Reviewerly/exHarmony huggingface dataset:
A Fuzzy based technique for Pattern Recognition &amp; Classification

Abstract A pattern can either be seen genuinely or it tends to be watched numerically by applying calculations. Pattern classification is worried about the capacity to discover absolute names for a lot of perceptions. A pattern classification task was viewed as an example determination issue where a meager subset of test from the marked preparing set was picked. We proposed a versatile learning calculation using the least square capacity to address this issue. Utilizing these chose tests, which we call educational vectors, a classifier equipped for perceiving the test tests was built up. This epic calculation is a mix of looking through systems that, in light of forward looking through advances, yet Adaptive finds a way to address the blunders presented by before forward advances. This paper reviews cost-delicate fuzzy standard based frameworks for pattern classification. Weighted preparing patterns are utilized to build cost-touchy fuzzy principle-based frameworks. A fuzzy classification framework is built from a given arrangement of preparing patterns. It is accepted that a weight is appointed to each preparation pattern from the earlier. The heaviness of preparing patterns can be determined dependent on their dispersion.
WITHDRAWN: Extracting the similar object sets using image threshold analysis techniques

Information extraction is one of the art for many researchers, especially extracting the images one of the most challenging for many researchers today, because the complexity of the data set. Image semantic features, because these features tell how to use the corresponding patterns during the process of editing the video. The video ontology which is a formal and explicit specification of events. Specifically, an event is combination of Location, time, moving, and shooting technique. Extracting the similar contentment from the stored data set is called interpretation template. Here, a semantic pattern is a combination of low –level features such color, motion and audio. The video ontology by extracting new semantic patterns from segments of videos which cannot be retrieved by previously extracted patterns.
Mobile Robot For Path Planning Using Firefly Algorithm

Firefly Algorithm is a meta-heuristic algorithm for global optimization, which is inventiveneted by flashing behavior of firefly insects. It has become an augmented optimization tool of Swarm Intelligence to solve the diverse problems. This is performed for any binary combination of fireflies in the population, on every iteration of algorithm.
Literature Review: Predicting Faults in Object-Oriented Software

Software fault prediction has become quite famous in the software engineering. If software faults are predicted earlier, it leads to good quality of software, and it reduces the resources and time required for testing, which ultimately leads to saving a considerable cost and effort that are used for testing purpose. In this literature study, we studied the major works done so far in the software defect prediction paradigm to find out the various benefit of predicting faults at initial phases of software product development. This paper aims to find answers to questions such as what are the merits and limitations of models developed so far, what are the possible areas of software fault prediction paradigm that are still open for research, what are the best suitable metrics used for predicting errors, and many more. From the literature survey, we analyzed that research conducted for predicting errors in object-oriented software so far was conducted on a small scale and using the limited metric suite. None of the studies gave a generalized model that could perform well for most of the datasets. They do not handle well the problem of imbalanced class distribution and noisy data. Many prediction models have already been developed so far, but most of them focus only on classification problem that is detecting faulty/not faulty classes.
A Two-Stage Cascaded Detection Scheme for Double HEVC Compression Based on Temporal Inconsistency

Nowadays, verifying the integrity of digital videos is significant especially for applications about multimedia communication. In video forensics, detection of double compression can be treated as the first step to analyze whether a suspicious video undergoes any tampering operations. In the last decade, numerous detection methods have been proposed to address this issue, but most existing methods design a universal detector which is hard to handle various recompression settings efficiently. In this work, we found that the statistics of different Coding Unit (CU) types have dissimilar properties when original videos are recompressed by the increased and decreased bit rates. It motivates us to propose a two-stage cascaded detection scheme for double HEVC compression based on temporal inconsistency to overcome limitations of existing methods. For a given video, CU information maps are extracted from each short-time video clip using our proposed value mapping strategy. In the first detection stage, a compact feature is extracted based on the distribution of different CU types and Kullback–Leibler divergence between temporally adjacent frames. This detection feature is fed into the Support Vector Machine classifier to identify abnormal frames with the increased bit rate. In the second stage, a shallow convolutional neural network equipped with dense connections is designed carefully to learn robust spatiotemporal representations, which can identify abnormal frames with the decreased bit rate whose forensic traces are less detectable. In experiments, the proposed method can achieve more promising detection accuracy compared with several state-of-the-art methods under various coding parameter settings, especially when the original video is recompressed with a low quality (e.g., more than 8%).
Generative Adversarial Network and Its Applications

