Miracle-dz/UniFAR-FTUs huggingface dataset:
Low Cost Smart House Implementation with Sensory Information Analysis and Face Recognition

A smart house prototype is presented in this study with home automation, safety, person identification and automatic notifications. Home automation is implemented using light sensor and actuators in the house such as LEDs and servo motors (for window control). Safety system is implemented using gas, humidity, and human infrared sensors that collect the data from the environment. The system developed allows the users to control actuators remotely, view environmental sensor readings and real-time video streaming using user-friendly web and mobile applications. The readings of environmental sensors are analyzed on the server for gas and water leak and potential intrusion using gas, humidity and human infrared sensors. Furthermore, a face recognition system is also incorporated to the smart home prototype which identifies whether the person is a resident of the house or an unknown person. An automatic notification system which uses the Telegram Bot, notifies the user in case an unknown person enters the house or if the system detects emergencies. Telegram Bot also provides options to the user for further actions.
Conclusive analysis and cause of the flyby anomaly

JPL’s own data correlate to 1% NEAR and Rosetta trajectory discrepancies to an unexpected doubling of path times in phase locked tracking. NEAR’s radar residuals illustrate the doubling to 5σ. Analysis of these and other NASA-tracked flybys shows that a distance sensitive anomalous signal generally exists.
Exponential Random Graph Modeling of Co-Offender Drug Crimes

Drug problem has contributed a rapid impact on today's society. It is not only a threat of human health but also causes a great impact on the social security issue. As drug abuse tends to organized crimes, we must consider the social network relationships among criminals to formulate better strategies against drugs. This research applied exponential random graph models (ERGMs) to analyze dynamic relations of drug crime. The strength of ERGMs is the ability to handle complicated dependency patterns which violate the basic assumption of traditional statistical methodologies. The homophily test and Monte Carlo Markov Chain (MCMC) estimation are used to explore the drug offenders' attributes and structural interactions, respectively. The experimental result shows that the homophily effect is significant on drug co-offenders relations regarding to occupation, education, nationality, drug type and recidivism. In addition, the positive 2-path coefficient indicates that drug offenders tend to share friends and form a cluster. The results of this paper reveal the advantages of structural implications in analyzing drug-related crime, as well as its ability to facilitate the cognition of crime prevention and intervention strategies.
A New Shopfloor Orchestration Approach for Collaborative Human-Robot Device Disassembly

We present a new approach to tackle the problem of task assignment and scheduling in human-robot teams that undertake collaborative device disassembly tasks. The proposed approach is a hybrid between a global search metaheuristic and an adaptive greedy operation assignment and scheduling algorithm. We propose the concept of an "Adaptive (work) Cell", (aCell), which becomes the basis for the hierarchical organization of the proposed search approach. At high level, metaheuristic search establishes resource constraints for each aCell and determines parameters for the task-level operation scheduling. At low level, the task-level scheduling algorithm produces feasible assignments and schedules within a single aCell by backtracking through feasible time slots using an adaptive score metric. The advantage of the proposed approach is that it clearly delineates between higher level state space exploration and focused, task-oriented exploitation. We validate the proposed approach on a class of novel multi-objective benchmark problems involving human-robot teams collaborating throughout a factory floor, addressing specifically for the first time the problem of device disassembly tasks, relevant to WEEE recycling, with additional constraints, where we obtain favorable results.
Supervised Machine Learning Algorithms for Credit Card Fraud Detection: A Comparison

In today’s economic scenario, credit card use has become extremely commonplace. These cards allow the user to make payments of large sums of money without the need to carry large sums of cash. They have revolutionized the way of making cashless payments and made making any sort of payments convenient for the buyer. This electronic form of payment is extremely useful but comes with its own set of risks. With the increasing number of users, credit card frauds are also increasing at a similar pace. The credit card information of a particular individual can be collected illegally and can be used for fraudulent transactions. Some Machine Learning Algorithms can be applied to collect data to tackle this problem. This paper presents a comparison of some established supervised learning algorithms to differentiate between genuine and fraudulent transactions.
Mechanisms of Reconfigurable Antenna: A Review

In today’s evolutionary world of wireless technology, reconfigurable antenna plays a very important role. Wireless technologies such as mobile communication, military, cognitive radio, radar, satellite communication are needed to be dynamic in their functions to improve the performance in changing scenario. This can be achieved using a single reconfigurable antenna where various performance parameters like resonant frequency, polarization and radiation pattern are altered as per user end requirement. Hence, a single reconfigurable antenna replaces multiple conventional antennas resulting in a compact, low cost system. In this paper, electrical and mechanical switching mechanism for reconfigurable antennas has been reviewed. In electrical switching mechanism, reconfigurability is obtained using the p-i-n diode, Radio Frequency Micro Electro Mechanical switch (RF-MEM’s) and varactor diode, where ON and OFF state of diode, activates or deactivates the part of antenna structure offering modified characteristics of antenna; while in mechanical mechanism actuators and motors mechanically modify the antenna structure. Apart from the reconfigurability mechanisms, the reviewed structures are analyzed with respect to their design theory and applications. Equivalent circuit of the diodes have been studied and presented in this paper, it being the key component in reconfigurable antennas.
Performance Evaluation of Sensors Lightweight Security Mechanism

