Reviewerly/exHarmony huggingface dataset:
Portable Convolution Neural Networks for Traffic Sign Recognition in Intelligent Transportation Systems

Deep convolutional neural networks (CNN) have the strength in traffic-sign classification in terms of high accuracy. However, CNN models usually contains multiple layers with a large number of parameters consequently leading to a large model size. The bulky model size of CNN models prevents them from the wide deployment in mobile and portable devices in Intelligent Transportation Systems. In this paper, we design and develop a portable convolutional neural network (namely portable CNN) structure used for traffic-sign classification. This portable CNN model contains a stacked convolutional structure consisting of factorization and compression modules. We conducted extensive experiments to evaluate the performance of the proposed Portable CNN model. Experimental results show that our model has the advantages of smaller model size while maintaining high classification accuracy, compared with conventional CNN models.
A distributed system for large-scale n-gram language models at Tencent

n-gram language models are widely used in language processing applications, e.g., automatic speech recognition, for ranking the candidate word sequences generated from the generator model, e.g., the acoustic model. Large n-gram models typically give good ranking results; however, they require a huge amount of memory storage. While distributing the model across multiple nodes resolves the memory issue, it nonetheless incurs a great network communication overhead and introduces a different bottleneck. In this paper, we present our distributed system developed at Tencent with novel optimization techniques for reducing the network overhead, including distributed indexing, batching and caching. They reduce the network requests and accelerate the operation on each single node. We also propose a cascade fault-tolerance mechanism which adaptively switches to small n-gram models depending on the severity of the failure. Experimental study on 9 automatic speech recognition (ASR) datasets confirms that our distributed system scales to large models efficiently, effectively and robustly. We have successfully deployed it for Tencent's WeChat ASR with the peak network traffic at the scale of 100 millions of messages per minute.
Who You’re Gonna Call? The Development of University Digital Leaders

In our hyper-connected digitised educational world, university tutors are interested in capitalising on affordances of digital trends in teaching and learning. Students, under the alias of preservice- teachers, walk among them equipped with digital skills in areas of their interest. How can we encourage collaboration between tutors and students that can promote the use of the digital force wisely, support the development of students’ professional identities further and extend tutors’ digital competences? The story of nine tutors and eleven undergraduate pre-service-teachers working together on digital partnerships is set against discussions around digital leadership and citizenship. This case study aims to highlight how universities can respond to technology-driven change by engaging students further and support their awareness of digital citizenship. The overall results showed that the informal learning that students have capitalised outside the classroom can be used to scaffold their development of digital citizenship through offline community engagement. It demonstrates the advantage of using such opportunities as a means to encourage citizenship practices among university student communities and the positive impact that such synergies can have on all the participants.
Mobile application development framework to support farming as a business via benchmarking: the case of Tanzania

In many developing countries, agricultural supplyside (i.e.smallholder farmers) is normally weak and cannot meet demands of formal markets [1].Problems include lack of mechanization; lack of irrigation schemes with too much reliance on rain; lack of skills and knowledge on farming-as-abusiness (FAAB); poor transportation and logistical networks; and lack or poor farm inputs (e.g.seeds, fertilizers, pests, etc.) [2, 3].In recent years, acceptance of agricultural and rural development (ARD) science has started to address challenges such as efficient production, managing profitability, sustaining the environment.
Conquering the CNN Over-Parameterization Dilemma: A Volterra Filtering Approach for Action Recognition

The importance of inference in Machine Learning (ML) has led to an explosive number of different proposals in ML, and particularly in Deep Learning. In an attempt to reduce the complexity of Convolutional Neural Networks, we propose a Volterra filter-inspired Network architecture. This architecture introduces controlled non-linearities in the form of interactions between the delayed input samples of data. We propose a cascaded implementation of Volterra Filtering so as to significantly reduce the number of parameters required to carry out the same classification task as that of a conventional Neural Network. We demonstrate an efficient parallel implementation of this Volterra Neural Network (VNN), along with its remarkable performance while retaining a relatively simpler and potentially more tractable structure. Furthermore, we show a rather sophisticated adaptation of this network to nonlinearly fuse the RGB (spatial) information and the Optical Flow (temporal) information of a video sequence for action recognition. The proposed approach is evaluated on UCF-101 and HMDB-51 datasets for action recognition, and is shown to outperform state of the art CNN approaches.
Phase effects in coherently stimulated down-conversion with a quantized pump field

