Novel lightweight signcryption-based key distribution mechanisms for MIKEY

Part 1: Authentication and Key ManagementInternational audienceMultimedia Internet KEYing (MIKEY) is a standard key management protocol, used to set up common secrets between any two parties for multiple scenarios of communications. As MIKEY becomes widely deployed, it becomes worthwhile to not confine its applications to real-time or other specific applications, but also to extend the standard to other scenarios as well. For instance, MIKEY can be used to secure key establishment in the Internet of Things. In this particular context, Elliptic Curve Cryptography-based (ECC) algorithms seem to be good candidate to be employed by MIKEY, since they can support equivalent security level when compared with other recommended cryptographic algorithms like RSA, and at the same time requiring smaller key sizes and offering better performance. In this work, we propose novel lightweight ECC-based key distribution extensions for MIKEY that are built upon a previously proposed certificateless signcryption scheme. To our knowledge, these extensions are the first ECC-based MIKEY extensions that employ signcryption schemes. Our proposed extensions benefit from the lightness of the signcryption scheme, while being discharged from the burden of the public key infrastructure (PKI) thanks to its certificateless feature. To demonstrate their performance, we implemented our proposed extensions in the Openmote sensor platform and conducted a thorough performance assessment by measuring the energy consumption and execution time of each operation in the key establishment procedure. The experimental results prove that our new MIKEY extensions are perfectly suited for resource-constrained device

Lightweight certificateless and provably-secure signcryptosystem for the internet of things

International audienceIn this paper, we propose an elliptic curve-based signcryption scheme derived from the standardized signature KCDSA (Korean Certificate-based Digital Signature Algorithm) in the context of the Internet of Things. Our solution has several advantages. First, the scheme is provably secure in the random oracle model. Second, it provides the following security properties: outsider/insider confidentiality and unforgeability; non-repudiation and public verifiability, while being efficient in terms of communication and computation costs. Third, the scheme offers the certificateless feature, so certificates are not needed to verify the user's public keys. For illustration, we conducted experimental evaluation based on a sensor Wismote platform and compared the performance of the proposed scheme to concurrent scheme

Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedback

A key technology for the development of large language models (LLMs) involves instruction tuning that helps align the models' responses with human expectations to realize impressive learning abilities. Two major approaches for instruction tuning characterize supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF), which are currently applied to produce the best commercial LLMs (e.g., ChatGPT). To improve the accessibility of LLMs for research and development efforts, various instruction-tuned open-source LLMs have also been introduced recently, e.g., Alpaca, Vicuna, to name a few. However, existing open-source LLMs have only been instruction-tuned for English and a few popular languages, thus hindering their impacts and accessibility to many other languages in the world. Among a few very recent work to explore instruction tuning for LLMs in multiple languages, SFT has been used as the only approach to instruction-tune LLMs for multiple languages. This has left a significant gap for fine-tuned LLMs based on RLHF in diverse languages and raised important questions on how RLHF can boost the performance of multilingual instruction tuning. To overcome this issue, we present Okapi, the first system with instruction-tuned LLMs based on RLHF for multiple languages. Okapi introduces instruction and response-ranked data in 26 diverse languages to facilitate the experiments and development of future multilingual LLM research. We also present benchmark datasets to enable the evaluation of generative LLMs in multiple languages. Our experiments demonstrate the advantages of RLHF for multilingual instruction over SFT for different base models and datasets. Our framework and resources are released at https://github.com/nlp-uoregon/Okapi

An in-situ thermoelectric measurement apparatus inside a thermal-evaporator

At the ultra-thin limit below 20 nm, a film's electrical conductivity, thermal conductivity, or thermoelectricity depends heavily on its thickness. In most studies, each sample is fabricated one at a time, potentially leading to considerable uncertainty in later characterizations. We design and build an in-situ apparatus to measure thermoelectricity during their deposition inside a thermal evaporator. A temperature difference of up to 2 K is generated by a current passing through an on-chip resistor patterned using photolithography. The Seebeck voltage is measured on a Hall bar structure of a film deposited through a shadow mask. The measurement system is calibrated carefully before loading into the thermal evaporator. This in-situ thermoelectricity measurement system has been thoroughly tested on various materials, including Bi, Te, and Bi$_2$Te$_3$, at high temperatures up to 500 K

Structural, Mechanical, Electronic and Thermodynamic Analysis of Calcium Aluminum Silicate Crystalline Phases in Stone Wool Insulation Materials: A first-principles study

