Reviewerly/exHarmony huggingface dataset:
Bayesian Optimization for Efficient Tuning of Visual Servo and Computed Torque Controllers in a Reinforcement Learning Scenario

Although the search for optimal parameters is a central concern for the design stage of control systems, this adjustment is generally not optimized in the design of visual servo controllers. However, for a classic position-based visual servo controller, the choice of the proportional gain that multiplies the computed error may directly affect the system&#x0027;s performance, and may even lead to instability. On the other hand, adjusting such a parameter can be a time-consuming and hard-working task. Thus, in this work, we propose to automate the search for the linear and angular gains of a visual servo controller through Bayesian optimization. We simulate the environment in Matlab with a Kinova GEN3 7DOF robot in a reinforcement learning scenario, in which the projected cost function is evaluated directly on the robot. We demonstrate that Bayesian optimization is capable of finding the visual servo controller gains, as well as the robot internal controller gains, with up to 13 and 14 times fewer iterations when compared to an on-police actor-critic model-free algorithm and the genetic algorithm respectively. Furthermore, we show that the obtained controller performs better considering different control performance parameters and in qualitative evaluations regarding the Cartesian and image spaces.
Modern Realities of Software Project Management

The need to maintain the workflow in conditions of forced disconnection has increased the need for software products. But it turned out that the quality of many of them is lower than expected. One of the reasons is that users with different quality criteria began to work with them. Another reason is that the products have objectively become unacceptably low quality, largely due to errors in the management of the project. The scale and significance of a modern software project require its implementation according to one of the classical models: it will ensure quality. However, due to the desire to get ahead of competitors, flexible methodologies are used, reducing deadlines and losing quality. And the larger the project, the higher the risk of quality loss and the higher the cost of losses. How to combine the benefits of both approaches without getting their disadvantages?
Breast Cancer Detection by Hybrid Techniques based on Deep Learning Networks

One of the cancers that gives high fatality rates in human life is breast cancer. The current method used to detect breast cancer needs radiologists, which makes it costly and time-consuming. A possible solution is to detect it early, which can be done by computer-aided diagnosis technologies. An end-to-end system that could automatically detect breast cancer is described in this paper. From the mammographic images, it was first undergone the pre-processing stages for noise elimination. The law's mask was then applied to the preprocessed image to filter out the secondary features further. The filtered image was segmented by the active contour to obtain the breast region before being fed into a deep convolutional neural network for feature extraction. Principle Component Analysis (PCA) technique was then applied to select the necessary features as input to the Support Vector Machine (SVM) for determining the class of cells (normal or abnormal). Lastly, k-fold cross-validation techniques were executed to validate the results and obtained the average reading for both training and testing datasets. The proposed system was tested on the Digital Database for Screening Mammography (DDSM) and Mammographic Image Analysis Society (MIAS) dataset, and attained 97.50%, 96.67%, 98.33%, and 0.99 for accuracy, sensitivity, specificity, and area under curve, respectively.
Modeling Agreement of Resources in a Distributed Information Environment

It is shown that the processes of information exchange in complex distributed structures require appropriate control mechanisms based on a proper description that takes into account various sequences of formation of message components, which should be described on the basis of streaming models of distributed computing that determine the general approach for solving problems of analysis and synthesis of functional architecture in the plane of properties a quantitative assessment of the probabilistic-temporal parameters of the information exchange process, taking into account the parameters of the procedures implemented at various levels by the relevant protocols, as well as determining the general nature of the relationship taking into account the parameters of the procedures.
Novelty Detection-Based Automated Anomaly Identification via Optimized Deep Generative Model

Novelty detection (ND) is a crucial task in machine learning to identify anomalies in the test data in some respects different from the training data. As an anomaly detection method, novelty detection only uses normal samples for model learning, which can well fit most of the natural scenes that the amount of abnormal samples is in fact strongly insufficient, such as network intrusion detection, industrial fault detection, and so on, due to the rareness of abnormal events or the high cost of abnormal samples collection. This paper proposes a reconstruction-based ND scheme by introducing an optimized deep generative model (ODGM), which combines the concept of Variational Auto-encoder (VAE) and the generative adversarial network (GAN) model jointly to efficiently and stably learn the essential characteristics from normal samples. A novelty index is established by combining signal reconstruction loss and feature loss between the original signal of the reconstructed signal based on the ODGM on normal samples for anomaly point identification in the test data. The effectiveness and superiority of the proposed model is validated and compared with other representative deep learning-based novelty detection models on two public data sets.
Privacy Preserving and Efficient Outsourcing Algorithm to Public Cloud

