Reviewerly/exHarmony huggingface dataset:
The Application of BP Neural Network Algorithm to Risk Evaluation of High-Tech Entrepreneurial Projects

Back propagation (BP) neural network is characterized by the advantages of large-scale parallel, distributed processing, self-organization, and self-learning. It is suitable for handling problems that need to consider multiple conditions and fuzzy information simultaneously. Firstly, this paper expounds on the construction and algorithm of the BP neural network. Then, the risk index system of high-tech entrepreneurial projects was designed, and the evaluation levels and scoring method for the risk factor indicators were determined. Finally, we developed a model for risk evaluation of high-tech entrepreneurial projects based on the BP neural network. The empirical results show that the BP neural network has strong feasibility and effectiveness, and can accurately fit the training values to derive accurate prediction results. This method provides a new approach for risk evaluation of entrepreneurial projects.
On Alternation, VC-dimension and k-fold Union of Sets

Alternation of Boolean functions is a measure of non-monotonicity of the function. In this paper, we asymptotically characterize the $$\mathsf{VC}\text {-dimension}$$ of family of Boolean functions parameterized by the maximum alternation of the Boolean functions in the family. Enroute to our main result, we show exact bounds for $$\mathsf{VC}\text {-dimension}$$ of functions which has alternation 1, which strictly contains monotone functions and hence generalizes the bounds in [3]. As an application, we show tightness of $$\mathsf{VC}\text {-dimension}$$ bounds for k-fold union, by explicitly constructing a family $$\mathcal{F}$$ of subsets of $$\{0,1\}^n$$ such that k-fold union of the family, $$\mathcal{F}^k = \left\{ \bigcup _{i=1}^k F_i \mid F_i \in \mathcal{F}\right\} $$ must have $$\mathsf{VC}\text {-dimension}$$ at least $$\varOmega (dk)$$ and that this bound holds even when the union is over disjoint sets from $$\mathcal{F}$$ . This provides a non-geometric set system achieving this bound.
Experimental verification of group non-membership in optical circuits

The class quantum Merlin–Arthur (QMA), as the quantum analog of nondeterministic polynomial time, contains the decision problems whose YES instance can be verified efficiently with a quantum computer. The problem of deciding the group non-membership (GNM) of a group element is conjectured to be a member of QMA. Previous works on the verification of GNM, which still lacks experimental demonstration, required a quantum circuit with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m1"> <mml:mi>O</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:msup> <mml:mi>n</mml:mi> <mml:mn>5</mml:mn> </mml:msup> <mml:mo stretchy="false">)</mml:mo> </mml:math> group oracle calls. Here, we provide an efficient way to verify GNM problems, in which each quantum circuit only contains <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m2"> <mml:mi>O</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:mn>1</mml:mn> <mml:mo stretchy="false">)</mml:mo> </mml:math> group of oracle calls, and the number of qubits in each circuit is reduced by half. Based on this protocol, we then experimentally demonstrate the new verification process with a four-element group in an all-optical circuit. The new protocol is validated experimentally by observing a significant completeness-soundness gap between the probabilities of accepting elements in and outside the subgroup. This work efficiently simplifies the verification of GNM and is helpful in constructing more quantum protocols based on the near-term quantum devices.
A Hybrid Unsupervised Feature Selection Algorithm

Due to the explosion of data, a vast amount of high-dimensional data like images, texts as well as medical microarray data are generated. In addition to exponentially raising measurement storage and processing strain on algorithms &#x0026; computer hardware, direct processing of high-dimensional data often results in poor performance because of irrelevant, noisy as well as duplicate dimensions. A large number of features present in the dataset used for machine intelligence purposes pose a big threat to researchers. Algorithms that use these large dimension features suffer in terms of computer time taken to make decisions and space required to store them in computer memory. In the proposed work we have developed a hybrid algorithm to select the highly discriminative features present in the dataset. Using the multicluster feature rank score and unsupervised discriminative feature ranking methods in selecting the most discriminative features, on some well-documented datasets like the ORL, we have carried out comprehensive experiments. Our experimental results have proven the superiority of our algorithms in comparison to some state-of-the-art algorithms.
Linear permutations and their compositional inverses over 𝔽qn

The use of permutation polynomials over finite fields has appeared, along with their compositional inverses, as a good choice in the implementation of cryptographic systems. As a particular case, the construction of involutions is highly desired since their compositional inverses are themselves. In this work, we present an effective way of how to construct several linear permutation polynomials over [Formula: see text] as well as their compositional inverses using a decomposition of [Formula: see text] based on its primitive idempotents. As a consequence, involutions are also constructed.
BSVMS: Novel Autonomous Trustworthy Scheme for Video Monitoring

