Reviewerly/exHarmony huggingface dataset:
Investigation of Time-delay Nonlinear Dynamic System in Batch Fermentation with Differential Evolution Algorithm

Differential evolution algorithm is an efficient computational method that uses population crossover and variation to achieve high-quality solutions. The algorithm is simple in principle and fast in solving global solutions, so it has been widely used in complex optimization problems. In this paper, we applied the differential evolution algorithm to a time-delay dynamic system for microbial fermentation of 1,3-propanediol and obtained an average error of 22.67% comparing to baseline error of 48.53%.
A Study on Blockchain Technology Implementation in the Logistics Sector of Pakistan

Blockchain technology provides a number of technological features, including high transparency, traceability, and trustiness. Its use in the logistics business increases technical support and ensures the logistics industry's effectiveness, as well as records, stores, and logistics information. The transmission ensures security, prevents logistics information from being distorted, and ensures the authenticity of logistics information. The use of blockchain technology in the logistics business has a positive impact on the industry's competitiveness and technical advantages. Blockchain has emerged as a key technology advancement with the ability to solve logistics problems. It's time for Pakistan to take advantage of this new technology. Blockchain has the potential to transform Pakistan's logistics sector.
RATEE - Resource Auction Trading at Edge Environments

Cloud providers host compute and storage resources used by multiple services. To be able to provide these services to millions of people with great reliability, large data centers are needed. However, some limitations of this centralized model are well known, such as bandwidth, because data has to be transferred from edge devices, and also latency, due to the distance between those data centers and personal devices. To overcome this, Edge Computing promotes computations or other types of operations on devices closer to users' personal devices. These devices have less power than data-centers but provide a shorter delay and as they are closer to end-users, data is immediately processed. Resources of these edge devices have the potential do host applications due to being underutilized. These applications can be allocated in various ways, voluntarily, or by exchange. This work focus on the trading process leading to the allocation of resources to an application. An auction-based trading algorithm is proposed, using an open-cry approach (i.e. all users knows the bids of others) and double side (i.e. buyers and sellers can bid and ask to a given item). The algorithm was implemented in a P2P overlay network and the Kademlia DHT. Results show a high success match rate, meaning all buyers found their respective sellers and vice-versa. Also, the memory consumption doesn't grow with the number of nodes that exist in the overlay.
Data Privacy Protection based on Feature Dilution in Cloud Services

Machine learning as a service (MLaaS) brings many benefits to people&#x0027;s daily life. However, the service mode of MLaaS will increase the risk of users&#x0027; privacy leakage. Existing works focusing on privacy-preserving based on encryption, differential privacy, and distributed framework require high computing resources or cannot be applied in MLaaS. In this paper, we propose feature dilution (FD), a noise-based desensitization algorithm to remove sensitive information in raw data. In particular, FD continuously adds raw data features to the random noise until it meets the minimum amount for an effective query, and we call this noise weak-feature noise (WFN). By fine-tuning the MLaaS architecture, we have realized that users can utilize WFN to get normal services without exposing their local private data. Meanwhile, noise addition technology is introduced by us to reduce the risk of privacy leakage caused by &#x201C;weak features&#x201D;. Extensive experiments have demonstrated that users can use FD to obtain effective services without exposing their private data. Finally, we conducted practical tests on weak-feature noises and found that these noises are difficult to use by malicious service providers.
Intertwining Binary Decision Trees and Probabilistic Neural Networks for Maximising Accuracy and Efficiency in Classification Tasks A Pilot /Proof–of–Concept Demonstration on the Iris Benchmark Classification Dataset

Intertwining binary decision trees (BDTs) and probabilistic neural networks (PNNs) is put forward as a powerful custom–made methodology for simultaneously maximising accuracy and efficiency in classification tasks. The proposed methodology brings together the complementary strengths of (I) the&nbsp;parametric, global, recursive, efficient as well as maximal dichotomisation of the BDT,&nbsp;and (II) the&nbsp;non–parametric, accurate–to–local–detail, multi–class identification of the PNN.&nbsp;The BDT/PNN combination&nbsp;evolves level–by–level, with each level comprising two steps:&nbsp;(step 1)&nbsp;the optimal BDT (hyper–)linear bi–partitioning of the entire dataset for the first level, and of every non–terminal partition thereof for the next levels, is determined, with each resulting partition being assigned to a new node of the next BDT level;&nbsp;(step 2)&nbsp;as many PNNs are created as there are&nbsp; multi–class partitions resulting from (step 1), implementing the non–parametric creation of the hyperplane that maximises class separability over each corresponding partition of the problem–space. BDT/PNN expansion is applied recursively – and independently – to each non–terminal partition of the dataset, concluding once the cumulative classification accuracy (CCA) of the terminal – BDT and PNN – nodes&nbsp;of the BDT/PNN is maximised. A proof–of–concept demonstration on the iris benchmark classification dataset (IBCD) illustrates the details of the BDT/PNN combination, attesting to its simplicity, effectiveness, and efficiency of operation. Follow–up research shall focus upon the classification of additional benchmark datasets, as well as of “hard” and “Big” real–world problems, for further evaluating and validating the proposed methodology.
Word Relationships are not Created Equally