We investigate the effect of the cumulative phase on the photon statistics of the three-mode state whose evolution is described by the trilinear Hamiltonian ${\stackrel{\ifmmode \hat{}\else \^{}\fi{}}{H}}_{I}=i\ensuremath{\hbar}\ensuremath{\kappa}\left(\stackrel{\ifmmode \hat{}\else \^{}\fi{}}{a}\stackrel{\ifmmode \hat{}\else \^{}\fi{}}{b}{\stackrel{\ifmmode \hat{}\else \^{}\fi{}}{c}}^{\ifmmode\dagger\else\textdagger\fi{}}\ensuremath{-}{\stackrel{\ifmmode \hat{}\else \^{}\fi{}}{a}}^{\ifmmode\dagger\else\textdagger\fi{}}{\stackrel{\ifmmode \hat{}\else \^{}\fi{}}{b}}^{\ifmmode\dagger\else\textdagger\fi{}}c\right)$, wherein the pump is taken to be quantized (and prepared in a coherent state) and the signal and idler modes are initially seeded with coherent states. We provide a brief review of the two-mode squeezed coherent states generated by nondegenerate coherently stimulated parametric down-conversion, whereby the nonlinear crystal is driven by a strong classical field. The statistics of the resulting two-mode state have been shown to depend greatly on the cumulative phase $\mathrm{\ensuremath{\Phi}}={\ensuremath{\theta}}_{s}+{\ensuremath{\theta}}_{i}\ensuremath{-}2\ensuremath{\phi}$ where ${\ensuremath{\theta}}_{s\left(i\right)}$ are the signal(idler) coherent state phases and $2\ensuremath{\phi}$ is the classical pump phase. Using perturbation theory, we analytically show for short times how the photon statistics and entanglement properties of the resultant state depends strictly on this phase combination. We also present numerical results of the relevant quantities to show the evolution of the three modes and provide a qualitative analysis of the near-thermal-like nature of the steady state valid for long times. Finally, we examine the effect of the cumulative phase $\mathrm{\ensuremath{\Phi}}$ on the correlations between the bipartite system of pump and signal/idler modes, and the effect of the quantized pump on the correlations between signal and idler modes.
A Distributed and Clustering-Based Algorithm for the Enumeration Problem in Abstract Argumentation

Computing acceptability semantics of abstract argumentation frameworks is receiving increasing attention. Large-scale instances, with a clustered structure, have shown particularly difficult to compute. This paper presents a distributed algorithm, AFDivider, that enumerates the acceptable sets under several labelling-based semantics. This algorithm starts with cutting the argumentation framework into clusters thanks to a spectral clustering method, before computing simultaneously in each cluster parts of the labellings. This algorithm is proven to be sound and complete for the stable, complete and preferred semantics, and empirical results are presented.
An Illegal Billboard Advertisement Detection Framework Based on Machine Learning

Illegal advertising constantly infringes on consumers' rights and interests. If we can't effectively suppress the spread of illegal advertisements, it will have a serious negative impact on the advertising industry and even the market environment. Because billboards are one of the main carriers of advertising, the harm of illegal billboard advertisements on consumers cannot be ignored. However, most of the existing illegal advertisement detection technologies focus on illegal Internet advertisements, and there is no effective detection technology for illegal billboard advertisements. Therefore, how to accurately and efficiently detect illegal billboard advertisements is one of the difficulties in our work of purifying advertising environment. In this paper, we propose FIBAD, a framework for illegal billboard advertising detection based on machine learning. FIBAD consists of two functional modules, including an advertisement information extraction module composed of a plurality of classifiers trained by the BERT, which is responsible for extracting advertisement information and judging unmarked data. And the same advertisement retrieval module based on CNN, which provides a channel for retrieving advertising information from a self-built database. In the experiment, we demonstrate the effectiveness of FIBAD through a real data set.
Privacy-Preserved Approximate Classification Based on Homomorphic Encryption

Privacy is a crucial issue for outsourcing computation, which means that clients utilize cloud infrastructure to perform online prediction without disclosing sensitive information. Homomorphic encryption (HE) is one of the promising cryptographic tools resolving privacy issue in this scenario. However, a bottleneck in application of HE is relatively high computational overhead. In this paper, we study the privacy-preserving classification problem. To this end, we propose a novel privacy-preserved approximate classification algorithm. It exploits a set of decision trees to reduce computational complexity during homomorphic evaluation computation formula, the time complexity of evaluating a polynomial is degraded from O n to O log n . As a result, for an MNIST dataset, the Micro- f 1 score of the proposed algorithm is 0 . 882 , compared with 0 . 912 of the standard method. For the Credit dataset, the algorithm achieves 0 . 601 compared with 0 . 613 of the method. These results show that our algorithm is feasible and practical in real world problems.
Comment on “Quantifying the informational value of classification images”: A miscomputation of the infoVal metric

