Reviewerly/exHarmony huggingface dataset:
Integration of Matched Filtering within the RF-DNA Fingerprinting Process

Radio-Frequency Distinct Native Attributes (RFDNA) fingerprinting is a Specific Emitter Identification (SEI) approach developed as a mechanism for enhancing wireless network security. RF-DNA fingerprinting exploits unintentional and distinctively unique Physical (PHY) Layer characteristics that are imparted upon the waveform during its generation and transmission. The RF-DNA fingerprinting approach specifically leverages those PHY Layer characteristics that color a fixed, known sequence of waveform symbols (e.g., IEEE 802.11a preamble). This makes the RF-DNA fingerprinting process well suited to matched filter (MF) integration, because (i) both are generated from a fixed sequence and (ii) the MF maximizes SNR while RF-DNA based radio identification performance is degraded as SNR decreases. In this work, the MF is applied prior to signal transformation, which results in four RF-DNA fingerprint generation scenarios: Fast Fourier Transform (FFT) with a MF (FFT-MF), FFT with an All-Pass Filter (FFT- APF), Gabor Transform with a MF (GT-MF), and GT with an APF (GT-APF). Performance of these four scenarios is assessed using average percent correct classification over degrading signal- to-noise channel conditions. When considering classification performance and IoT device constraints (e.g., memory, computation resources and time), RF-DNA fingerprints generated using the FFT-MF scenario proved superior to the other three.
A Framework for Detecting Intentions of Criminal Acts in Social Media: A Case Study on Twitter

Criminals use online social networks for various activities by including communication, planning, and execution of criminal acts. They often employ ciphered posts using slang expressions, which are restricted to specific groups. Although literature shows advances in analysis of posts in natural language messages, such as hate discourses, threats, and more notably in the sentiment analysis; research enabling intention analysis of posts using slang expressions is still underexplored. We propose a framework and construct software prototypes for the selection of social network posts with criminal slang expressions and automatic classification of these posts according to illocutionary classes. The developed framework explores computational ontologies and machine learning (ML) techniques. Our defined Ontology of Criminal Expressions represents crime concepts in a formal and flexible model, and associates them with criminal slang expressions. This ontology is used for selecting suspicious posts and decipher them. In our solution, the criminal intention in written posts is automatically classified relying on learned models from existing posts. This work carries out a case study to evaluate the framework with 8,835,290 tweets. The obtained results show its viability by demonstrating the benefits in deciphering posts and the effectiveness of detecting user’s intention in written criminal posts based on ML.
A Convolutional Neural Network Approach Towards Self-Driving Cars

A convolutional neural network (CNN) approach is used to implement a level 2 autonomous vehicle by mapping pixels from the camera input to the steering commands. The network automatically learns the maximum variable features from the camera input, hence requires minimal human intervention. Given realistic frames as input, the driving policy trained on the dataset by NVIDIA and Udacity can adapt to real-world driving in a controlled environment. The CNN is tested on the CARLA open-source driving simulator. Details of a beta-testing platform are also presented, which consists of an ultrasonic sensor for obstacle detection and an RGBD camera for real-time position monitoring at 10Hz. RRT*-Connect algorithm is used for path planning. Arduino Mega and Raspberry Pi are used for motor control and processing respectively to output the steering angle, which is converted to angular velocity for steering.
Real-Time Human Motion Detection, Tracking and Activity Recognition with Skeletal Model

Human activity recognition with 3Dskeletal model has been attracted in a lot of application area. Representations of human based on 3D perception have been occurred prevalent problems in activity recognition. In recent work with RGB-Depth cameras, expensive wearable sensors and illuminator array have been used to construct the 3D human skeleton model in recognition system. But these systems have been defined specific lightening condition, limited range. and great constraint in outdoor applications. To overcome this restriction, the proposed system is considered on the real-time video sequences of the human movement to understand human behavior in indoor and outdoor environment. The proposed method is constructed human detection and motion tracking by using framewise displacement and recognition is based on skeletal model with deep learning framework. The result is to become an efficient detection, tracking and recognition system for real-time human motion. The performance and accuracy of the system is analyzed with the various videos to show the results.
Bangla Intelligence Question Answering System Based on Mathematics and Statistics

The Bangla Informative Question Answering System (BIQAS) is a significant Machine Learning (ML) technique that helps a user to trace relevant information by Bengali Natural Language Processing (BNLP). In this research paper, we have applied three mathematical and statistical procedures for BIQAS based on question answering data. These procedures are cosine similarity, Jaccard similarity, and Naive Bayes algorithm. The cosine similarity has interacted with dimension reduction technique SVD on user questions and questions answering data in order to reduce the space and time complexity. These procedures of this research are separated into two parts: pre-processing data and establishment of a relationship between user’s questions and contained informative questions. We have got 93.22% accurate answer by using cosine similarity, 84.64% by Jaccard similarity and 91.31% by Naïve Bayes algorithm.
SC-DDPL: A Novel Standard-Cell Based Approach for Counteracting Power Analysis Attacks in the Presence of Unbalanced Routing

