Miracle-dz/UniFAR-FTUs huggingface dataset:
Strong Logic Obfuscation with Low Overhead against IC Reverse Engineering Attacks

Untrusted foundries pose threats of integrated circuit (IC) piracy and counterfeiting, and this has motivated research into logic locking. Strong logic locking approaches potentially prevent piracy and counterfeiting by preventing unauthorized replication and use of ICs. Unfortunately, recent work has shown that most state-of-the-art logic locking techniques are vulnerable to attacks that utilize Boolean Satisfiability (SAT) solvers. In this article, we extend our prior work on using silicon nanowire (SiNW) field-effect transistors (FETs) to produce obfuscated ICs that are resistant to reverse engineering attacks, such as the sensitization attack, SAT and approximate SAT attacks, as well as tracked signal attacks. Our method is based on exchanging some logic gates in the original design with a set of polymorphic gates (PLGs), designed using SiNW FETs, and augmenting the circuit with a small block, whose output is untraceable, namely, URSAT. The URSAT may not offer very strong resilience against the combined AppSAT-removal attack. Strong URSAT is achieved using only CMOS-logic gates, namely, S-URSAT. The proposed technique, S-URSAT + PLG-based traditional encryption, designed using SiNW FETs, increases the security level of the design to robustly thwart all existing attacks, including combined AppSAT-removal attack, with small penalties. Then, we evaluate the effectiveness of our proposed methods and subject it to a thorough security analysis. We also evaluate the performance penalty of the technique and find that it results in very small overheads in comparison to other works. The average area, power, and delay overheads of implementing 64 baseline key-bits of S-URSAT for small benchmarks are 5.03%, 2.60%, and −2.26%, respectively, while for large benchmarks they are 2.37%, 1.18%, and −1.93%.
Extracellular Vesicles in Renal Cell Carcinoma: Multifaceted Roles and Potential Applications Identified by Experimental and Computational Methods

Renal cell carcinoma (RCC) is the most common type of kidney cancer. Increasingly evidences indicate that extracellular vesicles (EVs) orchestrate multiple processes in tumorigenesis, metastasis, immune evasion, and drug response of RCC. EVs are lipid membrane-bound vesicles in nanometer size and secreted by almost all cell types into the extracellular milieu. A myriad of bioactive molecules such as RNA, DNA, protein, and lipid are able to be delivered via EVs for the intercellular communication. Hence, the abundant content of EVs is appealing reservoir for biomarker identification through computational analysis and experimental validation. EVs with excellent biocompatibility and biodistribution are natural platforms that can be engineered to offer achievable drug delivery strategies for RCC therapies. Moreover, the multifaceted roles of EVs in RCC progression also provide substantial targets and facilitate EVs-based drug discovery, which will be accelerated by using artificial intelligence approaches. In this review, we summarized the vital roles of EVs in occurrence, metastasis, immune evasion, and drug resistance of RCC. Furthermore, we also recapitulated and prospected the EVs-based potential applications in RCC, including biomarker identification, drug vehicle development as well as drug target discovery.
Context-sensitive Rewriting

The appropriate selection of the arguments of functions that can be evaluated in function calls is often useful to improve efficiency, speed, termination behavior, and so on. This is essential, e.g., in the conditional if - then - else operator. We can specify this by associating a set μ(f) of indices of evaluable arguments to each function symbol f. With μ(if - then - else)={1}, only the Boolean argument b in calls if b, then e else e' is evaluated. In the realm of term rewriting, this is called context-sensitive rewriting. It has been proven useful to improve the termination behavior of rewriting computations while it is still able to compute (or approximate) canonical forms like head-normal forms, (infinite) values, and (infinite) normal forms by requiring a few reasonable conditions. This article provides an overview of basic results to use context-sensitive rewriting in practice.
Internal Evaluation of Unsupervised Outlier Detection

