Reviewerly/exHarmony huggingface dataset:
Stacked Multi-head Attention for Multi-turn Response Selection in Retrieval-based Chatbots

In the field of deep learning, response selection is the key to retrieval-based chatbots. Faced with the challenge of contextual meaning comprehension and semantic matching, we propose matching a response through diverse information by stacked multi-head attention. First, we construct multi-granular representations on embedded input sentences by stacked multi-head attention. Based on these representations, we build two different matching matrices for context-response segment pairs with another multi-head attention stack. Next, we use a two-layer CNN to extract hidden information from the matching matrices. The results are matching scores which measure the correlation among every context and its candidate responses. According to the matching scores, one or more proper responses will be chosen from candidate responses. Multi-head attention reinforces the model’s ability to focus on different positions, as experiment on the Ubuntu Dialogue Corpus v1 and the Douban Conversation Corpus show that our model is superior to the baseline model.
Server-Aided Revocable Attribute-Based Encryption from Lattices

Attribute-based encryption (ABE) can support a fine-grained access control to encrypted data. When the user’s secret-key is compromised, the ABE system has to revoke its decryption privileges to prevent the leakage of encrypted data. Although there are many constructions about revocable ABE from bilinear maps, the situation with lattice-based constructions is less satisfactory, and a few efforts were made to close this gap. In this work, we propose the first lattice-based server-aided revocable attribute-based encryption (SR-ABE) scheme and thus the first such construction that is believed to be quantum resistant. In the standard model, our scheme is proved to be secure based on the hardness of the Learning With Errors (LWE) problem.
Topology of Networks in Generalized Musical Spaces

The abstraction of musical structures as mathematical objects in a geometrical space is one of the major accomplishments of contemporary music theory. The author generalizes the concept of musical spaces as networks and derives compositional design principles via network topology analysis. This approach provides a framework for analysis and quantification of similarity of musical objects and structures and suggests a way to relate such measures to human perception of different musical entities. Finally, network analysis provides alternative ways of interpreting the compositional process by quantifying emergent behaviors with well-established statistical mechanics. Interpreting the latter as probabilistic randomness in the network, the author develops novel compositional design frameworks.
Deadlift Recognition and Application based on Multiple Modalities using Recurrent Neural Network

Multiple Sclerosis (MS) is a chronic, often disabling, autoimmune disease affecting the central nervous system and characterized by demyelination and neuropathic alterations.Magnetic Resonance (MR) images plays a pivotal role in the diagnosis and the screening of MS.MR images identify and localize demyelinating lesions (or plaques) and possible associated atrophic lesions whose MR aspect is in relation with the evolution of the disease.We propose a novel MS lesions segmentation method for MR images, based on Convolutional Neural Networks (CNNs) and partial self-supervision and studied the pros and cons of using self-supervision for the current segmentation task.Investigating the transferability by freezing the firsts convolutional layers, we discovered that improvements are obtained when the CNN is retrained from the first layers.We believe such results suggest that MRI segmentation is a singular task needing high level analysis from the very first stages of the vision process, as opposed to vision tasks aimed at day-to-day life such as face recognition or traffic sign classification.The evaluation of segmentation quality has been performed on full image size binary maps assembled from predictions on image patches from an unseen database.
Adapted Tree Boosting for Transfer Learning

Secure online transaction is an essential task for e-commerce platforms. Alipay, one of the world’s leading cashless payment platform, provides the payment service to both merchants and individual customers. The fraud detection models are built to protect the customers, but stronger demands are raised by the new scenes, which are lacking in training data and labels. The proposed model makes a difference by utilizing the data under similar old scenes and the data under a new scene is treated as the target domain to be promoted. Inspired by this real case in Alipay, we view the problem as a transfer learning problem and design a set of revise strategies to transfer the source domain models to the target domain under the framework of gradient boosting tree models. This work provides an option for the cold-start and data-sharing problems.
The Implementation of Blockchain in Banking System using Ethereum

Our current banking system is based on a central server where every branch is connected to each other.If the server made any changes to the data of a branch then other branches get affected.In this system, Corruption can be easily occurred because of unauthorized access which is totally insecure in transaction systems.But, Blockchain is a secure system where the transactional history regarding crypto-currency cannot be modified or destructed.Since 2008, Blockchain has gained immense interest due to exclusion of third-party organization participation in monitoring of the transactions.Ethereum is a protocol which is based on Blockchain technology and has several benefits over other crypto-currency based system and is best suited for creating a secure lending system.Every Ethereum based system runs on 'Smart-Contracts' which are lines of code and makes the system automated.As the system gets automated, proper algorithms can make the system reliable and secure as each and every step of the system is maintained and executed by the algorithm inside the Smart-Contracts.Blockchain systems work with peer-to-peer networks and also uses a consensus algorithm that's why there is no possibility of data modification.
Blockchain Based Remote Patient Monitoring System

