Miracle-dz/UniFAR-FTUs huggingface dataset:
Artificial Intelligence Security Threat, Crime, and Forensics: Taxonomy and Open Issues

Advances in Artificial Intelligence (AI) have influenced almost every field including computer science, robotics, social engineering, psychology, criminology and so on. Although AI has solved various challenges, potential security threats of AI algorithms and training data have been stressed by AI researchers. As AI system inherits security threats of traditional computer system, the concern about novel cyberattack enhanced by AI is also growing. In addition, AI is deeply connected to physical space (e.g. autonomous vehicle, intelligent virtual assistant), so AI-related crime can harm people physically, beyond the cyberspace. In this context, we represent a literature review of security threats and AI-related crime. Based on the literature review, this article defines the term AI crime and classifies AI crime into 2 categories: AI as tool crime and AI as target crime, inspired by a taxonomy of cybercrime: Computer as tool crime and Computer as tool crime. Through the proposed taxonomy, foreseeable AI crimes are systematically studied and related forensic techniques are also addressed. We also analyze the characteristics of the AI crimes and present challenges that are difficult to be solved with the traditional forensic techniques. Finally, open issues are presented, with emphasis on the need to establish novel strategies for AI forensics.
Predicting Drug Risk Level from Adverse Drug Reactions Using SMOTE and Machine Learning Approaches

Adverse drug reactions (ADRs) are the major source of morbidity and mortality. The prediction of drug risk level based on ADRs is few. Our study aims at predicting the drug risk level from ADRs using machine learning approaches. A total of 985,960 ADR reports from 2011 to 2018 were attained from the Chinese spontaneous reporting database (CSRD) in Jiangsu Province. Among them, there were 887 Prescription (Rx) Drugs (84.72%), 113 Over-the-Counter-A (OTC-A) Drugs (10.79%) and 47 OTC-B Drugs (4.49%). An over-sampling method, Synthetic Minority Oversampling Technique (SMOTE), was applied to the imbalanced classification. Firstly, we proposed a multi-classification framework based on SMOTE and classifiers. Secondly, drugs in CSRD were taken as the samples, ADR signal values calculated by proportional reporting ratio (PRR) or information component (IC) were taken as the features. Then, we applied four classifiers: Random Forest (RF), Gradient Boost (GB), Logistic Regression (LR), AdaBoost (ADA) to the tagged data. After evaluating the classification results by specific metrics, we finally obtained the optimal combination of our framework, PRR-SMOTE-RF with an accuracy rate of 0.95. We anticipate that this study can be a strong auxiliary judgment basis for experts on the status change of Rx Drugs to OTC Drugs.
Physiological Growth and Yield of Two Groundnut Varieties as Influenced by Light and Boron

Light and boron play a significant role in the physiological growth and yield of crops such as groundnut. Even so, there has been little information on the application of boron in groundnut, while no information is available in Bangladesh regarding the imposition of light in groundnut. Therefore, two field experiments were conducted in order to evaluate the physiological growth and seed yield of groundnut. Treatments included two levels of light, viz . , L 0 = normal day length (≈12-h light) and L = normal day length + 6-h extended light at night (≈18-h light); three levels of boron, viz . , 0-kg B ha -1 (B 0 ), 1-kg B ha- 1 (B 1 ) and 2-kg B ha -1 (B 2 ), and two groundnut varieties, viz . , V 1 = ‘Dhaka-1’ and V 2 = ‘BARI Chinabadam-8’. The results obtained from the experiment revealed that the highest LAI, LAR, LMR and RWR were recorded in L, B 2 and V 2 variants, while NAR was the highest in L 0 and B 0 . Moreover, it was noted an inconsistent result for RGR found for boron application. Based on the results, it could be concluded that boron is one of the factors responsible for higher yield of groundnut; nevertheless, under the extended light, it is not possible to get the highest yield.
Implementation Voice Command System for Soccer Robot ERSOW

ERSOW is a wheeled soccer robot that is included in the Middle Size League (MSL) category in the Indonesian Wheeled Robot Soccer Contest division (Wheeled KRSBI). Wheeled soccer robot has Artificial Intelligent (AI) for kick the ball, receive the ball, feed the ball, recognize the ball, recognize the opponent, recognize the goal, receive instructions from the base station, and so forth. This research focuses on giving instructions to ERSOW through the base station using the voice command system. The system uses speech as input in the form of analog signals. Speech recognition is done by using a deep speech package so as to produce output in the form of text. The system will run on the Robot Operating System (ROS). The result of speech recognition using 13 trained speakers when tested by one speaker in different distance show average Word Error Rate (WER) 0.46% and Word Accuracy (W Acc) is 99.54%. When tested by five different speaker using trained speakers show average WER is 4.37% and W Acc is 95.63%, when using non trained speakers show average WER is 28.25% and W Acc is 71.75%. Base station implementation shows the simulation of the robot when the user gives instruction by his or her own voice.
A new features vector matching for big heterogeneous data in intrusion detection context

