Reviewerly/exHarmony huggingface dataset:
Towards Coordinated Multi-Robot Exploration under Bandwidth-constrained Conditions

Advances in robot locomotion algorithms, sensor technologies, planning frameworks and hardware designs have motivated the deployment of robot groups in unstructured environments. One of the most common tasks is autonomous exploration. During the multi-robot autonomous exploration task, it is desirable for robots to coordinate in order to steer them to different areas so as to increase the overall efficiency. Such coordination requires communication among peer robots. In some environments, such as tunnels, natural caves or urban underground spaces, it is not always feasible to maintain a stable communication network between robots. Thus, robust coordinated exploration solution must operate in unstable or constrained settings. In this work, we propose a decentralized and bandwidth-efficient algorithm to robustly coordinate robot groups to explore unknown unstructured environments. The proposed method's performance is evaluated in typical simulation environments and the experiment results show much reduced average bandwidth consumption compared with baseline algorithms. In addition, the proposed algorithms are implemented in a quadruped robot groups to demonstrate the effectiveness in a real world autonomous exploration task.
The Impact of Cascaded Optimizations in CNN Models and End-Device Deployment

Optimization techniques are vital in deploying CNN models to resource-constrained devices like IoT or handheld devices. The classic optimization techniques like quantization, pruning, and clustering have proved their impact on model compression by trading off the overall accuracy. However, the performance of cascaded optimizations like (i) sparsity preserving clustering (PC), (ii) sparsity preserving quantization (PQ), (iii) cluster preserving quantization (CQ), and (iv) sparsity and cluster preserving quantization (PCQ) techniques remain unexplored. This paper studies the inference efficiency of CNN models when optimization techniques are performed in a cascaded way and individually. The feasibility of the optimization techniques was tested on both NVIDIA GTX1650 GPU and Raspberry pi-4 by considering five factors: performance metrics like (a) inference latency and (b) accuracy and (c) computational complexity of the model, resource utilization metrics like (d) energy consumption during the inference and (e) memory requirement to store the model. The results show that among all the models generated from the optimized techniques, the fully cascaded (PCQ) outperforms in terms of accuracy, latency and memory requirement metrics.
Electric Vehicle as a Virtual Mobile Power Plant for Smart Grid

Blockchain is the underlying technology for cryptocurrencies. Reliable machine-to-machine automatic transactions, such as auctions, bidding, and payments, utilise the immense potential of blockchain technology. Researchers are exploring blockchain-based applications for automobiles and transportation, such as electric vehicle (EV) charging and highway user fee payment. The use of blockchain eliminates the need for third parties in transactions. This paper presents a proof of concept for using EVs as energy storage in a smart grid system. Generators, consumers, and distributed energy resources (DER), such as solar and wind, make up the elements of a smart grid. We propose storing the surplus power generated by DER in the electric vehicle's battery. When the generation is less, or there is high demand, these EVs can supply the stored energy back to the grid. We use blockchain smart contracts and Ethereum cryptocurrency to monitor and monetise the process. We also make a cost comparison of conventional internal combustion engine (ICE) vehicles and EVs, analysing the financial benefits of employing the suggested method in EV charging instead of more conventional charging methods.
Can VUI Turn-Taking Entrain User Behaviours?

This paper presents a study where human participants converse with a voice user interface (VUI) equipped with a unique turn-taking protocol. The protocol addresses constraints intrinsic to half-duplex systems. It uses tones to sonify the invisible seam of half-duplex turn-taking, encouraging users to adjust to this unnatural seam, and thus allowing turn-taking to perform more reliably. We control the VUI's turn-taking behaviour by manipulating the speech platform's temporal turn-taking parameters that have been applied to its automatic speech recognition (ASR). By analyzing the human participant's behaviour, we show that users do respond to the artificial turn-taking behaviours. Further, over a period, the users tend to entrain to the turn-taking of the VUI.
Recommendation of Smart Devices Using Collaborative Filter Approach

Consumers now have more options because to the growth of e-commerce, but there is also an abundance of information. Users are looking for technologies that will allow websites to automatically present goods that they may be interested in so they may swiftly locate preferred products from enormous resources. In order to automate the suggestion process, recommender systems are developed. The accuracy criterion of the suggestion in the area of collaborative filtering. One algorithm's implementation is never simple or straightforward due to an algorithm. We suggest a slope one technique that may be used in various recommender systems to address these issues. It is based on the combination of reliable data and user similarity. Three methods make up this algorithm. We should choose reliable facts first. The similarity between users should be calculated second. Third, we must the final suggestion is obtained by weighting this similarity with the modified slope one method equation. Using the [1] Kaggle dataset, we conducted a number of trials, and the findings show that our recommender superior to the conventional slope one method in terms of performance.
E-Supply Chain Issues in Internet Of Medical Things

