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1. SoK: "Plug & Pray" Today – Understanding USB Insecurity in Versions 1 Through C

   https://doi.org/10.1109/SP.2018.00037  

   Tian, Jing ; Scaife, Nolen ; Kumar, Deepak ; Bailey, Michael ; Bates, Adam ; Butler, Kevin ( May 2018 , 2018 IEEE Symposium on Security and Privacy (SP) (2018))

   USB-based attacks have increased in complexity in recent years. Modern attacks now incorporate a wide range of attack vectors, from social engineering to signal injection. To address these challenges, the security community has responded with a growing set of fragmented defenses. In this work, we survey and categorize USB attacks and defenses, unifying observations from both peer-reviewed research and industry. Our systematization extracts offensive and defensive primitives that operate across layers of communication within the USB ecosystem. Based on our taxonomy, we discover that USB attacks often abuse the trust-by-default nature of the ecosystem, and transcend different layers within a software stack; none of the existing defenses provide a complete solution, and solutions expanding multiple layers are most effective. We then develop the first formal verification of the recently released USB Type- C Authentication specification, and uncover fundamental flaws in the specification's design. Based on the findings from our systematization, we observe that while the spec has successfully pinpointed an urgent need to solve the USB security problem, its flaws render these goals unattainable. We conclude by outlining future research directions to ensure a safer computing experience with USB. 

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2. Comparative Approaches in Vertebrate Cartilage Histogenesis and Regulation: Insights from Lampreys and Hagfishes

   https://doi.org/10.3390/d13090435  

   Root, Zachary D. ; Gould, Claire ; Brewer, Margaux ; Jandzik, David ; Medeiros, Daniel M. ( September 2021 , Diversity)

   Jawed vertebrates (gnathostomes) have been the dominant lineage of deuterostomes for nearly three hundred fifty million years. Only a few lineages of jawless vertebrates remain in comparison. Composed of lampreys and hagfishes (cyclostomes), these jawless survivors are important systems for understanding the evolution of vertebrates. One focus of cyclostome research has been head skeleton development, as its evolution has been a driver of vertebrate morphological diversification. Recent work has identified hyaline-like cartilage in the oral cirri of the invertebrate chordate amphioxus, making cyclostomes critical for understanding the stepwise acquisition of vertebrate chondroid tissues. Our knowledge of cyclostome skeletogenesis, however, has lagged behind gnathostomes due to the difficulty of manipulating lamprey and hagfish embryos. In this review, we discuss and compare the regulation and histogenesis of cyclostome and gnathostome skeletal tissues. We also survey differences in skeletal morphology that we see amongst cyclostomes, as few elements can be confidently homologized between them. A recurring theme is the heterogeneity of skeletal morphology amongst living vertebrates, despite conserved genetic regulation. Based on these comparisons, we suggest a model through which these mesenchymal connective tissues acquired distinct histologies and that histological flexibility in cartilage existed in the last common ancestor of modern vertebrates. 

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3. Understanding liquid–liquid equilibria in binary mixtures of hydrocarbons with a thermally robust perarylphosphonium-based ionic liquid

   https://doi.org/10.1039/D1RA06268A  

   Bandlamudi, Santosh R. ; McGehee, Jimmie L. ; Mando, Albaraa D. ; Soltani, Mohammad ; Turner, C. Heath ; Davis, James H. ; West, Kevin N. ; Rabideau, Brooks D. ( September 2021 , RSC Advances)