Remote Patient Monitoring system is an approach of a health care system that enables the patient-user of performing a remote periodical check-up. Unfortunately, these types of systems usually don't provide the advantages of securely sharing the patient health information among different health providers. Many types of research aimed to solve this issue by applying the blockchain technique to the existing patient health information records at hospitals. However; none was found regarding the remote patient monitoring system's generated data. Therefore, this proposal aims to integrate the advantages of blockchain and the Remote Patient Monitoring (RPM) system by building a secure blockchain based RPM system.
Implementation of electronic stethoscope for online remote monitoring with mobile application

The stethoscope is a piece of medical standard equipment that isused by a physician for an initial examination of the patient. Generally, the stethoscopeis used for auscultating sounds which are generated by the workings of organ systems such as cardiac, lung or digestive. In the present condition with the growing number of the patient population, it has an impact on the burden of hospitals and medical practitioners. So that treatment is not optimal, especially patients who need continuous monitoring. Thus it needs a system that can work dynamically, flexibly and remotely based. This paper focuses on the implementation of the electronic stethoscope which is integrated with a mobile phone as the modality of online data transmission through the internet network. The prototype of an electronic stethoscope uses condenser mic, pre-amplifier, wide bandpass filter (20 Hz-1 KHz) and audio amplifier. The maximum gain is 28.63 dB in the 20 Hz-690 Hz frequency range. The signal output can be connected to the android mobile through the jacked phone to be stored in MP3 format and then sent to the cloud server for further monitoring and analysis. The application called “Steder” supports realtime communication between patient and physician for medical check-up, consultation, and discussion activities.
Spoken Multiple-Choice Question Answering Using Multimodal Convolutional Neural Networks

In a spoken multiple-choice question answering (MCQA) task, where passages, questions, and choices are given in the form of speech, usually only the auto-transcribed text is considered in system development. The acoustic-level information may contain useful cues for answer prediction. However, to the best of our knowledge, only a few studies focus on using the acoustic-level information or fusing the acoustic-level information with the text-level information for a spoken MCQA task. Therefore, this paper presents a hierarchical multistage multimodal (HMM) framework based on convolutional neural networks (CNNs) to integrate text- and acoustic-level statistics into neural modeling for spoken MCQA. Specifically, the acoustic-level statistics are expected to offset text inaccuracies caused by automatic speech recognition (ASR) systems or representation inadequacy lurking in word embedding generators, thereby making the spoken MCQA system robust. In the proposed HMM framework, two modalities are first manipulated to separately derive the acoustic- and text-level representations for the passage, question, and choices. Next, these clever features are jointly involved in inferring the relationships among the passage, question, and choices. Then, a final representation is derived for each choice, which encodes the relationship of the choice to the passage and question. Finally, the most likely answer is determined based on the individual final representations of all choices. Evaluated on the data of “Formosa Grand Challenge - Talk to AI”, a Mandarin Chinese spoken MCQA contest held in 2018, the proposed HMM framework achieves remarkable improvements in accuracy over the text-only baseline.
Deeper and Mixed Supervision for Salient Object Detection in Automated Surface Inspection

In recent years, researches in the field of salient object detection have been widely made in many industrial visual inspection tasks. Automated surface inspection (ASI) can be regarded as one of the most challenging tasks in computer vision because of its high cost of data acquisition, serious imbalance of test samples, and high real-time requirement. Inspired by the requirements of industrial ASI and the methods of salient object detection (SOD), a task mode of defect type classification plus defect area segmentation and a novel deeper and mixed supervision network (DMS) architecture is proposed. The backbone network ResNeXt-101 was pretrained on ImageNet. Firstly, we extract five multiscale feature maps from backbone and concatenate them layer by layer. In addition, to obtain the classification prediction and saliency maps in one stage, the image-level and pixel-level ground truth is trained in a same side output network. Supervision signal is imposed on each side layer to realize deeper and mixed training for the network. Furthermore, the DMS network is equipped with residual refinement mechanism to refine the saliency maps of input images. We evaluate the DMS network on 4 open access ASI datasets and compare it with other 20 methods, which indicates that mixed supervision can significantly improve the accuracy of saliency segmentation. Experiment results show that the proposed method can achieve the state-of-the-art performance.
Location Mapping of Hydrothermal Alteration Using Landsat 8 Data: A Case of Study in Prestea Huni Valley District, Ghana

