Miracle-dz/UniFAR-FTUs huggingface dataset:
Systematic and comparative analysis of techniques for SLAM development in mobile robotics

In recent decades, great researchers have focused on the development of simultaneous localization and mapping. Thus, SLAM technology (Simultaneous Localization and Mapping) has gone through a great technological evolution over the years. The present investigative work shows a chronology of milestones in the evolution of the SLAM of mobile robotics until today with a comparison of the main techniques that have been developed, aiming to identify the most suitable algorithm to perform a generation of 3D SLAM in real time in terms to improve accuracy, speed, and robustness among other aspects. These features will help to reduce mapping reconstructions errors, to improve the localization of a robot, and to provide greater reliability of the environment worked. This analysis will be established under some specific research criteria and as a prelude to a project focused on making a mobile robot capable of providing relevant information for search and rescue of people in critical situations, situations in which where it will be essential to perform SLAM 3D reliably and in real time. Thus, the present work it is focused to do an important systematic analysis regarding mobile robotics on the need to reason in a world that is sometimes confusing, sometimes dynamic and changing.
RingNet: A Memory-Oriented Network-On-Chip Designed for FPGA

In this paper, we identify the general requirements for network-on-chips (NoCs) and the general characteristics of field-programmable gate arrays (FPGAs) from leading producers. Based on the analysis provided, an FPGA-oriented NoC called RingNet is proposed. As a distinctive feature, RingNet uses communication through a centrally placed memory that aims at preventing network congestions and limiting the network buffer requirements. Optimal utilization of FPGA resources is one of the goals of RingNet development. Especially buffers are implemented in distributed RAM available in FPGAs, and the virtual cut-through is used as an efficient switching technique for FPGA. Simulations prove guaranteed throughput, predictable latency, and fair network access provided by RingNet. Synthesis results for sample FPGAs from Xilinx, Intel, and Lattice prove the universality of RingNet. The provided analysis of NoC implementations leads to the conclusion that RingNet needs fewer resources and supports higher clock frequencies than the widely used AXI4 architecture.
A Methodology for Improvement of RoBa Multiplier for Electronic Applications

In this paper, we propose an approximate multiplier that is high speed yet energy efficient. The approach is to round the operands to the closest exponent of 2. This way the machine intensive a part of the multiplication is omitted up speed and energy consumption. The potency of the planned multiplier factor is evaluated by comparing its performance with those of some approximate and correct multipliers using different design parameters. In this proposed approach combined the conventional RoBA multiplier with Kogge-stone parallel prefix adder. The results revealed that, in most (all) cases, the newly designed RoBA multiplier architectures outperformed the corresponding approximate (exact) multipliers. Thus improved the parameters of RoBA multiplier which can be used in the voice or image smoothing applications in the DSP.
"Majorly Adapted Translator": Towards Major Adaptation in ITS

Culturally Aware Learning Systems are intelligent systems that adapt learning materials or techniques to the culture of learners having different “country, hobbies, experiences, etc.”, helping them better understand the topics being taught. In higher education, many learning sessions involve students of different majors. As observed, many instructors tend to manually modify the exercises several times, once for every major to adapt to the culture, which is tedious and impractical. Therefore, in this paper we propose an approach to making learning sessions adaptable to the major of the learner. Specifically, this work introduces an Artificial Intelligent system, “Majorly Adapted Translator (MAT)”, which aims at translating and adapting exercises from one major to another. MAT has two main phases, the first identifies the parts of an exercise that needs changing and creates an exercise template. The second translates and adapts the exercise. This work, highlights the first phase, the Feature Extract phase, which relies on our own relation extraction method to identify variables which extracts relations specific to named entities by using dependency relations and shallow parsing. Moreover, we report the performance of the system that was tested on a number of probability exercises.
Secure Two-Party Computation: ABY versus Intel SGX

Secure Two-Party Computation (2PC) allows two parties to jointly evaluate a publicly known function without disclosing their respective private inputs. This can be realized using wellknown cryptographic protocols such as Yao’s garbled circuit protocol or the protocol of Goldreich, Micali and Wigderson (GMW), both of which obliviously evaluate a Boolean circuit that represents the desired functionality. The ABY framework (Demmler et al., NDSS’15) provides state-of-the-art implementations of these protocols. Nonetheless, they incur high computational and communication overheads compared to the unprotected computation. Our work explores an alternative approach to the problem of secure computation, which is based on Intel Software Guard Extensions (Intel SGX). Intel SGX allows applications to create trusted execution environments called enclaves, which are isolated from all other software running on the same machine. In a two-party computation scenario, the two cooperating parties can both submit their private input to the enclave, inside which the function can then be computed securely and from which they can afterwards retrieve the result. Unfortunately, Intel SGX does not provide protection against software side-channel attacks and researches have proven its vulnerability to them time and again. In contrast, Boolean circuits as used in cryptographic 2PC protocols are inherently secure against a number of side-channel attacks. To that effect, we propose evaluating a Boolean circuit representation of the function inside the enclave. We design an Intel SGX-based 2PC protocol and provide a proof-of-concept implementation putting it into practice. We benchmark our Intel SGX-based 2PC solution and compare it to an ABY-based solution. For the evaluation of a Boolean circuit with AND size 1 million and AND depth 1000, the communication of our implementation is four orders of magnitude lower compared to Yao’s garbled circuit protocol and the GMW protocol. In a high-latency network setting, the run-time for our protocol’s 2PC phase is reduced by factors 1.3× and 19×, respectively.
A Cost-Driven Approach to Caching-as-a-Service in Cloud-Based 5G Mobile Networks

