Miracle-dz/UniFAR-FTUs huggingface dataset:
Industrial Robot : An International Journal Real time 3 D localization and mapping for USAR robotic application

Purpose – The purpose of this paper is to demonstrate a real time 3D localization and mapping approach for the USAR (Urban Search and Rescue) robotic application, focusing on the performance and the accuracy of the General-purpose computing on graphics processing units (GPGPU)-based iterative closest point (ICP) 3D data registration implemented using modern GPGPU with FERMI architecture. Design/methodology/approach – The authors put all the ICP computation into GPU, and performed the experiments with registration up to 106 data points. The main goal of the research was to provide a method for real-time data registration performed by a mobile robot equipped with commercially available laser measurement system 3D. The main contribution of the paper is a new GPGPU based ICP implementation with regular grid decomposition. It guarantees high accuracy as equivalent CPU based ICP implementation with better performance. Findings – The authors have shown an empirical analysis of the tuning of GPUICP parameters for obtaining much better performance (acceptable level of the variance of the computing time) with minimal lost of accuracy. Loop closing method is added and demonstrates satisfactory results of 3D localization and mapping in urban environments. This work can help in building the USAR mobile robotic applications that process 3D cloud of points in real time. Practical implications – This work can help in developing real time mapping for USAR robotic applications. Originality/value – The paper proposes a new method for nearest neighbor search that guarantees better performance with minimal loss of accuracy. The variance of computational time is much less than SoA.
Cali-Sketch: Stroke Calibration and Completion for High-Quality Face Image Generation from Poorly-Drawn Sketches

Image generation task has received increasing attention because of its wide application in security and entertainment. Sketch-based face generation brings more fun and better quality of image generation due to supervised interaction. However, When a sketch poorly aligned with the true face is given as input, existing supervised image-to-image translation methods often cannot generate acceptable photo-realistic face images. To address this problem, in this paper we propose Cali-Sketch, a poorly-drawn-sketch to photo-realistic-image generation method. Cali-Sketch explicitly models stroke calibration and image generation using two constituent networks: a Stroke Calibration Network (SCN), which calibrates strokes of facial features and enriches facial details while preserving the original intent features; and an Image Synthesis Network (ISN), which translates the calibrated and enriched sketches to photo-realistic face images. In this way, we manage to decouple a difficult cross-domain translation problem into two easier steps. Extensive experiments verify that the face photos generated by Cali-Sketch are both photo-realistic and faithful to the input sketches, compared with state-of-the-art methods
Novel Attacks against Contingency Analysis in Power Grids

Contingency Analysis (CA) is a core component of the Energy Management System (EMS) in the power grid. The goal of CA is to operate the power system in a secure manner by analyzing the system subject to a contingency (e.g., the outage of a transmission line or a power generator) to determine the setpoints that will allow system operation without violation of constraints. The analysis in CA is conducted based on the output from State Estimation (SE), another core EMS module. However, it is also shown that an adversary can alter certain power measurements to corrupt the system states estimated by SE without being detected. Such a corrupted estimation can severely skew the results of the contingency analysis as it will provide a fake model to deal with. In this research, we formally model necessary interdependency relationships and systematically analyze these novel attacks on the contingency analysis. In particular, this research focuses on Security Constrained Optimal Power Flow (SCOPF) that finds out the optimal economic dispatches considering a single line failure (based on the $n - 1$ contingency analysis) and transmission line capacities. The proposed model is implemented and solved to find out potential threat vectors (i.e., a set of measurements to be altered) that can evade CA so that the system will face overloading situation on one or more transmission lines when some specific contingencies happen. We demonstrate our formal model on an IEEE 14 bus system-based case study and verify the results with a standard PowerWorld model. We further evaluate the model with respect to various attacks and grid characteristics.
Generalized Stueckelberg-Higgs gauge theories

The aim of this work is to discuss and explorer some generalized aspects of generation of photon mass respecting gauge symmetry. So with this intention we introduce the generalized Stueckelberg and Higgs gauge theories and present the classical and quantum conceptual aspects. We construct the quantum theory by writing the transition amplitude in the Fadeev-Senjanovic formalism and put it in a covariant form by the Fadeev-Popov method. Posteriorly we analyze the independence of physics by gauge choices via BRST symmetry. As we will see, the Stueckelberg structure has influence in the quantization process of the Higgs theory in the Gerardus 't Hooft shape, in which we see an intimate relationship between the Stueckelberg compensating field and the Goldstone boson. The degrees of freedom are explored not only in the quantization process, due to the constraints in gauge theories, but also in the Goldstone theorem, wherein we understand how the generalized gauge field eat the Goldstone boson and acquire mass.
Estimating Certain Integral Probability Metrics (IPMs) Is as Hard as Estimating under the IPMs