Although there is a large and growing literature that tackles the unsupervised outlier detection problem, the unsupervised evaluation of outlier detection results is still virtually untouched in the literature. The so-called internal evaluation, based solely on the data and the assessed solutions themselves, is required if one wants to statistically validate (in absolute terms) or just compare (in relative terms) the solutions provided by different algorithms or by different parameterizations of a given algorithm in the absence of labeled data. However, in contrast to unsupervised cluster analysis, where indexes for internal evaluation and validation of clustering solutions have been conceived and shown to be very useful, in the outlier detection domain, this problem has been notably overlooked. Here we discuss this problem and provide a solution for the internal evaluation of outlier detection results. Specifically, we describe an index called Internal, Relative Evaluation of Outlier Solutions (IREOS) that can evaluate and compare different candidate outlier detection solutions. Initially, the index is designed to evaluate binary solutions only, referred to as top-n outlier detection results. We then extend IREOS to the general case of non-binary solutions, consisting of outlier detection scorings. We also statistically adjust IREOS for chance and extensively evaluate it in several experiments involving different collections of synthetic and real datasets.
Design and Implementation of a Compressed Certificate Status Protocol

Trust in Secure Sockets Layer–based communications is traditionally provided by Certificate (or Certification) Authorities (CAs) in the form of signed certificates. Checking the validity of a certificate involves three steps: (i) checking its expiration date, (ii) verifying its signature, and (iii) ensuring that it is not revoked. Currently, such certificate revocation checks (i.e., step (iii) above) are done either via Certificate Revocation Lists (CRLs), or Online Certificate Status Protocol (OCSP) servers. Unfortunately, despite the existence of these revocation checks, sophisticated cyber-attackers can still trick web browsers to trust a revoked certificate, believing that it is still valid. Although frequently updated, nonced, and timestamped certificates can reduce the frequency and impact of such cyber-attacks, they add a huge burden to the CAs and OCSP servers. Indeed, CAs and/or OCSP servers need to timestamp and sign on a regular basis all the responses, for every certificate they have issued, resulting in a very high overhead. To mitigate this and provide a solution to the described cyber-attacks, we present CCSP : a new approach to provide timely information regarding the status of certificates, which capitalizes on a newly introduced notion called Signed Collections. In this article, we present in detail the notion of Signed Collections and the complete design, implementation, and evaluation of our approach. Performance evaluation shows that CCSP (i) reduces space requirements by more than an order of magnitude, (ii) lowers the number of signatures required by six orders of magnitude compared to OCSP-based methods, and (iii) adds only a few milliseconds of overhead in the overall user latency.
Centaur: A Novel Architecture for Reliable, Low-Wear, High-Density 3D NAND Storage

Due to the high density storage demand coming from applications from different domains, 3D NAND flash is becoming a promising candidate to replace 2D NAND flash as the dominant non-volatile memory. However, denser 3D NAND presents various performance and reliability issues, which can be addressed by the 3D NAND specific full-sequence program (FSP) operation. The FSP programs multiple pages simultaneously to mitigate the performance degradation caused by the long latency 3D NAND baseline program operations. However, the FSP-enabled 3D NAND-based SSDs introduce lifetime degradation due to the larger write granularities accessed by the FSP. To address the lifetime issue, in this paper, we propose and experimentally evaluate Centaur, a heterogeneous 2D/3D NAND heterogeneous SSD, as a solution. Centaur has three main components: a lifetime-aware inter-NAND request dispatcher, a lifetime-aware inter-NAND work stealer, and a data migration strategy from 2D NAND to 3D NAND. We used twelve SSD workloads to compare Centaur against a state-of-the-art 3D NAND-based SSD with the same capacity. Our experimental results indicate that the SSD lifetime and performance are improved by 3.7x and 1.11x, respectively, when using our 2D/3D heterogeneous SSD.
Disclosure, Privacy, and Stigma on Social Media