Utilization of multispectral satellite images is an excellent approach in the reconnaissance stage of gold prospecting due to its high effectiveness and low cost. This research used Landsat 8 Remote Sensing data and Geographical Information System to identify, delineate and map hydrothermal alteration zone relating to gold pattern mineralization in Prestea Huni Valley District in the Western Region of Ghana. Principle Component Analysis (PCA), three band combinations, spectral rationing and Crosta techniques are used in this study. For three band combinations, bands 5, 4, 3 and 5, 6, 7 are used to map location of hydrothermal alterations. Band ratios (7/5and6/7), (6/5) and (4/2) are used to identify presence of clay, ferrous and iron oxide minerals respectively. Sabin's ratio 4/2, 6/7 and 6/5, Kaufmann ratio 7/5, 5/4 and 6/7, and 4/2, 6/7 and 5 are used to identify vegetation, outcrop and hydrothermal alterations respectively. Crosta and PCA techniques were used to suppress the interference of vegetation in delineating the alteration zones. Results revealed that the three band combinations and spectral ratioing clearly depicts hydrothermal deposit of ferrous minerals, clay and iron oxide minerals. PCA identifies presence of iron-oxide and hydroxyl minerals as bright pixels. The first three high order principal components (PC1, PC2 and PC3) of input spectral bands gave more than 98% of the spectral information. Thus, the results from satellite images an effective and efficient way of mapping hydrothermal alteration zones at regional scale.
Analysis of user behavior in a large-scale internet video-on-demand(VoD) system

With the development and popularity of the Internet, Internet traffic has increased dramatically. Numerous studies have shown that video accounts for a large percentage of Internet traffic, and this percentage is still rising in the future. A good understanding of user behavior in online video systems can help us design, configure and manage video content distribution to alleviate network stress. In this paper, we did a detailed analysis of user behavior data for Internet video. Our research shows that the user's daily access and online pattern of users have a fixed pattern, and the user's access behavior conforms to Zipf's law. Besides, we optimized the fit of Zipf-like distribution of video's popularity. Finally, we built a reliable simulation system that simulates user behavior data. Overall, we believe that the results presented in this paper are very important and valuable to the whole network.
Information Security Risk Management of Research Information Systems: A hybrid approach of Fuzzy FMEA, AHP, TOPSIS and Shannon Entropy

The purpose of this paper is to implement information security risk management (ISRM) in research information systems (RIS).Appropriate identification and assessment of risks in different aspects such as software, communications, and human resources for RIS's besides providing efficient and effective preventive and corrective actions are other aims of this study.Furthermore, continual improvement of risk response processes in information technology environment is another aim of this study.In this study, potential risks of information security are identified using failure mode and effects analysis (FMEA).Also, detected failure modes are evaluated by multi-criteria decision-making method (MCDM) using a hybrid method of fuzzy logic, analytic hierarchy process (AHP), Shannon entropy scoring method, and technique for order preference by similarity to the ideal solution (TOPSIS).The result of this paper shows that information security software potential risks assessment by the proposed model is more accurate and reliable than non-fuzzy models.Unauthorized access to view or change the stored information of the server is the risk with the most important priority identified by MCDM approach.Confidentiality of information is more important than other information security criteria.Furthermore, failure modes in the category of the main server and internet have more priority in comparison to others.
Automating the Generation of High School Geometry Proofs using Prolog in an Educational Context

When working on intelligent tutor systems designed for mathematics education and its specificities, an interesting objective is to provide relevant help to the students by anticipating their next steps.This can only be done by knowing, beforehand, the possible ways to solve a problem.Hence the need for an automated theorem prover that provide proofs as they would be written by a student.To achieve this objective, logic programming is a natural tool due to the similarity of its reasoning with a mathematical proof by inference.In this paper, we present the core ideas we used to implement such a prover, from its encoding in Prolog to the generation of the complete set of proofs.However, when dealing with educational aspects, there are many challenges to overcome.We also present the main issues we encountered, as well as the chosen solutions.
Accelerating Convolutional Neural Network via Structured Gaussian Scale Mixture Models: A Joint Grouping and Pruning Approach