A large proportion of the Internet of Things devices are battery powered cheap units, designed to stay in the field for a long time. This is the case of connected sensors and actuators. In this demo, we analyze the performance of a system fully implementing an original cross-layer security mechanism for wireless sensor nodes, by precise energy measurement of different settings of the system.
Development of Huffman Code for Lora Technology

This research aims to implement the LoRa development boards to transmit measured sensor data. Two data transmission schemes are investigated, one using a traditional ASCII format and another exploiting the Huffman codes. Transmitted character sizes between these two coding representations are recorded and compared in terms of compression rate. To evaluate the error performance, various transmission distances up to 1,000 meters are investigated. At each receiving location, 250 sets of data are collected for both encoding formats. Experimental results show that the Huffman codes can reduce the transmitted character size on average. Furthermore, using compressed data via the Huffman algorithm can decrease error percentages of incorrect transmitted data.
Deep Learning Solutions for Motor Imagery Classification: A Comparison Study

Motor imagery classification has been widely applied in constructing brain computer interface to control the outside equipment as an alternative neural muscular pathway. EEG as the most popular non-invasive brain signal suffers from low signal to noise ratio and unpredictable pattern variation even for the same subject. To improve the classification accuracy of EEG based motor imageries, many deep learning based solutions have been developed, mainly including convolutional neural network (CNN) based methods and recurrent neural network (RNN) based methods. There is no unanimous acknowledgement of the most appropriate deep learning solution for motor imagery classification. In order to evaluate the performance of different deep learning solutions for motor imagery classification, a comprehensive comparison study has been conducted in this paper. CNN based method, RNN based method, temporal convolution network (TCN) based method, paralleled combination of CNN and SRU (Simple Recurrent Unit), cascaded combination of CNN and SRU have been constructed and compared based on extensive experiments. The experiments have been conducted on a fair basis with the same dataset, the same preprocessing of the data, and the same platform. Experiments have shown that TCN based method has obtained the best performance and the paralleled combination of CNN and RNN has obtained the second best performance, which inspired us to explore the spatial-temporal feature learning deep network solutions for further improvement.
Formal Methods for Solving Technological Problems in the Infocommunications Routines of Intelligent Decisions Making for Drilling Control

The proposed infocommunications routines for intellectual system based on constraints allows to conduct diagnostics of solutions to a technological problem (extraordinary unsheduled situation in drilling) submitted by the object of the decision support and on the basis of such analysis to generate the corresponding feedback. The feedback provides the benefits of a long-term and short-term decision-making process by reviewing the false knowledge of the oil and gas subject area submitted by the decision support object. An additional advantage of the constraints-based approach is the avoidance of detailed study and classification of errors made by decision support object, who tend to systematically use some unified procedure to solve current tasks. Each object uses one of the class of procedures that can be used for a certain class of tasks and the strategy of choosing procedures from the corresponding equivalence class and depends on the current task. Interfaces support the solution of a technological problem by visualizing its structure. When forming a request, the decision support system carries out a synchronous search using the keywords entered and highlights the errors in which such words are found, thus facilitating the choice of the object. Modeling on the basis of the analysis of the errors of the decision support object determines that for the study of the technological process of drilling of oil and gas wells, the significance of declarative knowledge are the dominant. Each constraint on the system has one or more predefined feedback messages that are provided to the object when the constraint is violated.
A Security Model of SCADA System Based on Attack Tree

In recent years, attacks against SCADA systems have emerged in endlessly. Therefore, it is of great significance to study the security issues of SCADA systems. Although many scholars have studied the security of SCADA systems, their research tends to focus on one aspect of security analysis or security protection design, lacking the organic combination of the two. Therefore, this paper proposes a security model of SCADA system based on attack tree—BPATSM, which contains two modules: security analysis model and security protection scheme. For the security analysis of SCADA systems, there are a lot of methods. Attack trees are a common method for security analysis. However, the traditional attack tree model can only analyze the structure of the attack, but cannot analyze the characteristics of the attack, nor it can analyze the bias of the attack that has occurred and predicted the possibility of the attack that will occurred, so its analysis efficiency is not high. Therefore, this paper proposes a preference attack tree (PAT) model based on the attack tree model, that is, adding the attributes of the number of attacks on the nodes of the attack tree to represent the preference, which can improve the efficiency of attack tree model analysis. Based on PAT analysis, this paper also proposes a security protection scheme for SCADA system, which can switch three levels of security protection according to different protect requirements. Finally, the BPATSM model is implemented in a semi-simulation system and tested its security.
Can Ce:Gd3Al2Ga3O12 scintillators detect thermal neutrons?

