Personalized Acoustic Modeling by Weakly Supervised Multi-Task Deep Learning using Acoustic Tokens Discovered from Unlabeled Data

It is well known that recognizers personalized to each user are much more effective than user-independent recognizers. With the popularity of smartphones today, although it is not difficult to collect a large set of audio data for each user, it is difficult to transcribe it. However, it is now possible to automatically discover acoustic tokens from unlabeled personal data in an unsupervised way. We therefore propose a multi-task deep learning framework called a phoneme-token deep neural network (PTDNN), jointly trained from unsupervised acoustic tokens discovered from unlabeled data and very limited transcribed data for personalized acoustic modeling. We term this scenario "weakly supervised". The underlying intuition is that the high degree of similarity between the HMM states of acoustic token models and phoneme models may help them learn from each other in this multi-task learning framework. Initial experiments performed over a personalized audio data set recorded from Facebook posts demonstrated that very good improvements can be achieved in both frame accuracy and word accuracy over popularly-considered baselines such as fDLR, speaker code and lightly supervised adaptation. This approach complements existing speaker adaptation approaches and can be used jointly with such techniques to yield improved results.Comment: 5 pages, 5 figures, published in IEEE ICASSP 201

Linear Insertion Deletion Codes in the High-Noise and High-Rate Regimes

This work continues the study of linear error correcting codes against adversarial insertion deletion errors (insdel errors). Previously, the work of Cheng, Guruswami, Haeupler, and Li [Kuan Cheng et al., 2021] showed the existence of asymptotically good linear insdel codes that can correct arbitrarily close to 1 fraction of errors over some constant size alphabet, or achieve rate arbitrarily close to 1/2 even over the binary alphabet. As shown in [Kuan Cheng et al., 2021], these bounds are also the best possible. However, known explicit constructions in [Kuan Cheng et al., 2021], and subsequent improved constructions by Con, Shpilka, and Tamo [Con et al., 2022] all fall short of meeting these bounds. Over any constant size alphabet, they can only achieve rate < 1/8 or correct < 1/4 fraction of errors; over the binary alphabet, they can only achieve rate < 1/1216 or correct < 1/54 fraction of errors. Apparently, previous techniques face inherent barriers to achieve rate better than 1/4 or correct more than 1/2 fraction of errors. In this work we give new constructions of such codes that meet these bounds, namely, asymptotically good linear insdel codes that can correct arbitrarily close to 1 fraction of errors over some constant size alphabet, and binary asymptotically good linear insdel codes that can achieve rate arbitrarily close to 1/2. All our constructions are efficiently encodable and decodable. Our constructions are based on a novel approach of code concatenation, which embeds the index information implicitly into codewords. This significantly differs from previous techniques and may be of independent interest. Finally, we also prove the existence of linear concatenated insdel codes with parameters that match random linear codes, and propose a conjecture about linear insdel codes

Erratum to: Architectural Growth of Cu Nanoparticles Through Electrodeposition

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Fast Post-placement Rewiring Using Easily Detectable Functional Symmetries

Timing convergence problem arises when the estimations made during logic synthesis can not be met during physical design. In this paper, an efficient rewiring engine is proposed to explore maximal freedom after placement. The most important feature of this approach is that the existing placement solution is left intact throughout the optimization. A linear time algorithm is proposed to detect functional symmetries in the Boolean network and is used as the basis for rewiring. Integration with an existing gate sizing algorithm further proves the effectiveness of our technique. Experimental results are very promising

Ginseng essence, a medicinal and edible herbal formulation, ameliorates carbon tetrachloride-induced oxidative stress and liver injury in rats

AbstractBackgroundGinseng essence (GE) is a formulation comprising four medicinal and edible herbs including ginseng (Panax ginseng), American ginseng (Panax quinquefolius), lotus seed (Nelumbo nucifera), and lily bulb (Lilium longiflorum). This study was aimed at investigating the hepatoprotective effect of GE against carbon tetrachloride (CCl4)-induced liver injury in rats.MethodsWe treated Wistar rats daily with low, medium, and high [0.625 g/kg body weight (bw), 1.25 g/kg bw, and 3.125 g/kg bw, respectively] doses of GE for 9 wk. After the 1st wk of treatment, rats were administered 20% CCl4 (1.5 mL/kg bw) two times a week to induce liver damage until the treatment ended.ResultsSerum biochemical analysis indicated that GE ameliorated the elevation of aspartate aminotransferase and alanine aminotransferase and albumin decline in CCl4-treated rats. Moreover, CCl4-induced accumulation of hepatic total cholesterol and triglyceride was inhibited. The hepatoprotective effects of GE involved enhancing the hepatic antioxidant defense system including glutathione, glutathione peroxidase, glutathione reductase, glutathione S-transferase, superoxide dismutase, and catalase. In addition, histological analysis using hematoxylin and eosin and Masson's trichrome staining showed that GE inhibited CCl4-induced hepatic inflammation and fibrosis. Furthermore, immunohistochemical staining of alpha-smooth muscle actin indicated that CCl4-triggered activation of hepatic stellate cells was reduced.ConclusionThese findings demonstrate that GE improves CCl4-induced liver inflammation and fibrosis by attenuating oxidative stress. Therefore, GE could be a promising hepatoprotective herbal formulation for future development of phytotherapy

