Reviewerly/exHarmony huggingface dataset:
CAs Based Student-Alumni Management System

Students and alumni management system of any university is a collaborative framework which integrates the knowledge between the students who are pursuing education and the students who have already successfully established themselves after graduation. There are various profession choices that are available wherein a person could choose from and this proposed framework is a predictive system which tries to predict the career option based on the success rate of the student in terms of his credentials CGPA, number of projects, number of publication, and so on and the credentials of the alumni like number of years of experience, proprietorship, number of recognitions, designation reached, and many more. The proposed system tries to collaborate students with alumni or the ones who have graduated out of the university and together they are provided an option to choose their career based on all the experiences they have had so far and the necessary credentials they own . Cognitive-Agents (CAs) which exhibits rational thinking which is applied in the paper to collaborate the students and alumni where in the beliefs over the students and alumni are formulated using the Behaviour-Observation-Belief (BOB) model. The performance achieved by the proposed CAs based framework towards alumni management in university is found to be good with respect to parameters like employment rate, student alumni association, and so on.
Improved method of selecting data in a nonrelational database

People surround themselves with data in many ways, which evokes the need for correct ways of storing the data. Nowadays, the trend tends to lean in favor of data storing in nonrelational (or NoSQL) databases. These databases are used in various user applications, which need a huge volume of the data highly accessible and do not require big data consistency. The problem of the data growth and its storing in the nonrelational databases results in the decreasing efficiency of searching in data. In the paper, we present use of very popular in-memory database in order to help us with this lack of efficiency of the data searching. This paper examines the data searching in applications hosted by Amazon cloud service while using nonrelational database DynamoDB. We develop new procedures to provide faster response to user and to obtain the data using nonrelational database DynamoDB. These procedures provide the queried data and subsequently, transfer them into the memory. The given procedures are based on two methods. The first method is a recognition of values, the user refers to and the provision of this data to the in-memory database. The second method is related to the automatic storing of the data transferred to the in-memory database. We perform number of experiments, which are describing a limitation of efficiency/inefficiency from a perspective computational time.
Eye gaze controlled adaptive virtual keyboard for users with SSMI

BACKGROUND: Users with Severe Speech and Motor Impairment (SSMI) often use a communication chart through their eye gaze or limited hand movement and care takers interpret their communication intent. There is already significant research conducted to automate this communication through electronic means. Developing electronic user interface and interaction techniques for users with SSMI poses significant challenges as research on their ocular parameters found that such users suffer from Nystagmus and Strabismus limiting number of elements in a computer screen. This paper presents an optimized eye gaze controlled virtual keyboard for English language with an adaptive dwell time feature for users with SSMI. OBJECTIVE: Present an optimized eye gaze controlled English virtual keyboard that follows both static and dynamic adaptation process. The virtual keyboard can automatically adapt to reduce eye gaze movement distance and dwell time for selection and help users with SSMI type better without any intervention of an assistant. METHODS: Before designing the virtual keyboard, we undertook a pilot study to optimize screen region which would be most comfortable for SSMI users to operate. We then proposed an optimized two-level English virtual keyboard layout through Genetic algorithm using static adaptation process; followed by dynamic adaptation process which tracks users’ interaction and reduces dwell time based on a Markov model-based algorithm. Further, we integrated the virtual keyboard for a web-based interactive dashboard that visualizes real-time Covid data. RESULTS: Using our proposed virtual keyboard layout for English language, the average task completion time for users with SSMI was 39.44 seconds in adaptive condition and 29.52 seconds in non-adaptive condition. Overall typing speed was 16.9 lpm (letters per minute) for able-bodied users and 6.6 lpm for users with SSMI without using any word completion or prediction features. A case study with an elderly participant with SSMI found a typing speed of 2.70 wpm (words per minute) and 14.88 lpm (letters per minute) after 6 months of practice. CONCLUSIONS: With the proposed layout for English virtual keyboard, the adaptive system increased typing speed statistically significantly for able bodied users than a non-adaptive version while for 6 users with SSMI, task completion time reduced by 8.8% in adaptive version than nonadaptive one. Additionally, the proposed layout was successfully integrated to a web-based interactive visualization dashboard thereby making it accessible for users with SSMI.
Optimization in Autonomic Computing and Resource Management