In this work the artificial neural networks robust design methodology was used to find the optimal parameters in backpropagation artificial neural network architecture applied to the solution of the inverse kinematics of a six degrees of freedom robotic manipulator. A systematic and experimental strategy is proposed according to the requirements of the problem. Among the various parameters that affect the performance of a backpropagation neural network, four design variables were selected since they are the variables that can be controlled by the user: the number of neurons in the first hidden layer, the number of neurons in the second hidden layer, the momentum, and the learning rate. The results show that the robust design methodology can be used to find a better configuration with high performance and generalization capability; significantly reducing the time spent determining the optimal architecture of the neural network compared to the trial and error approach, which requires long periods of time.
Searching a Video Database using Natural Language Queries

This paper describes an application that achieves voice based natural language query, search and extracted video segment playing after the search in order to query the content of the videos in a user-friendly manner. Two different models were explored for the same. The first model is implemented using an image captioning approach. Two different image captioning methods are used for creating tracklets, namely Densecap and NeuralTalk2. NeuralTalk2 generates a single appropriate caption for the entire image whereas Densecap generates multiple captions corresponding to specific regions of interest in the image. These captions are used to preprocess the video and create semantically similar tracklets. Given a video and a voice based natural language query, this system will produce video tracklets from the video that are semantically relevant to the query. The second model uses an audio processing approach. Here, first the transcripts generated by YouTube are collected. The voice query is taken as input and the most relevant segments of the video are retrieved using on-the-fly generation of tracks and merging if required. For finding Semantic similarity in both the models, first Universal Sentence Encoder (by Google) which uses a deep averaging network encoder (DAN) for converting the sentences into 512 dimensional vectors is used and then cosine similarity between the vectors is calculated.
TRANSLATIONS BETWEEN LINEAR AND TREE NATURAL DEDUCTION SYSTEMS FOR RELEVANT LOGICS

Abstract Anderson and Belnap presented indexed Fitch-style natural deduction systems for the relevant logics R , E , and T . This work was extended by Brady to cover a range of relevant logics. In this paper I present indexed tree natural deduction systems for the Anderson–Belnap–Brady systems and show how to translate proofs in one format into proofs in the other, which establishes the adequacy of the tree systems.
IUI 2021 Tutorial on Conversational Recommendation Systems

Recent years have witnessed the emerging of conversational systems, including both physical devices and mobile-based applications. Both the research community and industry believe that conversational systems will have a major impact on human-computer interaction, and specifically, the CHI/IR/DM/RecSys communities have begun to explore Conversational Recommendation Systems. Conversational recommendation aims at finding or recommending the most relevant information (e.g., web pages, answers, movies, products) for users based on textual- or spoken-dialogs, through which users can communicate with the system more efficiently using natural language conversations. Due to users’ constant need to look for information to support both work and daily life, conversational recommendation system will be one of the key techniques towards an intelligent web. The tutorial focuses on the foundations and algorithms for conversational recommendation, as well as their applications in real-world systems such as search engine, e-commerce and social networks. The tutorial aims at introducing and communicating conversational recommendation methods to the community, as well as gathering researchers and practitioners interested in this research direction for discussions, idea communications, and research promotions.
The Automatic Method of Endoscopic Images Analysis for Differential Diagnosis

Objectives: The two-stage method for automatic endoscopic images segmentation, which provides efficient polyps segmentation in condition of small database for training is proposed. Methods: Proposed method includes two stage. First stage is based on machine learning, second stage uses deep learning. Experimental research was conducted to verify and evaluate the proposed two-step method. We used for comparing with the proposed method a neural network U-net to segment 964 real endoscopic images of the open Kvasir database. The segmentation quality was estimated by the total average Dice coefficient. Results: The proposed method realizes automatic segmentation of polyp regions in endoscopic images. Compared with neural network segmentation method, this two-stage method improves the efficiency of polyp segmentation: The total average Dice coefficient of this two-stage method is 0.8, it is significant better than in the case of segmentation only with CNN (The Dice for U-net with extremely small data base for training is 0.3). Conclusions: Using the developed method can improve the efficiency of polyp automatic segmentation in endoscopic images.
Spatiotemporal Residual Network for Dynamic Scene Recognition based on ResNeXt