In this paper we present the Standard Cell Delay-based Dual-rail Pre-charge Logic (SC-DDPL), a novel logic style which is able to counteract Power Analysis Attacks (PAAs) also in the presence of capacitive mismatch at the output of dual-rail gates. The SC-DDPL is based on a standard-cell design flow and it is suitable to be implemented on ASICs or FPGAs without any routing constraint on differential lines, supporting the Time Enclosed Logic protocol along with a DPL structure. The security provided by SC-DDPL has been firstly investigated in simulation on some basic logic gates, designed adopting a commercial 40nm CMOS technology. Simulated experiments have highlighted the capability of SC-DDPL gates to guarantee a high-level of security also in presence of extreme capacitive mismatch, exhibiting strongly reduced NED/NSD metrics, as well as a reduction of the FED, compared to a reference RTZ-based WDDL implementation. In order to compare the proposed logic against other state-of-the-art countermeasures we have implemented a 4bit PRESENT crypto-core adopting several logic styles, evaluating different security metrics on a 65nm Intel Cyclone-IV FPGA. Experimental results have confirmed that the SC-DDPL outperforms other gate-level countermeasures in terms of security metrics with a reasonable area and power consumption overhead.
Unsupervised domain adaptation for activity recognition across heterogeneous datasets

Sensor-based human activity recognition is to recognise human daily activities through a collection of ambient and wearable sensors. It is the key enabler for many healthcare applications, especially in ambient assisted living. The advance of sensing and communication technologies has driven the deployment of sensors in many residential and care home settings. However, the challenge still resides in the lack of sufficient, high-quality activity annotations on sensor data, which most of the existing activity recognition algorithms rely on. In this paper, we propose an Unsupervised Domain adaptation technique for Activity Recognition, called UDAR, which supports sharing and transferring activity models from one dataset to another heterogeneous dataset without the need of activity labels on the latter. This approach has combined knowledge- and data-driven techniques to achieve coarse- and fine-grained feature alignment. We have evaluated UDAR on five third-party, real-world datasets and have demonstrated high recognition accuracy and robustness against sensor noise, compared to the state-of-the-art domain adaptation techniques.
MERR

This paper proposes a new defensive technique for memory, especially useful for long-living objects on Non-Volatile Memory (NVM), or called Persistent Memory objects (PMOs). The method takes a distinctive perspective, trying to reduce memory exposure time by largely shortening the overhead in attaching and detaching PMOs into the memory space. It does it through a novel idea, embedding page table subtrees inside PMOs. The paper discusses the complexities the technique brings, to permission controls and hardware implementations, and provides solutions. Experimental results show that the new technique reduces memory exposure time by 60% with a 5% time overhead (70% with 10.9% overhead). It allows much more frequent address randomizations (shortening the period from seconds to less than 41.4us), offering significant potential for enhancing memory security.
Teachers’ Attitudes Towards the Introduction of ICT in Ecuadorian Public Schools

This article focus on the attitudes of teachers about the implementation of information and communication technologies (ICT) in Ecuadorian schools. While the advantages of ICT in education are well researched, much less is known about the attitudes of teachers towards them. This article provides a qualitative interpretative analysis of the opinions and attitudes of 56 teachers in Ecuador. The produced results acknowledged that implementation of ICT in schools is difficult task due to a lack of materials and resources, high organisational and social demands perceived by teachers, competition caused by younger teachers and students, and job and personal insecurity. Finally, this article suggests some aspects that should be considered and some actions that should be taken in order to minimize these negative aspects.
An Abnormal Transaction Detection Mechanim on Bitcoin

In recent years, illegal activities such as money laundering using cryptocurrency represented by bitcoin have been emerging. The anonymity, two-way convertibility and transnational of bitcoin are used to "launder" illegal income. Some bitcoin theft incidents are also associated with money laundering. Hackers "launder" the stolen bitcoins and eventually convert them into legal property. In this paper, we explore whether these illegal activities can be detected. First, we mine the user characteristics from the original transaction data of bitcoin. Second, the user characteristics are classified to distinguish normal users from abnormal users. Third, Gaussian Mixture Model is used to cluster users to find suspicious users. Finally, we detect the abnormal transactions among the suspicious users.
Distant augmented reality: Bringing a new dimension to user experience using drones