With the continuous development and application of monitoring technology, which involves increasingly more sensitive information, the global demand for video monitoring systems has surged. As a result, video monitoring technology has received widespread attention both at home and abroad. Traditional video monitoring systems experience security threats, with differing levels of severity, in terms of attack, storage, transmission, etc., which results in different degrees of damage to users’ rights. Therefore, we propose a blockchain-SM-based video monitoring system called BSVMS. For the front-end device invasion risk, internal attack risk, and security storage problem of the monitoring system, we use commercial cryptography algorithms to complete the encryption processing of images through a visual change network in the imaging process, thereby ensuring the security of the video data from the source. To address the problem that the video monitoring application software and data are vulnerable to damage, we use blockchain technologies that are tamper-proof and traceable to build a trustworthy video monitoring system. In the system, no member can query the original monitoring data. To address the security issues in network transmission, we use a commercial cryptography algorithm for multilayer encryption to ensure the security of data during transmission, guarantee the confidentiality of the system, and realize domestic autonomous control. We then conduct tests and security analysis of the encryption and decryption efficiency of the SM4 algorithm used in the system, the blockchain performance, and the overall performance. The experimental results show that in this system environment, the SM4 algorithm encryption and decryption efficiency is better than other algorithms and that the blockchain used meets industry standards.
The Choice of Best Summer Job Based on Improved Fuzzy Comprehensive Evaluation

How to find a suitable summer job is a problem that plagues many high school students. In order to help high school students to choose the most suitable job for them, we proposed a personalized summer job selection evaluation model. Our model can give the best job selection according to the user's personal situation. In order to get people's considerations about choosing summer jobs, we proposed an influencing factor mining method based on the social media analysis. To provide users with personalized summer job selection evaluation, we proposed a personalized summer job selection evaluation model based on improved fuzzy comprehensive evaluation. We created 10 virtual users based on Holland's career interest theory. Our work is useful to find the best summer job.
Educational Advancements in the Field of Augmented Reality and Virtual Reality

Technology has constantly kept on evolving since many years. This has helped in the growth of many fields throughout the world. One such important field is Education. Different technologies have helped education to grow positively and help everyone learn and practice with ease and understanding. Technology has proven to be a useful aid in education, as it enables teaching methods to be eased and efficiency enhanced by incorporating economical and effective means of transmitting digital content. Researchers have shown us that Virtual Reality and Augmented Reality have great potential to support two types of users: students, on the one hand, increase productivity and from the other teachers, broaden their teaching techniques. Hence, it makes the experience of education and learning more effective and stimulating. Nonetheless, even for non-skilled developers, there is no specifically designed platform for the flexible creation of AR/VR content. There is therefore a need to develop new tools to allow users to easily become producers of such experiences.
An Attentional-based Capsule Neural Network for Sentiment Analysis on Online Reviews

As a fundamental task of the natural language processing, sentiment analysis has drawn much attention from researchers. In-depth research has been conducted by previous work in this field and great progress has been made. However, these works fail to capture the emotional information in the text accurately, so it is still difficult to achieve satisfactory results. To address this problem, attentional capsule network (ACN) is proposed, which leverages the attention mechanism and the properties of capsule network to capture sentiment information from online reviews accurately. The experimental results demonstrate that our model is superior to other baselines.
Integrating IOT, Game Engineering and Data Modeling with 2D-3D Design in Developing Virtual-Physical Interactive Cognition Game Box

In support of the gerontechnology development of university social responsibility for community and educational requirements, this study develops a virtual-physical interactive cognition game box for elders. After interviewing the care needs of community development association members, we propose a production prototype that combines science technology and art design with virtual-physical integration upon the considerations of technical feasibility and HCI design. The prototype was constructed by integrating the 3D shape design, laser engraving design/implementation, character design and animation, game engineering, data modeling, the internet of things (IoT), and so on. To further improve the Virtual-Physical Interactive Cognition Game Box prototype, several user experience activities were conducted in the community and the university. It has also been introduced in related courses in the form of cases integrating interdisciplinary works into virtual-physical interaction designs for students. Data engineering will be the next step in the future.
Automated Gland Detection in Colorectal Histopathological Images

Clinical morphological analysis of histopathological specimens is a successful manner for diagnosing benign and malignant diseases. Analysis of glandular architecture is a major challenge for colon histopathologists as a result of the difficulty of identifying morphological structures in glandular malignant tumours due to the distortion of glands boundaries, furthermore the variation in the appearance of staining specimens. For reliable analysis of colon specimens, several deep learning methods have exhibited encouraging performance in the glands automatic segmentation despite the challenges. In the histopathology field, the vast number of annotation images for training the deep learning algorithms is the major challenge. In this work, we propose a trainable Convolutional Neural Network (CNN) from end to end for detecting the glands automatically. More specifically, the Modified Res-U-Net is employed for segmenting the colorectal glands in Haematoxylin and Eosin (H&E) stained images for challenging Gland Segmentation (GlaS) dataset. The proposed Res-U-Net outperformed the prior methods that utilise U-Net architecture on the images of the GlaS dataset.
Novel DWT and PC-Based Profile Generation Method for Human Action Recognition