The supervision of supply disruptions, expiry, and fake products are just a few examples of crucial healthcare supply chain operations. Due to the competitive nature of the healthcare supply chain, which is vulnerable to paradoxical situation and unnecessary endeavors that may jeopardize patient safety and negatively affect medical outcomes, it is difficult to implement and carry out these operations in a reliable, successful, convenient, and checkable manner. A developing technology called blockchain and the Internet of Medical Things (IoMT) can provide a workable answer to these problems. They offer a great solution to monitor and trace things. This paper identifies the main problems with healthcare supply chain processes and shows how IoT blockchain technology might help solve these problems.
Precise Medical Diagnosis For Brain Tumor Detection and Data Sample Imbalance Analysis using Enhanced Kernel Extreme Learning Machine Model with Deep Belief Network Compared to Extreme Machine Learning

To identify the brain tumor according to the categorial identification by using the symptoms. Materials and Methods: To identify brain tumors using Kernel Extreme Learning Machine with improved accuracy over Extreme Machine Learning. The total number of samples that are evaluated on the proposed methodology is 10 in each of 2 groups. Results: The proposed hybrid Kernel Extreme Learning Machine approach gives accuracy 93.31% which is significantly better in classification when compared to Extreme Machine Learning which has less accuracy 81.91% and level of significance is 0.01 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathrm{p} &lt; 0.05)$</tex> . Conclusion: Identifying brain tumor was achieved significantly better by using a novel functional glioma innovation as Kernel Extreme Learning Machine compared to Extreme Machine Learning.
Prototyping in Software Quality Assurance: A Survey With Software Practitioners

User Interface Prototyping is a resource for exploring and communicating ideas, which is largely used in software development in activities like requirements elicitation or product discovery. Existing works indicate that the use of prototyping can be related to the improvement of software quality. Therefore, we conducted a survey with practitioners from the software industry to investigate how prototyping has been used in quality assurance activities. As a result, we identified different development process models that adopt prototyping to support various activities, such as the identification of new technologies to be adopted, the definition of user stories, planning activities, identification of business rules and navigation flows, front-end and back-end implementation, reviews, and tests. However, we also identified prototyping-related problems, such as teams from the same company using prototyping in a non-standard way, or the use of outdated prototypes during tests, approval, and training phases. In this way, we verified that the adoption of prototyping as a tool to support the software development process must follow usage rules explicitly defined in the development process and clearly communicated to developers, so they can positively influence the improvement of the quality of software.
Same Problems, Different Projects: Do the Same Solutions Work?

Software requirements management is a process that involves several activities that generate artifacts that serve as a basis for project development. This process occurs continuously and is responsible for performing analysis, tracking, documentation, prioritization and change control so that there is agreement on the requirements between all parties involved. However, these activities are not trivial and their form of execution varies according to the nature of each project. With this in mind, teams should be able to identify points for improvement, such as applying new practices, new technologies or tools and allocating resources to make the process more optimized and more productive. The paper presents an experience report related to the identification and application of improvements and good practices within two software development projects inside the same research group. In the first project, we address problems involving software requirements. In the second one we address the problem involving the measurement of the size of tasks. Both projects target to release the same “bigger” application but each one develops a minor part of it. Finally, difficulties and lessons learned, in relation to the changes made in the current requirements management process in both projects, are presented.
Beyond Double Ascent via Recurrent Neural Tangent Kernel in Sequential Recommendation

Overfitting has long been considered a common issue to large neural network models in sequential recommendation. In our study, an interesting phenomenon is observed that overfitting is temporary. When the model scale is increased, the trend of the performance firstly ascends, then descends (i.e., overfitting) and finally ascends again, which is named as double ascent in this paper. We therefore raise an assumption that a considerably larger model will generalise better with a higher performance. In an extreme case to infinite-width, performance is expected to reach the limit of this specific structure. Unfortunately, it is impractical to directly build a huge model due to the limit of resources. In this paper, we propose the Overparameterised Recommender (OverRec), which utilises a recurrent neural tangent kernel (RNTK) as a similarity measurement for user sequences to successfully bypass the restriction of hardware for huge models. We further prove that the RNTK for the tied input-output embeddings in recommendation is the same as the RNTK for general untied input-output embeddings, which makes RNTK theoretically suitable for recommendation. Since the RNTK is analytically derived, OverRec does not require any training, avoiding physically building the huge model. Extensive experiments are conducted on four datasets, which verifies the state-of-the-art performance of OverRec. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
AutoAssign: Automatic Shared Embedding Assignment in Streaming Recommendation