Augmented Reality (AR) is a live view that blends real world images with virtual information such as 3D models or sounds. AR is used to virtually reconstruct the ancient buildings of cultural heritage sites. Such sites can be presented to tourist groups in the form they were built in the past. AR can be implemented using mobile phones, tablets or smart glasses. Here, we present a new dimension to AR for cultural heritage using drones. We name it “Distant Augmented Reality”. The goal of this study is to augment video imagery acquired using drones with 3D models of the Roman Baths which is an important cultural heritage site in Turkey. For this purpose, two different tracking techniques were implemented: GPS based drone tracking and monocular camera based scene tracking. With these two techniques drone position and orientation was detected and tracked for augmenting the 3D model of the baths.
Digital Forensics: Maintaining Chain of Custody Using Blockchain

The fundamental aim of digital forensics is to discover, investigate and protect an evidence, increasing cybercrime enforces digital forensics team to have more accurate evidence handling. This makes digital evidence as an important factor to link individual with criminal activity. In this procedure of forensics investigation, maintaining integrity of the evidence plays an important role. A chain of custody refers to a process of recording and preserving details of digital evidence from collection to presenting in court of law. It becomes a necessary objective to ensure that the evidence provided to the court remains original and authentic without tampering. Aim is to transfer these digital evidences securely using encryption techniques.
A solution for priority-based multi-robot path planning problem with obstacles using ant lion optimization

This paper provides a Priority-based Optimized-Hybrid Asynchronous Centralized and Decentralized (POHACD) algorithm for multi-robot path planning (MRPP) issue, where multiple robots communicate with each other, as well as through collision check makes their decision. The priority-based approach provides priority to each robot thus bypassing another robot in conflict situation. Optimization lies in terms of minimal path length. The proposed hybrid asynchronous algorithm leverages the centralized approach to improve the overall system efficiency and distributed approach to reduce communication overhead, which automatically reduces execution time. This algorithm prevents unnecessary communication by transmitting global information and in the meantime limits specific information that is only in the interest of certain robots. By carrying out the simulations, it has been demonstrated that the ant lion optimization for prioritized planning performs better in all the roadmaps.
Use and Interpretation of Emoji in Electronic-Mediated Communication: A Survey

Among the pictorial representations that have succeeded in electronic-mediated communication (EMC), the rise of emoji is of special interest, as they have widely spread around the world. This article reports on the results of a survey conducted among students and staff of a large Spanish university (63% women) on the use of emoticons and emoji and their interpretation. Results confirm the preference for emoji over emoticons. The responses also disclosed emerging norms on the use and functions of these pictographs in EMC: Emoji are considered more “expressive” than emoticons, and messages without these cues are judged as “rude.” Regarding the interpretation of emoji, the survey shows that it heavily relies on preexisting iconography, even if new visual conventions are developing.
A Comparative Study of Deep Learning Methods for Spam Detection

Since the last decade, internet plays an imperative and vital role in the creation and retrieval of colossal amounts of information. With ever-increasing advancements in technological field and creation of data at an exponential rate, impertinent or irrelevant data is proliferating at a vast scale in commensuration with relevant data. Moreover, the usage of mobile phones has increased drastically, and phones are becoming an evident part of everyone's lives. With this, there is a notable increase in the number of spam messages from spammers. According to recent statistics, 96% of Indians receive unsolicited text messages every day. SMS spam is any unwanted or unsolicited text note in the form of weblink, promotional message or irrelevant text sent uncritically and non-selectively to your mobile phone, regularly for advertising purposes. The surge in unsolicited information across all platforms including mobile text messages and emails has created an expedited need for the advancement and refinement of more reliable filters to counteract the spam in these messages. Traditionally, rule-based approach is employed to counteract spam messages. According to this approach, a set of rules are employed on the messages by some authority manually. By this method, no favorable or assuring results will be shown because the rules need to regularly be restructured based on the source of spam messages, which is an arduous process. Instead, we use deep learning methods that are efficient and does not require any rules. Deep learning models require a set of training dataset samples to learn the rules from these SMS messages and build a text classifier that efficiently classifies spam from these messages. This paper presents a systematic review of employing deep learning methods namely, convolutional neural network and recurrent neural network on huge corpus of SMS texts to build a spam classifier that classifies messages as ham or spam.
The influence of the solar radiation database and the photovoltaic simulator on the sizing and economics of photovoltaic-diesel generators

