Reviewerly/exHarmony huggingface dataset:
An Analytical Review on Classification of IoT Traffic and Channel Allocation Using Machine Learning Technique

The growth of Internet of Things devices and technologies has given rise to a challenging new threat in the form of user data traffic flow. When there is insufficient channel allocation and network traffic measures in place, large volumes of sensitive data are at danger, and the transmission of data around the world can be slowed down by unwanted data. Cybercriminals have the potential to take use of this for evil ends. As a consequence of this, sophisticated mechanisms for assigning network channels and classifying network traffic are required. These mechanisms must be able to analyze and assign carriers to Internet of Things (IoT) network traffic in real time. We present a novel strategy based on machine learning for assigning channels in IoT networks and identifying data that is safe to use in order to get around this problem. The classification of Internet of Things (IoT) traffic networks and the allotment of channels for harmless data in huge network traffic could both benefit greatly from the application of this technology. The suggested approach makes use of deep learning technologies to perform operations at the network level, which results in a significant reduction in the amount of time spent on network classification and allocation of appropriate transmission medium for Benign traffic while also producing encouraging outcomes.
Deep CNN-GRU Based Human Activity Recognition with Automatic Feature Extraction Using Smartphone and Wearable Sensors

This article describes a method to Human Activity Recognition (HAR) challenges based on data from wearable and smartphone sensors. We introduced a deep learning model and recognition system that is a combination of CNN (Convolutional Neural Network) and GRU (Gated Recurrent Unit) to improve results. Preferably, the data have been collected from several wearables as the participants go about their everyday activities. The convolutional neural network (CNN) deployed to improve the extraction of features at various scales. The derived attributes are then inserted into the gated recurrent unit (GRU), which labels features and enhances feature representation by understanding temporal connections. The CNN-GRU model uses a fully inte-grated (FC) layer, which is employed to hook up the feature maps with the classification standard. Three publicly accessible datasets, UCIHAR, OPPORTUNITY, and MHEALTH, were used to test the model's performance, with accuracy rates of 98.74%, 99.05%, and 99.53%, respectively. The outcomes show that the proposed model transcends some of the notified results in terms of activity detection.
Self-Tracking to Do Less

Long COVID is a post-viral illness where symptoms are still experienced more than three months after an infection of COVID 19. In line with a recent shift within HCI and research on self-tracking towards first-person methodologies, I present the results of an 18-month long autoethnographic study of using a Fitbit fitness tracker whilst having long COVID. In contrast to its designed intentions, I misused my Fitbit to do less in order to pace and manage my illness. My autoethnography illustrates three modes of using fitness tracking technologies to do less and points to the new design space of technologies for reducing, rather than increasing, activity in order to manage chronic illnesses where over-exertion would lead to a worsening of symptoms. I propose that these “pacing technologies” should acknowledge the interoceptive and fluctuating nature of the user's body and support user's decision-making when managing long-term illness and maintaining quality of life.
Playful Co-Design: Creating an AR-Prototype with Nurses in Interlocking Remote and On-Site Workshops

Deeply engaging nurses in a participatory co-design process, especially in times of COVID-19, is challenging. In this case study, we shed light on the process of developing a prototype for AR-glasses in nursing. We show the challenges we faced, the methods we used and how they contribute to the core principles of participatory design. A special focus is laid on small-scale interventions with high-impact, that helped us to truly engage users. We introduce empathetic ways to connect contrasting work environments, establish mutual understanding, make the abstract more graspable with playful tools like PLAYMOBIL®, and support co-design development with online formats. Finally, we discuss the transferability to other projects.
Bees, Birds and Butterflies: Investigating the Influence of Distractors on Visual Attention Guidance Techniques

Visual attention guidance methods direct the viewer’s gaze in immersive environments by visually highlighting elements of interest. The highlighting can be done, for instance, by adding a colored circle around elements, adding animated swarms (HiveFive), or removing objects from one eye in a stereoscopic display (Deadeye). We contribute a controlled user experiment (N=30) comparing these three techniques under the influence of visual distractors, such as bees flying by. Our results show that Circle and HiveFive performed best in terms of task performance and qualitative feedback, and were largely robust against different levels of distractions. Furthermore, we discovered a high mental demand for Deadeye.
Finite-rate sparse quantum codes aplenty

