Reviewerly/exHarmony huggingface dataset:
Finding a ‘New’ Needle in the Haystack: Unseen Radio Detection in Large Populations Using Deep Learning

Radio frequency fingerprinting enhances security and privacy of wireless networks and communications by learning and extracting unique characteristics embedded in transmitted signals. Deep learning-based approaches learn radio fingerprints without hand-engineering features. One persisting drawback in deep learning methods is they identify only devices that are previously observed in a training set: if a radio signal from a new, unseen, device is passed through the classifier, the source device will be classified as one of the known devices. We propose a novel approach that facilitates new class detection without retraining a neural network, and perform extensive analysis of the proposed model both in terms of model parameters and real-world datasets. We accomplish this by first breaking down a longer transmission burst into smaller slices, and assessing classifier confidence on a new transmission based on per slice statistics: our approach detects a new device with 76% accuracy, while reducing the classification accuracy of 500 previously seen devices by no more than 10%.
The Impact of Age, Gender, and Educational level on the Cybersecurity Behaviors of Tertiary Institution Students: An Empirical investigation on Malaysian Universities

Abstract A lot of cybersecurity breaches occur due to human errors. Tertiary institutions need to enhance students’ cybersecurity awareness and capabilities, hence promoting safe cybersecurity behaviors. Socio-psychological factors affects students’ cybersecurity behaviors. We explore to what extent age, gender and educational level, plays a role in mediating the factors affecting tertiary institution students’ cybersecurity behaviors. A cross-sectional survey was conducted among 340 students and Structural Equation Modelling was employed for evaluating impacts. Data analysis was conducted via SPSS (V.25). Results show that students cybersecurity behaviors varies based on Age for factors such as: Perceived Severity, Peer Behavior, Familiarity with Cyber Threats, Response Efficacy and Perceived Vulnerability. Gender effects existed in Security Self Efficacy, Computer Skills, Cybersecurity Behaviors, Perceived Severity, and little effects in Prior Experiences with Computer Security Practices. Educational level differences existed in Cues to Action and Familiarity with Cyber-Threats. Age, gender and educational level are important factors in mediating students’ cybersecurity behaviors. Practically, our findings can instigate the need for specific/focused cybersecurity training and interventions for students in the tertiary institutions. It can also help cybersecurity training units in the tertiary institutions to target very vital components of cybersecurity behavior model, hence improving the cybersecurity behaviors of the students.
An optical-energy model for optimizing the geometrical layout of solar photovoltaic arrays in a constrained field

The number of rows of photovoltaic (PV) modules in a field are limited by the area available for installation. With the objective of achieving maximum solar energy collection by the modules, determining the geometric layout becomes an optimization problem that couples the tilt angle and inter-row spacing. The existing optimization methods are simplistic as they consider tilt angle and inter-row spacing as constant values for all rows. This paper presents an optical-energy model for determining the optimal geometrical layouts of PV arrays by considering different tilt angles and row spacings for every row, given a field constraint. It is comprised of two sub-models: (i) an optical sub-model that determines mutual shading and (ii) an energy sub-model that evaluates the yearly solar energy collection. Optimization was performed by proposing random layouts for the model and using the one that yielded the maximum yearly collection. A situation in which the proposed model can be used as a powerful tool for optimization is explained via a case study conducted in Auckland, New Zealand. The effects of maintaining either a constant tilt angle or a safe row-spacing on year-round performance are investigated.
Anticounterfeiting in Pharmaceutical Supply Chain by establishing Proof of Ownership

Drug counterfeiting is a serious and increasing critical issue worldwide, which puts the health of consumers and the general population at risk. WHO estimates that the world's counterfeit drug market make billions of dollars annually. It is due to the inadequate supply chain in the developing countries that one in the drug is counterfeit. People buying such medicines not only waste their money but also put their life and health in danger. A drug's ownership changes from manufacturer to distributor and then to the pharmacist before reaching the customer. The manufacturers don't know how their drug is being used. At the same time, consumers don't know whether the drug has come from the rightful source. It is due to the counterfeit drug in the supply chain manufacturing companies and countries face huge economic loss. The RFID technology has been effective for over a decade providing anti-counterfeit measures to the supply chain. However, the RFID tags cannot be guaranteed genuineness, since the tags can be cloned easily. In this paper, we propose how blockchain can be utilized to not only prevent drug counterfeiting but also add traceability, security, and visibility to the pharmaceutical supply chain. For this purpose, a permissioned blockchain is designed to which only trusted parties can join the network and push transactions to the blockchain.
Design of high transmission color filters for solar cells directed by deep Q-learning

