Reviewerly/exHarmony huggingface dataset:
Bi-Apriori-Based Association Discovery via Alarm Logs

The state grid information system is complex, the operation and maintenance information are diverse, involving a wide range of aspects. It becomes a key problem that how to use the alarm log of the operation and maintenance system to analyze the root cause of the fault. At present, there is a lack of alarm correlation technology for operation and maintenance system, and the traditional method lacks mature application systems for new scenarios. The reliability and efficiency of operation and maintenance system not only depends on the accurate collection of equipment fault information, but also depends on how to analyze massive fault information effectively and quickly, so as to grasp the key, handle the key, solve the key, three “keys”. In this context, a Bi-Apriori algorithm is proposed, to solve the problem of frequently scanning the dataset and generating a large number of candidate set. It adopts the vertical data structure to mine frequent items and then as for alarm logs the proposed Bi-Apriori can be used to mining frequent items from alarm logs. Experimental results show that the proposed Bi-Apriori algorithm is superior to the existing association rule discovery algorithms on several datasets in real applications, especially for the dataset of alarm logs.
Facial recognition system for security access control

Some companies require that only authorized personnel can enter certain restricted areas. Biometric systems normally use RFID (Radio Frequency Identification) cards. However, these systems are not immune to impersonation because cards can be stolen. Other alternatives for the development of security systems consist in the use of facial recognition techniques, which are safer since it is more difficult to impersonate someone, but photographs of the subject can still be used to try to vulnerate the system's security. Therefore, this work proposes the development of a facial recognition application that allows access only to those authorized persons who, during recognition, make a gesture to determine that it is indeed a real subject. The proposed technique comprises three serial stages: face recognition, mouth movement or blink detection, and liveness detection. Several algorithms were analyzed for each stage, choosing the models with which the best performance were obtained. In the first two stages, the Geitgey and Xie methods, respectively, were used for mouth detection, and the Geitgey and Soukupova-Cech algorithms for blinking. Since security systems demand the highest possible accuracy, a new technique for liveness detection from background analysis is proposed, which outperformed the results obtained with Rosebrock's technique, achieving 100% accuracy in a processing time of 6.76 seconds. To evaluate the methods, a database was constructed consisting of 46 videos of fake people and 40 videos of real people performing the opening and closing gesture of the mouth and blinking.
An Image Processing Based American Sign Language Fingerspelling Interpreter

Owing to limitations such as high cost, discomfort, and connectivity issues, existing fingerspelling interpreters fail to present a feasible and comfortable medium to efficiently communicate with communities that rely on sign language that includes hand gestures, facial expressions and orientations, for communication. Biological measurements, based on aspects such as hand or arm movements and acoustic variations at wrist, are unique to an individual and require customized systems to be designed for many groups of people. However, developing a general system that would not only incorporate sign language translation, but also additional features would bring a revolutionary change in differently abled community. In this article, we propose an image processing based system with machine learning algorithms, implemented as a WebApp on Raspberry Pi, which is an efficient, cost-effective, and user-friendly bridge between communities that understand sign language and the communities that do not.
Dedicated software agents in smart home system - Smarts

This paper describes a proposal for implementing complex, continuous control in a smart home system in a functional, but user-friendly way. The proposal assumes usage of software agents (called Smarts) running on the central controller, which operates on a given set of devices in a logical area. The proposed approach ensures that the system operates as expected even when without an internet connection. Additionally, the direct connection between smart devices and the central controller ensures much faster response times. To allow easy configuration, each agent is coupled with its own visual representation on mobile applications, in a similar way as devices. As a result, end-users can use the complex control functions on a large device set in a smart home in a very simple way, since the complexity is hidden behind the agents. For the concept validation and testing, pre-developed agents were used, but a future approach may also support a dynamic definition of agents.
DEDD: Deep Encoder with Dual Decoder Architecture for Stability and Specificity Preserving Encoding and Translation of Embedding between Domains

We propose a deep learning-based encoder with a dual decoder system to enrich the expressive power of embeddings pre-trained on two different corpora along with switching representation between domains. There are two scenarios: (a) Each of the corpora is pertaining to the different subject matter or topic of interests and (b) One corpus is a vast super-domain with generic and non-specific embeddings while the second one pertains to one specific sub-domain. In either case, the criterion for high-quality training would be to have enough common words between them. The mapping of contextual embeddings from both the corpus into the common latent space blends the semantic richness of both the corpus-specific learning while maintaining embedding stability. Furthermore, there is one dedicated decoder for either of the domains for generating the representation from common latent space. We evaluated our method for cross-learning between generalized GLOVE embedding and a very specialized skill-embedding developed by random-walk on a graph-based Skills Hierarchy. We demonstrate that our method preserves the stability of the generic embedding, the specificity of the skill domain as well as enriches the semantic representation of either domain through switching enabled by the encoder-to-duel-decoder path.
Generation of bitstream by moore machine from state machine

