Reviewerly/exHarmony huggingface dataset:
Do not get fooled: Defense against the one-pixel attack to protect IoT-enabled Deep Learning systems

With the rapid evolution and proliferation of Artificial Intelligence (AI) throughout the past decade, industry-wide Deep Learning (DL) applications within IIoT ecosystems are expected to rise exponentially within this coming decade. However, recent studies have highlighted the extreme sensitivity and vulnerability of modern Deep Neural Networks (DNNs) to adversarial examples. While being imperceptible and benign to the naked human eye, those adversarial perturbations crafted from one-pixel attacks can drastically impact the accuracy of DNNs. The consequences of those adversarial attacks in critical applications such as healthcare and autonomous vehicles can prove disastrous. Therefore, the urge to ensure the robustness and resilience of DL systems against one-pixel attacks has recently started attracting stupendous attention from both academicians and industry professionals. Throughout this paper, we first develop a one-pixel threat model within an IIoT ecosystem and propose a novel image recovery defense mechanism to detect and mitigate one-pixel attacks based on Accelerated Proximal Gradient approach. Experimental results proved that our proposed solution is highly effective and efficient in detecting and mitigating one-pixel attacks in state-of-the-art neural networks, more specifically LeNet and ResNet, using the CIFAR10 and MNIST datasets.
Fast and Effective Retrieval for Large Multimedia Collections

The indexing and retrieval of multimedia content is generally implemented by employing feature graphs. These graphs typically contain a significant number of nodes and edges to reflect the level of detail in feature detection. A higher level of detail increases the effectiveness of the results, but also leads to more complex graph structures. However, graph traversal-based algorithms for similarity are quite inefficient and computationally expensive, especially for large data structures. To deliver fast and effective retrieval especially for large multimedia collections and multimedia big data, an efficient similarity algorithm for large graphs in particular is desirable. Hence, in this paper, we define a graph projection into a 2D space (Graph Code) and the corresponding algorithms for indexing and retrieval. We show that calculations in this space can be performed more efficiently than graph traversals due to the simpler processing model and the high level of parallelization. As a consequence, we demonstrate experimentally that the effectiveness of retrieval also increases substantially, as the Graph Code facilitates more levels of detail in feature fusion. These levels of detail also support an increased trust prediction, particularly for fused social media content. In our mathematical model, we define a metric triple for the Graph Code, which also enhances the ranked result representations. Thus, Graph Codes provide a significant increase in efficiency and effectiveness, especially for multimedia indexing and retrieval, and can be applied to images, videos, text and social media information.
PNU Spoofing: a menace for biometrics authentication systems?

The Pixel Non-Uniformity noise (PNU noise, for short) is a characteristic noise of digital camera sensors that has been originally used a mean to perform Source Camera Identification (SCI), that is, to identify the digital camera that has been used to take an image under scrutiny. Actually, its usage has been extended to other relevant application domains, such as carrying out sensor identification in iris biometrics, to resolve the integrity of a biometric authentication system, in health monitoring systems based on bio-signal processing, and to prevent health fraud scams. As a consequence of this popularity, several counter-forensics techniques have been proposed in the scientific literature to deceive this identification approach and, thus, putting at risk its effectiveness. The goal of this paper is to experiment with some of these counter-forensics techniques and to assess whether they are able or not to deceive the PNU-based identification process. We focus our attention on the particular case where a target image is modified so as to appear as been taken by a different device than the original one (i.e., spoofing). The results of our experiments show that this kind of spoofing is apparently successful in deceiving the PNU-based identification process. However, we also show that the spoofed images retain traces of their originating camera. By leveraging on this information, it is still possible to trace back the device used to take that image. This finding may have a deep impact on the usage of Pixel Non Uniformity (PNU) noise for identification tasks, such as the ones carried out by biometric recognition and monitoring systems, and on the possibility to really cheat them by means of counter-forensics techniques.
Equalities between Combinators to Evaluate Expressions

One of the aims of this work is to revise the known issues of how to obtain the value of the expression using an applicative computing system. Computation and/or symbolic transformations have undoubtedly become one of the dominant trends in modern computer science. In particular, this refers to the execution of the processes of inference of an object with given properties, which is studied with the formation of sets of equalities leading to a target computational model. At the same time, the mathematical theory of computation gets a target equational description, making the process of studying its properties and capabilities more suitable. A model structure based on the method of indexed expressions is developed and applied using the notions of evaluation mapping and assignments. As shown, this may not be considered as generic notion, but derived using the combinators. Thus, the semantics of computation can be derived from the construction of a combinator-as-process. This is the embedding into a system of combinators. A linking system of equalities between combinatorsl is established, which serving as a generic computational model. This allows us to look differently at the previous cognitive ideas of the semantics of computation disabling an evaluation map and assignments and enabling their replacement by a set of equalities between combinators.
Robust Path Planning and Control For Polygonal Environments via Linear Programming