In this paper, we have used deep Q-learning networks (DQN) to find a colored coating for solar cells with high transmission. A basic structure with a huge range of possibilities was given to DQN, and it was designed to find the best structures fitting our purpose. The number of possibilities given to the model was more than 12 billion. Our model could find the structures with higher transmission and deeper colors compared to other human researchers in around 32,000 steps. Our numerical results cover a large area of color gamut which can be used for aesthetic purposes.
Codes, Differentially $\delta$ -Uniform Functions, and $t$ -Designs

Boolean functions, coding theory and t-designs have close connections and interesting interplay. A standard approach to constructing t-designs is the use of linear codes with certain regularity. The Assmus-Mattson Theorem and the automorphism groups are two ways for proving that a code has sufficient regularity for supporting t-designs. However, some linear codes hold t-designs, although they do not satisfy the conditions in the Assmus-Mattson Theorem and do not admit a t-transitive or t-homogeneous group as a subgroup of their automorphisms. The major objective of this paper is to develop a theory for explaining such codes and obtaining such new codes and hence new t-designs. To this end, a general theory for punctured and shortened codes of linear codes supporting t-designs is established, a generalized Assmus-Mattson theorem is developed, and a link between 2-designs and differentially δ-uniform functions and 2-designs is built. With these general results, binary codes with new parameters and explicit weight distributions are obtained, new 2-designs and Steiner system S(2, 4, 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> ) are produced in this paper.
Do FOMC and macroeconomic announcements affect Bitcoin prices?

Bitcoin, developed by Satoshi Nakamoto after the financial crisis in 2008, has risen in price since the beginning of 2010. To date, many investors have been participating in the Bitcoin market and trading. This study analyzes if the FOMC and macroeconomic announcements affect Bitcoin prices. Various studies have been actively conducted as the cryptocurrency market, including Bitcoin, rapidly develops. However, few studies on the effect of FOMC and macroeconomic announcements on Bitcoin prices are available. If the announcements affect the Bitcoin prices, then investors could consider the conclusions of this study to prepare for price changes.
D4I - Digital forensics framework for reviewing and investigating cyber attacks

Many companies have cited lack of cyber-security as the main barrier to Industrie 4.0 or digitalization. Security functions include protection, detection, response and investigation. Cyber-attack investigation is important as it can support the mitigation of damages and maturing future prevention approaches. Nowadays, the investigation of cyber-attacks has evolved more than ever leveraging combinations of intelligent tools and digital forensics processes. Intelligent tools (e.g., YARA rules and Indicators of Compromise) are effective only when there is prior knowledge about software and mechanisms used in the cyber-attack, i.e., they are not attack-agnostic. Therefore, the effectiveness of these intelligent tools is inversely proportional to the number of the never-seen-before software and mechanisms utilized. Digital forensic processes, while not suffering from such issue, lack the ability to provide in-depth support to a cyber-attack investigation mainly due to insufficient detailed instructions in the examination and analysis phases. This paper proposes a digital forensics framework for reviewing and investigating cyber-attacks, called D4I, which focuses on enhancing the examination and analysis phases. First, the framework proposes a digital artifacts categorization and mapping to the Cyber-Kill-Chain steps of attacks. Second, it provides detailed instructing steps for the examination and analysis phases. The applicability of D4I is demonstrated with an application example that concerns a typical case of a spear phishing attack.
Real-time UAV Detection based on Deep Learning Network

This paper presents deep learning-based YOLO (You only look once), for the detection of an unmanned aerial vehicle (UAV). In common practice, the creation of own data set is an extensive and hectic task, that takes a long time because it requires proper resolution images from different angles. These issues make the data set creation an important task. Implementation of YOLOv2 and YOLOv3 is done on the own created data set for the real-time UAV's detection and to benchmark the performance of both models in terms of mean average precision (MAP) and accuracy. For the specifically created data set made, YOLOv3 is outperforming YOLOv2 both in MAP and accuracy.
Acoustic data augmentation for Mandarin-English code-switching speech recognition

Code-switching (CS) is a multilingual phenomenon where a speaker uses different languages in an utterance or between alternating utterances. Developing large-scale datasets for training code-switching acoustic and language models is challenging and extremely expensive. In this paper, we focus on the acoustic data augmentation for the Mandarin-English CS speech recognition task. Effectiveness of conventional acoustic data augmentation approaches are examined. More importantly, we propose a CS acoustic event detection system based on the deep neural network to extract real code-switching speech segments automatically. Then, the semi-supervised and active learning techniques are investigated to generate transcriptions of these segments. Finally, code-switching speech synthesis system is introduced to further enhance the acoustic modeling. Experimental results on the OC16-CE80 data, a Mandarin-English mixlingual speech corpus, demonstrate the effectiveness of the proposed methods.
Deep discrete hashing with pairwise correlation learning