Nowadays, the volume of data considerably increasing, the data is exploding on the scale of the Exabyte and the Zettabyte at an exceptionally high rate. These can be characterized as big data. Hence, the security of the network, Internet, websites, Iot devices and the organizations, of this growth is indispensable. Detecting intrusions in such a big heterogeneous data environment is challenging. In this paper, we will present a new representation of data that can support this big heterogeneous environment. We will use three different datasets and propose an automatically matching algorithm that measures the semantic similarity between each two features existing on different datasets. Thereafter, an approximate vector is created that any type of coming data can be stored. With this representation, we can have subsequently an efficient intrusion detection system that can be able to acknowledge any instance of the existing data in the networks.
Adaptive Neuro-Fuzzy Inference System for Indoor Air Quality (IAQ) Assessment of a Korean Unlimited Grill Restaurant in Metro Manila

The recent developments in technology, especially in the field of computational intelligence, allows us to use its power in vast applications. Together with this notion, people’s growing curiosity and awareness of indoor air pollution has also risen in the past few years. Consequently, this baseline study was conceptualized to evaluate the IAQ of a Korean unlimited grill restaurant. The study is divided into three phases where phase one is the evaluation of the IAQ. The parameters considered in this the evaluation are the dry bulb temperature, relative humidity (RH), carbon dioxide (CO2) concentration, and particulate matter concentrations (PM2.5 and PM10). An integrated testing instrument was developed using the following equipment: Smart Sensor AR 807 and ASHRAE hygrometer (for dry bulb temperature and RH); Vernier CO2 gas sensor (for CO2); and LanBaoDeYuan’s LB-S06 formaldehyde detector (for PM2.5 and PM10). The second phase is to conduct a baseline assessment to determine if the IAQ parameters are within the standards. Overall results show that indoor temperature readings were above the standard temperature; RH readings exceeded its standard; CO2 concentrations both during lunch time and dinner time were lower than the satisfactory range; and both PM2.5 and PM10 were higher than the ASHRAE 62.1-2016 standard limit The next phase of this study was the development of an adaptive neuro-fuzzy inference system to determine the effective indoor air quality index (EIAQI), an index that was derived from the integration of air quality index and thermal comfort index, using the data gathered from the previous phase. The ANFIS model developed used difference sigmoidal membership function with RMSE of 0.0035. Training and testing validation and evaluation resulted to an average error of 0.0083 and 0.523 respectively.
LSTM-Based Intrusion Detection System for In-Vehicle Can Bus Communications

The modern automobile is a complex piece of technology that uses the Controller Area Network (CAN) bus system as a central system for managing the communication between the electronic control units (ECUs). Despite its central importance, the CAN bus system does not support authentication and authorization mechanisms, i.e., CAN messages are broadcast without basic security features. As a result, it is easy for attackers to launch attacks at the CAN bus network system. Attackers can compromise the CAN bus system in several ways including Denial of Service (DoS), Fuzzing and Spoofing attacks. It is imperative to devise methodologies to protect modern cars against the aforementioned attacks. In this paper, we propose a Long Short-Term Memory (LSTM)-based Intrusion Detection System (IDS) to detect and mitigate the CAN bus network attacks. We generate our own dataset by first extracting attack-free data from our experimental car and by injecting attacks into the latter and collecting the dataset. We use the dataset for testing and training our model. With our selected hyper-parameter values, our results demonstrate that our classifier is efficient in detecting the CAN bus network attacks, we achieved an overall detection accuracy of 99.995%. We also compare the proposed LSTM method with the Survival Analysis for automobile IDS dataset which is developed by the Hacking and Countermeasure Research Lab, Korea. Our proposed LSTM model achieves a higher detection rate than the Survival Analysis method.
A review of drought monitoring using remote sensing and data mining methods

Today, drought has become part of the identity as well as the fate of many countries. In fact, drought is considered among the most damaging natural disasters. The severe consequences resulting from drought affect the nature and society at different levels. Proper and efficient management is not possible without accurate prediction of drought and the identification of its various aspects. Thus, the existence of a considerable body of literature on drought monitoring. However, significant growth of remote sensing databases as will an increased amount of available data related to drought have been detected. Therefore, a more adequate approach should be developed. During the past decades, Data Mining (DM) methods have been introduced for drought monitoring. According to the best of our knowledge, a review of drought monitoring using remote sensing data and DM methods is lacking. Thereby, the purpose of this paper is to review and discuss the applications of DM methods. This paper consolidates the finding of drought monitoring, models, tasks, and methodologies.
Hold My Hand: Impact of Intimate Controllers on Player Experience