The exploding volumes of mobile video traffic call for deploying content caches inside mobile operator networks. With in-network caching, users’ requests for popular content can be served from a content cache deployed at mobile gateways in vicinity to the end user. This inherently reduces the load on the content servers and the backbone of operator's network. In light of the increasing trend in virtualization of network functions, we propose a cost-effective caching as a service (CaaS) framework for virtual video caching in 5G mobile networks. In order to evaluate the pros and cons of our CaaS approach, we formulate two virtual caching problems, namely maximum return on investment(MRI) and maximum offloaded traffic (MOT). MRI aims at maximizing return on caching investment by finding the best trade-off between the cost of cache storage and bandwidth savings from caching video contents in the mobile network operator (MNO)'s cloud. Likewise, MOT aims to maximize the traffic offloaded from the MNO's core and backhaul within given budget constraints. More specifically, taking the popularity and size of video contents into account, MRI and MOT aim to find the optimal caching tables which maximize the ratio of transmission bandwidth cost to storage cost and the offloaded traffic for a given budget, respectively. We reduce the complexity of the proposed problem formulated as a binary-integer programming (BIP) by using canonical duality theory (CDT). Experimental results obtained using the invasive weed optimization (IWO) have shown significant performance enhancement of the proposed system in terms of return on investment, quality, offloaded traffic, and storage efficiency.
Zero-Day Signature Extraction for High-Volume Attacks

We present a basic tool for zero day attack signature extraction. Given two large sets of messages, <inline-formula> <tex-math notation="LaTeX">$P$ </tex-math></inline-formula> the messages captured in the network at peacetime (i.e., mostly legitimate traffic) and <inline-formula> <tex-math notation="LaTeX">$A$ </tex-math></inline-formula> the messages captured during attack time (i.e., contains many attack messages), we present a tool for extracting a set <inline-formula> <tex-math notation="LaTeX">$S$ </tex-math></inline-formula> of strings that are frequently found in <inline-formula> <tex-math notation="LaTeX">$A$ </tex-math></inline-formula> and not in <inline-formula> <tex-math notation="LaTeX">$P$ </tex-math></inline-formula>, thus allowing the identification of the attack packets. This is an important tool in protecting sites on the Internet from worm attacks and distributed denial of service attacks and may also be useful for other problems, including command and control identification and the DNA-sequences analysis. The main contributions of this paper are the system we developed to extract the required signatures together with the string-heavy hitters problem definition and the algorithm for solving this problem. This algorithm finds popular strings of variable length in a set of messages, using, in a tricky way, the classic heavy-hitter algorithm as a building block. The algorithm runs in linear time requiring one-pass over the input. Our system makes use of this algorithm to extract the desired signatures. Furthermore, we provide an extended algorithm which is able to identify groups of signatures, often found together in the same packets, which further improves the quality of signatures generated by our system. Using our system, a yet unknown attack can be detected and stopped within minutes from attack start time.
An improved genetic algorithm-based optimal sizing of solar photovoltaic/wind turbine generator/diesel generator/battery connected hybrid energy systems for standalone applications

This paper proposes an improved GA-based new approach for the optimal design of hybrid energy systems comprises of solar PV, wind, diesel generator and battery connected systems for supplying the electrical power. The proposed system uses the meteorological data of wind speed, wind velocities and solar radiations, which were collected from NASA website. Optimisation of hybrid energy system includes minimisation of total cost, net present cost, loss of power system probability, reduced greenhouse gas emissions, cost of energy, annualised system cost to make the system more economically for the domestic house hold applications. The optimisation of results can be achieved by using improved Genetic Algorithm (GA), which uses the variables such as wind turbine capacity, PV array ratings, number of battery banks, diesel generator rated power, system initialisation cost and operation and maintenance cost were considered as the input chromosomes for the algorithm. A suitable improved GA programme has been formulated using MATLAB toolbox with the objective function of minimising the total/net present cost of the proposed hybrid energy system for electrifying the rural areas in standalone applications.
Artificial Intelligence, Algorithmic Pricing and Collusion