Hashing technology plays an important role in large-scale visual search due to its low memory and fast retrieval speed. Most existing deep hashing approaches first leverage the continuous relaxation strategy to learn continuous approximate codes, and then transform them into discrete hash codes by separating quantization operations, which results in the suboptimal problem of hash codes and ultimately affects the performance of image retrieval. To solve this problem, we propose a novel deep discrete hashing approach with pairwise labels, namely Pairwise Correlation Discrete Hashing (PCDH), to leverage the pairwise correlation of deep features and semantic supervised information to directly guide discrete hashing codes learning. Firstly, we integrate discrete hash code learning and deep features learning in a unified network framework, which can utilize the semantic supervision to guide discrete hash codes learning. Secondly, we design a novel pairwise correlation constraint to perform pairwise correlation learning of deep features. Thirdly, we develop a novel pairwise construction module to mine good pairwise samples for discrete hash codes learning. Extensive experimental results show that the proposed PCDH approach achieves superior performance over other recent state-of-the-art hashing approaches.
Analyzing the development of the third-generation biodiesel production from microalgae by a novel hybrid decision-making method: The case of Iran

Recently, the increase in energy demand motivated the researchers to study renewable energy sources. A significant portion of energy is consumed in transportation. Biofuels such as biodiesel are significant renewable sources of energy that can be utilized in this sector. In Asian countries such as Iran, with widespread use of diesel engines, biodiesel engines are likely to be combined in the system without modifications, since they share similar functionalities with diesel engines. Biodiesels can be extracted from different feedstocks such as microalgae that can grow in Iran. To perform a thorough analysis of the development of biodiesel in Iran, this paper applied strength-weakness-opportunity-threat (SWOT) structure to obtain suitable strategies. These strategies are ranked using a group decision-making method in fuzzy linguistic environment. Step-wise weight assessment ratio analysis (SWARA) is employed to weight the criteria, while fuzzy cognitive map (FCM) is utilized to consider casual relationships. Finally, weighted aggregated sum product assessment (WASPAS) is utilized to rank the strategies. As indicated in the results, a necessity in third-generation biodiesel production from microalgae is support by government with subsidy and financial policies. Additionally, developing technologies and encouraging private sectors to invest in this area are important strategies as well.
Internet of Things for Sustainability: Perspectives in Privacy, Cybersecurity, and Future Trends

In the sustainability IoT, the cybersecurity risks to things, sensors, and monitoring systems are distinct from the conventional networking systems in many aspects. The interaction of sustainability IoT with the physical world phenomena (e.g., weather, climate, water, and oceans) is mostly not found in the modern information technology systems. Accordingly, actuation, the ability of these devices to make changes in real world based on sensing and monitoring, requires special consideration in terms of privacy and security. Moreover, the energy efficiency, safety, power, performance requirements of these device distinguish them from conventional computers systems. In this chapter, the cybersecurity approaches towards sustainability IoT are discussed in detail. The sustainability IoT risk categorization, risk mitigation goals, and implementation aspects are analyzed. The openness paradox and data dichotomy between privacy and sharing is analyzed. Accordingly, the IoT technology and security standard developments activities are highlighted. The perspectives on opportunities and challenges in IoT for sustainability are given. Finally, the chapter concludes with a discussion of sustainability IoT cybersecurity case studies.
Adversarial Learning for Multiscale Crowd Counting Under Complex Scenes

In this article, a multiscale generative adversarial network (MS-GAN) is proposed for generating high-quality crowd density maps of arbitrary crowd density scenes. The task of crowd counting has many challenges, such as severe occlusions in extremely dense crowd scenes, perspective distortion, and high visual similarity between the pedestrians and background elements. To address these problems, the proposed MS-GAN combines a multiscale convolutional neural network (generator) and an adversarial network (discriminator) to generate a high-quality density map and accurately estimate the crowd count in complex crowd scenes. The multiscale generator utilizes the fusion features from multiple hierarchical layers to detect people with large-scale variation. The resulting density map produced by the multiscale generator is processed by a discriminator network trained to solve a binary classification task between a poor quality density map and real ground-truth ones. The additional adversarial loss can improve the quality of the density map, which is critical to accurately estimate the crowd counts. The experiments were conducted on multiple datasets with different crowd scenes and densities. The results showed that the proposed method provided better performance compared to current state-of-the-art methods.
Social Recommendation With Learning Personal and Social Latent Factors

