Miracle-dz/UniFAR-FTUs huggingface dataset:
Speaker recognizability evaluation of a voicefont-based text-to-speech system

We have developed a new text-to-speech system based on the VoiceFont technology. A VoiceFont is a voice dictionary for speech synthesis that holds the acoustic and prosodic characteristics extracted from the voice corpus of a speaker. The text-to-speech system using a VoiceFont is able to synthetically mimic the voice of the donor speaker. In this paper, we evaluated speaker recognizability of the synthetic speech, which means whether the synthetic speech can be recognized as the donor speaker’s voice. We conducted a subjective evaluation for five VoiceFonts and here report on the evaluation results. The results show that our text-to-speech system based on VoiceFonts can retain the acoustic and prosodic characteristics of the donor speaker and the synthetic speech can be recognized as the donor speaker’s voice. Furthermore, we report on how much the spectral characteristics, phoneme duration, and pitch frequency affect speaker recognizability.
System-level FPGA device driver with high-level synthesis support

We can exploit the standardization of communication abstractions provided by modern high-level synthesis tools like Vivado HLS, Bluespec and SCORE to provide stable system interfaces between the host and PCIe-based FPGA accelerator platforms. At a high level, our FPGA driver attempts to provide CUDA-like driver behavior, and more, to FPGA programmers. On the FPGA fabric, we develop an AXI-compliant, lightweight interface switch coupled to multiple physical interfaces (PCIe, Ethernet, DRAM) to provide programmable, portable routing capability between the host and user logic on the FPGA. On the host, we adapt the RIFFA 1.0 driver to provide enhanced communication APIs along with bitstream configuration capability allowing low-latency, high-throughput communication and safe, reliable programming of user logic on the FPGA. Our driver only consumes 21% BRAMs and 14% logic overhead on a Xilinx ML605 platform or 9% BRAMs and 8% logic overhead on a Xilinx V707 board. We are able to sustain DMA transfer throughput (to DRAM) of 1.47GB/s (74% peak) of the PCIe (x4 Gen2) bandwidth, 120.2MB/s (96%) of the Ethernet (1G) bandwidth and 5.93GB/s (92.5%) of DRAM bandwidth.
Collaborative Filtering for Multi-Class Data Using Bayesian Networks

As one of the most successful recommender systems, collaborative filtering (CF) algorithms are required to deal with high sparsity and high requirement of scalability amongst other challenges. Bayesian networks (BNs), one of the most frequently used classifiers, can be used for CF tasks. Previous works on applying BNs to CF tasks were mainly focused on binary-class data, and used simple or basic Bayesian classifiers.1,2 In this work, we apply advanced BNs models to CF tasks instead of simple ones, and work on real-world multi-class CF data instead of synthetic binary-class data. Empirical results show that with their ability to deal with incomplete data, the extended logistic regression on tree augmented naive Bayes (TAN-ELR)3 CF model consistently performs better than the traditional Pearson correlation-based CF algorithm for the rating data that have few items or high missing rates. In addition, the ELR-optimized BNs CF models are robust in terms of the ability to make predictions, while the robustness of the Pearson correlation-based CF algorithm degrades as the sparseness of the data increases.
BodyMap incorporated PCR-based expression profiling data and a gene ranking system

BodyMap is a human and mouse gene expression database that is based on site-directed 3'-expressed sequence tags generated at Osaka University. To date, it contains more than 300 000 tag sequences from 64 human and 39 mouse tissues. For the recent release, the precise anatomical expression patterns for more than half of the human gene entries were generated by introduced amplified fragment length polymorphism (iAFLP), which is a PCR-based high-throughput expression profiling method. The iAFLP data incorporated into BodyMap describe the relative contents of more than 12 000 transcripts across 30 tissue RNAs. In addition, a newly developed gene ranking system helps users obtain lists of genes that have desired expression patterns according to their significance. BodyMap supports complete transfer of unique data sets and provides analysis that is accessible through the WWW at http://bodymap.ims. u-tokyo.ac. jp.
Fostering the Development of Reading Skill Through Supplemental Instruction

