Reviewerly/exHarmony huggingface dataset:
Dynamic Adjusting ABC-SVM Anomaly Detection Based on Weighted Function Code Correlation

Under the tendency of interconnection and interoperability in Industrial Internet, anomaly detection, which has been widely recognized, has achieved modest accomplishments in industrial cyber security. However, a significant issue is how to effectively extract industrial control features which can accurately and comprehensively describe industrial control operations. Aiming at the function code field in industrial Modbus/TCP communication protocol, this paper proposes a novel feature extraction algorithm based on weighted function code correlation, which not only indicates the contribution of single function code in the whole function code sequence, but also analyzes the correlation of different function codes. In order to establish a serviceable detection engine, a dynamic adjusting ABC-SVM (Artificial Bee Colony - Support Vector Machine) anomaly detection model is also developed. The experimental results show that the proposed feature extraction algorithm can effectively reflect the changes of functional control behavior in process operations, and the improved ABC-SVM anomaly detection model can improve the detection ability by comparing with other anomaly detection engines.
Muti-Dimensional Reservoir Simulation Platform

Abstract The muti-dimensional reservoir simulation platform is a digital twin reservoir system that integrates seismic, geology, numerical simulation and production. Based on years of accumulation, we make full use of late-comer advantages and adapt to the tide of networking and cloudification to fully integrate with informatization. In the oil and gas industry applications, we regard integration and sharing as our goal. The muti-dimensional reservoir simulation platform will realize application integration on the basis of digital integration realized by informatization and maximize knowledge sharing on the basis of data sharing realized by informatization.
Problems of Cyber-Technical Systems’ Intelligent Consistent Real-Time Control

Railroad Transportation, Automated Manufactures, Nuclear Power Plants, Spacecrafts can be reviewed as examples of complex Cyber-Technical Systems. The subsystems of these systems contain dozens of sensors, actuators, etc. To complete the Cyber-Technical system's mission we need to implement the schedule of right coordinated processes, the wrong synchronization of the processes can lead to a catastrophe. Processes to be executed require various kinds of resources with limited available levels. The paper defines a notion of consistent intelligent real-time control implemented by the set of real-time control algorithms. This set should provide completion of the mission in changing environment and even in case of faults of the equipment. The paper analyses the aspects of complexity and requirements for 'intelligence' to provide consistent control. The paper also discusses ways of synthesis and verification of such intelligent real-time consistent control supported by the original software.
Exploring Quantization-Aware Training on a Convolution Neural Network

In this paper, a convolutional neural network is trained and tested on the MNIST handwritten digit recognition dataset. Quantization-aware training is performed on the model during training and the effects on model size and accuracy are explored and compared to the full precision model. The model is tested in full precision (32-bit), 8-bit precision, and selected quantized layers (mixed) as well. The results have shown a very slight reduction in accuracy of no more than 0.12%, with a compressed model size by 4X for precision of 8-bits compared to 32-bits model. Quantization of selected dense layers yielded a higher accuracy and a smaller model size than both the 32-bit model and the model with quantization of selected convolution layers, resulting in an MNIST handwritten digits classifier with 99.42% accuracy and a size of 0.2133 Mb.
A Partial Pre-composing Method for Composition of Web Services

In recent years, web service compositions are deemed to be one of the new technologies to build an application system. By composing independent web services, loosely coupled services are integrated into value-added service compositions. In this paper, we address the web service composition problem and propose a partial pre-composing approach. This approach picks and generates popular user requests as service combinations stored in the service repository. We compare our approach with QoS driven, extended A* and Graphplan methods, the experimental results indicate that our algorithm can return a solution with fewer web services or less execution time.
Modelling Solar Power Generation in Europe Using Support Vector Regression

In this work we modelled the solar power generation in Europe using support vector regression. The modelling also performed for three countries: Germany, France and United Kingdom. In the modelling the effect of using different kernels was observed. Radial kernel outperformed linear and polynomial kernels. Also using radial kernel, effect of modelling one country separately and using all Europe data together was investigated. It was observed that for Germany and United Kingdom using Europe data together with the countries data produced lower average error and root mean square error values. On the other hand, for France using only France data gave a better modelling compared with using France and Europe data together.
A Frequency-Category Based Feature Selection in Big Data for Text Classification

