Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementation issues in optimization algorithms: do they matter?

ABSTRACT Two factors that have a major impact on the performance of an optimization method are (1) formal algorithm specifications and (2) practical implementations. The impact of the latter is typically ignored, although it defines the results measured in experiments. We present an in-depth study of algorithm implementation issues and ask questions such as Does optimizing the implementation of an optimization algorithm pay off? Do bugs matter? and Is using more complicated but also more efficient data structures worth the effort? The intuitive answer to all of these questions is yes, but there is little published evidence. To bridge this gap, we use one of the most studied combinatorial optimization problems – the Traveling Salesman Problem – as a test bed and implement two state-of-the-art approaches for solving it – the Lin-Kernighan Heuristic and an Ejection Chain Method. We investigate implementation effort and performance gain, in order to provide further insights to the above questions.
Hypervisor-assisted Atomic Memory Acquisition in Modern Systems

: Reliable memory acquisition is essential to forensic analysis of a cyber-crime. Various methods of memory acquisition have been proposed, ranging from tools based on a dedicated hardware to software only solutions. Recently, a hypervisor-based method for memory acquisition was proposed (Qi et al., 2017; Martignoni et al., 2010). This method obtains a reliable (atomic) memory image of a running system. The method achieves this by making all memory pages non-writable until they are copied to the memory image, thus preventing uncontrolled modiﬁcation of these pages. Unfortunately, the proposed method has two deﬁciencies: (1) the method does not support multiprocessing and (2) the method does not support modern operating systems featuring address space layout randomization (ASLR). We describe a hypervisor-based memory acquisition method that solves the two aforementioned deﬁciencies. We analyze the memory usage and performance of the proposed method.
Planar Motion Bundle Adjustment

In this paper we consider trajectory recovery for two cameras directed towards the floor, and which are mounted rigidly on a mobile platform. Previous work for this specific problem geometry has focused on locally minimising an algebraic error between inter-image homographies to estimate the relative pose. In order to accurately track the platform globally it is necessary to refine the estimation of the camera poses and 3D locations of the feature points, which is commonly done by utilising bundle adjustment; however, existing software packages providing such methods do not take the specific problem geometry into account, and the result is a physically inconsistent solution. We develop a bundle adjustment algorithm which incorporates the planar motion constraint, and devise a scheme that utilises the sparse structure of the problem. Experiments are carried out on real data and the proposed algorithm shows an improvement compared to established generic methods.
Exploring the use of small group work as an effective teaching strategy to improve concept understanding

This article explores the use of small group work as a teaching strategy through investigating the conditions under which small groups can be conducted effectively and in what way can support concept understanding. This research was prompted due to public relations students struggling to understand and interpret new concepts in class when instructed to complete tasks autonomously. This article? support a critical realist approach to learning which encourages students to construct knowledge socially and lastly, recommendations are made on how small group work can be improved as a teaching strategy in the classroom to accommodate millennial students.
A New Method of Oppositional Gravitational Search Algorithm Using in Cloud Computing

Cloud computing is that the extended unreal vision of hard as a utility, wherever cloud users will distantly store their info into the cloud thus on relish the on-demand nice quality implementations and facilities from a communal pool of configurable hard sources. Cloud Computing develops predominant, additional and additional sensitive information square measure being central into the cloud. These work emphases on giving a dependable and safe cloud information sharing service that allows customers dynamic access to their info. For attaining this, during this article, we have a tendency to progress the privacy protective intermediate dataset on the idea of the Oppositional gravitative Search algorithmic program and Elliptic Curve Cryptography encoding technique customary (OGSA+ECC). within the preliminary part, the intermediate dataset is made and OGSA algorithmic program is enforced for ruling the optimum node assortment from the cloud. later, we have a tendency to work out the sensitive info on the idea of the information gain. Lastly, we have a tendency to smear the code for sensitive info encoding and investigation is performed by the datasets. The investigation is achieved with the assistance of the knowledge sets to guage the performance within the name of encoding time and memory use. From the investigation, it are often complete that the encoding time and memory usage cut back with upsurge in threshold
Gene Set Overlap: An Impediment to Achieving High Specificity in Over-representation Analysis

: Gene set analysis methods are widely used to analyze data from high-throughput “omics” technologies. One drawback of these methods is their low speciﬁcity or high false positive rate. Over-representation analysis is one of the most commonly used gene set analysis methods. In this paper, we propose a systematic approach to investigate the hypothesis that gene set overlap is an underlying cause of low speciﬁcity in over-representation analysis. We quantify gene set overlap and show that it is a ubiquitous phenomenon across gene set databases. Statistical analysis indicates a strong negative correlation between gene set overlap and the speciﬁcity of over-representation analysis. We conclude that gene set overlap is an underlying cause of the low speciﬁcity. This result highlights the importance of considering gene set overlap in gene set analysis and explains the lack of speciﬁcity of methods that ignore gene set overlap. This research also establishes the direction for developing new gene set analysis methods.
Design of Extended Kalman Filter for Object Position Tracking