Stone wool materials have gained considerable attention due to their effectiveness as thermal and acoustic insulation solutions. The comprehension of crystal structure properties is pivotal in determining the overall performance of these materials, as it enables us to optimize their composition for enhanced insulating capabilities. Crucial factors such as structural, mechanical, and thermodynamic characteristics of crystalline phases within stone wool are vital for evaluating its thermal and acoustic insulation properties. This study investigates the properties of calcium aluminum silicate crystal phases commonly present in stone wool, including anorthite, svyatoslavite, scolecite, and dehydrated scolecite using density functional theory (DFT) calculations. In comparison to previous works, this study provides a more comprehensive analysis using advanced DFT calculations. Our analysis reveals the complex interplay between the crystal structures and mechanical behavior of these phases. The calculated bulk modulus of the phases varies significantly, ranging from 38 to 83 GPa. We have compared the calculated elastic properties with available experimental data and found excellent agreement, confirming the accuracy of the computational approach. Moreover, we find that polymorphism has a significant impact on the mechanical strength, with anorthite exhibiting higher strength compared to svyatoslavite. Furthermore, dehydration is found to cause a reduction in unit volume and mechanical strength. The thermodynamic properties of dehydrated scolecite, including entropy and heat capacity, are significantly lower due to the absence of water molecules. These findings highlight the importance of understanding the structural and mechanical characteristics of calcium aluminum silicate phases in stone wool materials. Additionally, our findings have broader implications in various industries requiring effective insulation solutions such as to develop new materials or to enhance the energy efficiency of existing insulating products. © 2023 The Author(s)publishedVersio

Elastic and thermodynamic properties of the major clinker phases of Portland cement: Insights from first principles calculations

Portland based cement is one of the most popular materials used in the civil and construction applications. Reliable computational methods to provide an insight into the underlying mechanics of the major phases of this material are of great interest for cement design. The present work investigated the performance of density functional theory (DFT) calculations using the PBE-D2 method to predict the mechanical, thermodynamic properties of four major phases namely Alite C3S, Belite C2S, tricalcium aluminate C3A and tetracalcium aluminoferrite C4AF. The calculated elastic properties were in a good agreement with available experimental data. In addition, a deeper insight into the electron density of state, spin-polarization, atomic charge, as well as free energy and entropy properties were also presented. Further development is necessary to improve the established DFT models for predicting the mechanical properties of the ferrite phase of Portland clinker.publishedVersio

CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages

The driving factors behind the development of large language models (LLMs) with impressive learning capabilities are their colossal model sizes and extensive training datasets. Along with the progress in natural language processing, LLMs have been frequently made accessible to the public to foster deeper investigation and applications. However, when it comes to training datasets for these LLMs, especially the recent state-of-the-art models, they are often not fully disclosed. Creating training data for high-performing LLMs involves extensive cleaning and deduplication to ensure the necessary level of quality. The lack of transparency for training data has thus hampered research on attributing and addressing hallucination and bias issues in LLMs, hindering replication efforts and further advancements in the community. These challenges become even more pronounced in multilingual learning scenarios, where the available multilingual text datasets are often inadequately collected and cleaned. Consequently, there is a lack of open-source and readily usable dataset to effectively train LLMs in multiple languages. To overcome this issue, we present CulturaX, a substantial multilingual dataset with 6.3 trillion tokens in 167 languages, tailored for LLM development. Our dataset undergoes meticulous cleaning and deduplication through a rigorous pipeline of multiple stages to accomplish the best quality for model training, including language identification, URL-based filtering, metric-based cleaning, document refinement, and data deduplication. CulturaX is fully released to the public in HuggingFace to facilitate research and advancements in multilingual LLMs: https://huggingface.co/datasets/uonlp/CulturaX.Comment: Ongoing Wor

Growth of single crystals of methylammonium lead mixedhalide perovskites

We report the growth and characterization of different bulk single crystals of organo lead mixed halide perovskites CH3NH3PbI3−xBrx by two different crystal growth approaches: (i)anti-solvent diffusion, and (ii) inverse temperature crystallization. In order to control the size and the shape of crystals, we have investigated different experimental growth parameters such as temperature and precursor concentration. The morphology of obtained crystals was observed by optical microscope, whereas their intrinsic crystalline properties were characterized by single crystal as well as powder X-ray diffraction. The results illustrated that the growth and crystalline structure of mixed halide perovskites CH3NH3PbI3−xBrx could be easily tuned