Reviewerly/exHarmony huggingface dataset:
An analysis and forecasts of online product sales based on BP Neural Network and Pearson Coefficient

We construct two models to derive the optimal plan. We successfully utilize our models to determine the best online sales strategy and identify potentially important design features that would enhance product desirability. We establish the Genetic Algorithm-Optimized BP Neural Network Model to find the relationship between time-based measures within data sets and the reputation of products. After quantifying all the indicators, we take BP Neural Network Model as a fitting platform to fit and forecast the reputation-timeline figure. To degrade the influence because of the slow rate of convergence, we introduce Genetic Algorithm to seek optimal global solutions and optimize BP Neural Network Model. Finally, we arrive at the conclusion that the reputation of products has potential correlations with time-based measures within data sets. We establish the Pearson Correlation Model to get the correlation between indexes. Firstly, we quantified all the indicators. According to the model analysis, we find that specific star ratings do incite more reviews. Besides, we use VADER, an NLP Sentiment Analysis Model, to quantify the sentiment descriptors of the reviews. Finally, we conclude that specific quality descriptors of text-based reviews are strongly associated with star rating level but barely associated with the helpful rating level.
Resolve out of Vocabulary with Long Short-Term Memory Networks for Morphology

Out of vocabulary (OOV), which is a word that does not exist in a predefined vocabulary. How to deal with OOV is an important research topic in the field of natural language processing. The existence of OOV directly affects the performance of many NLP systems. For example, in some common scenarios such as machine translation, sentiment analysis, and intelligent question answering, the existence of OOV can greatly affect the key performance of the system. In recent years, with the advent of the word vector algorithm word2vec based on the principle of word morphology, the word embedding path of the NLP system has improved significantly. We combine LSTM with NLM, taking the morphemes of words as the basic processing unit, while taking into account the global context information. The results obtained are better than the existing OOV processing strategies, and the performance of commonly used NLP systems is generally improved. Finally, it is experimentally proved that our model is generally better than the existing models in the problem of unregistered word processing.
On Interactive Proof-Search for Constructive Modal Necessity

We present a dual sequent calculus for the necessity fragment of the constructive modal logic S4 and show its adequacy for proof-search in the style of modern interactive theorem provers. The main feature of dual systems is the use of two contexts to capture the notions of true and valid formulas, without using any formal semantics. This attribute allows us to give simple rules for the □ operator that, most of the time, grant the substitution of a strict modal reasoning by a pure propositional one, thus simplifying the proof-search process. Moreover, we introduce a formal notion of backward proof corresponding to a bottom-up construction of a derivation tree by means of a left-to-right depth-first proof-search.
Design and Implementation of Eye Pupil Movement Based PIN Authentication System

PINs (Personal Identification Systems) have been widely adopted worldwide as primary means of secure communication for user authentication and verification purposes. However, it's not a foolproof system since it can be easily forged. Since PINs needs to be entered manually, it provides an easy opportunity for an intruder to crack it. Thus it is susceptible to various intrusions such as shoulder surfing, key logger, tap print etc. In this paper, an eye pupil movement based PIN generation system has been devised. At first, the user enters sensitive authentication input (PIN) by using eye pupil movements in various directions (i.e. Left, Middle and Right), which further is internally mapped into various pattern of digits from 0 to 9. Thus eavesdropping by a malicious observer becomes practically impossible. It utilizes Haar-Cascade classifier for face and eye detection followed by combined approach of HOG features integrated with SVM classifier for eye blink detection. For pupil detection, canny operator is employed followed by fitting a circle to pupil using circular Hough Transform. Tracking the position of eye pupil is achieved using projection function algorithm. The accuracy of eye detection, eye blink detection and eye tracking is 98%, 92.51 % and 96.25 % respectively. The contribution of this paper is outlined along with a comparative study between proposed approach and traditional authentication systems like gaze, gaze - touch, eye movement CAPTCHA and such graphical image based authentication methodologies. Our devised system is simple, user friendly and works under low light conditions without involving any significant dependencies on the intricacies of the system.
Modification of Pitch Parameters in Speech Coding for Information Hiding