This paper represents generation of a particular sequence with the help of moore machine which is a sequential device. This was implemented with the help of state diagram. The various parameters required for the generation of the sequence are also discussed in this paper. The whole generation and analysis of sequence is done with moore machine as it is very efficient and high speed bit stream generator.
Hypervolume by Slicing Objective Algorithm: An Improved Version

The hypervolume remains a popular performance indicator in evolutionary multi-objective, mainly because of its nice mathematical properties (i.e., it's the only performance indicator known to be Pareto-compliant). However, its high computational cost (which grows polynomially on the population size but exponentially on the number of objectives) has severely limited its use in many-objective optimization. This has motivated a variety of proposals that attempt to overcome this limitation. One of the most popular proposals currently available is the so-called Hypervolume by Slicing Objectives (HSO) algorithm. Here, we show that the worst-case time complexity of the HSO algorithm, as obtained by its authors, is incorrect. Then, we provide an efficient implementation of the HSO algorithm, which guarantees that unique slices are generated to compute the hypervolume.
Key Protected Deputy Signature Scheme against the Deputy Signing Key Exposure

Key exposure is very harmful to a cryptographic system. To decrease the loss from the deputy signing key vulnerability in identity-based proxy signature systems, we propose the method of key protected deputy signature (IBKPDS) using the method of parallel key insulation. The proposed IBKPDS is based on identities and is shown to be secure with the cryptographic proof. In the proof, there is no random oracle. In an IBKPPS crypto-system, a user stores his short-lived deputy signing key by himself and saves two long-lived keys in two heavily guarded boxes respectively. The derived IBKPDS cryptographic system is heavily key-separated. A thief who wants to obtain crucial information can not corrupt the IBKPDS when he get only one long-lived key. In addition, the user can change the short-lived deputy signing keys frequently at low risk.
Convex Nonparanormal Regression

Quantifying uncertainty in predictions or, more generally, estimating the posterior conditional distribution, is a core challenge in machine learning and statistics. We introduce Convex Nonparanormal Regression (CNR), a conditional nonparanormal approach for coping with this task. CNR involves a convex optimization of a posterior defined via a rich dictionary of pre-defined non linear transformations on Gaussians. It can fit an arbitrary conditional distribution, including multimodal and non-symmetric posteriors. For the special but powerful case of a piecewise linear dictionary, we provide a closed form of the posterior mean which can be used for point-wise predictions. Finally, we demonstrate the advantages of CNR over classical competitors using synthetic and real world data.
Threshold reusable fuzzy extractor and an application to joint access control via biometric information

Fuzzy extractor can be used to convert the reading of a noisy non-uniform source into a reliably reproducible and almost uniform string, while reusable fuzzy extractor allows multiple extractions of the same source. In this paper, we propose multi-party fuzzy extractor, called threshold reusable fuzzy extractor, whose execution requires a threshold number of members. New definition and efficient construction are given with security proofs and experimental implementations. It preserves the inherent properties of fuzzy extractor with expanded use in multi-party scenarios. Based on the proposed threshold reusable fuzzy extractor, an application in privacy protection is given to store and share sensitive data by smart contract on blockchain, achieving joint access control via biometric information.
AHP-based Evaluation Model of Multi-modal Classroom Teaching Effect in Higher Vocational English

At this stage, modern teaching methods such as network technology and computers have been widely used in higher vocational colleges, which promotes richer teaching methods. Higher vocational English modal teaching mode is one of them, but it is not common to study evaluation indicators in this area. Analytic hierarchy process (AHP) is a kind of decision-making method which uses quantitative processing to deal with qualitative problems. Its principle is to divide all kinds of factors in complex problems into layers by using mutual relations to make them more orderly. In order to speed up the speed of data analysis, it is often used with statistical software and quantitative analysis software, and is often used to solve multi-objective, multi-criteria, multi-element, multi-level unstructured and complex decision-making problems. This article summarizes the specific data methods and research results based on previous work experience. The author discusses the AHP-based methods for enhancing the effect of multi-modal classroom teaching in higher vocational English from applying multi-modal teaching resources, creating a multi-modal teaching environment, designing multi-modal teaching activities, and students' attitudes and ideas on the application of multi-modal teaching four aspects.
An Improved SSD Network for Small Object Detection based on Dilated Convolution and Feature Fusion

