A Comprehensive Survey of Attacks without Physical Access Targeting Hardware Vulnerabilities in IoT/IIoT Devices, and Their Detection Mechanisms

With the advances in the field of the Internet of Things (IoT) and Industrial IoT (IIoT), these devices are increasingly used in daily life or industry. To reduce costs related to the time required to develop these devices, security features are usually not considered. This situation creates a major security concern. Many solutions have been proposed to protect IoT/IIoT against various attacks, most of which are based on attacks involving physical access. However, a new class of attacks has emerged targeting hardware vulnerabilities in the micro-architecture that do not require physical access. We present attacks based on micro-architectural hardware vulnerabilities and the side effects they produce in the system. In addition, we present security mechanisms that can be implemented to address some of these attacks. Most of the security mechanisms target a small set of attack vectors or a single specific attack vector. As many attack vectors exist, solutions must be found to protect against a wide variety of threats. This survey aims to inform designers about the side effects related to attacks and detection mechanisms that have been described in the literature. For this purpose, we present two tables listing and classifying the side effects and detection mechanisms based on the given criteria.

Total Synthesis of the Natural Chalcone Lophirone E, Synthetic Studies toward Benzofuran and Indole-Based Analogues, and Investigation of Anti-Leishmanial Activity

The potential of natural and synthetic chalcones as therapeutic leads against different pathological conditions has been investigated for several years, and this class of compounds emerged as a privileged chemotype due to its interesting anti-inflammatory, antimicrobial, antiviral, and anticancer properties. The objective of our study was to contribute to the investigation of this class of natural products as anti-leishmanial agents. We aimed at investigating the structure–activity relationships of the natural chalcone lophirone E, characterized by the presence of benzofuran B-ring, and analogues on anti-leishmania activity. Here we describe an effective synthetic strategy for the preparation of the natural chalcone lophirone E and its application to the synthesis of a small set of chalcones bearing different substitution patterns at both the A and heterocyclic B rings. The resulting compounds were investigated for their activity against Leishmania infantum promastigotes disclosing derivatives 1 and 28a,b as those endowed with the most interesting activities (IC50 = 15.3, 27.2, 15.9 μM, respectively). The synthetic approaches here described and the early SAR investigations highlighted the potential of this class of compounds as antiparasitic hits, making this study worthy of further investigation.

Machine Learning and data mining tools applied for databases of low number of records

The use of data mining and machine learning tools is becoming increasingly common. Their usefulness is mainly noticeable in the case of large datasets, when information to be found or new relationships are extracted from information noise. The development of these tools means that datasets with much fewer records are being explored, usually associated with specific phenomena. This specificity most often causes the impossibility of increasing the number of cases, and that can facilitate the search for dependences in the phenomena under study. The paper discusses the features of applying the selected tools to a small set of data. Attempts have been made to present methods of data preparation, methods for calculating the performance of tools, taking into account the specifics of databases with a small number of records. The techniques selected by the author are proposed, which helped to break the deadlock in calculations, i.e., to get results much worse than expected. The need to apply methods to improve the accuracy of forecasts and the accuracy of classification was caused by a small amount of analysed data. This paper is not a review of popular methods of machine learning and data mining; nevertheless, the collected and presented material will help the reader to shorten the path to obtaining satisfactory results when using the described computational methods

Electrophysiological Signatures of Numerosity Encoding in a Delayed Match-to-Sample Task

The number of elements in a small set of items is appraised in a fast and exact manner, a phenomenon called subitizing. In contrast, humans provide imprecise responses when comparing larger numerosities, with decreasing precision as the number of elements increases. Estimation is thought to rely on a dedicated system for the approximate representation of numerosity. While previous behavioral and neuroimaging studies associate subitizing to a domain-general system related to object tracking and identification, the nature of small numerosity processing is still debated. We investigated the neural processing of numerosity across subitizing and estimation ranges by examining electrophysiological activity during the memory retention period in a delayed numerical match-to-sample task. We also assessed potential differences in the neural signature of numerical magnitude in a fully non-symbolic or cross-format comparison. In line with behavioral performance, we observed modulation of parietal-occipital neural activity as a function of numerosity that differed in two ranges, with distinctive neural signatures of small numerosities showing clear similarities with those observed in visuospatial working memory tasks. We also found differences in neural activity related to numerical information in anticipation of single vs. cross-format comparison, suggesting a top-down modulation of numerical processing. Finally, behavioral results revealed enhanced performance in the mixed-format conditions and a significant correlation between task performance and symbolic mathematical skills. Overall, we provide evidence for distinct mechanisms related to small and large numerosity and differences in numerical encoding based on task demands.

