Reviewerly/exHarmony huggingface dataset:
Auto Generating Maps in a 2D Environment

Methods of algorithmic data generation, also known as Procedural Content Generation (PCG), consist of a striking vision within the gaming development industry. It is a way of creating enormously unique and diverse content, something that exponentially increases the game replayability. Although PCG in video games has a long history, there are also plenty of methods that have already been applied to levels, maps, models and textures among others. There is a variety of methods that have been used in video games, each with its own advantages and disadvantages. In the current study, an algorithm which generates 2D maps filled with rooms and some decorating items is presented. Map generation in commercial games heavily relies on constructive algorithms which do not evaluate and regenerate the output if something goes wrong. They do not demand heavy processing power and they can be used in real time situations, such as generating big worlds with fauna and flora. However, the playability of the generated map is examined by an agent which is usually created to access every corridor, room, and the start to finish pathway.
A Study on Cascade R-CNN-Based Dangerous Goods Detection Using X-Ray營mage

X-ray inspection equipment is divided into small baggage inspection equipment and large cargo inspection equipment. In the case of inspection using X-ray scanning equipment, it is possible to identify the contents of goods, unauthorized transport, or hidden goods in real-time by-passing cargo through X-rays without opening it. In this paper, we propose a system for detecting dangerous objects in X-ray images using the Cascade Region-based Convolutional Neural Network (Cascade R-CNN) model, and the data used for learning consists of dangerous goods, storage media, firearms, and knives. In addition, to minimize the overfitting problem caused by the lack of data to be used for artificial intelligence (AI) training, data samples are increased by using the CP (copy-paste) algorithm on the existing data. It also solves the data labeling problem by mixing supervised and semi-supervised learning. The four comparative models to be used in this study are Faster Region-based Convolutional Neural Networks Residual2 Network-101 (Faster R-CNN_Res2Net-101) supervised learning, Cascade R-CNN_Res2Net-101_supervised learning, Cascade Region-based Convolutional Neural Networks Composite Backbone Network V2 (CBNetV2) Network-101 (Cascade R-CNN_ CBNetV2Net-101)_supervised learning, and Cascade R-CNN_ CBNetV2-101_semi-supervised learning which are then compared and evaluated. As a result of comparing the performance of the four models in this paper, in case of Cascade R-CNN_CBNetV2-101_ semi-supervised learning, Average Precision (AP) (Intersection over Union (IoU) = 0.5): 0.7%, AP (IoU = 0.75): 1.0% than supervised learning, Recall: 0.8% higher.
Summarization of Comic Videos

With the increasing amount of video data generated everyday, it has become important to summarize the videos for faster retrieval and quick surfing of videos so that users can select the more relevant video for viewing as per their requirement. The importance of video summarization lies in the fact that it helps in efficient storage and allows quick browsing through a large number of videos. We propose a method to generate summaries for videos by utilising the audio component. Our video summarization approach involves generating the audio transcript, using speech recognition, if it is not readily available. It is based on assigning scores to sentences in the transcripts and selecting the ones with highest scores. Then from the original video, segments corresponding to the selected sentences are extracted and merged to obtain the final summary.
Pedestrian Head Detection and Tracking via Global Vision Transformer

In recent years, pedestrian detection and tracking have significant progress in both performance and latency. However, detecting and tracking pedestrian human-body in highly crowded environments is a complicated task in the computer vision field because pedestrians are partly or fully occluded by each other. That needs much human effort for annotation works and complex trackers to identify invisible pedestrians in spatial and temporal domains. To alleviate the aforementioned problems, previous methods tried to detect and track visible parts of pedestrians (e.g., heads, pedestrian visible-region), which achieved remarkable performances and can enlarge the scalability of tracking models and data sizes. Inspired by this purpose, this paper proposes simple but effective methods to detect and track pedestrian heads in crowded scenes, called PHDTT (Pedestrian Head Detection and Tracking with Transformer). Firstly, powerful encoder-decoder Transformer networks are integrated into the tracker, which learns relations between object queries and image global features to reason about detection results in each frame, and also matches object queries and track objects between adjacent frames to perform data association instead of further motion predictions, IoU-based methods, and Re-ID based methods. Both components are formed into single end-to-end networks that simplify the tracker to be more efficient and effective. Secondly, the proposed Transformer-based tracker is conducted and evaluated on the challenging benchmark dataset CroHD. Without bells and whistles, PHDTT achieves 60.6 MOTA, which outperforms the recent methods by a large margin. Testing videos are available at https://bit.ly/3eOPQ2d .
Enhancement of Aes Cryptography with Fast Key Generation Technique to Improve Cyber Security

