Reviewerly/exHarmony huggingface dataset:
Women Safety and Alertness in Instagram using Deep Learning

This research investigates the social media concern about social judgement. Therefore, immediate utilization to enhance women safety. Unique references are widely distributed in social networking sites and apps like Facebook, Twitter, and Instagram. Instagram typically offers opportunities in a range of fields by using images and videos to educate users. Instagram manages the hash tag messages that are extensively viewed and serves as a forum for women to express their emotions and thoughts. The quotes focusing on the protection of women may be used to read a message from the youth culture and therefore strict measures can be initialized to outlaw the messaging priority using deep learning and actions can be taken against those who abuse women. Instagram and other Instagram handles that post messages with hash tags are useful to establish a global communication. This research study utilized deep learning model to investigate the security and privacy of women social media users.
Secure smart meter data access with a distributed data storage based on Ethereum

This paper describes the pilot implementation of blockchain technology (Ethereum) for smart grid data management in the IT environment of electrical distribution company Elektro Celje. The work focuses on the decentralised notation of smart meter data to enable secure access and prevent data misuse. The procedure for setting up the pilot project and the operational functions as well as the results of the performance tests are presented and discussed.
A Comparison of AdaBoost and SVC for Fake Hotel Reviews Detection

Customers increasingly rely on hotel reviews for their bookings whereas hotel administrators use reviews for evaluating their service. Hence, a system that can detect fake reviews can help both customers and hotels administrators obtain accurate information to support their respective decision-making. In this study, we proceed to compare the performance of three machine learning models to classify fake hotel reviews. We used a dataset consisting of a total of 1024 reviews and 1600 pre-labeled reviews from Trip-Advisor. The collected data was preprocessed and split into 80% training data and 20% testing data. The three machine learning models evaluated are AdaBoost classifier (ABC) model with two base estimators: decision tree classifier and Random Forest classifier, and Support Vector classifier (SVC). Training was based on the linguistic features of the training set. During the model validation phase, it is seen that the SVC model provides the best classification performance with an F1-score of 97%. We deduce that SVM can positively contribute to the detection of fake reviews by using the linguistic features of text data.
Utilization of AI for Streamlining and Optimizing Credit Decision Process and Security in Banking Sector

The rapid growth of credit card service in banking is a challenge because of user level decision to provide loan risk. More specific real-time data analysis through Artificial Intelligence (AI), banking, and malware penetration application data analysis through other areas needs security identification. The efficiency gain of the method has been verified by experiments on real data sets from commercial banks. Representative behavior models can help improve online banking credit fraud detection performance significantly. Previous approaches of individual and population models find the single level of detecting fraud deposits, access levels, loan deployment etc. It will not efficient in containing customer behavior faced with online payment fraud detection. The proposed TRUBSV rule-based fraud detection is carried out by a precision algorithm and behavior-based group-level or general-level calculation system to provide loans. The recommended method is to evaluate by doing behavioral factors depends on security analysis in user level to provide loan. It can be easily discovered that the original and fraudulent transactions. Differential analysis is used to gather Group level evidence where fraud indicates significant deviations from normal behavior. Evaluating fraud possibilities is a fraud detection method based on effective identification by tracking the number of different accounts, accessing our main contributing accounts, and accessing each other's devices. Provide a systematic and detailed overview of these issues and challenges that may disrupt Target Request Based User Behavioral Session Verification (TRUBSV).The TRUBSVs promising for the e-commerce system. These e-commerce systems have been introduced closely to the types of fraud that are widely used. Most of the e-commerce systems have been able to process financial transactions. Behavior-based methods are considered a promising method for online payment fraud detection. It is to develop a high-resolution behavior model using the details of low-quality behavior.
AHP Evaluation Software for Intelligent Reform based on Interactive Intelligent Testing Algorithm

Mathematics and other cross-composition, its content is very abstract, theoretically strong, students have great difficulty in the learning process. In the new era, in order to further improve the teaching quality of data structure courses for computer majors in colleges and universities, when generating test cases, the one. test-at-a-time strategy is used to generate test cases that cover more total gains through sequential optimization ant colony algorithm. The pheromone update adopts a phased approach. The AHP method is used to evaluate the importance of each influencing factor, and then according to the weight of each influencing factor, the curriculum setting of computer majors in higher vocational colleges is modularized and refined, and a computer curriculum setting plan suitable for the characteristics of higher vocational colleges is formulated accordingly.
Optimization of an Archives Information System based on Intelligent Information Sorting Algorithm