Disclosures of distress and stigma on identified social media can be beneficial. Yet, many who may benefit from such disclosures do not engage in them. I examine factors that inform decisions to not disclose stigmatized experiences on identified social media. I conducted in-depth interviews with women in the US who used social media, had experienced pregnancy loss, and had not disclosed about their loss on identified social media. I detail six types of factors related to the self, audience, network, society, platform, and temporality that contribute to non-disclosure decisions. I show that the Disclosure Decision-Making (DDM) framework introduced in prior work explaining disclosures when they do occur, also explains non-disclosure decisions on social media. I show how DDM builds from and bridges prior privacy theories, namely, Communication Privacy Management and Contextual Integrity. I discuss design implications around removing barriers to disclosure to facilitate beneficial disclosures and reduce stigma.
A Secure Multimedia Processing through Blockchain in Smart Healthcare Systems

The rapidly growing, complex and key area of multimedia processing is medical area where the huge amount of patient? s data is disregarded. Further, medial fields used to interrelate with multimedia formats because of their daily real communication. Healthcare associations are renovating themselves into added efficient, coordinated and user-centered methods through several upcoming techniques. Though, the management of large information such as patient information, medical reports escorts to augments the human labors and security hazards. Therefore, to conquer these problems, IoT-healthcare came into existence that enhances the patients care by reducing the cost of resources in an effective way. Along with lot of IoT benefits, associations are afraid to use them because of its compromise initiated by several intruders. For benefiting their own purposes, intruders hack the patient? s online record for selling to third party for doing the researchers analysis. Further, doctors also make the money for their concession by forcing the patients to buy the medicines from their recommended firms. So as to avert these problems, Blockchain technology has been came across in healthcare systems that may keep track of every activity of the entities. The patients and other entities are recognized through bio metric methods so that even if hackers try to compromise the devices or to access the stored information; the patients may easily identify the intruders or alteration in stored record. In this paper, we have projected a secure IoT healthcare mechanism using Blockchain technique that initially identify the patients and other entities using bio metric passwords and further can analyze the modifications in their records. The proposed scheme results have been validated against traditional method by offering 88% success rate over certain parametric scenarios such as grey hole attack, falsification attack and probabilistic authentication scenarios.
Sparse Graph Connectivity for Image Segmentation

It has been demonstrated that the segmentation performance is highly dependent on both subspace preservation and graph connectivity. In the literature, the full connectivity method linearly represents each data point (e.g., a pixel in one image) by all data points for achieving subspace preservation, while the sparse connectivity method was designed to linearly represent each data point by a set of data points for achieving graph connectivity. However, previous methods only focused on either subspace preservation or graph connectivity. In this article, we propose a Sparse Graph Connectivity (SGC) method for image segmentation to automatically learn the affinity matrix from the low-dimensional space of original data, which aims at simultaneously achieving subspace preservation and graph connectivity. To do this, the proposed SGC simultaneously learns a self-representation affinity matrix for subspace preservation and a sparse affinity matrix for graph connectivity, from the intrinsic low-dimensional feature space of high-dimensional original data. Meanwhile, the self-representation affinity matrix is pushed to be similar to the sparse affinity as well as be the final segmentation results. Experimental result on synthetic and real-image datasets showed that our SGC method achieved the best segmentation performance, compared to state-of-the-art segmentation methods.
Efficient Discovery of Matching Dependencies

Matching dependencies (MDs) are data profiling results that are often used for data integration, data cleaning, and entity matching. They are a generalization of functional dependencies (FDs) matching similar rather than same elements. As their discovery is very difficult, existing profiling algorithms find either only small subsets of all MDs or their scope is limited to only small datasets. We focus on the efficient discovery of all interesting MDs in real-world datasets. For this purpose, we propose HyMD, a novel MD discovery algorithm that finds all minimal, non-trivial MDs within given similarity boundaries. The algorithm extracts the exact similarity thresholds for the individual MDs from the data instead of using predefined similarity thresholds. For this reason, it is the first approach to solve the MD discovery problem in an exact and truly complete way. If needed, the algorithm can, however, enforce certain properties on the reported MDs, such as disjointness and minimum support, to focus the discovery on such results that are actually required by downstream use cases. HyMD is technically a hybrid approach that combines the two most popular dependency discovery strategies in related work: lattice traversal and inference from record pairs. Despite the additional effort of finding exact similarity thresholds for all MD candidates, the algorithm is still able to efficiently process large datasets, e.g., datasets larger than 3 GB.
Experiences derived from the Carabid Beetle Laboratory Test

