Miracle-dz/UniFAR-FTUs huggingface dataset:
Social Engineering: The Art of Human Hacking

The first book to reveal and dissect the technical aspect of many social engineering maneuversFrom elicitation, pretexting, influence and manipulation all aspects of social engineering are picked apart, discussed and explained by using real world examples, personal experience and the science behind them to unraveled the mystery in social engineering.Kevin MitnickвЂ”one of the most famous social engineers in the worldвЂ”popularized the term вЂњsocial engineering. вЂќ He explained that it is much easier to trick someone into revealing a password for a system than to exert the effort of hacking into the system. Mitnick claims that this social engineering tactic was the single-most effective method in his arsenal. This indispensable book examines a variety of maneuvers that are aimed at deceiving unsuspecting victims, while it also addresses ways to prevent social engineering threats.Examines social engineering, the science of influencing a target to perform a desired task or divulge informationArms you with invaluable information about the many methods of trickery that hackers use in order to gather information with the intent of executing identity theft, fraud, or gaining computer system accessReveals vital steps for preventing social engineering threatsSocial Engineering: The Art of Human Hacking does its part to prepare you against nefarious hackersвЂ”now you can do your part by putting to good use the critical information within its pages.
EXAMiner.

As an alternative to the Fibonacci heap, and a variation of the 2-3 heap data structure by Tadao Takaoka, this research presents the 3-4 heap data structure. The aim is to prove that the 3-4 heap, like its counter-part 2-3 heap, also supports n insert, n delete-min, and m decrease-key operations, in O(m + n log n) time. Many performance tests were carried out during this research comparing the 3-4 heap against the 2-3 heap and for a narrow set of circumstances the 3-4 heap outperformed the 2-3 heap. The 2-3 heap has got a structure based on dimensions which are rigid using ternary linking and this path is made up of three nodes linked together to form a trunk, and the trunk is permitted to shrink by one. If further shrinkage is required then an adjustment is made by moving a few nodes from nearby positions to ensure the heaps rigid dimensions are retained. Should this no longer be the case, then the adjustment will trigger a makeup event, which propagates to higher dimensions, and requires amortised analysis. To aid amortised analysis, the trunk is given a measurement value called potential and this is the number of comparisons required to place each node into its correct position in ascending order using linear search. The divergence of the 3-4 heap from the 2-3 heap is that the trunk maximum is increased by one to four and is still permitted to shrink by one. This modified data structure will have a wide range of applications as the data storage mechanism used by graph algorithms such as Dijkstra’s ‘Single Source Shortest Path’.
Fabrication and characterization of room temperature operating single electron transistors using focused ion beam technologies

The single electron transistor (SET) is a Coulomb blockade device, whose operation is based on the controlled manipulation of individual electrons. Single electron transistors show immense potential to be used in future ultra lowpower devices, high density memory and also in high precision electrometry. Most SET devices operate at cryogenic temperatures, because the charging energy is much smaller than the thermal oscillations. The room temperature operation of these devices is possible with sub10nm nano-islands due to the inverse dependance of charging energy on the radius of the conducting nano-island. The fabrication of sub-10nm features with existing lithographic techniques is a technological challenge. Here we present the results for the first room temperature operating SET device fabricated using Focused Ion Beam deposition technology. The SET device, incorporates an array of tungsten nano-islands with an average diameter of 8nm. The SET devices shows clear Coulomb blockade for different gate voltages at room temperature. The charging energy of the device was calculated to be 160.0 meV; the capacitance per junction was found to be 0.94 atto F; and the tunnel resistance per junction was calculated to be 1.26 G Ω. The tunnel resistance is five orders of magnitude larger than the quantum of resistance (26 k Ω) and allows for the localization of electrons on the tungsten nano-island. The lower capacitance of the device combined with the high tunnel resistance, allows for the Coulomb blockade effects observed at room temperature. Different device configurations, minimizing the total capacitance of the device have been explored. The effect of
Cross-Domain Recommendation Via User-Clustering and Multidimensional Information Fusion

