Reviewerly/exHarmony huggingface dataset:
Variational Student: Learning Compact and Sparser Networks In Knowledge Distillation Framework

The holy grail in deep neural network research is porting the memory- and computation-intensive network models on embedded platforms with a minimal compromise in model accuracy. To this end, we propose Variational Student where we reap the benefits of compressibility of the knowledge distillation framework, and sparsity inducing abilities of variational inference (VI) techniques. Essentially, we build an accurate and sparse student network, whose sparsity is induced by the variational parameters found via optimizing a loss function based on VI, leveraging the knowledge learnt by an accurate but complex pre-trained teacher network. Further, for sparsity enhancement, we also employ a Block Sparse Regularizer on a concatenated tensor of teacher and student network weights. We benchmark our results on MLP and CNN variants and illustrate an improved performance in lowering the memory footprint up to ~ 213× without a need to retrain the teacher network.
Persuasive Mobile Apps for Sustainable Waste Management: A Systematic Review

This paper provides a systematic review of mobile apps for waste management with the main aim of uncovering the persuasive strategies employed, their operationalizations, the relationship between the number of persuasive strategies employed and the apps' effectiveness to achieve specific target behavior. Specifically, we systematically investigated 125 mobile apps for waste management and identified distinct persuasive strategies, from the primary task support category of the PSD model. Furthermore, we classified these strategies based on the kind of waste management activities that the app was designed to support. Secondly, we uncovered how each of the persuasive strategies was implemented in the waste management apps to achieve the targeted outcome. Thirdly, we evaluated the relationship between the number of persuasive strategies employed in the apps design and the effectiveness of the apps (measured by user ratings). The results show that the apps cumulatively employed 251 persuasive strategies spread across the seven distinct primary task support persuasive strategies as follows: reduction (n = 76), tunneling (n = 9), tailoring (n = 37), personalization (n = 75), self-monitoring (n = 31), simulation (n = 7) and rehearsal (n = 16). In addition, our findings show that appropriate waste disposal, collection, recycling, and general waste management challenges were some of the waste management issues that the mobile apps targeted. Based on our results, we offer some design recommendations for operationalizing persuasive strategies in waste management app to increase their effectiveness.
Detecting Adversarial Attacks In Time-Series Data

In recent times, deep neural networks have seen increased adoption in highly critical tasks. They are also susceptible to adversarial attacks, which are specifically crafted changes made to input samples which lead to erroneous output from such models. Such attacks have been shown to affect different types of data such as images and more recently, time-series data. Such susceptibility could have catastrophic consequences, depending on the domain.We propose a method for detecting Fast Gradient Sign Method (FGSM) and Basic Iterative Method (BIM) adversarial attacks as adapted for time-series data. We frame the problem as an instance of outlier detection and construct a normalcy model based on information and chaos-theoretic measures, which can then be used to determine whether unseen samples are normal or adversarial. Our approach shows promising performance on several datasets from the 2015 UCR Time Series Archive, reaching up to 97% detection accuracy in the best case.
An Empirical Investigation of the Impact of the Communication and Employee Motivation on the Project Success Using Agile Framework and Its Effect on the Software Development Business

Throughout the years, project management has evolved tremendously. As technological innovations occur, project management framework also evolved. As the project management framework evolved, project success became more and more difficult. This article examines the impact of communication and employee motivation on the success of the project and evaluates the impact on the software development business. Even though there is a considerable amount of research being conducted on project success, the communication and employee motivation attribute still lack research, especially in the field of software development. The data gathered through this research were empirically tested using SPSS, and a significant relationship was observed between the dependent and the independent variables. It was found out that communication and employee motivation indeed play a role in the success of the project. It is crucial to train employees in an agile framework and keep on motivating them so that they can deal with the continuous changes. The results of this research indicated that organizations should implement a proper communication channel between the customers and the development team. Furthermore, the more the employees are motivated, the more chances the project will have on success, making the business profitable.
What an Agile Leader Does: The Group Dynamics Perspective

When large industrial organizations change to (or start with) an agile approach to operations, managers and some employees are supposed to be “agile leaders” often without being given a clear definition of what that comprises when building agile teams. An inductive thematic analysis was used to investigate what 15 appointed leaders actually do and perceive as challenges regarding group dynamics working with an agile approach. Team maturity, Team design, and Culture and mindset were all categories of challenges related to group dynamics that the practitioners face and manage in their work-life that are not explicitly mentioned in the more process-focused agile transformation frameworks. The results suggest that leader mitigation of these three aspects of group dynamics is essential to the success of an agile transformation.
Motion-Aware Feature Enhancement Network for Video Prediction

