Reviewerly/exHarmony huggingface dataset:
Statistical Analysis &amp;amp; Survey of Research Work in Opinion Mining

Social Networks or online inquiry takes part in a fundamental role in getting people’s perspective. Opinion mining regulates a reckoning exercise of the opinions, attitudes and determination with respect to an object and its aspects. The object may be a product, service, organization or to a given person and the aspects refers to attributes of the object. Nowadays, if someone willing to purchase a product one is no longer limited to asking a person who is using that product for their view because there are many user’s reviews and discussions in public convention or website on the Web about the product. Opinion mining plays important role in decision making. To mine valuable content from such sources is exigent task. This has made the focus of researchers in the field of opinion mining. This survey paper describes the various techniques used in opinion extraction & investigated opinion mining mainly at three granularity level i.e. sentence, document & feature level .In this work various datasets available, technique used is shown. We also computed the entire quantity of research articles available in journal and also shown statistical analysis of research work on opinion mining for last five years.
A dynamic term discovery strategy for automatic speech recognizers with evolving dictionaries

We present a dynamic term discovery (TD) strategy that is capable of automatically adapting the dictionaries managed by ASR systems to the input speech, in terms of lexicon and language model (LM). The adaptation tries to solve the problem of out-of-vocabulary (OOV) words that are likely to appear in most realistic scenarios and uses external knowledge sources for extending the capabilities of the LMs present in the systems. The handling of the OOV words is made by existing TD strategies that are able to detect and solve OOVs, plus special word selection processes that decide which words are to be added or deleted, so as to update the vocabulary constantly. We also propose a mathematical model for controlling the vocabulary size of the ASR system as well as the word addition and deletion rates that are involved. Then, the update of the overall LM is based on an interpolation scheme with smaller LMs built with external language knowledge that depends on the current speech and the words to be added at each time. We designed a realistic experimental framework for evaluating the strategy, employing ASR systems with moderated vocabulary sizes and a couple of test speech corpora with very distinct features. The results show that the dynamic TD strategy is able to offer a general positive tendency in WER improvement over systems without it, being able indeed to reach a significant difference after few hours of speech processing.
ThanosNet: A Novel Trash Classification Method Using Metadata

Recent progress in deep neural networks has spurred significant development of image-based trash classification literature. These methods predominately use transfer learning to achieve state-of-the-art results. In this contribution, a new methodology is introduced that uses metadata fields such as location and time-based traffic intensity to assist existing image-based classifiers. We curated ISBNet, a dataset which contains 889 images and their associated metadata, distributed over 5 classes (paper, plastic, cans, tetra pak, and landfill). This dataset was used to develop our model, ThanosNet, which is superior to current state-of-the-art, image-based, trash classification models. Although ISBNet is localized to one user community, the general methodology developed here is applicable to a wide array of consumer contexts.
Application of AO* Algorithm in Recognizing the Optimum Refactoring sequence for examining the effect on Maintainability: An Empirical Study

Bad smells are an indication of deeper problems in source code that need to be identified in order to decrease the accumulation effect in the SDLC which implies that at each stage smells may transform into bugs, faults or even failure of the working software resulting in loss of efforts. Refactoring, on the other hand, helps in removal of smells without affecting the external attributes of the software. Nowadays, researchers are focusing on the detection of optimum refactoring sequences well in advance so that software maintenance cost can be reduced and subsequently the efforts may minimize. In this paper, authors have identified a total of eleven bad smells present in the critically affected class selected on the basis of prioritization technique. After that, an attempt have been made to find optimum refactoring technique sequence using AO* algorithm which will eliminate identified bad smells and thereby helping the team to complete project within budget and time constraints. The obtained results showed that there is a considerable amount of improvement in maintainability value after applying optimum refactoring sequence on every class. This approach will help researchers and practitioners to use heuristic algorithms in finding the sequences in the early phase and hence maintain the source code under surveillance.
A Survey of Teaching PDC Content in Undergraduate Curriculum

Parallel and Distributed Computing (PDC) is included in the Computer Science Curriculum 2013 from ACM and IEEE Computer Science and hence required for ABET Computer Science programs, 2019 to 2020. It is a challenge to incorporate PDC into an existing full Computer Science program. In this paper, we examine the feasibility and challenges of introducing PDC thinking at the lower level or upper level and evaluate the effectiveness of various PDC learning and teaching methodologies. Further, we propose possible teaching strategies and techniques for introducing PDC topics for both lower and upper-level undergraduate students.
Analysis of Exertion with Relative Grading Mechanism in Academics Using Computational Technique

