Reviewerly/exHarmony huggingface dataset:
3D Lidar Reconstruction with Probabilistic Depth Completion for Robotic Navigation

Safe motion planning in robotics requires planning into space which has been verified to be free of obstacles. However, obtaining such environment representations using lidars is challenging by virtue of the sparsity of their depth measurements. We present a learning-aided 3D lidar reconstruction framework that upsamples sparse lidar depth measurements with the aid of overlapping camera images so as to generate denser reconstructions with more definitively free space than can be achieved with the raw lidar measurements alone. We use a neural network with an encoder-decoder structure to predict dense depth images along with depth uncertainty estimates which are fused using a volumetric mapping system. We conduct experiments on real-world outdoor datasets captured using a handheld sensing device and a legged robot. Using input data from a 16-beam lidar mapping a building network, our experiments showed that the amount of estimated free space was increased by more than 40% with our approach. We also show that our approach trained on a synthetic dataset generalises well to real-world outdoor scenes without additional fine-tuning. Finally, we demonstrate how motion planning tasks can benefit from these denser reconstructions.
Unsupervised Continual Learning via Pseudo Labels

Continual learning aims to learn new tasks incrementally using less computation and memory resources instead of retraining the model from scratch whenever new task arrives. However, existing approaches are designed in supervised fashion assuming all data from new tasks have been manually annotated, which are not practical for many real-life applications. In this work, we propose to use pseudo label instead of the ground truth to make continual learning feasible in unsupervised mode. The pseudo labels of new data are obtained by applying global clustering algorithm and we propose to use the model updated from last incremental step as the feature extractor. Due to the scarcity of existing work, we introduce a new benchmark experimental protocol for unsupervised continual learning of image classification task under class-incremental setting where no class label is provided for each incremental learning step. Our method is evaluated on the CIFAR-100 and ImageNet (ILSVRC) datasets by incorporating the pseudo label with various existing supervised approaches and show promising results in unsupervised scenario.
Distributed Continuation Stealing is More Scalable than You Might Think

The need for load balancing in applications with irregular parallelism has motivated research on work stealing. An important choice in work-stealing schedulers is between child stealing or continuation stealing. In child stealing, a newly created task is made stealable by other processors, whereas in continuation stealing, the caller's continuation is made stealable by executing the newly created task first, which preserves the serial execution order. Although the benefits of continuation stealing have been demonstrated on shared memory by Cilk and other runtime systems, it is rarely employed on distributed memory, presumably because it has been thought to be difficult to implement and inefficient as it involves migration of call stacks across nodes. Akiyama and Taura recently introduced efficient RDMA-based continuation stealing, but the practicality of distributed continuation stealing is still unclear because a comparison of its performance with that of child stealing has not previously been performed. This paper presents the results of a comparative performance analysis of continuation stealing and child stealing on distributed memory. To clarify the full potential of continuation stealing, we first investigated various RDMA-based synchronization (task join) implementations, which had not previously been fully inves-tigated. The results revealed that, when the task synchronization pattern was complicated, continuation stealing performed better than child stealing despite its relatively long steal latency due to stack migration. Notably, our runtime system achieved almost perfect scaling on 110,592 cores in an unbalanced tree search (UTS) benchmark. This scalability is comparable to or even better than that of state-of-the-art bag-of-tasks counterparts.
Fast Obstacle Avoidance Based on Real-Time Sensing

Humans excel at navigating and moving through dynamic and complex spaces, such as crowded streets. For robots to do the same, it is crucial that they are endowed with highly reactive obstacle avoidance which is adept at partial and poor sensing. We address the issue of enabling obstacle avoidance based on sparse and asynchronous perception. The proposed control scheme combines a high-level input command provided by either a planner or a human operator with fast reactive obstacle avoidance (FOA). The sampling-based sensor data can be combined with an analytical reconstruction of the obstacles for real-time collision avoidance. Thus, we can ensure that the agent does not become stuck when a feasible path exists between obstacles. Our algorithm was evaluated experimentally on static laser data from cluttered, indoor office environments. Additionally, it was used in shared-control mode in a dynamic and complex outdoor environment in the center of Lausanne. The proposed control scheme successfully avoided collisions in both scenarios. During the experiments, the controller took 1 ms to evaluate over 30000 data points.
Econometric modeling of job satisfaction in education

The purpose of the study is to establish a causal relationship between job satisfaction in the field of education in the Russian Federation, depending on the satisfaction with: working conditions; opportunities for professional growth; remuneration. The article deals with the problem of modeling socio-economic satisfaction with work in the field of education. Based on the correlation analysis, linear and nonlinear models of the interrelationships of factors related to this problem are formed. Econometric models have been developed to effectively assess the level of job satisfaction of employees employed in the field of education. The scientific novelty lies in the study of new models that are statistically superior to domestic developments in this field of activity. As a result, the most significant factors affecting the level of job satisfaction were identified.
Three constructions of asymptotically optimal codebooks via multiplicative characters of finite fields

In this paper, we give three constructions of codebooks with multiplicative characters of finite fields.Our constructions generalize the first construction in A. X. Zhang and K. Q. Feng (IEEE Trans.Inf.Theory 58(4), 2507-2511, 2012) from one dimension to two dimensions.We determine the maximum cross-correlation amplitude of these codebooks by the orthogonal relation of multiplicative characters and the properties of Jacobi sum.We prove that all the codebooks we constructed are asymptotically optimal with respect to the Welch bound.The parameters of these codebooks are new.
Maturing the Scrum Framework for Software Projects Portfolio Management: A Case Study-Oriented Methodology