The growth of the cloud computing services and its proliferation in business and academia has triggered enormous opportunities for computation in third-party data management settings. This computing model allows the client to outsource their large computations to cloud data centers, where the cloud server conducts the computation on their behalf. But data privacy and computational integrity are the biggest concern for the client. In this article, the authors attempt to present an algorithm for secure outsourcing of a covariance matrix, which is the basic building block for many automatic classification systems. The algorithm first performs some efficient transformation to protect the privacy and verify the computed result produced by the cloud server. Further, an analytical and experimental analysis shows that the algorithm is simultaneously meeting the design goals of privacy, verifiability and efficiency. Also, found that the proposed algorithm is about 7.8276 times more efficient than the direct implementation.
A two-stream heterogeneous network for action recognition based on skeleton and RGB modalities

Recent years, skeleton based action recognition with graph convolutional network (GCN) has achieved great success. However, since skeleton data only includes human body joints coordinates, other key information on actions is missing such as the subtle motion of hands, the objects the human is interacting, leading to an unsatisfactory performance. In this respect, the RGB data can offer help to recognize actions that skeleton-based methods have limitations on. In this work, we propose a novel two-stream heterogeneous network consisting of GCN and CNN networks for action recognition. Specifically, the GCN network takes the skeletal sequence as input to exploit skeleton information. For the RGB video, the CNN model, ResNet (2+1)D, is adapted to exploit RGB information. Afterwards, the discriminant canonical correlation analysis (DCCA) method is utilized to integrate the output feature maps from the skeleton and RGB streams, resulting in improved performance. Experimental results on the large-scale dataset NTU RGB+D show that the proposed model outperforms state-of-the-art models.
Background Modeling

This chapter provides complete working for the background modeling algorithm and its various algorithms. The working of background subtraction and foreground detection in digital video. Also, the chapter describes a modified Gaussian mixture model for background modeling for the video surveillance system.
Algebraic structure of parametric elliptic curves

The advantage of elliptic curve (EC) cryptographic systems is that they provide equivalent security even with small key lengths. However, the development of modern computing technologies leads to an increase in the length of keys. In this case, it is recommended to use a secret parameter to ensure sufficient access without increasing the key length. To achieve this result, the initiation of an additional secret parameter R into the EC equation is used to develop an EC-based key distribution algorithm. The article describes the algebraic structure of an elliptic curve with a secret parameter.
Discriminative Multi-feature Representation for Renal Cancer Detection based on Histopathology Images

Renal cancer is one of the most common cancers in the world, and early diagnosis can increase the possibility of successful treatment and survival rate. However, manual detection is time-consuming and relies heavily on the experience of pathologists. Therefore, it is desirable to employ a computer-aided approach to automate the diagnostic process thereby saving time and labor. To date, a substantial amount of research with common deep learning methods have been applied to address this issue. However, deep learning methods require large numbers of images to train the model. Alternatively, traditional machine learning methods such as texture feature extractors can reach a reasonable result with a smaller computing cost. In this paper, we extensively study the efficiency of texture features extracted from histopathology images at detecting kidney cancer by adopting a weighted fusion method of HOG and GLCM, which includes both local structural features and full texture information from the histopathology images. We applied the proposed method on a histopathology image data set containing 93 patients with renal cancer and 150 patients with normal kidneys. The experimental results indicate that our method can achieve a similar outcome to deep learning methods, while reducing the computing time.
Histological Discrimination Using Fractal Analysis and Refractive Index Variance

Histopathology presents artifacts, delays or subjective errors. Digital histology contributes to automation and diagnosis improvement. In this work we employ label-free tissue slices by phase contrast fractal dimension and refractive index variance.
Implementation of a Network-Centric Production Storage System in Distributed High-Performance Computing Systems

The main components of a network production data storage system in distributed high-performance computing systems are defined. A hybrid method of organizing a data storage network is considered. A feature of distributed data storage is the semantic distribution of data blocks. The interoperability of local data storage (LDS) and data export utilities is to transform the low-level organization of LDS data. When organizing a resource center, LDS plays the role of a DBMS, and the data export utility serves to organize data in a form suitable for processing by the main storage module. Thus, the interoperability of local data stores and data export utilities implies the formalization of DBMS (LDS) data for their subsequent processing. The data of interaction of the storage system when accessing computing clusters is reduced to the form of a JSON file, divided into “entities”, “actions” and “links”.
Accelerating 2-D Image Convolution Using a Graphics Processing Unit

