm namely GG-XGBoost by integrating GRU-GAN with XGBoost model. Experimentation have been performed on Twitter dataset to verify the efficiency of GG-XGBoost algorithm.
Prediction of Immunotherapy Success Rate: Particle Swarm Optimization Approach

Cancer is a disease that ranks second as a cause of death, one of which is warts caused by the Human Papilloma Virus. One of the warts treatment techniques that can be done is Immunotherapy, this method is another treatment that increases the human immune system. Naive Bayes Classifier or often called Bayesian Classification is a statistical classification method that can be used to predict the probability of membership of a class, Naive Bayes Classifier can be optimized by assigning a weight value to each attribute using the Particle Swarm Optimization method which aims to increase accuracy. Particle Swarm Optimization has advantages in selecting features and has superior performance in many optimization problems that can be solved more quickly, and the convergence rate tends to be more stable. The results of processing the dataset using this algorithm show a good accuracy value, which is 92.59% and AUC is 0.841. This research is useful in the field of health sciences, as information that needs to be followed up in testing the success rate of immunotherapy.
Differentially Private Occupancy Monitoring from WiFi Access Points

Accurately monitoring the number of individuals inside a building is vital to limiting COVID-19 transmission. Low adoption of contact tracing apps due to privacy concerns has increased pervasiveness of passive digital tracking alternatives. Large arrays of WiFi access points can conveniently track mobile devices on university and industry campuses. The CrowdMap system employed by the University of Southern California enables such tracking by collecting aggregate statistics from connections to access points around campus. However, since these devices can be used to infer the movement of individuals, there is still a significant risk that even aggregate occupancy statistics will violate the location privacy of individuals. We examine the use of Differential Privacy in reporting statistics from this system as measured using point and range count queries. We propose discretization schemes to model the positions of users given only user connections to WiFi access points. Using this information we are able to release accurate counts of occupants in areas of campus buildings such as labs, hallways, and large discussion halls with minimized risk to individual users' privacy.
IoT in the Healthcare Sector

Internet of Things (IoT)-enabled services and wearable devices have transformed the lives of patients suffering from chronic diseases and other diseases where patients need intensive care. Mostly, IoT enabled devices are wearable and equipped with activity monitors and measurement devices such as digital scales and digital thermometers. The related data is stored, analysed at the patient’s end, and then transmitted through computing clouds to the doctors in the hospital. Patients get feedback from the doctor and make better health and well-being decisions in real-time in a very economical way. The personalized health of the population is greatly benefited in the whole process. Doctors, hospitals, health insurance companies, and artificial intelligence software developers are also providing their services very efficiently. Undoubtedly, the new IoT technology has revolutionized the healthcare facilities, but at the same time, service providers are also facing some issues, such as personal sensitive data security, difficulties with regular updates, and global healthcare regulations.
Active Learning for Imbalanced Data: The Difficulty and Proportions of Class Matter

Deep learning methods have achieved great success in many fields, but it is severely limited by the quantity and quality of training data. When the number of labeled samples is small and the datasets is class-imbalanced, the model is difficult to perform well. In this paper, an active learning method for the small and class-imbalanced labeled datasets is proposed. In addition to the uncertainty of instances which traditional active learning methods take into account, the difficulty and the proportion of samples of different classes are also considered in our method too, so that our active learning method is better oriented to the dataset with unbalanced classes. This paper first explains the motivation of the proposed method and then introduces the frameworks of the method in detail. Finally, experiments on three datasets prove that our method can obtain better results than traditional active learning methods based on uncertainty.
Design of Financial Information Management System and IoT Application Based on Fuzzy Comprehensive Evaluation

In light of the current financial information management system’s poor performance, this paper proposes a design method for a financial information management system based on fuzzy comprehensive evaluation, which improves the system’s operation performance by improving the system hardware structure setting and improving the system software function combined with the principle of fuzzy comprehensive evaluation. Finally, the experiment demonstrates that the financial information management system based on fuzzy comprehensive evaluation has a high practicability in the process of practical application, allowing it to effectively avoid financial management risk while also improving financial information management quality.
Based on the Application of Deep Learning in College Education Evaluation