The most successful method of securing data is cryptographic encryption. Modern cryptographic techniques significantly contribute towards today's accelerated cybersecurity needs. With the advent of data, it has become more important to secure the data from external interference to improve Cyber security. Various algorithms have been deployed with a penultimate goal of securing data of which Advanced Encryption Standard (AES) is one. The paper provides a substantial modification over Advanced Encryption Standard, with the aim of Cyber security and easy implementation focusing on the Symmetric Key Cryptosystem and AES algorithm. The performance was compared using different parameters such as data block size, encryption/decryption speed, run-time, and compile time. The main reason for this experiment was to exploit the effectiveness of the newly modified AES over the actual AES using the real-life application. A new key implementation process, the right shift key technique, and the elimination of the Mix-Column process were introduced. This paper provides calculations based on the AES-128-bit version, but the proposed algorithm can work on other versions as well. The goal was to achieve fast execution time and less memory usage while keeping the security hard to breach. The result depicts that our method achieved 88% throughput and better net execution time. This is a substantial improvement in terms of efficiency from the traditional AES.
Implementation of Remote Control of a Robot Arm by Hand Gestures

Recently, the coronavirus pandemic has led to a new normal lifestyle, and reducing contact with humans by 70% is necessary in order for preventing infection. Therefore, in order to reduce the chances of contact between humans to the greatest degree possible, we propose a prototype method for remotely controlling a robot arm using human hand gestures in a non-contact manner. By multithread processing of involving hand gesture recognition using a Kinect device, and conversion between the camera coordinate system and the robot coordinate system, smooth operation of the robot arm is realized by asynchronous communication using exclusive control (mutex class in C++) and queue buffering (queue class on C++).
Comparison of Selected Algorithms on Breast Cancer Classification

Over the years, machine learning techniques have been exhaustively used to diagnose the presence of several diseases from medical images. This has achieved acceptable results which has resulted in the use of computer-aided design (CAD) systems as decision support systems for disease diagnosis. However, the best machine learning technique to use for a particular disease diagnosis still remains an issue of argument as many factors are responsible for the accuracy results obtained from these machine learning techniques. This study explored the use of support vector machine (SVM), decision tree classifier (DTC), Naive Bayes (NB), K nearest neighbors (KNNs), sequential model (SM), and discriminant analysis algorithm for the detection and classification of tumors in patients into benign and malignant. Wisconsin breast cancer dataset was used to train and test the machine learning techniques while accuracy, precision, and sensitivity were used as the performance evaluation metrics. Among the techniques used, DAL and SM achieved the highest classification accuracy of 95.80% which was closely followed by SVM and Naïve Bayes.
Sentiment Analysis of Twitter Tweet Using Machine Learning

To express emotions, the users are using emoticons. Previously, for the classification of text, emoticons, or image machine learning techniques, are being used, but emoticons to text being ignored, resulting in the misrepresentation of many emotions. Sentiment analysis is the evaluation of people’s attitudes, thoughts, and feelings in order to determine whether they are optimistic, negative, or neutral. In recent years, the use of such emoticons on social media has nearly doubled. This study proposed an algorithm and pattern for sentiment analysis that makes use of both text and emoticons, this study shows that when emoticons are used, the emotion associated with them outweighs the sentiment expressed by textual data processing. This paper defines a social media sentiment analysis scheme that categorizes posts as positive or negative depending on the general polarity of the message. These classifiers often give a judgment score very similar to the decision boundary for a significant number of posts, implying that they are clearly confused rather than absolutely incorrect about these tweets. This paper uses different techniques like Naïve Bayes, SVM, LSTM, logistic regression model to analyze the sentiment.
A Lightweight Convolutional Neural Network for Breast Cancer Diagnosis with Histology Images