Human action recognition in recordings acquired from reconnaissance cameras discovers application in fields like security, health care and medicine, sports, programmed gesture-based communication acknowledgment, and so on. The task is challenging due to variations in motion, recording settings, and inter-personal differences. In this paper, novel DWT & PC-based profile generation algorithm is proposed which incorporates notion of energy in extracting features from video frames. Seven energy-based features are calculated using unique energy profiles of each action. Proposed algorithm is applied to three widely used classifiers—SVM, Naive bayes, and J48 to classify video actions. Algorithm is tested on Weizmann’s dataset & performance is measured with evaluation metrics such as precision, sensitivity, specificity, and accuracy. Finally, it is compared with the existing method of template matching using MACH filter. Simulation results give good accuracy than existing method.
An ObsPy Library for Event Detection and Seismic Attribute Calculation: Preparing Waveforms for Automated Analysis

We have implemented an extension for the observational seismology <em>obspy</em> software package to provide a streamlined tool tailored to the processing of seismic signals from non-earthquake sources, in particular those from deforming systems such as glaciers and landslides. This <em>seismic attributes</em> library provides functionality to: (1) download and/or pre-process seismic waveform data; (2) detect and catalogue seismic events using multi-component signals from one or more seismometers; and (3) calculate characteristics (‘attributes’/‘features’) of the identified events. The workflow is controlled by three main functions that have been tested for the breadth of data types expected from permanent and campaign-deployed seismic instrumentation. A selected STA/LTA-type (short-term average/long-term average), or other, event detection algorithm can be applied to the waveforms and user-defined functions implemented to calculate any required characteristics of the detected events. The code is written in Python 2/3 and is available on GitHub together with detailed documentation and worked examples.
A New Density Clustering Method Using Mutual Nearest Neighbor

Density-based clustering algorithms have become a popular research topic in recent years. However, most these algorithms have difficulties identifying all clusters with greatly varying densities and arbitrary shapes or have considerable time complexity. To tackle this issue, we propose a novel density assessment method by using mutual nearest neighbor, and then propose a relative density clustering algorithm (RDC). RDC can get the right number of clusters for the datasets, which include varying densities and arbitrary shapes; in addition, the time complexity of it is O(nlog n).
Web-Based Bag And Luggage Sales Information System At CV. Purnama

This study aims to design and create a web-based information system that functions to help manage the sales process at CV. Purnama. This research was conducted in March 2020 at the Informatics Laboratory, University of Muhammadiyah Sidoarjo and CV. Purnama. This research was conducted using the waterfall method, data were collected through interviews, literature study, and problem analysis. The results of this study are the design and manufacture of Web-Based Bag and Suitcase Sales Information Systems at CV. Purnama which aims to increase promotion and facilitate the operational management in CV. Purnama. This information system has two actors, namely admin as manager of information systems and customers as consumers. Based on this research, the conclusion is that with the sales system (e-commerce), CV. Purnama can facilitate the conventional transaction process to become more modern with the availability of online transactions.
Electronic System of Language Skills Formation: Psychological Features of Foreign Language Teaching

In the world, there is always a need to improve the quality of communication between people who speak different languages. This urgent task constantly requires the search for new ways of teaching the language, which should be based on the creative cooperation of specialists in different fields. Any language is a complex multi-level system, and language acquisition is a process that includes a large number of parameters. The article provides a brief description of the psychological aspects that allow us to justify the possibility of using a visual approach to the development of grammatical competencies. The relevance of the topic is determined by the need to create a new detailed technology for the application of the most effective methods for the formation of speech skills of foreign language proficiency in adults. The study is based on models of cognitive psychology. Using the extension of the areas of Bloom's taxonomy and the representation of the model of interaction between the psyche and reality, it is possible to demonstrate patterns that are difficult to explain in words due to restrictions on the amount of verbal RAM of the student, which is especially important in the process of mastering a foreign language. The latest developments are aimed at combining new methods aimed at simultaneously developing language skills and grammatical competence within the same activity. Each of the methods can be improved through the use of computer technologies, and their joint use in one system will lead to a synergistic effect in the learning process. The proposed learning management system should work in combination with a methodologically precisely integrated linguistic system and ensure the formation of the speech zone of a new language in the consciousness of an adult student in the learning process.
GSLD: A Global Scanner with Local Discriminator Network for Fast Detection of Sparse Plasma Cell in Immunohistochemistry