Video prediction is challenging, due to the pixel-level precision requirement and the difficulty in capturing scene dynamics. Most approaches tackle the problems by pixel-level reconstruction objectives and two decomposed branches, which still suffer from blurry generations or dramatic degradations in long-term prediction. In this paper, we propose a Motion-Aware Feature Enhancement (MAFE) network for video prediction to produce realistic future frames and achieve relatively long-term predictions. First, a Channel-wise and Spatial Attention (CSA) module is designed to extract motion-aware features, which enhances the contribution of important motion details during encoding, and subsequently improves the discriminability of attention map for the frame refinement. Second, a Motion Perceptual Loss (MPL) is proposed to guide the learning of temporal cues, which benefits to robust long-term video prediction. Extensive experiments on three human activity video datasets: KTH, Human3.6M, and PennAction demonstrate the effectiveness of the proposed video prediction model compared with the state-of-the-art approaches.
Improving Spoken Question Answering Using Contextualized Word Representation

While question answering (QA) systems have witnessed great breakthroughs in reading comprehension (RC) tasks, spoken question answering (SQA) is still a much less investigated area. Previous work shows that existing SQA systems are limited by catastrophic impact of automatic speech recognition (ASR) errors [1] and the lack of large-scale real SQA datasets [2]. In this paper, we propose using contextualized word representations to mitigate the effects of ASR errors and pretraining on existing textual QA datasets to mitigate the data scarcity issue. New state-of-the-art results have been achieved using contextualized word representations on both the artificially synthesised and real SQA benchmark data sets, with 21.5 EM/18.96 F1 score improvement over the sub-word unit based baseline on the Spoken-SQuAD [1] data, and 13.11 EM/10.99 F1 score improvement on the ODSQA data [2]. By further fine-tuning pre-trained models with existing large scaled textual QA data, we obtained 38.12 EM/34.1 F1 improvement over the baseline of fine-tuned only on small sized real SQA data.
Sequential Deep Unrolling With Flow Priors For Robust Video Deraining

Video deraining has attracted wide attention since the urgent demand of high-quality video in recent years. The indistinct details and nonideal deraining effects are the most common defects in existing techniques, whose cause lies in the insufficient usage of single-frame image and temporal information. To effectively settle video deraining, we establish a new deraining model with flow priors to simultaneously introduce spatial and temporal information for accurately depicting the enhancement model of the current frame. A sequential deep unrolling framework is substantially presented by solving this model based on optimization techniques. The ablation study indicates our effectiveness as far as the design of architecture. Plenty of subjective and objective evaluations fully demonstrate our superiority in detail recovery and deraining effects against other state-of-the-are video deraining approaches.
What can blockchain do and cannot do?

This paper studies the economic functions of blockchain. First, by explaining blockchain technologies from an economic perspective, it introduces the Token Paradigm to summarize mainstream blockchain systems, discusses the true meanings of consensus and trustlessness in the blockchain field, and analyzes the functions of smart contracts. Next, it categorizes major blockchain applications according to how they use tokens and discusses relevant economic problems such as tokens' monetary features, tokens' impacts on blockchain platforms, blockchain's governance functions, and the efficiency and security of blockchain systems. Finally, it discusses the concept of Blockchain as a Financial Infrastructure (BaaFI), which is represented by central bank digital currencies (CBDC) and global stable coins.
Learning Product Graphs from Multidomain Signals

In this paper, we focus on learning the underlying product graph structure from multidomain training data. We assume that the product graph is formed from a Cartesian graph product of two smaller factor graphs. We then pose the product graph learning problem as the factor graph Laplacian matrix estimation problem. To estimate the factor graph Laplacian matrices, we assume that the data is smooth with respect to the underlying product graph. When the training data is noise free or complete, learning factor graphs can be formulated as a convex optimization problem, which has an explicit solution based on the water-filling algorithm. The developed framework is illustrated using numerical experiments on synthetic data as well as real data related to air quality monitoring in India.
Remote detection and interpretation of structural style of the Zuru schist belt, northwest Nigeria

Mapping of the major structures in the Zuru schist belt and preliminary evaluation of distribution of the locations of gold mineralization relative to these structures was achieved using remote sensing and field data. On the basis of interpretation of remote sensing and field data, four deformational episodes are identified. The first two deformational episodes are ductile in nature, with the first resulting in the development of tight to isoclinal folds with E-W trending axial planes. These folds are refolded during the second folding event, producing open to tight and tight to isoclinal folds with N-S to NE-SW trending axial planes. These folding events are then followed by two brittle deformation events, the first leading to the development of NE-SW strike-slip faults with the second producing NW-SE dip-slip faults. Mineralized veins in the area are found to occur in and around major structures, notably the brittle NE-trending strike-slip faults and ductile E-W and N-S to NE-SW trending folds. However, further studies using geostatistical methods is required to quantitatively ascertain role of the structures on gold mineralization and their distribution within the area.
Privacy-Preserving Machine Learning as a Tool for Secure Personalized Information Services

