Reviewerly/exHarmony huggingface dataset:
Bangla Document Categorization Using Deep RNN Model with Attention Mechanism

Text classification is a strategy where the document categories are decided. Report classification is the assignment of relegating archives to a predefined space category. In this paper, a new method has classified Bangla text documents which consist of two deep learning models in terms of Bangla text categorization. The main purpose of this research is to text classification from Bangla text documents. This system can easily categorize from a large number of text documents. Firstly, the collected data was processed for the Bangla text document. Secondly, by designing the model architecture training data it was fitted out into the model. Finally, by calculating the accuracy and F1-score on the testing dataset, the model performance was evaluated. The deep learning recurrent neural network attention layer and recurrent neural network with BiLSTM attained 97.72% and 86.56% accuracy, respectively, which is compared to the other well-known classification algorithms in Bangla text classification.
Verifiable Isogeny Walks: Towards an Isogeny-Based Postquantum VDF

In this paper, we investigate the problem of constructing postquantum-secure verifiable delay functions (VDFs), particularly based on supersingular isogenies. Isogeny-based VDF constructions have been proposed before, but since verification relies on pairings, they are broken by quantum computers. We propose an entirely different approach using succinct non-interactive arguments (SNARGs), but specifically tailored to the arithmetic structure of the isogeny setting to achieve good asymptotic efficiency. We obtain an isogeny-based VDF construction with postquantum security, quasi-logarithmic verification, and requiring no trusted setup. As a building block, we also construct non-interactive arguments for isogeny walks in the supersingular graph over $$\mathbb F_{p^2}$$ , which may be of independent interest.
Metaverse research propositions: Online intermediaries

One of the sectors with the fastest growth rates worldwide is the hospitality and tourism industry, although the pandemic caused losses and setbacks for the industry. The potential of the metaverse and virtual travel could lead to the emergence of a brand-new sector. Thus, traditional online travel agencies may need to change their utility model in the future to accommodate the new technology. This virtual transformation in the metaverse entails more flexible travel options, customized consumer services, and improved entertainment. The employment of augmented and virtual reality technology in the metaverse may enable seamless interaction between users in real and simulated surroundings. As such future developments in metaverse technology may enable fully immersive experiences. Customers can now get real-time price changes, availability, and promotions bypassing third-party distribution platforms. This research note aims to introduce the metaverse’s enormous potential and to define three research proposals for additional investigation.
Strength in Numbers: The WWARN Case Study of Purpose-Driven Data Sharing

Data are the basis for all scientific output. The sharing of data supporting that output is an important aspect of scientific communication, and is increasingly required by funders and publishers. Yet, academic advancement seldom recognizes or rewards data sharing. This article argues that although mandating data sharing will increase the amount of data available, this will not necessarily enable or encourage the secondary analyses needed to achieve its purported public good. We, therefore, need to build models that maximize the efficiency of processes for data collation and curation, and genuinely reward those engaged in data sharing and reuse. The WorldWide Antimalarial Resistance Network has 10 years of experience as a data platform, and its study group approach provides an example of how some of the challenges in equitable and impactful data-sharing and secondary use can be addressed, with a focus on the priorities of researchers in resource-limited settings.
Machine vision benefits from human contextual expectations

Abstract Scene context is known to facilitate object recognition in both machines and humans, suggesting that the underlying representations may be similar. Alternatively, they may be qualitatively different since the training experience of machines and humans are strikingly different. Machines are typically trained on images containing objects and their context, whereas humans frequently experience scenes without objects (such as highways without cars). If these context representations are indeed different, machine vision algorithms will be improved on augmenting them with human context representations, provided these expectations can be measured and are systematic. Here, we developed a paradigm to measure human contextual expectations. We asked human subjects to indicate the scale, location and likelihood at which cars or people might occur in scenes without these objects. This yielded highly systematic expectations that we could then accurately predict using scene features. This allowed us to predict human expectations on novel scenes without requiring explicit measurements. Next we augmented decisions made by deep neural networks with these predicted human expectations and obtained substantial gains in accuracy for detecting cars and people (1–3%) as well as on detecting associated objects (3–20%). In contrast, augmenting deep network decisions with other conventional computer vision features yielded far smaller gains. Taken together, our results show that augmenting deep neural networks with human-derived contextual expectations improves their performance, suggesting that contextual representations are qualitatively different in humans and deep neural networks.
End‐to‐end elasticity control of cloud‐network slices