- This study present the design of extended Kalman filter (EKF) for object position tracking. It is required to accurately track the position of an object amidst noisy measurements. The state variables and nonlinear output equations were obtained for a flying object at a fixed point position. An extended Kalman filter and its algorithm was developed in the embedded Matlab/Simulink function block. The measurement noise was introduced in the filter using the random noise block of the Matlab/Simulink block code. Simulations were performed at 0.1s sampling intervals. The output error standard deviation was varied between 0.05 and 1. This results to optimal selection of the system noise covariance matrix. The simulation results obtained showed that the designed extended Kalman filter accurately tracked object position with improved filtering performance by effective tuning of the noise covariance matrix.
On the Problem about Optical Flow Prediction for Silhouette Image

We address a problem of finding an algorithm to handle unclear flows in the inner area of silhouette image. In this study, after getting the region of interest from an implemented algorithm of dual TV-L1 optical flow, we conducted experiments to predict the new flow in the unclear inner areas. The experiments include perspective transform methods. Five experiments were performed using these methods. As a result, an algorithm that uses double refining of perspective transform method obtained the optimal result. It might be useful for analyzing the motion of a unicolored animal (e.g. a black cat).
Facilitating Keyboard Use While Wearing a Head-Mounted Display

Virtual reality (VR) headsets are becoming more common and will require evolving input mechanisms to support a growing range of applications. Because VR devices require users to wear head-mounted displays, there are accomodations that must be made in order to support specific input devices. One such device, a keyboard, serves as a useful tool for text entry. Many users will require assistance towards using a keyboard when wearing a head-mounted display. Developers have explored new mechanisms to overcome the challenges of text-entry for virtual reality. Several games have toyed with the idea of using motion controllers to provide a text entry mechanism, however few investigations have made on how to assist users in using a physical keyboard while wearing a head-mounted display. As an alternative to controller based text input, I propose that a software tool could facilitate the use of a physical keyboard in virtual reality. Using computer vision, a user’s hands could be projected into the virtual world. With the ability to see the location of their hands relative to the keyboard, users will be able to type despite the obstruction caused by the head-mounted display (HMD). The viability of this approach was tested and the tool released as a plugin for the Unity development platform. The potential uses for the plugin go beyond text entry, and the project can be expanded to include many physical input devices.
Constraint Maximal Inter-molecular Helix Lengths within RNA-RNA Interaction Prediction Improves Bacterial sRNA Target Prediction

Efficient computational tools for the identification of putative target RNAs regulated by prokaryotic sRNAs rely on thermodynamic models of RNA secondary structures. While they typically predict RNA–RNA interaction complexes accurately, they yield many highly-ranked false positives in target screens. One obvious source of this low specificity appears to be the disability of current secondary-structure-based models to reflect steric constraints, which nevertheless govern the kinetic formation of RNA–RNA interactions. For example, often—even thermodynamically favorable—extensions of short initial kissing hairpin interactions are kinetically prohibited, since this would require unwinding of intra-molecular helices as well as sterically impossible bending of the interaction helix. In consequence, the efficient prediction methods, which do not consider such effects, predict over-long helices. To increase the prediction accuracy, we devise a dynamic programming algorithm that length-restricts the runs of consecutive inter-molecular base pairs (perfect canonical stackings), which we hypothesize to implicitely model the steric and kinetic effects. The novel method is implemented by extending the state-of-the-art tool INTARNA. Our comprehensive bacterial sRNA target prediction benchmark demonstrates significant improvements of the prediction accuracy and enables 3-4 times faster computations. These results indicate—supporting our hypothesis—that length-limitations on inter-molecular subhelices increase the accuracy of interaction prediction models compared to the current state-of-the-art approach.
Using Ontology To Define Decision Problems Of Enterprises

Competitive Intelligence (CI) allows companies to collect information relevantly and to treat them while ensuring the legality and safety. The definition phase of information needs is an essential step in the process of CI, as it identifies the field to monitor the business and define the scope of the strategic watch. We propose to make a model for the business and its internal and external environment to help decision makers and watchers in the definition of decision problems. We choose the notion of ontology as a way of modeling, as it provides the tools to describe explicitly the conceptualization of knowledge in a specific field. Our goal is to build enterprise ontology which can be used in companies for the definition of information needs.
q-Rung Orthopair Fuzzy Hypergraphs with Applications