The article deals with the problems of cryptographic protection of data processing algorithms and techniques. They are novel techniques allowing to process private information without disclosing it to persons engaged in processing. One of the main applications of such security tools is the creation of personalized information services, which opens up new opportunities for business and reduces the risks of unauthorized access to personal data. We review important building blocks for cryptographic protection of data processing, such as zero-knowledge proofs, secure multi-party computations, and homomorphic encryption. Often, personalized information services are based on data mining and machine learning, so privacy-preserved machine learning is a very important building block for them. We analyze the concept of differential privacy which serves as the basis for privacy-preserving machine learning and some other cryptographic schemes. At the end of the paper, we forecast the perspectives of encrypted data processing.
Region-based Convolutional Neural Network as Object Detection in Images

In this study, the Region-based Convolutional Neural Network (R-CNN) is utilised for object detection in the MATLAB simulation environment. This involves the basic R-CNN model that uses the SVM algorithm for classification. Firstly, total images of 911 acquired and captured from desert environment acted as the database in this study. The reason for choosing this type of images are due to the unique features specifically variances between objects and background and this is indeed different from city or urban environment images. Next, from these 911 images, 70% are used as training data for the basic R-CNN model and the remainder as testing. Experimental results showed that the accuracy is indeed promising with 83.1% for single object detection, 77.8% for two objects detection and 67.6% for three objects detection with average accuracy of 69%.
Document Image Classification using SqueezeNet Convolutional Neural Network

SqueezeNet networks perform well on image classification tasks, achieving accuracies comparable to state of the art convolutional neural networks. In this research we evaluate the performance of SqueezeNet networks in document image classification, showing that an ImageNet pretrained SqueezeNet achieves an accuracy of approximately 75 percent over 10 classes on the Tobacco-3482 dataset, which is comparable to other state of the art convolutional neural networks in terms of accuracy, while containing 50 times less weights compared to them. We then visualize saliency maps of the gradient of our networks output to input, which shows that the network is able to learn meaningful features that are useful for document classification. Features such as the existence of handwritten text, document titles, text alignment and tabular structures, which are proof that the network does not overfit to redundant representations of the dataset itself.
Assured Runtime Monitoring and Planning: Toward Verification of Neural Networks for Safe Autonomous Operations

Autonomous systems operating in uncertain environments under the effects of disturbances and noises can reach unsafe states even while using finetuned controllers and precise sensors and actuators. To provide safety guarantees on such systems during motion planning operations, reachability analysis (RA) has been demonstrated to be a powerful tool. RA, however, suffers from computational complexity, especially when dealing with intricate systems characterized by high-order dynamics, making it hard to deploy for runtime monitoring.
avtmNet:Adaptive Visual-Text Merging Network for Image Captioning

Recently, researchers have made extensive research on the technology of automatically generating descriptions for an image. Various technologies for image captioning have been proposed, among which attention-based encoder-decoder framework achieved great success. Two different types of attention models are proposed to generate image captions respectively, i.e., model based visual attention that is good at describing details, and model based text attention that is good at comprehensive understanding. In order to integrate and make full use of visual information and text information to generate more accurate captions for images, in this paper, we firstly introduce a visual attention model to generate the visual information and a text attention model to form the text information respectively, and then propose an adaptive visual-text merging network(avtmNet). This merging network can effectively merge the visual information and text information, and automatically determine the proportion of both visual information and text information to generate the next caption word. Extensive experiments are performed on the datasets named COCO2014 and Flickr30K respectively, and show the effectiveness and superiority of our proposed approach.
Trajectory Optimization of Autonomous Agents With Spatio-Temporal Constraints

This article addresses the problem of optimally controlling trajectories of autonomous mobile agents (e.g., robots) so as to jointly minimize travel time and energy consumption in the presence of multiple spatio-temporal constraints on these trajectories. In addition to state and input constraints, we impose spatial equality and temporal inequality constraints viewed as interior-point constraints. We address this problem by first identifying the structure of the optimal agent controllable acceleration profile and showing that it is characterized by several parameters subsequently used for trajectory design optimization. Therefore, the infinite dimensional optimal control problem is transformed into a finite dimensional parametric optimization problem. The proposed algorithm is applied to the eco-driving problem of autonomous vehicles approaching multiple signalized intersections. We include simulation results to show quantitatively the advantages of the proposed solution.
Review on Evaluation Criteria of Machine Learning Based on Big Data