Image processing is an important technique that is used in many fields, such as self-driving vehicles or facial recognition. One method is called image convolution, which involves many calculations that manipulate the pixels of an image to produce a new image with a desired effect. This is computation intensive and requires a significant amount of time when run on a traditional computer processing unit (CPU). Since image processing is used for real-time applications, such as those mentioned above, it is essential that convolution algorithms run as quickly as possible. A common way to speed up image convolution algorithms is to take advantage of the highly parallel structure of graphical processing units (GPU) to perform concurrent calculations. One problem with GPU applications is that they are often limited by the latency delays associated with transferring data between the CPU and the GPU. Previous works have looked into different ways to address this issue and optimize GPU programs. This research aims to explore different memory implementations and compare them to see which is best at optimizing data transfers.
Appunti di etimologia greca e semitica dello sciclitano. Sull’etimologia di alcuni grecismi e semitismi in nomi, antroponimi e toponimi attestati nel territorio di Scicli (Sicilia)

[Notes on Greek and Semitic Etymology of the Sciclitano Dialect: About the Etymology of Some Hellenisms and Semitisms in Names, Anthroponyms and Toponyms Attested in Scicli (Sicily)] This study aims to analyze and report in a single text a set of terms (anthroponyms, toponyms and common nouns) of the Sciclitano dialect - one of the different forms in which Sicilian language manifests itself -, whose etymology dates back to terms of Greek origin (classical, Byzantine and Neo-Greek) and Semitic (Arabic and, to a lesser extent, Hebrew) as evidence of the linguistic layers that have settled in the dialect from the origins down into the course of the Middle Ages and beyond, under various dominations, and are still present in the territory. As far as surnames are concerned, the Arabic ones attested in recent decades due to the phenomenon of immigration have not been taken into consideration. Where possible, epichoric peculiarities have been taken into account (especially for toponyms) and an etymological reconstruction has been attempted even in the absence of archival documents, always having regard to the history of Scicli (and the County of Modica to which it belonged) and various sources handed down by local scholars and also to the oral tradition. Some of the items analyzed are present in other parts of Sicily, while others are found only locally and, on the whole, they represent a part worthy of notice and study.
Dementia Prediction Using OASIS Data for Alzheimer’s Research

The role of data science and machine learning in the medical field has increased manifold in recent years. However there lies a vast scope in conditions like dementia. Complex machine learning models are in place to analyze brain images but these fail to perform on numeric biological and social data of the patients. This work analyses the longitudinal brain data of patients collected by OASIS for Alzheimer research. A graphical analysis is performed on the data and several conclusions regarding dementia have been drawn. Multilayer Perceptron and Decision Tree both provided an accuracy of 0.839 and a recall of 0.836 and 0.800 respectively thereby providing the most efficient model for dementia prediction. Machine learning models can help predict dementia using social and biological data of patients to a fairly accurate degree without the requirement of brain MRI images.
Neural Network for Monitoring Design of Science and Technology Talent Flow System

Based on the information of highly cited scientists and high-strength patent inventors obtained by neural network, this paper uses the complex network big data analysis method to build a global science and technology leader talent flow network model, system detection research, and measure and analyze the characteristics of global leading talent flow in major countries (regions) and the whole world. Through the analysis of the model, we can find that the United States and other western developed countries attract a large number of leading talents in science and technology, while the loss of leading talents in developing countries is more serious, and the flow of leading talents between developed countries is very frequent. From the global perspective, there is a central node in the talent flow network, that is, the whole talent flow network has strong anti-interference.
Empowering Youth For Sustainable Development Through Vocational Guidance

Vocational guidance is intended to aid young people in choosing an occupation, preparing them for it, finding an opening in it, and building up a career of efficiency and success. It is apparent in Nigeria that young people are ill-prepared for entry into the world of work. They are inadequately oriented in educational institutions to the extent that they do not understand their own abilities, interest, and values. As a result of the failure of the educational system in helping the youths realise their potentialities, they prefer choosing high-level jobs or prestigious careers. The youths, being the future hope of a nation's development, require information that will make them take initiative and take decisions to solve problems and improve service and performance. This paper, therefore, examines the concepts of vocational guidance as well as empowerment against the backdrop of its significance for sustainable development. An attempt was also made to highlight the roles of a guidance counselor, that of parents and teachers in youth empowerment for national development. The government at all levels should focus on job creation and invest in the development of youths for them to exhibit their talents maximally for effective participation in democratic governance for sustainable development.
Hofs: Higher Order Mutual Information Approximation for Feature Selection in R