This article reports the effects of a 2-year supplemental reading program for students in kindergarten through third grade that focused on the development of decoding skills and reading fluency. Two hundred ninety-nine students were identified for participation and were randomly assigned to the supplemental instruction or to a no-treatment control group. Participants' reading ability was assessed in the fall, before the first year of the intervention, and again in the spring of Years 1, 2, 3, and 4. At the end of the 2-year intervention, students who received the supplemental instruction performed significantly better than their matched controls on measures of entry-level reading skills (i.e., letter—word identification and word attack), oral reading fluency, vocabulary, and comprehension. The benefits of the instruction were still clear 2 years after instruction had ended, with students in the supplemental-instruction condition still showing significantly greater growth on the measure of oral reading fluency. Hispanic students benefited from the supplemental reading instruction in English as much as or more than non-Hispanic students. Results support the value of supplemental instruction focused on the development of word recognition skills for helping students at risk for reading failure.
Protozoa: adaptive granularity cache coherence

State-of-the-art multiprocessor cache hierarchies propagate the use of a fixed granularity in the cache organization to the design of the coherence protocol. Unfortunately, the fixed granularity, generally chosen to match average spatial locality across a range of applications, not only results in wasted bandwidth to serve an individual thread's access needs, but also results in unnecessary coherence traffic for shared data. The additional bandwidth has a direct impact on both the scalability of parallel applications and overall energy consumption. In this paper, we present the design of Protozoa, a family of coherence protocols that eliminate unnecessary coherence traffic and match data movement to an application's spatial locality. Protozoa continues to maintain metadata at a conventional fixed cache line granularity while 1) supporting variable read and write caching granularity so that data transfer matches application spatial granularity, 2) invalidating at the granularity of the write miss request so that readers to disjoint data can co-exist with writers, and 3) potentially supporting multiple non-overlapping writers within the cache line, thereby avoiding the traditional ping-pong effect of both read-write and write-write false sharing. Our evaluation demonstrates that Protozoa consistently reduce miss rate and improve the fraction of transmitted data that is actually utilized.
Increase of glycosaminoglycans and metalloproteinases 2 and 9 in liver extracellular matrix on early stages of extrahepatic cholestasis.

CONTEXT Cholestasis produces hepatocellular injury, leukocyte infiltration, ductular cells proliferation and fibrosis of liver parenchyma by extracellular matrix replacement. OBJECTIVE Analyze bile duct ligation effect upon glycosaminoglycans content and matrix metalloproteinase (MMPs) activities. METHODS Animals (6-8 weeks; n = 40) were euthanized 2, 7 or 14 days after bile duct ligation or Sham-surgery. Disease evolution was analyzed by body and liver weight, seric direct bilirubin, globulins, gamma glutamyl transpeptidase (GGT), alkaline phosphatase (Alk-P), alanine and aspartate aminotransferases (ALT and AST), tissue myeloperoxidase and MMP-9, pro MMP-2 and MMP-2 activities, histopathology and glycosaminoglycans content. RESULTS Cholestasis caused cellular damage with elevation of globulins, GGT, Alk-P, ALT, AST. There was neutrophil infiltration observed by the increasing of myeloperoxidase activity on 7 (P = 0.0064) and 14 (P = 0.0002) groups which leads to the magnification of tissue injuries. Bile duct ligation increased pro-MMP-2 (P = 0.0667), MMP-2 (P = 0.0003) and MMP-9 (P<0.0001) activities on 14 days indicating matrix remodeling and establishment of inflammatory process. Bile duct ligation animals showed an increasing on dermatan sulfate and/or heparan sulfate content reflecting extracellular matrix production and growing mitosis due to parenchyma depletion. CONCLUSIONS Cholestasis led to many changes on rats' liver parenchyma, as so as on its extracellular matrix, with major alterations on MMPs activities and glycosaminoglycans content.
Extracting the Representative API Call Patterns of Malware Families Using Recurrent Neural Network