In view of the fact that the feature layers of different scales in the traditional single shot multibox detector (SSD) are independent of each other, resulting in poor detection performance for small objects. We propose an improved SSD network for small object detection based on dilated convolution and feature fusion, which is called DFSSD. Specifically, by introducing dilated convolution, we enhance the receptive field of the third-level feature map in the network, which enables the feature map to obtain more global information. At the same time, we designed feature fusion module to fuse low-level feature map with detailed information and high-level feature map with rich semantic information. We adjust the prediction box scale of the DFSSD network prediction layer. Our proposed network obtains 78.9% mAP on PASCAL VOC2007 test at 40 FPS and 74.7% AP on difficult objects in car class of KITTI dataset. The results outperform the original SSD model by 1.4 and 1.2 points respectively.
Enhanced Deep Learning Approach For IoT Enabled Sensitive Clinical Data Analytics

Abstract With the enormous upgrade of clinical sensors, it has acquired developing interest to investigate the Healthcare Internet of Things (H-IoT) because of its wide relevance for wellbeing of patient. Current medical services are providing only a limited significance with the emergence of a new strain Covid-19. People are dealing with an issue of unforeseen demise because of different ailment which is a direct result of absence of clinical consideration to the patients at perfect time. Hence in this situation, an IoT based smart healthcare checking framework is necessary during this pandemic. In this paper, an enhanced deep learning framework is proposed that efficiently deep belief network perform IoT-enabled clinical data analytics. The proposed framework is utilized to PIMA Indian physiological parameter dataset quantify to the actual boundaries like internal heat level, heart beat rate, and oxygen level checking with the assistance of sensors. The proposed framework involves the secured cloud infrastructure where the clinical sensor data are acquired and stored. For secured cloud storage and to provide an improved security, Improved Elliptic Curve Cryptography (IECC) algorithm utilized set up a transfer a medical records trust between the doctors and patients centralised repository trailed by enabling a typical session key for communication. The proposed solution automatically predicts any abnormalities exist in the patient and it is communicated to the medical experts for disease diagnosis and appropriate medical consultancy is provided for the patient through secured channel. The proposed framework is deployed in real-time environment for remote healthcare especially in rural area.
Enhancing Deep Fingerprinting Attack On Tor Network Using Lottery Ticket Hypothesis

Tor is one of the popular choices as an anonymity tool to protect the identity of users in digital-based communication. It is used to avoid any eavesdropping that could be performed by local adversaries for monitoring purposes. Website Fingerprinting is a technique by which an eavesdropper determines which websites a user is visiting over an encrypted connection such as Tor. In an encrypted and anonymized network observing patterns of data flows such as packet size and direction helps in identifying a webpage that is being accessed by a user. A new Fingerprinting attack called Deep Fingerprinting makes use of deep learning for finding out the best features to identify a website. It makes use of a Convolutional neural network with a sophisticated architecture design that achieves high accuracy even against the modern defenses proposed for minimizing the effect of fingerprinting. In this paper, we propose to apply The Lottery Ticket Hypothesis on Deep Fingerprinting Attack to reduce the complexity of the model. Thus, the revised model will be efficient in terms of computation costs, storage, and latency. The improvised model is not expected to degrade the accuracy of the model.
Analysis of Dimensionality Reduction Techniques for Effective Text Classification

In this paper, the different dimensionality reduction techniques are presented for effective text classification. The feature selection technique and feature transformation fallowed by feature selection techniques are recommended to reduce the dimension of the features. For more compactness of data, the symbolic interval data type is recommended. The techniques are evaluated using SVM classifier and Symbolic classifier on standard benchmark datasets viz., Reuters-21578 and TDT2. The effectiveness of the techniques is verified with the performance F-1 score measure. The dimensionality reduction techniques which perform better are recommended when compared to the others.
Producing Online-Courses for Higher Education: The Marketing Approach

Rapid development of digital technologies expectedly will transform the overall system of higher and further education. Massive open online courses are widely presented in Russia through state and private e-platforms. Large and medium-sized Russian higher education institutions have been deeply involved in the preparation and integration of new educational content. This article considers educational online platforms and online-courses from the perspective of an independent product that requires full-fledged marketing support. Common instruments of strategic and tactical marketing planning should be utilized for the more accurate production of educational online-courses in higher education. The main marketing activities and instruments that have been considered cover segmentation, targeting, positioning, product management, pricing, distribution, promotion, etc. Particular attention has been focused on methodological considerations of online production that involve structuring, syllabus planning, presentation, and knowledge assessment system. The empirical background of the study is an audit of 10 Russian universities from 5–100 project rating providing online courses in addition to educational services. They are aggressively developing distance learning and employing various approaches to it.
Analogue Optical Simulation of the 2D Ising Model in an External Magnetic field