Optimization of an archives management information system based on intelligent information sorting algorithm is studied in the paper. The innovative development and wide application of blockchain technology has become an important core of social life and production mode to digital transformation. Whether the blockchain can get a higher development depends on three factors: cognition, technology and policy. Further analysis will be carried out by utilizing the intelligent sorting algorithm. The semantic similarity measurement module is used to then calculate the semantic similarity between the relevant text information and the corresponding fact statement and the information model is then adjusted. Through the simulation, the ideas are tested.
Dense Captioning of Videos using Feature Context Integrated Deep LSTM with Local Attention

Dense captioning is a fast emerging area in video processing in natural language, that construe semantic contents present in an input video and. A traditional deep learning algorithm faces more challenges in solving this problem because it requires optimizing not just one set of values, but two sets, namely (1) event proposals, which are the timestamps for detecting an activity in a particular temporal region, and (2) natural language annotations for the detected proposals. Bidirectional LS TMs are used to predict event proposals based on information from the past and future of the event. Captions for detected events are also generated based on the past and future information associated with the event. The context vectors are augmented with original C3D video features in the decoder network in order to optimize the encoder network for proposals instead of captions. In this way, all the information necessary for the decoding network is provided. A local attention mechanism is added to the model so that it can focus on the relevant parts of the data to improve its performance. As a final step, captions will be generated with deep LSTMs. In order to verify the effectiveness of proposed model, a rigorous experiments have been conducted on the suggested innovations and demonstrated that it is remarkably effective at dense captioning events in videos with significant gains across a variety of metrics when it uses Feature Context Integrated (FC1) Deep LS TM with local attention.
Fact-Checking Algorithm in Regional Network Media

The article raises the problem of fake news verification in mass media. Regional media are particularly notorious for poor fact-checking because their reporters often prefer promptness to quality. The analysis involved random news published on the local Kuzbass news platforms. The authors developed their own unified fact-checking algorithm that can facilitate the work of online media journalists. It works as a multicomponent verification method. The procedure includes checking proper names, information sources, quotations, details, and visual elements.
Ontology of the complex rare-earth elements mineral system

ABSTRACT The ree-ont ontology formally represents the semantics of the complex interactions in the rare-earth elements (REE) mineral system from a self-organized criticality perspective. This ontology models various primarily heat-driven, melt-led, ore-forming processes that contribute to the formation of the REE-bearing alkaline intrusive bodies and related hydrothermal deposits. It also models the processes that form sediment-hosted concentrations of the REE from the weathering and erosion of the primary REE deposits. The ontology explicitly formalizes the interactions among the members of various magmatic, hydrothermal, basinal, regolith, and supergene subsystems. It represents the principal geodynamic, tectonic, and geological driver, trigger, carrier, conduit, and barrier roles that the components of the REE mineral system recursively play in enriching the REE during the subcritical and critical stages of the evolution of the complex system in and on the surface of the Earth’s crust. ree-ont establishes a logical machine processable basis for understanding the dynamic interactions among various components of the REE mineral system by extending the upper-level Basic Formal Ontology (BFO) and its underlying mid-level Common Core Ontologies (CCO).
A scalable solution for running ensemble simulations for photovoltaic energy

ABSTRACT This chapter provides an in-depth discussion of a scalable solution for running solar energy production ensemble simulations. Generating a forecast ensemble is computationally expensive. But with the help of Analog Ensemble, forecast ensembles can be generated with a single deterministic run of a weather forecast model. Weather ensembles are then used to simulate 11 10 KW photovoltaic solar power systems to study the simulation uncertainty under a wide range of panel configurations and weather conditions. This workflow has been developed and tested at scale on the National Center for Atmospheric Research supercomputer, Cheyenne, with more than 7000 concurrent cores. Results show that spring and summer are typically associated with greater simulation uncertainty. Optimizing the panel configuration based on the individual performance of simulations under changing weather conditions can improve the accuracy of simulations by more than 12%. This work also shows how panel configuration can be optimized based on geographic locations.
Distributed Denial of Wallet Attack on Serverless Pay-as-you-go Model

