Reviewerly/exHarmony huggingface dataset:
Secure and Traceable Copyright Management System Based on Blockchain

Blockchain technology can be used to solve the problem of the management of the digital copyright. Though some related ideas have been proposed, few papers propose a complete digital copyright management system based on public chain and simulation. A system based on public chain enables copyright owners and users to trade directly without resorting to a centralized organization. In this paper, a secure digital copyright management system based on Ethereum is proposed. Firstly, a model is established based on smart contracts and InterPlanetary File System. Secondly, use the improved ELGamal encryption algorithm to encrypt session data and protect privacy and evaluate the efficiency of the improved algorithm. Lastly, a transaction watermark that is consistent with the transaction information on the chain is added to the image data, providing proof for the piracy.
A Practical Two-Stage Training Strategy for Multi-Stream End-to-End Speech Recognition

The multi-stream paradigm of audio processing, in which several sources are simultaneously considered, has been an active research area for information fusion. Our previous study offered a promising direction within end-to-end automatic speech recognition, where parallel encoders aim to capture diverse information followed by a stream-level fusion based on attention mechanisms to combine the different views. However, with an increasing number of streams resulting in an increasing number of encoders, the previous approach could require substantial memory and massive amounts of parallel data for joint training. In this work, we propose a practical two-stage training scheme. Stage-1 is to train a Universal Feature Extractor (UFE), where encoder outputs are produced from a single-stream model trained with all data. Stage-2 formulates a multi-stream scheme intending to solely train the attention fusion module using the UFE features and pretrained components from Stage-1. Experiments have been conducted on two datasets, DIRHA and AMI, as a multi-stream scenario. Compared with our previous method, this strategy achieves relative word error rate reductions of 8.2-32.4%, while consistently outperforming several conventional combination methods.
In-situ sulfur isotopes analysis as an exploration tool for orogenic gold mineralization in the Granites-Tanami Gold Province, Australia: Learnings from the Callie deposit

Orogenic gold deposits are commonly associated with a small alteration and geochemical footprint that often hinders the exploration process. In the Granites-Tanami Gold Province; however, we demonstrate that sulfur isotope analyses represent a powerful exploration tool for orogenic gold mineralization. The Granites-Tanami Gold Province, situated in the Northern Territory (Australia), hosts numerous gold occurrences including the world-class Callie deposit (c. 14.2 Moz Au). In this paper, we use sulfur isotopes to: 1) characterize the δ34S signature of the sulfides associated with gold mineralization in order to assist with exploration in the Granites-Tanami Gold Province, and 2) monitor the temporal fluid evolution of the Callie deposit. We present in-situ sulfur isotope analyses of sulfides associated to gold mineralization, diagenetic sulfides, and sulfides associated to a barren, post-mineralization hydrothermal event. Our results show that the sulfides associated with mineralization yield δ34S values that range from +4‰ to +11.5‰ (n = 114), the diagenetic sulfides yield δ34S values that range from +0.0‰ to +1.7‰ (n = 41) and the post-mineralization sulfides yield δ34S values that range from +2.3‰ to +3.3‰ (n = 20). The range of δ34S values obtained from mineralized samples at the Callie deposit coincides with δ34S values reported in sulfides associated to other orogenic gold deposits of the Granites-Tanami Gold Province. The mineralization-related δ34S signature in the Granites-Tanami Gold Province is distinct from both diagenetic and post-mineralization sulfides providing a unique isotopic signature for the sulfides associated to gold mineralization. Therefore, we propose that the application of routine sulfur isotope analysis may represent a valuable tool for mineral exploration in the area. We also report a temporal shift toward lighter δ34S values from various sulfides associated to the stratabound mineralization at the Callie deposit. We interpret the shift in δ34S values as the expression of a progressive increase in the oxygen fugacity of the ore fluid resulting from progressive alteration of the host-rock.
Training Spoken Language Understanding Systems with Non-Parallel Speech and Text

End-to-end spoken language understanding (SLU) systems are typically trained on large amounts of data. In many practical scenarios, the amount of labeled speech is often limited as opposed to text. In this study, we investigate the use of non-parallel speech and text to improve the performance of dialog act recognition as an example SLU task. We propose a multiview architecture that can handle each modality separately. To effectively train on such data, this model enforces the internal speech and text encodings to be similar using a shared classifier. On the Switchboard Dialog Act corpus, we show that pretraining the classifier using large amounts of text helps learning better speech encodings, resulting in up to 40% relatively higher classification accuracies. We also show that when the speech embeddings from an automatic speech recognition (ASR) system are used in this framework, the speech-only accuracy exceeds the performance of ASR-text based tests up to 15% relative and approaches the performance of using true transcripts.
Privacy-protected Electronic Voting System Based on Blockchin and Trusted Execution Environment