We study the minimax optimal rates for estimating a range of Integral Probability Metrics (IPMs) between two unknown probability measures, based on $n$ independent samples from them. Curiously, we show that estimating the IPM itself between probability measures, is not significantly easier than estimating the probability measures under the IPM. We prove that the minimax optimal rates for these two problems are multiplicatively equivalent, up to a $\log \log (n)/\log (n)$ factor.
Dynamic Graph Embedding via LSTM History Tracking

Many real world networks are very large and constantly change over time. These dynamic networks exist in various domains such as social networks, traffic networks and biological interactions. To handle large dynamic networks in downstream applications such as link prediction and anomaly detection, it is essential for such networks to be transferred into a low dimensional space. Recently, network embedding, a technique that converts a large graph into a low-dimensional representation, has become increasingly popular due to its strength in preserving the structure of a network. Efficient dynamic network embedding, however, has not yet been fully explored. In this paper, we present a dynamic network embedding method that integrates the history of nodes over time into the current state of nodes. The key contribution of our work is 1) generating dynamic network embedding by combining both dynamic and static node information 2) tracking history of neighbors of nodes using LSTM 3) significantly decreasing the time and memory by training an autoencoder LSTM model using temporal walks rather than adjacency matrices of graphs which are the common practice. We evaluate our method in multiple applications such as anomaly detection, link prediction and node classification in datasets from various domains.
Scale invariance and criticality in nuclear spectra

A Detrended Fluctuation Analysis (DFA) method is applied to investigate the scaling properties of the energy fluctuations in the spectrum of 48 Ca obtained with (a) a large realistic shell model calculation (ANTOINE code) and (b) with a random shell model (TBRE) calculation. We compare the scale invariant properties of the energy fluctuations with similar analyses applied to the RMT ensembles GOE and GDE. A comparison with the related power spectra calculations is made. The possible consequences of these results are discussed.
Utilization of wheat bran and dried Acacia saligna (Labill) H.L.Wendl leaves by highland rams

This study was conducted to evaluate body weight gain, feed intake and digestibility of highland sheep supplemented with wheat bran and dried Acacia saligna leaf five treatments, namely grass hay as a control (T1), 100 g per day A. saligna, (T2), 200 g per day A. saligna (T3), 300 g per day A. saligna (T4) and 400 g per day A. saligna (T5) with a fixed amount (200 g per day) of wheat bran was provided to the supplemented groups. Ram lambs in the control group gained 7.8 g/day while ram lambs placed under T2, T3, T4 and T5 gained 42.8, 63.9, 62.2 and 57.8 g/day. Thus, supplementation of 200 g/day dried A. saligna and 200 g/day wheat bran is biologically more efficient and economically more profitable and thus recommended for highland ram lambs fattening.
Disaggregated approach to government spending shocks: a theoretical analysis

We calibrate a dynamic stochastic general equilibrium model that features a transmission mechanism with different types of government spending, while the literature usually treats government spending as a homogenous compound. In this regard, we manage to distinguish between different types of government spending (namely: government investment, government wage component consumption and non-wage component consumption) where each type of spending has a varied role in the economy. The government wage increase has the largest positive effect both on private consumption and output by affecting the economy through the government production. This is a natural consequence of government production being complementary to private consumption in our model. Other two government spending types, namely government non-wage consumption and government investment, also have positive effects on output, whereas their responses on (private) consumption are mostly negative. These results provide an alternative explanation for the wide range of multipliers existing in the literature as our setup enables them to produce different effects on macroeconomic variables.
The Relationship between the Consistency of Users' Ratings and Recommendation Calibration

Fairness in recommender systems has recently received attention from researchers. Unfair recommendations have negative impact on the effectiveness of recommender systems as it may degrade users' satisfaction, loyalty, and at worst, it can lead to or perpetuate undesirable social dynamics. One of the factors that may impact fairness is calibration, the degree to which users' preferences on various item categories are reflected in the recommendations they receive. The ability of a recommendation algorithm for generating effective recommendations may depend on the meaningfulness of the input data and the amount of information available in users' profile. In this paper, we aim to explore the relationship between the consistency of users' ratings behavior and the degree of calibrated recommendations they receive. We conduct our analysis on different groups of users based on the consistency of their ratings. Our experimental results on a movie dataset and several recommendation algorithms show that there is a positive correlation between the consistency of users' ratings behavior and the degree of calibration in their recommendations, meaning that user groups with higher inconsistency in their ratings receive less calibrated recommendations.
A Model for Spatial Outlier Detection Based on Weighted Neighborhood Relationship