In big data era, text classification is considered as one of the most important machine learning application domain. However, to build an efficient algorithm for classification, feature selection is a fundamental step to reduce dimensionality, achieve better accuracy and improve time execution. In the literature, most of the feature ranking techniques are document based. The major weakness of this approach is that it favours the terms occurring frequently in the documents and neglects the correlation between the terms and the categories. In this work, unlike the traditional approaches which deal with documents individually, we use mapreduce paradigm to process the documents of each category as a single document. Then, we introduce a parallel frequency-category feature selection method independently of any classifier to select the most relevant features. Experimental results on the 20-Newsgroups dataset showed that our approach improves the classification accuracy to 90.3%. Moreover, the system maintains the simplicity and lower execution time.
Optimising engagement between forensics &amp; policing: Avoiding the dialogue of the deaf

Forensic science is increasing in the scale and scope of available or emergent capabilities to aid criminal investigations. Concurrently, investigations are increasing in complexity, with disciplines such as forensic ecology having an input into police work. The many specialisms comprising forensic ecology are helping authorities to counteract the trans-national and inter-jurisdictional criminal operations which are driven by global interconnectedness and the Internet. These factors, when combined with a rise in public oversight, and the media’s unrealistic expectation that all crimes are solvable, produce a challenging environment for police investigators. This paper explores the author’s experience of effective communication between police investigators and forensic practitioners and reflects on an emergent consultancy model of engagement. This option presents a change in the police operating environment, often with improved investigative outcomes.
Active discovery information systems

As new forms of information are increasingly “active” (i.e., media can change form and is not limited by location, user, or purpose) traditional information practices are shifting to meet the nature of media and the search expectations patrons bring to the library. Although not found in library literature, active discovery information systems encompass how we organize and make discoverable the evolving and emerging materials we handle. This paper addresses a gap in the literature for real-world applications of libraries implementing non-traditional organization and discovery systems. Specifically, the Sitterwerk Library in St. Gallen, Switzerland, is profiled as an example of a library that utilizes patron engagement and technology to change how information is organized and accessed. The systems reflect how we internally process and organize information in order to learn. Provided is a discussion on the implications of active discovery information systems, as well as possible active discovery information systems.
Elucidation of Velvet Hand Illusion using LABNIRS toward a New Haptic Display

VHI (Velvet Hand Illusion) is an illusion phenomenon to make someone feel a smooth sensation like velvet fabric and it is expected to be applied to haptic devices. To achieve this goal, it is very important to elucidate the mechanism of VHI. In this research, we investigate whether the brain activation related to VHI can be measured with NIRS. As a result, it is confirmed that the right middle frontal gyrus plays an important role in illusion occurrence which is same as the previous fMRI research.
Design and Implementation of Phylotastic, a Service Architecture for Evolutionary Biology

Access and reuse of authoritative phylogenetic knowledge have been a longstanding challenges in the evolutionary biology community — leading to a number of research efforts (e.g. focused on interoperation, standardization of formats, and development of minimum reporting requirements). The Phylotastic project was launched to provide an answer to such challenges — as an architectural concept collaboratively designed by evolutionary biologists and computer scientists. This paper describes the first comprehensive implementation of the Phylotastic architecture, based on an open platform for Web services composition. The implementation provides a portal, which composes Web services along a fixed collection of workflows, as well as an interface to allow users to develop novel workflows. The Web services composition is guided by automated planning algorithms and built on a Web services registry and an execution monitoring engine. The platform provides resilience through seamless automated recovery from failed services.
Quantum computing : problems and exercises

Quantum technology is one of the most promising and challenging fields in contemporary science. Quantum computing, quantum cryptography, and more generally quantum information technologies claim that they, in the short term, will change our paradigm of classical computing and communications. Concepts such as duality, superposition, entanglement, teleportation and many others seem to be closer to magic or science fiction than to everyday human experience. However, herein lies also the unlimited power of human thought, which mathematics and physics help us to go beyond our immediate experience of the world. This book, oriented toward undergraduate students pursuing Bachelor’s Degrees in Engineering, draws on the experience of the authors during nearly ten years of work in this field. The book consists of a comprehensive list of exercises with increasing degrees of dificulty. Additional material such as solved problems and a basic compilation of theory is also included in each chapter. The list of topics, which include among others, quantum computing, quantum teleportation, superdense coding, quantum processors or quantum communication protocols, intended for opening doors that will motivate students to continue delving into the field of quantum technologies, which has only just begun to be explored.
Cloud-Assisted Privacy Protection for Data Retrieval Against Keyword Guessing Attacks