The concept of q-rung orthopair fuzzy sets generalizes the notions of intuitionistic fuzzy sets and Pythagorean fuzzy sets to describe complicated uncertain information more effectively. Their most dominant attribute is that the sum of the q th power of the truth-membership and the q th power of the falsity-membership must be equal to or less than one, so they can broaden the space of uncertain data. This set can adjust the range of indication of decision data by changing the parameter q, q ≥ 1 . In this research study, we design a new framework for handling uncertain data by means of the combinative theory of q-rung orthopair fuzzy sets and hypergraphs. We define q-rung orthopair fuzzy hypergraphs to achieve the advantages of both theories. Further, we propose certain novel concepts, including adjacent levels of q-rung orthopair fuzzy hypergraphs, ( α , β ) -level hypergraphs, transversals, and minimal transversals of q-rung orthopair fuzzy hypergraphs. We present a brief comparison of our proposed model with other existing theories. Moreover, we implement some interesting concepts of q-rung orthopair fuzzy hypergraphs for decision-making to prove the effectiveness of our proposed model.
Robustness Analysis of Iterative Learning Control for a Class of Mobile Robot Systems With Channel Noise

This paper considers the robust tracking problem of a unicycle type mobile robot, which is controlled remotely by an iterative learning controller over the wireless network. First, taking the effect of the channel noise into account, the nonlinear system model is formulated to describe the mobile robot controlled by a differential-type iterative learning controller. After that, the relation between the input error at the controller side and the channel noise is derived. Based on this relation, the norm of output error is derived and analyzed theoretically. The analysis reveals that the effect of channel noise is accumulated in both the iteration and time domains, and is ruled simultaneously by the sampling time in the nonlinear function of the system. Due to the sampling time that is far less than one, the accumulated effect of channel noise on the robustness of the system is suppressed significantly by the sampling time, which means that the mobile robot can track the desired trajectory without any processing. Finally, the simulation results are given to corroborate the theoretical analysis.
Vulnerabilities in SBAS and RTK Positioning in Intelligent Transport Systems: An Overview

As Intelligent Transport Systems (ITS) become more automated and more demanding, ITS positioning integrity is becoming a key performance parameter. ITS relies on GNSS technology for absolute positioning. In order to develop efficient models and methods that can provide high levels of integrity, it is necessary to study the vulnerabilities of the GNSS-based positioning systems intended for use in ITS applications, in particular those which require positioning accuracy at the sub-metre level. These vulnerabilities are attributed to several sources and include biases and errors in the GNSS measurements, and in the corrections applied to the measurements for augmented performance, as well as those induced by the operating environment. The vulnerabilities also comprise possible anomalies that may affect each component of the system, including disturbances or disruption in the communications between the service provider and users, data latency, to name a few. In this paper a preliminary overview of possible vulnerabilities is presented for two widely-used GNSS positioning techniques envisioned for ITS applications: the Satellite-Based Augmentation System (SBAS) and low-cost RTK. Some examples are given, including the source of these errors, e.g. satellite or receiver hardware, environment, external communications, the error magnitude, temporal and spatial behaviour, their deterministic and stochastic characteristics, and their impact on estimated positions. Furthermore, some of the corresponding mathematical models that can be used to describe these vulnerabilities in the integrity monitoring algorithms are presented.
Keyword based Data Indexing and Receiving in Protected Cloud Environment

A Keyword Based Data Indexing and Receiving In Protected Cloud Environment due to the increasing popularity of cloud computing, more and more data owners are motivated to outsource their data to cloud servers for great convenience and reduced cost in data management. However, sensitive data should be encrypted before outsourcing for privacy requirements, which obsoletes data utilization like keyword-based document retrieval. In this paper, we present a secure multi-keyword ranked search scheme over encrypted cloud data, which simultaneously supports dynamic update operations like deletion and insertion of documents. Specifically, the vector space model and the widely-used tf idf model are combined in the index construction and query generation. We construct a special tree-based index structure and propose a “greedy depth-first search” algorithm to provide efficient multi-keyword ranked search. The secure knn algorithm is utilized to encrypt the index and query vectors, and meanwhile ensure accurate relevance score calculation between encrypted index and query vectors. In order to resist statistical attacks, phantom terms are added to the index vector for blinding search results. Due to the use of our special tree-based index structure, the proposed scheme can achieve sub-linear search time and deal with the deletion and insertion of documents flexibly. Extensive experiments are conducted to demonstrate the efficiency of the proposed scheme.
A Review on Cloud Computing-It's SecurityProblem & Solutions

