Reviewerly/exHarmony huggingface dataset:
Action-Improved Actor-Critic Tracking for Accurate Object Tracking

Abstract Visual object tracking is a fundamental problem, which tracks the target in the whole sequence with the given object in the first frame. Numerous tracking frameworks have been proposed. Actor-Critic Tracking (ACT) is based on RL, which is a popular framework with real-time speed. However, ACT cannot handle scale estimation effectively, since it cannot change the ratio of target size. In this paper, we aim to improve the scale estimation of ACT and propose the Action-improved ACT(AIACT) model. This work is based on the basic framework of ACT. ACT builds the ‘Actor’ deep neural network to track the target and the ‘Critic’ network as an inspection module to improve the model. We add a new element to the bounding box of ‘Actor’ model to change the aspect ratio. The experiment result shows that our model achieves better accuracy than the previous model. Our method achieves 0.646 and 0.912 score of success rate and precision rate on OTB dataset, which means AIACT model can handle the scale variation of the target. Therefore, it will benefit more applications.
Memoized Pull-Tabbing for Functional Logic Programming

Pull-tabbing is an evaluation technique for functional logic programs which computes all non-deterministic results in a single graph structure. Pull-tab steps are local graph transformations to move non-deterministic choices towards the root of an expression. Pull-tabbing is independent of a search strategy so that different strategies (depth-first, breadth-first, parallel) can be used to extract the results of a computation. It has been used to compile functional logic languages into imperative or purely functional target languages. Pull-tab steps might duplicate choices in case of shared subexpressions. This could result in a dramatic increase of execution time compared to a backtracking implementation. In this paper we propose a refinement which avoids this efficiency problem while keeping all the good properties of pull-tabbing. We evaluate a first implementation of this improved technique in the Julia programming language.
Analysis of Positional Tracking Space Usage when using Teleportation

Teleportation is a widely used virtual locomotion technique that allows users to navigate beyond the confines of available tracking space with a low possibility of inducing VR sickness. Because teleportation requires little physical effort and lets users traverse large distances instantly, a risk is that over time users might only use teleportation and abandon walking input. This paper provides insight into this risk by presenting results from a study that analyzes tracking space usage of three popular commercially available VR games that rely on teleportation. Our study confirms that positional tracking usage is limited by the use of teleportation.
In the Field of Quantum Technologies

Quantum technologies are an emerging field one can think of in two categories. The first generation, which used principles of quantum physics to deliver technologies of the last century, and the second generation, which harnesses quantum phenomena to achieve novel device functionalities in the current century. In general, various sophisticated tools for the control and manipulation of quantum states are required and under development, fundamental concepts based on nonclassical behaviour or properties are used, or new capabilities are enabled which would have been out of reach for classical devices. Indeed, the role of semiconductor photonics as well as quantum structures is pronounced. In this chapter, two aspects of work in the field of quantum technologies are summarised. The first part will deal with coherent light sources, ranging from semiconductor lasers to novel sources of coherent light. The second part provides an introduction into quantum optics based on quantum light generation and tailored light–matter coupling. Serving as a starting point, here given examples shall motivate further reading in the dedicated literature on these subjects.
Detection Jellyfish Attacks Against Dymo Routing Protocol on Manet Using Delay Per-Hop Indicator (Delphi) Method

Mobile Ad Hoc Network (MANET) is one of the types of Ad-hoc Network which is comprised of wireless in a network. The main problem in this research is the vulnerability of the protocol routing Dymo against jellyfish attack, so it needs detection from a jellyfish attack. This research implements the DELPHI method to detect jellyfish attacks on a DYMO protocol which has better performance because the Delay Per-Hop Indicator (DELPHI) gathers the amount of hop and information delay from the disjoint path and calculates the delays per-hop as an indicator of a jellyfish attack. The evaluation results indicate an increase in the end-to-end delay average, start from 112.59s in 10 nodes increased to 143.732s in 30 nodes but reduced to 84,2142s in 50 nodes. But when the DYMO routing did not experience any jellyfish attacks both the delivery ratio and throughput are decreased. The delivery ratio, where decreased from 10.09% to 8.19% in 10 nodes, decreased from 20.35% to 16.85%, and decreased from 93.5644% to 82.825% in 50 nodes. As for the throughput, for 10 nodes decreased from 76.7677kbps to 68.689kbps, for 30 nodes decreased from 100kbps to 83.5821kbps and for 50 nodes decreased from 18.94kbps to 15.94kbps.
Children Longitudinal Face Recognition Using Random Forest