In streaming recommender systems, the traditional approach for handling new user IDs or item IDs is to assign randomly initialized ID embedding, leading to two practical issues: (i) Items or users with insufficient interactive data can result in suboptimal prediction performance; and (ii) The embedding of new IDs or low-frequency IDs will consistently increase the size of the embedding table, thereby consuming unnecessary memory. To this end, we propose a reinforcement learning-based Automatic Shared Embedding Assignment framework, AutoAssign. To be specific, an Identity Agent serves to (i) field-wisely represent low-frequency IDs by utilizing a small number of shared embeddings, so as to enhance the embedding initialization; and (ii) dynamically identify the ID features that need to be retained or eliminated in the embedding table. We conduct extensive experiments on three public benchmark datasets and observe that AutoAssign can significantly improve the recommendation performance by alleviating the cold-start problem. Besides, AutoAssign reduces the memory space by 20-30 %, which demonstrates the effectiveness and efficiency of our framework in practical streaming recommender systems.
AutoAttention: Automatic Field Pair Selection for Attention in User Behavior Modeling

In Click-through rate (CTR) prediction models, a user’s interest is usually represented as a fixed-length vector based on her history behaviors. Recently, several methods are proposed to learn an attentive weight for each user behavior and conduct weighted sum pooling. However, these methods only manually select several fields from the target item side as the query to interact with the behaviors, neglecting the other target item fields, as well as user and context fields. Directly including all these fields in the attention may introduce noise and deteriorate the performance. In this paper, we propose a novel model named AutoAttention, which includes all item/user/context side fields as the query, and assigns a learnable weight for each field pair between behavior fields and query fields. Pruning on these field pairs via these learnable weights lead to automatic field pair selection, so as to identify and remove noisy field pairs. Though including more fields, the computation cost of AutoAttention is still low due to using a simple attention function and field pair selection. Extensive experiments on the public dataset and Tencent’s production dataset demonstrate the effectiveness of the proposed approach.
Pruning Adversarially Robust Neural Networks without Adversarial Examples

Adversarial pruning compresses models while preserving robustness. Current methods require access to adversarial examples during pruning. This significantly hampers training efficiency. Moreover, as new adversarial attacks and training methods develop at a rapid rate, adversarial pruning methods need to be modified accordingly to keep up. In this work, we propose a novel framework to prune a previously trained robust neural network while maintaining adversarial robustness, without further generating adversarial examples. We leverage concurrent self-distillation and pruning to preserve knowledge in the original model as well as regularizing the pruned model via the Hilbert-Schmidt Information Bottleneck. We comprehensively evaluate our proposed framework and show its superior performance in terms of both adversarial robustness and efficiency when pruning architectures trained on the MNIST, CIFAR-10, and CIFAR-100 datasets against five state-of-the-art attacks..
Extremely Fast Hoeffding Adaptive Tree

Many real-world data streams are non-stationary. Subject to concept drift, the distributions change over time. To retain accuracy in the face of such drift, online decision tree learners must discard parts of the tree that are no longer accurate and replace them by new subtrees that reflect the new distribution. The longstanding state-of-the-art online decision tree learner for non-stationary streams is Hoeffding Adaptive Tree (HAT), which adds a drift detection and response mechanism to the classic Very Fast Decision Tree (VFDT) online decision tree learner. However, for stationary distributions, VFDT has been superseded by Extremely Fast Decision Tree (EFDT), which uses a statistically more efficient learning mechanism than VFDT. This learning mechanism needs to be coupled with a compensatory revision mechanism that can compensate for circumstances where the learning mechanism is too eager. The current work develops a strategy to combine the best of both these state-of-the-art approaches, exploiting both the statistically efficient learning mechanism from EFDT and the highly effective drift detection and response mechanism of HAT. To do so requires decoupling of the EFDT splitting and revision mechanisms, as the latter incorrectly triggers the HAT drift detection mechanism. The resulting learner, Extremely Fast Hoeffding Adaptive Tree, responds to drift more rapidly and effectively than either HAT or EFDT, and attains a statistically significant advantage in accuracy even on stationary streams.
GNN-Adv: Defence Strategy from Adversarial Attack for Graph Neural Network