Machine learning is more closely linked to data privacy and has obtained rapid development in recent years. As for data privacy, searchable encryption (SE) is widely used as a ciphertext search technology, protecting the privacy of users. However, existing schemes usually support single keyword search, so the remote cloud server (CS) may return some irrelevant results. To address this problem, we propose a server-aided public key encryption scheme with multi-keyword search (SA-PEMKS) scheme. The scheme allows a data user to conduct multiple keyword search in a single search query. Security and performance analysis show that the SA-PEMKS scheme is secure and efficient.
<i>STILLLEBEN</i>with Symbionts

‘STILLLEBEN. Becoming Symbionts’ proposes to value milk and its microbial constituents as primordial assets and currencies - along with cells, sperm, blood, water, and oxygen. The latest scientific research, which has suggested that there is an intimate unseen interplay between mothers and their babies via the transfer of breast milk and microbes, which actually increases the value of the currency with each exchange. The co-operation of Irini Athanassakis and David Berry is an invitation to perceive the transfer of milk not only as an interaction visible to the naked eye, but also on the microscopic level of cells and bacteria. In order to challenge us with this unseen perspective, Athanassakis encourages us to step forward and take a performative and procreative role in expanding our perception. As we are home to billions of microscopic entities, we continuously cast this part of ourselves into our surroundings, impacting and interacting with everything around us. We leave a microbial trace, a lingering residue of cells on the objects, rooms, and people that we encounter. Breast milk and formula are part of such an exchange process. If we look at the logic of microbial exchange in nursing as ‘giving’ and knowledge about symbionts for a future holo-economy based on co-operation and mutualism for collective survival.
Distributional Reinforcement Learning with Ensembles

It is well known that ensemble methods often provide enhanced performance in reinforcement learning. In this paper, we explore this concept further by using group-aided training within the distributional reinforcement learning paradigm. Specifically, we propose an extension to categorical reinforcement learning, where distributional learning targets are implicitly based on the total information gathered by an ensemble. We empirically show that this may lead to much more robust initial learning, a stronger individual performance level, and good efficiency on a per-sample basis.
Machine Learning Agricultural Application Based on the Secure Edge Computing Platform

Machine learning (ML) is fast becoming a powerful tool for increasing agricultural production, for instance, ML predicts weather and yield through satellite images. Such approaches, however, most applications are based on expensive cloud servers to handle massive calculations, which will easily lead to important data leakage (e.g. crop cultivation methods, annual output, etc.), and increase the economic burden of farmers. Motivated by the status of agriculture, especially small farms that are underfunded and require data confidentiality. We propose an application for agricultural object detection based on a secure edge computing platform that can be used for agricultural output statistics and automated spraying agriculture. Compared with cloud-based ML agricultural applications, edge-based computing platforms do not require data uploading and downloading, which protects data security and reduces capital costs. We use some techniques in the training model and compression model to solve the problems of model accuracy and real-time, as follows: (1) Category-based Assisted Excitation model is proposed to improve the YOLOv3 accuracy. (2) Use layer pruning and channel pruning together to achieve a small-scale structure search and maximize real-time performance. We have evaluated our optimized model based on NVIDIA Jetson TX2 and Fruit Dataset. Its frames per second (FPS) is 11.63, mean average precision (MAP) is 89.6%.
A Revisit to Le Cam’s First Lemma

Le Cam’s first lemma is of fundamental importance to modern theory of statistical inference: it is a key result in the foundation of the Convolution Theorem, which implies a very general form of the optimality of the maximum likelihood estimate and any statistic that is asymptotically equivalent to it. This lemma is also important for developing asymptotically efficient tests. In this note we give a relatively simple but detailed proof of Le Cam’s first lemma. Our proof allows us to grasp the central idea by making analogies between contiguity and absolute continuity, and is particularly attractive when teaching this lemma in a classroom setting.
Comparison of activation functions on radial basis function neural network in predicting dengue hemorrhagic fever incidents in DKI Jakarta

Dengue Hemorrhagic Fever (DHF) is still one of the main public health problems in Indonesia. The number of DHF cases and the spread of this disease is increasing along with mobility and population density. The Radial Basis Function Neural Network (RBFNN) in this research was implemented to predict the number of weekly DHF incidents in DKI Jakarta. RBFNN is a Feed Forward Neural Network model that has a single hidden layer. The hidden layer of RBFNN is constructed by an activation function. K-means clustering algorithm is used to improve the performance of RBFNN to determine the center and width of the activation function. This research used two different activation functions, which are the Gaussian and multiquadratic functions. The final results of this research indicate that the selection of the best activation function depends on the data and RBFNN structure used. The value of MSE contributes to the graph of the predicted results of weekly DHF incidents.
LSTM BASED EXTREME MACHINE LEARNING TO IMPROVE THE ACCURACY FOR MOBILITY PREDICTION FRAMEWORK