We propose a novel approach for navigating in polygonal environments by synthesizing controllers that take as input relative displacement measurements with respect to a set of landmarks. Our algorithm is based on solving a sequence of robust min-max Linear Programming problems on the elements of a cell decomposition of the environment. The optimization problems are formulated using linear Control Lyapunov Function (CLF) and Control Barrier Function (CBF) constraints, to provide stability and safety guarantees, respectively. The inner maximization problem ensures that these constraints are met by all the points in each cell, while the outer minimization problem balances the different constraints in a robust way. We show that the min-max optimization problems can be solved efficiently by transforming it into regular linear programming via the dualization of the inner maximization problem. We test our algorithm to agents with first and second order integrator dynamics, although our approach is in principle applicable to any system with piecewise linear dynamics. Through our theoretical results and simulations, we show that the resulting controllers: are optimal (with respect to the criterion used in the formulation), are applicable to linear systems of any order, are robust to changes to the start location (since they do not rely on a single nominal path), and to significant deformations of the environment.
Touch Sensing for a Projected Screen Using Slope Disparity Gating

This paper introduces a novel touch sensing system for a projected screen with a single camera. To achieve touch sensing with camera image, there are two challenges to overcome. One is to establish robustness against image processing being misled by projected contents. The other is to realize both touch detection and fingertips localization with a single-camera image. In this work, we use Slope Disparity Gating and capture only the region slightly above the projected screen. This selected region is set geometrically so that a part of the finger is captured when touching, and no finger is captured when not touching. A key feature of this study is that the projector can enable this selective imaging capability in the camera while still displaying a normal projected image to the user's eyes. The projector is both a programmable light source for capturing system and an interface display device for the users. We built a prototype system and touch sensing algorithm, evaluated the touch accuracy, demonstrated robustness to a projected content.
Detecting Anomalies in Semantic Segmentation with Prototypes

Traditional semantic segmentation methods can recognize at test time only the classes that are present in the training set. This is a significant limitation, especially for semantic segmentation algorithms mounted on intelligent autonomous systems, deployed in realistic settings. Regardless of how many classes the system has seen at training time, it is inevitable that unexpected, unknown objects will appear at test time. The failure in identifying such anomalies may lead to incorrect, even dangerous behaviors of the autonomous agent equipped with such segmentation model when deployed in the real world. Current state of the art of anomaly segmentation uses generative models, exploiting their incapability to reconstruct patterns unseen during training. However, training these models is expensive, and their generated artifacts may create false anomalies. In this paper we take a different route and we propose to address anomaly segmentation through prototype learning. Our intuition is that anomalous pixels are those that are dissimilar to all class prototypes known by the model. We extract class prototypes from the training data in a lightweight manner using a cosine similarity-based classifier. Experiments on StreetHazards show that our approach achieves the new state of the art, with a significant margin over previous works, despite the reduced computational overhead. Code is available at https://github.com/DarioFontanel/PAnS.
Putting wake words to bed

'Wake words' such as "Alexa" or "Hey Siri", as conversation design elements, mimic the interactionally rich 'summons-answer' sequence in natural conversation, but their function amounts to little more than a button-push: simply activating the interface. In practice, however, users vocally overdesign their wake words with all the detail of a 'real' interactional summons. We hear users uttering wake words with a specific prosody and intonation, as though for a particular recipient in a particular social/pragmatic context. This presents a puzzle for designers of conversational user interfaces (CUIs). Previous research suggests that expert users simplify their talk when interacting with CUIs, but with wake words we observe the opposite. When users do the extra interactional work of varying their wake words in ways that seem 'recipient designed' for a specific other, does that suggest that designers are successfully eliciting natural interaction from users, or is it violating user expectations? Our two case studies highlight how the mismatch between user expectations and the limitations of how wake words are currently implemented can lead to cascades of interactional trouble, especially in multi-party conversations. We argue that designers should find new ways to activate CUIs that align users' expectations with conversational system design.
On the Role of Sensor Fusion for Object Detection in Future Vehicular Networks