Blockchain technology is more and more popular for its advantage of decentralization, transparency, and nonmodifiable. In an electronic voting system, voting can be fair and secure by blockchain technology without a trusted third party. In this paper, we propose an economical and efficient electronic voting system based on the Ethereum Smart Contract that can also protect voters' privacy. The privacy of voters and votes can be protected by blind signature and homomorphic encryption. More importantly, the encryption algorithm is implemented in the Trusted Execution Environment (TEE), which can effectively reduce the complex operations in the contract and make the cost of Gas as low as possible. In addition, we also evaluated the system's implementation cost and designed a proposal to better protect voters' privacy for board-scale elections.
Quantum algorithms for hash preimage attacks

Cryptographic hash functions are important building blocks of many security systems. A preimage attack on hash functions tries to find a message that has a specific hash value. A cryptographic hash function should resist attacks on its preimage. The current paper presents a new quantum algorithm for hash preimage attacks, which can break the preimage resistance with circuit complexity O(2n/3) using O(2n/3) running times, where n is the input bit length of the hash function. The quantum circuit complexity is apparently reduced from O(2n/2) to O(2n/3) at the cost of O(2n/3) running times requirements compared with the existing algorithms.
Distilling Attention Weights for CTC-Based ASR Systems

We present a novel training approach for connectionist temporal classification (CTC) -based automatic speech recognition (ASR) systems. CTC models are promising for building both a conventional acoustic model and an end-to-end (E2E) ASR model. However, CTC models make it difficult to capture the correct timing of each output label because timing is not given explicitly in the training data. In this paper, we propose a new auxiliary task with frame-wise targets for CTC model enhancement. We utilize attention weights generated by an attention-based encoder-decoder model (S2S) for making the targets, called the attention matrix. The attention matrix is the sum of the products of the attention weights (spike timing information) and the corresponding target vectors (probability information), and used for S2S-to-CTC knowledge distillation loss computation. Therefore, the attention matrix makes the CTC models jointly train-able as regards spike timings and their posteriors. Experiments on Japanese ASR tasks demonstrate that our proposal is effective for CTC model training; it achieves a 10.2% (E2E) / 9.4% (acoustic model) relative reduction in the character/kana-syllable error rates compared to models trained using only CTC loss.
Personalized Video Summarization Based Exclusively on User Preferences

We propose a recommender system to detect personalized video summaries, that make visual content interesting for the subjective criteria of the user. In order to provide accurate video summarization, the video segmentation provided by the users and the features of the video segments' duration are combined using a Synthetic Coordinate based Recommendation system.
Multiple Points Input For Convolutional Neural Networks in Replay Attack Detection

The models based on convolutional neural network (CNN) have shown remarkable performance in spoofing detection for automatic speaker verification. In order to input data into CNN-based models in mini-batch unit, the shape of all data in each mini-batch must be equal. Therefore, the method to make all data have the same length should be preceded because speeches have variable lengths. Segmentation is one of the methods to make the lengths of all data be equal. It divides the data into multiple segments using sliding window. Then, the models take one segment as input. However, it means that the amount of information that can be considered at one time is limited. We proposed the multiple points input method to increase the amount of information that can be considered at one time. The CNNs get input from multiple points in an utterance that are separated far enough to have different characteristics. The experimental results on ASVspoof 2019 physical access scenarios showed that our proposed method reduced the relative equal error rate by about 44% compared to the baseline.
Cyber Threat Intelligence for Improving Cyber Supply Chain Security

Cyber supply chain (CSC) systems provide operational efficiency and business continuity due to the integrated nature of various network system nodes. Such integration has made the overall system vulnerable to various cyber attacks and malware propagation is one of the common attacks for CSC. Cyber threat intelligence (CTI) provides an organization the capability to identify, gather, analyze threats and the associated risks so that CSC organization can forecast the existing and future threat trends and manage the cybersecurity risk in a proactive manner. A threat actor may attack the system and propagate a malware. The purpose is to manipulate, alter, or change delivery mechanisms. It is imperative to integrate CTI into the existing cybersecurity practice to detect and understand the threat actor's intents and motive. In our previous paper, we used threat analysis gathering to provide us an understanding of the adversaries' capabilities, actions, and intents. This paper contributes to improving the cybersecurity of CSC by using CTI. In particular, we extend our previous work which identifies and analysis CSC attacks and adopts CTI approach to understand the attack trends so that appropriate control can determine proactively. We use the malware a smart grid case study as CSC context to demonstrate our approach. The result demonstrations how CTI approach is applied to assist in preventing cyberattacks and to disseminate threat information sharing.
Indylstms: Independently Recurrent LSTMS