   Binary mixtures of hydrocarbons and a thermally robust ionic liquid (IL) incorporating a perarylphosphonium-based cation are investigated experimentally and computationally. Experimentally, it is seen that excess toluene added to the IL forms two distinct liquid phases, an “ion-rich” phase of fixed composition and a phase that is nearly pure toluene. Conversely, n -heptane is observed to be essentially immiscible in the neat IL. Molecular dynamics simulations capture both of these behaviours. Furthermore, the simulated composition of the toluene-rich IL phase is within 10% of the experimentally determined composition. Additional simulations are performed on the binary mixtures of the IL and ten other small hydrocarbons having mixed aromatic/aliphatic character. It is found that hydrocarbons with a predominant aliphatic character are largely immiscible with the IL, while those with a predominant aromatic character readily mix with the IL. A detailed analysis of the structure and energetic changes that occur on mixing reveals the nature of the ion-rich phase. The simulations show a bicontinuous phase with hydrocarbon uptake akin to absorption and swelling by a porous absorbent. Aromatic hydrocarbons are driven into the neat IL via dispersion forces with the IL cations and, to a lesser extent, the IL anions. The ion–ion network expands to accommodate the hydrocarbons, yet maintains a core connective structure. At a certain loading, this network becomes stretched to its limit. The energetic penalty associated with breaking the core connective network outweighs the gain from new hydrocarbon–IL interactions, leaving additional hydrocarbons in the neat phase. The spatially alternating charge of the expanded IL network is shown to interact favourably with the stacked aromatic subphase, something not possible for aliphatic hydrocarbons. 

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4. The Temple University Digital Pathology Corpus: The Breast Tissue Subset

   https://doi.org/10.1109/SPMB52430.2021.9672275  

   Wevodau, Z. ; Doshna, B. ; Jhala, N. ; Akhtar, I. ; Obeid, I. ; Picone, J. ( December 2021 , Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB))

   Obeid, I. (Ed.)