Human Mobility estimation is crucial in wide extent of current applications in different fields, for instance, customized proposition systems, the fifth-age (5G) portable correspondence structures, etc. Generally, the estimate objective shifts through numerous implementations circumstances. For the utilizations about 5G organize including source task just as versatility organization, it is major to foresee the spots of portable customers for the future a few minutes in order toward implement course of action early, which is actually a bearing expectation issue. Right now, explicit point of convergence on multi-client multi-step heading expectation, we first arrangement a fundamental significant learn -base estimate structure where the Long Short-Term Memory (LSTM) mastermind be authentically apply the essential fragment toward take in client explicit portability plan from the customer's irrefutable bearings and foresee his/her improvement slants later on. Roused by the related disclosures subsequent to affirming along with analyze thatcrucialstructure upon method based datarecords, we extended this through a territory situated forecast plot and plan a multi-customer multi-step course desire structure through additional merging the Sequence-toward-Sequence (Seq2Seq) learning. Consequences upon a reasonable datarecords delineate so the planned structure basic upgrades for hypothesis limit and diminishes mistake aggregation sway for multi-step conjecture. As an extension concept we are adding the Extreme Machine Learning to improve the performance of our planned work.
A Method for Combination and Ranking Hypotheses Under Conditions of Partial Uncertainty

The urgent problem of remote sensing information analysis is to form the most adequate statements, that is, hypotheses with the highest values of probability of their realization. The paper deals with the problem of combining and ranking hypotheses with interval estimates of their probability under conditions of uncertainty, the application of which will allow to estimate the states of the objects of interest in remotely sensed image. A numerical example is used to illustrate proposed method.
A Joint Model for Graph-Based Chinese Dependency Parsing

In Chinese dependency parsing, the joint model of word segmentation, POS tagging and dependency parsing has become the mainstream framework because it can eliminate error propagation and share knowledge, where the transition-based model with feature templates maintains the best performance. Recently, the graph-based joint model [19] on word segmentation and dependency parsing has achieved better performance, demonstrating the advantages of the graph-based models. However, this work can not provide POS information for downstream tasks, and the POS tagging task was proved to be helpful to the dependency parsing according to the research of the transition-based model. Therefore, we propose a graph-based joint model for Chinese word segmentation, POS tagging and dependency parsing. We designed a character-level POS tagging task, and then train it jointly with the model of [19]. We adopt two methods of joint POS tagging task, one is by sharing parameters, the other is by using tag attention mechanism, which enables the three tasks to better share intermediate information and improve each other's performance. The experimental results on the Penn Chinese treebank (CTB5) show that our proposed joint model improved by 0.38% on dependency parsing than the model of [19]. Compared with the best transition-based joint model, our model improved by 0.18%, 0.35% and 5.99% respectively in terms of word segmentation, POS tagging and dependency parsing.
A Reversible Watermarking and Encryption for ensuring the Authenticity, Integrity and Protection of Medical Images

For various purposes such as diagnosis, consultation between clinicians and radiologists, expert opinion from a doctor in another country, and treatment, the medical images are transmitted from one place to another. The medical images may be exposed to unauthorized sources during transmission, and may get modified deliberately or accidentally. Such changes to the diagnostic images may result in a misdiagnosis. False diagnosis can lead to serious repercussions. Hence, authenticity and integrity of the image should be checked and its security should be assured. To achieve this, watermarking is used in the proposed method, in conjunction with encryption. The watermark is created and inserted into the medical image's region of non interest (NROI). The watermarked image is then encrypted to ensure that it is confidential. The encrypted medical image is decrypted and the embedded watermark is retrieved upon reaching the receiving hand. The image authenticity and integrity verification is then performed.
A comprehensive survey on Neural Network based Image Data hiding Scheme

The advancement in science and technology has become a significant bottleneck to the information/data. In order to overcome the threats, a number of information hiding methodologies have been proposed. Among such techniques, the most commonly used technique is information hiding in an image due to the presence of secret information in an image, where it will be hard to be identified by the attacker. The appearance of both the normal image and the image that contains the secret information will be similar. Also, with a number of deep learning and AI techniques, data hiding can be done in an increasingly better way. This paper summarizes various neural network-based image steganography techniques and also the methodology used in each paper. Also, this research paper discusses the advantages and drawbacks faced by each technique.