The Carabid Beetle Laboratory Test using Poecilus cupreus (L.) was developed for the assessment of effects of pesticides on beneficial arthropods and is now available as an official guideline published by the BBA and the IOBC for the registration of pesticides. In this contribution some of the experiences in performing the test are summarized. In total, 126 compounds (mainly herbicides) were tested in 190 test runs (including water controls). In this acute laboratory test, the three parameters mortality, behaviour and feeding rate are investigated. Due to experiences with other ecotoxicological tests a one-way Analysis of Variance or ANOVA, followed by a Bonferroni Test, was used for the statistical evaluation of the feeding rates. The results presented here show that a statistical evaluation of the feeding data is possible and that this parameter is useful for the assessment of side-effects of chemicals for these predatory arthropods. In testing the reference substance pyrazophos (formulation : 'Afugan' 294 g litre -1 EC) the mortality varied considerably. It is proposed that at a given value (30%) for effects on the mortality and the feeding rate, a test substance should be tested in a semi-field or field situation.
Pop Music Generation

Music plays an important role in our daily life. With the development of deep learning and modern generation techniques, researchers have done plenty of works on automatic music generation. However, due to the special requirements of both melody and arrangement, most of these methods have limitations when applying to multi-track music generation. Some critical factors related to the quality of music are not well addressed, such as chord progression, rhythm pattern, and musical style. In order to tackle the problems and ensure the harmony of multi-track music, in this article, we propose an end-to-end melody and arrangement generation framework to generate a melody track with several accompany tracks played by some different instruments. To be specific, we first develop a novel Chord based Rhythm and Melody Cross-Generation Model to generate melody with a chord progression. Then, we propose a Multi-Instrument Co-Arrangement Model based on multi-task learning for multi-track music arrangement. Furthermore, to control the musical style of arrangement, we design a Multi-Style Multi-Instrument Co-Arrangement Model to learn the musical style with adversarial training. Therefore, we can not only maintain the harmony of the generated music but also control the musical style for better utilization. Extensive experiments on a real-world dataset demonstrate the superiority and effectiveness of our proposed models.
Pathogenesis of infectious hematopoietic necrosis virus in adult sockeye salmon (Oncorhynchus nerka).

The concentration of infectious hematopoietic necrosis (IHN) virus was determined in eight organs and two body fluids from each of 60 adult sockeye salmon (Oncorhynchus nerka). Included in the sample were 4 males and 56 prespawning, spawning, or spent female fish. All fish were infected, and virus was present in nearly all organs. There was an overall tendency for the mean concentration to increase in many of the organs over time as the fish progressed in ripeness. In prespawning females, IHN virus could be detected in all organs and in ovarian fluid but not in serum; the incidences were highest in the gills, spleen, and pyloric ceca, and the titers were highest in the pyloric ceca and liver. Incidences of infection in the organs were higher in spawning than in prespawning females and higher still in spent females in which the incidence of virus was 100% in all organs except brains (78%) and sera (67%). Virus concentrations in organs or fluids ranged from 5 to 4.0 × 109 plaque-forming units per millilitre.
A time- and space-optimal algorithm for the many-visits TSP