Alternative neutron detectors are actively being researched to replace 3He gas detectors for neutron scattering and homeland security. Neutron scintillators for this purpose must have high light output, short decay time, low sensitivity to gamma-rays, and possibly be non-hygroscopic. Gd-containing oxide scintillators are excellent at detecting gamma-rays, but their thermal-neutron detectability has not been proven yet, because of similar pulse-shapes and pulse-heights generated under thermal neutrons and gamma irradiation. Here we show that a non-negative matrix factorization (NMF) algorithm for digital signal processing greatly improves neutron-gamma discrimination ratio (the ratio of neutron and gamma detection efficiencies) of a Gd-containing scintillator, Ce:Gd3Al2Ga3O12 (GAGG). In the case of Ce:GAGG as a gamma detector, better discrimination against ambient background events was observed compared to results from a traditional pulse-height method. It was also found that the secondary integration area (S2) of a waveform in the time window of 0-50 ns is an energy measure of gamma-rays. Therefore, Gd-containing scintillators are potentially applicable in not only neutron detection but also in gamma detection with higher signal-to-noise ratio.
Simultaneously Improving Accuracy and Precision within Dynamic Kernelized PET Reconstruction

We propose two kernelized reconstruction methods for simultaneously improving accuracy and precision for dynamic PET imaging, followed by validations using 4D simulations. One of the proposed methods utilizes an effective kernel matrix which consists of an accuracy component that accounts for partial volume effect and a precision component which achieves 4D noise reduction (i.e. RBV-HYPR4D-K-OSEM), while the other method incorporates the standard resolution modeling within our previously proposed 4D de-noised reconstruction method (i.e. PSF-HYPR4D-K-OSEM). It was observed that the inclusion of the accuracy kernel improves the convergence rate in contrast recovery coefficient (CRC) for relatively small regions, whereas the inclusion of resolution modeling slows down the convergence rate. As compared to the standard OSEM with post filter, both proposed methods achieved better CRC vs noise trade off and mean absolute percent error across the time-activity curves, while RBV-HYPR4D outperforms PSF-HYPR4D in terms of accuracy.
Research on the Impact of Attacks on Security Characteristics

Confidentiality, Integrity and Availability, essential security characteristics, are getting more attention. By analyzing the relationship between network attacks and security characteristics, we can deepen our understanding of network security. This paper proposes a method that evaluates the impact of attacks on the security characteristics for the first time by machine learning. The evaluation method is a step towards objectification. It based on intrusion detection system data set, includes data set process, extraction of critical features, evaluation value design, and other processes. This method can evaluate the impact of each attack record on the security characteristics in the data set. The evaluation result, enriching the content of the data set, can be used as a data label and an aspect of network security analysis. Through experimentation, the rationality of this evaluation method is proved.
Mitigating Cyber Supply Chain Risks in Cyber Physical Systems Organizational Landscape

Cyber supply chain (CSC) provide an organizationwith the ability to align its business processes, informationflows and data structures with other organization. However, the increase interdependencies have brought about inherent, threats, risks, attacks and vulnerabilities that adversaries maybe able to exploit when not properly mitigated. Additionally, every cyberattack on each organization increases theprobability of the risk cascading to others. The CSC risk hasincreased exponentially due to uncertainties surroundingcyberattacks and the cyber threat landscape. Recent CSCthreats have been disruptive and impacting on the smooth flowof delivery of products and services. CSC risk has beenobserved as one of the areas that impact greatly and causesbudget overruns. The aim of this paper is to mitigate CSCrisks in an organizational landscape. In particular, the paperidentifies supply inbound and outbound chain threatlandscape using a risk breakdown structure. Further, weassess the risk to gather cyber threat intelligence. Furthermore, we use the probability distribution method todetermine the CSC risks and analyze the risk probabilities andlikelihood of risk cascading impact. Our results show thatCSC risk can be neutralized using probability distributionmethods to detect and mitigate the risks and their impactlevels.
Educational Technology in the Age of Natural Interfaces and Deep Learning

