Reviewerly/exHarmony huggingface dataset:
Deep Depth Fusion for Black, Transparent, Reflective and Texture-Less Objects

Structured-light and stereo cameras, which are widely used to construct point clouds for robotic applications, have different limitations on estimating depth values. Structured-light cameras fail in black, transparent, and reflective objects, which influence the light path; stereo cameras fail in texture-less objects. In this work, we propose a depth fusion model that complements these two types of methods to generate high-quality point clouds for short-range robotic applications. The model first determines the fusion weights from the two input depth images and then refines the fused depth using color features. We construct a dataset containing the aforementioned challenging objects and report the performance of our proposed model. The results reveal that our method reduces the average L1 distance on depth prediction by 75% and 52% compared with the original depth output of the structured-light camera and the stereo model, respectively. A noticeable improvement on the Iterative Closest Point (ICP) algorithm can be achieved by using the refined depth images output from our method.
Ensuring Privacy-Aware Data Release: An Analysis of Applicability of Privacy Enhancing Techniques to Real-world Datasets

The internet and media platforms are store and share user-generated data as a means to provide an intelligent and personalized experience to the user. This user data is the new currency which is powering commercial interests through profiling of user behavior. But as users are becoming increasingly aware of implications due to data breaches, there has been a greater need for privacy preservation methods. The need is further enforced due to legal and commercial implications caused by the privacy compromises. We have analyzed the existing privacy ensuring techniques and their applicability to real-world data. It has been established that while privacy preservation techniques are dependent on the data attributes, the scheme of data release also plays an important role. We have therefore explored the applicability of syntactic and semantic privacy preservation techniques and shown their effects on a dataset.
Automatic Sun-Tracking System

The traditional sources of energy in the world are beginning to fade and the issue of finding alternative sources has become an urgent and fundamental. Hence, alternative sources of energy have become the desired solution for a prosperous future and a clean environment. Thus, solar energy is considered one of the most important renewable sources of energy. This paper describes an automatic sun tracking system, based on two stepper motors, and moving solar panel. To gain more energy from the sun, the active surface of the solar cells should be perpendicular to solar radiation, which means that the panel must follow the path of the sun all the time. The orientation of the solar panel towards the sun is achieved by using a C++ program. An experimental comparison between a fixed panel and a moving one is presented.
Supervised Discriminative Sparse PCA with Adaptive Neighbors for Dimensionality Reduction

Dimensionality reduction is an important operation in information visualization, feature extraction, clustering, regression, and classification, especially for processing noisy high dimensional data. However, most existing approaches preserve either the global or the local structure of the data, but not both. Approaches that preserve only the global data structure, such as principal component analysis (PCA), are usually sensitive to outliers. Approaches that preserve only the local data structure, such as locality preserving projections, are usually unsupervised (and hence cannot use label information) and uses a fixed similarity graph. We propose a novel linear dimensionality reduction approach, supervised discriminative sparse PCA with adaptive neighbors (SDSPCAAN), to integrate neighborhood-free supervised discriminative sparse PCA and projected clustering with adaptive neighbors. As a result, both global and local data structures, as well as the label information, are used for better dimensionality reduction. Classification experiments on nine high-dimensional datasets validated the effectiveness and robustness of our proposed SDSPCAAN.
High-Level Classification for Multi-Label Learning

Multi-label learning (MLL) addresses the problem of learning from data items which can be associated with multiple labels simultaneously. As MLL techniques are usually derived of single-label ones, they also share common drawbacks. For example, most MLL techniques perform a low-level classification, i.e., they consider only the physical features of the input data (e.g., distance, distribution, etc) in the classification process, having troubles to detect semantic relationships among the data items, like the formation pattern for example. Recent studies have shown that learning systems based on complex networks have the ability to consider not only the physical features of the data, but also structural and topological features extracted from the network connection patterns, which is known as high-level classification. In this paper, we investigate a MLL framework which combines both low-level and high-level techniques in order to improve the predictive performance of existing MLL techniques. Experiments conducted on artificial and real-world data sets highlighted the salient features of the MLL framework and also attested its good predictive performance in comparison with widely used MLL techniques, indicating that our framework may considerable improve their predictive performance.
Continual Learning Of Predictive Models In Video Sequences Via Variational Autoencoders