The serverless pay-as-you-go model in the cloud enables payment of services during execution and resources used at the smallest, most granular level, as was the initial idea when setting the foundations and concepts of the pay-as-you-go model in the cloud. The disadvantage of this method of payment during execution and the resources used is that it is subject to financial damage if we have an attack on serverless services. This paper defines notions for three types of attacks that can cause financial damage to the serverless pay-as-you-go model and are experimentally validated. The first attack is Blast DDoW - Distributed Denial of Wallet, the second attack is Continual Inconspicuous DDoW, and the third one is Background Chained DDoW. We discussed financial damages and the consequences of each type of attack.
Optimal guest file system for type-2 hypervisor-based virtualization in Virtual box

The main objective of this paper is to find the optimal guest file system for hypervisor-based virtualization with the Oracle’s VirtualBox virtual machines. For this research, we decided to measure file system performances, in equal conditions and apply the same test method. For testing purposes, we used Filebench software, which guarantees equality and independence of the hardware and operating system characteristics. CentOS 7.9 with the latest updates is used as the guest operating system, while VirtualBox 6.1 is used as the hypervisor. Performances are compared for the cases of one and two virtual machines running simultaneously. Expected behaviors are analyzed with Filebench, while the results are used to verify the assumption.
Deep Vision based Vehicle Retrieval for Automated Smart Traffic Surveillance Systems

Traffic systems form a significant part of a city and automated smart traffic surveillance systems have attracted significant research attention in recent years. Tasks such as vehicle detection, vehicle tracking, vehicle retrieval, anomaly detection in traffic flow, vehicle type detection, and other automated monitoring tasks are of essential importance in this ecosystem. In this work, we explore the problem of vision based vehicle retrieval, a major challenge in automated traffic surveillance systems in smart city environments. Vision based vehicle retrieval or vehicle re-identification is the process of identifying instances of the target vehicle given a gallery of vehicle images. We present a comprehensive review of existing works in this area, encompassing the evolution of vehicle retrieval models. We divide the existing work on vision based vehicle retrieval into four categories based on the techniques adopted and explore them in a systemic manner. Through this, we hope to provide the readers with an understanding of the techniques adopted in the existing works and how these can be further improved, the identified challenges and research gaps.
Software Tool for Parallel Generation of Cryptographic Keys Based on Elliptic Curves

Public key cryptography plays an important role in secure communications over insecure channels. Elliptic curve cryptography, as a variant of public key cryptography, has been extensively used in the last decades for such purposes. In this paper, we present a software tool for parallel generation of cryptographic keys based on elliptic curves. Binary method for point multiplication and C++ threads were used in parallel implementation, while secp256k1 elliptic curve was used for testing. Obtained results show speedup of 30% over the sequential solution for 8 threads. The results are briefly discussed in the paper.
LoRa-Powered Energy-Effcient Object Detection Mechanism in Edge Computing Nodes

The ongoing accomplishments in the decades-long realization of computer vision have infused new dimensions in various research areas such as smart mobility, smart healthcare, education, finance, and so forth. Research works relating to automated object detection, deep learning-assisted data pipelines, and energy-efficient end-to-end solutions have enabled newer perceptions among researchers, albeit the existence of challenges. This paper proposes an object detection system using energy-efficient Long Range (LoRA) communication media on edge nodes such as Raspberry Pi, Coral DevBoard, and Nvidia Jetson Nano. The proposed approach utilized energy-efficient methods to collaboratively offload object detection-related tasks such as capturing images, training images, and inferring objects across a compendium of computing nodes using LoRA. In addition, this research study has attempted to reveal the inference capabilities of images on three different edge nodes. The proposed work has achieved a power difference of at least 1.2 watts during the inference period of the deep learning models without challenging the prediction accuracy with respect to the base model.
Pattern Recognition Assisted Translation Training System

The pattern recognition and computer-assisted translation teaching under the school-enterprise cooperation mode is centered on computer-assisted translation technology, and the implementation of networked interactive translation teaching will help translation students improve translation quality and efficiency, become familiar with the construction of smart undergraduates, and form project management Ideas, including the choice of CAT software, the combined application of CAT and corpus technology in translation teaching, the setting of CAT technology modules, etc., in order to promote the application and development of CAT technology in undergraduate translation training.
Using Smartphone Sensors for Encrypting Mobile Data Storage to Withstand Quantum Cryptanalysis