The design of efficient elasticity control mechanisms for dynamic resource allocation is crucial to increase the efficiency of future cloud‐network slice‐defined systems. Current elasticity control mechanisms proposed for cloud‐ or network‐slicing, only consider cloud‐ or network‐type resources respectively. In this paper, we introduce the elaSticity in cLOud‐neTwork Slices ( SLOTS ) which aims to extend the horizontal elasticity control to multi‐providers scenarios in an end‐to‐end fashion, as well as to provide a novel vertical elasticity mechanism to deal with critical insufficiency of resources by harvesting underused resources on other slices. Finally, we present a preliminary assessment of the SLOTS prototype in a real testbed, revealing outcomes that suggest the viability of the proposal.
Efficient Chaotic Encryption Scheme with OFB Mode

Data confidentiality is mandatory during transmission or when storing sensitive information, especially in financial, medical and military applications. In this context, several cipher solutions and techniques have been presented in the literature. However, existing solutions are mainly based on static structures, where the confusion and diffusion primitives are fixed and independent of the secret key. In this article, we propose a new block cipher scheme that is based on the Substitution Permutation Networks (SPN). The proposed cipher consists of three operations: round-key addition, substitution, and bits’ permutation. Moreover, the substitution operation is applied at the byte level and it is based on a dynamically generated S-box, while the diffusion primitives are applied at the bit level using a dynamically generated P-box. Such key-dependent design ensures better cryptographic strength and system performance when compared, for instance, to DES, 3DES, RC5, and PRESENT schemes, among others, due to its key expansion algorithm. Thorough analysis show that the proposed scheme exhibits a high degree of randomness, key and plain-text sensitivity, and it satisfies the avalanche effect. From a theoretical perspective, we have formulated the Output Feedback mode of operation as a discrete dynamical system on a topological space. We prove that the dynamics of this system (in terms of sensitivity to the initial vector, etc.) are directly related to the strong connectivity of a graph. By doing so, we are able to characterize the conditions under which this mode evolves chaotically, as defined in Devaney’s theory. In particular, such theoretical investigations allow us to link the avalanche effect and key sensitivity of the cipher with the sensitivity of the whole process, that is, with the mode of operation.
Fast, accurate normalized cross-correlation image matching

Normalized cross-correlation (NCC) is fast to compute but its accuracy is low. In this paper, we propose a fast, highly accurate NCC image matching algorithm. First, a wavelet pyramid is constructed to reduce feature point searching and matching times. Then, an NCC image matching algorithm is used to acquire the coarse matching points in the original image. Next, an improved iterative relaxation algorithm is used to remove false matching points, and an adaptive winner-takes-all strategy is introduced to improve the algorithm’s iteration speed and obtain more one-to-one matching points. Our experimental results show that the proposed algorithm can improve not only matching speed, but also matching accuracy.
Multi-Layer Abstraction Saliency for Airport Detection in SAR Images

The detection of airports using synthetic aperture radar (SAR) images has attracted considerable attention. Traditional methods easily result in inaccurate detection due to the complex scenes and multiplicative speckle noise. Therefore, airport detection from SAR images is still a challenging task. In order to limit the influence of unnecessary and attractive details and noise, we propose a multi-layer abstraction saliency model for airport detection in SAR images in this paper. Specifically, we first obtain airport support regions and superpixels in the first layer. According to the dis-similarity between foreground and background superpixels, airport components are explored by iterative refinement for each airport support region in the second layer. In the third layer, airport adobes are produced by clustering. Based on the characteristics of an airport in SAR images, we propose three saliency cues, including local contrast (LC), adobe deformation (AD), and global uniqueness (GU), to obtain adobe-level saliency. Furthermore, we assign saliency to each pixel by Bayesian inference. Finally, we can explore airport location using integrated saliency map. The proposed approach is tested on an airport data set collected from Gaofen-3, TerraSAR, and RadarSat. Our method achieves 88.89% detection rate. The experimental results demonstrate that the proposed algorithm is effective and outperforms the previously airport detection methods. The code will be available at https://github.com/NengyuanLiu/MyAirportSaliency.
Towards Real-Time User QoE Assessment via Machine Learning on LTE Network Data