This paper proposes a method for performing continual learning of predictive models that facilitate the inference of future frames in video sequences. For a first given experience, an initial Variational Autoencoder, together with a set of fully connected neural networks are utilized to respectively learn the appearance of video frames and their dynamics at the latent space level. By employing an adapted Markov Jump Particle Filter, the proposed method recognizes new situations and integrates them as predictive models avoiding catastrophic forgetting of previously learned tasks. For evaluating the proposed method, this article uses video sequences from a vehicle that performs different tasks in a controlled environment.
Unsupervised Fault Diagnosis of Machine via Multiple-Order Graphical Deep Extreme Learning Machine

The intelligent fault diagnosis powered deep learning (DL) is widely applied in various real industrial fields. However, the classical intelligent fault diagnosis methods cannot fully juggle the manifold structure information with multiple-order similarity from the massive industrial data. At the same time, the scarcity of the labeled information can also result in inferior generalization performance. To this end, a new multiple-order graphical deep extreme learning machine (MGDELM) algorithm for unsupervised fault diagnosis (UFD) of rolling bearing is designed in this study. By jointly optimizing the multiple-order objective function, the proposed MGDELM algorithm can synchronously extract local and global structural information from the raw industrial data. Empirically, rolling bearing failure data validates the effectiveness of the designed algorithm and fault diagnosis method.
Application of single chip technology in internet of things electronic products

Taking the rapid development of electronic science and technology as an opportunity, MCU which undertakes the equipment control task is developing towards the direction of intelligence, self-learning and multi-function integration. They have been applied to all aspects of human production and life. Driven by computer network technology, the development of Internet of Things technology is promoted. In this era, only by strengthening the research and development and improvement of MCU control system can we promote the development of the entire society and economy. This article mainly studies the application of MCU Technology in IoT electronics. This article first briefly explains the definition of MCU, and then summarizes the entire development process of MCU. On this basis, it is effectively combined with the actual situation, and puts forward the practical application of the MCU Technology in the Internet of Things electronic products. On the basis of ensuring that the personalized needs of modern people are met, it can lay a good foundation for the future development of electronic products. The research experiments in this paper found that up to 70 meters, and found that a large number of packet loss has affected the basic communication. It is believed that communication can be performed at 70 meters but the communication quality is poor. It is not recommended to use, and the test is terminated. It can be seen from the results that the communication distance of the terminal node is finally within 30 meters, which can ensure that the data is almost 100% received. The packet loss rate within 60 meters is within 2%, and the communication quality is good. Guarantee basic communication functions.
Recognition Performance of Selected Speech Recognition APIs – A Longitudinal Study

Within the last five years, the availability and usability of interactive voice assistants have grown. Thereby, the development benefits mostly from the rapidly increased cloud-based speech recognition systems. Furthermore many cloud-based services, such as Google Speech API, IBM Watson, and Wit.ai, can be used for personal applications and transcription tasks. As these tasks vary in their domain, their complexity as well as in their interlocutor, it is challenging to select a suitable cloud-based speech recognition service. As the update-process of online-services can be completely handled in the back-end, client applications do not need to be updated and thus improved accuracies can be expected within certain periods. This paper contributes to the field of automatic speech recognition, by comparing the performance of speech recognition between the above-mentioned cloud-based systems on German samples of high-qualitative spontaneous human-directed and device-directed speech as well as noisy device-directed speech over a period of eight months.
Unleashing the Potential of Attention Model for News Headline Generation

Headline generation is a special summarization generation task and the difficulty lies in requiring the generated headline to be concise, fluent and informative. Limited by the ability of commonly used encoder and decoder modules to capture long-term dependencies in seq2seq tasks, previous work rarely researched headline generation by end-to-end methods. However, the recent success of Transformer model and its subsequent improved versions demonstrate their remarkable performance on seq2seq tasks, which provide us with a feasible solution. In this paper, we propose a novel model Transformer(XL)-CC to generate headline from the perspective of understanding the whole text, the segment-level recurrence mechanism and relative positional encoding make our model learn ultra-long-term dependencies. In addition, we combine the copy and coverage mechanisms to generate more readable titles. Experimental results on the NYT and Chinese LSCC news datasets also confirm that our method significantly achieves better performance on the headline generation task.
Towards Designing Linguistic Assessments Aggregation as a Distributed Neuroalgorithm

