Robust Multi-bit Natural Language Watermarking through Invariant Features

Recent years have witnessed a proliferation of valuable original natural language contents found in subscription-based media outlets, web novel platforms, and outputs of large language models. However, these contents are susceptible to illegal piracy and potential misuse without proper security measures. This calls for a secure watermarking system to guarantee copyright protection through leakage tracing or ownership identification. To effectively combat piracy and protect copyrights, a multi-bit watermarking framework should be able to embed adequate bits of information and extract the watermarks in a robust manner despite possible corruption. In this work, we explore ways to advance both payload and robustness by following a well-known proposition from image watermarking and identify features in natural language that are invariant to minor corruption. Through a systematic analysis of the possible sources of errors, we further propose a corruption-resistant infill model. Our full method improves upon the previous work on robustness by +16.8% point on average on four datasets, three corruption types, and two corruption ratios. Code available at https://github.com/bangawayoo/nlp-watermarking.Comment: ACL 2023 lon

Optical Air-Gap Attacks:Analysis and IoT Threat Implications

Since 2008, the Korean government has instituted network separation technology, which physically isolates external internet networks from internal networks, aiming to thwart cyber-attacks. Consequently, the domestic financial sector was largely unaffected during global crises (2017 WannaCry ransomware outbreak and the 2021 Log4j vulnerability incident). However, there exist certain vulnerabilities owing to the presumption of their relative safety against cyber intrusions and the integration of cloud and Internet of Things (IoT) technologies in the current smart revolution. The existing network separation measures only mitigate one facet of potential cyber threats, rendering a comprehensive defense elusive. The rise of “air-gap” attacks, which exploit the isolated space between closed and external networks to illicitly transfer data and the existing research primarily substantiating the potential for data breaches from closed networks to their external counterparts are problems yet to be addressed. Thus, our study proposed a tangible optical air-gap attack methodology, harnessing readily available optical mediums within closed networks. Intricate measurement metrics that consider vital factors of the transmission environment were proposed. Moreover, acknowledging the proliferating integration of IoT devices, such as smart bulbs, to facilitate automation within closed networks, this study demonstrated the viability of optical air-gap attacks using these devices

Self-Distilled Self-Supervised Representation Learning

State-of-the-art frameworks in self-supervised learning have recently shown that fully utilizing transformer-based models can lead to performance boost compared to conventional CNN models. Striving to maximize the mutual information of two views of an image, existing works apply a contrastive loss to the final representations. Motivated by self-distillation in the supervised regime, we further exploit this by allowing the intermediate representations to learn from the final layer via the contrastive loss. Through self-distillation, the intermediate layers are better suited for instance discrimination, making the performance of an early-exited sub-network not much degraded from that of the full network. This renders the pretext task easier also for the final layer, lead to better representations. Our method, Self-Distilled Self-Supervised Learning (SDSSL), outperforms competitive baselines (SimCLR, BYOL and MoCo v3) using ViT on various tasks and datasets. In the linear evaluation and k-NN protocol, SDSSL not only leads to superior performance in the final layers, but also in most of the lower layers. Furthermore, positive and negative alignments are used to explain how representations are formed more effectively. Code will be available.Comment: 15 page

Translating Hanja Historical Documents to Contemporary Korean and English

The Annals of Joseon Dynasty (AJD) contain the daily records of the Kings of Joseon, the 500-year kingdom preceding the modern nation of Korea. The Annals were originally written in an archaic Korean writing system, `Hanja', and were translated into Korean from 1968 to 1993. The resulting translation was however too literal and contained many archaic Korean words; thus, a new expert translation effort began in 2012. Since then, the records of only one king have been completed in a decade. In parallel, expert translators are working on English translation, also at a slow pace and produced only one king's records in English so far. Thus, we propose H2KE, a neural machine translation model, that translates historical documents in Hanja to more easily understandable Korean and to English. Built on top of multilingual neural machine translation, H2KE learns to translate a historical document written in Hanja, from both a full dataset of outdated Korean translation and a small dataset of more recently translated contemporary Korean and English. We compare our method against two baselines: a recent model that simultaneously learns to restore and translate Hanja historical document and a Transformer based model trained only on newly translated corpora. The experiments reveal that our method significantly outperforms the baselines in terms of BLEU scores for both contemporary Korean and English translations. We further conduct extensive human evaluation which shows that our translation is preferred over the original expert translations by both experts and non-expert Korean speakers.Comment: 2022 EMNLP Finding