Recently, recommendation systems have been widely usedin online business scenarios, which can improve the online experience by learning the user or item characteristics to predict the user’s future behavior and to realize precision marketing. However, data sparsity and cold-start problems limit the performance of recommendation systems in some emerging fields. Thus, cross-domain recommendation has been proposed to handle the abovementioned problems. Nonetheless, many cross-domain recommendations only consider modeling a single user’s representation and ignore user-group information (this group has similar behavior and interests). Additionally, most studies are based on matrix factorization for generating embeddings, which results in a weak generalization ability of user latent features. In this paper, we propose a novel cross-domain recommendation model via User-Clustering and Multidimensional information Fusion (UCMF) that attempts to enhance user representation learning in a data sparsity scenario for accurate recommendation. In addition, we consider a user’s individual information and cross-domain feature information. A novel multidimensional information fusion is proposed to guarantee the robustness of the user features. In particular, we apply a graph neural network to learn the user-group features, which can effectively save the correlation among users’ information and guarantee feature performance. In other words, the Wasserstein autoencoder is utilized to learn the cross-domain user features, which can guarantee the consistency of user features from different domains. Experiments conducted on real-world datasets empirically demonstrate that our proposed method outperforms the state-of-the-art methods in cross-domain recommendation.
A Biological Signature for the Inhibition of Outer Membrane Lipoprotein Biogenesis

Gram-negative bacteria have an outer membrane, which acts as a protective barrier and excludes many antibiotics. The limited number of antibiotics active against Gram-negative bacteria, along with rising rates of antibiotic resistance, highlights the need for efficient antibiotic discovery efforts. ABSTRACT The outer membrane (OM) of Gram-negative bacteria is an essential organelle that acts as a formidable barrier to antibiotics. Increasingly prevalent resistance to existing drugs has exacerbated the need for antibiotic discovery efforts targeting the OM. Acylated proteins, known as lipoproteins, are essential in every pathway needed to build the OM. The central role of OM lipoproteins makes their biogenesis a uniquely attractive therapeutic target, but it also complicates in vivo identification of on-pathway inhibitors, as inhibition of OM lipoprotein biogenesis broadly disrupts OM assembly. Here, we use genetics to probe the eight essential proteins involved in OM lipoprotein maturation and trafficking. We define a biological signature consisting of three simple assays that can characteristically identify OM lipoprotein biogenesis defects in vivo. We find that several known chemical inhibitors of OM lipoprotein biogenesis conform to the biological signature. We also examine MAC13243, a proposed inhibitor of OM lipoprotein biogenesis, and find that it fails to conform to the biological signature. Indeed, we demonstrate that MAC13243 activity relies entirely on a target outside of the OM lipoprotein biogenesis pathway. Hence, our signature offers simple tools to easily assess whether antibiotic lead compounds target an essential pathway that is the hub of OM assembly. IMPORTANCE Gram-negative bacteria have an outer membrane, which acts as a protective barrier and excludes many antibiotics. The limited number of antibiotics active against Gram-negative bacteria, along with rising rates of antibiotic resistance, highlights the need for efficient antibiotic discovery efforts. Unfortunately, finding the target of lead compounds, especially ones targeting outer membrane construction, remains difficult. The hub of outer membrane construction is the lipoprotein biogenesis pathway. We show that defects in this pathway result in a signature cellular response that can be used to quickly and accurately validate pathway inhibitors. Indeed, we found that MAC13243, a compound previously proposed to target outer membrane lipoprotein biogenesis, does not fit the signature, and we show that it instead targets an entirely different cellular pathway. Our findings offer a streamlined approach to the discovery and validation of lead antibiotics against a conserved and essential pathway in Gram-negative bacteria.
Developing a Repository for Component-Based Energy-Efficient Software Development