Effective or optimal cloud resource management is included in the major challenges of the cloud computing environment from both the provider perspective and the user perspective. Many optimization algorithms have been introduced and redefined for the same. Every algorithm gives optimum results under certain conditions. When it comes to comparison among them, then no specific studies show that a particular algorithm behaves best in all types of problems. Research has shown that meta-heuristic algorithms are best fitted for resource management, especially for an automated environment where self-configuration and self-healing play a vital role. In this chapter, different meta-heuristic algorithms are initially classified based on different parameters and then they are described. A comparative analysis in the form of a table is also proposed which gives a clear classification among all.
An Effective Voting and Priority based Technique for Deadlock Prevention in Distributed &amp;amp; Cloud Systems

Deadlock, starvation and concurrency have always been a problem in single, multiple, distributed and cloud computing performance. Voting based approach is a process of getting the majority of votes and will only be allowed to enter into the critical section (CS). But disadvantage of this method is that if no process remains in the pre-requisite range of majority of votes then the system will remain in idle state and a considerable number of minority processes will remain in waitlist. In prioritized distributed mutual exclusion based algorithms, the process having highest amount of priority will be allowed to enter into the critical section (CS while the processes having lower priority in waitlist will result in an increase in the length of waitlist.Hence, for solving the concurrency and starvation related problems of distributed and cloud computing systems, we have tried to present an NMI based voting and priority algorithm with optimal & feasible solution through our study. Here, we have tried to propose a clubbed solution for voting and priority based approach by adding some other factors such as space for ‘non-mask-able interrupts’ and ‘Shortest Job Scheduling First (SJSF)’ methods has been used for upgrading the throughput of the system. The method will allow the creation of multiple critical sections in cloud, single and multi-servers based distributed systems preventing the serving processors from entering into starvation and in achieving optimum output from the system.
Comparison of TTS System Efficiency: A Pilot Study of Word Intelligibility between Siri and Google Translate with Thai Language

This study presents a comparative study of the word intelligibility of two popular applications, Siri and Google Translate, that are widely used on smartphones. Both of them have text-to-speech synthesis (TTS) functions. This is a pilot study based on Thai words, both general and often-misread words. Each group consists of 500 words. After conducting the word intelligibility tests, it appears that both Siri and Google Translate (GT) are good at synthesizing general Thai words with 95.4 % and 98.8 % efficiency respectively. However, for commonly misread words, Siri shows its TTS efficiency of 81.8%, while Google Translate shows its TTS performance of 72.2%. Focusing on the often-misread words, the results that are investigated using the t-test shows that there are significant differences between both applications with the p-value of less than 0.001. However, to confirm the findings from this study, the future work with a huge number of Thai words and the sentence intelligibility should be conducted.
Content-Based Image Retrieval Using Energy-Based Frequency Domain Features

Content-Based Image Retrieval (CBIR) has become one of the trending areas of research in computer vision. In traditional CBIR, the features in a spatial domain, such as color, texture, shape, and point features, are extracted. It is often considered that apart from the spatial features, the features extracted from the frequency domain of the images can give further information on the features of an image. In this paper, we are proposing two novel methods for the purpose of feature extraction from the 2-dimensional Discrete Cosine Transform (DCT) of an image: DCT_256_Zigzag and DCT_256_2 × 2. These methods take into consideration the lower frequencies in order to determine the features in the frequency domain. The advantage of using the zigzag scanning is to have the maximum low frequency values having Higher Energies comparatively. These two features are combined with two of the existing spatial domain features: Local Binary Patterns (LBP) and Interchannel voting features to generate a global feature vector for an image. For a query image, its feature vector is compared with feature vectors of every other image in the database using d1-distance, and the images with least distance are considered most similar images to the query image. To evaluate the efficiency of these two methods, five standard performance measures such as Average Precision Rate (APR), Average Recall Rate (ARR), F-Measure, Average Normalized Modified Retrieval Rank (ANMRR), and Total Minimum Retrieval Epoch (TMRE) are used. Six benchmark image datasets Corel-1000, Corel-5000, Corel-10000, VisTex, STex, and Color-Brodatz are used to corroborate the performance of these methods.
Multimodal biometric system based on feature source compaction and the proposed VCG (Virtual Center of Gravity) feature