We all know cloud computing is the most modern word in IT sector and it having huge demand in these days. So most of the IT companies like Google, IBM, Microsoft, Amazon, Yahoo and others develop cloud computing systems and products related to it for customers. But still customers are having difficulties for adopting the cloud computing technique that is only because of its security issues. Data of the customers are stored and processed in cloud, not in a local machine. Though cloud computing is considered mature for practical application, there is a need for more research. So in this paper I have discussed some of the Security Problems and also provide some solutions related to these problems
Arp Spoofing Detection via Wireshark and Veracode

In current scenario, static and DHCP both has addressing schemes which also protect large number of end users without any burden on administrator. Also performance study need real network[1] and the final result shows that end user take not more than one millisecond to register himself or herself for protected ARP cache. Lastly server can hinder any external attacker in only a moment.
A LOW ENERGY CONSUMPTION SMART ANTENNA ADAPTIVE ARRAY SYSTEM FOR MOBILE AD HOC NETWORKS

Smart Antenna Systems (SAS) are largely employed in wireless networks for their capabilities to provide very high performances with respect to omnidirectional antennas. The use of adaptive array Smart Antenna could bring to enhance aspects such as energy management, especially in Mobile Ad Hoc Network (MANET), when the network performance closely depends on the battery life of the mobile nodes as well as on the routing protocol features. In this paper, we propose a new adaptive array Smart Antenna System that improves some Ad Hoc Distance Vector (AODV) network protocol features through a mechanism of energy consumption optimization. The system implements the Least Mean Square (LMS) as adaptive algorithm; for this purpose, we perform a co-simulation between Matlab, that implements the LMS algorithm and Omnet++, that provides a complete simulation environment for MANET.
A Model-Based AI-Driven Test Generation System

OF THE DISSERTATION A MODEL-BASED AI-DRIVEN TEST GENERATION SYSTEM by Dionny Santiago Florida International University, 2018 Miami, Florida Professor Peter J. Clarke, Major Professor Achieving high software quality today involves manual analysis, test planning, documentation of testing strategy and test cases, and development of automated test scripts to support regression testing. This thesis is motivated by the opportunity to bridge the gap between current test automation and true test automation by investigating learning-based solutions to software testing. We present an approach that combines a trainable web component classifier, a test case description language, and a trainable test generation and execution system that can learn to generate new test cases. Training data was collected and hand-labeled across 7 systems, 95 web pages, and 17,360 elements. A total of 250 test flows were also manually hand-crafted for training purposes. Various machine learning algorithms were evaluated. Results showed that Random Forest classifiers performed well on several web component classification problems. In addition, Long Short-Term Memory neural networks were able to model and generate new valid test flows.
Three-dimensional computer vision: a geometric viewpoint

Projective geometry modelling and calibrating cameras edge detection representing geometric primitives and their uncertainty stereo vision determining discrete motion from points and lines tracking tokens over time motion fields of curves interpolating and approximating three-dimensional data recognizing and locating objects and places answers to problems. Appendices: constrained optimization some results from algebraic geometry differential geometry.
Fuzzy Logic Decision based Collaborative Privacy Management Framework for Online Social Networks

Online Social Networks (OSNs) have become one of the most popular implement for interacting with people all over the world and sharing data with them. These data sometimes may be a co-owned data which involves multiple users, sharing co-owned data can cause privacy violation if co-owners are not happy with the owner’s sharing privacy settings. To tackle privacy issues on co-owned data, collaborative privacy management has become a popular research area in recent years. In this work, we provide a fuzzy logic decision based collaborative privacy management framework for OSNs. We use data sensitivity value and confidence value in targeted group as input variables of fuzzy system. We also use trust values between users since our framework needs to calculate trust loss and gains for reputation value.
Efficient MIMO Detection with Imperfect Channel Knowledge - A Deep Learning Approach

Multiple-input multiple-output (MIMO) system is the key technology for long term evolution (LTE) and 5G. The information detection problem at the receiver side is in general difficult due to the imbalance of decoding complexity and decoding accuracy within conventional methods. Hence, a deep learning based efficient MIMO detection approach is proposed in this paper. In our work, we use a neural network to directly get a mapping function of received signals, channel matrix and transmitted bit streams. Then, we compare the end-to-end approach using deep learning with the conventional methods in possession of perfect channel knowledge and imperfect channel knowledge. Simulation results show that our method presents a better trade-off in the performance for accuracy versus decoding complexity. At the same time, better robustness can be achieved in condition of imperfect channel knowledge compared with conventional algorithms.