Intimate controllers are defined as devices that require players to breach their comfort zone and interpersonal boundaries to establish a physical contact between players. We developed a game that requires players to hold each other’s hands to perform a special action. The main purpose was to investigate the impact that this alternative controller has on player experience. Two version of the game were created: with and without intimate controllers. We had two groups playing with each version and then fill a survey to evaluate player experience. Player experience was assessed using the Game Experience Questionnaire (Core Module, Social Presence Module and Post-game Module) but given some serious concerns regarding this model, we analyzed the data bypassing the factors and instead focusing on individual answers. The data shows that intimate controllers can lead to a much more engaging experience and feelings of closeness to other players, while eliciting stronger emotions, both positive and negative. There are also unexpected results in terms of increased engagement with the narrative aspect of the game while also reporting a decreased feeling of mastery and skills and increased weariness.
On Dissipativity Conditions for Linearized Models of Locally Active Circuit Blocks

This paper generalizes the concept of dissipativity of linear models to linearized models of nonlinear circuit blocks that may also include locally active behavior. We show that such models can be guaranteed to behave as dissipative, provided they are subjected to certain bounds on the small-signal input amplitude, and provided that total voltage and current signals (including bias) are considered in the energy balance. Potentially severe incorrect dynamic behaviour can result from the violation of such bounds, as illustrated through a linearized reduced-order model of a low dropout voltage regulator.
Assistive Device for Visual Impaired Person Based on Real Time Object Detection

This research presents a design system that can assist visual impaired persons. The Visual impaired person has difficulty seeing things or objects in their environment where most information is displayed visually. In addition to requiring object detection, the visually impaired person also requires real-time detection to receive information. In this paper, we develop a design system to assist the visually impaired person in a shopping place. This system performs image recognition equipped with a GPS tracking system that can detect lots of object in one scene. We conducted the preliminary experiments to verify performance of the proposed method and confirm validity of the system.
Diode Based Reconfigurable Microwave Filters for Cognitive Radio Applications: A Review

The cognitive radio paradigm for developing next-century wireless communication systems is rapidly entering the mainstream, and various aspects of it are currently being applied in 5G technology, aeronautical engineering, military communications, emergency, and public safety applications, satellite communication, and healthcare. Cognitive radio focuses on the existence of software defined radio architectures that allow dynamic reconfiguration. Many researchers have taken initiatives in the last decade to achieve the reconfiguration ability in cognitive radio systems to support the concept of dynamic spectrum access. As cognitive radio adapts dynamic spectrum allocation for its users, the physical implementation requires reconfigurable filters that can alter the carrier frequencies and bandwidth. Although there are many ways to reconfigure filter operation, diode based reconfiguration has received utmost attention among researchers because of its shorter response delay and easy implementation. In the last decade, researchers have reported several diode-based reconfigurable filters, including their characteristics such as filter function, filter combination, tuning range, variation in bandwidth, isolation, and resonance. However, to examine the potential of these filters in the application of cognitive radio, a comprehensive review needs to be pursued. In this review article, the descriptions of several diode based reconfigurable filters are illustrated with their exhibiting characteristics. The detailed information provided in this article has disclosed that primarily three different types diode based reconfigurable filters have been reported by researchers: Tunable, Switchable, and Hybrid (Both Tunable and Switchable). It is also found that each type of reconfiguration can further be segregated in terms of filter function, centre frequency variation, and bandwidth variation. The detailed categorization of the reconfiguration presented in this paper provides a systematic approach to select the correct reconfigurable filter for the desired frequency reconfiguration in cognitive radio.
Multi-Objective Adaptive Robust Voltage/VAR Control for High-PV Penetrated Distribution Networks

In active distribution networks, high penetration of distributed photovoltaic power generation may cause voltage fluctuation and violation issues. To conquer the challenges, this paper firstly proposes a load-weighted voltage deviation index (LVDI) to quantify network voltage deviation. Secondly, this paper proposes a multi-objective adaptive voltage/VAR control (VVC) framework which coordinates multiple devices in multiple timescales to minimize voltage deviation and power loss simultaneously. Then, a multi-objective adaptive robust optimization method is proposed to obtain robust Pareto solutions under uncertainties. Accordingly, solution algorithms based on different multi-objective programming algorithms and a column-and-constraint generation algorithm are developed and systematically compared. The proposed method is verified through comprehensive tests on the IEEE 123-bus system and simulation results demonstrate high effectiveness of the LVDI, high efficiency of the solution algorithms and full operating robustness of the proposed VVC method against any uncertainty realization.
Spatial GNSS Spoofing Against Drone Swarms With Multiple Antennas and Wiener Filter

