Reviewerly/exHarmony huggingface dataset:
A Glimpse of Physical Layer Decision Mechanisms: Facts, Challenges, and Remedies

Communications are realized as a result of successive decisions at the physical layer, from modulation selection to multi-antenna strategy, and each decision affects the performance of the communication systems. Future communication systems must include extensive capabilities as they will encompass a wide variety of devices and applications. Conventional physical layer decision mechanisms may not meet these requirements, as they are often based on impractical and oversimplifying assumptions that result in a trade-off between complexity and efficiency. By leveraging past experiences, learning-driven designs are promising solutions to present a resilient decision mechanism and enable rapid response even under exceptional circumstances. The corresponding design solutions should evolve following the lines of learning-driven paradigms that offer more autonomy and robustness. This evolution must take place by considering the facts of real-world systems and without restraining assumptions. In this paper, the common assumptions in the physical layer are presented to highlight their discrepancies with practical systems. As a solution, learning algorithms are examined by considering the implementation steps and challenges. Furthermore, these issues are discussed through a real-time case study using software-defined radio nodes to demonstrate the potential performance improvement. A cyber-physical framework is presented to incorporate future remedies.
Intention to Create Meaningful Outcomes: Tenders, Bids and Client Pitches

Responding to a brief – a specific response structure – despite the rigor of compliance, has the opportunity for compelling content, focused on addressing the audience requirements in an engaging, informative and convincing way. The aim is to persuade, whether by visual appeal, content structure or clarity of message. Very often this requires a creative, agile work environment focused on targeted delivery within a short timeframe of weeks or sometimes months. The message needs to be highly adapted/tailored to the brief and strategy of the client – whether internal to the organization or an external client, the purpose is to compel the client to accept the pitch or tender and “close the deal”, making the offered solution their preferred choice.
Resource Efficient AI: Exploring Neural Network Pruning for Task Specialization

This paper explores the use of neural network pruning for transfer learning applications for more resource-efficient inference. The goal is to focus and optimize a neural network on a smaller specialized target task. With the advent of IoT, we have seen an immense increase in AI-based applications on mobile and embedded devices, such as wearables and other smart appliances. However, with the ever-increasing complexity and capabilities of machine learning algorithms, this push to the edge has led to new challenges due to the constraints imposed by the limited availability of resources on these devices. Some form of compression is needed to allow for state-of-the-art convolutional neural networks to run on edge devices.In this work, we adapt existing neural network pruning methods to allow them to specialize networks to only focus on a subset of what they were originally trained for. This is a transfer learning use-case where we optimize large pre-trained networks. This differs from standard optimization techniques by allowing the network to forget certain concepts and allow the network's footprint to be even smaller. We compare different pruning criteria, including one from the field of Explainable AI (XAI), to determine which technique allows for the smallest possible network while maintaining high performance on the target task. Our results show the benefits of using network specialization when executing neural networks on embedded devices both with and without GPU acceleration.
A Framework for Verifiable and Auditable Collaborative Anomaly Detection

Collaborative and Federated Leaning are emerging approaches to manage cooperation between a group of agents for the solution of Machine Learning tasks, with the goal of improving each agent&#x2019;s performance without disclosing any data. In this paper we present a novel algorithmic architecture that tackle this problem in the particular case of Anomaly Detection (or classification of rare events), a setting where typical applications often comprise data with sensible information, but where the scarcity of anomalous examples encourages collaboration. We show how Random Forests can be used as a tool for the development of accurate classifiers with an effective insight-sharing mechanism that does not break the data integrity. Moreover, we explain how the new architecture can be readily integrated in a blockchain infrastructure to ensure the verifiable and auditable execution of the algorithm. Furthermore, we discuss how this work may set the basis for a more general approach for the design of collaborative ensemble-learning methods beyond the specific task and architecture discussed in this paper.
BRITISH SOJOURNER’S BARRIER DAN ADJUSTMENT TERHADAP INTENTION TO STAY NATIVE SPEAKER