Increasingly, algorithms are supplanting human decision-makers in pricing goods and services. To analyze the possible consequences, we study experimentally the behavior of algorithms powered by Artificial Intelligence (Q-learning) in a workhorse oligopoly model of repeated price competition. We find that the algorithms consistently learn to charge supracompetitive prices, without communicating with one another. The high prices are sustained by collusive strategies with a finite phase of punishment followed by a gradual return to cooperation. This finding is robust to asymmetries in cost or demand, changes in the number of players, and various forms of uncertainty. (JEL D21, D43, D83, L12, L13)
MultiD-AMP: match up Accuracy and Fast Computation by Dynamically Denoising Data

—Denoising AMP (D-AMP) is as an iterative algorithm where at each iteration a non-linear denoising function is applied to the signal estimate. We aim to design a hierarchical denoising model (MultiD-AMP) in order to minimise the complexity given the expected risk. Our intuition is that at earlier iterations we can exploit less complex denoisers since the estimate is far from the true signal. This idea has been tested on i.i. d. random Gaussian measurements with Gaussian noise and the results show the effectiveness of the proposed reconstruction algorithm
The Vicinity Package for Opportunistic Networks

Opportunistic networks have grown into a major trend in the wireless network research field. In our work, we relaxed the traditional definition of contact and intercontact times by bringing the notion of vicinity into the game. We first propose to analyze opportunistic/disruption-tolerant networks (DTN) using a node-centered vicinity point of view i.e. we consider that nodes are in k-contact when they remain within a few hops from each other and in k-intercontact otherwise (where k is the maximum number of hops characterizing the vicinity). Second, we focused on the inner vicinity behaviors namely Vicinity Motion and on how we could reproduce these patterns to generate dataset-based synthetic pairwise vicinity movements. Finally, since the main use of opportunistic networks is to transmit data, we investigated the use of vicinity knowledge into pairwise shortest distance prediction and found out a very interesting heuristic relying on Vicinity Motion. We packaged most of our contributions concerning vicinity knowledge for opportunistic networks in the Vicinity Package written using Python 2.7 and libraries such as NetworkX and NumPy. This paper reviews the theoretical knowledge required to use the Vicinity Package.
Uncertain Event Processing Using Prediction Correction Paradigm

The systems which react automatically to events must be capable of handling abundant load of incoming events the problem resides in the designing of the system is handling events associated with uncertainty and materializing the events by the occurrence of the relevant events at active functionality in higher rate of accuracy. Wide spread paradigm for such materializing is Complex Event Processing, a rule based paradigm, the rule definition is purely depended on the domain experts, the domain experts have to provide the necessary event for the processing and those rules will be processing the incoming events and pass out the input to the system. While it is reasonable to expect that domain experts will be able to provide partial rules specification, providing all the required details is a hard task, even for domain experts [2]. Moreover, in many active systems, rules may change over time, due to the dynamic nature of the domain. Such changes complicate even further the specification task, as the expert must constantly update the rules. To lower the burden of the domain expert to constantly update the rule we device this mechanism of both defining of rules at the initial stage and update the rule over the time. It combines both the information provided by the domain expert with machine learning technique it is aimed at improving the accuracy of event specification and materialization
Liftoff – ReaderBench introduces new online functionalities

Natural Language Processing (NLP) became a trending domain within recent years for many researches and companies due to its wide applicability and the new advances in technology. The aim of this paper is to introduce an updated version or our open-source NLP framework, ReaderBench (http://readerbench.com/), designed to support both students and tutors in multiple learning scenarios that encompass one or more of the following dimensions: Cohesion Network Analysis of discourse, textual complexity assessment, keywords’ extraction using Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA) and word2vec semantic models, as well as the analysis of online communities and discussions. The latest version of our ReaderBench framework (v4.1) includes: a) new features, Application Programing Interfaces (APIs) and visualizations (e.g., sociograms, analysis of interaction between participants inside a community), b) a new web interface written in Angular 6, and c) the integration of new technologies to increase performance (i.e., spaCy and AKKA), as well as modularity and ease of deployment (i.e., Artifactory and Maven modules). ReaderBench is a fully functional framework capable to enhance the quality of learning processes conducted in multiple languages (English, French, Romanian, Dutch, Spanish, and Italian), and covering both individual and collaborative assessments.
Bi-Directional Attention Flow for Video Alignment

