Reviewerly/exHarmony huggingface dataset:
A Study on Design and Development of Framework for Content-based Image Retrieval

CBIR is one of the most widely used approaches for detecting images from an extensive image database. The advanced integration and deployment of computer networking technologies enabled a sudden explosion in the number of ever increasing different types of internet based contents eg: digital images, audio and video content etc. Therefore it leads to a situation where retrieval of those complex data in a short period of time become more challenging. As a primary consequence there is an immense need to develop a novel technique well capable of retrieving such complex information based on their respective content or features. Howere, taking above consideration into account. In this paper, we discuss the reviews on the proposed study formulates an efficient Content Based Image Retrieval (CBIR) framework. The framework also implements a conceptual modelling based on biomedical image retrieval and classification. The study outcomes, found to exhibit better accuracy in retrieving similar images with very less processing time.
The online attendance system models for educational institutions

Global COVID-19 pandemic embraces educational institutions to adaptatively implement long-distance learning. Needless to say, there are difficulties and limitations during the process. Besides the learning process, the attendance system plays an indispensable role, notably in Indonesian education. Currently, lecturers need to do extra workload to perform attendance checks to prevent cheating (proxy attendance). An attendance system can be used to help lecturers focus on academic deliverance. The attendance system should focus on user as part of the ecosystem by adhere good user interface to produces good user experiences. The system must reduce lecturer workload and be easy to implement and use. In this paper, we introduce three online attendance system models that adhere to those criteria. Our models have been tested with an actual working prototype that follows interaction design principles. Finally, the end product has undergone an evaluation process based on heuristic usability principles to ensure the models are feasible to be implemented and applied to educational institutions.
Telecom-band Hyperentangled Photon Pairs from a Fiber-based Source

We experimentally demonstrate the generation of telecom-band biphotons hyperentangled in both the polarization and frequency DoFs using a periodically-poled silica fiber and observe entanglement concurrences above 0.95 for both DOFs.
Development of Artificial Intelligence Weapon System Test and Evaluation Methods: Performance Evaluation of the Classification Model

Purpose: As artificial intelligence (AI) systems are actively deployed in the defense field, the need for the test and evaluation of AI weapon systems is increasing. This paper proposes test and evaluation methods for AI systems.<BR>Methods: In this paper, improvements to test and evaluation methods and procedures for AI weapon systems are proposed. In addition, based on case studies, a test and evaluation metric suitable for classification models is suggested.<BR>Results: A method for testing and evaluating AI weapon systems by unifying terminology and using the confusion matrix as a performance evaluation method is presented.<BR>Conclusion: This paper forms a consensus for the necessity of improving as well as implementing guides to institutionalize AI weapon system test and evaluation.
Framework for Context-Based Intelligent Search Engine for Structured and Unstructured Data

Sawale, Ritendra R. Nagar, Lokesh Khatavkar, Vaibhav K.In today’s time, determining the user’s exact need through search queries is really a significant challenge as there is a tremendous amount of structure and unstructured data which is being produced daily. In structured data, it is easy to extract what we need as compared to unstructured data. There is a need to understand the semantics of the text given in such textual data. Natural language processing helps us to extract useful information from such unstructured textual data. Word embedding is one of the ways to overcome this issue. The implemented system aims to build a framework that will search based on the context hidden in the user query. As the context of keywords plays a vital role while extracting relevant search results from the database, the implemented system works on determining the context of the keyword in the query by using the GloVe word embedding technique. The embedded query is used to find out the most relevant documents from the database. This database consists of various text documents of different formats like pdf, word files, excel sheets, online crawled data, etc. All this data is stored in a database name as ElasticSearch. The proposed system can be used as an intranet searching system. The most relevant data is sent as an output quickly. The existing entity-based search engine is not having contextual capability which is provided by the implemented system. The result for search queries are based on the combination of entity and context-based search system.
Sumcheck-based delegation of quantum computing to rational server