Fully autonomous driving systems require fast detection and recognition of sensitive objects in the environment. In this context, intelligent vehicles should share their sensor data with computing platforms and/or other vehicles, to detect objects beyond their own sensors' fields of view. However, the resulting huge volumes of data to be exchanged can be challenging to handle for standard communication technologies. In this paper, we evaluate how using a combination of different sensors affects the detection of the environment in which the vehicles move and operate. The final objective is to identify the optimal setup that would minimize the amount of data to be distributed over the channel, with negligible degradation in terms of object detection accuracy. To this aim, we extend an already available object detection algorithm so that it can consider, as an input, camera images, LiDAR point clouds, or a combination of the two, and compare the accuracy performance of the different approaches using two realistic datasets. Our results show that, although sensor fusion always achieves more accurate detections, LiDAR only inputs can obtain similar results for large objects while mitigating the burden on the channel.
Tuning Language Representation Models for Classification of Turkish News

Pre-trained language representation models are very efficient in learning language representation independent from natural language processing tasks to be performed. The language representation models such as BERT and DistilBERT have achieved amazing results in many language understanding tasks. Studies on text classification problems in the literature are generally carried out for the English language. This study aims to classify the news in the Turkish language using pre-trained language representation models. In this study, we utilize BERT and DistilBERT by tuning both models for the text classification task to learn the categories of Turkish news with different tokenization methods. We provide a quantitative analysis of the performance of BERT and DistilBERT on the Turkish news dataset by comparing the models in terms of their representation capability in the text classification task. The highest performance is obtained with DistilBERT with an accuracy of 97.4%.
An Efficient Real-Time Object Detection Framework on Resource-Constricted Hardware Devices via Software and Hardware Co-design

The fast development of object detection techniques has attracted attention to developing efficient Deep Neural Networks (DNNs). However, the current state-of-the-art DNN models can not provide a balanced solution among accuracy, speed, and model size. This paper proposes an efficient real-time object detection framework on resource-constricted hardware devices through hardware and software co-design. The Tensor Train (TT) decomposition is proposed for compressing the YOLOv5 model. By unitizing the unique characteristics given by the TT decomposition, we develop an efficient hardware accelerator based on FPGA devices. Experimental results show that the proposed method can significantly reduce the model size and improve the execution time.
Visual-Semantic Transformer for Face Forgery Detection

This paper proposes a novel Visual-Semantic Transformer (VST) to detect face forgery based on semantic aware feature relations. In face images, intrinsic feature relations exist between different semantic parsing regions. We find that face forgery algorithms always change such relations. Therefore, we start the approach by extracting Contextual Feature Sequence (CFS) using a transformer encoder to make the best abnormal feature relation patterns. Meanwhile, images are segmented as soft face regions by a face parsing module. Then we merge the CFS and the soft face regions as Visual Semantic Sequences (VSS) representing features of semantic regions. The VSS is fed into the transformer decoder, in which the relations in the semantic region level are modeled. Our method achieved 99.58% accuracy on FF++(Raw) and 96.16% accuracy on Celeb-DF. Extensive experiments demonstrate that our framework outperforms or is comparable with state-of-the-art detection methods, especially towards unseen forgery methods.
Does class size matter? An in-depth assessment of the effect of class size in software defect prediction

In the past 20 years, defect prediction studies have generally acknowledged the effect of class size on software prediction performance. To quantify the relationship between object-oriented (OO) metrics and defects, modelling has to take into account the direct, and potentially indirect, effects of class size on defects. However, some studies have shown that size cannot be simply controlled or ignored, when building prediction models. As such, there remains a question whether, and when, to control for class size. This study provides a new in-depth examination of the impact of class size on the relationship between OO metrics and software defects or defect-proneness. We assess the impact of class size on the number of defects and defect-proneness in software systems by employing a regression-based mediation (with bootstrapping) and moderation analysis to investigate the direct and indirect effect of class size in count and binary defect prediction. Our results show that the size effect is not always significant for all metrics. Of the seven OO metrics we investigated, size consistently has significant mediation impact only on the relationship between Coupling Between Objects (CBO) and defects/defect-proneness, and a potential moderation impact on the relationship between Fan-out and defects/defect-proneness. Other metrics show mixed results, in that they are significant for some systems but not for others. Based on our results we make three recommendations. One, we encourage researchers and practitioners to examine the impact of class size for the specific data they have in hand and through the use of the proposed statistical mediation/moderation procedures. Two, we encourage empirical studies to investigate the indirect effect of possible additional variables in their models when relevant. Three, the statistical procedures adopted in this study could be used in other empirical software engineering research to investigate the influence of potential mediators/moderators.
Deep Learning Approach for Credit Card Fraud Detection