It is well known that current reactive network management would be unable to support the exponential increase in complexity and rapidity of change in future cellular networks. Keeping this in perspective, the goal of this paper is to investigate applicability of machine learning and predictive models to assess cell-level user quality of experience (QoE) in real-time. For this purpose, we leverage a 5 week LTE metrics data collected at cell level granularity for a national LTE network operator. Domain knowledge is applied to assess user QoE with network key performance indicators (KPIs), namely scheduled user throughput, inter-frequency handover success rate and intra-frequency handover success rate. Results indicate that applying boosted trees model on a subset of carefully selected non-collinear features allows high accuracy threshold-based estimation of user throughput and inter-frequency handover success rate. We also exploit the periodic nature of cell data characteristics and apply a recently developed time series prediction model known as PROPHET for future QoE estimation. By employing machine learning and data analytics on network data within an end-to-end framework, network operators can proactively identify low performance cell sites along with the influential factors that impact the cell performance. Based on the root cause analysis, appropriate corrective measures may then be taken for low performance cell sites.
Human Fall Detection Using Machine Learning Methods: A Survey

Human fall due to an accident can cause heavy injuries which may lead to a major medical issue for elderly people. With the introduction of new advanced technologies in the healthcare sector, an alarm system can be developed to detect a human fall. This paper summarizes various human fall detection methods and techniques, through observing people’s daily routine activities. A human fall detection system can be designed using one of these technologies: wearable based device, context-aware based and vision based methods. In this paper, we discuss different machine learning models designed to detect human fall using these techniques. These models have already been designed to discriminate fall from activities of daily living (ADL) like walking, moving, sitting, standing, lying and bending. This paper is aimed at analyzing the effectiveness of these machine learning algorithms for the detection of human fall.
Medi-Care AI: Predicting medications from billing codes via robust recurrent neural networks

In this paper, we present an effective deep prediction framework based on robust recurrent neural networks (RNNs) to predict the likely therapeutic classes of medications a patient is taking, given a sequence of diagnostic billing codes in their record. Accurately capturing the list of medications currently taken by a given patient is extremely challenging due to undefined errors and omissions. We present a general robust framework that explicitly models the possible contamination through overtime decay mechanism on the input billing codes and noise injection into the recurrent hidden states, respectively. By doing this, billing codes are reformulated into its temporal patterns with decay rates on each medical variable, and the hidden states of RNNs are regularized by random noises which serve as dropout to improved RNNs robustness towards data variability in terms of missing values and multiple errors. The proposed method is extensively evaluated on real health care data to demonstrate its effectiveness in suggesting medication orders from contaminated values.
Givs: Fine-Grained Gesture Control for Mobile Devices in Driving Environments

New media and communication technologies like mobile devices are nowadays widely used everywhere for providing rich functionalities and highly personalized services.However, using such a device in a driving environment is still very inconvenient and unsafe to be controlled by the driver.The touchscreen operations are one major obstacle since multi-touchscreen is optimized for hand-held usage scenarios.To overcome this limitation, we propose to replace some most used touch operations with gesture controls for mobile devices in a driving environment.Gesture control is simple, more flexible and requires less eye focus, which makes it more suitable for in-vehicle usages.In this paper, we design Givs, a fully functional gesture control system for mobile devices in a driving environment.Givs leverages the latest motion sensing technology to enable ubiquitous and driving-friendly gestures.Compared to other off-the-shelf gesture recognition solutions, Givs is optimized for in-vehicle use cases and is designed to overcome various limitations caused by real driving conditions, including bumpy road conditions, significant noise introduced by car vibration and technical limitations of motion sensors.Our extensive in-vehicle tests and participant experience experiments demonstrate that Givs well assists users in accomplishing various types of tasks and support human-machine interaction in driving environments such as personal vehicle and public transport, with high accuracy and fast responsiveness, while promoting drivnig convenience and safety.
Dynamic Data Mining of Sensor Data

The research of data mining has aroused widespread concern in academia and industry. However, an important mark of the Internet of Things era is that sensor data replaces artificially compiled data. How to extract valuable knowledge and patterns from a large amount of data generated by sensors is a meaningful research topic. This paper proposes a dynamic data mining framework for processing sensor data. A sensor data mining model which can be used in the process of dynamic change is constructed. In this model, different sensor network environments are considered as different physical systems. The physical system and its parameters are trained by collecting and mining historical changes in sensor data; the associations between different sensor network environments are discovered by mining the associations between the parameters of different physical systems. In our limited experimental environment, the physical quantities considered included transmission distance, transmission delay, sensor data, data changes, and so on. Experiments were carried out on the designated experimental platform, and the results showed that the model could mine the dynamic data and find stable patterns. Through the analysis of the experimental results, it was found that the model had reference value for the dynamic mining of sensor data, and was expected to construct new methods for industrial big data analysis.
Synthetic CT-aided MRI-CT image registration for head and neck radiotherapy