Counteracting Phishing Page Polymorphism: An Image Layout Analysis Approach

Abstract. Many visual similarity-based phishing page detectors have been developed to detect phishing webpages, however, scammers now cre-ate polymorphic phishing pages to breach the defense of those detectors. We call this kind of countermeasure phishing page polymorphism. Poly-morphic pages are visually similar to genuine pages they try to mimic, but they use different representation techniques. It increases the level of difficulty to detect phishing pages. In this paper, we propose an effective detection mechanism to detect polymorphic phishing pages. In contrast to existing approaches, we analyze the layout of webpages rather than the HTML codes, colors, or content. Specifically, we compute the sim-ilarity degree of a suspect page and an authentic page through image processing techniques. Then, the degrees of similarity are ranked by a classifier trained to detect phishing pages. To verify the efficacy of our phishing detection mechanism, we collected 6, 750 phishing pages and 312 mimicked targets for the performance evaluation. The results show that our method achieves an excellent detection rate of 99.6%.

Sediment transport following water transfer from Yangtze River to Taihu Basin

AbstractTo meet the increasing need of fresh water and to improve the water quality of Taihu Lake, water transfer from the Yangtze River was initiated in 2002. This study was performed to investigate the sediment distribution along the river course following water transfer. A rainfall-runoff model was first built to calculate the runoff of the Taihu Basin in 2003. Then, the flow patterns of river networks were simulated using a one-dimensional river network hydrodynamic model. Based on the boundary conditions of the flow in tributaries of the Wangyu River and the water level in Taihu Lake, a one-dimensional hydrodynamic and sediment transport numerical model of the Wangyu River was built to analyze the influences of the inflow rate of the water transfer and the suspended sediment concentration (SSC) of inflow on the sediment transport. The results show that the water transfer inflow rate and SSC of inflow have significant effects on the sediment distribution. The higher the inflow rate or SSC of inflow is, the higher the SSC value is at certain cross-sections along the river course of water transfer. Higher inflow rate and SSC of inflow contribute to higher sediment deposition per kilometer and sediment thickness. It is also concluded that a sharp decrease of the inflow velocity at the entrance of the Wangyu River on the river course of water transfer induces intense sedimentation at the cross-section near the Changshu hydro-junction. With an increasing distance from the Changshu hydro-junction, the sediment deposition and sedimentation thickness decrease gradually along the river course

Exploration of Building Information Modeling and Integrated Project Cloud Service in early architectural design stages

[EN] In the evolving Architecture, Engineering, and Construction (AEC) industry, the use of Building Information Modeling (BIM) and Integrated Project Cloud Service (IPCS) has become crucial. These tools are particularly essential during the early design stages, as they enable comprehensive management and integration of project information, thus promoting effective decision-making throughout project lifecycles. This combined approach enhances inter-organizational collaborations, improves design and construction practices, and creates a communal data platform for stakeholders. This research explores the effectiveness of the BIM-IPCS system in streamlining data exchange and information flow during early design, suggesting ways to minimize errors, speed up processes, and reduce construction costs through dependable networks. Conclusively, this study underscores the significant impact of the BIM-IPCS system on project management, ensuring well-coordinated and informed construction while advocating for its role in driving innovative and efficient project delivery in the AEC industry.Grateful acknowledgment is extended to the National Taiwan University of Science and Technology, the Public Works Information Institute of the Republic of China (CPWEIA), and Luxor Digital Co., Ltd. (LUXOR) for their substantial support and contributions to this research.Wagiri, F.; Shih, S.; Harsono, K.; Cheng, T.; Lu, M. (2023). Exploration of Building Information Modeling and Integrated Project Cloud Service in early architectural design stages. VITRUVIO - International Journal of Architectural Technology and Sustainability. 8(2):26-37. https://doi.org/10.4995/vitruvio-ijats.2023.2045326378

Bi-Objective simplified swarm optimization for fog computing task scheduling

In the face of burgeoning data volumes, latency issues present a formidable challenge to cloud computing. This problem has been strategically tackled through the advent of fog computing, shifting computations from central cloud data centers to local fog devices. This process minimizes data transmission to distant servers, resulting in significant cost savings and instantaneous responses for users. Despite the urgency of many fog computing applications, existing research falls short in providing time-effective and tailored algorithms for fog computing task scheduling. To bridge this gap, we introduce a unique local search mechanism, Card Sorting Local Search (CSLS), that augments the non-dominated solutions found by the Bi-objective Simplified Swarm Optimization (BSSO). We further propose Fast Elite Selecting (FES), a ground-breaking one-front non-dominated sorting method that curtails the time complexity of non-dominated sorting processes. By integrating BSSO, CSLS, and FES, we are unveiling a novel algorithm, Elite Swarm Simplified Optimization (EliteSSO), specifically developed to conquer time-efficiency and non-dominated solution issues, predominantly in large-scale fog computing task scheduling conundrums. Computational evidence reveals that our proposed algorithm is both highly efficient in terms of time and exceedingly effective, outstripping other algorithms on a significant scale