Abstract Evaluation process is an integral part of academic system in order to monitor the performance of students from many perspectives. Since, recent past lots of methodological changes has been taken places in evaluation process from traditional one. Many experts from concerned domain claims the superiority of one method over other but still dilemma continue with reference to optimum or appropriate method. In this connection even very recently being used to a maximum extent in Indian context too is the “Grade Method”. However, even “Grade Method” is also not completely free from certain controversies. That is, though grades were initially meant to serve various pedagogical purposes, grading systems remain controversial and hotly debated when those doesn’t give recompensing results. Some argued that grades are psychologically harmful. Thus, given certain limitations of existing methods in some respect, the current study has been undertaken in order to address few issues associated with minimum passing grade and cut-off mark for highest grade - resulting out of relative grading, Therefore, in this article, through mathematical computation we propose acceptable ranges for the parameters mean and standard deviation-respectively to have appropriate mechanism and hence can make evaluation process more effective and applicable in various conditions.
Fuzzy High-Utility Pattern Mining based on the Hadoop Framework

In this paper, fuzzy-set theory is first used and a new algorithm called efficient fuzzy high-utility itemset mining (EFUPM) algorithm is designed to discover the fuzzy high-utility patterns from a single machine. Two upper-bounds are then estimated to early prune the unpromising candidates in the search space. To handle the large-scale of big datasets, the Hadoop-based fuzzy high-utility pattern mining (HFUPM) algorithm is then developed to discover the fuzzy high-utility patterns based on the Hadoop framework. Experimental results show that the proposed algorithms can perform well to mine the required fuzzy high-utility patterns whether in a single machine or a large-scale environment compared to the state-of-the-art approaches.
A Framework for Lecture Video Segmentation from Extracted Speech Content

Increasing demand for lecture videos in digital libraries has raised the challenge of automatic annotation of lecture content for effective navigation of lectures by users. One direction is the prior segmentation of lecture videos to simplify several applications such as indexing, keyword spotting, and targeted search. In this study, we present a lecture video segmentation framework based on the speech content of the instructors. The framework is built upon a model that extracts textual and acoustic features from speech and uses them to identify topical segment boundaries of the lecture video. To evaluate our proposed model, we collected our own dataset containing a diverse set of 37 lecture videos and also manually created ground truth. The performance was measured by using metrics like Precision, Recall, and F1 Score and obtained 0.69, 0.58, and 0.63 respectively. We also compared our model with some previously known similar models where our model outperformed others. The overall results of the study are presented as a lecture video segmentation model, integrating various tools and techniques, and showing promising performance. Findings can be used further for research in content-based search and retrieval using speech content.
A Comparative Study on CNN based Low-light Image Enhancement

Images collected under low-light conditions have an intrinsic property of poor illumination, due to the environment that they're captured in. Improving the quality of images pro-poses various challenges such as low contrast, low brightness, noise etc. Simply increasing the brightness of the dark regions will inevitably amplify the hidden artifacts. Deep Learning has shown great promise in this task, and it's black-box nature is used heavily for the enhancement of under-exposed images. In this paper we present a comparative analysis of various techniques based on Convolutional Neural Networks (CNN) that aim to enhance under-exposed images and perform extensive experiments on these techniques. The performance of these algorithms are analyzed subjectively and quantitatively using established metrics.
Procedural Memory Augmented Deep Reinforcement Learning

Inspired by the human brain, we propose an external memory-augmented decision-making architecture for video processing. A self-organizing object detector is employed as a frontend to deconstruct the environment. This is done by extracting events from the flow of time and detecting objects within the frames. By employing an extra working memory where objects are temporarily stored, the system can extract properties of the stored objects related to the task. We propose a deep reinforcement learning (RL) neural network to learn affordances, i.e., a sequence of actions to manipulate these objects. The RL network and object detector are trained alternatively. After both the network and detector are trained, the objects and their affordances are transferred to an external memory. They are then utilized when the same objects are detected in input frames. Here, we use a combination of a dictionary and a linked list for the external memory that can be accessed by either content or temporal order. This dual access is motivated by the temporal property of human procedural memory. The proposed memory-augmented RL framework brings advantages of transferability, explainability and computational efficiency with respect to conventional deep learning architectures. We validate the framework on the video game Super Mario Brothers to show superiority to some classical deep RL architectures and exemplify these three advantages.
Assessment of Data Augmentation Techniques for Firearm Detection in Surveillance Videos