Delegated quantum computing enables a client with weak computational power to delegate quantum computing to a remote quantum server in such a way that the integrity of the server can be efficiently verified by the client. Recently, a new model of delegated quantum computing has been proposed, namely, rational delegated quantum computing. In this model, after the client interacts with the server, the client pays a reward to the server depending on the server's messages and the client's random bits. The rational server sends messages that maximize the expected value of the reward. It is known that the classical client can delegate universal quantum computing to the rational quantum server in one round. In this paper, we propose novel one-round rational delegated quantum computing protocols by generalizing the classical rational sumcheck protocol. An advantage of our protocols is that they are gate-set independent: the construction of the previous rational protocols depends on gate sets, while our sumcheck technique can be easily realized with any local gate set (each of whose elementary gates can be specified with a polynomial number of bits). Furthermore, as with the previous protocols, our reward function satisfies natural requirements (the reward is non-negative, upper-bounded by a constant, and its maximum expected value is lower-bounded by a constant). We also discuss the reward gap. Simply speaking, the reward gap is a minimum loss on the expected value of the server's reward incurred by the server's behavior that makes the client accept an incorrect answer. The reward gap should therefore be large enough to incentivize the server to behave optimally. Although our sumcheck-based protocols have only exponentially small reward gaps as in the previous protocols, we show that a constant reward gap can be achieved if two noncommunicating but entangled rational servers are allowed. We also discuss whether a single rational server is sufficient under the (widely believed) assumption that the learning-with-errors problem is hard for polynomial-time quantum computing. Apart from these results, we show, under a certain condition, the equivalence between rational and ordinary delegated quantum computing protocols. This equivalence then serves as a basis for a reward-gap amplification method.
Crime Analysis Using Artificial Intelligence

The crime rate in India is increasing rapidly. According to the India Today report, the crime rate was over 540 in 2013, which increased to 581 in 2014 and then went up to almost 582 in 2015. The crime rate then dropped to 379.3 in 2016. Further, it increased to 388.6 in 2017. According to data provided by National Crime Records Bureau (NCRB), in 2018 a total of 50.74 lakh crimes were registered. Our project helps in automated crime detection and recognition of dangerous situations and crimes using three modules. Modules implemented by us help in the detection of weapons, posture analysis, and audio classification of various weapons, which further improves the process of crime detection and generates more accurate results. Detecting crimes like theft, robbery, and assault can be easily done and will increase the speed of solving crimes. Our project comprises algorithms that will notify the human administrator/director when any weapon like a gun, knife, or firearm is visible in the CCTV. Along with weapon detection, we are also implementing posture analysis and audio classification to avoid false alarms. A notification will be generated to the security personnel about any suspicious activity or a person committing a crime using real-time analysis. This idea will lead to quick and effective response times. Also, it will reduce the number of potential victims. The audio classification will help in the analysis of a crime in situations where CCTV footage is a blur or with less resolution.
Events of Interest Extraction from Forensic Timeline Using Natural Language Processing (NLP)

Forensic timeline performs a critical role in the investigation of computer forensic incident. It provides a summary of events that happened in and around a specific security incident. The events found in log files can help the investigator to construct a forensic timeline. We can consider events of interest as abnormalities in the timeline. An investigator can manually identify interesting events in a forensic timeline by entering keywords that have a high possibility of occurrence, but this process can be rather time-consuming. Some systems work on predefined rules, but those do not provide any help for previously unseen log messages. There are many useful methods and tools for creating a forensic timeline, but nothing significant for detecting events of interest. This paper presents a methodology that will help an investigator in extracting interesting events from a timeline. We propose the use of natural language processing (NLP) for identifying the sentiments of log messages. After spotting a log message with negative sentiment, the examiner can study the neighboring events recorded in that forensic timeline. In this way, the time required for investigation will reduce. This paper also presents a comparative study of learning algorithms used for sentiment analysis on forensic logs.
Stochastic Based Part of Speech Tagging in Mizo Language: Unigram and Bigram Hidden Markov Model

The process of assigning words in a corpus to the corresponding specified tags based on the context and its definition is part of speech (POS) tagging. It is always been a big challenge yet a very important task in language processing. The task of a part of speech tagging is more challenging in low resource languages, for example, Mizo language. This paper presents the development of a data-driven part of speech tagging system for the Mizo language. This research work includes creating of tagset and annotated Mizo corpus, development of stochastic based taggers such as unigram and bigram Hidden Markov model. The highest accuracy obtained using the proposed unigram and bigram Hidden Markov Model (HMM) based part of speech (POS) taggers are 70.61% and 75.19% respectively.
Swarm activity-based dynamic PSO for distribution decision