The article presents a method of using F0 parameter in speech coding to transmit hidden information. It is an improved approach, which uses interpolation of pitch parameters instead of transmitting exact original values. Using an example of the Speex codec, we describe six variants of this method, named originally as HideF0, and we compare them by analyzing the capacity of the hidden channels, their detectability and the decrease in quality introduced by pitch manipulation. In particular, we perform listening tests using 20 participants to verify how perceptible the pitch manipulations are. The results are presented and discussed. We prove that minor modifications of pitch parameters are hardly perceptible, what can be used to create hidden transmission channels. One of the best proposed variants, called HideF0-FM, is shown to enable hidden transmission at the bitrate of over 120 bps at no speech quality degradation at all. Higher bitrates are also possible, only with minor quality degradation and limited detectability.
Conjunctive Keywords Searchable Encryption Scheme Against Inside Keywords Guessing Attack from Lattice

Public-key encryption with conjunctive keywords search (PECKS) is an extension of public-key encryption with keywords search (PEKS), which can realize the efficient search of the encrypted data stored in the cloud server and keep the privacy of these data during the search phase. However, there exists an inherent security issue for the typical PEKS schemes that they cannot prevent the inside keywords guessing attack (KGA). Moreover, most PEKS schemes were constructed based on the hardness of some number theory problems which can be solved in polynomial time using the quantum computer. Thus once large-scale quantum computers are built, these PEKS schemes aren't secure in the future quantum era. To address the above issues, this paper proposes a lattice-based PECKS scheme which can resist inside KGA. Its security can be reduced to the hardness of LWE problem and ISIS problem, thus it can resist quantum computing attack. We also give a comparison with other searchable encryption schemes on the computational cost of the main algorithms and the size of related parameters.
Convolution of Images Using Deep Neural Networks in the Recognition of Footage Objects

In the problems of image recognition, various approaches used when the image is noisy and there is a small sample of observations. The paper discusses nonparametric recognition methods and methods based on deep neural networks. This type of neural network allows you to collapse images, to perform downsampling as many times as necessary. Moreover, the image recognition speed is quite high, and the data dimension is reduced by using convolutional layers. One of the most important elements of the application of convolutional neural networks is training. The article gives the results of work on the application of convolutional neural networks. The work was carried out in several stages. In the first stage was carried out the modeling of the convolutional neural network and was developed its architecture. In the second stage, the neural network was trained. The third phase produced Python software. The software health check and video processing speed were then performed.
An Empirical Study on the Persistence of SpotBugs Issues in Open-Source Software Evolution

Background. Static analyzers can be useful to software developers in detecting and locating code issues and, in addition, classifying their nature. The main problem of static analyzers, however, is that they may signal too many false alarms. Objective. In this paper, we investigate whether code issues that are detected by SpotBugs persist in software code, or if they get removed. We chose SpotBugs because it is one of the best-known and most used static analyzers. Method. We carried out an empirical study on five open-source Java programs and took into account two versions of each of them, to check whether the issues signaled by SpotBugs on the older version had been removed by the time the newer version was released. A total of 1,006 issues were signaled by SpotBugs. Results. Our results show that about half of the issues signaled disappeared between the two versions, but the correction rate was uneven across projects. Issues about the correctness of software code were more likely to be no longer present in the newer version than other types of warnings. Conclusions. Further investigations are required, to understand why some projects appear more active than others in correcting SpotBugs issues, and why very few high-severity warnings were observed in the analyzed code. Nonetheless, the fact that about half of the issues flagged by SpotBugs were removed indicates that the tool is effective in detecting incorrect or otherwise problematic code.
Mental Health Data Analysis Using Cloud

In health care related research studies, there exists a need for retrieving patient's health record from multiple sites. So here comes the digitization of health records, which leads to a wide range of access to various users such as doctors, patients, psychiatrists and pharmacists. The sensitive nature of individual health care data pose a threat to security. Moreover, the increased access of health information by the users threatens the privacy and confidentiality of the stored data. Notwithstanding the existing privacy protection approaches used for mental health records, we suggest a privacy preserving data analysis methodology enabling protection of health records, once user access to records are granted. This paper mainly focuses on utilizing the data analysis approach in preserving privacy of personal health records to overcome the drawbacks of existing approaches.
DNNStream: Deep-learning based Content Adaptive Real-time Streaming

With the advent of modern smartphones, AR, VR services and advancement in display resolution of mobile devices coupled with real-time streaming services, the demand for high-resolution video has boomed. To fulfill this requirement, a variety of Adaptive Bit-Rate Streaming methods for Video-on-Demand applications are employed. However, the use of multi-pass encoding in the aforementioned methods renders them obsolete when it comes to real-time video streaming due to latency restrictions. In this work, we bypass the conventional multiple-encoding used in Video-on-Demand applications and present a novel machine learning-based approach that estimates the optimal video resolution for a given content at a particular bit-rate for ultra low latency applications. A new feature that captures temporal as well as spatial correlation in video sequence has been used to train the Deep Neural Network (DNN) model. A python-based testbed is designed to evaluate the proposed scheme. Experiment results corroborate the viability and effectiveness of the proposed method for real-time mobile video streaming applications.
A Novel Named Entity Recognition Approach of Judicial Case Texts Based on BiLSTM-CRF