Construction of knowledge graph from plain text entails identifying relationships among the words. Downstream tasks like finding similarity is sensitive to these relationships. From PubMed abstracts, words are extracted. “Nearest neighbor” words are identified as candidate words using Word2Vec, Glove and FastText. Conceptnet is a popular knowledge graph using which we find relationship between these words. Similarity for each word pair is calculated. Random Effects Model (REM) is applied to study this relationship strata using the similarity scores. Analysis shows that there is heterogeneity among the relationships independent of the base similarity metrics used.
Bandwidth-Optimal Random Shuffling for GPUs

Linear-time algorithms that are traditionally used to shuffle data on CPUs, such as the method of Fisher-Yates, are not well suited to implementation on GPUs due to inherent sequential dependencies, and existing parallel shuffling algorithms are unsuitable for GPU architectures because they incur a large number of read/write operations to high latency global memory. To address this, we provide a method of generating pseudo-random permutations in parallel by fusing suitable pseudo-random bijective functions with stream compaction operations. Our algorithm, termed “bijective shuffle” trades increased per-thread arithmetic operations for reduced global memory transactions. It is work-efficient, deterministic, and only requires a single global memory read and write per shuffle input, thus maximising use of global memory bandwidth. To empirically demonstrate the correctness of the algorithm, we develop a statistical test for the quality of pseudo-random permutations based on kernel space embeddings. Experimental results show that the bijective shuffle algorithm outperforms competing algorithms on GPUs, showing improvements of between one and two orders of magnitude and approaching peak device bandwidth.
Considerations on Monitoring the Drowsiness of Drivers Through Video Detection and Real-Time Warning

Microsomnia, decreased concentration and fatigue at the wheel are particularly dangerous and are the cause of many accidents. However, the initial signs can be detected in advance: tired, low-attention drivers perform less precise steering maneuvers and have to make minor path corrections more often. The willingness to take over the vehicle control in driving scenarios, in autopilot mode, is an important factor for road safety. This paper presents a low-cost system for automatic recognition of driver activity by eye monitoring. Thus, an architecture based on eye movement and blink tracking data is introduced in this system, thus analyzing several features. It is estimated that this technology will help prevent acci-dents caused by drivers who become drowsy. Various studies have suggested that about 20% of all road accidents are related to fatigue.
Private Censorship on Social Media: A Comparative Analysis of the Horizontal Application of Fundamental Rights

This paper examines the legal obligations of social media platforms in relation to the human rights of their users through the application of the horizontal effect of national constitutional rights. It establishes that through intricate systems of private governance developed due to economic and legal incentives, transnational social media companies have acquired the status of duty bearers of certain fundamental rights in their processes of content moderation. The grounds for such normative qualification are explored with respect to the function of fundamental rights and the social power inherent to platforms' regulatory ability and social power. Finally, a comparative analysis of national constitutional doctrines’ of the horizontal application of fundamental rights responses to those normative exigencies is conducted.
Cardinal Connect: A Student Organization Events Management System

Student organizations and the office are greatly affected by the COVID 19 pandemic, forcing universities to shift into the online setting. The organizations continue to adapt to the new normal by conducting online events and activities. However, managing events and activities can be challenging, especially without the use of a common platform. The paper proposes an event management system that would ease the transition to the online setting and reduce workload. A web application that focuses on event application, event implementation, and post-activity evaluation would benefit the coordination between the involved parties. The study aims to automate necessary document processes, provide an online platform for student organizations to execute proposals, generate and summarize the entirety of the events and activities. The study will be using a Likert Scale survey questionnaire for the Office of the Students Affairs and Student organizations to measure the usability of the Cardinal Connect Event Management System. The data develop a web-based application that would improve the event management at Mapua University. The researchers will be testing the serviceability of the system by using a Usability Test to test its serviceability. This is because the study requires a specific population of student organizations. The prototype is possible using HTML, CSS, and Javascript for the front-end and PHP, Python, Javascript for the back-end development. Through a prototype, the users can execute design features of the web application and conduct usability testing to improve the overall user experience. The web application aids the needs of the university and how the different organizations would engage students to maximize their university life.
An optimized genetic algorithm for single UAV mission planning

