Reviewerly/exHarmony huggingface dataset:
Achieving Agility in IT Project Portfolios – A Systematic Literature Review

Over the past two decades, enterprise IT functions have enjoyed continued success in projects using agile development methods. However, the lack of ample empirical research on achieving portfolio level agility can potentially inhibit their ability to effectively govern IT investments while scaling agile practices to derive more significant benefits. This study examines the impact of agile delivery efforts on project portfolio management at the enterprise level and identifies approaches adopted to foster agility in portfolio practices. We conducted a systematic literature review to explore existing scientific knowledge around agile methods and portfolio management in an enterprise IT context. An analysis of the 21 primary studies found relevant to this research identified six portfolio management aspects impacted by agile delivery practices and a variety of approaches adopted to support them. While these identified portfolio management aspects guide practitioners on areas to focus on while scaling agile efforts across an enterprise, the specific practices/approaches observed present opportunities to consider within their respective organizational contexts. Portfolio processes need an exploratory focus to sense environmental change to support agility, utilize a systems-thinking approach for a holistic view of potential interactions within and across portfolio components, and consider the effect of existing organizational processes to support portfolio agility. This study contributes to academic knowledge by synthesizing current knowledge on how portfolio management contributes to IT agility while incorporating agile delivery efforts and by identifying a set of future research directions in this space.
Simple generate-evaluate strategy for tight-budget parameter tuning problems

Good hyperparameter selection is essential for metaheuristic algorithm performance. Tuning is usually a time-consuming and tedious task that requires user expertise for the best results. Automated tuning algorithms can help to speed up this process and even lead to better parameter configurations; however, it requires a vast amount of computing resources. This is especially true for complex real-world problems where a single evaluation of configuration can take minutes, hours or even days. To overcome the problem, the eTuner and eTunerAlgo have been proposed. The distinctive feature of eTunerAlgo is that both algorithm selection and parameter tuning is done automatically. We evaluate proposed algorithms using three metaheuristics-Ant Colony optimization (ACO), Evolutionary Strategy (ES) and Imperialist Competitive Algorithm (ICA) and two NP-hard problems - Aerial Surveying Problem (ASP) and Multiple Knapsack Problem (MKP). Furthermore, a metaheuristic tuning benchmark containing 18,760 configurations is generated for efficient method evaluation. The experimental results show that our proposed approach is best suited for a low tuning budget, where within a given time only a small size of configurations can be evaluated.
Data-driven Learning for Approximation of Nonlinear Functions with Stochastic Disturbances

In this paper, a data-driven learning approach is proposed for approximation of nonlinear functions with stochastic disturbances. A neural network is built and trained so as to approximate nonlinear mappings whose measured outputs are perturbed by stochastic disturbances. In the proposed approach, an adaptive-based learning algorithm is employed to update the weights of the neural network. Mathematical formulation is developed with the consideration of the stochastic disturbances, and the stability of the system for functional approximation tasks is ensured, even in the presence of the stochastic disturbances. Simulations on the approximation of arbitrary functions with different nonlinearities are then performed to illustrate the effectiveness of the proposed data-driven learning approach.
Research on Blockchain Smart Contracts Vulnerability and A Code Audit Tool based on Matching Rules

As an emerging service architecture of multi-technology integration, blockchain draws the attention of public because of smart contracts which implement a secure and tamper-proof programming. However, due to the complexity of this architecture, there exists endless attacks against blockchain. Smart contracts as the application layer prototype of blockchain, face more severe security risks. This paper detailly lists the security events of smart contracts in recent years, summarizes several common attack modes, then introduces their principles and analyzes the commonness. Finally, an original version of the contract code audit tool based on matching rules is given. The tool can ensure that the contract has a complete audit process before deployment, so as to decrease the DApp vulnerability caused by poor programming. Moreover, the matching rule library supports customization, the tool can be updated timely to enhance its audit ability.
Single Image Dehazing Using Dark Channel Prior With Median Filter and Contrast Enhancement

Aerosols such asdust particles, water droplets, haze, fog degraded the standard of an image taken by the camera. In many applications of an image such as a video surveillance system, a navigation application, remote sensing use image as an input. These images are affected by environmental pollution due to the scattering of aerosols. Image disorientated its standard and not suitable for the vision system. There is a need to restore the image taken as input to the vision system. There are a lot of techniques, which can restore the image as its natural content. Dark Channel Prior is a key attribute to the restoration of degrade image. In this paper we use DCP method and median filter to rectify the transmission map and use AGCWD (adaptive gamma correction with weighting distribution) to enhance the contrast of final dehaze image. On seeing this quality of an output image this method provide finer and efficient outcomes. For the quality evaluation of these outcomes we use different parameter PSNR, MSE and IE. We compare these results with existing algorithm.
Progress on the National Solar Radiation Data Base (NSRDB): A new DNI computation

