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1. PRShare: A Framework for Privacy-preserving, Interorganizational Data Sharing

   https://doi.org/10.1145/3531225  

   Idan, Lihi; Feigenbaum, Joan (November 2022, ACM Transactions on Privacy and Security)

   We consider the task of interorganizational data sharing, in which data owners, data clients, and data subjects have different and sometimes competing privacy concerns. One real-world scenario in which this problem arises concerns law-enforcement use of phone-call metadata: The data owner is a phone company, the data clients are law-enforcement agencies, and the data subjects are individuals who make phone calls. A key challenge in this type of scenario is that each organization uses its own set of proprietary intraorganizational attributes to describe the shared data; such attributes cannot be shared with other organizations. Moreover, data-access policies are determined by multiple parties and may be specified using attributes that are not directly comparable with the ones used by the owner to specify the data. We propose a system architecture and a suite of protocols that facilitate dynamic and efficient interorganizational data sharing, while allowing each party to use its own set of proprietary attributes to describe the shared data and preserving the confidentiality of both data records and proprietary intraorganizational attributes. We introduce the novel technique ofAttribute-Based Encryption with Oblivious Attribute Translation (OTABE), which plays a crucial role in our solution. This extension of attribute-based encryption uses semi-trusted proxies to enable dynamic and oblivious translation between proprietary attributes that belong to different organizations; it supports hidden access policies, direct revocation, and fine-grained, data-centric keys and queries. We prove that our OTABE-based framework is secure in the standard model and provide two real-world use cases. 

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2. Language contact or linguistic micro-engineering? Feature pools, social semiosis, and intentional language change in the Cameroonian Grassfields

   Di Carlo, Pierpaolo; Good, Jeff (November 2023, Linguistic Typology at the Crossroads)

   With more than seventy named languages, and many more locally distinctive varieties, the Cameroonian Grassfields are known for their impressive linguistic diversity. At the same time, the languages of the Grassfields also show a considerable degree of structural homogeneity and lexical similarity which is suggestive of both genealogical relatedness and prolonged processes of contact-induced convergence. However, fine-grained comparative analyses reveal puzzling situations of similarities and differences among neighboring languages and varieties. Often left unaddressed or viewed as “irregularities”, these cases might in fact provide insights into low-level language dynamics that have contributed significantly to the development of the regional linguistic configuration. In this paper, we focus on two such cases involving noun classes and tense-aspect marking and propose a model of language change based on a notion that we term the social semiosis layer, which is viewed as a specific part of a linguistic feature pool. When paired with the existing notion of neighbor opposition, it can account for situations where there is evidence that specific forms have been deliberately manipulated to create salient distinctions among varieties in a given local sociolinguistic context. 

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3. One model for the learning of language

   https://doi.org/10.1073/pnas.2021865119  

   Yang, Yuan; Piantadosi, Steven T. (February 2022, Proceedings of the National Academy of Sciences)

   A major goal of linguistics and cognitive science is to understand what class of learning systems can acquire natural language. Until recently, the computational requirements of language have been used to argue that learning is impossible without a highly constrained hypothesis space. Here, we describe a learning system that is maximally unconstrained, operating over the space of all computations, and is able to acquire many of the key structures present in natural language from positive evidence alone. We demonstrate this by providing the same learning model with data from 74 distinct formal languages which have been argued to capture key features of language, have been studied in experimental work, or come from an interesting complexity class. The model is able to successfully induce the latent system generating the observed strings from small amounts of evidence in almost all cases, including for regular (e.g., a n , ( a b ) n , and { a , b } + ), context-free (e.g., a n b n ,   a n b n + m , and x x R ), and context-sensitive (e.g., a n b n c n ,   a n b m c n d m , and xx ) languages, as well as for many languages studied in learning experiments. These results show that relatively small amounts of positive evidence can support learning of rich classes of generative computations over structures. The model provides an idealized learning setup upon which additional cognitive constraints and biases can be formalized. 

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4. Foundations of plasma standards

   https://doi.org/10.1088/1361-6595/acb810  

   Alves, Luís L.; Becker, Markus M.; van Dijk, Jan; Gans, Timo; Go, David B.; Stapelmann, Katharina; Tennyson, Jonathan; Turner, Miles M.; Kushner, Mark J. (February 2023, Plasma Sources Science and Technology)

   Abstract The field of low-temperature plasmas (LTPs) excels by virtue of its broad intellectual diversity, interdisciplinarity and range of applications. This great diversity also challenges researchers in communicating the outcomes of their investigations, as common practices and expectations for reporting vary widely in the many disciplines that either fall under the LTP umbrella or interact closely with LTP topics. These challenges encompass comparing measurements made in different laboratories, exchanging and sharing computer models, enabling reproducibility in experiments and computations using traceable and transparent methods and data, establishing metrics for reliability, and in translating fundamental findings to practice. In this paper, we address these challenges from the perspective of LTP standards for measurements, diagnostics, computations, reporting and plasma sources. This discussion on standards, or recommended best practices, and in some cases suggestions for standards or best practices, has the goal of improving communication, reproducibility and transparency within the LTP field and fields allied with LTPs. This discussion also acknowledges that standards and best practices, either recommended or at some point enforced, are ultimately a matter of judgment. These standards and recommended practices should not limit innovation nor prevent research breakthroughs from having real-time impact. Ultimately, the goal of our research community is to advance the entire LTP field and the many applications it touches through a shared set of expectations. 

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5. Error Resilient Transformer Networks: A Novel Sensitivity Guided Approach to Error Checking and Suppression

   Ma, Kwondo.; Amarnath, Chandramouli.; Chatterjee, Abhijit. (May 2023, European Test Symposium)

   Transformer networks have achieved remarkable success in Natural Language Processing (NLP) and Computer Vision applications. However, the underlying large volumes of Transformer computations demand high reliability and resilience to soft errors in processor hardware. The objective of this research is to develop efficient techniques for design of error resilient Transformer architectures. To enable this, we first perform a soft error vulnerability analysis of every fully connected layers in Transformer computations. Based on this study, error detection and suppression modules are selectively introduced into datapaths to restore Transformer performance under anticipated error rate conditions. Memory access errors and neuron output errors are detected using checksums of linear Transformer computations. Correction consists of determining output neurons with out-of-range values and suppressing the same to zero. For a Transformer with nominal BLEU score of 52.7, such vulnerability guided selective error suppression can recover language translation performance from a BLEU score of 0 to 50.774 with as much as 0.001 probability of activation error, incurring negligible memory and computation overheads. 

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