Brinkman et al. (2019) recently introduced an innovative metric—infoVal—to assess the informational value of classification images (CIs) relative to a random distribution. Although this measure constitutes a valuable tool to distinguish random from nonrandom CIs, we identified two noteworthy discrepancies between the mathematical formalization of the infoVal metric and the authors’ computation. Specifically, the computation was based on the one norm instead of the Euclidean norm, and the k constant was omitted in the denominator of the ratio that produces infoVal. Accordingly, the simulations and experimental results reported by Brinkman et al. do not build on the correct infoVal computation but on a biased index. Importantly, this discrepancy in the computation affects the statistical power and Type I and error rate of the metric. Here we clarify the nature of the discrepancies in the computation and run Brinkman et al.’s Simulation 1 anew with the correct values, to illustrate their consequences. Overall, we found that relying on the miscomputed infoVal metric can lead to misguided conclusions, and we urge researchers to use the correct values.
3D Singing Head for Music VR

We propose a real-time 3D singing head system to enhance the talking head on model integrity, keyframe generation and song synchronicity. The individual head appearance meshes are first obtained by matching multi-view visible images with face prior for accuracy, and then used to reconstruct entire head model by integrating with generic internal articulatory meshes for efficiency. After embedding physiology, the keyframes of each phoneme-music note correspondence are substantially synthesized from real articulation data. The song synchronicity of articulators is learned using a deep neural network to train visual co-articulation model (VCM) on parallel audio-visual data. Finally, the keyframes of adjacent phoneme-music note correspondences are blended by VCM to produce song synchronized animation. Compared to state-of-the-art baselines, our system can not only clearly distinguish phonemes and notes, but also significantly reduce the dependence on training data.
Digital Forensics Study of a Cloud Storage Client: A Dropbox Artifact Analysis

The rapid development of cloud storage technology paired with the prevalence of smartphone usage presents wide-ranging challenges for digital forensics practitioners. Data are more easily uploaded and shared between multiple devices and across multiple platforms. So, it has increased the opportunities for criminality. Criminality undertaken in cloud computing can be directly seen on logs stored on the cloud storage server, which records user activity. However, because of user privacy protection, these logs cannot be easily used as evidence in court. This issue emphasizes the need for a reliable means of identifying, acquiring, and preserving evidential data from the client-side. This study identifies the data artifacts of a user accessing Dropbox via smartphone (Android Lollipop and Android Nougat). The data are from performing several common activities such as installing, signing up, uploading, downloading, sharing, and others. About 14 artifacts are identified by documenting the Dropbox client database changing contents as these activities are carried out. This study increases knowledge of the artifacts that are leftover by Dropbox client on Android smartphones. The results propose that these methods can be used by digital forensics investigators in carrying out investigations and cyberlaw practitioners as guidance in criminal cases.
Item Analysis for a Better Quality Test

This study is a small-scale study of item analysis of a teacher’s own-made summative test. It examines the quality of multiple-choice items in terms of the difficulty level, the discriminating power, and the effectiveness of distractors. The study employed a qualitative approach which also used a simple quantitative analysis to analyze the quality of the test items through the document analysis of the teacher’s English summative test and the students’ answer sheets. The result shows that the summative test has more easy items than difficult items with the ratio of 19:25:6 while they should be 1:2:1 for easy, medium, and difficult. In terms of the Discriminating Power, there are 3, 13, and 16 for excellent, Good, and satisfactory level, but there are 17 and 2 for poor and bad levels of Discriminating Power. There are 43 (21.5%) of all distractors which are dysfunctional which, in turns, makes the items too easy which also makes the items fail to discriminate the upper-group students from the lower ones. Therefore, the 43 dysfunctional distractors should be revised to alter the difficulty level and improve the discriminating power. This research is expected to serve as a reflective means for teachers to examine their own-made test to ensure the quality of their test items.
Content-based large-scale medical image retrieval

This chapter introduces content-based image retrieval (CBIR) and its key components including image feature extraction, similarity comparison, the indexing scheme, and the interactive query interface. The need for CBIR in the medical domain (CBMIR) and its related challenges are discussed and followed by a detailed review of the current major CBMIR techniques in four different categories of retrieval: based on physical visual features (color and texture); based on geometric spatial features (shape, 3-D volumetric features, and spatial relationships); by combination of semantic and visual features (semantic pathology interpretation and generic models); and based on physiological functional features. The success of CBMIR could open up many new vistas in medical services and research, such as disease tracking, differential diagnosis, noninvasive surgical planning, clinical training, and outcomes research.
Digital Video Stabilization: Algorithms and Evaluation