Dynamic scene recognition is a fundamental task in computer vision, focusing on the method of classifying videos by analyzing dynamic changes of different scenes. Modeling the spatiotemporal information of dynamic scenes in videos is the main challenge of the task. To solve this problem, this paper proposed a spatiotemporal residual network model based on ResNeXt. Our model uses the 2D deep convolutional network for spatial information extraction, and the residual units of ResNeXt are transformed to spacetime to increase the network's temporal receptive field making it possible to extract temporal features in videos. In addition, to improve the generalization ability and prevent overfitting, we extend the global pooling strategy from spatial to temporal. On both of the static and moving subsets of the YUP++ dataset, the classification accuracies are improved compared with state-of-the-art methods, which indicates that our optimized method could make better use of spatiotemporal information for dynamic scene recognition.
A New Training Idea of Machine Learning in NLP

Abstract Natural language processing is an important direction in the field of computer science and artificial intelligence. It studies various theories and methods that enable effective communication between humans and computers in natural language. Generally speaking, NLP can be divided into the following areas, including text retrieval, sentiment analysis, semantic role labeling, machine translation and dialogue systems. Nowadays, although artificial intelligence has achieved a certain degree of achievements in the dialogue system, there is still room for improvement. This article proposes a way to deal with multiple interactive dialogues between man and machine, so that machine dialogue can take more about the content of the conversation before and after into account and the performance of machine is closer to the real human dialogue.
Automatic Generation of Annotated Collection for Recognition of Sentiment Frames

While addressing the challenge of sentiment analysis, it is crucial to consider not only the polarity of certain words but also the polarity between them, particularly between the arguments of a predicate. For this purpose, the RuSentiFrames lexicon was created. But the training of the ML model requires an annotated collection of data, and since the manual annotation is laborious and expensive, the automation of the process is preferable. In this paper, we describe a rule-based approach to automatic annotation of semantic roles for the predicates of the RuSentiFrames lexicon. The implementation of the algorithm includes the search of the entities with certain morpho-syntactic features in the order that depends on the case of the entity and is based on calculation of the posterior probabilities of the co-occurrence of a certain case and a certain type of predicate arguments. The results of the algorithm evaluation, based on several different characteristics, were relatively high. The solutions of problematic cases have been suggested and are expected to be implemented in further research.
Flappy Bird in Space: An Impulse Minimization Problem

In 2013, the mobile game Flappy Bird was released and quickly became a sensation. In this game, the player navigates a cartoon bird through an obstacle course by repeatedly applying upward boosts a...
Compression in Communication Security using Huffman Encoding and Cipher Block Chaining

In virtually every communication system, security is the most significant concern. To protect incoming and outgoing information, communication media such as social media today must apply different types of cryptographic algorithms. But adding security is also one of the overhead for the communication. Thus to improve the performance of the network, it is imperative to reduce the size of the data and at the same time ensure the security. This work involves emulating an amalgam of security and compression scenarios using Huffman Encoding for compression and CBC (Cipher Block Chaining) for security. An addition to these is the visualization of the Huffman Tree that is done using JavaScript.
An Observer for Infinite Dimensional 3D Surface Reconstruction that Converges in Finite Time

This paper proposes a method of reconstructing the dense structure of scenes from visual or depth sensors that provably converges in finite time. We represent the scene as a superlevel set of a function that resides within some potentially infinite-dimensional function space. The observer state is determined by the parameters of the function that represents the scene. Preliminary experiments show that the observer exhibits convergence behaviour on a variety of different function spaces both in simulation and with real light-field camera data.
PENGARUH KINERJA PEGAWAI DAN TEKNOLOGI INFORMASI TATA USAHA SECARA SIMULTAN TERHADAP PELAYANAN ADMINISTRASI DI SMA NEGERI 10 KERINCI

This study aims to determine the effect of employee performance and administrative information technology simultaneously on administrative services in SMA Negeri 10 Kerinci, and to determine how much influence the performance of employees and administrative information technology simultaneously on administrative services in SMA Negeri 10 Kerinci. This type of research is a quantitative study to see the influence between the independent variable and the dependent variable. In this study, the researcher wants to see the effect of employee performance and administrative information technology simultaneously on administrative services at SMA Negeri 10 Kerinci. The respondents in this study amounted to 32 people. The results of this study are: The sig value for the effect of administrative staff performance (X1) on administrative services (Y) is 0.000 2.750 so it can be concluded that H1 is accepted, which means that there is a significant influence between employee performance. uasha governance (X1) on administrative services (Y) at SMA Negeri 10 Kerinci. The sig value for the effect of administrative information technology (X2) on administrative services (Y) is 0.001 t table 2.750 so it can be concluded that H1 is accepted, which means that there is a significant influence between governance information technology (X2) on administrative services (Y) in SMA Negeri 10 Kerinci. The sig value for the effect of employee performance (X1) and administrative information technology (X2) simultaneously on administrative services (Y) is 0.005 ftabel 3.32 so it can be concluded that H3 is accepted, which means there is an influence between the performance of employees (X) and information technology governance (X2) simultaneously on administrative services (Y) in SMA Negeri 10 Kerinci
Exploring Methods for Tracing an Object Througn Its Enviroment and Predicting Trajectories Using Interpolation