With thousands of malware samples pouring out every day, how can we reduce malware analysis time and detect them effectively? Malware family classification provides one of good measures to predict characteristics of unknown malware since malware belonging to the same family can have similar features. Static analysis and dynamic analysis are techniques to obtain features to be used for classifying malware samples to their families. Static analysis performs analysis based on specific signatures included in the malware. Static analysis has the advantages that the scope of the analysis covers the entire code, and the analysis can be performed without executing the malware. However, it is very difficult to detect or classify malware variants with only the results of the static analysis, because malware developers use polymorphic or encryption techniques to avoid static analysis-based detection of anti-virus software. Dynamic analysis analyzes malware behaviors, so the results of dynamic analysis can be used to detect or classify malware variants. One of dynamic features that can be used to detect or classify malware variants is API call sequences. In this paper, we propose a novel method to extract representative API call patterns of malware families using Recurrent Neural Network (RNN). We conducted experiments with 787 malware samples belonging to 9 families. In our experiments, we extracted representative API call patterns of 9 malware families on 551 samples as a training set and performed classification on the 236 samples as a test set. Classification accuracy results using API call patterns extracted from RNN were measured as 71% on average. The results show the feasibility of our approach using RNN to extract representative API call pattern of malware families for malware family classification.
Full state tracking and internal dynamics of nonholonomic wheeled mobile robots

Output tracking control and internal stability are analyzed for restricted mobile robots. Tracking error dynamics offers insights into the properties and stability of the input-output subsystem as well as the internal dynamics. Sufficient conditions for the full state tracking are developed. A type (1,1) mobile robot is studied in details and simulation results are presented to confirm the theory.
A community-driven hypertension treatment group in rural Honduras

Background We formed a self-funded hypertension treatment group in a resource-poor community in rural Honduras. After training community health workers and creating protocols for standardized treatment, we used group membership fees to maintain the group, purchase generic medications in bulk on the local market, and hire a physician to manage treatment. We then assessed whether participation in the group improved treatment, medication adherence, and hypertension control. Design This is a program evaluation using quasi-experimental design and no control group. Using data from the 86 members of the hypertension treatment group, we analyzed baseline and follow-up surveys of members, along with 30 months of clinical records of treatment, medication adherence, and blood pressure readings. Results Our initial hypertension needs assessment revealed that at baseline, community hypertensives relied on the local Ministry of Health clinic as their source of anti-hypertensive medications and reported that irregular supply interfered with medication adherence. At baseline, hypertension group members were mainly female, overweight or obese, physically active, non-smoking, and non-drinking. After 30 months of managing the treatment group, we found a significant increase in medication adherence, from 54.8 to 76.2% (p<0.01), and hypertension control (<140/90 mmHg), from 31.4 to 54.7% (p<0.01). We also found a mean monthly decrease of 0.39 mmHg in systolic blood pressure (p<0.01). At the end of the 30-month observation period, the local Ministry of Health system had increased provision of low-cost anti-hypertensive medications and adopted the hypertension treatment group's treatment protocols. Conclusions Formation of a self-funded, community-based hypertension treatment group in a rural, resource-poor community is feasible, and group participation may improve treatment, medication adherence, and hypertension control and can serve as a political driver for improving hypertension treatment services provided by the public system.
Towards generic framework for tabular data extraction and management in documents