In this paper, we propose a Faster R-CNN based model for detection of firearms in surveillance and Closed-Circuit Television (CCTV) video. Due to the lack of a publicly available benchmark surveillance video database containing firearms, we created our own firearm database composed of only real surveillance footage from scenes with people holding firearms. Portable firearms such as rifles and pistols, often referred to as small arms, have a high degree of variation in surveillance footage in terms of shape, illumination, scale, pose, and occlusion. To deploy a fast and accurate firearm detection system for real world scenarios, it is important to detect all types of common small arms. To accomplish this, we collected images for our database that contain both handguns (pistols and revolvers) and long guns (rifles and shotguns) to capture as much variation of commonly used small arms as possible. To train our firearm detector we first, assess 18 common geometric and photometric data augmentation techniques in order to identify which ones improve detection performance. Next, we identify deep learning based small arm detection techniques that improve detection performance. We reuse these techniques in another more comprehensive augmentation assessment in order to identify the most efficient combination in terms of detection performance. Finally, we optimize the hyper-parameters of all the tested firearm detection models and apply cross-validation. Our proposed model can accurately detect firearms in video frames taken from real surveillance footage close to real-time, yielding precision and recall scores of 93.9% and 96.4% for handguns, and 95.2% and 94.6% for long guns.
Revolutionizing the Stock Market With Blockchain

This chapter explores the drawbacks of conventional centralized share exchange frameworks, like those of higher transaction costs, central and vulnerable regulation to exploitation, and lack of revelation to business behavior and practices by introducing a revolutionary model that utilizes blockchain to establish a decentralized stock exchange and a transparent persistent economy. The suggested model utilizes exclusive contracts to implement the validity of the privileges of the owner and the proper accomplishment and settlement of the transactions, thereby mitigating the need for a centralized authority to ensure the accuracy of the stock exchange mechanism. The experimental findings convincingly demonstrate that the decentralized solution can provide lower transaction costs by progressively replacing brokerage costs and centralized officials' commissions with mining charges, which reward the miners for their backbreaking work in maintaining the system and enforcing the laws.
Quran content representation in NLP

Word representation is a starting point for Natural Language Processing (NLP). These representations transform words into symbolic vectors of a given length that reveal the hidden linguistic and semantic similarities. This paper presents a study of the various word representation tools used for the content of the texts of the holy Quran in Arabic, which include the two main representation forms: Local representation and Distributed representation, with the aim of using them in different artificial intelligence subsets such as "machine learning" and "deep learning" algorithms that require NLP.
E-SGX: Effective Cache Side-Channel Protection for Intel SGX on Untrusted OS

Cache side-channels are among the major weaknesses of Intel SGX. We mitigate this weakness with E-SGX, an effective defensive approach against all known access-driven/trace-driven cache side-channel attacks from privileged code. The core idea of E-SGX is to monopolize the whole CPU during security-critical executions, breaking the concurrent execution condition of access-driven/trace-driven cache side-channel attacks. To achieve this, E-SGX employs several SGX threads within the same enclave: one application thread and a few dummy threads together hold all CPU cores. A key challenge is to ensure all those enclave threads are scheduled exclusively to occupy all CPU cores with an untrusted OS scheduler. E-SGX addresses this challenge by providing effective mechanisms to detect violations of exclusive scheduling: challenge-response check of dummy threads aliveness and detection of asynchronous enclave exits, both performed with a carefully selected period. Comparing to existing approaches, E-SGX is capable of defending against access-driven/trace-driven cache side-channel attacks not only from the sibling logical core but from across all physical cores.
Mining Social Media (Twitter) Data for Corporate Image Analysis: A Case Study in the Indonesian Mining Industry

Abstract PT Freeport Indonesia (PTFI) is the largest multinational mining company in Indonesia which frequently becomes trending topics on social media. Since the continuation of the company’s business operations depends on many stakeholders including the regulators, it is important for the company to listen to public opinion in order to develop a solid public relation strategy to maintain its corporate reputation. The objective of this study is to gather insights from social media as input towards a data-driven public relations strategy development. As a case study, we analysed Twitter conversations mentioning PTFI between 20 to 30 August 2020 during which there was a strike in the company. We performed text mining techniques using Drone Emprit system specifically sentiment analysis, bot analysis, and descriptive analysis to get quantitative measures of the corporate image on social media. The findings suggest several improvement opportunities of the current social media strategy of the company including the lack of engagement from local communities, the absence of specific strategy to handle negative sentiment, and the lack of awareness of social media topics not reported by mainstream media. As the implication of the findings, we propose several recommendations including nurturing engagement from employees and local communities, addressing the most influential tweets, and choosing better time periods to get more engagement.
Discovering Closed Periodic-Frequent Patterns in Very Large Temporal Databases