In this research, we have explored methods for tracing objects in video: using neural networks, capturing points inside an object, using video filters, and tracking and capturing points outside the object itself. Also, this research talks about predicting the trajectory of objects using interpolation and object capture using one of the previously described methods. An experiment was carried out, because of which we figured out the best method for tracing objects, and also evaluated the accuracy of the prediction of our trajectories.
Analysis of Software Security level based on system clustering and correlation

Abstract With the wide use of electronic devices, passwords are used increasingly frequently. How to effectively evaluate password security needs our further research. In this paper, the known leaked passwords obtained from the network are analyzed by cluster analysis, frequency analysis, and correlation analysis by using system clustering and correlation, the strength grade model of the password is established, and the relationship among the factors of the password is analyzed. It shows that most people’s personal information plays an important role in building passwords.
Development of Vehicular Robots With Machine Learning Capability

A prototype of any vehicle/robot is developed for having a better understanding of functional parts and behavior under restricted environment. In the present work an electric moving vehicle that resembles a robot called rover is designed and developed. The study consists of findings during the development of a rover with machine learning through Tensor Flow Lite, running on a Raspberry Pi 4B. The designed electronics rover has a 12 kg structure, 10 kg battery load, and ability to take 8 kg of load with a speed of 6.7 cm/sec when tested on flat surface. The same can also be maneuvered over different gradient terrains. This rover has independent suspension and two 4-Axis arms, one on each side. The power to the wheels is supplied by 6 motor of 12 V 1.2 A having rated torque of 80 kg-cm and stall torque 400 kg-cm. They provide details of part selection and its fitments to work as a combined unit. Once the prototype is scaled on a bigger scale it can be used for heavy industrial applications without compromising human safety.
Inexact-Proximal Accelerated Gradient Method for Stochastic Nonconvex Constrained Optimization Problems

Stochastic nonconvex optimization problems with nonlinear constraints have a broad range of applications in intelligent transportation, cyber-security, and smart grids. In this paper, first, we propose an inexact-proximal accelerated gradient method to solve a nonconvex stochastic composite optimization problem where the objective is the sum of smooth and nonsmooth functions, the constraint functions are assumed to be deterministic and the solution to the proximal map of the nonsmooth part is calculated inexactly at each iteration. We demonstrate an asymptotic sublinear rate of convergence for stochastic settings using increasing sample-size considering the error in the proximal operator diminishes at an appropriate rate. Then we customize the proposed method for solving stochastic nonconvex optimization problems with nonlinear constraints and demonstrate a convergence rate guarantee. Numerical results show the effectiveness of the proposed algorithm.
A knowledge based system for the management of a time stamped uncertain observation set with application on preserving mobility

The aim of this study is to maintain up-to-date information about the current state of elderly people that are medically followed for risks of fall. Our proposal consists of an individual information database management system that can provide information on-demand on various variables. Such a system has to deal with several sources of uncertainty: lack of information, evolving information and reliability of the information sources. We consider that the features of the person may evolve with time causing uncertainty due to obsolete information. Our context includes new information received bit by bit, with no possibility to collect all required information at once. This paper establishes a first proposal to manage a set of uncertain observations, in order to reduce erroneous and obsolete information while keeping the benefit of previously collected information. We propose an architecture of the system based on a probabilistic knowledge model about the characteristics of interest, a set of decay functions that help to evaluate the confidence degree in previous observations, and a reasoning module to manage new observations, maintain the compatibility and the quality of the observation set. We detail the algorithms of the reasoning module, and the algorithm to update the confidence degree of the observations.
The Effects of Data Breaches on Public Companies: A Mirage or Reality?

Public companies are often criticized for their lackluster approach to cybersecurity investments. Critics assert that there are no financial incentives for public companies to invest in cybersecurity. Previous studies have mostly focused on the effects of data breaches on the market value of public companies. Nonetheless, this study focused on the impact of data breaches on revenue, net income and marketing expenses of public companies. Results of the study indicated that data breaches have significant impact on revenue but not on net income and marketing expenses. These results affirm the assertion that public companies do not have sufficient financial incentives to invest against data breaches. Therefore, we assert that public companies not only need to look at their bottom line but also consider non-financial incentives when making decisions regarding cybersecurity investments.