Tables are one of the common data presentation structures in documents. However, the task of automatic recognition and extraction of tables embedded in documents is still a significant challenge, and data contained within tables still remains under-utilised. Although some common steps can be defined for table extraction, there is no generic approach for table extraction tasks which can be applied to different sources and provide an end-to-end repeatable work-flow. This paper looks at the table extraction problem from the process point of view and proposes a table extraction workflow, which can be considered as a plug-and-play architecture for table extraction. Also, we present an overview of our complete system where the extracted tables are stored and managed. Table extraction is considered in the context of financial statements in this work, but the methods apply generally.
Multidimensional analysis of the insoluble sub‐proteome of Oceanobacillus iheyensis HTE831, an alkaliphilic and halotolerant deep‐sea bacterium isolated from the Iheya ridge

We report the first proteomic analysis of the insoluble sub‐proteome of the alkaliphilic and halotolerant deep‐sea bacterium Oceanobacillus iheyensis HTE831. A multidimensional gel‐based and gel‐free analysis was utilised and a total of 4352 peptides were initially identified by automated MS/MS identification software. Automated curation of this list using PROVALT reduced our peptide list to 467 uniquely identified peptides that resulted in the positive identification of 153 proteins. These identified proteins were functionally classified and physiochemically characterised. Of 26 proteins identified as hypothetical conserved, we have assigned function to all but four. A total of 41 proteins were predicted to possess signal peptides. In silico investigation of these proteins allowed us to identify three of the five bacterial classes of signal peptide, namely: (i) twin‐arginine translocation; (ii) Sec‐type and (iii) lipoprotein transport. Our proteomic strategy has also allowed us to identify, at neutral pH, a number of proteins described previously as belonging to two putative transport systems believed to be of importance in the alkaliphilic adaptation of O. iheyensis HTE831.
A Personal Identification Framework based on Facial Image and Fingerprint Fusion Biometric

Biometric based person identity verification is gaining more and more attention. Several studies have shown that multimodal biometric identification systems improve the recognition accuracy and reliability compared with recognition using a single biometric. The present paper introduces a new personal identification framework that is based on the fusion of face and fingerprint biometrics. The proposed framework overcomes the limitations of face recognition systems as well as fingerprint verification systems. The gray-level co-occurrence matrix and the minutiae extraction are used to represent the features of face and fingerprint image respectively. This framework uses the correlation coefficient as a similarity measure to retrieve the closest face and the corresponding fingerprint images with a query image. Experimental results performed on a given database of face and fingerprint images show that the proposed framework improved greatly the security and recognition rate. General Terms Multimodal biometric, Personal identification, Fingerprint verification, Face recognition.
Spectroscopic studies of surface chemical composition and wettability of modified wood

Abstract Recent advances in spectroscopic methods used in the surface science field may provide new valuable information about the surface chemical composition of engineering materials. Such methods, combined with wettability analyses, have been applied in the development of well-designed adhesives and coating systems for newly developed and commercially available modified wood materials. The main objective of this paper is to demonstrate and present some aspects on the application of two different state-of-the-art spectroscopic methods for surface chemical composition studies of a complex material such as modified wood. The methods are X-ray photoelectron spectroscopy (XPS) and time-of-flight secondary ion mass spectrometry (ToF-SIMS), the former generating more quantitative data and the latter more qualitative data. The spectroscopic data are also combined with wettability data obtained from contact angle measurements using the Wilhelmy method. Modified wood samples were prepared from pilot plant or commercially produced acetylated, furfurylated and thermally modified wood. Effects of wood surface ageing, i.e. the time after machining, on the surface chemical composition and wettability were also studied. Results clearly indicate a hydrophobization process due to ageing of the unmodified and certain modified wood, probably mainly related to a migration and reformation of extractives in the surface. The surface composition and wettability of acetylated wood was not appreciably affected by the ageing process. Such findings could be quantified by the XPS measurements, which is further discussed and related to the different wood modification routes. ToF-SIMS is a powerful tool and complementary to XPS for identification of, for example, specific hydrophobic substances in the wood surfaces. In addition, this method provides ion images, mapping the lateral distribution of selected secondary ions signals within an analysed wood surface area.
The Baseline Speech Recognition System 2 . 1 Speech and