We introduce a methodology for generating random multi-qubit stabilizer codes based on solving a constraint satisfaction problem (CSP) on random bipartite graphs. This framework allows us to enforce stabilizer commutation, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>X</mml:mi><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo>/</mml:mo></mml:mrow><mml:mi>Z</mml:mi></mml:math> balancing, finite rate, sparsity, and maximum-degree constraints simultaneously in a CSP that we can then solve numerically. Using a state-of-the-art CSP solver, we obtain convincing evidence for the existence of a satisfiability threshold. Furthermore, the extent of the satisfiable phase increases with the number of qubits. In that phase, finding sparse codes becomes an easy problem. Moreover, we observe that the sparse codes found in the satisfiable phase practically achieve the channel capacity for erasure noise. Our results show that intermediate-size finite-rate sparse quantum codes are easy to find, while also demonstrating a flexible methodology for generating good codes with custom properties. We therefore establish a complete and customizable pipeline for random quantum code discovery.
Training circuit-based quantum classifiers through memetic algorithms

Among the ready-to-implement quantum algorithms, Variational Quantum Circuits (VQCs) play a key role in several applications, including machine learning. Their strength lies in the use of a parameterized quantum circuit that is trained by means of an optimization algorithm run on a classical computer. In such a scenario, there is a strong need to design appropriate classical optimization schemes that deal efficiently with VQCs and pave the way for quantum advantage in machine learning. Among possible optimization schemes, those based on evolutionary computation are finding increasing interest, given the unconventional and nonanalytical nature of the problem to be solved. This paper proposes to apply memetic algorithms to train VQCs used as quantum classifiers and shows the benefits of exploiting this evolutionary optimization technique through a comparative experimental session.
Research on Tractor Optimal Obstacle Avoidance Path Planning for Improving Navigation Accuracy and Avoiding Land Waste

Obstacle avoidance operations of tractors can cause parts of land to be unavailable for planting crops, which represents a reduction in land utilization. However, land utilization is significant to the increase in agricultural productivity. Traditional obstacle avoidance path planning methods mostly focus on automatic tractor navigation with small errors, ignoring the decrease in land utilization due to obstacle avoidance operations. To address the problem, this paper proposed an obstacle avoidance path planning method based on the Genetic Algorithm (GA) and Bezier curve. In this paper, a third-order Bezier curve was used to plot the obstacle avoidance path, and the range of control points for the third-order Bezier curve was determined according to the global path and the location of the obstacle. To target the navigation error and land utilization problems, GA was used to search for the optimal point from the selection range of the control point under multiple constraints for automatic tractor navigation such as the obstacle collision avoidance, the minimum turning radius, and the maximum turning angle. Finally, the optimal obstacle avoidance path was determined based on the selected control points to minimize the navigation error and maximize land utilization. The algorithm proposed in this paper was compared with existing methods and the results showed that it has generally favorable performance on obstacle avoidance path planning.
A new approach for cancer prediction based on deep neural learning

We know today that numerous factors play a significant role as causes of cancer. Because of this, a doctor's opinion alone cannot be used to classify cancer. Intelligent algorithms providing medical assistance are therefore necessary. In addition, many researchers have adopted them for estimating the likelihood of patient survival, and others have employed predictive methodologies like machine learning and deep learning to forecast prognoses for cancer. The accuracy of predictive cancer prognosis is currently of widespread concern. Since deep neural learning (DNL) methods can quickly predict outcomes from a significant amount of clinical and genetic data, they are essential for predicting various diseases. Deep neural learning is the foundation of our suggested approach. Our deep neural learning cancer prediction model (DNLC) has the following stages. In the first stage, Deep Network (DN) is used to select the best collection of features from datasets. In the second stage, we train genomic or clinical data samples with a deep neural network (DNN). In the third stage, we evaluate the capabilities of the DNLC model of predicting cancer in its earlier stages. For classification, DNLC uses five cancer datasets, which are for colon, lung adenocarcinoma, squamous cell carcinoma, breast, and leukaemia cancers. The five cancer datasets are used in experiments to predict how well the suggested model will perform. The dataset is divided into two parts: training sets, which make up 80% of the dataset, and testing sets, which make up 20%. The experimental results show that the suggested model performs better in terms of accuracy than earlier CNN and RNN models. Our findings demonstrate that the DNLC technique, with an average accuracy of 93%, outperforms other methods in all circumstances.
Integrated Silicon Photonic Reservoir Computing With PSO Training Algorithm for Fiber Communication Channel Equalization