Advancement of encryption standards in mobile communication technology has also paved the way for the emergence of different encryption standards for encrypting mobile storage. But even the most sophisticated encryption algorithms and mobile data encryption software exist today, found to consist of 4 main security and design flaws through the studies conducted during this research, which hinders their performance in achieving the ultimate security in mobile data encryption. They are, Inability to achieve True Randomness in key generation, the Possibility of repeating using the same encryption key, Limited key-length which is not enough to cover the entire message, the Inability to increase security without compromising performance. Because of these reasons the confusion and diffusion rounds used in the middle of the algorithm will be useless in face of quantum computing power because the key selection is ultimately predictable within a selected domain if the domain is pre-defined already. Through this research, solutions to all of these research problems were being able to overcome to produce an unprecedented One-Time Pad encryption algorithm and utilize it in mobile software to provide the ultimate data privacy for static mobile data storage, with the ability even to withstand the most sophisticated quantum cryptanalysis attacks.
Empirical Analysis of Sentence Embedding Techniques for Answer Retrieval in Marathi Question Answering

It is vital that any type of text can be portrayed in a machine-readable language so that it can be processed and interpreted. Text embeddings, which allow a machine to interpret any type of text by mathematically expressing it, naturally minimise this work. To extract the concept of interdependence across words or sentences, the degree of connection, synonym recognition, idea segmentation, selectional preferences, and analogies, text embedding are very useful. Using closest neighbor lookup of vectors, a vector representation of a text in a semantic vector space may be utilized for answer retrieval according to the vectors’ similarity and proximity. Traditional QA systems require a huge number of high-quality language-specific resources to perform multiple activities. The precision of several embedding techniques presently supporting Marathi, an Indo-Aryan language, is explored in this research. The performance of several sentence embedding algorithms is evaluated to select the most relevant and related answer text within the corpus given a natural language query in Marathi. Pragmatic tests are performed across supervised machine learning models to perform categorization and sentence embeddings in order to evaluate and compare the veracity of various embedding techniques. The proposed research findings concentrate on identifying the most effective embedding approaches from the restricted resources available for the Marathi language, laying the groundwork for future study in this language.
Search for Social Smart Objects Constituting Sensor Ontology, Social IoT and Social Network Interaction

An emerging constituent of Internet of Things is the Social IoT, which aids creation of Social relationships amongst interacting objects. SIoT attempts to moderate the shortcomings of IoT in the areas of trust, resource discovery and scalability by taking a cue from social computing. In this paper, we have proposed the OntoSSSO framework for recommending Socially Similar Smart objects to users, which is knowledge-centric, ontology-driven and dataset-driven. It incorporates Semantic Intelligence. The proffered model is compared for performance along with the baseline models using sundry performance metrics. Our model outperforms the other models, yielding a precision of 95.83 %.
The 1955 setback to the plan to establish the Ministry of Health and Welfare Sport Bureau, focusing on interests, institutions and ideas

This study examined the 1955 setback to the plan aimed at establishing the Sport Bureau of the Ministry of Health and Welfare (MHW) as part of the policymaking process related to establishing the Physical Education Bureau of the Ministry of Education, Science and Culture (MESC) in 1958. By focusing on related interests, institutions, and ideas, the following points emerged. From the viewpoint of mutual interests, the various parties involved in physical education and sports were united about the need to establish an administrative organization. However, when the plan for MHW Sports Bureau emerged, a conflict of interest between the MESC and the MHW arose. Furthermore, the Japan Sports Association carefully observed trends in the MESC and MHW and acted to maximize its own interests. With regard to the institutions involved, during this period the government was basically not in a position to establish new administrative organizations because of the restrictions imposed by simplification of the administrative structure. In exceptional cases, political and public support was essential for the establishment of new administrative organizations. On the other hand, the partial revision of the Ministry of Education, Science and Culture establishment act in 1952 changed the organizational principle of the MESC internal bureaus, thereby eliminating one of the restrictions that prevented the establishment of the Physical Education Bureau. This was an important change in the reestablishment of the Bureau in 1958. From the viewpoint of ideas, the Minister Matsumura of the MESC was cautious about administrative intervention, whereas Minister Kawasaki of the MHW was proactive. However, the concept of the government actively intervening in public health and sanitation in postwar Japan was unfamiliar and provoked opposition. In addition, the Health and Physical Education Council proposed the concept of a Physical Education Bureau of the MESC, and ideas subsequently accumulated. On the other hand, there was no accumulation of ideas for the Sports Bureau of the MHL. Therefore, Kawasaki's statement was regarded as “just an idea”. Moreover, the opposition of the MESC to this plan meant that it created a “dualization” problem and was not supported by the public.