The children face recognition system plays a vital role in application towards the track and recognize the children who are missing at a young age due to child trafficking or kidnapping. In this study, an attempt is made to find the rate of recognition of a young child face images from age 1 to 15 years, where the frequency of facial growth is higher in this interval. To study the effectiveness of the problem, a dimensionality reduction technique is adopted such as PCA followed by random forest classifier as one method and deep convolutional neural network as another method. The proposed model was vindicated with a relatively large dataset that was created to address this problem. The total number of longitudinal face images of 47 children each 12-15 years was 685 and each year consists of a single sample. Experimentation was carried out by varying the number of decision trees and the number of classes for efficacious analysis of the problem. The procured results were promising with deep learning classification techniques with 93% of mini-batch accuracy.
Learning Object Deformation and Motion Adaption for Semi-supervised Video Object Segmentation

We propose a novel method to solve the task of semi-supervised video object segmentation in this paper, where the mask annotation is only given at the first frame of the video sequence. A mask-propagation-based model is applied to learn the past and current information for segmentation. Besides, due to the scarcity of training data, image/mask pairs that model object deformation and shape variance are generated for the training phase. In addition, we generate the key flips between two adjacent frames for motion adaptation. The method works in an end-to-end way, without any online fine-tuning on test videos. Extensive experiments demonstrate that our method achieves competitive performance against state-of-the-art methods on benchmark datasets, covering cases with single object or multiple objects. We also conduct extensive ablation experiments to analyze the effectiveness of our proposed method.
Directed Variational Cross-encoder Network for Few-shot Multi-image Co-segmentation

In this paper, we propose a novel framework for multi-image co-segmentation using class agnostic meta-learning strategy by generalizing to new classes given only a small number of training samples for each new class. We have developed a novel encoder-decoder network termed as DVICE (Directed Variational Inference Cross Encoder), which learns a continuous embedding space to ensure better similarity learning. We employ a combination of the proposed DVICE network and a novel few-shot learning approach to tackle the small sample size problem encountered in co-segmentation with small datasets like iCoseg and MSRC. Furthermore, the proposed framework does not use any semantic class labels and is entirely class agnostic. Through exhaustive experimentation over multiple datasets using only a small volume of training data, we have demonstrated that our approach outperforms all existing state-of-the-art techniques.
SpeedChain: A framework for Monitoring and Alerting blockchain projects

Blockchain is an emerging technology for decentralized transactional data sharing networks of untrusted participants. Monitoring and alerting are crucial functionalities when it comes to blockchain projects running in production. In the meantime, different blockchain monitoring systems lack detailed and real-time metrics. In addition to that, the adoption of different DLTs (Distributed Ledger Technologies) is poor. To solve this problem, we propose a realtime-based information framework to monitor blockchain applications. Our monitoring framework is not only a log-based method but also gathers other system metrics (e.g., CPU throughput, latency). The main benefits of our approach are lower configuration setup, more detailed metrics, and, larger DLTs adoption than the existing monitoring tools. We implement the framework to monitor three well-known blockchain systems. The experimental results show that our framework can make a detailed and realtime monitoring of blockchain projects.
Memory-Free Stochastic Weight Averaging by One-Way Variational Pruning

Recent works on convolutional neural networks (CNN) have attempted to find the local optima with ensemble-based approaches. Fast Geometric Ensemble (FGE) showed that captured weight points at the end of training time circulate local optima. This led to the Stochastic Weight Averaging (SWA) approach, which averages multiple model weights to find the local optima. However, they are limited by their output of fully-parameterized models, including needless parameters, after the training procedure. To solve this problem, we propose a novel training procedure: Stochastic Weight Averaging by One-way Variational Pruning (SWA-OVP). SWA-OVP reduces the number of model parameters by variationally updating the mask of weights for pruning. SWA-OVP variationally generates a mask for pruned weights in each iteration while recent pruning approaches produce the mask at the end of each training. In addition, our SWA-OVP prunes the model in a one-way training procedure, while other recent approaches prune the model weights in iterative training or require additional computation. Our experiment shows that SWA-OVP using only a 0.5x% ~ 0.7x% parameter size achieves even higher accuracy than SWA and FGE on several networks, such as Pre-ResNet110, Pre-ResNet164 and WideResNet28x10 on CIFAR10 and CIFAR100 datasets. SWA-OVP also achieves better performance compared to state-of-the-art pruning approaches.
Dr.supER: Intubation and Ventilator Troubleshooting VR Simulation

This paper presents Virtual Reality 3D interaction and simulation techniques in the context of the 2021 3DUI contest of the IEEE VR conference. In our implementation, by taking into consideration the current COVID-19 pandemic situation, we developed a VR gamified simulation regarding the intubation and ventilator troubleshooting procedures, taking place in an Emergency Room (E.R.) In this paper, we present the design and development of the overall experience, aiming to train the end-user on these actions to enhance their possibilities of achieving optimal performance under stressful situations. By gamifying the above education system, we also expect to empathize the layman regarding the daily challenges that the medical staff is experiencing in an Intensive Care Unit.
Continuous modeling supports from business analysis to systems engineering in IoT development

