Reviewerly/exHarmony huggingface dataset:
The Impact of Cloud Computing on Network Security and the Risk for Organization Behaviors

Cloud computing is currently provided consumer or business IT support via social media or by using the internet. Cloud computing on the other hand, is increasing the level of the network security risk due to the services are basically presented by a third party. This results in hard to control the privacy and data security. In addition, to maintain the service availability and support data collections. Cloud computing clouts several technologies such as virtualization, SOA, and Web 2.0, it is also claimed their security risk matters. In this paper, the most serious and important risks and threats of cloud computing are discussed. The main vulnerability is identifying through a review of the published works on the cloud computing environment with possible solutions to overcome these threats and risks.
What to buy, Pepper? – Bridging the Physical and the Digital World with Recommendations from Humanoid Robots

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Comparison of Naive Bayes and Support Vector Machine with Grey Wolf Optimization Feature Selection for Cervical Cancer Data Classification

The cervix is part of the female reproductive system, and it can be affected by cancer disease. On a global scale, cervical cancer causes death and malignancy in women. However, it has a considerable chance of recovery when detected early. Along with the development of technology in various fields such as the medical field, the early detection of cervical cancer can be conducted through the use of machine learning classification methods. Therefore, this research aims to solve cervical cancer classification problems using Naive Bayes (NB) and Support Vector Machine (SVM) with Grey Wolf Optimization (GWO) feature selection. GWO feature is a wrapper feature selection method that was used to eliminate irrelevant features in classifying cervical cancer allowing NB and SVM to classify accurately. Thus, these methods are referred to as NB-GWO and SVM-GWO. The cervical cancer dataset used was numerical data from MRI obtained from Dr. Cipto Mangunkusumo Hospital. Furthermore, performance results from both methods were compared to find out which one is better in classifying cervical cancer data. The results indicated the highest average accuracy of NB-GWO and SVM-GWO with 96.30% and 95.37% respectively. Therefore, NB-GWO performed better in classifying cervical cancer data compared to SVM-GWO.
Leveraging Trajectory Prediction for Pedestrian Video Anomaly Detection

Video anomaly detection is a core problem in vision.Correctly detecting and identifying anomalous behaviors in pedestrians from video data will enable safety-critical applications such as surveillance, activity monitoring, and human-robot interaction.In this paper, we propose to leverage trajectory localization and prediction for unsupervised pedestrian anomaly event detection.Different than previous reconstruction-based approaches, our proposed framework rely on the prediction errors of normal and abnormal pedestrian trajectories to detect anomalies spatially and temporally.We present experimental results on real-world benchmark datasets on varying timescales and show that our proposed trajectory-predictor-based anomaly detection pipeline is effective and efficient at identifying anomalous activities of pedestrians in videos.Code will be made available at https://github.com/akanuasiegbu/Leveraging-Trajectory
User-Centric Federated Learning

Data heterogeneity across participating devices poses one of the main challenges in federated learning as it has been shown to greatly hamper its convergence time and generalization capabilities. In this work, we address this limitation by enabling personalization using multiple user-centric aggregation rules at the parameter server. Our approach potentially produces a personalized model for each user at the cost of some extra downlink communication overhead. To strike a trade-off between personalization and communication efficiency, we propose a broadcast protocol that limits the number of personalized streams while retaining the essential advantages of our learning scheme. Through simulation results, our approach is shown to enjoy higher personalization capabilities, faster convergence, and better communication efficiency compared to other competing baseline solutions.
Construction of Graduate Behavior Dynamic Model Based on Dynamic Decision Tree Algorithm

With the research of machine learning technology and big data intelligent processing technology in engineering application becoming more and more mature, people gradually combine machine learning technology and big data intelligent processing technology. Aiming at the problem of innovative employment in colleges and universities, this paper proposes a dynamic decision tree algorithm based on these two technologies and constructs a dynamic model of graduates’ behavior. Through the analysis of dynamic decision tree algorithm, a big data analysis system is formed. Finally, simulation experiments verify whether the dynamic model can correctly reflect the behavior of college graduates. The results show that the big data integration system based on big data and dynamic decision tree algorithm has high adaptability. Incremental adaptive optimization of the traditional decision tree model can significantly improve the prediction effect and prediction time of dynamic data and provide theoretical support for the industrialization and social significance of big data technology. The dynamic decision tree algorithm of college employment proposed in this paper has good predictability and provides a certain theoretical reference for college graduates’ entrepreneurship.
Metaheuristic optimization techniques