The identification of named entity in judicial case texts is the critical phrase of knowledge graph information extraction in judicial fields. This paper proposes a neural network model based on bi-directional long-short term memory-conditional random field algorithm for the judicial case texts, named BiLSTM-CRF-JCT. Firstly, data preprocessing utilizes BIO notation to label sentences containing key named entities in judicial case texts, and then transfers sentences containing related entities into character vectors as input data. Afterward, the BiLSTM model is utilized to process vectors in order to obtain the sentence features. Finally, the proposed approach uses the CRF model to label and extract entities to realize named entity recognition. The experimental results show that the accuracy and recall rate of the proposed model are significantly improved compared with other NER algorithm models, F1 value is about 16% higher, which certifies that the BiLSTM-CRF-JCT model has good NER performance.
Legal Department 4.0: Operating Model of the Future

Legal departments today face multiple challenges, including cost pressure and an increase in workload due to a rapidly changing regulatory environment. The reaction to the challenges will have to be rethinking the way a legal department operates: The authors assess the current state of legal departments and their processes and paint a picture of the legal department 4.0—the future legal department with a new operating model, new processes and new sourcing strategy underpinned by technology.
Jointly learning to align and transcribe using attention-based alignment and uncertainty-to-weigh losses

End-to-end Automatic Speech Recognition (ASR) models with attention, especially the Joint Connectionist Temporal Classification (CTC) and Attention in Encoder-Decoder models have shown promising results. In this joint CTC and Attention framework, misalignment of attention with the ground truth is not penalised, as the focus is on optimising only the CTC and Attention cost functions. In this paper, a function that additionally minimizes alignment errors is introduced. This function is expected to enable the ASR system to attend to the right part of the input sequence, and in turn, minimize alignment and transcription errors. We also implement a dynamic weighting of losses corresponding with the tasks of CTC, attention, and alignment. We demonstrate that in many cases, the proposed design framework results in better performance and faster convergence. We show results on two datasets - TIMIT and Librispeech 100 hours for the phone recognition task by taking the alignments from a previously trained monophone Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) model.
Performance Comparison of Various Machine Learning Algorithms for Ultrasonic Fetal Image Classification Problem

The machine learning uses statistical algorithms to provide the ability to learn with and without data programming. Image classifier is used to categorize a subject or an object present in an image into predefined classes. A statistical algorithm used in numerous fields like pattern classification, regression, control systems, identification, and prediction. This research paper presents a novel approach performance comparison of machine learning classifiers used for the classification of ultrasonic fetal images. It presents Gabor feature extraction and various classification techniques to classify three different classes of ultrasound images. The proposed method is presented as follows. Initially, the Gabor features are obtained from raw images. To remove redundancy in features and dimensionality reduction principal component analysis (PCA) is applied. Finally, the features obtained from PCA are fed to the various machine learning classifiers and its performances are evaluated. The simulations results are carried out using MATLAB image processing toolbox. From the results, it is observed that decision tree (DT) algorithm and multi-layer perceptron (MLP) perform to be closer and this classifier outperforms all other classifiers.
The Human Factors of the Agile Software Tester

Although there has been extensive research on the technological aspects of the software testers, little investigation has been conducted within human factors determinant for the software testing role. The current paper explores the human factors of the software testers working in agile projects, through a qualitative study focusing on the perception of these factors, in a software service company. We interviewed 13 agile team members; 6 testers, 5 developers, and 2 designers. Additionally, we observed 11 meetings and observed the teams at work. Our results show that the following six traits are pivotal for the software testing role, seen by working agile software professionals: able to see the whole picture, having good communication skills, being detailed-oriented, structured, creative, and adaptable. Based on our results, we propose how organizations may think when recruiting agile software testers, how the traits may be used as a reference for those considering a software testing career.
Distributed Adaptive Video Streaming using Inter-Server Data Distribution and Agent-based Adaptive Load Balancing