Periodic-frequent pattern mining (PFPM) is an important data mining model having many real-world applications. However, this model's prosperous industrial use has been hindered by the problem of combinatorial explosion of patterns, which is the generation of too many redundant patterns, most of which may be useless to the user. We propose a novel model of closed periodic-frequent patterns that may exist in a temporal database to address this problem. Closed periodic-frequent patterns represent a concise lossless subset that uniquely preserves the complete information of all periodic-frequent patterns in a database. An efficient depth-first search algorithm, called Closed Periodic-Frequent Pattern Miner (CPFP-Miner), has been introduced to find all the database's desired patterns. Experimental results demonstrate that CPFP-Miner is not only memory, runtime, and energy-efficient, but also highly scalable. The usefulness of our model has also been shown with a case study on traffic congestion analytics.
Peace Speech Identification Using ABBYY Fine Reader and HathiTrust

Exploring help speech through computational methods is a counterbalance to similar research for offensive speech. A lack of agreement in sentiment dictionaries around the value of help speech suggests a gap in this research. Using HathiTrust, an online library that exposes digital copies of copyrighted works for token-based analysis, the researchers explore help speech in eighty-seven (87) artifacts authored by the Dalai Lama. This work confirms that the sentiment of peace artifacts is generally positive and suggests further work to explore where help speech and offensive speech may overlap.
Interleaving Shifted Versions of a PN-Sequence

The output sequence of the shrinking generator can be considered as an interleaving of determined shifted versions of a single PN -sequence. In this paper, we present a study of the interleaving of a PN-sequence and shifted versions of itself. We analyze some important cryptographic properties as the period and the linear complexity in terms of the shifts. Furthermore, we determine the total number of the interleaving sequences that achieve each possible value of the linear complexity.
App Success Classification Using Machine Learning Models

Millions of mobile apps are downloaded and uploaded to the Google Play Store platform every day. Various features of an app, features that are either internal to or external to it, can play a significant role in determining whether the app will be successful or not. Using a dataset containing data extracted from the Google Play Store, this paper uses various features external to an app in order to make classifications on the app's success. This research therefore deals with a classification problem by classifying apps based on various degrees of success. Various classification models are created and compared with an aim to get a better understanding of classifying an app's success. The models used are Logistic Regression, K-Nearest Neighbors, Stochastic Gradient Descent, Decision Trees, Random Forest and Support Vector Machine. The models are compared based on their performance measures and run times. Lastly, the paper presents the classification model with the best classification performance while also exploring the relationship between the number of target categories and the performance of each classification model.
Identifying Thyroid Nodules in Ultrasound Images Through Segmentation-Guided Discriminative Localization

In this paper, we propose a novel segmentation-guided network for thyroid nodule identification from ultrasound images. Accurate diagnosis of thyroid nodules through ultrasound images is significant for cancer detection at the early stage. Many Computer-Aided Diagnose (CAD) systems for this task ignore the inherent correlation between nodule segmentation task and classification task (i.e. cancer grading). Actually, segmentation results could be used as localization cues of thyroid nodules for facilitating their classifications as benign or malignant. Accordingly, we propose a two-stage thyroid nodule diagnosis method through 1) nodule segmentation and 2) segmentation-guided diagnosis. Specifically, in the segmentation stage, we use an ensemble strategy to integrate segmentations from diverse segmentation networks. Then, in the classification stage, the obtained segmentation result is integrated as additional information along with its corresponding original ultrasound images as the input of the classification network. Meanwhile, the segmentation result is further served as guidance to refine the attention map of the features used for classification. Our method is applied to the TN-SCUI 2020, a MICCAI 2020 Challenge, with the largest set of thyroid nodule ultrasound images according to our knowledge. Our method achieved the 2nd place in its classification challenge.
An Innovative Algorithm for Improved Quality Multipath Delivery of Virtual Reality Content

This paper describes and evaluates an Innovative Algorithm for Improved Quality Multipath Delivery of Virtual Reality Content (QM4VR) that addresses the stringent communication requirements of Virtual Reality (VR) applications.Making use of the Multipath TCP (MPTCP) built-in multipath delivery features (subflows), QM4VR explores the subflows' characteristics, evaluates their performance (e.g., delay, throughput or loss) and proposes a new management scheme to improve the Quality of Service (QOS) of the VR applications.glsqm4vr adopts a Machine Learning (ML)-based approach to evaluate the subflows' performance which is implemented in two steps: 1) a linear regression scheme to forecast the subflow's performance for a given feature; and 2) a linear classification scheme to arrange the results obtained in step 1.Based on these results QM4VR selects the most appropriate subflows for data delivery in order to achieve improvement of VR QOS levels.