Spoofing of global-navigation satellite system (GNSS) signals to induce a target position estimate is a relevant security threat to the navigation of drones. However, spoofing multiple drones simultaneously as they move in a swarm, without disrupting their formation, is a complex task. In this paper, we propose to transmit spoofing signals from the ground, such that the fake position can be estimated in any point of an area of the plane where the swarm is moving. To this end we filter the satellite-generated GNSS signals with a multidimensional linear filter, and transmit the filtered signal with multiple ground antennas. The multidimensional filter is designed according to a generalized Wiener-filter criterion, such that the fake signal is accurately reproduced (in terms of mean squared error (MSE)) in the whole spoofed area. We investigate the impact of various design parameters (among others, the size of the spoofed area, the number of ground antennas, and the number of spoofed satellites) on both the MSE, and the probability of acquisition of the spoofed signal by the drones.
Convolutional Neural Network Based Classification of App Reviews

An app store (i.e., Google Play) is a platform for mobile apps for almost every software and service. App stores allow users to browse and download apps and facilitate developers to keep an eye on their apps by providing ratings and reviews of the apps. App reviews may include the user’s experience, information about bugs, request for new features, or rating of the app in word. The manual categorization of app reviews is critical and time-consuming for developers. Automatic classification of app reviews may help developers especially for fixing bugs on time. In this perspective, several approaches have been proposed for the automatic classification of reviews. However, none of them exploits the non-textual information of app reviews. In this paper, we propose a deep learning based approach for the classification of app reviews. It does not only leverage non-textual information of app reviews but also exploits a deep learning technique that has proved more accurate for the text classification in various domains. The approach first extracts textual and non-textual information of each app review, preprocesses the textual information, computes the sentiment of app reviews using Senti4SD, and determines the history of the reviewer includes the total number of reviews posted by the reviewer, and his submission rate (i.e., what percentages of his review have been submitted for the associated app). Second, we create a digital vector against each app review. Finally, we train a deep learning based multi-class classifier to classify app reviews. The proposed approach is evaluated on a public dataset, and the results suggest that it significantly improves the state of the art. It improves average precision from 75.72% to 95.49%, average recall from 69.40% to 93.94%, and f-measure from 72.41% to 94.71%, respectively.
Cost-Effective Implementation of Air-Filled Waveguides on Printed Circuit Boards

This paper presents a new cost-effective method to produce air-filled waveguides out of a printed circuit board (PCB) with a milling machine and how to implement them with standard PCB technology. For this purpose, low cost substrates like FR-4 are used. For the baseboard to waveguide transition in the E-Band (60-90GHz) a WR12 waveguide connector [1] was used. The WR12 waveguide was manufactured and analyzed on mechanical deviations. The RF performance in the E-Band was measured and compared to common PCB waveguides like microstrip lines (MS) and grounded coplanar waveguides (GCPW).
Multitask Deep Neural Networks for Tele-Wide Stereo Matching

In this article, we propose deep learning solutions for the estimation of the real world depth of elements in a scene captured by two cameras with different field of views. We consider a realistic smart-phone scenario, where the first field of view (FOV) is a wide FOV with $1 \times $ the optical zoom, and the second FOV is contained in the first FOV captured by a tele zoom lens with $2 \times $ the optical zoom. We refer to the problem of estimating the depth for all elements in the union of the FOVs which corresponds to the Wide FOV as ‘tele-wide stereo matching’. Traditional approaches can only estimate the disparity or depth in the overlapped FOV, corresponding to the Tele FOV, using stereo matching algorithms. To benchmark this novel problem, we introduce a single-image inverse-depth estimation (SIDE) solution to estimate the disparity from the image corresponding to the union Wide FOV only. We also design a novel multitask tele-wide stereo matching deep neural network (MT-TW-SMNet), which is the first to combine the stereo matching and the single image depth tasks in one network. Moreover, we propose multiple methods for the fusion between the above networks. For example, we have input feature fusion, that utilizes the disparity estimated by stereo-matching as an additional input feature for SIDE. We also designed networks for decision fusion, that deploys a stacked hour glass (SHG) network for fusion and refinement of the disparity maps from both the SIDE network and the MT-TW-SMNet. These fusion schemes significantly improve the accuracy. Experimental results on KITTI and SceneFlow datasets demonstrate that our proposed approaches provide a reasonable solution to the tele-wide stereo matching problem. We demonstrate the effectiveness of our solutions in generating the Bokeh effect on the full Wide FOV.