Several English course institutes started to employ sojourners as their native speaker tutor. The degree of intention to stay from the sojourner defines whether they choose to stay and work at their companies in Indonesia, move from their initial companies to work for other companies, or go back to their countries. The purpose of this study is to analyze effect of perceived inability to adjust and unwillingness to adjust toward general adjustment and work adjustment. This study then analyzes the effect of general adjustment and work adjustment towards intention to stay of british sojourners who works for the English course. This is a quantitative study, sample used in this study were all the natiove speakers who work in “K” and “BI” English course institution. This study used questionnaire, then to further analyze the relationship among all variables, PLS was used through SmartPLS software. This study found that there was negative effect of perceived inability to adjust and unwillingness to adjust towards general adjustment and work adjustment, among these negative effects only unwillingness to adjust towards general adjustment that showed significant result. This study also found that general adjustment and work adjustment had a positive significant impact towards intention to stay.
Operating System Convergence: An Example via the Maru OS Project

Converging multiple form-factors and devices into one cohesive system is not necessarily a new idea; however, there has been a growing interest in achieving the design goal of convergence in recent years. This growing interest is due partly to a desire to maintain only one operating system. Some implementations also include the ability to run both in desktop mode and mobile mode simultaneously. These mobile systems attach to a lapdock or monitor via one or more cables to activate the desktop mode. These converged systems have mobile hardware constraints; however, current system on chip (SOC) improvements may make running these systems much more realistic. Several techniques are available to achieve convergence. This paper attempts to outline these design principles while also presenting an example architecture for using virtualization techniques to achieve operating system convergence. Additionally, this paper proposes a solution to attach the mobile device to the lapdock wirelessly using Bluetooth and Miracast, The virtualization techniques used to achieve this convergence can apply to various form-factors, application ecosystems, and computational platforms.
Loss Curve Approximations for Fast Neural Architecture Ranking &amp; Training Elasticity Estimation

Two key questions for any deep learning task involve questions around its optimization. First, when should we stop training or, alternatively, how long should we train for before the gains are not worth the continued training (i.e. early or optimal stopping)? Secondly, what is the “right” or best model: what training settings, hyper-parameters, and model architecture are best for the task at hand to maximize performance (i.e. architecture search)? Though essential, these questions are arguably also the most expensive parts of deep learning experimentation and the most unclear. Moreover, these expensive, exhaustive searches require large computational budgets that can carry large environmental footprints and significant energy expenditure. In this paper, we introduce a new method we call the Loss Curve Gradient Approximation (LCGA) that ranks model performance with minimal training. Using a wide variety of popular deep vision models, we test its predictive power and performance across different neural architectures and training settings. For a comparative analysis, we benchmark the performance of LCGA against an existing technique, Training Speed Estimation (TSE), used in architecture search and performance ranking and show that LCGA can significantly outperform TSE while still holding the same advantages in terms of ease, speed, and efficiency. Lastly, we describe potential applications of LCGA beyond its primary application: namely, (1) combining collected experimental data with LCGA to develop train-less NAS and (2) presenting a framework to more rigorously guide early stopping in training by borrowing concepts of demand elasticity from economics.
Motion planning for mobile robots using the Collidable Velocity Obstacles method

The collision-free motion planning for mobile robots is a challenging task in dynamic environment. There are situations when the robot has no chance to select a velocity vector that would cause no collision in the next steps of the motion. Most of the motion planning algorithms for mobile robots cannot generate an appropriate solution for that problem. At the autonomous systems, the only acceptable solution in this situation is emergency braking which could cause huge damage between the robot and the obstacle. The main focus of this paper is to introduce a novel motion planning method for mobile robots that could generate a velocity vector for the robot that would cause the least damage between the robot and the environment. It can be assumed that the obstacles in the environment are passive, and low damage collisions would be attempted only when avoidance is not possible at the sampling time. If the robot has an opportunity to select a velocity vector from the Reachable Avoidance Velocities set, then a cost-function-based algorithm can be used considering different aspects.
Attack-Resilient Multi-Agent Flocking Control Using Graph Neural Networks

Flocking control of a group of mobile agents has been recently investigated using Graph Convolution Networks (GCNs). The design relies on training using a centralized controller but the resulting GCN controller is based on communication between the agents. The agents receive sensor measurements which are incorporated into the states and shared between the neighbors. However, the paradigm is prone to adversarial attacks. In this paper, we consider the problem of designing GCN-based distributed flocking control that is resilient to attacks on the communicated information. We consider an attack model that is used to compromise the inter-agent communication and may inject arbitrary signals. Our control design uses a coordinate-wise median-based aggregation function. It is shown that the GCN-based controller using the proposed aggregation method is resilient against attacks on the communication between the agents, whereas the typical average-based aggregation fails to maintain the flock structure. Robustness analysis is performed to show that the proposed method is resilient whenever a majority of the agents in the neighborhood can be trusted. Simulation results and analysis are presented that validate the merits of the proposed approach.
The case study about effect of a curriculum of mathematics education at the college of education on the preservice mathematics teacher’s teaching perspectives