Due to leveraging social relationships between users as well as their past social behavior, social recommendation becomes a core component in recommendation systems. Most existing social recommendation methods only consider direct social relationships among users (e.g., explicit and observed social relations). Recently, researchers proved that indirect social relationships can be effective to improve the recommendation quality when users only have few social connections, because it can identify the user interesting group even though the users have no observed social connection. In the literature, separate two-stage methods are studied, but they cannot explicitly capture the natural relationship between indirect social relations and latent user/item factors. In this paper, the main contribution is to propose a new joint recommendation model taking advantage of the Indirect Social Relations detection and Matrix Factorization collaborative filtering on social network and rating behavior information, which is called as InSRMF. In our work, the user latent factors can simultaneously and seamlessly capture user's personal preferences and social group characteristics. To optimize the InSRMF model, we develop a parallel graph vertex programming algorithm for efficiently handling large scale social recommendation data. Experiments based on four real-world datasets (Ciao, Epinions, Douban and Yelp) are conducted to demonstrate the performance of the proposed model. The experimental results have shown that InSRMF has ability to mine the proper indirect social relations and improve the recommendation performance compared with the testing methods in the literature, especially on the users with few social neighbors, Near-cold-start Users, Pure-cold-start Users and Long-tail Items.
3D object detection based on sparse convolution neural network and feature fusion for autonomous driving in smart cities

People in cities are suffering from traffic congestion and air pollution in daily life partly due to a great number of private cars, and always face the danger of accidents, so autonomous driving is developed by many institutes and companies especially in recent years. Autonomous driving will play an import role in the future smart cities, reduce the time and economic cost of the whole society, and be helpful for the sustainability of the city and society. A significant task for autonomous driving is to detect surrounding objects accurately in real-time, including car, pedestrian, cyclist, etc. In this paper, we propose one end-to-end three dimensional (3D) object detection method based on voxelization, sparse convolution, and feature fusion. The proposed method exploits only point cloud as input, and it has two key components—small voxels and efficient feature fusion. Instead of utilizing extra networks to transform voxels, we directly average the points within each voxel as their feature representation. To enrich features for prediction, we have designed a two-step feature fusion method called fusion of fusion network that can combine information of multiple scales and 3D space. We have submitted to the official test server of the 3D detection benchmark—KITTI, and achieved state-of-the-art performance especially in the Cyclist class. Besides, detection speed of our method achieves 0.05 s/frame with a 2–4 fold runtime improvement against state-of-the-art methods due to its simple and compact architecture.
Methods of AI for Multimodal Sensing and Action for Complex Situations

Artificial intelligence (AI) seeks to emulate human reasoning, but is still far from achieving such results for actionable sensing in complex situations. Instead of emulating human situation understanding, machines can amplify intelligence by accessing large amounts of data, filtering unimportant information, computing relevant context, and prioritizing results (for example, answers to human queries) to provide human‐machine shared context. Intelligence support can come from many contextual sources that augment data reasoning through physical, environmental, and social knowledge. We propose a decisions‐to‐data multimodal sensor and action through contextual agents (human or machine) that seek, combine, and make sense of relevant data. Decisions‐to‐data combines AI computational capabilities with human reasoning to manage data collections, perform data fusion, and assess complex situations (that is, context reasoning). Five areas of AI developments for context‐based AI that cover decisions‐to‐data include: (1) situation modeling (data at rest), (2) measurement control (data in motion), (3) statistical algorithms (data in collect), (4) software computing (data in transit), and (5) human‐machine AI (data in use). A decisions‐to‐data example is presented of a command‐guided swarm requiring contextual data analysis, systems‐level design, and user interaction for effective and efficient multimodal sensing and action.
The blended learner: digitalisation and regulated freedom - neoliberalism in the classroom

The digital revolution in education is sustained by the belief that digital technologies carry with them the potential for an ethical renewal of learning according to the neoliberal principle of freedom. In this article we problematise the ethical effects of the encounter between Blended Learning as a techno-educational form and neoliberalism, focusing as an exemplary case on the introduction of an innovative Blended Learning model in a US Charter School funded by the Michael and Susan Dell Foundation. Connecting the literatures on the neoliberalisation and digitalisation of education, we show how in this case it is possible to observe a paradoxical kind of ethical renewal, emerging from the intertwining between the individualizing possibilities opened up by digital technologies, freedom production as a neoliberal project of world-making and the related search for new and specific mechanisms of security. We argue that digital technologies are in a paradoxical relationship with neoliberalism, crystallising the conditions of possibility for a neoliberalisation of education, but also having the potential to betray the promise of educational freedom and differentiation. Far from making any claim of determination, we outline the need for future research that explores the enactment of Blended Learning and questions its effects on education.