   The Neural Engineering Data Consortium (NEDC) is developing the Temple University Digital Pathology Corpus (TUDP), an open source database of high-resolution images from scanned pathology samples [1], as part of its National Science Foundation-funded Major Research Instrumentation grant titled “MRI: High Performance Digital Pathology Using Big Data and Machine Learning” [2]. The long-term goal of this project is to release one million images. We have currently scanned over 100,000 images and are in the process of annotating breast tissue data for our first official corpus release, v1.0.0. This release contains 3,505 annotated images of breast tissue including 74 patients with cancerous diagnoses (out of a total of 296 patients). In this poster, we will present an analysis of this corpus and discuss the challenges we have faced in efficiently producing high quality annotations of breast tissue. It is well known that state of the art algorithms in machine learning require vast amounts of data. Fields such as speech recognition [3], image recognition [4] and text processing [5] are able to deliver impressive performance with complex deep learning models because they have developed large corpora to support training of extremely high-dimensional models (e.g., billions of parameters). Other fields that do not have access to such data resources must rely on techniques in which existing models can be adapted to new datasets [6]. A preliminary version of this breast corpus release was tested in a pilot study using a baseline machine learning system, ResNet18 [7], that leverages several open-source Python tools. The pilot corpus was divided into three sets: train, development, and evaluation. Portions of these slides were manually annotated [1] using the nine labels in Table 1 [8] to identify five to ten examples of pathological features on each slide. Not every pathological feature is annotated, meaning excluded areas can include focuses particular to these labels that are not used for training. A summary of the number of patches within each label is given in Table 2. To maintain a balanced training set, 1,000 patches of each label were used to train the machine learning model. Throughout all sets, only annotated patches were involved in model development. The performance of this model in identifying all the patches in the evaluation set can be seen in the confusion matrix of classification accuracy in Table 3. The highest performing labels were background, 97% correct identification, and artifact, 76% correct identification. A correlation exists between labels with more than 6,000 development patches and accurate performance on the evaluation set. Additionally, these results indicated a need to further refine the annotation of invasive ductal carcinoma (“indc”), inflammation (“infl”), nonneoplastic features (“nneo”), normal (“norm”) and suspicious (“susp”). This pilot experiment motivated changes to the corpus that will be discussed in detail in this poster presentation. To increase the accuracy of the machine learning model, we modified how we addressed underperforming labels. One common source of error arose with how non-background labels were converted into patches. Large areas of background within other labels were isolated within a patch resulting in connective tissue misrepresenting a non-background label. In response, the annotation overlay margins were revised to exclude benign connective tissue in non-background labels. Corresponding patient reports and supporting immunohistochemical stains further guided annotation reviews. The microscopic diagnoses given by the primary pathologist in these reports detail the pathological findings within each tissue site, but not within each specific slide. The microscopic diagnoses informed revisions specifically targeting annotated regions classified as cancerous, ensuring that the labels “indc” and “dcis” were used only in situations where a micropathologist diagnosed it as such. Further differentiation of cancerous and precancerous labels, as well as the location of their focus on a slide, could be accomplished with supplemental immunohistochemically (IHC) stained slides. When distinguishing whether a focus is a nonneoplastic feature versus a cancerous growth, pathologists employ antigen targeting stains to the tissue in question to confirm the diagnosis. For example, a nonneoplastic feature of usual ductal hyperplasia will display diffuse staining for cytokeratin 5 (CK5) and no diffuse staining for estrogen receptor (ER), while a cancerous growth of ductal carcinoma in situ will have negative or focally positive staining for CK5 and diffuse staining for ER [9]. Many tissue samples contain cancerous and non-cancerous features with morphological overlaps that cause variability between annotators. The informative fields IHC slides provide could play an integral role in machine model pathology diagnostics. Following the revisions made on all the annotations, a second experiment was run using ResNet18. Compared to the pilot study, an increase of model prediction accuracy was seen for the labels indc, infl, nneo, norm, and null. This increase is correlated with an increase in annotated area and annotation accuracy. Model performance in identifying the suspicious label decreased by 25% due to the decrease of 57% in the total annotated area described by this label. A summary of the model performance is given in Table 4, which shows the new prediction accuracy and the absolute change in error rate compared to Table 3. The breast tissue subset we are developing includes 3,505 annotated breast pathology slides from 296 patients. The average size of a scanned SVS file is 363 MB. The annotations are stored in an XML format. A CSV version of the annotation file is also available which provides a flat, or simple, annotation that is easy for machine learning researchers to access and interface to their systems. Each patient is identified by an anonymized medical reference number. Within each patient’s directory, one or more sessions are identified, also anonymized to the first of the month in which the sample was taken. These sessions are broken into groupings of tissue taken on that date (in this case, breast tissue). A deidentified patient report stored as a flat text file is also available. Within these slides there are a total of 16,971 total annotated regions with an average of 4.84 annotations per slide. Among those annotations, 8,035 are non-cancerous (normal, background, null, and artifact,) 6,222 are carcinogenic signs (inflammation, nonneoplastic and suspicious,) and 2,714 are cancerous labels (ductal carcinoma in situ and invasive ductal carcinoma in situ.) The individual patients are split up into three sets: train, development, and evaluation. Of the 74 cancerous patients, 20 were allotted for both the development and evaluation sets, while the remain 34 were allotted for train. The remaining 222 patients were split up to preserve the overall distribution of labels within the corpus. This was done in hope of creating control sets for comparable studies. Overall, the development and evaluation sets each have 80 patients, while the training set has 136 patients. In a related component of this project, slides from the Fox Chase Cancer Center (FCCC) Biosample Repository (https://www.foxchase.org/research/facilities/genetic-research-facilities/biosample-repository -facility) are being digitized in addition to slides provided by Temple University Hospital. This data includes 18 different types of tissue including approximately 38.5% urinary tissue and 16.5% gynecological tissue. These slides and the metadata provided with them are already anonymized and include diagnoses in a spreadsheet with sample and patient ID. We plan to release over 13,000 unannotated slides from the FCCC Corpus simultaneously with v1.0.0 of TUDP. Details of this release will also be discussed in this poster. Few digitally annotated databases of pathology samples like TUDP exist due to the extensive data collection and processing required. The breast corpus subset should be released by November 2021. By December 2021 we should also release the unannotated FCCC data. We are currently annotating urinary tract data as well. We expect to release about 5,600 processed TUH slides in this subset. We have an additional 53,000 unprocessed TUH slides digitized. Corpora of this size will stimulate the development of a new generation of deep learning technology. In clinical settings where resources are limited, an assistive diagnoses model could support pathologists’ workload and even help prioritize suspected cancerous cases. ACKNOWLEDGMENTS This material is supported by the National Science Foundation under grants nos. CNS-1726188 and 1925494. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. REFERENCES [1] N. Shawki et al., “The Temple University Digital Pathology Corpus,” in Signal Processing in Medicine and Biology: Emerging Trends in Research and Applications, 1st ed., I. Obeid, I. Selesnick, and J. Picone, Eds. New York City, New York, USA: Springer, 2020, pp. 67 104. https://www.springer.com/gp/book/9783030368432. [2] J. Picone, T. Farkas, I. Obeid, and Y. Persidsky, “MRI: High Performance Digital Pathology Using Big Data and Machine Learning.” Major Research Instrumentation (MRI), Division of Computer and Network Systems, Award No. 1726188, January 1, 2018 – December 31, 2021. https://www. isip.piconepress.com/projects/nsf_dpath/. [3] A. Gulati et al., “Conformer: Convolution-augmented Transformer for Speech Recognition,” in Proceedings of the Annual Conference of the International Speech Communication Association (INTERSPEECH), 2020, pp. 5036-5040. https://doi.org/10.21437/interspeech.2020-3015. [4] C.-J. Wu et al., “Machine Learning at Facebook: Understanding Inference at the Edge,” in Proceedings of the IEEE International Symposium on High Performance Computer Architecture (HPCA), 2019, pp. 331–344. https://ieeexplore.ieee.org/document/8675201. [5] I. Caswell and B. Liang, “Recent Advances in Google Translate,” Google AI Blog: The latest from Google Research, 2020. [Online]. Available: https://ai.googleblog.com/2020/06/recent-advances-in-google-translate.html. [Accessed: 01-Aug-2021]. [6] V. Khalkhali, N. Shawki, V. Shah, M. Golmohammadi, I. Obeid, and J. Picone, “Low Latency Real-Time Seizure Detection Using Transfer Deep Learning,” in Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB), 2021, pp. 1 7. https://www.isip. piconepress.com/publications/conference_proceedings/2021/ieee_spmb/eeg_transfer_learning/. [7] J. Picone, T. Farkas, I. Obeid, and Y. Persidsky, “MRI: High Performance Digital Pathology Using Big Data and Machine Learning,” Philadelphia, Pennsylvania, USA, 2020. https://www.isip.piconepress.com/publications/reports/2020/nsf/mri_dpath/. [8] I. Hunt, S. Husain, J. Simons, I. Obeid, and J. Picone, “Recent Advances in the Temple University Digital Pathology Corpus,” in Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB), 2019, pp. 1–4. https://ieeexplore.ieee.org/document/9037859. [9] A. P. Martinez, C. Cohen, K. Z. Hanley, and X. (Bill) Li, “Estrogen Receptor and Cytokeratin 5 Are Reliable Markers to Separate Usual Ductal Hyperplasia From Atypical Ductal Hyperplasia and Low-Grade Ductal Carcinoma In Situ,” Arch. Pathol. Lab. Med., vol. 140, no. 7, pp. 686–689, Apr. 2016. https://doi.org/10.5858/arpa.2015-0238-OA. 

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5. AI for a Generative Economy: The Role of Intelligent Systems in Sustaining Unalienated Labor, Environment, and Society

   Eglash, R. ; Robert, L. ; Bennett, A. ; Robinson, K. ; Lachney, M. ; & Babbitt, W. ( November 2019 , AAAI fall symposium series)

   Extractive economies pull value from a system without restoring it. Unsustainable extraction of ecological value includes over-fishing, clear-cut logging, etc. Extraction of labor value is similarly objectionable: assembly line jobs for example increase the likelihood of cardiovascular disease, depression, suicide and other problems. Extraction of social value-vacuuming up online personal information, commodification of the public sphere, and so on-constitutes a third form. But all three domains-ecological value, labor value, and social value-can thrive in unalienated forms if we can create a future of work that replaces extraction with generative cycles. AI is a key technology in developing these alternative economic forms. This paper describes some initial experiments with African, African American, and Native American artisans who were willing to experiment with the introduction of computational enhancements to their work. Following our report on these initial results, we map out a vision for how AI could scale up labor that sustains "heritage algorithms", ecologically situated value chains and other hybrid forms that prevent value alienation while flourishing from its robust circulation. 

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