We demonstrate a method to implement a Zeeman term to the 2D-Ising Hamiltonian in a spatial Ising machine. An NP-complete problem of the 2D-Ising spin-glass in an external magnetic field is studied.
Improving and Redesigning the Online Complaint System of the National Bureau of Investigation (NBI)

Despite the decrease of overall crime rate in the Philippines during the lockdown, a worrying growth in the number of crimes happening in the homes of Filipino has been observed during this period. Since the current pandemic restricts people from the usual procedure of filing complaints physically in the NBI office, the official website of NBI must be utilized. This paper aims to present the proposed interface and processing system of the NBI website created through Wix. This developed processing system is designed to be user- friendly and efficient for people who want to file a complaint online. The result in the flow process chart shows that one transportation process sets to be the difference between the existing and proposed layout. It denotes that the existing system is more focused on the physical processing of complaints whereas the proposed system leans on the utilization of the online submission of complaints. Nonetheless, eliminating the transportation process yields a possible decrease of the total time of the overall process, and the new layout presents a more convenient interface for complaint filing which is beneficial for most people who have limited resources in their respective homes.
A Hybrid Approach Towards Color Image Segmentation

Nowadys, image processing plays vital role in real time application by enhancing image clarity, removes noise, compresses and decompresses it etc. Image segmentation is one of the techniques that process an image by dividing it into its component regions or objects. There are many algorithms proposed for color image segmentation. Watershed method results in over segmentation of image. Enhanced method, Marker-controlled watershed solves this problem. Mean Shift algorithm results in blurring of image. This paper proposes a hybrid approach to solve the above problems. We carried out our experiment on different colored images. Firstly, mean shift is implemented which finds density regions which are highest that represents clusters centered on modes, followed by Marker controlled watershed procedure. Results were compared against watershed and mean-shift method and conclusion was drawn that best results were obtained for hybrid approach.
Innovation and Exploration of Practical Education Mode of New Engineering Subject Driven by BIM Technology

BIM is the abbreviation of Building Information Modeling, which refers to the technology of creating and maintaining digital information of construction products and its engineering application in the whole life cycle of construction projects. It has five characteristics: visualization, simulation, coordination, plotting and optimization. All engineering majors are independent in teaching and practice, which is easy to form an information island. According to the requirements of new engineering construction, the upgrading and transformation of BIM based engineering multi specialty collaborative new engineering industry education integration talent training mode has been put on the agenda. To build a multi professional collaborative BIM new engineering practice education platform of Tianjin University Ren'ai College - intelligent construction innovation college, and form the “433” BIM new engineering practice education mode, change the simple confirmatory practice teaching form, solve the problem that practice teaching is not closely linked with theory, and form a benign “interactive mechanism” with theoretical teaching, so as to improve the quality of applied talents training.
Collecting and evaluating large volumes of bibliographic metadata aggregated in the WorldCat database: a proposed methodology to overcome challenges

Purpose This paper aims to discuss the challenges encountered in collecting, cleaning and analyzing the large data set of bibliographic metadata records in machine-readable cataloging [MARC 21] format. Possible solutions are presented. Design/methodology/approach This mixed method study relied on content analysis and social network analysis. The study examined subject representation in MARC 21 metadata records created in 2020 in WorldCat – the largest international database of “big smart data.” The methodological challenges that were encountered and solutions are examined. Findings In this general review paper with a focus on methodological issues, the discussion of challenges is followed by a discussion of solutions developed and tested as part of this study. Data collection, processing, analysis and visualization are addressed separately. Lessons learned and conclusions related to challenges and solutions for the design of a large-scale study evaluating MARC 21 bibliographic metadata from WorldCat are given. Overall recommendations for the design and implementation of future research are suggested. Originality/value There are no previous publications that address the challenges and solutions of data collection and analysis of WorldCat’s “big smart data” in the form of MARC 21 data. This is the first study to use a large data set to systematically examine MARC 21 library metadata records created after the most recent addition of new fields and subfields to MARC 21 Bibliographic Format standard in 2019 based on resource description and access rules. It is also the first to focus its analyzes on the networks formed by subject terms shared by MARC 21 bibliographic records in a data set extracted from a heterogeneous centralized database WorldCat.