A challenging problem which arises in the domain of integrating symbolic and sub-symbolic computations within a massively parallel computational environments like Internet of Things is considered in application to the Linguistic Decision Making tasks. A novel theoretical idea is proposed on expressing linguistic operators in dynamics of an artificial neural network. The proposal consists of two consequent stages: expressing linguistic operators as structural manipulations and translating them in a neuroalgorithm. The theoretical foundation is Tensor Product Representation (TPR) that provides a generic framework of designing a neural network that does not require training and produces an exact result equivalent to the result of symbolic algorithms. This paper discusses viability of the proposed idea, demonstrates design of TPR-based arithmetic as a basic building block for construction of such a method and elaborates directions of further research.
Improved Resection Margins in Surgical Oncology Using Intraoperative Mass Spectrometry

PURPOSE: Incomplete tumor resections leads to the presence of cancer cells on the resection margins demanding subsequent revision surgery and poor outcomes for patients. Intraoperative evaluations of the tissue pathology, including the surgical margins, can help decrease the burden of repeat surgeries on the patients and healthcare systems. In this study, we propose adapting multi instance learning (MIL) for prospective and intraoperative basal cell carcinoma (BCC) detection in surgical margins using mass spectrometry. METHODS: Resected specimens were collected and inspected by a pathologist and burnt with iKnife. Retrospective training data was collected with a standard cautery tip and included 63 BCC and 127 normal burns. Prospective data was collected for testing with both the standard and a fine tip cautery. This included 130 (66 BCC and 64 normal) and 99 (32 BCC and 67 normal) burns, respectively. An attention-based MIL model was adapted and applied to this dataset. RESULTS: Our models were able to predict BCC at surgical margins with AUC as high as 91%. The models were robust to changes in cautery tip but their performance decreased slightly. The models were also tested intraoperatively and achieved an accuracy of 94%. CONCLUSION: This is the first study that applies the concept of MIL for tissue characterization in perioperative and intraoperative REIMS data.
Improving on and assessing ethical guidelines for digital tracking and tracing systems for pandemics

Abstract So-called digital tracking and tracing systems (DTTSs) have been proposed as a means to prevent the spread of SARS-CoV-2. There are ethical guidelines and evaluations of such systems available. As part of a research project, I will build on and critically evaluate the foundations of such guidelines. The goal is to provide both incremental improvements of the specific requirements for DTTSs and to present and discuss more fundamental challenge, the risk for indirect effects and slippery slopes. The nature of slippery slopes makes ethical guidelines more difficult since it requires a more complex analysis than, for example, using a checklist allows for.
Automatic Region Selection For Objective Sharpness Assessment Of Mobile Device Photos

Mobile devices are the source of a vast majority of digital photos today. Photos taken by mobile devices generally have fairly good visual quality. When evaluating high-quality mobile device photos, people have to manually zoom in to local regions to discern the subtle difference. Understandably, a global objective quality assessment method cannot perform well on such task. Therefore, local region selection is widely recognized as a prerequisite for the following quality evaluation. Clearly, subjective regions selection suffers from the drawbacks in terms of productivity, reproducibility and optimality. In this paper, we propose an automatic local region selection algorithm for sharpness measurement of mobile device photos. Specifically, local texture statistics, depth, saliency, as well as inter-pictures difference, are used as main features to select an optimal local region, in which the sharpness is then measured. For validation, we have built a largescale database for sharpness evaluation of mobile device photos, with 100 different scenes shot by several flagship mobile phones. The experimental results show that the performance of classic sharpness evaluation algorithms can be substantially improved with the region selected by the proposed algorithm.
Extracting archival-quality information from software-related chats

Software developers are increasingly having conversations about software development via online chat services. Many of those chat communications contain valuable information, such as code descriptions, good programming practices, and causes of common errors/exceptions. However, the nature of chat community content is transient, as opposed to the archival nature of other developer communications such as email, bug reports and Q&A forums. As a result, important information and advice are lost over time.
Multi-Label Learning with Local Similarity of Samples

Multi-label learning has been successfully applied to solve instance multi-semantics problems. Moreover, the topology information of samples is often adopted in existing works to improve the prediction performance, in which the similarity of samples is usually calculated in the entire feature space. However, in real-world applications, each label is often determined by a subset of the original features, so when we focus on different labels, the similarity of two instances may be different. In this paper, we propose a multi-label learning method by exploiting the local similarity of samples. Specifically, the smoothness assumption is applied to assume that if the feature subset is similar between samples, the corresponding label should be similar. In addition, L1 regularization is also adopted to sparse the weight coefficients when constraining the output space of the instance. The experimental results on several data sets validate the effectiveness of the proposed method.
Crowd Counting from Unmanned Aerial Vehicles with Fully-Convolutional Neural Networks