Spatial outliers are used to discover inconsistent objects producing implicit, hidden, and interesting knowledge, which has an effective role in decision-making process. In this paper, we propose a model to redefine the spatial neighborhood relationship by considering weights of the most effective parameters of neighboring objects in a given spatial data set. The spatial parameters, which are taken into our consideration, are distance, cost, and number of direct connections between neighboring objects. This model is adaptable to be applied on polygonal objects. The proposed model is applied to a GIS system supporting literacy project in Fayoum governorate.
Decoding of visual-related information from the human EEG using an end-to-end deep learning approach

There is increasing interest in using deep learning approach for EEG analysis as there are still rooms for the improvement of EEG analysis in its accuracy. Convolutional long short-term (CNNLSTM) has been successfully applied in time series data with spatial structure through end-to-end learning. Here, we proposed a CNNLSTM based neural network architecture termed EEG_CNNLSTMNet for the classification of EEG signals in response to grating stimuli with different spatial frequencies. EEG_CNNLSTMNet comprises two convolutional layers and one bidirectional long short-term memory (LSTM) layer. The convolutional layers capture local temporal characteristics of the EEG signal at each channel as well as global spatial characteristics across channels, while the LSTM layer extracts long-term temporal dependency of EEG signals. Our experiment showed that EEG_CNNLSTMNet performed much better at EEG classification than a traditional machine learning approach, i.e. a support vector machine (SVM) with features. Additionally, EEG_CNNLSTMNet outperformed EEGNet, a state-of-art neural network architecture for the intra-subject case. We infer that the underperformance when using an LSTM layer in the inter-subject case is due to long-term dependency characteristics in the EEG signal that vary greatly across subjects. Moreover, the inter-subject fine-tuned classification model using very little data of the new subject achieved much higher accuracy than that trained only on the data from the other subjects. Our study suggests that the fine-tuned inter-subject model can be a potential end-to-end EEG analysis method considering both the accuracy and the required training data of the new subject.
The impact of privatization on earnings management in a developing country like Iran

One of the important reasons for privatization is increasing management performance because privatization is often associated with a change in the management of the company, on the other hand, managers in the private companies have more freedom than public companies, also in the private companies giving reward is based on performance, and then managers have enough incentive to manipulate earnings. The main purpose of this study is to investigate if this theoretical literature is true in practice especially in developing country like Iran. For this purpose this paper investigates the effect of privatization on earnings management to see if EM changes after privatization toward before it. In fact, it tests earnings management behavior in the period surrounding the privatization. The results show that firms with low earnings before accruals have relatively high earnings management than firms with high earnings before accruals, and the private companies involved in more aggressive earnings management practices or in detail, private companies tend to manipulate earnings upward more aggressively when their earnings before discretionary accruals are poor, and they downplay their earnings more than public companies when their earnings before discretionary accruals are exceptionally high. Also, the government action to restructure the company before inviting prospective investors is considered consistent with earnings management. It is opined that the findings of this study are indicative of what could be the situation surrounding privatization in developing country like Iran. Also, these results show earnings management after privatization removes quickly upward than the period before privatization. Key words: earnings management, privatization, private company, public company, earnings before accruals, discretionary accruals, total accruals.
Self-Playing Labyrinth Game Using Camera and Industrial Control System

In this master’s thesis, an industrial control system together with a network camera and servo motors were used to automate a ball and plate labyrinth system. The two servo motors, each with its own servo drive, were connected by joint arms to the plate resting on two interconnected gimbal frames, one for each axis. A background subtraction-based ball position tracking algorithm was developed to measure the ball-position using the camera. The camera acted as a sensor node in a control network with a programmable logical controller used together with the servo drives to implement a cascaded PID control loop to control the ball position. The ball reference position could either be controlled with user input from a tablet device, or automatically to make the labyrinth self-playing. The resulting system was able to control the ball position through the labyrinth using the camera for position feedback.
Context-Aware Local Differential Privacy

Local differential privacy (LDP) is a strong notion of privacy for individual users that often comes at the expense of a significant drop in utility. The classical definition of LDP assumes that all elements in the data domain are equally sensitive. However, in many applications, some symbols are more sensitive than others. This work proposes a context-aware framework of local differential privacy that allows a privacy designer to incorporate the application's context into the privacy definition. For binary data domains, we provide a universally optimal privatization scheme and highlight its connections to Warner's randomized response (RR) and Mangat's improved response. Motivated by geolocation and web search applications, for $k$-ary data domains, we consider two special cases of context-aware LDP: block-structured LDP and high-low LDP. We study discrete distribution estimation and provide communication-efficient, sample-optimal schemes and information-theoretic lower bounds for both models. We show that using contextual information can require fewer samples than classical LDP to achieve the same accuracy.
NFV-based IoT Security for Home Networks using MUD