Feature selection is a process of choosing a subset of relevant features so that the quality of prediction models can be improved. An extensive body of work exists on information-theoretic feature selection, based on maximizing Mutual Information (MI) between subsets of features and class labels. The prior methods use a lower order approximation, by treating the joint entropy as a summation of several single variable entropies. This leads to locally optimal selections and misses multi-way feature combinations. In this article we present a higher order MI based approximation technique called Higher Order Feature Selection (HOFS) implemented in R software. Instead of producing a single list of features, our method produces a ranked collection of feature subsets that maximizes MI, giving better comprehension (feature ranking) as to which features work best together when selected, due to their underlying interdependent structure. Our experiments demonstrate that the proposed method performs better than existing feature selection approaches while keeping similar running times and computational complexity.
Malay Single Sentence Parser

Parser is a process of classifying sentence structures of a language. Parser receives a sentence and breaks it up into correct phrases. The purpose of this research is to develop a Malay single sentence parser that can help primary school students to learn Malay language according to the correct phrases. This is because research in Malay sentence parsing has not gotten enough attention from researchers to the extent of building parser prototypes. This research used top-down parsing technique, and grammar chosen was context-free grammar (CFG) for Malay language. However, to parse a sentence with correct phrase was a difficult task due to lack of resources for obtaining Malay lexicon. Malay lexicon is a database that stores thousands of words with their correct phrases. Therefore, this research developed a Malay lexicon based on an article from Dewan Masyarakat magazine. In conclusion, this research can provide help to the primary school students to organize correct Malay single sentences.
Outcomes of Speech to Speech Translation for Broadcast Speeches and Crowd Source Based Speech Data Collection Pilot Projects

Speech-to-Speech Machine Translation (SSMT) applications and services use a three-step process. Speech recognition is the first step to obtain transcriptions. This is followed by text-to-text language translation and, finally, synthesis into text-speech. As data availability and computing power improved, these individual steps evolved. However, despite significant progress, there is always the error of the first stage in terms of speech recognition, accent, etc. Having traversed the speech recognition stage, the error becomes more prevalent and decreases very often. This chapter presents a complete pipeline for transferring speaker intent in SSMT involving humans in the loop. Initially, the SSMT pipeline has been discussed and analyzed for broadcast speeches and talks on a few sessions of Mann Ki Baat, where the source language is in Hindi, and the target language is in English and Telugu. To perform this task, industry-grade APIs from Google, Microsoft, CDAC, and IITM has been used for benchmarking. Later challenges faced while building the pipeline are discussed, and potential solutions have been introduced. Later this chapter introduces a framework developed to collect a crowd-sourced speech database for the speech recognition task.
An Analysis of Physical and Environmental Security in Communication and Information Department Mojokerto

This study aims to provide an overview to the Communication and Information Department Mojokerto regarding the maturity level of physical and environmental security management at the agency, as well as to provide future recommendations. The results of research related to physical and environmental safety using the ISO 27002 standard, indicate that the level of physical and environmental security at the Communication and Information Department Mojokerto is still relatively low. Things that are still lacking include the lack of protection from external threats such as natural disasters, as well as the lack of care and maintenance of infrastructure. The maturity level of physical and environmental security control is 0.85 which is still at level 1 or Initial Ad Hoc from a maximum value of 5, which is at the Optimized level. It can be concluded that Communication and Information Department Mojokerto only knows that there are things that need attention but there is no standardization of the process. With this research, it is hoped that the Communication and Information Department Mojokerto can make improvements to improve physical and environmental security. In addition, it is also a consideration to obtain ISMS Certification with the ISO 27002 standard in the future.
Coreference Resolution and Meaning Representation in a Legislative Corpus

This paper addresses the application and integration of coreferences resolution tasks in a legislative corpus by using SpanBERT, which is an improvement of the BERT (Bidirectional Encoder Representations from Transformers) model and semantic extraction by Abstract Meaning Representation (AMR) for reducing text complexity, meaning preservation and further applications. Our main processes are divided into four subparts: legal text pre-processing, coreference resolution, AMR, evaluation for meaning preservation, and complexity reduction. Smatch evaluation tool and Bilingual Evaluation Understudy (BLEU) scores are applied to evaluate overlapped meaning between resolved and unresolved coreference sentences. The AMR graphs after complexity have been reduced can be applied for further processing tasks with Neural Network such as legal inferencing and legal engineering tasks.