An optical channel distortion equalization method based on silicon photonic reservoir computing (PhRC) structure with particle swarm optimization (PSO) algorithm is proposed. The weights training scheme of the photonic readout layer of PhRC is trained by PSO algorithm towards a lower bit error rate (BER) and a better eye diagram, simultaneously. The self-organizing evolutionary PSO algorithm enables fast convergence and iterative optimization for training optical weights. Without the necessary the reservoir signal states obtained from the monitors, it eliminates the noise introduced by the optical monitors. We implement a system-level simulation, including RC structure, training algorithm, and fiber communication construction. Because of the excellent performance of the PSO training algorithm, a BER of 9.15 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−5</sup> is obtained with the 25 Gb/s on-off keying (OOK) input signal in the case of 25 km single-mode transmission. This BER result is three orders of magnitude lower than that before the equalization. This equalizer mitigates the processing bandwidth limit and provides a distorted signals equalization method in the optical domain with simple and effective training strategy. This method shows much potential in high speed optical communication channel equalization in the future.
A Smaller and Better Word Embedding for Neural Machine Translation

Word embeddings play an important role in Neural Machine Translation (NMT). However, it still has a series of problems such as ignoring the prior knowledge of the association between words, relying on specific task constraints passively in parameter training, and isolating individual embedding’s learning process from one another. In this paper, we propose a new word embedding method to add the prior knowledge of the association between words to the training process, and at the same time to share the iterative training results among all word embeddings. This method is applicable to all mainstream NMT systems. In our experiments, it achieves an improvement of +0.9 BLEU points on the WMT’14 English→German task. On the Global Voices v2018q4 Spanish→Czech low-resource translation tasks, it leads to a more prominent performance improvement over the strong baselines (a +2.6 BLEU improvement on average). As another “bonus”, the new word embedding has far fewer parameters than the traditional word embedding, even as low as 15% of the parameters of the baselines.
Acoustic properties of subtypes of creaky voice

“Creaky voice” is a term that covers multiple kinds of voicing, and there is no single defining acoustic property shared by all subtypes of creaky voice. Here we explore the distinct characteristics of each subtype. We identify three main properties of creaky voice: low f0, irregular f0, and constricted glottis (as shown by electroglottography). Prototypical creaky voice has all of these properties; other subtypes are characterized by different subsets of properties. We will describe, with reference to previous literature, multiply-pulsed creak (a special case of irregular f0, along with low f0), open-glottis creak (low and often irregular f0, but unconstricted glottis), vocal fry (low but regular f0, constricted glottis), and creak with such irregular pulsing that no f0 can be recovered. Building on our previous work [Keating et al. 2015 Proc. ICPhS], we show how various acoustic measures pattern for each subtype. Results from parametric speech synthesis provide support for our acoustic observations.
Deceptive review detection using GAN enhanced by GPT structure and score of reviews

These days, in online E-commerce platforms, the most of users utilize the purchase experience of other users reported in the reviews to make a right decision. As much as the positive effect of this phenomena, there would be a motivation to produce deceptive reviews toward some malicious goals. Therefore, the detection and deletion of these deceptive reviews from online platforms will be an essential task to keep them safe. Different approaches from Machine Learning (ML)-based methods to the recent Neural Networks (NN)-based ones have been proposed to detect deceptive reviews. Here, the lack of sufficient labeled data is an essential barrier, to obviate that Generative Adversarial Networks (GAN) have been utilized to generate some data with a distribution close to original ones. In this regard and along with the successful results of Generative Pre-trained Transformers (GPT) in textual tasks, it have also been used besides GAN framework to detect deceptive reviews. Despite a lot of efforts on this matter, there is no efficient study for considering metadata or behavioral features besides powerful generative models. In this paper, we have proposed a new approach called Score_Gpt2ganto consider the scores of reviews as a regularization concept besides the GPT-based GAN approach. Evaluation results in comparison between different methods have shown an increase in the accuracy of 1.4% on the TripAdvisor dataset and 3.8% on the YelpZip dataset by our proposed method.
Shot Optimization in Quantum Machine Learning Architectures to Accelerate Training

Quantum Machine Learning (QML) has recently emerged as a rapidly growing domain as an intersection of Quantum Computing (QC) and Machine Learning (ML) fields.Hybrid quantumclassical models have demonstrated exponential speedups in various machine learning tasks compared to their classical counterparts.On one hand, training of QML models on real hardware remains a challenge due to long wait queue and the access cost.On the other hand, simulation-based training is not scalable to large QML models due to exponentially growing simulation time.Since the measurement operation converts quantum information to classical binary data, the quantum circuit is executed multiple times (called shots) to obtain the basis state probabilities or qubit expectation values.Higher number of shots worsen the training time of QML models on real hardware and the access cost.Higher number of shots also increase the simulation-based training time.In this paper, we propose shot optimization method for QML models at the expense of minimal impact on model performance.We use classification task as a test case for MNIST and FMNIST datasets using a hybrid quantum-classical QML model.First, we sweep the number of shots for short and full versions of the dataset.We observe that training the full version provides 5-6% higher testing accuracy than short version of dataset with up to 10X higher number of shots for training.Therefore, one can reduce the dataset size to accelerate the training time.Next, we propose adaptive shot allocation on short version dataset to optimize the number of shots over training epochs and evaluate the impact on classification accuracy.We use a (a) linear function where the number of shots reduce linearly with epochs, and (b) step function where the number of shots reduce in step with epochs.We note around 0.01 increase in loss and ∼4% (1%) reduction in testing accuracy for reduction in shots by up to 100X (10X) for linear (step) shot function compared to conventional constant shot function for MNIST dataset, and 0.05 increase in loss and ∼5-7% (5-7%) reduction in testing accuracy with similar reduction in shots using linear (step) shot function on FMNIST dataset.For comparison, we also use the proposed shot optimization methods to perform ground state energy estimation of different molecules and observe that step function gives the best and most stable ground state energy prediction at 1000X less number of shots.
Doing Academia Differently: Taking Care of Humans, Technologies and Environments in the Digital Age