Abstract In machine learning in the context of big data, It is indispensable link for the evaluation results and indicators of prediction results. Although it is not as important as the core algorithm, it plays a role in related applications and research fields. The role of horizontal comparison of learning algorithms. The article briefly summarizes and reviews the research results of basic machine learning algorithm evaluation standards. Different evaluation methods are applied to different machine learning problems, and the trends and difficulties of machine learning evaluation methods in the future are analyzed.
On the Blockchain-Based Decentralized Data Sharing for Event Based Encryption to Combat Adversarial Attacks

It is human nature to anticipate events in the future and plan accordingly. As such, the possibility of letting future events trigger the decryption of a message is a form of an encryption mechanism we deem to be significant in today's information age. In this paper, we propose a variant of Attribute-Based Encryption (ABE), called Event-Based Encryption (EBE), that will help avoid adversarial attacks. In EBE, we attempt to decrypt the messages in the future after an event is confirmed. We illustrate how EBE can be employed by presenting a scenario where a will is decrypted when an individual has been confirmed dead. To achieve this, we introduce a decentralized data sharing network powered by the blockchain technology that ensures data undergoes a thorough vetting process before it is accepted to the network. This vetted data is useful in determining the facts needed for the confirmation of the occurrence of events and is also helpful in restricting adversarial attacks. Our approach utilizes multi-authority ABE schemes, Natural Language Processing (NLP) techniques, and a decentralized data-sharing platform to achieve our goal. The main contribution of this research is the enabling of multiple parties, each with thoroughly vetted proprietary data, to collaborate to confirm the occurrence of an event. This event confirmation should trigger the decryption of a message. We highlight the various applications of our approach and illustrate the practical and secure nature of EBE using numerical results.
Digit Recognition in Sign Language Based on Convolutional Neural Network and Support Vector Machine

Hearing and speech impaired individuals communicate using sign language among themselves and to the normal people. But it is challenging for non-sign language speaker to understand the sign language. Thus, sign language speaker or signer often finds difficulties in expressing their feelings. Hence, in this paper we have presented a method by combining convolutional neural network (CNN) and support vector machine (SVM) to recognize digits in sign language. We have employed the proposed model with two renewable Sign Language dataset namely MU_Handlmages_ASL and standard databases-SLD in order to measure efficacy of the model. The proposed model achieved average test accuracy 98.20% on ASL data and 98.30% on SLD data that indicates the model is very dependable and convincing to aid to communicate between signers and non-signers.
RMoR-Aion: Robust Multioutput Regression by Simultaneously Alleviating Input and Output Noises

Multioutput regression, referring to simultaneously predicting multiple continuous output variables with a single model, has drawn increasing attention in the machine learning community due to its strong ability to capture the correlations among multioutput variables. The methodology of output space embedding, built upon the low-rank assumption, is now the mainstream for multioutput regression since it can effectively reduce the parameter numbers while achieving effective performance. The existing low-rank methods, however, are sensitive to the noises of both inputs and outputs, referring to the noise problem. In this article, we develop a novel multioutput regression method by simultaneously alleviating input and output noises, namely, robust multioutput regression by alleviating input and output noises (RMoR-Aion), where both the noises of the input and output are exploited by leveraging auxiliary matrices. Furthermore, we propose a prediction output manifold constraint with the correlation information regarding the output variables to further reduce the adversarial effects of the noise. Our empirical studies demonstrate the effectiveness of RMoR-Aion compared with the state-of-the-art baseline methods, and RMoR-Aion is more stable in the settings with artificial noise.
Wavelet Based Image DE-Noising with Optimized Thresholding Using HHO Algorithm

This paper presents image de-noising in wavelet domain utilizing optimization algorithm combined with thresholding function. In this study, we utilized the nature inspired metaheuristic optimizer in order to obtain the optimal solutions for the parameters of thresholding function. Despite the TNN based noise reduction, while applying optimization algorithm there is no need to use LMS learning algorithm to find the optimal threshold value. Applying Harris Hawk Optimization (HHO) algorithm which includes threshold function leads to find the optimized thresholded wavelet coefficients. In this research, smooth sigmoid based shrinkage (SSBS) has been utilized to be combined with the HHO algorithm as the proposed method. Then, applying inverse discrete wavelet transform on the optimized wavelet coefficients provides us with the output de-noised image. The results have shown the efficiency of the proposed technique over other alternative methods in the literature.