The many-visits traveling salesperson problem (MV-TSP) asks for an optimal tour of $n$ cities that visits each city $c$ a prescribed number $k_c$ of times. Travel costs may not be symmetric, and visiting a city twice in a row may incur a non-zero cost. The MV-TSP problem finds applications in scheduling, geometric approximation, and Hamiltonicity of certain graph families. The fastest known algorithm for MV-TSP is due to Cosmadakis and Papadimitriou (SICOMP, 1984). It runs in time $n^{O(n)} + O(n^3 \log \sum_c k_c )$ and requires $n^{\Omega(n)}$ space. The interesting feature of the Cosmadakis-Papadimitriou algorithm is its \emph{logarithmic} dependence on the total length $\sum_c k_c$ of the tour, allowing the algorithm to handle instances with very long tours, beyond what is tractable in the standard TSP setting. However, the \emph{superexponential} dependence on the number of cities in both its time and space complexity renders the algorithm impractical for all but the narrowest range of this parameter. In this paper we significantly improve on the Cosmadakis-Papadimitriou algorithm, giving an MV-TSP algorithm that runs in \emph{single-exponential} time with \emph{polynomial} space. More precisely, we obtain the running time $2^{O(n)} + O(n^3 \log{\sum_c k_c})$, with $O(n^2 \log{\sum_c k_c})$ space. Assuming the Exponential-time Hypothesis (ETH), both the time and space requirements of our algorithm are optimal. Our algorithm is deterministic, and arguably both simpler and easier to analyse than the original approach of Cosmadakis and Papadimitriou. It involves an optimization over oriented spanning trees of a graph and employs a recursive, centroid-based decomposition of trees.
Does It Ping or Pong? Auditory and Tactile Classification of Materials by Bouncing Events

Two experiments studied the role of impact sounds and vibrations in classification of materials. The task consisted of feeling on an actuated surface and listening through headphones to the recorded feedback of a ping-pong ball hitting three flat objects respectively made of wood, plastic, and metal, and then identifying their material. In Experiment 1, sounds and vibrations were recorded by keeping the objects in mechanical isolation. In Experiment 2, recordings were taken while the same objects stood on a table, causing their resonances to fade faster due to mechanical coupling with the support. A control experiment, where participants listened to and touched the real objects in mechanical isolation, showed high accuracy of classification from either sounds (90% correct) or vibrations (67% correct). Classification of reproduced bounces in Experiments 1 and 2 was less precise. In both experiments, the main effect of material was statistically significant; conversely, the main effect of modality (auditory or tactile) was significant only in the control. Identification of plastic and especially metal was less accurate in Experiment 2, suggesting that participants, when possible, classified materials by longer resonance tails. Audio-tactile summation of classification accuracy was found, suggesting that multisensory integration influences the perception of materials. Such results have prospective application to the nonvisual design of virtual buttons, which is the object of our current research.
Bioensaios de atividade alelopática dos esteroides espinasterol, espinasterona e glicopiranosil espinasterol

Tres esteroides (espinasterol, espinasterona e glicopiranosil espinasterol) foram isolados do caule de Moutabea guianensis, planta de origem amazonica. Suas estruturas foram determinadas a partir das analises de RMN e por comparacao com dados espectroscopicos da literatura. Foram avaliadas as atividades alelopaticas das tres substâncias em duas plantas invasoras de pastagens comuns da regiao amazonica: Mimosa pudica (malicia) e Senna obtusifolia (mata-pasto). A substância glicopiranosil espinasterol foi mais ativa no ensaio de desenvolvimento da radicula, inibindo a especie Senna obtusifolia em 75%; o espinasterol inibiu em 22% o desenvolvimento do hipocotilo de Senna obtusifolia; e no bioensaio de germinacao de sementes as substâncias espinasterol e espinasterona proporcionaram 10% de inibicao em Mimosa pudica. A partir desses resultados, observou-se que a diferenca de substituintes observada na posicao-3 dos esteroides e capaz de alterar a atividade alelopatica. O grupo glicosil na posicao-3 elevou a atividade alelopatica de forma mais expressiva no bioensaio de desenvolvimento de radicula da especie malicia, quando comparado aos esteroides hidroxilado e carbonilado. Este e o primeiro estudo quimico e alelopatico do genero Moutabea.
Rare Case of Isolated Dural Marginal Zone Lymphoma

PCNSL accounts for <1% of all non‐Hodgkin’s lymphoma cases and approximately 4% of newly diagnosed primary CNS tumors. [1] Most cases of PCNSL are aggressive, especially in the settings of underlying immunocompromised state. [2] Dural lymphomas (DLs) are a rare subtype of PCNSL that involve the dura mater without infiltration to the brain parenchyma. Low‐grade lymphomas, especially marginal zone subtype, constitute majority of DLs. Herein, we report a case of primary dural marginal zone lymphoma (MZL).