Despite the great success of deep convolutional neural networks (DCNNs), their heavy computational complexity remains a key obstacle to the wide use in practical applications. To meet this challenge, DCNN pruning has been recently developed as a technique of compressing DCNNs to facilitate their applications in the real world. In this paper, we propose a hybrid network compression technique for exploiting the prior knowledge of network parameters by Gaussian scale mixture (GSM) models. Specifically, the collection of network parameters are characterized by GSM models and network pruning is formulated as a maximum a posteriori (MAP) estimation problem with a sparsity prior. The key novel insight brought by this work is that groups of parameters associated with the same channel are similar, which is analogous to the grouping of similar patches in natural images. Such observation inspires us to leverage powerful structured sparsity prior from image restoration to network compression - i.e., to develop a flexible filter-grouping strategy that not only promotes structured sparsity but also can be seamlessly integrated with the existing network pruning framework. Extensive experimental results on several popular DCNN models including VGGNet, ResNet and DenseNet have shown that the proposed GSM-based joint grouping and pruning method convincingly outperforms other competing approaches (including both pruning and non-pruning based methods).
Performance evaluation of Map-reduce jar pig hive and spark with machine learning using big data

Big data is the biggest challenges as we need huge processing power system and good algorithms to make an decision. We need Hadoop environment with pig hive, machine learning and hadoopecosystem components. The data comes from industries. Many devices around us and sensor, and from social media sites. According to McKinsey There will be a shortage of 15000000 big data professionals by the end of 2020. There are lots of technologies to solve the problem of big data Storage and processing. Such technologies are Apache Hadoop, Apache Spark, Apache Kafka, and many more. Here we analyse the processing speed for the 4GB data on cloudx lab with Hadoop mapreduce with varing mappers and reducers and with pig script and Hive querries and spark environment along with machine learning technology and from the results we can say that machine learning with Hadoop will enhance the processing performance along with with spark, and also we can say that spark is better than Hadoop mapreduce pig and hive, spark with hive and machine learning will be the best performance enhanced compared with pig and hive, Hadoop mapreduce jar.
Motivation as the Mediator in Relationship between Non-verbal Communication of Arabic Language Teachers and Student Learning Outcomes

Effective communication requires a strong combination between verbal and nonverbal communication which is one of the skills that need to be mastered by a teacher.The estimate of 65-70 percent of message in communication is obtained by nonverbal and only 30-35 percent is obtained through speech.This study is aimed at investigating the impact of NVC practice of Arabic language teachers on the motivation of students and their learning outcomes in Arabic language in Malaysia.Quantitative study used a survey questionnaire and was involved a population of 84,841 students.Sampling was done through stratified random sampling and only 750 students were chosen in all over Malaysia as respondents.The SEM model was analysed using the approach by Baron and Kenny (1986).The findings show that the relationship NVCmotivation in students was significant (β= .133,p < 0.01), NVClearning outcome (β = .350,p<0.01), motivationlearning outcome (β = .439,p<0.01).In the test of indirect effect (mediator), the findings show that motivation was statistically proven through the Sobel test (β= 0.133, Sobel Test = 4.68, SE = 0.01, p < 0.01) as the mediator in the relationship between NVC and learning outcome.Hence, the result shows that, the higher the motivation possessed by the students the better their learning outcome in Arabic language.Therefore, inevitably the teachers play vital roles during teaching and learning to use more NVC approach to enhance the higher learning outcomes for students in Arabic language learning.
A GMS-Guided Approach for 2D Feature Correspondence Selection

Feature correspondence selection, which aims to seek as many true matches ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , inliers) as possible from a given putative set while minimizing false matches ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , outliers), is crucial to many feature-matching based tasks in computer vision. It remains a challenging problem how to deal with putative sets with low inlier ratios. To address this problem, in this paper, we propose a novel correspondence selection strategy, which is guided by Grid-based Motion Statistics (GMS). We first adopt the GMS to generate a small correspondence set with a high inlier ratio. Then, an accurate geometric model is built using the above correspondence set. Finally, the built geometric model is used to filter the given putative correspondence set to obtain true correspondences. The experimental results on benchmark datasets demonstrate that our proposed approach outperforms the state-of-the-art approaches for putative sets with various inlier ratios, especially for cases with low inlier ratios.
Discrete logarithm for nilpotent groups and cryptanalysis of polylinear cryptographic system

We present an efficient algorithm to compute a discrete logarithm in a finite nilpotent group, or more generally, in a finitely generated nilpotent group.Special cases of a finite p-group (p is a prime) and a finitely generated torsion free nilpotent group are considered.Then we show how the derived algorithm can be generalized to an arbitrary finite or finitely generated nilpotent group respectively.We suppose that group is presented by generating elements and defining relators or as a subgroup of a triangular matrix group over a prime finite field (in finite case) or over the ring of integers (in torsion-free case).On the base of the derived algorithm we give a cryptanalysis of some schemes of polylinear cryptography known in the literature.