In this study, we propose a synthetic CT (sCT) aided MRI-CT deformable image registration for head and neck radiotherapy. An image synthesis network, cycle consistent generative adversarial network (CycleGAN), was first trained using 25 pre-aligned CT-MRI image pairs. Using the MR head and neck images, the trained CycleGAN then predicts sCT images, which were used as MRI’s surrogate in MRI-CT registration. Demons registration algorithm was used to perform the sCT-CT registration on 5 separate datasets. For comparison, the original MRI and CT images were registered using mutual information as similarity metric. Our results showed that the target registration errors after registration were on average 1.31 mm and 1.02 mm for MRI-CT and sCT-CT registration, respectively. The mean normalized cross correlation between the sCT and CT after registration was 0.97, indicating that the proposed method is a viable way to perform MRI-CT image registration for head neck patients.
A Survey of Scalable Deep Learning Frameworks

Machine learning models recently have seen a large increase in usage across different disciplines. Their ability to learn complex concepts from the data and perform sophisticated tasks combined with their ability to leverage vast computational infrastructures available today have made them a very attractive choice for many challenges in academia and industry. In this context, deep Learning as a sub-class of machine learning is specifically becoming an important tool in modern computing applications. It has been successfully used for a wide range of different use cases, from medical applications to playing games. Due to the nature of these systems and the fact that a considerable portion of their use-cases deal with large volumes of data, training them is a very time and resource consuming task and requires vast amounts of computing cycles. To overcome this issue, it is only natural to try to scale deep learning applications to be able to run them across in order to achieve fast and manageable training speeds while maintaining a high level of accuracy. In recent years, a number of frameworks have been proposed to scale up ML algorithms to overcome the scalability issue, with roots both in the academia and the industry. With most of them being open source and supported by the increasingly large community of AI specialists and data scientists, their capabilities, performance and compatibility with modern hardware have been honed and extended. Thus, it is not easy for the domain scientist to pick the tool/framework best suited for their needs. This research aims to provide an overview of the relevant, widely used scalable machine learning and deep learning frameworks currently available and to provide the grounds on which researchers can compare and choose the best set of tools for their ML pipeline.
Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks

In this paper, we study distributed algorithms for large-scale AUC maximization with a deep neural network as a predictive model. Although distributed learning techniques have been investigated extensively in deep learning, they are not directly applicable to stochastic AUC maximization with deep neural networks due to its striking differences from standard loss minimization problems (e.g., cross-entropy). Towards addressing this challenge, we propose and analyze a communication-efficient distributed optimization algorithm based on a {\it non-convex concave} reformulation of the AUC maximization, in which the communication of both the primal variable and the dual variable between each worker and the parameter server only occurs after multiple steps of gradient-based updates in each worker. Compared with the naive parallel version of an existing algorithm that computes stochastic gradients at individual machines and averages them for updating the model parameters, our algorithm requires a much less number of communication rounds and still achieves a linear speedup in theory. To the best of our knowledge, this is the \textbf{first} work that solves the {\it non-convex concave min-max} problem for AUC maximization with deep neural networks in a communication-efficient distributed manner while still maintaining the linear speedup property in theory. Our experiments on several benchmark datasets show the effectiveness of our algorithm and also confirm our theory.
Sample and time efficient policy learning with CMA-ES and Bayesian Optimisation

In evolutionary robot systems where morphologies and controllers of real robots are simultaneously evolved, it is clear that there is likely to be requirements to refine the inherited controller of a ‘newborn’ robot in order to better align it to its newly generated morphology. This can be accomplished via a learning mechanism applied to each individual robot: for practical reasons, such a mechanism should be both sample and time-efficient. In this paper, We investigate two ways to improve the sample and time efficiency of the well-known learner CMA-ES on navigation tasks. The first approach combines CMA-ES with Novelty Search, and includes an adaptive restart mechanism with increasing population size. The second bootstraps CMA-ES using Bayesian Optimisation, known for its sample efficiency. Results using two robots built with the ARE project's modules and four environments show that novelty reduces the number of samples needed to converge, as does the custom restart mechanism; the latter also has better sample and time efficiency than the hybridised Bayesian/Evolutionary method.