Tools are an essential support in any human activity. As technology advances, we are able to design more advanced tools that help us in doing activities more efficiently. Recently, we have seen breakthroughs in the two main components of tools, namely the interface and the computing engine behind. Natural interfaces allow us to communicate with tools in a way better adapted for humans. In relation to the engine, we are shifting from a computing paradigm to another one based on artificial intelligence, which learns as it is used. In this paper, we examine how these technological advances have an impact on education, leading to smart learning environments.
60 GHz Dual-Polarized High-Gain Planar Aperture Antenna Array Based on LTCC

Novel designs of planar aperture antenna (PAA) arrays are proposed in this article, which achieve dual-polarized and high-gain performances in the 60 GHz unlicensed band. In order to achieve high gain, the original PAA is expanded to its two-dimensional array form, and the two linear polarizations share the same apertures. The two polarizations are realized by using orthogonal coupling slots excited by substrate-integrated waveguide (SIW) power dividers below. Two array prototypes with <inline-formula> <tex-math notation="LaTeX">$8 \times 8$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$16 \times 16$ </tex-math></inline-formula> radiation cells are designed and manufactured based on low-temperature co-fired ceramic (LTCC) technology for ease of integration. The simulated results show that the two antennas have −10 dB impedance bandwidth and 3 dB gain bandwidth of 57.5–65 and 57–64 GHz, respectively, with stable, no-squint, and symmetrical beams in band, which is verified by measurements. Compared with other reported high-gain and dual-polarized millimeter-wave antennas, the proposed antennas feature a low-profile, simple structure while keeping high aperture efficiency. The proposed antennas are very suitable for integration and are promising in 60 GHz short-range communication applications.
Security Issues With Fog Computing

Fog computing or edge computing or fogging extends cloud computing to the edge of the network. It operates on the computing, storage and networking services between userend devices and cloud computing data centres. However, in the process of caring out these operations, fog computing is faced with several security issues. These issues may be inherited from cloud computing systems or may arise due to fog computing systems alone. Some of the major gaps in providing a secure platform for the fog computing process arise from interim operational steps like authentication or identification, which often expands to large scale performance issues in fog computing. Thus, these issues and their implications on fog computing databases, and the possible available solutions are researched and provided for a better scope of future use and growth of fog computing systems by bridging the gaps of security issues in it.
Framework for Sizing of Energy Storage System Supplementing Photovoltaic Generation in Consideration of Battery Degradation

There is growing interest in the use of energy storage systems (ESS) to create combined “renewable energy plus storage” power plants. ESS based on lithium-ion batteries have drawn much attention due to their high energy density and low self-discharge. However, as lithium-ion batteries are still costly, a power producer should determine ESS capacity in a sophisticated manner to ensure profitability of the PV plus storage projects. During the project horizon, lithium-ion batteries undergo severe capacity degradation, which must be considered in ESS planning. The degradation rate depends on various stress factors which are affected by ESS sizes and operation. Therefore, this paper aims to propose an advanced framework for calculating the capacity of an ESS supplementing a photovoltaic system considering the effect of the size and operation of ESS on battery degradation while maximizing profitability. Depending on how batteries are used during the project horizon, two scenarios are discussed and an ESS sizing framework for each scenario is suggested. To deal with non-convexity and black-box parameters of the optimal ESS sizing problems, we introduce an iterative algorithm that finds a solution by accessing battery degradation and optimizing profitability repetitively. We adopted the South Korean market for analysis and simulation of the frameworks.
An Identity Based-Identification Scheme With Tight Security Against Active and Concurrent Adversaries

Identification schemes are used by machines to securely authenticate the identity of other machines or their users over computer networks. As conventional public key schemes require a trusted third party (TTP) or a public file to ensure the corresponding public key matches with the identity, identity-based cryptosystems emerged as a form of certificate-free system. The entity’s identity is the public key itself, therefore eliminating the need for a TTP. The identity-based identification (IBI) scheme introduced by Kurosawa and Heng using their transform in 2004 remains as the only IBI derived from the Boneh-Lynn-Shacham (BLS) short signature scheme which has the advantage of shorter keys. We show tight security reduction against active and concurrent attackers (imp-aa/ca) on our scheme that is obtained from the same transform. As the transform will only produce schemes that are only secure against passive attackers (imp-pa), security against imp-aa/ca scheme relies on a strong One-More interactive assumption and therefore resulted in weak security. While the OR-proof method allows schemes secure against imp-pa to be secure against imp-aa/ca, the resulting security against imp-aa/ca will suffer from loose bounds in addition to the user secret keys being doubled in size. Our work avoids both OR-proof and strong interactive assumptions by showing an ad-hoc proof for our construction which utilizes the weaker well-studied co-computational Diffie-Hellman assumption and yet still has tight security against imp-aa/ca. We demonstrate the tight security of our scheme which allows usage of even shorter key sizes.