A Breast cancer diagnosis provides prevention and treatment to save lives or improve the life quality of patients, and a recent tool with good performance for this diagnosis is deep learning methods to process breast histology images. However, these methods are based on Convolutional Neural Networks (CNN) with a high computational cost that reduces usability. Therefore, this paper proposes an optimized CNN for breast cancer diagnosis named Lightweight CNN for Histology Image Processing (LCIP). LCIP is based on the MobileNet V2 architecture adapted with four inverted residual convolutions to find cell features. LCIP was validated with the BreakHis database, reporting an accuracy of 99.73%, the best result in the literature. Additionally, LCIP is the Histology Image Processing Deep learning method with fewer parameters than recent state-of-the-art methods. These results demonstrate that LCIP is a method that can be used as a feasible, portable, and accessible method to develop novel tools for breast cancer diagnosis.
A Hierarchical Approach for Lossy Light field Compression with Multiple Bit Rates Based on Tucker Decomposition via Random Sketching

Recently, there has been extensive progress in developing autostereoscopic platforms for display purposes to present real-world 3D scenes. Light fields are the best emerging choice for computational multi-view autostereoscopic displays since they provide an optimized solution to support direction-dependent outputs simultaneously without sacrificing the resolution. We present a novel light field representation, coding and streaming scheme that efficiently handles large tensor data. Intrinsic redundancies in light field subsets are eliminated through low-rank representation using Tucker decomposition with tensor sketching for various ranks and sketch dimension parameters, making it ideal for streaming and transmission. Apart from removing spatial redundancies, the approximated light field is used to construct a Fourier disparity layers representation to further exploit other non-linear, temporal, intra and inter-view correlations present among the approximated sub-aperture images. Four scanning or view prediction patterns are utilized and the subsets in each pattern hierarchically construct the FDL representation and synthesize subsequent views. Iterative refinement and encoding with HEVC are followed by the final light field reconstruction. The complete end-to-end processing pipeline can flexibly work for multiple bitrates and is adaptable for a variety of multi-view autostereoscopic platforms. The compression performance of the proposed scheme is analyzed on real light fields. We achieved substantial bitrate savings compared to state-of-the-art codecs, while maintaining good reconstruction quality.
Reasoning About Order Crossover in Genetic Algorithms

The formal modeling and verification of algorithms is a challenging task, but it is a necessary requirement for the proof of correctness. Evolutionary computation and theorem proving approach of formal methods are two different domains in theoretical computer science. Using Prototype Verification System (PVS), this paper presents a method of formal specification, reasoning and verification for order crossover operator in Genetic Algorithms (GAs) and their rudimentary properties. Order crossover operator is first formally specified in PVS specification language. Some other operators used in the definitions of order crossover are also specified. PVS theorem prover is then used to prove some properties of order crossover and operators.
Open-Source Data Collection for Activity Studies at Scale

Activity studies range from detecting key indicators such as steps, active minutes, or sedentary bouts, to the recognition of physical activities such as specific fitness exercises. Such types of activity recognition rely on large amounts of data from multiple persons, especially with deep learning. However, current benchmark datasets rarely have more than a dozen participants. Once wearable devices are phased out, closed algorithms that operate on the sensor data are hard to reproduce and devices supply raw data. We present an open-source and cost-effective framework that is able to capture daily activities and routines and which uses publicly available algorithms, while avoiding any device-specific implementations. In a feasibility study, we were able to test our system in production mode. For this purpose, we distributed the Bangle.js smartwatch as well as our app to 12 study participants, who started the watches at a time of individual choice every day. The collected data was then transferred to the server at the end of each day.
A Recipe for Low-Resource NMT

Incorporating theoretical information into the dataset, tokenization and subword splitting improves translation quality in low-resource settings. Previous research has shown that one can train a reasonably good translation model by training a model with small subword vocabularies and high dropout parameters. And backtranslation and multilingual translation further improve translation quality. But just as a textbook helps a student learn a language, it also helps a machine learn a language. Theoretical information allows us to make more efficient use of a given dataset and train a better model.
A Comprehensive Analysis of Data Breaches and Data Security Challenges in Cloud Environment

COVID 19 health crisis has increased cloud consumers rapidly with attractive features provided by cloud service providers (CSP). The network of resources obtained by the consumers over the internet on demand. In particular, data storage is the most prominent feature available in cloud computing. Data owners prefer storing data in the cloud environment rather than storing it in local machine or physical system. Even organization, government sectors, industries move data to cloud storage. Although cloud utilization has many advantages for end users, few challenges arise in storing sensitive and non sensitive data. Cloud Data security is a growing phenomenon which is primarily caused by intruders, hackers and attackers. Based on the security issues, many researchers have proposed various techniques and methods to safeguard data stored in cloud environment. In this paper, detailed study and investigation is performed on few of the research papers that is published over period of time in this field. This literature paper gives insights about data breaches and data security challenges in cloud environment. Finally discussed about a research problem to be addressed in future to securely store data in cloud.
Abstractive Text Summarization Using Machine Learning

