Reviewerly/exHarmony huggingface dataset:
Technical Analysis of Fall Detection Techniques

Fall has become the most imminent public health problem in the world. Each year thousands of people over age 65 experience falls. Because of that a large number of elderly people gets injured. Even some of them die if not rescued on time. With such immense increase in the falls that harms the individuals physically and/or mentally Researchers found ways to tackle the problem by devising different techniques for the purpose of fall detection. Most of techniques being used rely on Threshold based algorithms (TBA). While some of them brought the Machine Learning (ML) into the field to fight detect falls. The use of TBA and ML combinedly for fall detection has been reported. But still every technique has its advantages and disadvantages. We are doing analysis of techniques that are totally based on either TBA's, ML or the combination of both TBA and ML.
Text-To-Speech quality evaluation based on LSTM Recurrent Neural Networks

Nowadays, the Text-To-Speech (TTS) system has developed to quite a high level, but there has not been an objective assessment method to evaluate the synthesized speech effectively. Research on the objective assessment method is around predicting the mean opinion score(MOS) of the speech in general. In this paper, a mandarin TTS evaluation method using LSTM+LR to predict the MOS is proposed. To the best of our knowledge, this is the first research in evaluating mandarin TTS. Compared with other methods such as the CNN+LR, which is the previous best method, this method achieves much higher accuracy with the root mean square(RMSE) of 0.40 and the correlation $\mathbf { \rho } _ { \mathbf { S } }$ of 0.68.
A Deep Dictionary Model to Preserve and Disentangle Key Features in a Signal

We propose a deep dictionary model for single image super-resolution (SISR) made of multiple layers of analysis dictionaries interlaced with corresponding soft-thresholding operations and a single synthesis dictionary. In this paper, we introduce a novel method for learning analysis dictionary and thresholding pairs as building block for the deep dictionary model. Each analysis dictionary contains two sub-dictionaries: an information preserving analysis dictionary (IPAD) and a clustering analysis dictionary (CAD). The IPAD and thresholding pair passes the key information from the previous layer, while the CAD and thresholding pair gives a sparse representation of its input data that facilitates discrimination of key features. Simulation results show that the proposed deep dictionary model achieves comparable performance with a deep neural network which has the same structure and is optimized using backpropagation.
Epithelial Segmentation From In Situ Hybridisation Histological Samples Using A Deep Central Attention Learning Approach

The assessment of pathological samples by molecular techniques, such as in situ hybridization (ISH) and immunohistochemistry (IHC), has revolutionised modern Histopathology. Most often it is important to detect ISH/IHC reaction products in certain cells or tissue types. For instance, detection of human papilloma virus (HPV) in oropharyngeal cancer samples by ISH products is difficult and remains a tedious and time consuming task for experts. Here we introduce a proposed framework to segment epithelial regions in oropharyngeal tissue images with ISH staining. First, we use colour deconvolution to obtain a counterstain channel and generate input patches based on superpixels and their neighbouring areas. Then, a novel deep attention residual network is applied to identify the epithelial regions to produce an epithelium segmentation mask. In the experimental results, comparing the proposed network with other state-of-the-art deep learning approaches, our network provides a better performance than region-based and pixel-based segmentations.
The Role of Blockchain in Supporting Critical National Infrastructure

Cyber-attacks have become more sophisticated over recent years with the different configuration types and various industry sectors have suffered from a range of these different attack vectors resulting in some devastating outcomes. These have manifested in the shape of ransomware, malware, manipulation methods, phishing and spear-phishing. Whilst data breaches are a serious incident, in most organisations, there is a growing concern regarding attacks that are designed to have a more destructive effect such as the Ukraine cyber-attack in 2015 that resulted in a shutdown of the power grid. Or the WannaCry ransomware attack in 2017 that caused widespread chaos with healthcare institutions unable to carry out any tasks since access to data/systems was unavailable. These critical national infrastructure (CNI) attacks into sectors such as healthcare cause data breaches/disruptions and are also able to leverage vulnerabilities in the industrial processes containing ICS and SCADA systems. Perhaps the state sponsored cyber-attacks cause the most concern as they tend to be at the more sophisticated level of the spectrum and maximize on amount of potential harm that is delivered. There is growing interest in how to protect CNI besides just using traditional methods such as regular patching, Intrusion Detection and Prevention systems (IDPS), up to date compliance policies, etc., and blockchain can be the mechanism that gives another protection layer to protect mission critical data. Blockchain, a decentralized network, offers the features of immutability, non-tampering, security encryption, auditability, and can be a permissioned type of environment where the set of users are invited and not open for all. Blockchain can complement these traditional systems by offering another layer of protection to the sensitive and mission critical data.
English – Indonesian Phrase Translation Using Recurrent Neural Network and ADJ Technique