— Software components are one reusable asset which can contain other kinds of software assets like requirement specifications, design patterns, source codes, documents, and so on. It can be used for designing software architecture or implementing a software system as an element like a building block. Therefore, software can be developed easily and quickly by assembling those building blocks. Focused on this nature of component-based development, energy-efficient software development can also be achieved with reusable software components. In particular, low-energy software has become a critical component for embedded and mobile software systems. Therefore, we have to consider energy efficiency to develop embedded software when developing the software based on reusable components. This paper, firstly, proposes how to represent the energy characteristics of the components and how to select a component for energy-efficient software development. We developed a component repository, ECoReS to support the selection of low-energy software components.
An Optimized Continuous Dragonfly Algorithm Using Hill Climbing Local Search to Tackle the Low Exploitation Problem

Optimization problems are usually solved using heuristic algorithms such as swarm intelligence algorithms owing to their ability of providing near-optimal solutions in a feasible amount of time. An example of an optimization problem is the training of artificial neural networks to obtain the most optimal connection weights. Artificial Neural Network (ANN), being the most prominent machine learning algorithm, has a multitude of applications in a myriad of areas. Recently, the use of ANNs has risen exponentially owing to its effective ability of making conclusions based on certain inputs. This ability is primarily achieved during the training phase of the ANN, which is a vital process prior to being able to use the ANN. Gradient descent-based algorithms, which are usually used for the training process, often encounter the problem of local optima, thus being unable to obtain the optimal connection weights of the ANN. Metaheuristic algorithms, including swarm intelligence algorithms, have been found to be a better alternative to train ANNs. The Dragonfly Algorithm (DA) is a swarm intelligence algorithm that has been found to be more effective than multiple swarm intelligence algorithms. However, despite having a good performance, it still suffers from low exploitation. In this paper, we propose to further improve the performance of DA by using hill climbing as a local search technique so as to enhance its low exploitation. The optimized DA algorithm is then used for training artificial neural networks which are employed for classification problems. Based on the experimental results, the optimized DA algorithm has higher effectiveness than the original DA and some other swarm intelligence algorithms as the ANNs trained by the optimized DA have a lower root mean squared error and a higher classification accuracy.
Capacity for Meaningful Relationship in Severe Substance Use Recovery: A Qualitative Study

ABSTRACT The purpose of this study was to qualitatively explore the lived experiences of meaningful relationships among persons in 12-step recovery. The distinction of recovery is important as our study focused less on relational patterns during active substance use and more on their perception within their recovery. This phenomenological study followed Van Manen’s six step approach of qualitative research. Data were collected from 7 participants through semi-structured one-on-one participant interviews. Participants were asked open-ended questions regarding their SUD recovery (e.g., “What is your experience of your capacity to be in meaningful relationships? ”). Resulting from the analysis, four themes were extracted from the overarching theme of the Capacity for Meaningful Relationships in Recovery. The four emergent themes included Building the Foundation in Recovery, Sharing a Common Ground of Support, Giving Back – Helping Others, and Bridging Recovery to Family – The Biggest Accomplishment. Limitations and future research are discussed as well as the potential impact and application of these findings as they relate to recovery research and practice.
Unsupervised Summarization of Privacy Concerns in Mobile Application Reviews

The proliferation of mobile applications (app) over the past decade has imposed unprecedented challenges on end-users privacy. Apps constantly demand access to sensitive user information in exchange for more personalized services. These—mostly unjustifiable—data collection tactics have raised major privacy concerns among mobile app users. Such concerns are commonly expressed in mobile app reviews, however, they are typically overshadowed by more generic categories of user feedback, such as app reliability and usability. This makes extracting user privacy concerns manually, or even using automated tools, a challenging and time-consuming task. To address these challenges, in this paper, we propose an effective unsupervised approach for summarizing user privacy concerns in mobile app reviews. Our analysis is conducted using a dataset of 2.6 million app reviews sampled from three different application domains. The results show that users in different application domains express their privacy concerns using domain-specific vocabulary. This domain knowledge can be leveraged to help unsupervised automated text summarization algorithms to generate concise and comprehensive summaries of privacy concerns in app review collections. Our analysis is intended to help app developers quickly and accurately identify the most critical privacy concerns in their domain of operation, and ultimately, alter their data collection practices to address these concerns.
Automatic Identification of Functionally Untestable Cell-Aware Faults in Microprocessors