The most significant material in teaching quality assessment is the teacher’s teaching evaluation; nevertheless, as education informatization accelerates, the current teaching management challenge is how to use network technology to evaluate instructors’ teaching quality in conventional teaching. The question of how to evaluate the network teaching environment is a crucial issue in the development of network teaching. The key to educational evaluation lies in the quality of teaching. Therefore, the effective processing and analysis of the huge original data collected in the teaching process of colleges and universities can provide decision support for the evaluation of teaching quality and the formulation of relevant improvement measures. Given the variety and large quantity of original teaching data, this paper proposes a deep learning-based teaching quality evaluation model that organically integrates various original data by constructing deep neural networks and can achieve more accurate teaching quality evaluation, which has practical value. Education evaluation is a complex system engineering, which needs to consume a lot of manpower, material, and financial resources. Through the use of the system to obtain a large number of statistical data, it will provide a basis for in-depth analysis and decision-making.
Improved Parallel Scanner for the Concurrent Execution of Lexical Analysis Tasks on Multi-Core Systems

Purpose: The processing power of machines will continue to accelerate massively. Modern eras of computing are driven by elevated parallel processing by the revolution of multi-core processors. This continuing trend toward parallel architectural paradigms facilitates parallel processing on a single machine and necessitates parallel programming in order to utilize the machine's enormous processing power. As a consequence, scanner generator applications will eventually need to be parallelized in order to fully leverage the throughput benefits of multi-core processors. This article discusses the way of processing the tasks in parallel during the scanning stage of lexical analysis. This is done by recognizing tokens in different lines of the source program in parallel along with auto detection of keyword in a character stream. Tasks are allocated line-by-line to the multiple instances of the lexical analyzer program. Then, each of the instances is run in parallel to detect tokens on different cores that are not yet engaged. Design/Methodology/Approach: Developing a theoretical and experimental approach for parallelizing the lexical scanning process on a multi-core system. Findings/Result: Based on the developed model, the theoretical and practical results indicate that the suggested methodology outperforms the sequential strategy in terms of tokenization consistently. It significantly decreases the amount of time spent on lexical analysis during the compilation process. It is clearly observed that the speedup should increase at or close to the same rate as the number of cores and keywords in the source program increases. This enhancement would improve the overall compilation time even more. Originality/Value: A hybrid model is developed for the concurrent execution of a lexical analyzer on multi-core systems using a dynamic task allocation algorithm and an auto-keyword detection method. Paper Type: Experimental Research.
Calibration method for a structured light system based on DIC

Structured light is a non-contact three-dimensional shape measurement method. The structured light system based on diffractive optical elements (DOEs) is widely used due to its low cost and compact structure. However, compared with a time sequence coded structured light system based on a digital projector, its projector cannot change the content, so it cannot be calibrated with a phase-shift-based method. This paper proposes a calibration method based on the pseudo-camera method and digital image correlation (DIC). It is suitable not only for the calibration of systems based on time series coding but also for the calibration of a speckle structured light system based on DOEs. Validation experiments were conducted in which our method achieved a reprojection error of 0.68 pixels in calibration and a radius error of less than 1% in a measurement of a cylinder in a <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>20</mml:mn> </mml:mrow> <mml:mspace width="thickmathspace" /> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">c</mml:mi> <mml:mi mathvariant="normal">m</mml:mi> </mml:mrow> <mml:mo>×<!-- × --></mml:mo> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>10</mml:mn> </mml:mrow> <mml:mspace width="thickmathspace" /> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">c</mml:mi> <mml:mi mathvariant="normal">m</mml:mi> </mml:mrow> </mml:math> field. Compared with existing calibration methods, this method does not need to set a datum plane, takes fewer photos, operates simply, and has higher calibration efficiency.