This study introduces a new technique to compute direct normal irradiance (DNI) for improving the National Solar Radiation Data Base (NSRDB). A finite-surface integration algorithm is developed to compute solar radiation in differential solid angles and efficiently infer its contribution to a surface perpendicular to the solar direction. A lookup table of cloud bi-directional transmittance distribution function (BTDF) is developed by use of the discrete ordinates radiative transfer (DISORT) model for possible solar and observing directions and various cloud optical and microphysical properties. In each solar incident direction, DNI is given by the cloud BTDFs from approximately 200 differential solid angles. The simulated DNI is calibrated and evaluated using surface observations at the National Renewable Energy Laboratory's (NREL's) Solar Energy Research Laboratory (SRRL) and the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) facility.
APPLICATION OF HYBRID NEURAL NETWORK SYSTEM IN IMAGE PROCESSING

The article describes the use of feedforward two-layer neural network and recursive neural network with feedback, i.e. Hopfield network within a system capable of performing associative memory functions. It has been shown that these structures can be used successfully alone, however, using them together in series in the listed above order significantly improves system performance.
Minimal PD-sets for codes associated with the graphs $$Q^m_2$$, m even

For $$m\ge 4$$ even, the duals of p-ary codes, for any prime p, from adjacency matrices for the m-ary 2-cube $$Q^m_2$$ are shown to have subcodes with parameters $$[m^2,2m-2,m]$$ for which minimal PD-sets of size $$\frac{m}{2}$$ are constructed, hence attaining the full error-correction capabilities of the code, and, as such, the most efficient sets for full permutation decoding.
Learning Summarised Messaging Through Mediated Differentiable Inter-Agent Learning

In recent years, notable research has been done in the area of communication in multi-agent systems. When agents have a partial view of the environment, communication becomes essential for collaboration. We propose a Deep Q-Learning based multi-agent communication approach: Mediated Differentiable Inter-Agent Learning (M-DIAL), where messages produced by individual agents are sent to a mediator that encodes all the messages into a global embedding. The mediator essentially summarises the crux of the messages it receives into a single global message that is then broadcasted to all the participating agents. The proposed technique allows the agents to receive only essential abstracted information and also reduces the overall bandwidth required for communication. We analyze and evaluate the performance of our approach over several collaborative multi-agent environments.
Phishing Website Detection Using Machine Learning

Phishing costs around billions of dollars per year to the Internet users. Novel phishing techniques for instance spoofing in between trusted websites on the Internet are leveraged to phish target’s account information, login credentials and personally identifiable information such as email Id, date of birth, biometrics and passwords. Most commonly attackers use phishing software and spam emails for stealing personal information to collect financial accounts details like credit card details and credentials. In the recent study, it was identified that phishing attacks account for more than 80% of reported security incidents and 94% of them are via email. The research work presented here is on a multitude of strategies which are used for detection of malicious and phishing sites depending on their various lexical features. Information gathered from the study of malicious and phishing sites is then used for lexical features assessment, and further, to analyze and to improve upon the algorithm used for the detection of malicious and phishing sites. Most organizations today use rule-based engines for phishing detection that do not proactively scale for phishing attacks without additional rules deployment. From the recent study, one could gather that for an organization, an improvement in phishing detection does have a positive impact on net revenue. An associated data point here is that $17,700 is lost every minute due to phishing attacks, thus a need for a comprehensive solution to phishing attacks. The paper provides an analysis of various methods used for detecting phishing websites by using machine learning and classification techniques based on lexical features. Machine learning-based techniques leverage natural language processing and other classification techniques like logistic regression, support vector machines and random forest [, ]. In order to have a comprehensive machine learning-based solution, training data is required to possess lots of relevant and non-correlated features. A comprehensive learning algorithm can effectively determine not previously classified URLs with a better accuracy. Here, our purpose is to study various machine learning algorithms that can be leveraged for safeguarding users from spoofed websites and help them not fall in the trap of phishing by detecting these websites early. Previous work done on the subject is also studied and compared against for accuracy.
Reducing T-count When Decomposing Many MPMCT Gates Simultaneously