We introduce Independently Recurrent Long Short-term Memory cells: IndyLSTMs. These differ from regular LSTM cells in that the recurrent weights are not modeled as a full matrix, but as a diagonal matrix, i.e. the output and state of each LSTM cell depends on the inputs and its own output/state, as opposed to the input and the outputs/states of all the cells in the layer. The number of parameters per Indy-LSTM layer, and thus the number of FLOPS per evaluation, is linear in the number of nodes in the layer, as opposed to quadratic for regular LSTM layers, resulting in potentially both smaller and faster models.We evaluate their performance experimentally by training several models on the popular IAM-OnDB and CASIA online handwriting datasets, as well as on several of our in-house datasets. We show that IndyLSTMs, despite their smaller size, consistently outperform regular LSTMs both in terms of accuracy per parameter, and in best accuracy overall. We attribute this improved performance to the IndyLSTMs being less prone to overfitting.
The impact of regional institutional quality on firm innovation: evidence from Africa

The main aim of this study is to empirically examine the role of regional institutional quality, and compare the relative importance of three different institutional arrangements, namely regulatory quality, rule of law, and corruption, on propensity of firm innovation in African countries. To this end, we used the most recent World Bank Enterprise Survey (WBES) of firms from 19 African countries. Our empirical investigation using multilevel logistic regression has drawn three important conclusions. First, there is no significant relationship between firm-specific factors, such as age, ownership, and exporting, and firm innovation. Nonetheless, firms’ propensity to innovate is significantly determined by the size of the firm, R&D expenditures, on-the-job training (OJT), and capability of managers/owners to adopt new technologies. Second, the innovation performance of firms is significantly affected by regional institutional quality. Third, among different regional institutional arrangements, reducing corruption and maintaining rule of law appeared to matter the most for firm innovation.
Generalized Graph Spectral Sampling with Stochastic Priors

We consider generalized sampling for stochastic graph signals. The generalized graph sampling framework allows recovery of graph signals beyond the bandlimited setting by placing a correction filter between the sampling and reconstruction operators and assuming an appropriate prior. In this paper, we assume the graph signals are modeled by graph wide sense stationarity (GWSS), which is an extension of WSS for standard time domain signals. Furthermore, sampling is performed in the graph frequency domain along with the assumption that the graph signals lie in a periodic graph spectrum subspace. The correction filter is designed by minimizing the mean-squared error (MSE). The graph spectral response of the correction filter parallels that in generalized sampling for WSS signals. The effectiveness of our approach is validated via experiments by comparing the MSE with existing approaches.
Integrated Head-Tilt and Electromyographic Cursor Control

This study evaluated the performance of two alternate computer access methods that could be used for two-dimensional cursor control. The first method, ACC/sEMG, integrates head acceleration and facial surface electromyography. The second method, Camera Mouse, is a free-to-use, computer vision-based access method. Twenty-four healthy adults performed a target acquisition task using each computer access method across two lighting conditions and three computer orientations. Performance in the task was evaluated using metrics of target selection accuracy, movement time, and path efficiency. Using ACC/sEMG resulted in better mean path efficiency and target selection accuracy, whereas using Camera Mouse resulted in faster target selection. Moreover, performance in the task when using Camera Mouse depended on lighting conditions in the room. The findings of this study show that the ACC/sEMG system is an effective computer access method across different lighting conditions and computer orientations. However, there is a tradeoff between speed and accuracy: ACC/sEMG system provided higher target selection accuracy compared to Camera Mouse, while the latter provided faster target selection. Future development should focus on evaluating performance of each method in populations with limited motor abilities.
Comparative Study Between Decision Tree, SVM and KNN to Predict Anaemic Condition

Anemia, a disease which is caused by an inadequacy of hemoglobin or red blood cells in the blood. It is very risky at the time of pregnancy, menstruation and in ICU sometimes causing death. So, it is a need of hemoglobin and detects anemia quickly. Usually, doctors examine the eye conjunctiva color and confirmed by a blood test which is painful, time-consuming and costly. In this study, a total of 104 people (54 males and 50 females) are collected with their clinical blood hemoglobin level, anemic condition and taken palpebral conjunctiva image. The images are captured with a cell phone camera of good resolution. By using the images, the percentage of the red, green and blue pixels are extracted in MATLAB, image processing method. Taking those features, the Hemoglobin level is plotted. A total of 81 data is taken for training purposes and 23 data for testing. For Anemia detection, the 81 data are trained with a used different classifier such as Linear SVM, Coarse Tree, and Cosine KNN and have been got highest accuracy of 82.61% in Decision Tree (Coarse) by testing 23 data.
Sequence-to-Sequence Automatic Speech Recognition with Word Embedding Regularization and Fused Decoding