The multimodal biometric system is a biometric system that uses more than one biometric characteristic and vice versa for unimodal biometric systems. There are also two types of multimodal systems regarding their authenticity techniques: serial and parallel authentication. In this study, the proposed multimodal biometric system uses a different technique, namely the feature source compaction technique. Each recorded biometric characteristic data (voice, face, and fingerprint) is converted into a grayscale image (called a feature source image). All feature source images of the same sample are arranged into a 3D image. The proposed VCG feature is extracted from the texture image resulting from applying the feature source compaction technique. In this way, only one generated feature for each data sample (as opposed to other serial or parallel multimodal systems). This study uses three genuine users with five data samples for each and 25 fake users. The authentication stage has tested using all data samples from all users as guest users. It means that there will be a total of 35 guest users for each of the three prototypes of VCG features. There are three test scenarios (in addition to the proposed method) used to determine the compaction sub-stage's effect on the resulting VCG feature prototype. The study results showed that the proposed method has a FAR range with the smallest limit value (0 – 17.33%) and the highest accuracy (85.56% - 100%). It has proven that the proposed method is much better than the other three scenarios.
Using Text to Teach Image Retrieval

Image retrieval relies heavily on the quality of the data modeling and the distance measurement in the feature space. Building on the concept of image manifold, we first propose to represent the feature space of images, learned via neural networks, as a graph. Neighborhoods in the feature space are now defined by the geodesic distance between images, represented as graph vertices or manifold samples. When limited images are available, this manifold is sparsely sampled, making the geodesic computation and the corresponding retrieval harder. To address this, we augment the manifold samples with geometrically aligned text, thereby using a plethora of sentences to teach us about images. In addition to extensive results on standard datasets illustrating the power of text to help in image retrieval, a new public dataset based on CLEVR is introduced to quantify the semantic similarity between visual data and text data. The experimental results show that the joint embedding manifold is a robust representation, allowing it to be a better basis to perform image retrieval given only an image and a textual instruction on the desired modifications over the image.
Answer Extraction Method Based on BiLSTM and CRF in Q-A System

Answer extraction is an important part of the field of automatic question answering (QA). The traditional answer extraction method relies heavily on contextual semantics, which makes the extraction process consume a lot of time and manpower. Aiming at the question of answer extraction, a method based on the bidirectional long-term memory network conditional random field (Bi-LSTM-CRF) is proposed. This question is extracted in parts from the answer segment. Extract the entity that contains the answer, and then extract the final answer from it. On a valid data set, the accuracy of the experimental results can reach more than 0.6050.
A General Pseudo-Random Number Generator Based on Chaos

In view of the shortcomings and problems of the commonly used C language pseudo-random number generation function, the existing C language pseudo-random number generation function is improved, a general pseudo-random number generator based on chaos is proposed, and multiple types of random number acquisition interface are introduced. While improving the randomness of the original C language pseudo-random number function, the procedure of improvement also enhances its versatility and can better meet the needs of different types of random numbers. The test results of the pseudo-random number generator show that the random number generated by it has good randomness, and the function call is convenient and flexible.
An Approach Based on Tunicate Swarm Algorithm to Solve Partitional Clustering Problem

The tunicate swarm algorithm (TSA) is a newly proposed population-based swarm optimizer for solving global optimization problems. TSA uses best solution in the population in order improve the intensification and diversification of the tunicates. Thus, the possibility of finding a better position for search agents has increased. The aim of the clustering algorithms is to distributed the data instances into some groups according to similar and dissimilar features of instances. Therefore, with a proper clustering algorithm the dataset will be separated to some groups with minimum similarities. In this work, firstly, an approach based on TSA algorithm has proposed for solving partitional clustering problem. Then, the TSA algorithm is implemented on ten different clustering problems taken from UCI Machine Learning Repository, and the clustering performance of the TSA is compared with the performances of the three well known clustering algorithms such as fuzzy c-means, k-means and k-medoids. The experimental results and comparisons show that the TSA based approach is highly competitive and robust optimizer for solving the partitional clustering problems.
RADICS: Runtime Assurance of Distributed Intelligent Control Systems

We describe RADICS: Runtime Assurance of Distributed Intelligent Control Systems, which combines a Simplex-based, black-box monitor with a white-box monitor to ensure correct behavior and good performance of AI systems. The black-box monitor allows the system to detect when the AI controller is on a failing trajectory and use a provably safe, but less performant algorithm, to right the system. The white-box monitor predicts when the AI controller will be put on such a trajectory before it happens and helps maximize the performance of the overall system. We describe the overall approach in detail and implement a simple version of it on a case study into controlling the lights in a small traffic grid.
Cluster Purging: Efficient Outlier Detection based on Rate-Distortion Theory