Development of Practical Design Approaches for Water Distribution Systems

The optimal design of water distribution systems (WDSs) should be economical, consider practical field applicability, and satisfy hydraulic constraints such as nodal pressure and flow velocity. However, the general optimal design of a WDSs approach using a metaheuristic algorithm was difficult to apply for achieving pipe size continuity at the confluence point. Although some studies developed the design approaches considering the pipe continuity, these approaches took many simulation times. For these reasons, this study improves the existing pipe continuity search method by reducing the computation time and enhancing the ability to handle pipe size continuity at complex joints that have more than three nodes. In addition to more practical WDSs designs, the approach considers various system design factors simultaneously in a multi-objective framework. To verify the proposed approach, the three well-known WDSs to apply WDS design problems are applied, and the results are compared with the previous design method, which used a pipe continuity research algorithm. This study can reduce the computation time by 87% and shows an ability to handle complex joints. Finally, the application of this practical design technique, which considers pipe continuity and multiple design factors, can reduce the gap between the theoretical design and the real world because it considers construction conditions and abnormal situations.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Peningkatan Kualitas Pelet Tandan Kosong Kelapa Sawit melalui Torefaksi Menggunakan Reaktor Counter-Flow Multi Baffle (COMB)

Oil palm (Elaeis guineensis) empty fruit bunches (EFB) have not been utilized optimally. Currently, it is considered as a resource with low economic value. This biomass can be converted into bioenergy through a torrefaction process. Torrefaction is a mild pyrolysis at temperatures ranging between 200 and 300 °C, and it is generally performed under an inert atmosphere. The objective of this study was to evaluate the effects of torrefaction using Counter-Flow Multi Baffle (COMB) on the properties of oil palm EFB pellets. Torrefaction was conducted at 280 °C temperature with a residence time of 4 minutes. The results showed a decrease in the equilibrium moisture content and an increase in hydrophobicity after torrefaction using the COMB reactor. The change in the hygroscopic property could make the oil palm EFB pellet more stable against chemical oxidation and microbial degradation, hence self-heating and auto-ignition during storage could be prevented. The heating value of biomass increased after torrefaction. Torrefaction with the COMB reactor resulted in a heating value of 17.90 MJ/kg, which is comparable with the results of oxidative torrefaction (with longer residence time) of 18.28 MJ/kg. The results suggested that torrefaction using the COMB reactor could provide a great improvement in the quality of the bioenergetic properties of oil palm EFB pellets. However, the high ash content of the EFB pellets implied that the EFB pellets suitable for a small-scale application, but not yet for cofiring in power plants or as a feedstock for gasification.Keywords: Counter-Flow Multi Baffle; oil palm empty fruit bunches; renewable; torrefactionA B S T R A KTandan kosong kelapa sawit (Elaeis guineensis) belum dimanfaatkan secara optimal. Saat ini bahan tersebut masih dianggap sebagai sumber daya bernilai ekonomi rendah. Tandan kosong kelapa sawit (TKKS) dapat dikonversi menjadi bioenergi melalui proses torefaksi. Torefaksi merupakan proses pirolisis ringan pada suhu berkisar antara 200 dan 300 °C dan umumnya dilakukan di bawah kondisi inert. Penelitian ini bertujuan untuk mengetahui pengaruh torefaksi dengan reaktor Counter-Flow Multi Baffle (COMB) terhadap sifat-sifat pelet TKKS. Torefaksi dilakukan pada suhu 280 °C dengan waktu tinggal 4 menit. Hasil penelitian menunjukkan bahwa torefaksi menyebabkan penurunan kadar air kesetimbangan dan menjadi hidrofobik setelah torefaksi dengan reaktor COMB. Perbaikan sifat higroskopis dapat membuat pelet TKKS lebih stabil terhadap oksidasi kimia dan degradasi mikroba, sehingga pemanasan sendiri dan pembakaran spontan selama penyimpanan dapat dicegah. Nilai kalor biomassa meningkat setelah torefaksi. Torefaksi dengan reaktor COMB menghasilkan nilai kalor 17,90 MJ/kg, yang sebanding dengan hasil torefaksi oksidatif dengan waktu tinggal lebih lama, sebesar 18,28 MJ/kg. Hasil penelitian menunjukkan bahwa torefaksi dengan reaktor COMB dapat meningkatkan kualitas energi pelet TKKS. Tetapi pelet TKKS masih memiliki kadar abu yang tinggi sehingga biomassa hasil torefaksi belum sesuai untuk cofiring di pembangkit listrik atau sebagai bahan baku untuk gasifikasi.Kata kunci: Counter-Flow Multi Baffle; tandan kosong kelapa sawit; terbarukan; torefaksi