Recurrent Neural Network (RNN) and annotated disjunction are discussed to develop English – Indonesian phrase based. Phrase – based English – Indonesian machine translation becomes important because there are differences between two languages and it can be a sub system for English – Indonesian machine translation. Automatic translation can be done using RNN and ADJ Technique. The main process in this research are preprocessing, determination of phrase type, and translation the phrase. In the preprocessing has process such as case folding, tokenizing, pos tagging, and stemming. In the determination of phrase type, system counts weight of input phrase using RNN. The translation phrase is entered to target language using ADJ Technique. The software experiment is tested by using 70 English phrases. The result shows accuracy of RNN and ADJ Technique for English – Indonesian phrase translation is 88.57 %.
Crossover Service Phenomenon Analysis Based on Event Evolutionary Graph

Nowadays, crossover and convergence between services has become a new phenomenon in service market, especially in China. This work aims at analyzing the inner and external formation mechanism of crossover service, which have great significance to the sustainable development of this new ecosystem. Previously, a traditional concept based knowledge graph extracted from crossover service News was constructed for crossover service event analysis. The crossover modes and the statistics of appearances were found in that experiment. Due to the limitation of motivation analysis of the inference method based on static concept structure, the Event Evolutionary Graph (EEG) is introduced to improve the event analysis, and moreover to achieve the event prediction in this undergoing experiment. Generic EEG can represent the event evolution rules and patterns, but due to the characteristics of crossover services, this experiment will adapt event modeling method and its training method. This work in progress methodology would explain the inner and external formation reason of crossover service, and furthermore achieve the assisted decision-making for potential crossover organizations based on event prediction.
Boundary Information Matters More: Accurate Temporal Action Detection with Temporal Boundary Network

Temporal action detection in untrimmed videos is an important yet challenging task. How to locate complex actions accurately is still an open question due to the ambiguous boundaries between action instances and the background. Recently a newly proposed work exploits Structured Segment Networks (SSN) for temporal action detection, which models temporal structure of action instances via structured temporal pyramids, and comprises two classifiers, respectively for classifying actions and determining proposal completeness. In this paper we attempt to delve the temporal boundary information when modeling temporal structure of action instance, by introducing to SSN the structured temporal boundary attention pyramid. On top of the pyramid, we add another set of classifiers for unit-wise completeness evaluation, which enables proposal recycling for efficient action detection. Experimental results on two challenging benchmarks, THUMOS’14 and ActivityNet, indicate that our Temporal Boundary Network shows a significant performance improvement compared with SSN, and achieves a competitive performance compared with state-of-the-arts.
World Perception of the Latest Events in Egypt Based on Sentiment Analysis of the Guardian’s Related Articles

In order to infer how the world has perceived the unfolding of events in Egypt during the last eight years, we take the Guardian newspaper as a sample study to extract valuable information about the world viewpoints on the big events in Egypt during this period. We perform a sentiment analysis on all the articles in the ‘World’ section of the newspaper from the beginning of 2010 till the end of 2017 based on just the keyword ‘Egypt’. We extracted Unigram tokens from each article and used them for making inference using three lexicons dictionaries: afinn, nrc, and bing. The results show that the general trend is slightly negative over all the selected period. Many conflicting feelings were prevalent during this period such as positive, negative, trust, fear, anger and anticipation. The results show also that years 2011 and 2013, where the world witnessed the two uprisings in Egypt, have witnessed the peaks in both positive and negative emotions.
Contrast enhancement of medical images using fuzzy set theory and nonsubsampled shearlet transform

Abstract Noises and artifacts are introduced in medical images during the process of imaging and transmission, resulting in reduced definition and lack of detail. Therefore, a contrast enhancement method, based on fuzzy set theory and nonsubsampled shearlet transform (NSST), is proposed. First, the original image is decomposed into several high‐frequency components and a low‐frequency component by NSST. Then, the threshold method is used to remove noises in the high‐frequency components. In addition, a linear stretch is used to improve the overall contrast in the low‐frequency component. Then, the reconstruct image is reconstructed by applying the inverse NSST to the processed high‐frequency and low‐frequency components. Finally, the fuzzy contrast is used to improve the detail information and global contrast in the reconstruct image. Experimental results indicate that, relative to contrast algorithms, the peak signal‐to‐noise ratio of the proposed method is improved by approximately 18%, and the root mean square error (RMSE) is optimized to approximately 48%. The proposed method also improves the image definition and texture information. Moreover, when compared with the Improved Fuzzy Contrast Combined Adaptive Threshold in NSCT for Medical Image Enhancement, the processing time (time) of this proposed method optimizes about 86%, which can obviously improve the computational efficiency of this method.
Experimenting with Real Application-specific QoS Guarantees in a Large-scale RINA Demonstrator