Developing Internet of Things(IoT) systems is non-trivial because diverse solution spaces must be simultaneously satisfied due to the intrinsic nature of IoT systems. To address this challenge, we proposed a method chain approach called the continuous modeling support process for business analysis and solution requirements in IoT development(COMP4BA-IoT) consisting of Business Analysis Body of Knowledge (BABOK), GQM+Strategies, GSN (goal structuring notation) and systems modeling language SysML.
Search for Lepton Flavor, Lepton Number, and Baryon Number Violating Tau Decay $$\tau \rightarrow p \mu \mu $$ at Belle

Lepton flavor violation (LFV) is forbidden in the standard model. However, the observation of neutrino oscillations tells us that lepton flavor is not a good quantum number, implying LFV could also show up in the charged lepton sector. Baryon number violation (BNV) is believed to have occured in the early universe. BNV in charged lepton decays automatically implies both lepton number and lepton flavor violation. Any observation of BNV or LFV would be clear signal for beyond-the-standard-model physics. We present a search for the LFV and BNV decay channel $$\tau \rightarrow p \mu \mu $$ performed with the data recorded by the Belle experiment at the KEKB asymmetric-energy $$e^+e^-$$ collider.
Interactive Virtual Learning in the Virtual World

Today, virtual reality (VB) is widely used in various fields, especially in computer systems, engineering and medicine. However, it remains one of the main barriers to education due to the high cost of conducting experimental research. This article discusses how VB and virtual entity technologies are used in education, and the advantages and disadvantages of using these technologies in the classroom. This interactive virtual learning is very convenient and effective. Today, it offers great opportunities to improve the quality of education by creating new methods and techniques. Undoubtedly, this increases the cognitive activity of students, requiring them to work on themselves in their teaching and research activities. Makes the learning process brighter and more interesting.
Channel-Wise Mix-Fusion Deep Neural Networks for Zero-Shot Learning

Zero-shot learning (ZSL), with the assistance of the seen class image and additional semantic knowledge, generalizes its classification ability to the unseen class by aligning the visual-semantic space embeddings. Few previous methods have researched whether discriminative visual features are helpful to recognize different classes while neglecting the rich semantic information from the surrounding background. This paper proposes a channel-wise mix-fusion ZSL model (CMFZ) to contextualize the ZSL classifier's discriminative information by incorporating much richer visual semantic information from both objects and their semantic surrounding environments. In particular, the channel-wise connection module (CCM) learns to construct the relationship between the object and its surroundings. A collaborative channel-wise activation module (CAM) is adopted to learn from a more delicate scale image attained from the cropping module. It highlights the most distinct channels representing the object’s discriminative regions to eliminate inadvertently introduced background noise. Furthermore, the representation ability of the learned mapping is enhanced by integrating the visual semantic features processed by CCM and CAM. Experimental results show that CMFZ outperforms the state-of-the-art ZSL methods and verifies the effectiveness of incorporating visual semantic information.
On the enumeration of polynomials with prescribed factorization pattern

We use generating functions over group rings to count polynomials over finite fields with the first few coefficients and a factorization pattern prescribed. In particular, we obtain different exact formulas for the number of monic n-smooth polynomials of degree m over a finite field, as well as the number of monic n-smooth polynomials of degree m with the prescribed trace coefficient.
Channel Planting for Deep Neural Networks using Knowledge Distillation

In recent years, deeper and wider neural networks have shown excellent performance in computer vision tasks, while their enormous amount of parameters results in increased computational cost and overfitting. Several methods have been proposed to compress the size of the networks without reducing network performance. Network pruning can reduce redundant and unnecessary parameters from a network. Knowledge distillation can transfer the knowledge of deeper and wider networks to smaller networks. The performance of the smaller network obtained by these methods is bounded by the predefined network. Neural architecture search has been proposed, which can search automatically the architecture of the networks to break the structure limitation. Also, there is a dynamic configuration method to train networks incrementally as sub-networks. In this paper, we present a novel incremental training algorithm for deep neural networks called <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">“planting”</i> . Our planting can search the optimal network architecture with smaller number of parameters for improving the network performance by augmenting channels incrementally to layers of the initial networks while keeping the earlier trained parameters fixed. Also, we propose using the knowledge distillation method for training the channels planted. By transferring the knowledge of deeper and wider networks, we can grow the networks effectively and efficiently. We evaluate the effectiveness of the proposed method on different datasets such as CIFAR-10/100 and STL-10. For the STL-10 dataset, we show that we are able to achieve comparable performance with only 7% parameters compared to the larger network and reduce the overfitting caused by a small amount of the data.
Congestion Strategies for Clustered Central Place Foraging