Most of the real-world optimization problems involve various complexities, including nonlinearity, nonconvexity, discontinuity, and mixed variable types. In such cases, the classical algorithms are found to be either impractical or ineffective. In response, the search and optimization algorithms developed using heuristics offer a reasonable alternative. They are usually stochastic in nature and mimic the natural, physical, or biological principles. However, the metaheuristic techniques do not claim to achieve the exact global optimal solutions, but they always provide the near-optimal solution in a competitive time. Therefore, their practical appeal and ease of implementation, and the devise of high-speed computational devices made them popular in many application domains. This chapter provides the backgrounds, working principles, flowcharts, and pseudocodes of several popular and widely adopted metaheuristic algorithms. The discussion of the chapter shall assist the readers in understanding the fundamental features and differences of the explored algorithms that eventually guide them to choose the most appropriate algorithms for their problems. Moreover, many of the discussed algorithms are employed in solving several optimization problems throughout this book.
Region-based Neighbor Selection in Blockchain Networks

Blockchain networks are mainly used in financial verification processes, the most famous of which is the Bitcoin network consisting of a huge number of nodes. Expansion of blockchain technology, however, to other areas, such as healthcare, arts, culture, and entertainment, is being considered internationally. Especially important challenges for ensuring the reliability of blockchain networks and the security of peer-to-peer networks are shortening the block propagation times throughout a blockchain network to reduce fork and preventing malicious eclipse attacks against targeted nodes in a network. Previous methods have tried to increase the block propagation speed at the expense of imposing a higher burden on each node and a higher risk of eclipse attack. This paper proposes a new neighbor selection method based on the neighbor's regional information. That is, each node has a relatively small number neighbors located outside its region. By using this simple method, the distribution of blocks throughout the network becomes faster and the random neighbor selection nature in a blockchain network is kept intact; thus, risk of eclipse attack is low.
Algebraic Soft Decoding of Elliptic Codes

This paper proposes the algebraic soft decoding (ASD) for one-point elliptic codes, where the interpolation problem is solved from the perspective of module basis reduction. In ASD, the interpolation polynomial <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {Q}(x, y, z)$ </tex-math></inline-formula> is the minimum candidate of a Gröbner basis. Based on a multiplicity matrix, an interpolation ideal can be defined. With the decoding output list size, an equivalent interpolation module can be led to. By further defining the set of interpolation points, a sequence of modules from the elliptic curve coordinate ring can be obtained. Based on the Lagrange interpolation functions over elliptic function field, a basis of the interpolation module can be constructed. The desired Gröbner basis that contains <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {Q}$ </tex-math></inline-formula> can be determined by reducing the module basis. Re-encoding transform (ReT) is further introduced to reduce the basis reduction complexity. It is also shown that the interpolation can be facilitated by assessing the degree of the Lagrange interpolation polynomials. The decoding complexity is analyzed, which is verified by numerical results. That shows the advantage of this interpolation technique over the conventional Kötter’s interpolation. The ASD performance of elliptic codes is also presented.
Tracking of Scalpel Motions With an Inertial Measurement Unit System

Surgical planning to visualize a complete procedure before surgical intervention, paired with the advanced surgical techniques of a surgeon, has been shown to improve surgical outcomes. Efforts to improve surgical planning have included tracking real-time surgeon movements via surgical instruments in a confined body cavity space in the human body to enhance specific techniques when performing minimally invasive surgery. In this work, a surgical tool tracking approach is presented that leverages small scale electronics to enable real-time position capture for use in iterative surgical planning. By integrating a lightweight 9 degree-of-freedom Inertial Measurement Unit (IMU), our system captures both spatial and temporal information of the surgical tool without requiring a direct line-of-sight. The IMU is printed on a flexible film and attached to and integrated with a surgical tool demonstrating its tracking capabilities. Data from the IMU is analyzed to determine the full range of motion during angular displacement for measurement and tracking. The results show an accuracy of 2.2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> , 2.9 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> and 3.1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> of the full range of motion of the X (Yaw), Y (Roll) and Z (Pitch) Euler angle coordinate system respectively demonstrating the potential for surgical tool tracking measurement without the need for a direct line of sight and with future impact including flexible electronics and motion tracking. This work will be helpful in a diversity of fields including surgery, surgical training, biomaterials, and motion tracking.
Three-Way Ensemble Prediction for Workload in the Data Center