As the number and hours of videos available within an organisation increases, as well as it's demand, the need for fast video streaming applications arises. Cloud based services are not cost effective and are not an ideal choice for storing the ever-increasing video data that is usually stored and used only within a particular organisation, like a University. Hence, this paper proposes a web based system design to store and stream videos at a small-scale within an organisation. To improve the video viewing experience for the user, the system is flexible to handle sudden changes, like increase in number of requests. The system requires the use of a cluster of servers to deliver the content as a single server cannot handle the load as number of requests increases. This requires effective load distribution among the servers. This paper proposes a way to design this system for adaptive video streaming. This system is highly scalable and can handle high loads, i.e. a higher number of users connecting to the application simultaneously. This paper proposes an algorithm called inter-server load balancing algorithm with Adaptive Agent-based load balancing to solve this problem. The algorithms also incorporates dynamic video resolution delivery techniques to ensure smooth viewing experience in the whole user experience irrespective of the network speed and bandwidth.
On Feature Models of Home Automation Systems towards Smart Sensing

The growing need for innovation in software products and an increase in their complexity has demanded an evolution in the software industry in recent years. Domain Analysis assists software developers in understanding the domains leading to significant improvement in their software development. Domain Analysis can be easily carried out using Feature Modelling by identifying the commonalities and variations among attributes of the domain. On the other hand, Home Automation is a developing domain and is now seeing an increase in the number and complexity of products. The heterogeneity of the home automation system elements introduced a lot of complexity to the developers. The commonly observable features and variations arising from HA systems allow us to visualize Home Automation (HA) systems as a Software Product Line (SPL). In this paper, we aim to adopt the Feature Modelling technique for Domain Analysis of the HA system. The efficacy and validity of this approach is evaluated with the help of proof of concepts.
Clinical Predictive Keyboard using Statistical and Neural Language Modeling

A language model can be used to predict the next word during authoring, to correct spelling or to accelerate writing (e.g., in sms or emails). Language models, however, have only been applied in a very small scale to assist physicians during authoring (e.g., discharge summaries or radiology reports). But along with the assistance to the physician, computer-based systems which expedite the patient's exit also assist in decreasing the hospital infections. We employed statistical and neural language modeling to predict the next word of a clinical text and assess all the models in terms of accuracy and keystroke discount in two datasets with radiology reports. We show that a neural language model can achieve as high as 51.3% accuracy in radiology reports (one out of two words predicted correctly). We also show that even when the models are employed only for frequent words, the physician can save valuable time.
Application Evaluation of Self-Explaining Intersections Based on Visual Information

More than 80% of the traffic information acquired by drivers comes from the visual channel. Therefore, visual information provided by traffic environment has great effects on driving safety. Data of drivers’ driving trajectories, driving velocities, and eye movement characteristics in experimental scenes with different traffic environmental visual information and levels of self-explaining characteristic were collected via a driving simulator and eye tracker experiment. This paper analyzed the effects of visual information on drivers’ behaviors, evaluated the safety of self-explaining roads, and designed the methods to improve the capacity of fault tolerance and safety of the self-explaining intersection.
AI4AR: An AI-Based Mobile Application for the Automatic Generation of AR Contents

Augmented reality (AR) is the process of using technology to superimpose images, text or sounds on top of what a person can already see. Art galleries and museums started to develop AR applications to increase engagement and provide an entirely new kind of exploration experience. However, the creation of contents results a very time consuming process, thus requiring an ad-hoc development for each painting to be increased. In fact, for the creation of an AR experience on any painting, it is necessary to choose the points of interest, to create digital content and then to develop the application. If this is affordable for the great masterpieces of an art gallery, it would be impracticable for an entire collection. In this context, the idea of this paper is to develop AR applications based on Artificial Intelligence. In particular, automatic captioning techniques are the key core for the implementation of AR application for improving the user experience in front of a painting or an artwork in general. The study has demonstrated the feasibility through a proof of concept application, implemented for hand held devices, and adds to the body of knowledge in mobile AR application as this approach has not been applied in this field before.
Calculating and displaying key labels: the texts, sections, authors and neighbourhoods where words and collocations are likely to be prominent

Corpora are usually not only made up of words, sentences and plain texts; they usually also have metadata, background information and structural features which can be used to filter searches or provide additional information about the context of specific concordance lines. This paper presents a new approach which uses the information about the texts in which words and collocations occur, generating clouds and tables of what are called Key Labels. The procedure can be likened to looking at key words ( Scott, 1997 ; and Scott and Tribble, 2006 ) from the opposite starting point: beginning with a word of interest and exploring the features of texts and the parts of text in which it occurs. The paper explains the background to the procedure, how it is carried out, and how these Key Labels are integrated into The Prime Machine corpus tool for English language learning.