This paper studies the mission planning of a single UAV, and proposes a rapid mission planning method for UAVs based on the Voronoi diagram genetic algorithm. Taking the shortest coordinated strike path as the objective function, the Voronoi diagram is used to divide the task environment space, which effectively reduces the space complexity of the solution. On the basis of the centralized mission planning architecture, the genetic algorithm based on the Voronoi diagram is used to realize the rapid solution of the problem and the mission planning of the UAV. The simulation results show that this method can effectively select the initial base and complete the coordinated strike mission of multiple targets.
Background Study and Analysis

In this chapter, a detailed literature review about recent digital copy-move forgery detection techniques is given. The primary contributions of the chapter are.
Design and Implementation of Benchmarks for Multimodal Databases

With the continuous development and change of application scenarios, multimodal databases have become increasingly important, and subsequent testing benchmarks for multimodal databases have also become the research focus. This paper proposes a multimodal benchmark test with a distributed architecture, which aims to reduce the requirements for single-machine performance and to conduct a reasonable evaluation of the multimodal database. We design a set of workloads covering common multimodal queries and transactions and use multimodal datasets that cover relational, graph, key-value, JSON, and XML types. Finally, we tested recent common multimodal databases to show the feasibility of the system and the test performance of each multimodal database.
Work in Progress: Educational Ecosystem of Teaching Russian as a Foreign Language in Technical Universities

Creating educational ecosystems is an increasing tendency in modern education. In this paper, the authors propose a variant of developing an effective educational ecosystem for organizing the teaching of a foreign language, including Russian as a foreign language, to technical students. Such an ecosystem is based on the integration of knowledge and practice, implements mandatory educational standards, and responds flexibly to students’ individual requests. Such an educational ecosystem has been created at Samara State Technical University. It implements long-term personal-level goals of technical students as well as their local goals in mastering Russian as a foreign language, which corresponds to the concept of the ecosystem approach. The presented model meets both technological requirements of the time, and most social expectations and needs. It fits into the concept of an individual educational path, creates conditions for students’ self-development and educational creativity, as well as for the development of personal professionally significant qualities required of engineers. The creation of an ecosystem model at the university is aimed at implementing the strategic development programme, creates conditions for the integration of teachers and students into this programme, and makes it possible to attract new resources. The obtained results are of interest for studying current trends in Educational (Pedagogical) Sciences. The results are also aimed at improving conventional methods of teaching foreign languages (including Russian as a foreign language) and adapting these methods to today's needs.
A secure biometric authentication system for smart environment using reversible data hiding through encryption scheme

Biometric authentication is used in its fullest potential in all smart environments. Face recognition and fingerprint recognition are the two most popular approaches for biometric authentication. In some places, where a higher level of security is required can be equipped with the combination of face recognition and fingerprint recognition. In general, from the client side, the biometric data will be collected using a camera and fingerprint scanner, and that information will be transmitted to a cloud service provider to do the complex tasks, such as face recognition and fingerprint recognition. In such situations, the transmission of face images and fingerprints from the client side to a server is a major concern since an intruder may try to grab that information, and later, they can use that information to control the authentication system. In this chapter, we discuss a secure mechanism to send the face images and the fingerprints with the aid of a reversible data hiding (RDH) through the encryption scheme. RDH is a process of hiding some data by using a concealing medium in such a way that later the extraction of the hidden message is possible along with the recovery of the original images. In a RDH through encryption scheme, the RDH process and the image encryption process will be combined into a single task to hide the secret message in an image. In this chapter, we propose a new model in which the compressed fingerprint data as a secret message will be embedded into the face image through a reversible data hiding through the encryption scheme. The encrypted image obtained after RDH through encryption will be transmitted to the cloud service provider for further processing.
Location Privacy Protection Scheme Without Trusted Third Party

With the rapid development of the Internet of things (IoT), location-based services (LBS) have become an indispensable tool. It has brought great convenience to people’s daily life, such as car navigation, online taxis, takeaway, and ticketing services. [1]. However, with the widespread application of LBS, personal location privacy may be leaked. [2], which will limit the development of LBS severely.
Conclusion

In keeping with the rapid pace of data explosion, efficient and failsafe methods for the retrieval and analysis of relevant data have attained importance. Data mining pertains to the techniques and procedures to unearth relevant trends, patterns, and correlations in these huge amounts of data in order to extract meaningful information. Thus, data mining techniques typically resort to cluster analysis of the data under consideration with the aim to retrieve strikingly similar as well as dissimilar features. This chapter draws a conclusion of the achievable of this volume as regards to the recent practices in mining of various forms and exhibits of data ranging from text to videos.
IT design and development of multilingual terminology resource management platform