The distribution decision is a complicated constrained optimisation problem that plays a key role in the production planning and inventory scheduling at the current era of intelligent big data, since few studies have developed new models integrating intelligent supply chain management. With the aim at the limitation of traditional methods which are difficult to obtain feasible solutions in large-scale search space with limited time, a new swarm activity-based intelligent optimisation algorithm, called PSO-SAW, is reconstructed in this paper by improving the particle swarm optimisation (PSO). The methodology is validated through several benchmarks and experimental applications to some distribution decision problems adopted from the literatures. Empirical results have implied the feasibility, effectiveness and robustness of the proposed method. Moreover, the experimental results of the algorithm have also verified the promising performances and applicability to distribution decision problems by comparing with other considered stochastic algorithms.
Empirical software engineering for evaluating adaptive task-oriented personal assistants: a case study in human/machine event extraction and coding

This paper concerns methodology for evaluating task-oriented personal assistants, where users perform a complex task that has objective success criteria, independent of personal preferences, and the agent provides suggestions to help users repeatedly perform the task consistently and accurately. We develop a systematic approach to evaluating task-oriented personal assistants in normal contexts of use through extending the methodology of empirical software engineering to evaluate effectiveness, efficiency and satisfaction. The approach allows the evaluation of both the human-agent system and of the personal assistant using data obtained by observations of user and system behaviour. A key element of our approach is to define empirically observable conditions that separate the learning period, when users and the agent are learning to perform the task, from the evaluation period, when performance benefits are measured. The methodology is illustrated using the example of a system for users to extract, annotate and code events from news articles.
A combined authentication strategy for public cloud using attribute based encryption

Whenever we are deploying data in public cloud, we are doing with semi-reliable cloud servers present outside the data owner's reliable domain. To avoid hazardous service providers trying to access delicate data from data owners we are encrypting that data. In this situation conducting the authentication approach over the data becomes a very tough task. So, by using Attribute -Based encryption we can provide access policies among the attributes which can proved flexibility, secured access controls over the deployed data. But we have a problem with ABE that it does not support the access control mechanisms by combination. Here in this proposed paper, we are applying a scheme where different users who are having different attribute sets can approach the data using collaboration provided if the data owner accepts the combination with that access policy. If the combination is not designed in access policy, then it is considered as conspiracy and request is denied. So, we introduce attribute-based combined authentication strategy through assigning translation nodes in the access format.
Research on the Quality Evaluation of Innovation and Entrepreneurship Education in Applied Universities based on Analytic Hierarchy Process

Abstract: The goal of application universities is to cultivate application-oriented talents with applicability, practicality and innovation. Therefore, college students' innovation and entrepreneurship is an extremely important course in applied universities. The quality of innovation and entrepreneurship course is the result of many influences. According to the characteristics of application universities, this paper first constructs the quality evaluation index system of innovation and entrepreneurship education, then uses analytic hierarchy process (AHP) to determine the weight of each index, and finally establishes a comprehensive evaluation model of innovation and entrepreneurship education quality in application universities. This model can be used to evaluate the quality of innovation and entrepreneurship education in application universities.
Short Introduction to Python 3.10

This appendix presents some of the enhancements implemented in Python 3.10, which was released in October 2021. Therein are features that may be relevant to you. I start with improvements to error messages. Then I briefly look at match for convenient design and formulation of case distinctions. Finally, I briefly touch on performance improvements as well as type checks and other details.
Language Training Improvement of Engineering Students Through New Technologies

Quick changes are very much in evidence in engineering education. They are becoming real challenges and requiring to transform a traditional model of higher education. In the context of Industry 4.0, E-Learning, which was previously one of the possible forms of an educational process, is becoming one of the most significant functional technologies. E-Learning is considered as a knowledge acquisition in electronic form using personal computers, smartphones, and tablets. E-Learning allows to improve engineering students’ digital skills and their ability to analyze and manage information. E-Learning develops engineering students’ independence and self-control. Moreover, E-Learning contributes to improving an education level in whole. The purpose of this study is to present arguments to prove language training improvement of engineering students on the basis of Foreign Language e-course, designed by the authors of the paper, in Learning Management System Moodle at Samara State Transport University. Flexible usage of e-content and interactive tools of the e-course make it possible to personalize E-Learning and, as a result, contribute to the efficient management of the engineering learning process.
Data-Based Insights for the Masses: Scaling Natural Language Querying to Middleware Data

In this demonstration, we focus on middleware data obtained from devices like the network routers and power meters which may be of interest to a technician fixing a customer complaint or a user trying to self-diagnose their utility usage. The users in our case are often unaware of both the data details and database querying language which is in contrast to typical natural language to structured query (NL2SQL) situations where the business analyst knows their domain data but not the querying techniques. We adapt the rule-based NL2SQL approach to our problem and in particular, focus on queries about users, devices and spatio-temporal properties that are unique to this setting. We demonstrate an Alexa-based system, implemented using open-source Rasa, that can answer router usage queries in a home setting and easily extended for power usage or other utilities.
Optical Flow-Guided Multi-level Connection Network for Video Deraining