Rate-distortion theory-based outlier detection builds upon the rationale that a good data compression will encode outliers with unique symbols. Based on this rationale, we propose Cluster Purging, which is an extension of clustering-based outlier detection. This extension allows one to assess the representivity of clusterings, and to find data that are best represented by individual unique clusters. We propose two efficient algorithms for performing Cluster Purging, one being parameter-free, while the other algorithm has a parameter that controls representivity estimations, allowing it to be tuned in supervised setups. In an experimental evaluation, we show that Cluster Purging improves upon outliers detected from raw clusterings, and that Cluster Purging competes strongly against state-of-the-art alternatives.
An Evolutionary Computation Based Model for Testing Transfer Learning Strategies

To study how Transfer Learning (TL) works and what are effective strategies for transfer learning, we propose to model the TL process using Evolutionary Computation. EC provides a clear model for a problem as searching through a set of potential solutions. We are able to more easily control and measure problem difficulty, problem similarity, and methods of information transfer and relate these to success. As a proof of concept, we will use a static source problem and three fixed target problems with simple known relationships (see Section III). We compare the effectiveness of several ways to transfer knowledge learned from solving one problem to solving the new problems in the context of the relationship between the problems. This we hope will demonstrate that using our EC model is a fruitful way to investigate TL. The results show there is an improvement for using some sampled methods representing the "learned knowledge" of the source problem S. Also, the results show that the diversity of the transferred population has some positive effect on finding the optimal solution depending on the relationship between source and target problems.
Multi-Clue Reconstruction of Sharing Chains for Social Media Images

The amount of multimedia content shared everyday, combined with the level of realism reached by recent fake-generating technologies, threatens to impair the trustworthiness of online information sources. The process of uploading and sharing data tends to hinder standard media forensic analyses, since multiple re-sharing steps progressively hide the traces of past manipulations. At the same time though, new traces are introduced by the platforms themselves, enabling the reconstruction of the sharing history of digital objects, with possible applications in information flow monitoring and source identification. In this work, we propose a supervised framework for the reconstruction of image sharing chains on social media platforms. The system is structured as a cascade of backtracking blocks, each of them tracing back one step of the sharing chain at a time. Blocks are designed as ensembles of classifiers trained to analyse the input image independently from one another by leveraging different feature representations that describe both content and container of the media object. Individual decisions are then properly combined by a late fusion strategy. Results highlight the advantages of employing multiple clues, which allow accurately tracing back up to three steps along the sharing chain.
Application of BERT+Attention Model in Emotion Recognition of Metizens during Epidemic Period

Abstract In 2020, SARS-CoV-2 will affect the hearts of people all over the country, and Weibo will become the representative of people expressing their feelings on the Internet. Traditional emotion dictionary and machine learning methods have poor text emotion recognition effect, while BERT pre-training model is based on bidirectional Transformer model, which can better obtain the emotion expressed by the text and effectively improve the accuracy of the model. On the basis of improving BERT pre-training model, attention mechanism is introduced, and the key features are weighted to make emotion classification more accurate. According to the analysis of emotions expressed by netizens on Weibo during the epidemic, compared with textCNN model, BILSTM model and BILSTM+Attention model, the accuracy rate has increased by 6.25%, 4.69% and 2.67% respectively. The overall performance of this model is the best, and it can effectively recognize text emotion.
Toward Building an Academic Search Engine Understanding the Purposes of the Matched Sentences in an Abstract

This paper introduces an automatic approach to understand the purposes of each sentence in the abstract of an academic document. Specifically, computers can label each sentence in the abstract as being related to one or several of six aspects - “BACKGROUND”, “OBJECTIVES”, “METHODS”, “RESULTS”, “CONCLUSIONS”, and “OTHERS”. Experimental results obtained on a real dataset show that the labeling methodology outperforms baseline methods. We also build a prototype academic search engine to demonstrate the use of this new design. Users may search for articles containing keywords related to any of these six aspects to better meet their search goals.
Landmark Recognition Using Ensemble-Based Machine Learning Models

Recognizing landmarks in images with machine learning is an excellent topic for research today. Landmark recognition is an important field in computer vision. In this field, we train the machine learning models to identify and recognize the closed distinctly distinguishable objects in a digital image. In general, if we consider a digital image to be a set of coordinates of different pixels, a landmark is said to be enclosed in that closed polygon formed by the pixels that may be considered as a distinct and distinguishable thing in one or the other sense. Landmark recognition is an important subject area of image classification since it is considered as one of the first steps towards reaching complete computer vision. The extremely broad definition of a landmark makes it eligible to be considered as one of the leading problems in image classification tasks. Since the task is considered to be a very broad one, the solutions to the task hold no easy procedures. This chapter explores landmark recognition using ensemble-based machine learning models.