Accurate prediction of data center workload, an important technology of cloud computing, is particular in improving resource utilization and reducing energy consumption.However, the workload presents a quasi-volatile, is challenging to obtain accurate results in cloud resource management.In this paper, the three-way ensemble prediction for workload in the data center is first proposed to improve the accuracy of the prediction.Moreover, we first defined the workload as the stable period, the volatility period, and the jitter period and adopted a simulated annealing algorithm to learn the optimal threshold to divide the workload.Then, according to the basic idea of the three-way decision, we assigned various prediction models based on workload characteristics and a priori error prediction to improve the prediction accuracy further.Finally, all the experimental results carried on the CPU load monitoring logs from Google cluster trace and compared with ARIMA, NN, and DMASVR-3WD, TWD-RCPM improve the accuracy of workload prediction by 69.0%, 68.6% and 72.6%, respectively.
Continual Learning for Multivariate Time Series Tasks with Variable Input Dimensions

We consider a sequence of related multivariate time series learning tasks, such as predicting failures for different instances of a machine from time series of multi-sensor data, or activity recognition tasks over different individuals from multiple wearable sensors. We focus on two under-explored practical challenges arising in such settings: (i) Each task may have a different subset of sensors, i.e., providing different partial observations of the underlying ‘system’. This restriction can be due to different manufacturers in the former case, and people wearing more or less measurement devices in the latter (ii) We are not allowed to store or re-access data from a task once it has been observed at the task level. This may be due to privacy considerations in the case of people, or legal restrictions placed by machine owners. Nevertheless, we would like to (a) improve performance on subsequent tasks using experience from completed tasks as well as (b) continue to perform better on past tasks, e.g., update the model and improve predictions on even the first machine after learning from subsequently observed ones. We note that existing continual learning methods do not take into account variability in input dimensions arising due to different subsets of sensors being available across tasks, and struggle to adapt to such variable input dimensions (VID) tasks. In this work, we address this shortcoming of existing methods. To this end, we learn task-specific generative models and classifiers, and use these to augment data for target tasks. Since the input dimensions across tasks vary, we propose a novel conditioning module based on graph neural networks to aid a standard recurrent neural network. We evaluate the efficacy of the proposed approach on three publicly available datasets corresponding to two activity recognition tasks (classification) and one prognostics task (regression). We demonstrate that it is possible to significantly enhance the performance on future and previous tasks while learning continuously from VID tasks without storing data.
Prevention of SQL Injection Attack by Using Black Box Testing

SQL injection attacks are diverse and have fast variations, making it convenient to handle them. An SQL attack can affect the entire system to a great extent, adversely affecting the website, as well as data loss and leaking. In the past, enormous efforts have been put forth into handling the attack. This attack consists of a variety of heterogeneous components, which has always made research a challenging attempt. A web application injection attack allows hackers to gain control of a web application by entering invalid input. In the past, researchers found that many applications were vulnerable to such attacks as a result of less efficient entry validation. We propose an automated black box testing program for automating the task of SQL Injection Vulnerability to eradicate SQL injection attacks. The SQLIV is automatically assessed when such attacks are carried out. Also, it uses object-oriented methodology to develop the SQLIV Scanner so that false +ve and -ve results can be reduced. The algorithm's performance can be compared with existing algorithms and an experimental analysis can be conducted to test and validate its results. A new algorithm is expected to provide more accurate results.
Generating Context-Aware API Calls from Natural Language Description Using Neural Embeddings and Machine Translation

API calls can be described using natural language and can be implemented using a programming language. A programming environment that can process natural language descriptions within source code and provide context aware code suggestions can significantly improve productivity of developers and make programming more accessible to non-programmers and less experienced programmers. This paper proposes a context-aware tool called API Call Programming Interface (ACPI) which allows developers to write a natural language description for methods within source code and get correct and compilable API Call (AC) based on the text description and surrounding code. Existing work and code suggestion tools only consider the user's natural language description as input and ignore the contextual surrounding code. We take surrounding code into account and include information about local variable names within the code suggestion. Our approach consists of three modules. First, Method Name Generator, an unsupervised neural-embeddings-based algorithm to map the natural language description of methods to a list of most likely method names. Second, an AST Generator, a supervised Machine Translation model that predicts the structure of the AST from the list of the method names. And third, a Code Synthesizer that assigns local variables names to the AST to get the final method calls. Further, we include a Ranking Module that ranks the list of suggested method names based on their completeness. We evaluated our approach on data from 1000 high-quality Java projects and achieved an accuracy of 61% for API calls suggestions from natural language descriptions, which outperforms prior work and demonstrates the potential of our approach. We also conducted productivity experiments with 148 undergraduate participants to measure the usefulness of ACPI. The experiment showed that programming with ACPI can reduce programming time by 45% and increase programming accuracy from 19% to 83% when compared to programming without ACPI in four code completion tasks.
A Peer-to-Peer Ownership-Preserving Data Marketplace