As technology evolves rapidly, the world is using credit cards instead of cash in its everyday lives, opening up a new way for fraudulent people to abuse them. Credit card fraud losses reached approximately $28.65 billion in 2019, according to Nilsson’s report, and global card fraud is expected to reach around $32.96 billion by 2023. Providers should therefore develop an efficient model to detect and prevent fraud early. In this paper, we used deep learning techniques as an effective way to detect fraudsters in credit card transactions. Therefore, we present a model for predicting legitimate transactions or fraud on Kaggle's credit card dataset. The proposed model is OSCNN (Over Sampling with Convolution Neural Network) which is based on over-sampling preprocessing and CNN (convolution neural network). The MLP (Multi-layer perceptron) was also applied to the dataset. Comparing the MLP-OSCNN results, they proved that the proposed model achieved better results with 98% accuracy.
Primary geochemical haloes and alteration zoning applied to gold exploration in the Zarshuran Carlin-type deposit, northwestern Iran

The Zarshuran gold deposit as sediment-hosted Carlin-type has a total reserve of 120 million metric tonnes and an average grade of 3.5 g/t Au. The dominant hydrothermal alterations associated with gold mineralization are argillic and silicic as well as decalcification of the carbonate host rocks. The exploration geochemistry was applied to characterize the mineralization in the subsurface and peripheries of the open-pit mine at Zarshuran in order to enhance the proved reserves of the gold ores. A sampling network of a 10 × 10 m grid (n = 1720) was applied to almost unweathered surface outcrops to determine the primary geochemical haloes in the study area. Initially, the cut-off grades utilized in the exploratory data analysis were applied to identify the threshold values of the trace elements with lognormal distribution. Then, the central log ratio transformation was used to determine the robustness against outliers to characterize the closure problems with compositional data during Principle Component Analysis (PCA). The supra-ore and near-ore geochemical haloes are recognized by the indicator elements like As-Sb-Hg-Te-Au-Ag-Fe-Mn-Ba-F which are associated with mineral assemblages such as arsenical pyrite, orpiment, realgar, cinnabar, barite, and fluorite. The sub-ore geochemical haloes are characterized by index element association like Cu-Mo-Bi-W-Sn-Co-Ni-V-Cr. The ores at Zarshuran are enriched in Au, As, Sb, Ag, Te, Tl, Zn, Pb, and F but depleted in Cu, W, and Sn, as were observed in many other Carlin-type gold deposits. The enrichment factor of indicator elements (except S, Ag, and Sb) gradually decreases in value from the highly altered and mineralized zones (in proximity of the mine) toward the zones with least alteration and mineralization (in the distal parts of the mine). According to PCA analysis, two main types of ore mineralization occurred at Zarshuran, (1) Au mineralization associated with As, Sb, and Hg, and (2) Cu-Zn-Pb base-metal mineralization related to Ag. The ore-forming index was used to determine the primary axial haloes in both shallow (<−80 m) and deep (>−120 m) levels. The results of this study show that the main ore body may extend eastwardly and downwardly to greater depths, as was verified by the data obtained from diamond drill cores in the eastern part of the mine.
Blockchain-based identity and authentication scheme for MQTT protocol

The publish and subscribe messaging model has proven itself as a dominant messaging paradigm for IoT systems. An example of such is the commonly used Message Queuing Telemetry Transport (MQTT) protocol. However, the security concerns with this protocol have presented vital security challenges in most IoT applications. For example, the MQTT protocol does not have secure authentication mechanisms implemented and leaves that task to the developer as all the included native security services are fragile. This paper will present a well-thought approach involving a lightweight authentication and authorization scheme together with a decentralized identity system to manage the users' identities. This mechanism helps in facilitating the authentication for both subscribers and publishers by utilizing a smart contract in Ethereum blockchain to guarantee trust, accountability and preserve user privacy. We provided a proof-of-concept implementation to prove our work, which involves a decentralized MQTT platform and dashboard using our approach. The usability of this approach was further analyzed, particularly concerning CPU and memory utilization. Our analysis proved that our approach satisfies IoT applications' requirements since it reduces the consumption of resources and that smart contracts help in the automation of data management processes.
The tripartite quantum-memory-assisted entropic uncertainty relation and upper bound on shareability of quantum discord

Quantum discord and quantum uncertainty are two important features of the quantum world. In this work, the relation between entropic uncertainty relation and the shareability of quantum discord is studied. By using tripartite quantum-memory-assisted entropic uncertainty relation, an upper bound for the shareability of quantum discord among different parties of a composite system is obtained. It is also shown that, for a specific class of tripartite states, the obtained relation could be expressed as monogamy of quantum discord. Moreover, it is illustrated that the relation could be generalized and an upper bound for the shareability of quantum discord for multipartite states is derived.