Extracting DNN Architectures Via Runtime Profiling On Mobile GPUs

Due to significant investment, research, and development efforts over the past decade, deep neural networks (DNNs) have achieved notable advancements in classification and regression domains. As a result, DNNs are considered valuable intellectual property for artificial intelligence providers. Prior work has demonstrated highly effective model extraction attacks which steal a DNN, dismantling the provider’s business model and paving the way for unethical or malicious activities, such as misuse of personal data, safety risks in critical systems, or spreading misinformation. This thesis explores the feasibility of model extraction attacks on mobile devices using aggregated runtime profiles as a side-channel to leak DNN architecture. Since mobile devices are resource constrained, DNN deployments require optimization efforts to reduce latency. The main hurdle in extracting DNN architectures in this scenario is that optimization techniques, such as operator-level and graph-level fusion, can obfuscate the association between runtime profile operators and their corresponding DNN layers, posing challenges for adversaries to accurately predict the computation performed. The thesis presents a novel approach for identifying the original architecture of a Deep Neural Network (DNN) based on analyzing its GPU call profile as a side-channel. Even when the optimization process has obscured layer information and introduced noise, the proposed approach can effectively determine the original structure. Additionally, we propose extraction of hyperparameters layer-by-layer from sub-layer patterns. No existing solution has extracted architectures from optimized DNN models deployed on mobile GPUs, especially in the presence of obfuscation or optimization. This research is the first to do so

Crash simulation and development of automotive bumper impact beam design for weight reduction

Increasing fuel economy has been a principal issue in the development of new cars. Decrease in weight of vehicle components is one of the important approaches to solving the problem. In order to obtain the goal, researches sought for ways to design lighter and stronger bumper impact beams relinquishing neither impact absorbing capacity of bumpers nor the safety of vehicles. In this paper, a possible weight reduction of impact beam is examined by substituting conventional type of steel with high-strength steel (boron steel having tensile strength 1.5 GPa) with regard to variables such as frontal, offset and corner impact crash capability of a bumper impact beam. In addition, the effects of variation in structural differences in bumper impact beams (open section, closed section, open section with 5 stays) were carefully considered. Arguably, this analysis presents a good guide for an optimum design and development of bumper impact beams

Determination of 11 Phenolic Endocrine Disruptors using Gas Chromatography/Mass Spectrometry-Selected Ion Monitoring in Five Selected Wastewater Influents

Abstract An efficient method for the simultaneous determination of eleven phenolic endocrine-disrupting chemicals (EDCs) present in wastewater influent samples was described. The 11 phenolic EDCs including alkylphenols, chlorophenols, and bisphenol A were determined by gas chromatography/mass spectrometry-selected ion monitoring (GC/MS-SIM) following two work-up methods for comparison; isobutoxycarbonyl (isoBOC) derivatization and tert-butyldimethylsilyl (TBDMS) derivatization. The wastewater influent samples containing the 11 EDCs were adjusted to pH 2 with H2SO4 and then cleaned up with n-hexane. Next, they were subjected to solid-phase extraction (SPE) with XAD-4 resin and subsequently converted to isoBOC or TBDMS derivatives for sensitivity analysis with gas chromatography/mass spectrometry-selected ion monitoring (GC/MS-SIM). Following isoBOC derivatization and TBDMS derivatization, the recoveries were 86.6-105.2% and 97.6-142.7%, the limits of quantitation (LOQ) for the 11 phenolic EDCs for SIM was 0.001-0.050 ng/mL and 0.003-0.050 ng/mL, and the SIM responses were linear with the correlation coefficient varying by 0.9717-0.9995 and 0.9842-0.9980, respectively. When these methods were applied to five selected wastewater influent samples, for isoBOC derivatization and TBDMS derivatization the ranges of concentration detected were 0.2-99.6 ng/mL and 0.4-147.4 ng/mL, respectively

Crash simulation and development of automotive bumper impact beam design for weight reduction

Increasing fuel economy has been a principal issue in the development of new cars. Decrease in weight of vehicle components is one of the important approaches to solving the problem. In order to obtain the goal, researches sought for ways to design lighter and stronger bumper impact beams relinquishing neither impact absorbing capacity of bumpers nor the safety of vehicles. In this paper, a possible weight reduction of impact beam is examined by substituting conventional type of steel with high-strength steel (boron steel having tensile strength 1.5 GPa) with regard to variables such as frontal, offset and corner impact crash capability of a bumper impact beam. In addition, the effects of variation in structural differences in bumper impact beams (open section, closed section, open section with 5 stays) were carefully considered. Arguably, this analysis presents a good guide for an optimum design and development of bumper impact beams

Hydroxyapatite Humidifier Vibrator Housing Fabrication and Characteristics

The humidifier vibrator housing is difficult to clean and prone to contamination due to its metallic material. To overcome these shortcomings, the humidifier vibrator housing was manufactured using Hydroxyapatite as a raw material. Although hydroxyapatite has excellent antibacterial properties and biocompatibility, it is difficult to manufacture a sintered body due to its weak fracture toughness. Therefore, hydroxyapatite sintered compacts were prepared according to the amount of plasticizer added and their physical properties were compared. The average compressive strength was 395.1 N·mm-2 at 8 % of the amount of added plasticizer, and the average bending strength was 61.8 N·mm-2 at 6 % of the amount of added plasticizer. The hydroxyapatite sintered compact showed the effect of inhibiting the production of bacteria regardless of the amount of plasticizer added. As a result of this physical property study, it was possible to develop a humidifier vibrator housing with excellent antibacterial properties and maintaining mechanical strength