Several devices have allowed the acquisition and editing of videos in various circumstances, such as digital cameras, smartphones and other mobile devices. However, the use of cameras under adverse conditions usually results in non-precise motion and occurrence of shaking, which may compromise the stability of the obtained videos. To overcome such problem, digital stabilization aims to correct camera motion oscillations that occur in the acquisition process, particularly when the cameras are mobile and handled in adverse conditions, through software techniques - without the use of specific hardware - to enhance visual quality either with the intention of enhancing human perception or improving final applications, such as detection and tracking of objects. This is important in order to avoid hardware cost and indispensable for videos already recorded. This work proposed three methods to perform digital video stabilization and two other techniques to evaluate video stabilization quality.
Using Fuzzy Sets in Surgical Treatment Selection and Homogenizing Stratification of Patients with Significant Chronic Ischemic Mitral Regurgitation

We present three (main one and two auxiliary) fuzzy algorithms to stratify observations in homogenous classes.These algorithms modify, upgrade and fuzzify crisp algorithms from our earlier works on a medical case study to select the most appropriate surgical treatment for patients with ischemic heart disease complicated with significant chronic ischemic mitral regurgitation.Those patients can be treated with either surgical revascularization and mitral valve repair (group A) or with isolated surgical revascularization (group B) depending on their health status.The main algorithm results in a fuzzy partition of patients in two fuzzy sets (groups A and B) through identification of their degrees of membership.The resulting groups are highly nonhomogenous, which impedes subsequent proper comparisons.So, the two auxiliary algorithms further stratify each group into two homogenous subgroups with comparatively preserved medical condition (A 1 and B 1 ) and with comparatively deteriorated medical condition (A 2 and B 2 ).Those two algorithms perform fuzzy partition of patients from A and B respectively into A 1 , A 2 , B 1 and B 2 by identifying their conditional degrees of membership to those subgroups.We then utilize the product t-norm to calculate the degree of membership of patients to their respective subgroup as an intersection of two fuzzy sets.We demonstrate how to form fuzzy samples for medical parameters for any subgroup.We also compare the performance of the fuzzy algorithms with their preceding crisp version, as well as with eight Bayesian classifiers.We then assess the quality of classification by modified confusion matrices, summarized further into four criteria.The fuzzy algorithms show total superiority over the other methods, and excellent differentiation of typical patients and outliers.On top, only the fuzzy algorithms provide a measure of how typical a patient is to its subgroup.The fuzzy algorithms clearly outline the role of the Heart Team, which is missing in the Bayesian classifiers.
SMS Security Improvement using RSA in Complaints Application on Regional Head Election’s Fraud

In the campaign period of regional heads election, fraud can occur, such as money politics, blaming campaign facilities, campaign time violations, and black campaign. This study implemented a secure SMS application for election fraud complaints as a tool for the society to report all forms of election fraud that have occurred to the election supervisory department safely. The RSA algorithm was applied to encrypt the messages for sender privacy protection. The application was able to perform the message randomization function properly with a 10.44% avalanche effect. Brute force attack using a 16-bit key length needs 3.7 milliseconds for each try to find 32.768 possible private keys.
PT-ResNet: Perspective Transformation-Based Residual Network for Semantic Road Image Segmentation

Semantic road region segmentation is a high-level task, which paves the way towards road scene understanding. This paper presents a residual network trained for semantic road segmentation. Firstly, we represent the projections of road disparities in the v-disparity map as a linear model, which can be estimated by optimizing the v-disparity map using dynamic programming. This linear model is then utilized to reduce the redundant information in the left and right road images. The right image is also transformed into the left perspective view, which greatly enhances the road surface similarity between the two images. Finally, the processed stereo images and their disparity maps are concatenated to create a set of 3D images, which are then utilized to train our neural network. The experimental results illustrate that our network achieves a maximum F1-measure of approximately 91.19%, when analyzing the images from the KITTI road dataset.
Outlier Detection Using Improved Support Vector Data Description in Wireless Sensor Networks

Wireless sensor networks (WSNs) are susceptible to faults in sensor data. Outlier detection is crucial for ensuring the quality of data analysis in WSNs. This paper proposes a novel improved support vector data description method (ID-SVDD) to effectively detect outliers of sensor data. ID-SVDD utilizes the density distribution of data to compensate SVDD. The Parzen-window algorithm is applied to calculate the relative density for each data point in a data set. Meanwhile, we use Mahalanobis distance (MD) to improve the Gaussian function in Parzen-window density estimation. Through combining new relative density weight with SVDD, this approach can efficiently map the data points from sparse space to high-density space. In order to assess the outlier detection performance, the ID-SVDD algorithm was implemented on several datasets. The experimental results demonstrated that ID-SVDD achieved high performance, and could be applied in real water quality monitoring.