Crowd analysis is receiving an increasing attention in the last years because of its social and public safety implications. One of the building blocks of crowd analysis is crowd counting and the associated crowd density estimation. Several commercially available drones are equipped with onboard cameras and embed powerful GPUs, making them an excellent platform for real-time crowd counting tools. This paper proposes a light-weight and fast fully-convolutional neural network to learn a regression model for crowd counting in images acquired from drones. A robust model is derived by training the network from scratch on a subset of the very challenging VisDrone dataset, which is characterized by a high variety of locations, environments, perspectives and lighting conditions. The derived model achieves an MAE of 8.86 and an RMSE of 15.07 on the test images, outperforming models developed by state-of-the-art light-weight architectures, that are MobileNetV2 and YOLOv3.
Performance Evaluation of Queue Algorithms for Video-on-Demand Application

Nowadays, there are real-time internet applications that require low latency and high bandwidth usage. In order to satisfy this requirement, many active queuing algorithms are offered, such as RED, BLUE, and controlled delay (CoDel). The number of publications aimed at evaluating the performance of the active queue management (AQM) algorithm is still limited by using a network simulator and only comparing two algorithms. The combination of AQM and connection types (fairness and no-fairness) in real network implementation has not been discussed much. In this paper, we offer a comparison of the performance of three queuing algorithms, namely DropTail, BLUE, and CoDel, with two types of connections, namely fairness and no-fairness using real network devices and internet video streaming access. Experiments carried out in the real network environment using Sandvine PL8840's bandwidth management device. The quality of services (QoS) parameters that will be compared are throughput, latency, and packet loss. The comparison results show that fairness connections on all queuing algorithms can maintain low latency values; throughput is smaller than no-fairness connections. The absence of packet loss in streaming video based on the TCP protocol does not guarantee that the video being played runs well because it depends on the available bandwidth capacity.
Optimization of Multi-Extreme Multidimensional Functions: Population-Based Nature-Inspired Algorithm

The article presents a population-based algorithm for solving multidimensional optimization problems using a hierarchical multi-population approach. Specific operators are used to supporting a diverse population of solutions, as well as expanding the search space by less promising solutions. The authors evaluate the effectiveness of the proposed algorithm on a set of multivariate functions of Schwefel, Rosenbrock, Rastrigin, and Grivank. The performance of the developed algorithm is compared with the performance of competing algorithms. The researchers register here significant statistical differences. In its turn, it proves in favor of a scalable evolutionary algorithm. Such a tendency is observed for all the considered functions with an increasing dimension of the problem.
GCN-LRP explanation: exploring latent attention of graph convolutional networks

Graph convolutional networks (GCNs) have been successfully applied to many graph data on various learning tasks such as node classification. However, there is limited understanding of the internal logic and decision patterns of GCNs. In this paper, we propose a layer-wise relevance propagation based explanation method for GCNs, namely GCN-LRP, to explore the latent pattern of GCNs. Then, we use three well-known citation network data sets and synthetic graph data sets for node classification tasks with GCN-LRP explanation, and experimentally identify latent attentions when GCNs aggregates information from the node and its neighboring nodes: (i) GCNs pay more attention to the classified node when comparing with its neighbors; (ii) GCNs do not pay attention to all the neighboring nodes equally, and a few neighboring nodes received more attention than others. Moreover, we further theoretically analyze and find that: (i) the latent attentions come from the recursively aggregating of GCNs; (ii) the neighboring nodes, which share enough neighbors with classified node, would receive more attention than other neighbors; (iii) the latent attention could hardly be changed by model training. We also discuss the advantage and limitations of GCNs introduced by the latent attentions, and implications of our findings for graph data learning with GCNs.
Unknown Class Label Cleaning For Learning With Open-Set Noisy Labels

Deep neural networks (DNNs) trained on large-scale annotated datasets have achieved impressive results in the area of image classification. Many large-scale datasets have been collected from websites; however, such data are inevitably corrupted with noise. In this study, we researched the open-set noisy label problem, where some outliers are contained in a dataset and annotated through a noisy label but do not belong to any class of training data. To address this problem, we propose a novel unknown class label cleaning framework for the training of DNNs with open-set noisy labels. In addition to general image classification, we also estimate the probability of an input being from an unknown class by assigning a pseudo unknown label to all of the data and correct these labels through an alternating update of the network parameters and labels. The results of experiments conducted on the noisy CIFAR-10 datasets demonstrate that our approach can robustly train DNNs with a high proportion of noisy labels.