Learning to extract relations for protein annotation

Motivation: Protein annotation is a task that describes protein X in terms of topic Y. Usually, this is constructed using information from the biomedical literature. Until now, most of literature-based protein annotation work has been done manually by human annotators. However, as the number of biomedical papers grows ever more rapidly, manual annotation becomes more difficult, and there is increasing need to automate the process. Recently, information extraction (IE) has been used to address this problem. Typically, IE requires pre-defined relations and hand-crafted IE rules or annotated corpora, and these requirements are difficult to satisfy in real-world scenarios such as in the biomedical domain. In this article, we describe an IE system that requires only sentences labelled according to their relevance or not to a given topic by domain experts. Results: We applied our system to meet the annotation needs of a well-known protein family database; the results show that our IE system can annotate proteins with a set of extracted relations by learning relations and IE rules for disease, function and structure from only relevant and irrelevant sentences. Contact: [email protected]

Christianity in Kim Hyun-seung’s poetry

This article attempts to analyze how Kim Hyun-seung’s thoughts about Christianity have changed throughout his poetic works. Considered to be a Puritan-like-figure, Kim Hyun-seung is regarded as the most representative Christian poet in Korea. His works, often written in a solemn mood, are characterized by prayer and devotion to God. In Autumn, the significant season for Kim, he gets immersed in religious contemplation. As a solely wandering bird in his works, the raven symbolizes his religious soul. However, Kim was skeptical about the religion during his middle period due to the limitations of Christianity. His frustration was accompanied by solid solitude with the loss of God, which is in opposition to Kierkegaardian solitude. After suffering from a crisis of death, he returned to Christianity with absolute faith in his final years. His oscillation between the religious faith and the recognition of reality reflects his persistent pursuit of religious ethics, which cannot be fully satiated

Evaluation and cross-comparison of lexical entities of biological interest (LexEBI)

MOTIVATION: Biomedical entities, their identifiers and names, are essential in the representation of biomedical facts and knowledge. In the same way, the complete set of biomedical and chemical terms, i.e. the biomedical "term space" (the "Lexeome"), forms a key resource to achieve the full integration of the scientific literature with biomedical data resources: any identified named entity can immediately be normalized to the correct database entry. This goal does not only require that we are aware of all existing terms, but would also profit from knowing all their senses and their semantic interpretation (ambiguities, nestedness). RESULT: This study compiles a resource for lexical terms of biomedical interest in a standard format (called "LexEBI"), determines the overall number of terms, their reuse in different resources and the nestedness of terms. LexEBI comprises references for protein and gene entries and their term variants and chemical entities amongst other terms. In addition, disease terms have been identified from Medline and PubmedCentral and added to LexEBI. Our analysis demonstrates that the baseforms of terms from the different semantic types show only little polysemous use. Nonetheless, the term variants of protein and gene names (PGNs) frequently contain species mentions, which should have been avoided according to protein annotation guidelines. Furthermore, the protein and gene entities as well as the chemical entities, both do comprise enzymes leading to hierarchical polysemy, and a large portion of PGNs make reference to a chemical entity. Altogether, according to our analysis based on the Medline distribution, 401,869 unique PGNs in the documents contain a reference to 25,022 chemical entities, 3,125 disease terms or 1,576 species mentions. CONCLUSION: LexEBI delivers the complete biomedical and chemical Lexeome in a standardized representation (http://www.ebi.ac.uk/Rebholz-srv/LexEBI/). The resource provides the disease terms as open source content, and fully interlinks terms across resources

Evaluating gold standard corpora against gene/protein tagging solutions and lexical resources

Motivation The identification of protein and gene names (PGNs) from the scientific literature requires semantic resources: Terminological and lexical resources deliver the term candidates into PGN tagging solutions and the gold standard corpora (GSC) train them to identify term parameters and contextual features.Ideally all three resources, i.e.~corpora, lexica and taggers, cover the same domain knowledge, and thus support identification of the same types of PGNs and cover all of them.Unfortunately, none of the three serves as a predominant standard and for this reason it is worth exploring, how these three resources comply with each other.We systematically compare different PGN taggers against publicly available corpora and analyze the impact of the included lexical resource in their performance.In particular, we determine the performance gains through false positive filtering, which contributes to the disambiguation of identified PGNs. RESULTS: In general, machine learning approaches (ML-Tag) for PGN tagging show higher F1-measureperformance against the BioCreative-II and Jnlpba GSCs (exact matching), whereas the lexicon basedapproaches (LexTag) in combination with disambiguation methods show better results on FsuPrgeand PennBio. The ML-Tag solutions balance precision and recall, whereas the LexTag solutions havedifferent precision and recall profiles at the same F1-measure across all corpora. Higher recall isachieved with larger lexical resources, which also introduce more noise (false positive results). TheML-Tag solutions certainly perform best, if the test corpus is from the same GSC as the trainingcorpus. As expected, the false negative errors characterize the test corpora and - on the other hand- the profiles of the false positive mistakes characterize the tagging solutions. Lex-Tag solutions thatare based on a large terminological resource in combination with false positive filtering produce betterresults, which, in addition, provide concept identifiers from a knowledge source in contrast to ML-Tagsolutions. CONCLUSION: The standard ML-Tag solutions achieve high performance, but not across all corpora, and thus shouldbe trained using several different corpora to reduce possible biases. The LexTag solutions havedifferent profiles for their precision and recall performance, but with similar F1-measure. This resultis surprising and suggests that they cover a portion of the most common naming standards, but copedifferently with the term variability across the corpora. The false positive filtering applied to LexTagsolutions does improve the results by increasing their precision without compromising significantlytheir recall. The harmonisation of the annotation schemes in combination with standardized lexicalresources in the tagging solutions will enable their comparability and will pave the way for a sharedstandard