This paper reports the definition, setup and obtained results of the Fed4FIRE + medium experiment ERASER, aimed to evaluate the actual Quality of Service (QoS) guarantees that the clean-slate Recursive InterNetwork Architecture (RINA) can deliver to heterogeneous applications at large-scale. To this goal, a 37-Node 5G metro/regional RINA network scenario, spanning from the end-user to the server where applications run in a datacenter has been configured in the Virtual Wall experimentation facility. This scenario has initially been loaded with synthetic application traffic flows, with diverse QoS requirements, thus reproducing different network load conditions. Next,their experienced QoS metrics end-to-end have been measured with two different QTA-Mux (i.e., the most accepted candidate scheduling policy for providing RINA with its QoS support) deployment scenarios. Moreover, on this RINA network scenario loaded with synthetic application traffic flows, a real HD (1080p) video streaming demonstration has also been conducted, setting up video streaming sessions to end-users at different network locations, illustrating the perceived Quality of Experience (QoE). Obtained results in ERASER disclose that, by appropriately deploying and configuring QTA-Mux, RINA can yield effective QoS support, which has provided perfect QoE in almost all locations in our demo when assigning video traffic flows the highest (i.e., Gold) QoS Cube.
Fuzzy Personalized Scoring Model for Recommendation System

In this research, we aim to propose a data preprocessing framework particularly for financial sector to generate the rating data as input to the collaborative system. First, clustering technique is applied to cluster all users based on their demographic information which might be able to differentiate the customers' background. Then, for each customer group, the importance of demographic characteristics which are highly associated with financial products purchasing are analyzed by the proposed fuzzy integral technique. The importance scores across items and customers are generated either on customer groups and individuals. The analysis shows the proposed method is able to differentiate customers based on their demographic and purchasing behaviors. Also, the generated rating matrix can be directly used for collaborative filtering model.
A Survey of Mobile Learning Approaches for Teaching Internet of Things

Over the past few years, the interest in the Internet of Things (IoT) as well as the integration of mobile technology into education—called mobile learning—is rapidly rising. The purpose of this paper is to review mobile learning approaches that have been applied to teach IoT or related subjects and to derive lessons learned for designing mobile-learning-based IoT courses. To achieve this, we performed a systematic interdisciplinary literature review. The contribution of this paper is threefold. First, we present a survey of all identified publications in the research area, providing insights into the development of this field and showing its emerging importance. Second, we discuss different mobile learning approaches, focusing on approaches concerned explicitly with the special demands of these concepts in the context of IoT and related Computer Science topics. Third, by deriving lessons learned we provide a foundation for the informed selection of suitable mobile learning concepts as well as for evaluating future research in this area.
Auditable zerocoin scheme with user awareness

With the development of Blockchain, more and more distributed cryptocurrencies continue to emerge. Most of these currencies use cryptographic techniques (such as zero-knowledge proofs, ring signatures, etc.) to enhance privacy protection, but at the same time the regulatory authorities have no access to audit transactions. Most existing auditing schemes are based on centralized system and cannot be directly applied to distributed cryptocurrencies. Therefore, an Auditable Zerocoin with User Awareness (AZUA) scheme which supports privacy protection and auditability simultaneously is proposed in this paper. Aiming to ensure that auditors can audit Zerocoin's transactions, auditability is defined in this paper. Meanwhile, in order to prevent auditors from abusing their power, a new security property is proposed: user awareness auditing. Moreover, taking transaction privacy issue into consideration, indistinguishability of audit information is defined. The transaction auditing from auditors is achieved in AZUA through the encryption scheme, which ensures the honesty of the auditors by introducing a commitment mechanism and that the users can be aware of whether they have been audited according to randomized public keys. Finally, it is proved that the scheme can satisfy auditability, user awareness auditing and indistinguishability of audit information simultaneously.
On the Achievable Rate and Capacity for a Sample-Based Practical Photon-Counting Receiver