IT terminology resource for natural language processing research is a prerequisite for technology, and standardized terminology corpus is the premise of the research work. Multilingual language term IT resource management platform for building multilingual information processing oriented language knowledge. Research paper and IT design and development of multilingual terminology resource management platform for multilingual terminology corpus editing, processing, indexing, statistics, job analysis, translation, interpretation and other aspects of the platform in building multilingual information processing, multilingual terminology libraries play an important role in the construction.
PROBLEMS OF DETECTING ANOMALIES USING UNCONTROLLED AND SELF-CONTROLLED MODELS

The purpose and aims of this work is to consider the theoretical basis of the anomaly detection process, to conduct an overview of existing methods, modification of the selected algorithms and their application to the open dataset of hourly energy consumption. Formalized a problem of anomaly detection application, and defined the steps necessary to solve it.
Simultaneous Sampling for Robust Markov Chain Monte Carlo Inference

We propose an extension to Markov Chain Monte Carlo methods for inferences in the imprecise probability framework. The algorithm is based on simultaneous sampling from all the Markov chains targeting the distributions in the credal set. The algorithm constructs a chain of random sets, which can be used for conservative estimation of lower and upper expected values of derived random variables. Tight bounds on the set of estimators arising from the set of admitted stochastic models can be obtained when the credal set is finite for general models. Conservative bounds can be obtained for some classes of models also when the credal set is uncountable. Computational complexity for uncountable credal sets is not bounded, and heuristic fixes need to be implemented.
Optimization of Software System Models

Most object-oriented development technologies are based on the use of the universal modeling language; class diagrams are especially widely used in the design process, which serve to build a model of a software system. Modern CASE-tools, which are basic object-oriented project instruments, do not have tools for optimizing UML diagrams. This contribution shows how you can use design patterns to validate and optimize class diagrams. It is shown that some transformations can be carried out automatically; in other cases, the designer will be pointed out to the potential inefficiency of the model and will be offered recommendations on how to correct it. It is proposed to extend the modern CASE-tool Rational Software Architect Designer from IBM with the capabilities of validation and optimization of class diagrams. For this purpose, a plugin has been developed that parses an XMI file containing a description of class diagrams. Since the class diagram view in Rational Software Architect Designer contains redundant information, a more optimal view was chosen to make it easier to implement algorithms for finding erroneous and sub-optimal class diagram fragments.
Dynamic of Two-Level System in the F-Deformed Jaynes-Cummings Model Beyond the Rotating-Wave Approximation

Dynamic of Two-Level System in the F-Deformed Jaynes-Cummings Model Beyond the Rotating-Wave Approximation
The Concurrent Learned Indexes for Multicore Data Storage

We present XIndex, which is a concurrent index library and designed for fast queries. It includes a concurrent ordered index (XIndex-R) and a concurrent hash index (XIndex-H). Similar to a recent proposal of the learned index, the indexes in XIndex use learned models to optimize index efficiency. Compared with the learned index, for the ordered index, XIndex-R is able to handle concurrent writes effectively and adapts its structure according to runtime workload characteristics. For the hash index, XIndex-H is able to avoid the resize operation blocking concurrent writes. Furthermore, the indexes in XIndex can index string keys much more efficiently than the learned index. We demonstrate the advantages of XIndex with YCSB, TPC-C (KV), which is a TPC-C-inspired benchmark for key-value stores, and micro-benchmarks. Compared with ordered indexes of Masstree and Wormhole, XIndex-R achieves up to 3.2× and 4.4× performance improvement on a 24-core machine. Compared with hash indexes of Intel TBB HashMap, XIndex-H achieves up to 3.1× speedup. The performance further improves by 91% after adding the optimizations on indexing string keys. The library is open-sourced. 1
Designing a Real-Time ASL Auto-Recognition Tool Using Deep Learning

American Sign Language comprises more than 500 signs and gestures. Assessing sign-language for each individual learner is often time-consuming, especially when instructors assess learners&#x0027; work regularly. In this project, we describe the design of an ASL auto-recognition tool, which can evaluate students&#x0027; signed videos automatically and hence help with self-evaluation. This tool uses deep learning methods to recognize ASL gestures and provides immediate feedback to ASL learners. The results suggest that the accuracy of the model is acceptable in conditions with adequate lighting. Hence, the tool can potentially be used to support instructors with sign language assessment.