In-field test of microprocessors is a major topic for the industry, especially in the safety-critical domain, where the respective standards mandate high test coverage thresholds. The dominant fault models used are the transition delay and the stuck-at fault model. However, the adoption of very advanced semiconductor technologies to manufacture devices used in safety-critical applications pushes toward considering new fault models that are better suited to catch subtle and age-related defects. Among the other phenomena, latent cell-internal defects emerged as relevant causes for several failures. Hence, the necessity for the Cell-Aware Test (CAT) was born, and the inclusion of the CAT fault model in the latest safety standards. Although CAT amends the issue of the numerous test escapes, it may suffer as well from the presence of functionally untestable faults that may pollute the overall test efficiency with their presence. In this paper, we propose a solution, based on formal methods, for the automatic identification of functionally untestable faults under the Cell-Aware fault model for the case where the DUT is a fully pipelined processor. As a case study, we used the RISC-V processor RI5CY for which we applied the minimum constraints required to ensure a functional behavior to demonstrate the effectiveness and impact of the approach. With the considered constraints, a significant percentage of functionally untestable faults was located in the several modules within the processor. Furthermore, the method allows to flexibly take into account any constraint stemming from the system configuration and the application. The obtained results have been validated by resorting to commercial EDA tools.
Transformation from temporal to rate coding in a somatosensory thalamocortical pathway

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A New Approach of Modified Submerged Patch Clamp Recording Reveals Interneuronal Dynamics during Epileptiform Oscillations

Highlights Simultaneous epileptiform LFPs and single-cell activity can be recorded in the membrane chamber. Interneuron firing can be linked to epileptiform high frequency activity. Fast ripples, unique to chronic epilepsy, can be modeled in ex vivo tissue from TeNT-treated rats. Traditionally, visually-guided patch clamp in brain slices using submerged recording conditions has been required to characterize the activity of individual neurons. However, due to limited oxygen availability, submerged conditions truncate fast network oscillations including epileptiform activity. Thus, it is technically challenging to study the contribution of individual identified neurons to fast network activity. The membrane chamber is a submerged-style recording chamber, modified to enhance oxygen supply to the slice, which we use to demonstrate the ability to record single-cell activity during in vitro epilepsy. We elicited epileptiform activity using 9 mM potassium and simultaneously recorded from fluorescently labeled interneurons. Epileptiform discharges were more reliable than in standard submerged conditions. During these synchronous discharges interneuron firing frequency increased and action potential amplitude progressively decreased. The firing of 15 interneurons was significantly correlated with epileptiform high frequency activity (HFA; ~100–500 Hz) cycles. We also recorded epileptiform activity in tissue prepared from chronically epileptic rats, treated with intrahippocampal tetanus neurotoxin. Four of these slices generated fast ripple activity, unique to chronic epilepsy. We showed the membrane chamber is a promising new in vitro environment facilitating patch clamp recordings in acute epilepsy models. Further, we showed that chronic epilepsy can be better modeled using ex vivo brain slices. These findings demonstrate that the membrane chamber facilitates previously challenging investigations into the neuronal correlates of epileptiform activity in vitro.
Interfacing constraints of distributed maximum power point tracking converters in photovoltaic applications

Long strings of photovoltaic (PV) modules have been observed to be prone to shading effects causing significant reduction in the system power output. To overcome this, distributed maximum power point-tracking (DMPPT) schemes have been proposed, in which individual dc-dc converters are connected to each PV module to enable maximum power extraction out of the PV modules. The output terminals of these converters are connected either in series or in parallel, followed by a grid-connected inverter. The series concept has been under intensive study with novel topologies and control schemes, yet operational problems compromising its feasibility have been widely reported. The parallel concept does not contain similar operational limitations, but suffers mainly from higher voltage levels. This paper discusses the feasibility of the series and the parallel schemes and introduces fundamental constraints governing the operation of DMPPT converter systems. These constraints have not been truly recognized to be key issues in designing high-performance, high-efficiency photovoltaic systems.
Prognosis of Severe Nosocomial Sepsis in 33 Older Adults Not Eligible for Intensive Care Units

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