Application of deep learning artificial intelligence technique to the classification of clinical orthodontic photos

Abstract Background Taking facial and intraoral clinical photos is one of the essential parts of orthodontic diagnosis and treatment planning. Among the diagnostic procedures, classification of the shuffled clinical photos with their orientations will be the initial step while it was not easy for a machine to classify photos with a variety of facial and dental situations. This article presents a convolutional neural networks (CNNs) deep learning technique to classify orthodontic clinical photos according to their orientations. Methods To build an automated classification system, CNNs models of facial and intraoral categories were constructed, and the clinical photos that are routinely taken for orthodontic diagnosis were used to train the models with data augmentation. Prediction procedures were evaluated with separate photos whose purpose was only for prediction. Results Overall, a 98.0% valid prediction rate resulted for both facial and intraoral photo classification. The highest prediction rate was 100% for facial lateral profile, intraoral upper, and lower photos. Conclusion An artificial intelligence system that utilizes deep learning with proper training models can successfully classify orthodontic facial and intraoral photos automatically. This technique can be used for the first step of a fully automated orthodontic diagnostic system in the future

Synthesis and Characterization of Polycarbonate Copolymers Containing Benzoyl Groups on the Side Chain for Scratch Resistance

The purpose of this study was to enhance the scratch resistance of polycarbonate copolymer by using 3,3′-dibenzoyl-4,4′-dihydroxybiphenyl (DBHP) monomer, containing benzoyl moieties on the ortho positions. DBHP monomer was synthesized from 4,4′-dihydroxybiphenyl and benzoyl chloride, followed by the Friedel-Craft rearrangement reaction with AlCl3. The polymerizations were conducted following the low-temperature procedure, which is carried out in methylene chloride by using triphosgene, triethylamine, bisphenol-A, and DBHP. The chemical structures of the polycarbonate copolymers were confirmed by 1H-NMR. The thermal properties of copolymers were investigated by thermogravimetric analysis and differential scanning calorimetry, and also surface morphologies were assessed by atomic force microscopy. The scratch resistance of homopolymer film (100 μm) changed from 6B to 1B, and the contact angle of a sessile water drop onto the homopolymer film also increased

The assessment of efficacy of porcine reproductive respiratory syndrome virus inactivated vaccine based on the viral quantity and inactivation methods

<p>Abstract</p> <p>Background</p> <p>There have been many efforts to develop efficient vaccines for the control of porcine reproductive and respiratory syndrome virus (PRRSV). Although inactivated PRRSV vaccines are preferred for their safety, they are weak at inducing humoral immune responses and controlling field PRRSV infection, especially when heterologous viruses are involved.</p> <p>Results</p> <p>In all groups, the sample to positive (S/P) ratio of IDEXX ELISA and the virus neutralization (VN) titer remained negative until challenge. While viremia did not reduce in the vaccinated groups, the IDEXX-ELISA-specific immunoglobulin G increased more rapidly and to significantly greater levels 7 days after the challenge in all the vaccinated groups compared to the non-vaccinated groups (<it>p </it>< 0.05). VN titer was significantly different in the 10⁶PFU/mL PRRSV vaccine-inoculated and binary ethylenimine (BEI)-inactivated groups 22 days after challenge (<it>p </it>< 0.05). Consequently, the inactivated vaccines tested in this study provided weak memory responses with sequential challenge without any obvious active immune responses in the vaccinated pigs.</p> <p>Conclusions</p> <p>The inactivated vaccine failed to show the humoral immunity, but it showed different immune response after the challenge compared to mock group. Although the 10⁶PFU/mL-vaccinated and BEI-inactivated groups showed significantly greater VN titers 22 days after challenge, all the groups were already negative for viremia.</p