This study evaluated the outcome differences when adopting five different solar irradiation databases on the sizing of hybrid solar photovoltaic-diesel generators designed to supply electricity to isolated minigrids. To do this, the two most widely adopted photovoltaic (PV) simulation packages in the market, namely PVsyst® and HOMER Energy® were used. The different origins, data timespan, space and time resolution, of the five most relevant solar irradiation databases available for the region were compared. A case study was presented to illustrate the influences of the solar irradiation database and the solar simulation tool on the resulting PV generator. Furthermore, the hourly behavior of the energy supply to an existing load in a minigrid in the Brazilian Amazon was evaluated, together with the savings in diesel obtained with the resulting PV generator. Evaluating the five options of solar radiation databases, for the same PV plant configuration, variations of up to 19.7% were found in the expectation of PV generation. When the simulation software package was varied, the combined effect (radiation database X PV system sizing tool) showed differences of up to 20.6%. This demonstrates that despite having different algorithms, computational tools have a small influence (less than 1%) on results. These combined differences, taking into account the load curve behavior and the total diesel generation capacity installed at the site, resulted in over 100% differences in the optimum PV generator size in the case study. The total savings in diesel fuel, over a 15-years period, ranged from $ 6.5 million to over $ 16 million (>2.5 times) for the smallest PV system. This demonstrates the importance of the correct choice of database. These evaluations can be extended to minigrids of any size elsewhere. The novelty and originality of this study is to demonstrate and quantify for the first time the influence of the solar radiation database and the PV simulator package on the sizing of PV-diesel generators. The consequences of this study are not only of scientific and academic importance, but of economic and commercial interest as well.
Parallelization of the Honeybee Search Algorithm for Object Tracking

Object tracking refers to the relocation of specific objects in consecutive frames of a video sequence. Presently, this visual task is still considered an open research issue, and the computer science community attempted solutions from the standpoint of methodologies, algorithms, criteria, benchmarks, and so on. This article introduces a GPU-parallelized swarm algorithm, called the Honeybee Search Algorithm (HSA), which is a hybrid algorithm combining swarm intelligence and evolutionary algorithm principles, and was previously designed for three-dimensional reconstruction. This heuristic inspired by the search for food of honeybees, and here adapted to the problem of object tracking using GPU parallel computing, is extended from the original proposal of HSA towards video processing. In this work, the normalized cross-correlation (NCC) criteria is used as the fitness function. Experiments using 314 video sequences of the ALOV benchmark provides evidence about the quality regarding tracking accuracy and processing time. Also, according to these experiments, the proposed methodology is robust to high levels of Gaussian noise added to the image frames, and this confirms that the accuracy of the original NCC is preserved with the advantage of acceleration, offering the possibility of accelerating latest trackers using this methodology.
Opinion Spam Detection in Online Reviews Using Neural Networks

People of the Internet era usually rely on online reviews to make decisions about an online purchase, a hotel booking or a car rental and much more, since people believe that making decisions based on other's opinions lead to making the right choice. As writing fake reviews come with monetary gain, the opinion spam activities have increased dramatically on online review websites. Thus opinion spam in reviews has become a big challenge to people to make purchase decisions as well as damages the reputation of review websites. Hence the deceptive opinion spam detection is an essential task in the field of natural language processing. Most of the existing research on opinion spam detection uses the traditional bag-of-words model to represent the review text features and apply standard machine learning models such as Support-Vector Machines or Naïve Bayes as classifiers. There is only a few state of the art methods, which have utilized neural network based methods for spam detection. With the recent advancements in deep learning, there are successful applications of Convolutional Neural Networks (CNN) for natural language processing problems which have achieved improved performance. In this study, a CNN model is developed to detect opinion spam using the features extracted from the pre-trained GloVe: Global Vectors for Word Representation model. Moreover, some word and character level features used in existing research work, are extracted from the text and concatenated with a feature set extracted by the convolutional layers of the model to improve the performance. The proposed model found to outperform the state-of-the-art method and the inclusion of additional features improved the performance further.
Systematic Analysis of the PMBM, PHD, JPDA and GNN Multi-Target Tracking Filters

Multiple-target tracking has increasingly gained attention over the last 60 years, with the data association task being one of the most challenging aspects in sensor data fusion due to its computational burden. Hence, a plethora of algorithms has been proposed to solve this data association problem. However, most approaches are solely evaluated in comparison to algorithms of the same class. Therefore, this paper tries to give an overview and intends to evaluate systematically the four main classes of multiple-target tracking filters, namely the non-Bayesian data association filters, the Bayesian data association filters, the intensity filters and the multi-Bernoulli filters. These four classes are exemplified by respective filters, namely the Global Nearest Neighbor filter, the Joint Probabilistic Data Association filter, the Probability Hypothesis Density filter and the Poisson multi-Bernoulli mixture filter. These four filters are evaluated on two challenging simulated scenarios comprising situations with false measurements as well as birth and death of targets. The performance is assessed using the well-established GOSPA metric. It is shown that the Poisson multi-Bernoulli mixture filter outperforms the other three filters regarding the GOSPA metric in these scenarios, yielding a smaller mean error and deviation in its position estimates. This accuracy comes at the cost of a higher runtime performance, as the other three filter types require less computational time to produce their state estimates.