Objectives This study analyzed the effect of the curriculum of mathematics education at the College of Education on the preservice mathematics teacher’s teaching perspectives.&#x0D; Methods To the purpose of this study, the teaching perspective of the preservice mathematics teachers was investigated using TPI in the first week of the freshman of mathematics education, and was investigated once again in the final exam week of December, the senior just before completion. The subjects of this study were three groups, group 1 was the freshman in 2015 and the senior in 2018, group 2 was the freshman in 2016 and the senior in 2019. The results of the study were analyzed by chi-square homogeneity test using IBM SPSS Statistics 26 program.&#x0D; Results As a result, both groups showed numerical changes in teaching perspective, but no statistically significant changes were found.&#x0D; Conclusions There are a numerical change on the superior and inferior teaching perspcetives of freshmen and seniors in both groups. However, the mathematics education curriculum of the study subjects did not have a statistically significant effect on the preservice mathematics teacher’s teaching perspectives.&#x0D; &#x0D;
Simulation system of lighting environment for optical imaging test

Abstract In order to overcome the shortcomings of the solar simulator in the aspect of the performance testing for optical imaging system, a lighting environment simulation system is designed. We select dysprosium lamp, xenon lamp and multiple LEDs as light sources of the solar simulator, and the change of the sun zenith angle is simulated by using the symmetrical method of the light source. Also, the light intensity is controlled by adjusting the output current of the power supply. Lighting environment simulation system not only can simulate spectral irradiance and intensity change of real solar, but also can simulate the real sun depending on the season and the angle of illumination period. This system provided a technical support for the performance testing of optical imaging systems.
Generating Targeted Adversarial Attacks and Assessing their Effectiveness in Fooling Deep Neural Networks

Deep neural network (DNN) models have gained popularity for most image classification problems. However, DNNs also have numerous vulnerable areas. These vulnerabilities can be exploited by an adversary to execute a successful adversarial attack, which is an algorithm to generate perturbed inputs that can fool a well-trained DNN. Among various existing adversarial attacks, DeepFool, a white-box untargeted attack is considered as one of the most reliable algorithms to compute adversarial perturbations. However, in some scenarios such as person recognition, adversary might want to carry out a targeted attack such that the input gets misclassified in a specific target class. Moreover, studies show that defense against a targeted attack is tougher than an untargeted one. Hence, generating a targeted adversarial example is desirable from an attacker’s perspective. In this paper, we propose ‘Targeted DeepFool’, which is based on computing a minimal amount of perturbation required to reach the target hyperplane. The proposed algorithm produces minimal amount of distortion for conventional image datasets: MNIST and CIFAR10. Further, Targeted DeepFool shows excellent performance in terms of adversarial success rate.
Automated Recognition of Hand Gestures for Crane Rigging using Data Gloves in Virtual Reality

Construction safety training through Virtual Reality (VR) environments offers workers an interactive and immersive experience.Many complex interactions and realistic scenarios are possible in VR using accessories such as data gloves and trackers which allow data recording on incidents.For example, misinterpretation of hand signals construction workers give during operations may result in severe accidents on construction sites.This study proposes automatic gesture recognition to identify hand signals for crane rigging operations in VR training.The developed gesture recognition algorithm tracks information from a data glove and a tracker.Preliminary data on movements and orientation of hands and fingers were recorded.Mathematical models of hand gestures were created based on finger movement data.Gesture rules were created based on the rotation and orientation of the hand.The gesture models were combined with the gesture rules to develop the algorithm for automated gesture recognition.Final experiments estimated the efficacy of the proposed method in automatically recognizing crane rigging signals in real-time.The performance is evaluated by comparing the identified hand gestures with independently created ground truth labels.The proposed method identified static hand gestures with an average accuracy of 96.55 percent.This method recognizes the gestures along with the hand movements and displays the results in real-time equivalent to dynamic gesture recognition.More refined dynamic gesture recognition based on this method is in progress.
Research based on image inpainting

This paper studies the current research status of image inpainting, and proposes the Unet network to solve the problem of image holes of objects wearing reflective balls captured by the Azure Kinect camera and other damaged images that extend to the field.
Eye Localisation using Cascaded U-Net for Autism Spectrum Disorder