Video deraining, which aims at removing rain streaks from video, has drawn increasing attention in computer vision task. In this paper, we present an optical flow guided multi-level connection network for video deraining which can effectively take advantage of additional information in frames of the video sequence. Firstly, we utilize optical flow estimation method to extract the motion between two neighboring frames and perform warp operation according to inter-frame motion information to make neighboring frames align with target frame. Then, a multi-level connection network based on encoder-decoder residual block architecture is proposed for removing rain streaks from preceding aligned frames. To better recover background details, multi-level connection is used to effectively utilize the information between different scales. Furthermore, we also introduce hard example mining loss which is beneficial for promoting the performance enhancement of video deraining. Extensive experiments on three public datasets demonstrate that the proposed model achieves the state-of-the-art performance.
Representation Learning in Heterogeneous Information Networks Based on Hyper Adjacency Matrix

Heterogeneous information networks(HINs), which usually contain different kinds of nodes and interactions are very common in real world. The richer semantic information and complex relationships have posed great challenges to current representation learning in HINs. Most existing approaches which use predefined meta-paths suffer from high cost and low coverage. In addition, most of the existing methods cannot capture and learn influential high-order neighbors precisely and effectively. In this paper, we attempt to tackle the problem of meta-paths and influential high-order neighbors by proposing an original method HIN-HAM. HIN-HAM captures influential neighbors of target nodes precisely and effectively by generating the hyper adjacency matrix of the HIN. Then it uses convolutional neural networks with weighted multi-channel mechanism to aggregate different types of neighbors under different relationships. We conduct extensive experiments and comparisons on three real datasets and experimental results show the proposed HIN-HAM outperforms the state-of-the-art methods.
Comparison of clustering in tuberculosis using fuzzy c-means and k-means methods

Tuberculosis (TB) is a health problem that has yet to be resolved in Indonesia. Based on WHO data, in 2021 Indonesia will still be in the third rank of the highest TB cases in the world. This study aims to determine how many groups of TB patients are based on age, gender, HIV status, history of diabetes mellitus, chest X-ray, and the results of the Molecular Rapid Test (TCM). The data used in this study were 985 from 2017 to 2020. The method used in this research is K-Nearest Neighbor (KNN) in carrying out the imputation process, as well as comparing the k-means and Fuzzy C-Means (FCM) methods in classifying TB data. Before doing the grouping, the data cleaning process is carried out by an imputation process which is useful for filling in the missing data in this case, using the KNN method. To produce maximum results of data grouping or clustering, it is necessary to determine the right number of clusters. For this reason, this study tries to compare the elbow, silhouette coefficient, and Davies Bouldin Index (DBI) methods. The application of the KNN method in the data imputation process in this study is to use k=5. The application of the K-Means algorithm is to form groups of TB patients based on six features. Determination of the optimal number of clusters using the K-means and FCM methods shows the optimal number of clusters, namely K = 2 but with different values. The results of the clustering test using the elbow method with the K-means and FCM methods are 93288.49. The DBI value for the K-means and FCM methods is 0.4937. Meanwhile, the clustering trial with the silhouette coefficient on K-means yields a value of 0.6318 which is better than the FCM which produces a value of 0.6321. This shows that the results of clustering k-means with silhouette coefficients produce better cluster quality because they have a lower silhouette coefficient value than FCM.
Modeling the Costas Array Problem in QUBO for Quantum Annealing

We present experiments in solving constrained combinatorial optimization problems by means of Quantum Annealing. We describe how to model a hard combinatorial problem, the Costas Array Problem, in terms of QUBO (Quadratic Unconstrained Binary Optimization). QUBO is the input language of quantum computers based on quantum annealing such as the D-Wave systems and of the “quantum-inspired” special-purpose hardware such as Fujitsu’s Digital Annealing Unit or Hitachi’s CMOS Annealing Machine. We implemented the QUBO model for the Costas Array Problem on several hardware solvers based on quantum annealing (D-Wave Advantage, Fujitsu DA3 and Fixstars AE) and present some performance result for these implementations, along those of state-of-the-art metaheuristics solvers on classical hardware.