We investigate the achievable rate and capacity of a non-perfect photon-counting receiver. For the case of long symbol duration, the achievable rate under on-off keying modulation is investigated based on Kullback-Leibler (KL) divergence and Chernoff $\alpha$-divergence. We prove the tightness of the derived bounds for large peak power with zero background radiation with exponential convergence rate, and for low peak power of order two convergence rate. For large peak power with fixed background radiation and low background radiation with fixed peak power, the proposed bound gap is a small positive value for low background radiation and large peak power, respectively. Moreover, we propose an approximation on the achievable rate in the low background radiation and long symbol duration regime, which is more accurate compared with the derived upper and lower bounds in the medium signal to noise ratio (SNR) regime. For the symbol duration that can be sufficiently small, the capacity and the optimal duty cycle are is investigated. We show that the capacity approaches that of continuous Poisson capacity as $T_s=\tau\to0$. The asymptotic capacity is analyzed for low and large peak power. Compared with the continuous Poisson capacity, the capacity of a non-perfect receiver is almost lossless and loss with attenuation for low peak power given zero background radiation and nonzero background radiation, respectively. For large peak power, the capacity with a non-perfect receiver converges, while that of continuous Poisson capacity channel linearly increases. The above theoretical results are extensively validated by numerical results.
A Key Exchange Based on the Short Integer Solution Problem and the Learning with Errors Problem

Short integer solution (SIS) and learning with errors (LWE) are two hard lattice problems. These two problems are believed having huge potential in application of cryptography. In 2012, Ding et al. [5] introduced the first provably secure key exchange based on LWE problem. On the other hand, we believe that it is very difficult to do key exchange on SIS problem only. In 2014, Wang et al. [6] did an attempt, but it was not successful. Mao et al. [7] broke the protocol by an attack based on CBi-SIS problem in 2016. However, their attack is not efficient. In this paper, we present a extremely straightforward and simple attack to Wang's key exchange and then we will construct a key exchange based on SIS and LWE problems.
Boundary Value Analysis Testing on Augmented Reality of Indonesian Fruit Recognition at Mekarsari Tourist Park using Cloud Method on Android Mobile Devices

Software testing is a process of implementing a program with the aim of finding an error. A good test case is if the test has the possibility of finding an uncovered error. A successful test is if the test finds an error that was not initially found. One of the testing types available is black box testing. This paper proposes testing using black box testing technique. The black box testing method consists of several ways including equivalence partitioning, boundary value analysis, comparison testing, sample testing, robustness testing, and others. Among the many methods of testing, Boundary Value Analysis was chosen in this study. Boundary Value Analysis is a method of testing by determining the value of the lower limit and upper limit of the data that will be tested. This test is performed on the functions of Augmented Reality prototype of Indonesia fruit recognition by using the cloud method on Android mobile devices. From testing the distance of marker objects to Android mobile devices on cloud recognition using an Android camera shows that the higher the augmentable rating of the target image and the more the number of markers features detected, the easier the image will be tracked by AR. If the distance between the camera and the real object gets farther away, then the virtual object cannot be displayed. Testing with mobile devices using HSDPA, 3G and WIFI networks connecting a cloud database server to display virtual objects shows the average results of devices that use WIFI networks provide the fastest performance.
Investigating Domain Sensitivity of DNN Embeddings for Speaker Recognition Systems

A speaker embeddings framework achieves state-of-the-art speaker recognition performance by modeling speaker discriminant information directly using deep neural networks (DNNs). After the introduction of neural network based speaker embeddings, researchers have explored the requirements for training an effective embeddings network. However, the domain of the data used for system development should match the domain of operation for optimal performance. In this paper, we investigate the sensitivity of domain mismatch in the embeddings space. Specifically, degradation in performance is observed when back-end scoring with embeddings is performed with out-domain data. To compensate for the domain mismatch, we propose two novel deep domain adaptation techniques based on autoencoder architectures trained on embeddings in an unsupervised fashion. The results show that domain mismatch can be compensated effectively using autoencoders to adapt the out-domain data to in-domain.
On the Matrix, neuroscience and dialogue

The author poses a position statement that Foulkes underplayed the importance of emotions as an organizing principle in the mental life of groups, and indeed, paid very little attention to emotions in his published works. Evidence from the field of neuroscience is drawn upon to identify seven emotional neuro-chemical systems in the sub-cortical region of the brain. The fundamental emotions are: rage, fear, seeking, panic/grief, lust, care and play. The article makes a distinction between emotions, as being physiological responses triggered by neurochemical release caused by perception of both external and internal realities, whereas feelings are a higher order level of functioning at cognitive levels that can articulate cause, meaning and symbolism of the more visceral emotional experiences. The article puts forward the idea that emotions are the biological substrate of the Foundation Matrix, linking all of humanity and the mammalian world. In turn, groups and individuals have valences to particular emotional responses and this will be demonstrated with examples from the Personal, Social and Dynamic Matrices. An elaboration of the concept of dialogue, within the wider sphere of communication, is put forward, which the author argues is the group analytic expression of love (care). Love is mutative and facilitates the plasticity of the brain/mind relationship encouraging new neuronal connections to be made, linking sub-cortical brain regions with the neo-cortex thinking cap.