In this paper, a novel technique is introduced to address the video alignment task which is one of the hot topics in computer vision. Specifically, we aim at finding the best possible correspondences between two overlapping videos without the restrictions imposed by previous techniques. The novelty of this work is that the video alignment problem is solved by drawing an analogy between it and the machine comprehension (MC) task in natural language processing (NLP). Simply, MC seeks to give the best answer to a question about a given paragraph. In our work, one of the two videos is considered as a query, while the other as a context. First, a pre-trained CNN is used to obtain high-level features from the frames of both the query and context videos. Then, the bidirectional attention flow mechanism; that has achieved considerable success in MC; is used to compute the query-context interactions in order to find the best mapping between the two input videos. The proposed model has been trained using 10k of collected video pairs from ”YouTube”. The initial experimental results show that it is a promising solution for the video alignment task when compared to the state of the art techniques.
Phase-Locked Loop based Resonant Sensors: A Rigorous Theory and General Analysis Framework for Deciphering Fundamental Sensitivity Limitations due to Noise

Nanomechanical resonators are used in building ultra-sensitive mass and force sensors. In a widely used resonator based sensing paradigm, each modal resonance frequency is tracked with a phase-locked loop (PLL) based system. There is great interest in deciphering the fundamental sensitivity limitations due to inherent noise and fluctuations in PLL based resonant sensors to improve their performance. In this paper, we present a precise, first-principles based theory for the analysis of PLL based resonator tracking systems. Based on this theory, we develop a general, rigorously-derived noise analysis framework for PLL based sensors. We apply this framework to a setting where the sensor performance is mainly limited by the thermomechanical noise of the nanomechanical resonator. The results that are deduced through our analysis framework are in complete agreement with the ones we obtain from extensive, carefully run stochastic simulations of a PLL based sensor system. We compare the conclusions we derive with the recent results in the literature. Our theory and analysis framework can be used in assessing PLL based sensor performance with other sources of noise, e.g., from the electronic components, actuation and sensing mechanisms, and due to the signal generator, as well as for a variety of PLL based sensor configurations such as multi-mode and nonlinear sensing.
Remote Attestation of Sensor Nodes on Node Personalization: One Case Study

For the verifier has to identify the attester which introduces more computation, homogenous node is obstacle for remote attestation of sensor nodes. In this paper, we firstly propose node personalization, which means that every node is different from others in the sensor network, to save the computation energy. Then, we discuss the principle of node personalization. Lastly, we present a practical case. More details, we develop node personalization with obfuscated code, and verify code integrity with cascading hashing. Our discussion shows the solution we present is feasible.
A Theoretical Study on Improvement of Public Delivery System According to IT Service Cost Estimation

Forecasting ordering size and performance cost correctly in the ordering of IT service projects and the initial stage of performance is a very important factor not only to the ordering body but also the company who performs the project. Middle standing IT service companies receive/perform public IT service project, substituting large companies, after 2013 amendment of Software Industry Promotion Act. However, the business competence accumulated by middle standing IT service companies is relatively insufficient in project management ability compared with large companies, and the style that project companies bear all risks of the project, such as Turn-key order in the market focused on large companies in the past, has great risk to the ordering body, project company, and service beneficiaries in quality, costs, profit and delivery date. Currently, the market centered on middle standing IT service companies deteriorates the profitability of interested parties, which makes IT service ecosystem as an element of cost increase due to reduction of ordering size, low earnings yield, and increase of management expenses. Therefore, there is necessity that the cost calculation and order style structure for IT services, in which project companies should bear all the risk of project performance, should be improved. This treatise is going to analyze the IT service market ecosystem and agreement, project performance style upon current IT service project cost calculation method based on SW lifespan process and suggest improvement methods.
Recognizing factors affecting decline in groundwater level using wavelet-entropy measure (case study: Silakhor plain aquifer)

The most important approach to identify the behavior of hydrological processes is time series analysis of this process. Wavelet-entropy measure has been considered as a criterion for the degree of time series fluctuations and consequently uncertainty. Wavelet-entropy measure reduction indicates the reduction in natural time series fluctuations and thus, the occurrence of an unfavorable trend in time series. In this way, to identify the main cause of declining aquifer water level in the Silakhor plain, monthly time series of rainfall, temperature and output discharge were divided into three different time periods. Then, these time series were decomposed to multiple frequent time series by wavelet transform and then, the wavelet energies were computed for these decomposed time series. Finally, wavelet-entropy measure was computed for each different time period. Given the entropy reduction of about 71, 13 and 10.5% for discharge, rainfall and temperature time series respectively, it can be concluded that fluctuation decrease of discharge time series has relatively more effect on groundwater level oscillation patterns with respect to the rainfall and temperature time series. In this regard, it could be concluded that the climate change factors are not facing significant changes; thus, human activities can be regarded as the main reason for the declining groundwater level in this plain.