To realize fault-tolerant quantum circuits, one of the important task is to reduce the number of T gates (T-count) when we decompose MPMCT (Mixed Polarity Multiple-Control Toffoli) gates into elementary gates. When we have enough auxiliary qubits, we have an efficient decomposition of a single MPMCT gate. However, it has not been studied intensively how to decompose many MPMCT gates efficiently at the same time. Thus, this paper studies what is an efficient decomposition method when we have many MPMCT gates. We consider two situations to reduce T-count when we need to decompose many MPMCT gates. Then, we propose a heuristic method to reorder MPMCT gates if possible so that we can find the two situations as much as possible. Our preliminary experiments show that our heuristic can reduce T-count drastically.
Learning hierarchical face representation to enhance HCI among medical robots

In this paper, we propose a hierarchical framework for face recognition by learning deep representation. In order to exploit key patches for face recognition, we separate the entire image into several patches including eyes, nose, and mouth. A binary facegrid is generated to indicate the accurate position of the key patches in face image. The patches are fed into the hierarchical framework to learn the deep representation of the image. We leverage the PCA and SVM method for face recognition. Our face representation can enhance many medical robot applications. Comprehensive experiments have demonstrated that our proposed method can effectively recognize real human faces from fake samples.
Network security assessment based on full host-based attack graph

Network security assessment is an important method of evaluating network security. The existing researches of network security assessment have the problem that the method is not comprehensive and accurate. In order to solve this problem, we proposed a method of network security assessment based on a full host-based attack graph from the "point" and "plane" perspective. This method constructed a full host-based attack graph model, and proposed a splitting algorithm for weakly connected components of the full host-based attack graph. The algorithm is used to evaluate the network security from the "plane" perspective. Meanwhile, based on the full host-based attack graph and the theory of degree centrality and betweenness centrality, the key nodes in the network are evaluated. Therefore, this method accomplished the comprehensive and accurate security measurement of the network. The experimental results showed the effectiveness of the method.
A Guided Evaluation Method for Robot Dynamic Manipulation

It is challenging for reinforcement learning (RL) to solve the dynamic goal tasks of robot in sparse reward setting. Dynamic Hindsight Experience Replay (DHER) is a method to solve such problems. However, the learned policy DHER is easy to degrade, and the success rate is low, especially in complex environment. In order to help agents learn purposefully in dynamic goal tasks, avoid blind exploration, and improve the stability and robustness of policy, we propose a guided evaluation method named GEDHER, which assists the agent to learn under the guidance of evaluated expert demonstrations based on the DHER. In addition, We add the Gaussian noise in action sampling to balance the exploration and exploitation, preventing from falling into local optimal policy. Experiment results show that our method outperforms original DHER method in terms of both stability and success rate.
A theorem of Joseph-Alfred Serret and its relation to perfect quantum state transfer

In this paper we recast the Serret theorem about a characterization of palindromic continued fractions in the context of polynomial continued fractions. Then, using the relation between symmetric tridiagonal matrices and polynomial continued fractions we give a quick exposition of the mathematical aspect of the perfect quantum state transfer problem.
AADGen: Automatic Annotated Data Generation for Training Text Detection and Recognition Models

<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">_</sup> One of the biggest challenges faced in deep learning community is the image data annotation, we know how data is essential for any machine learning problems but when it comes to object detection and segmentation (ODS) problems the data preparation process becomes extremely difficult and time-consuming. The data preparation becomes two steps process, first the data collection itself and second the data annotation. In this paper I present AADGen, a novel way of automatic data collection and annotation (DCA) for text detection and segmentation (TDS) problems. This method reduces the time taken for DCA to almost zero and provides error-free data annotation process. The DCA process will help us in building online learning model training by providing on demand continuous stream of training data. I have done extensive experiments on the data annotation process and achieved more than 99% annotation accuracy. I present a fast, scalable, customizable and error free data annotation approach.
Acquisition of Periodic Events with Person Attributes

In this paper, we aim to acquire “periodic events” that represent significant actions that are happened by groups of people in particular seasons or timing. Recently, the importance of knowledge acquisition is increasing. Many studies about human action knowledge and temporal commonsense knowledge have been carrying out. We need a dataset to acquire periodic events. However, manually building the dataset with human attribute labels is costly. Therefore, we construct a human attribute classifier of Twitter users and create a large labeled tweets dataset automatically. Periodic events with specific human attributes are collected with our proposed method. Finally, we obtained commonsense event knowledge; e.g., “Students often go to college at 1 P.M.” and “Workers often work overtime on weekdays”.
A Quantum Photonic TRNG based on Quaternary Logic