This paper presents the application of morpheme-based and factored language models in an Amharic speech recognition task. Since using morphemes in both acoustic and language models results, mostly, in performance degradation due to acoustic confusability and since it is problematic to use factored language models in standard word decoders, we applied the models in a lattice rescoring framework. Lattices of 100 best alternatives for each test sentence of the 5k development test set have been generated using a baseline speech recognizer that uses a word-based backoff bigram language model. The lattices have then been rescored with various morpheme-based and factored language models and a slight improvement in word recognition accuracy has been observed.
Sparse and unique nonnegative matrix factorization through data preprocessing

Nonnegative matrix factorization (NMF) has become a very popular technique in machine learning because it automatically extracts meaningful features through a sparse and part-based representation. However, NMF has the drawback of being highly ill-posed, that is, there typically exist many different but equivalent factorizations. In this paper, we introduce a completely new way to obtaining more well-posed NMF problems whose solutions are sparser. Our technique is based on the preprocessing of the nonnegative input data matrix, and relies on the theory of M-matrices and the geometric interpretation of NMF. This approach provably leads to optimal and sparse solutions under the separability assumption of Donoho and Stodden (2003), and, for rank-three matrices, makes the number of exact factorizations finite. We illustrate the effectiveness of our technique on several image data sets.
Predicting Author Blog Channels with High Value Future Posts for Monitoring

The phenomenal growth of social media, both in scale and importance, has created a unique opportunity to track information diffusion and the spread of influence, but can also make efficient tracking difficult. Given data streams representing blog posts on multiple blog channels and a focal query post on some topic of interest, our objective is to predict which of those channels are most likely to contain a future post that is relevant, or similar, to the focal query post. We denote this task as the future author prediction problem (FAPP). This problem has applications in information diffusion for brand monitoring and blog channel personalization and recommendation. We develop prediction methods inspired by (naive) information retrieval approaches that use historical posts in the blog channel for prediction. We also train a ranking support vector machine (SVM) to solve the problem. We evaluate our methods on an extensive social media dataset; despite the difficulty of the task, all methods perform reasonably well. Results show that ranking SVM prediction can exploit blog channel and diffusion characteristics to improve prediction accuracy. Moreover, it is surprisingly good for prediction in emerging topics and identifying inconsistent authors.
Segmentation of multiple sclerosis lesions from MR brain images using the principles of fuzzy-connectedness and artificial neuron networks

Segmentation is an important step for the diagnosis of multiple sclerosis. In this paper, a method for segmentation of multiple sclerosis lesions from magnetic resonance (MR) brain image is proposed. The proposed method combines the strengths of two existing techniques: fuzzy connectedness and artificial neural networks. From the input MR brain image, the fuzzy connectedness algorithm is used to extract segments which are parts of cerebrospinal fluid (CSF), white matter (WM) or gray matter (GM). Segments of the MRI image which are not extracted as part of CSF, WM or GM are processed morphologically, and features are computed for each of them. Then these computed features are fed to a trained artificial neural network, which decides whether a segment is a part of a lesion or not. The results of our method show 90% correlation with the expert's manual work.
The nature of end-user relationships in the development of electronic commerce applications

The effectiveness of IT departments, and more specifically the developers of electronic commerce (EC) applications, and the value they are adding to their organizations in many cases are hampered by poor relationships between EC professionals and their end users. This situation impacts on EC professionals' ability to produce service and support of high quality that delivers competitive advantage for the company in the electronic commerce environment. This paper looks into the working relationship between EC developers and its end users. It describes EC-end user relationships as intriguing and complex and that these relationships should be seen and managed as multidimensional entities. Two such dimensions, namely the physical and abstract dimensions which form the basis of EC-end user relationships, enable one to fully describe the holistic nature of such relationships and encapsulate the important elements of a support-oriented organization, namely mutuality, belonging, and connection.