Taking “Use Case” Inventory of Available Open Shared Visuals for Teaching and Learning From Searches in the Federated Creative Commons Search (Old)

In instructional design, there are a number of common “use cases” for acquiring open-source shared visuals and images: breaking up gray text, driving attention, sparking the imagination, illustrating concepts, providing examples, explaining phenomena, representing reality, depicting models, and others. The instating of licensure and open-source releases has meant that there are literally hundreds of millions of such visuals available online, with varying levels of releases (with variations on the following dimensions: editability, [non]crediting, [non]commercial usages, [non]required sharing, all the way up to full release into the public domain with no restrictions). The federated Creative Commons Search (old) enables exploration and acquisition across a range of web-based platforms for digital images based on text search. When pursuing actual images for particular usage, the abundance of shared imagery suddenly becomes small-set and limited. This work explores this phenomenon and provides some ideas for mitigation.

Dependence of the nature of the Holstein polaron motion in a polynucleotide chain subjected to a constant electric field on the initial polaron state and the parameters of the chain

In this work, we consider the motion of a polaron in a polynucleotide Holstein molecular chain in a constant electric field. It is shown that the character of the polaron motion in the chain depends not only on the chosen parameters of the chain, but also on the initial distribution of the charge along the chain. It is shown that for a small set value of the electric field intensity and for fixed values of the chain parameters, changing only the initial distribution of the charge in the chain, it is possible to observe either a uniform movement of the charge along the chain, or an oscillatory mode of charge movement.

A meshless local Galerkin integral equation method for solving a type of Darboux problems based on the radial basis functions

The main goal of this paper is to solve a class of Darboux problems by converting them into the two-dimensional nonlinear Volterra integral equation of the second kind. The scheme approximates the solution of these integral equations using the discrete Galerkin method together with local radial basis functions, which use a small set of data instead of all points in the solution domain. We also employ the Gauss–Legendre integration rule on the influence domains of shape functions to compute the local integrals appearing in the method. Since the scheme is constructed on a set of scattered points and does not require any background meshes, it is meshless. The error bound and the convergence rate of the presented method are provided. Some illustrative examples are included to show the validity and efficiency of the new technique. Furthermore, the results obtained demonstrate that this method uses much less computer memory than the method established using global radial basis functions. doi:10.1017/S1446181121000377

LargeGraph

Many out-of-GPU-memory systems are recently designed to support iterative processing of large-scale graphs. However, these systems still suffer from long time to converge because of inefficient propagation of active vertices’ new states along graph paths. To efficiently support out-of-GPU-memory graph processing, this work designs a system LargeGraph . Different from existing out-of-GPU-memory systems, LargeGraph proposes a dependency-aware data-driven execution approach , which can significantly accelerate active vertices’ state propagations along graph paths with low data access cost and also high parallelism. Specifically, according to the dependencies between the vertices, it only loads and processes the graph data associated with dependency chains originated from active vertices for smaller access cost. Because most active vertices frequently use a small evolving set of paths for their new states’ propagation because of power-law property, this small set of paths are dynamically identified and maintained and efficiently handled on the GPU to accelerate most propagations for faster convergence, whereas the remaining graph data are handled over the CPU. For out-of-GPU-memory graph processing, LargeGraph outperforms four cutting-edge systems: Totem (5.19–11.62×), Graphie (3.02–9.41×), Garaph (2.75–8.36×), and Subway (2.45–4.15×).

Query-focused multi-document text summarization using fuzzy inference

The present paper proposes a fuzzy inference system for query-focused multi-document text summarization (MTS). The overall scheme is based on Mamdani Inferencing scheme which helps in designing Fuzzy Rule base for inferencing about the decision variable from a set of antecedent variables. The antecedent variables chosen for the task are from linguistic and positional heuristics, and similarity of the documents with the user-defined query. The decision variable is the rank of the sentences as decided by the rules. The final summary is generated by solving an Integer Linear Programming problem. For abstraction coreference resolution is applied on the input sentences in the pre-processing step. Although designed on the basis of a small set of antecedent variables the results are very promising.

Ensemble-based deep meta learning for medical image segmentation

Deep learning methods have led to a state of the art medical applications, such as image classification and segmentation. The data-driven deep learning application can help stakeholders to collaborate. However, limited labelled data set limits the deep learning algorithm to generalize for one domain into another. To handle the problem, meta-learning helps to learn from a small set of data. We proposed a meta learning-based image segmentation model that combines the learning of the state-of-the-art model and then used it to achieve domain adoption and high accuracy. Also, we proposed a prepossessing algorithm to increase the usability of the segments part and remove noise from the new test image. The proposed model can achieve 0.94 precision and 0.92 recall. The ability to increase 3.3% among the state-of-the-art algorithms.