Deep learning-based models have demonstrated exceptional performances in diverse fields. However, recent research has revealed that adversarial attacks and minor input perturbations may easily deceive DNNs. Graph Neural Networks (GNNs) inherit this weakness. An opponent can persuade GNNs to generate inaccurate predictions by influencing a few edges in the graph. It results in severe consequences of adopting GNNs in safety-critical applications. The research focus has shifted in recent years to make GNNs more robust to adversarial attacks. This article proposes GNN-Adv, a novel approach for defending against numerous attacks that disturb the graph structure during training. Experiments demonstrate that GNN-Adv surpasses current peer approaches by an average of 15 % across five GNN approaches, four datasets, and three defense techniques. Remarkably, GNNs-Adv can successfully restore their current performance in the face of terrifying, directly targeted attacks.
Leveraging the Learnable Vertex-Vertex Relationship to Generalize Human Pose and Mesh Reconstruction for In-the-Wild Scenes

We present MeshLeTemp, a powerful method for 3D human pose and mesh reconstruction from a single image. In terms of human body priors encoding, we propose using a learnable template human mesh instead of a constant template as utilized by previous state-of-the-art methods. The proposed learnable template reflects not only vertex-vertex interactions but also the human pose and body shape, being able to adapt to diverse images. We conduct extensive experiments to show the generalizability of our method on unseen scenarios.
Predicting the Optimizability for Workflow Decisions

With the rapid advancement of quantum technologies, the integration between classical and quantum computing systems is an active area of research critical to future development. The coupling between these systems requires both to be as efficient as possible. One of the key elements to increase efficiency on the quantum side is circuit optimization. The goal is to execute the circuit on the desired hardware in less time and with less complexity, thereby reducing the impact of noise on the quantum system. However, the optimization process does not guarantee to generate improved results, yet it is always a computationally highly complex task that can create significant load for the classical computing side. To mitigate this problem, we propose a novel approach to predict the optimizability of any circuit using a Machine Learning-based algorithm within the decision workflow. This optimizes the most suitable circuits thereby increasing efficiency of the optimization process itself.
Forensic Timeline Analysis of iOS Devices

One of the steps in a forensic investigation is to build a timeline. A timeline is required to discover activities that occurred in a forensic image. A forensic image is an acquisition result of an iOS device, such as the iPhone and iPad. One of the de facto tools for creating forensic timelines is the log2time1ine plaso. However, the plaso cannot extract all the time data on iOS device artifacts. In this study, a method is proposed to complete log2time1ine in order to extract all-time data on iOS devices. We create a parser plugin for the log2time1ine plaso for missing artifacts, such as a plist or an SQLite database. The proposed method is briefly described as follows. First, the procedure constructs a forensic timeline using the plaso tool from an iOS image which has been acquired beforehand. We then examine missing artifacts from the timeline. After that, we create a plaso plugin to parse missing artifacts. Finally, we rerun the plaso with new plugins to build a more comprehensive timeline. Thus, a complete forensic timeline is obtained from the forensic image of an iOS device. Experiments show that additional plugins can provide a more comprehensive forensic timeline extracted from an iOS device.
Effective Use of Embedded Platforms in the Development of Experiments for Enhancing the Interests of STEAM Students in Mexico

Technological advances are motivated, in most cases, for the search for fulfilling a necessity. However, behind of all technological advances, there is at least one person that is developing an adequate technological solution. In this regard, education is a pillar for technological advancements, but also, inspiration, aesthetical appreciation, curiosity, or even the desire to reach a goal. These are reasons why an education that focus on both human and technological aspects is required. With the aim to provide such an education, the STEAM (science, technology, engineering, arts, and math) approach was born. Among the tools used in STEAM-teaching, microcontrollers are basic elements that are affordable for most students. In this chapter, an overview of STEAM education in Mexico and the use of microcontrollers for experimentation under the STEAM philosophy is offered.
An intervention strategy for mitigating the prevalence of syntax errors during task exercise engagements

The success of a pedagogical intervention is determined by the level of knowledge and corresponding success achieved by recipients of the intervention. While knowledge is visibly measurable in sport-related activities and in most stem courses by an individual’s ability to critically evaluate a problem, execute well-defined procedures and come up with an answer or range of possible propositions, evaluating knowledge acquisition in a programming task is a much complex task. Complex in the sense that a learner must master the syntactic, semantic, and logical details of a given language before being asserted as to have mastered the language. Ergo, it becomes evident, the difficulty of structuring a learner’s engagement process. Owing that the pattern of engagement is a determinant for the degree of knowledge acquired by a learner in programming courses. We adopted a tutorial walk-through strategy, which enables us to guide students in adopting a structured method of learning engagement. To appraise the effectiveness of our strategy, we evaluate our learner’s engagement activities using the count of errors they generate in the course of their learning activities. Engagement comparison across 3 semesters shows a sharp reduction in the count of syntax errors, which is indicative of an increase in the degree of knowledge acquisition, retention, and increased adoption of a structured pattern of learning engagement.