We present a new system to protect IoT devices in multiple premises by a single Virtual Network Function (VNF) deployed in the ISP network. The system is based on the Manufacturer Usage Description (MUD) framework, a white-list IoT protection scheme that has been proposed in recent years.While MUD is designed for on-premise deployment, here we adapt it to work as a scalable, managed service in the ISP level. Our service does not require any cooperation or installation on the client premise or on the IoT devices themselves. Furthermore, it monitors the IoT traffic and detects malicious behavior, including outgoing DDoS traffic, without being on the critical path, and it filters bad traffic by ACLs on either the POP router or the client CPE. The CPE itself is considered an IoT device and traffic destined or that originates at the CPE is monitored as well. For the white-list method we extend the MUD architectural framework to support peer to peer communicating IoT devices (e.g., direct mobile device to IoT device communication). The system includes a mechanism to distinguish between flows of different devices at the ISP level despite the fact that most home networks (and their IoT devices) are behind a NAT and all the flows from the same home come out with the same source IP address. Moreover, the NFV system needs to receive only the first packet of each flow/connection at the VNF, and rules space is proportional to the number of unique types of IoT devices rather than the total number of IoT devices (which is much larger). A PoC with a large national level ISP proves that our technology works as expected, identifying the various IoT devices that are connected to the network and detecting any unauthorized communications.
Effect of carboxymethylcellulose on thermal properties of batter systems formulated with different flour combinations

Abstract: Batters are complex systems with a wide range of ingredients. Interactions between the ingredients determine batter performance and the final quality of the products coated with them. The influence of carboxymethylcellulose (CMC) on thermal properties of batter systems was studied. The combinations of wheat and rice, wheat and corn, and rice and corn flours, with CMC added at the levels of 0.5, 1.0 and 1.5% (weight basis) were studied. Two different temperature profiles were used to simulate cooking and storage processes. Differential scanning calorimetry (DSC) was used to determine thermal properties (namely glass transition temperature, gelatinization temperature, ice melting temperature, and enthalpy) and to describe the phase transitions that occur during heating and cooling processes. CMC greatly influenced the thermal properties of batter systems. It shifted the gelatinization temperatures and decreased the total enthalpies of gelatinization. Further, CMC depressed the glass transition temperature and the total enthalpies of ice melting. The results revealed possible interactions between CMC and the other batter constituents, thus partially explaining its effect in complex batter systems.
Prioritization of Evacuation of Solid Waste at Municipal Solid Waste Disposal Centers

Disposal of solid waste is one of the means of managing the waste in our urban centers which requires huge amount of money and other resources such as time. Most agencies face the problem of limited resources for waste disposal and in most cases, evacuation of the waste at waste collection centers are not given equal priority by the agencies. In this study, Analytical Hierarchy Process (AHP) was applied to prioritize evacuation of solid waste in collection centers in Abuja Municipal Council (Federal Capital of Nigeria). Abuja has thirteen (13) waste collection centers and two (2) disposal sites. Three criteria were identified as being used during the waste evacuation exercise. These are Population Density (PD) areas, Very Important Personalities (VIP) areas and Road Network/Drainage Channel (RNDC) areas. A nine point scale of AHP was used to quantify the verbal judgments obtained from the Officers of the Solid Waste Management Agency in respect of the comparable importance of the criteria in relation to waste evacuation, and comparable importance of the waste collection centers in relation to each of the criteria. An excel spreadsheet program was developed and used to generate the synthesized matrices, consistency indices and priority weights. The results reveal that for waste evacuation in Abuja, Nigeria, Wuse 1 waste collection center should be given the highest priority while Wuye waste collection center should be given the least priority.
EFFECTS OF STRAIN RATE AND CO 2 ON NO FORMATION IN CH 4 / N 2 / O 2 COUNTER-FLOW DIFFUSION FLAMES by

The reduction of nitrogen oxides in the high temperature flame is the key factor affecting the oxygen-enriched combustion performance. A numerical study using an OPPDIF code with detailed chemistry mechanism GRI 3.0 was carried out to focus on the effect of strain rate (25-130 s–1) and CO2 addition (0-0.59) on the oxidizer side on NO emission in CH4 / N2 / O2 counter-flow diffusion flame. The mole fraction profiles of flame structures, NO, NO2 and some selected radicals (H, O, OH) and the sensitivity of the dominant reactions contributing to NO formation in the counter-flow diffusion flames of CH4\/ N2 /O2 and CH4 / N2 / O2 / CO2 were obtained. The results indicated that the flame temperature and the amount of NO were reduced while the sensitivity of reactions to the prompt NO formation was gradually increased with the increasing strain rate. Furthermore, it is shown that with the increasing CO2 concentration in oxidizer, CO2 was directly involved in the reaction of NO consumption. The flame temperature and NO production were decreased dramatically and the mechanism of NO production was transformed from the thermal to prompt route.