In central place foraging tasks, multiple robots search for and gather resources in an environment. The robots then proceed to deposit the collected resources in a single central location. The performance of central place foraging approaches is reduced due to congestion around the central collection point. The congestion problem is made worse in the case where resources are distributed in clusters whereby several robots collect resources in the same area and deliver those resources to the collection point along the same path. The approach proposed here seeks to alleviate this congestion problem through simple path planning strategies that reduce the number of inter-robot collisions. Path Planning And Collision Avoidance Algorithm For Clustered Central Place Foraging (PPCA-CCPFA) addresses congestion by detecting possible inter-robot collisions and finding alternate collision free paths for each robot. We compare our approach to the Distributed Deterministic Spiral Search Algorithm (DDSA). This approach provides a notable increase in the performance of DDSA in cases where resources are distributed in a single cluster. A simulation study was conducted using the swarm robotics simulation tool ARGoS to measure the effectiveness of the proposed approach as measured by the number of resources collected per unit time and by the number of inter-robot collisions per unit time.
Attentive Hybrid Collaborative Filtering for Rating Conversion in Recommender Systems

Recommendation models that use collaborative filtering consider the influence of friends and neighbors when recommending suitable items for a target user. Most of these neighborhood-based models use the actual ratings from neighbors to predict the ratings of the target user toward target items, which often leads to a low accuracy prediction caused by the improper rating-range problem. Recently, rating conversion methods have been proposed to address this issue. Because each friend/neighbor can have a different level of influence on the target user, we propose a friend module, which converts their ratings to match the target user’s perspective and assigns different weight to each user before modeling latent relations and predictions. In rating conversion, ratings that involve explicit feedback are important. Instead of the traditional approach to user embedding, we propose a novel approach that uses explicit feedback. This can express user features better than traditional methods and can then be used to convert ratings to match the target user’s perspective. For better representation and recommendation, we also learn latent relations between each user and item by adopting knowledge graph ideas, which leads to more accurate results. The FilmTrust and MovieLens datasets are used in experiments comparing the proposed method with existing methods. This evaluation showed that our model is more accurate than existing methods.
Cam-softmax for discriminative deep feature learning

Deep convolutional neural networks are widely used to learn feature spaces for image classification tasks. We propose cam-softmax, a generalisation of the final layer activations and softmax function, that encourages deep feature spaces to exhibit high intra-class compactness and high inter-class separability. We provide an algorithm to automatically adapt the method's main hyperparameter so that it gradually diverges from the standard activations and softmax method during training. We report experiments using CASIA-Webface, LFW, and YTF face datasets demonstrating that cam-softmax leads to representations well suited to open-set face recognition and face pair matching. Furthermore, we provide empirical evidence that cam-softmax provides some robustness to class labelling errors in training data, making it of potential use for deep learning from large datasets with poorly verified labels.
Prediction of Wi-Fi Comfort in Buildings using Support Vector Machines

Comfort of occupants in semi-public buildings depends on ambient parameters such as temperature, relative humidity, carbon dioxide concentration, light intensity, noise, cleanliness, etc. In addition to the above parameters, occupants' indoor comfort is observed to be significantly influenced by the quality of Wi-Fi connection provided for ubiquitous internet access in buildings. Hence, in this work, another important dimension to indoor comfort is introduced-Wi-Fi comfort. A novel comfort management architecture is proposed which employs machine learning to recognize the level of Wi-Fi comfort. The proposed architecture complements the extensive research done in the area of network planning, by providing Quality of Experience (QoE) of Wi-Fi as an effective input to such mechanisms. Comfort is predicted based on the Wi-Fi signal strength, Wi-Fi access permissions, steady-state occupancy and applications used by the occupants. A case study is presented in which occupants use a specific set of applications, such as email, voice and/or video for a given duration. The Support Vector Machines (SVM) method is used for classification of comfort levels. The performance evaluation of the classifier and of the overall system is discussed in detail.
Multi-criteria and Review-Based Overall Rating Prediction

An overall rating cannot reveal the details of user’s preferences toward each feature of a product. One widespread practice of e-commerce websites is to provide ratings on predefined aspects of the product and user-generated reviews. Most recent multi-criteria works employ aspect preferences of users or user reviews to understand the opinions and behavior of users. However, these works fail to learn how users correlate these information sources when users express their opinion about an item. In this work, we present Multi-task & Multi-Criteria Review-based Rating (MMCRR), a framework to predict the overall ratings of items by learning how users represent their preferences when using multi-criteria ratings and text reviews. We conduct extensive experiments with three real-life datasets and six baseline models. The results show that MMCRR can reduce prediction errors while learning features better from the data.