Many computer vision applications Computer Aided Diagnosis require an accurate and efficient eye detector. We represent, in this work, an efficient approach for determining the position of the eye in images presenting faces. First, a series of candidate regions are proposed; next, a set of cascaded U-net is used to detect the eye regions; then edge detection methods are used to detect eyes lids and iris boundary and thus helping determining the gaze direction of person having ASD. Our proposed approach achieved a precision of detection that is better than the current most recent methods in trials utilizing GI4E, BioID, and columbiaGaze datasets. The proposed approach is robust to picture alterations, such as changes in external illumination, facial occlusion, and image modality.
SeEn: Sequential enriched datasets for sequence-aware recommendations

Abstract The recommendation of items based on the sequential past users’ preferences has evolved in the last few years, mostly due to deep learning approaches, such as BERT4Rec. However, in scientific fields, recommender systems for recommending the next best item are not widely used. The main goal of this work is to improve the results for the recommendation of the next best item in scientific domains using sequence aware datasets and algorithms. In the first part of this work, we present the adaptation of a previous method (LIBRETTI) for creating sequential recommendation datasets for scientific fields. The results were assessed in Astronomy and Chemistry. In the second part of this work, we propose a new approach to improve the datasets, not the algorithms, to obtain better recommendations. The new hybrid approach is called sequential enrichment (SeEn), which consists of adding to a sequence of items the n most similar items after each original item. The results show that the enriched sequences obtained better results than the original ones. The Chemistry dataset improved by approximately seven percentage points and the Astronomy dataset by 16 percentage points for Hit Ratio and Normalized Discounted Cumulative Gain.
Library Resource Construction and Interconnection Model Based on Linked Data

Heterogeneous and self-distributed massive data has always been the core issue of knowledge openness and knowledge sharing. Because the library has a wide range of data sources, various types of resources, and many operating platforms, a large amount of heterogeneous data is stored in it, which greatly affects the sharing of resources in the library. Linked Data, as one of the implementation methods of Semantic Web technology, can promote the interconnection and interoperability of data in different fields and promote the opening and sharing of knowledge. Therefore, this paper proposes the construction of library resources based on Linked Data, aiming to study how to optimize the association rules of Linked Data in the context of the Semantic Web and promote the continuous realization of open sharing of information resources. Experiments show that, under the action of the optimized association rules, the support degree between data can reach 79%, and the data association degree can be increased to 84%.
The Influence of Service Quality and Corporate Image of Royal Brunei Airlines: A Partial Least Square Approach

The hospitality and tourism industries have become the pivotal industries that contribute to the growth of the nation's economy. However, fewer studies pay attention to the aviation industry where this industry has faced a lot of challenges and crises in the past decade. Hence, this study aims to examine the effects of service quality on the corporate image of Royal Brunei Airlines. The study utilized the SERVQUAL model to guide the study. A quantitative (survey) research design was employed using a total of 178 completed questionnaires obtained from the customers of Royal Brunei Airlines through purposive sampling. The data were then analyzed using Partial Least Squares Structural Equation Modelling through Smart-PLS 3.2.7. The results revealed that three (3) dimension of SERVQUAL (assurance, responsiveness, tangibles) has a positive and significant relationship with corporate image. However, empathy and reliability were found not significant to corporate image. Conclusion, implications, and suggestions for future study were also discussed.
COST OF ILLNESS DIABETES MELITUS TIPE 2: NARRATIVE REVIEW

Diabetes mellitus (DM) is chronic disease that requires routine lifelong treatment. Uncontrolled blood sugar levels in DM patients can cause complications. This has an impact on the burden of health costs. The aim of this study to conduct a narrative review of the cost of illness in type 2 DM patients in various countries. The English articles used in this study were obtained from Pubmed, ScienceDirect, and Wiley that published in the period 2011 to 2021. The cost reported in the articles were local currency that later converted into US dollars according to the time of the study. Based on the narrative review, there are 19 articles that meet the criteria. In the article, there are varied perspectives, methods, and cost categories. The total cost per year for type 2 DM ranged from US$246.1 to US$5894.1, direct, and indirect costs per patient per year ranged from US$ 104.6 to US$5246.4 and US$82.9 to US$2299.4. Type 2 diabetes mellitus increases the burden of health costs. The presence of complications in type 2 DM patients requires higher health costs compared to uncomplicated type 2 DM patients and non-DM patient