A data marketplace enables trading among those who expect to monetize their data and those interested in gaining insights from it. Unfortunately, the paradigm that drives current marketplaces suffers from data leakage: one who buys data can, in principle, resell the acquired data as many times as he wants, even despising non-disclosure agreements. This work proposes a data marketplace, which allows to sell data that can be computed, though not unveiled. First, an owner provides encrypted data to a buyer, who can perform arbitrary operations on this encrypted data as if it were regular data. Thanks to homomorphic encryption, the encrypted results obtained in the buyer-side can then be decrypted in the seller-side, in a second and definitive data exchange. A smart contract is in charge of registering each step of the transaction to guarantee a safe exchange for the two parties involved.
Impact of Delay Classes on the Data Structure in IOTA

In distributed ledger technologies (DLTs) with a directed acyclic graph (DAG) data structure, a message-issuing node can decide where to append that message and, consequently, how to grow the DAG. This DAG data structure can typically be decomposed into two pools of messages: referenced messages and unreferenced messages (tips). The selection of the parent messages to which a node appends the messages it issues, depends on which messages it considers as tips. However, the exact time that a message enters the tip pool of a node depends on the delay of that message. In previous works, it was considered that messages have the same or similar delay; however, this generally may not be the case. We introduce the concept of classes of delays, where messages belonging to a certain class have a specific delay, and where these classes coexist in the DAG. We provide a general model that predicts the tip pool size for any finite number of different classes. This categorisation and model is applied to the first iteration of the IOTA 2.0 protocol (a.k.a. Coordicide), where two distinct classes, namely value and data messages, coexist. We show that the tip pool size depends strongly on the dominating class that is present. Finally, we provide a methodology for controlling the tip pool size by dynamically adjusting the number of references a message creates.
Hyperspectral Image Restoration via Spatial-Spectral Residual Total Variation Regularized Low-Rank Tensor Decomposition

To eliminate the mixed noise in hyperspectral images (HSIs), three-dimensional total variation (3DTV) regularization has been proven as an efficient tool. However, 3DTV regularization is prone to losing image details in restoration. To resolve this issue, we proposed a novel TV, named spatial domain spectral residual total variation (SSRTV). Considering that there is much residual texture information in spectral variation image, SSRTV first calculates the difference between the pixel values of adjacent bands and then calculates a 2DTV for the residual image. Experimental results demonstrated that the SSRTV regularization term is powerful at changing the structures of noises in an original HSI, thus allowing low-rank techniques to get rid of mixed noises more efficiently without treating them as low-rank features. The global low-rankness and spatial–spectral correlation of HSI is exploited by low-rank Tucker decomposition (LRTD). Moreover, it was demonstrated that the l2,1 norm is more effective to deal with sparse noise, especially the sample-specific noise such as stripes or deadlines. The augmented Lagrange multiplier (ALM) algorithm was adopted to solve the proposed model. Finally, experimental results with simulated and real data illustrated the validity of the proposed method. The proposed method outperformed state-of-the-art TV-regularized low-rank matrix/tensor decomposition methods in terms of quantitative metrics and visual inspection.
Deep Segmentation of Corrupted Glyphs

Historical documents and archaeological artifacts are hard to process due to natural degradation, fading, spills, tears, overlaid data,, and so on. In this work, we focus on the task of recovering characters and symbols from images of corrupted archaeological artifacts where data is partially erased, occluded, or overwritten by other data. Such phenomena can be widely observed in image datasets of palimpsests and petroglyphs consisting of erased, overwritten, and in general heavily degraded data. Segmentation and binarization are typically applied to such images to detect and recover characters and symbols from their background. However, these methods mainly focus on the visible data while in our case, due to large corruption, both visible and invisible information should be considered. For example, computing the segmentation mask of an occluded character requires also labeling invisible pixels and missing parts. In this work, we introduce a deep neural network that computes character segmentation in palimpsests and petroglyphs while overcoming occlusions, missing parts, and degradation. Our network has inference abilities, thus, not only segmenting the symbol’s foreground pixels but also inferring and completing missing and corrupted parts. Since palimpsests and petroglyphs have very limited annotated ground-truth data, we also introduce data augmentation tools to properly train our network. We demonstrate both qualitative and quantitative performance of our method also including a user study involving expert evaluation.