Compared with abundant application of deep learning on hematoxylin and eosin (H&E) images, the study on immunohistochemical (IHC) images is almost blank, while the diagnosis of chronic endometritis mainly relies on the detection of plasma cells in IHC images. In this paper, a novel framework named Global Scanner with Local Discriminator (GSLD) is proposed to detect plasma cells with highly sparse distribution in IHC whole slide images (WSI) effectively and efficiently. Firstly, input an IHC image, the Global Scanner subnetwork (GSNet) predicts a distribution map, where the candidate plasma cells are localized quickly. Secondly, based on the distribution map, the Local Discriminator subnetwork (LDNet)discriminates true plasma cells by adopting only local information, which greatly speeds up the detection. Moreover, a novel grid-oversampling strategy for WSI preprocessing is proposed to relieve sample imbalance problem. Experimentas show that the proposed framework outperforms the representative object detection networks in both speed and accuracy.
Design of an Ultrasound-Navigated Prostate Cancer Biopsy System for Nationwide Implementation in Senegal

This paper presents the design of NaviPBx, an ultrasound-navigated prostate cancer biopsy system. NaviPBx is designed to support an affordable and sustainable national healthcare program in Senegal. It uses spatiotemporal navigation and multiparametric transrectal ultrasound to guide biopsies. NaviPBx integrates concepts and methods that have been independently validated previously in clinical feasibility studies and deploys them together in a practical prostate cancer biopsy system. NaviPBx is based entirely on free open-source software and will be shared as a free open-source program with no restriction on its use. NaviPBx is set to be deployed and sustained nationwide through the Senegalese Military Health Service. This paper reports on the results of the design process of NaviPBx. Our approach concentrates on "frugal technology", intended to be affordable for low-middle income (LMIC) countries. Our project promises the wide-scale application of prostate biopsy and will foster time-efficient development and programmatic implementation of ultrasound-guided diagnostic and therapeutic interventions in Senegal and beyond.
Performance Improvement of Content-Based Video Retrieval using Thepade’s Sorted n-ary Block Truncation Coding(TSnBTC) with Various Color Spaces

Content-Based Video Retrieval is used in several applications across sectors such as Education, Medical, and Agriculture. In this world, each end-user has a different perspective when elaboration of a video is expected, due to which the text description/keywords-based methods of video retrieval do face difficulties. Content-Based Video Retrieval (CBVR) has become more popular in the last few years; which attempts to describe each video, based on the contents represented as features. Various feature extraction methods can be used for CBVR. This paper attempts CBVR with the features extracted from videos using TSn-BTC - Thepade’s Sorted n-ary Block Truncation Coding n-ary method along-with different color spaces. The different types of an extended version of TSnBTC are enlisted in this paper ,Versions are - Thepade’s Sorted Ternary, Quarternary, Pentanary, Septanary, Octanary, Nonary, and Denary Block Truncation Coding Technique. Plenty of color spaces used in this paper such as RGB, LUV, YCbCr, YIQ, YUV, CIEXYZ, CIEXYZrec, CIEXYZccir, CMY, and CMYK, in Which YCbCr gives Higher Accuracy followed by RGB. The similarity criteria used is Mean Squared Error(MSE) for calculating similarity. The Feature Vector generation process can be done by using the 20 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> Frequency Frame of every video. The experimentation environment in Content-Based Video Retrieval based on TSnBTC Technique is done of 500 various videos which consist into Various 10 unique categories. Each category consists of 50 videos. The YCbCr Color Space achieved higher performance. For each query video fired onto no .of database Video dataset, and the Mean Exactness is calculated For different TSnBTC Versions.
Learnability in the Context of Lexicalized Parameters: A Compositional Approach to Language Acquisition

Language acquisition is inextricably linked to the structure of language itself. Consequently, syntactic acquisition must address the structure of syntactic representation. Research on cross-linguistic syntactic variation and acquisition has repeatedly demonstrated the various difficulties in modeling a Principles and Parameters-style grammar and learning mechanism. Perhaps, such difficulty stems from the inadequacy of a Principles and Parameters-style representation of language itself. This paper combines relevant aspects of Minimalist syntax and a learning-by-parsing approach, in the vein of Fodor and Teller (2000), in order to motivate a reanalysis of learnability using lexicalized parameter values. The learner’s goal is no longer to set grammar-wide parameter values, but rather to learn how individual lexical items are merged. In this paper, a new psychologically-plausible learnability model is mapped as the foundation for future research. Furthermore, by abstracting away from mental primitives, this paper also notes how tree-adjoining grammars may be used as computational proxies for lexicalized parameters, providing empirical ways to further study this proposed learning algorithm.