Text summarization creates a brief and succinct summary of the original text. The summarized text highlights the main text’s most interesting points without omitting crucial details. There is a plethora of applications on the market that include news summaries, such as Inshort and Blinklist which not only save time but also effort. The method of manually summarizing a text can be time-consuming. Fortunately, using algorithms, the mechanism can be automated. We apply three text summarization algorithms on the Amazon Product Review dataset from Kaggle: extractive text summarization using NLTK, extractive text summarization using TextRank, and abstractive text summarization using Seq-to-Seq. We present the advantages and disadvantages for these three methods.
Planning and Scheduling Scheme Based on Fuzzy Finite State Machine Model

Planning and scheduling is an important optimization problem in many transportation and robotic applications. To solve planning problems, the main approaches are based on optimization methods, sampling-based methods, and usually such kinds of problems are NP-hard and high dimensional. In this paper, the method for planning and scheduling is introduced; the implementation of fuzzy finite state machine model is suggested. Graph-based presentation of the scheduling problem and operation planning is given. The algorithm for planning based on decision tree and the state enumeration is developed. Using this idea, the scheduling problem can be formulated as a state-decision problem with operations to be planned. The idea of temporal-ordered partial schedule associated with the planning state of the system is discussed. And the concept of fuzzy finite state machine model for the planning system under is suggested.
Error-Diffusion Based Speech Feature Quantization for Small-Footprint Keyword Spotting

Neural network based keyword spotting (KWS) system is a critical component for user interaction in current smart devices. Although small-footprint networks have been widely explored to reduce deployment overhead, low-precision input feature representation still lacks in-depth research. In this letter, an error-diffusion based speech feature quantization method is proposed. Specifically, our algorithm adapts image processing to quantize the input speech feature maps in arbitrary bits. Experiments show that in the 10-keyword KWS task, our 3-bit representation only brings a 0.45% average accuracy drop compared to the full-precision log-Mel spectrograms while others drop over 3%. In the 2 keywords task, our 3-bit representation produces no significant differences, while 1-bit quantization only leads to an average of 1.7% accuracy drop and is even capable of handling similar keywords and imbalanced data distribution. The result proves our method, to the best of our knowledge, is the first practical method that supports as low as 1-bit quantization for single-channel speech features in small-footprint KWS. In addition, we analyze the impact of error-diffusion directions and conclude that time-direction diffusion is more suitable for temporal convolutional networks.
Word Segmentation as Unsupervised Constituency Parsing

Word identification from continuous input is typically viewed as a segmentation task.Experiments with human adults suggest that familiarity with syntactic structures in their native language also influences word identification in artificial languages; however, the relation between syntactic processing and word identification is yet unclear.This work takes one step forward by exploring a radically different approach of word identification, in which segmentation of a continuous input is viewed as a process isomorphic to unsupervised constituency parsing.Besides formalizing the approach, this study reports simulations of human experiments with DIORA (Drozdov et al., 2019), a neural unsupervised constituency parser.Results show that this model can reproduce human behavior in word identification experiments, suggesting that this is a viable approach to study word identification and its relation to syntactic processing.
An Efficient Approach for Sentiment Analysis using Data Mining Algorithms

Last few years have delivered a huge boom in the field of research in Sentiment Analysis, totally on incredibly subjective textual content kinds like films or products evaluations. This research work design a feature selection technique with Bagged Random Forest classification to predict the sentiments of news articles. This work represents comparison among C4. 5, random forest algorithm, bagged random forest algorithm and proposed algorithm by taking various parameters into consideration. Results are evaluated on the basis of various attributes namely correctly classified instances, incorrectly classified instances, error comparison. The basis of this comparison was done by taking various parameters into light such as mean absolute error, root mean squared error, relative absolute error, root relative squared error, average true positive rate, average false positive rate, recall and F-measure. These new results have proved that the proposed technique is having better results than the previous one.