In this paper, we investigate the benefit that off-the-shelf word embedding can bring to the sequence-to-sequence (seq-to-seq) automatic speech recognition (ASR). We first introduced the word embedding regularization by maximizing the cosine similarity between a transformed decoder feature and the target word embedding. Based on the regularized decoder, we further proposed the fused decoding mechanism. This allows the decoder to consider the semantic consistency during decoding by absorbing the information carried by the transformed decoder feature, which is learned to be close to the target word embedding. Initial results on LibriSpeech demonstrated that pre-trained word embedding can significantly lower ASR recognition error with a negligible cost, and the choice of word embedding algorithms among Skip-gram, CBOW and BERT is important.
Constrained language use in Finnish: A corpus-driven approach

Abstract It has been suggested that second languages and translated languages are constrained by an interplay of several linguistic systems. This paper reports on a data-driven quantitative study on constrained Finnish. We detect linguistic phenomena that distinguish constrained from non-constrained Finnish across constrained varieties, first/source languages, and registers. Implementing a two-phase method, we first detect key quantitative differences of syntactically defined POS bigrams between each variety-, language-pair- and register-specific constrained dataset and its non-constrained counterpart, using Boruta feature selection. We then use the results as variables in a Multi-dimensional Analysis. The results show that both nominal complexity and verbal/clausal complexity distinguish constrained from non-constrained Finnish. These differences interact with both type of constraint and register: the constrained varieties are less sensitive to register differences, and this tendency is more pronounced in learner Finnish than in translated Finnish. Leaving out any of these variables from the analysis would blur our view of this multi-faceted phenomenon.
Multi-Range Supported Oblivious RAM for Efficient Block Data Retrieval

Data locality exists everywhere in the memory hierarchy. Most applications show temporal and spatial locality, and computer system and architecture designers utilize this property to improve the system performance with better data layout, prefetching, and scheduling. The locality property can be represented by memory access patterns, which records the time and frequency of accessed addresses. From the security perspective, if an attacker can trace the access pattern, sensitive information inside of the application could be observed and leaked. Oblivious RAM is one of the most effective solutions to mitigate the access pattern leakage on the system, which adds redundant data blocks in space and time. With ORAM protection, the intrinsic data locality is broken by the randomly stored data. Therefore, the application cannot gain any performance benefits from locality if the ORAM protocol is used. In this work, we would like to study the potential to support multi-range accesses with new storage and access efficient ORAM construction. Our proposed designs include two major schemes: Lite-rORAM, which minimize the storage overhead of existing rORAM; and Hybrid-rORAM, which support multiple ranges accesses with minimum storage overhead. We achieve the goal to preserve the locality for consecutive data blocks with different ranges in the application while obfuscates the access pattern as well. We tested our proposed schemes with different workloads on local and remote backends. The experimental results show that, in the best case, our proposed ORAM construction can reduce the data block retrieval time to 0.24x of the baseline Path ORAM, with 87.5% storage overhead reduction compared to rORAM.
A compact and understandable associative classifier based on overall coverage

Associative classification is a machine learning approach that aims to build accurate, effective and compact classification models (classifiers) by combining paradigms from classification and association rule mining. Research studies show that associative classification approaches could achieve higher accuracy than some of the traditional classification methods. In this paper, we propose a simple and accurate classification method by selecting "strong" class association rules that highly contribute to improve the overall coverage of the classifier. The advantage of our proposed classifier is that it generates reasonably less rules on bigger datasets compared to traditional rule-based classifiers. We also discuss how the overall coverage of such classifiers affects their classification accuracy. We have performed experiments on 15 real-life datasets from the UCI Machine Learning Database Repository and compared our proposed associative classifier with other 8 well-known classification algorithms on accuracy and the number of classification rules (all differences were tested for statistical significance). Experimental results show that our proposed method was comparative with other well-known classification algorithms on accuracy, it achieved the fourth-highest average accuracy (82.7%) among all classification methods, and tends to outperform the other algorithms in terms of average number of rules (especially on bigger datasets). Although not achieving the best results in terms of classification accuracy, our approach is relatively simple and produces a compact and understandable classifier by exhaustively searching the entire example space.
Towards comparison of real time stream processing engines

The real time stream processing engines are developed for different specific use cases, which incorporates various domains such as, IOT, finance, advertisement, telecommunications, healthcare etc. These stream processing engines are based on distributed processing models, where unbounded data streams are processed. Semantics of data stream is determined after complete scanning of whole data sets, which becomes inconvenient in real time stream processing to process entire data stream at once. Windowing mechanisms are used for processing data stream in a predefine topology with fixed number of operations such as, join, aggregate, filter etc. In this paper, a comparative study is performed with existing stream processing engines. This comparison provides a direction for choosing an appropriate stream processing engine. A modified master-slave model for stream processing is discussed for reducing latency, improving scalability and fault tolerance.