In this SoTL undertaking, we reflect on two of the most prominent issues of our time: new algorithmic ecologies and the ecological crisis. In particular, we are interested in what these mean for teaching, and teaching differently. To do so – and using the ‘Socratic method’, a dialogic technique of questioning and cooperative cross-examination that provokes critical thinking – we consider the refrains of death immanent to digitality and the Anthropocene, and how they affect not only subjective formation, but also knowledge production. Reflecting on these issues in our classrooms, for example the widespread disaffection prevalent in universities, aided by internet addiction, dopamine loops, the disintegration of societal rituals and bonds, and the looming ‘end of the world’ (at least as we know it), we consider the role of practices of care, drawing on the work of Stiegler, and Deleuze and Guattari. Our argument is that what is needed, against the intensified bureaucratisation and neoliberalisation of academic institutions, is careful experimentation for the production and circulation of healthier intensities that tend to and nurture life in the face of the refrains of death all around us.
Confidence Ranking for CTR Prediction

Model evolution and data updating are two common phenomena in large-scale real-world machine learning applications, e.g. ads and recommendation systems. To adapt, the real-world system typically retrain with all available data and online learn with recently available data to update the models periodically with the goal of better serving performance. In this paper, we propose a novel framework, named Confidence Ranking, which designs the optimization objective as a ranking function with two different models. Our confidence ranking loss allows direct optimization of the logits output for different convex surrogate functions of metrics, e.g. AUC and Accuracy depending on the target task and dataset. Armed with our proposed methods, our experiments show that the introduction of confidence ranking loss can outperform all baselines on the CTR prediction tasks of public and industrial datasets. This framework has been deployed in the advertisement system of JD.com to serve the main traffic in the fine-rank stage.
Analyzing and Detecting the De-Authentication Attack by Creating an Automated Scanner using Scapy

with the rapid spread of internet technologies around the world, the number of people that are using the internet is increasing enormously in the last 10 years. with the increase in the number of people that are using the internet and the increase in the devices that depend on the internet such as computers, tablets, and mobile phones are raised the challenges of internet security against hackers who can steal sensitive information and exploits personal data. In this paper, we’re focusing on the home security threads and one of its famous attacks called the De-authentication attacks. The de-authentication frame is one of the Management frames that is transmitted between the AP and the connected devices and it can be used by attackers to apply a Dos attack and deny the devices from connecting to the network. In this paper. We will analyze the normal de-authentication frame and compare it with the attacking de-authentication frames to create an automated Scanner to identify whether it’s an attack, or it's a normal frame transmitted between AP and its connected devices, or vice versa.
Pose Based Multi View Sign Language Recognition through Deep Feature Embedding

Sign language recognition in real time has been leveraged by the continuously varying hand movements in both shape and orientation across Spatio-Temporal dimensions.This is accomplished by either independent view or shared view feature learning.However, information movement between views is neither total nor restricted to achieve view insensitivity during testing where all views are needed.The objective is to perform pose based multi view sign language recognition by applying triplet loss on a pair of specific and shared view features.Shared view features are obtained using view compatibility matrix which maps within class between view features and between class within view features.This mapping helps in increasing information flow between views from the same class and restricting it between classes thereby making highly discriminative feature representation for all views.Subsequently, metric learning enables to build a view invariant feature embedding by stacking view specific and shared features from different layers for training deep models.In the end, a blended view feature representation is obtained per class.Experiments were designed on our multi view skeletal sign language video dataset and three benchmark action datasets.The results of the experimentation have shown that the performance of the classifier has improved by 8% over the linear view combiners such as Laplacian eigenmaps.Further, the proposed model is useful in constructing a view invariant feature for recognition of multi view sign language.