An architecture for a quantum photonic true random number generator (TRNG) that takes advantage of higher-radix encoding schemes is presented. The TRNG is based upon two statistically independent sources of entropy: generated photon sequences at randomly distributed time intervals and the measurement of radix-4 superimposed quantum states. The two-source TRNG comprises of recently developed structures and extractor functions that enable it to be implemented in a quantum photonic integrated circuit (QPIC). We show that the amount of entropy extracted from the dual weakly random sources exceeds the amount of entropy that can be extracted from either of the single physical sources. We also describe several features of the TRNG architecture that enhance the data output rates.
SIDE: Security-aware Integrated Development Environment

An effective way for building secure software is to embed security into software in the early stages of software development. Thus, we aim to study several evidences of code anomalies introduced during the software development phase, that may be indicators of security issues in software, such as code smells, structural complexity represented by diverse software metrics, the issues detected by static code analysers, and finally missing security best practices. To use such evidences for vulnerability prediction and removal, we first need to understand how they are correlated with security issues. Then, we need to discover how these imperfect raw data can be integrated to achieve a reliable, accurate and valuable decision about a portion of code. Finally, we need to construct a security actuator providing suggestions to the developers to remove or fix the detected issues from the code. All of these will lead to the construction of a framework, including security monitoring, security analyzer, and security actuator platforms, that are necessary for a security-aware integrated development environment (SIDE).
Enhancement in Cloud Security for Web Application Attacks

Nowadays many application programs are on remote server and accessed through internet called as web applications. All web applications have web services mostly used in integration of business. Web applications are always targeted by different types of malicious attacks. Many users upload their data on cloud so web application security is very important issue. To avoid cloud based web application attacks rules to be specified using ontology. Protégé tool is used to implement ontology. Semantic rules are written to show ontology. Attacks on web services have direct impact on organizations private data. Identity based encryption is used to create master login. System uses publisher and subscriber model to share data among various specified users. The publisher has credentials to publish events with two attributes. The user has a credential to receive events with an attribute. This paper focuses on identification of web application attacks, publisher subscriber model architecture, implementation of publisher subscriber algorithm, ontology logic, how the encryption is maintained with confidentiality and authentication.
Factors affecting the number of dengue fever cases in West Sumatra province using the Multivariate Adaptive Regression Splines (MARS) approach

Abstract The Multivariate Adaptive Regression Splines (MARS) approach is a multivariate nonparametric regression analysis that assumes the form of a functional relationship between response variable and predictors whose patterns are unknown. MARS is a combination of Recursive Partitioning Regression (RPR) and the Spline method which is able to process high-dimensional data (data that has many predictor variables 3 ≤ p ≤ 20), and large data samples (50 ≤ n ≤ 1000). The MARS model is obtained from a combination of Basis Function values (BF), Maximum Interaction (MI), and Minimum Observation (MO) by trial and error. In this study, we describe the use of MARS to analyze the factors that influence the number of dengue cases in West Sumatra Province. The response variable (Y) used was the number of dengue fever cases in West Sumatra Province with several predictor variables, namely the number of health workers (X1), the number of health facilities (X2), the height of an area (X3), and the density of settlements (X4). The data used is secondary data from the Central Statistics Agency (BPS) in 2019. Based on the calculation, the best model for this problem is the model with a combination of BF = 16, MI = 3, and MO = 2 with GCV value = 279.1654. The results showed that all predictor variables had an effect on the number of dengue fever cases according to the order of importance: the number of health workers (X1), the number of health facilities (X2), the density of settlements (X4) and finally the height of an area (X3).
Detection Technique of Software-induced Rowhammer Attacks

Side-channel attacks have recently progressed into software-induced attacks. In particular, a rowhammer attack, which exploits the characteristics of dynamic random access memory (DRAM), can quickly and continuously access the cells as the cell density of DRAM increases, thereby generating a disturbance error affecting the neighboring cells, resulting in bit flips. Although a rowhammer attack is a highly sophisticated attack in which disturbance errors are deliberately generated into data bits, it has been reported that it can be exploited on various platforms such as mobile devices, web browsers, and virtual machines. Furthermore, there have been studies on bypassing the defense measures of DRAM manufacturers and the like to respond to rowhammer attacks. A rowhammer attack can control user access and compromise the integrity of sensitive data with attacks such as a privilege escalation and an alteration of the encryption keys. In an attempt to mitigate a rowhammer attack, various hardware- and software-based mitigation techniques are being studied, but there are limitations in that the research methods do not detect the rowhammer attack in advance, causing overhead or degradation of the system performance. Therefore, in this study, a rowhammer attack detection technique is proposed by extracting common features of rowhammer attack files through a static analysis of rowhammer attack codes.