In the modern era, information technology-based solution providers are encountering a growing request for satisfying the versatile requirements of their customers in terms of software applications. To this end, specific approaches have been designed to streamline the way of accomplishing software projects in an efficient manner, i.e., agile-oriented frameworks. Even though previous studies have highlighted variations of such a framework, the literature has not addressed the adaptations required in response to the gradual maturity of a wide-ranging case study dealing with software applications. Following a case study-oriented methodology, this paper focuses on elaborating a set of workable maneuvers to mature the Scrum framework when applied to portfolio management. Particularly, we highlight how Scrum should be adapted from its basic setting to a vision and goal-oriented configuration or Scrumban under certain conditions. As a maturing practice, we propose a heuristic scoring technique to determine the sprint length of subprojects with different characteristics in the context of a portfolio. The study also introduces a multi-level refinement structure to enhance the monitoring of the teams’ performance under the proposed mature framework. The results obtained display a considerable spike in the realization rate of release planning in light of the actual performance.
Securing the transfer and controlling the piracy of digital files using Blockchain

With the increasing Digitalisation, everything is made available online from books to movies, from tickets to songs, from photographs and motion pictures to software. To fully utilise the benefits of this digitalisation, the online transfer of documents is also in boom. This improvement has led to advancement in file sharing technology that results into information distribution in much convenient way. But this has also resulted in the unethical way of downloading and distributing the materials. This unethical way of digital content distribution is known as Digital Piracy. To control the digital piracy, many copyright protection systems have been adopted like digital watermarking, digital fingerprinting, digital rights management, encryption etc. these systems have some loopholes, that can be removed by using the Blockchain Technology. This chapter focuses on reviewing the capabilities of the existing copyright protection system, related works in this area using Blockchain technology and proposing the idea of applying Blockchain technology with the license generation in order to provide more protection to the existing copyright protection system.
On Autonomous Drone Navigation Using Deep Learning and an Intelligent Rainbow DQN Agent

Drones are intelligent devices that offer solutions for a continuously expanding variety of applications. Therefore, there would be a significant improvement if these systems could explore space automatically and without human-supervision. This work integrates cutting-edge artificial intelligence techniques that allow drones to travel independently. Following an overview of reinforcement learning methods built for discrete action space settings, a multilayer Perceptron model is constructed for feature extraction along with Hybrid neural networks. The agent employed in the experiments is a Rainbow DQN agent trained on the AirSim simulator. The experimental results are encouraging as the agent was tested for 16 missions and the accuracy was higher than 93%. In particular, the success for action selection was 97% and 93 for mission success. Finally, future work related to the navigation of autonomous drones is discussed including current concepts and methods of integration with more sophisticated algorithmic approaches.
The DataScope: A Mixed-Initiative Architecture for Data Labeling

Machine learning promises many advantages, but achieving these promises requires methods that smooth the interaction between humans and machine learning. Machine learning systems require meticulous training on large, labeled datasets. Labeling data is a tedious expensive process, and many times requires complex human-judgment skills. Mixed-initiative designs divide the labor between the artificial agent and the human to make the task at hand more efficient and effective. This paper proposes a mixed-initiative method for efficient coding of video and image data in general and emotion data in particular. We integrate an unsupervised dimensionality reduction algorithm and the R Shiny platform to develop an interactive method that leverages human expertise to label the data more efficiently and effectively. The method, through the interactive web tool, allows the user to explore the data interactively, examine similarities and dissimilarities in the data, and label clusters of many images and video frames at once. The combination of the unsupervised learning algorithm and the R Shiny platform enables interactive exploration and annotation of high-dimensional, complicated data. This method can be used to annotate large data sets faster and can advance research in machine vision.
A Labeled Dataset for Investigating Cyberbullying Content Patterns in Instagram

As online communication continues to become more prevalent, instances of cyberbullying have also become more common, particularly on social media sites. Previous research in this area has studied cyberbullying outcomes, predictors of cyberbullying victimization/perpetration, and computational detection models that rely on labeled datasets to identify the underlying patterns. However, there is a dearth of work examining the content of what is said when cyberbullying occurs and most of the available datasets include only basic labels (cyberbullying or not). This paper presents an annotated Instagram dataset with detailed labels about key cyberbullying properties, such as the content type, purpose, directionality, and co-occurrence with other phenomena, as well as demographic information about the individuals who performed the annotations. Additionally, results of an exploratory logistic regression analysis are reported to illustrate how new insights about cyberbullying and its automatic detection can be gained from this labeled dataset.
Teacher's Digital Literacy to Improve Quality in Learning

The post-covid-19 pandemic is a new era that affects several sectors in Indonesia, one of which is education. Education has changed from conventional learning to online learning. The use of technology in education is still needed to improve the quality of education. Therefore, digital literacy is the main key and foundation that must be owned. This study aims to describe teachers' digital literacy to improve the quality of learning. This study uses a qualitative descriptive method to explore the level of teacher ability, driving and constraining factors, efforts of teachers and schools as well as teacher challenges in improving teacher digital literacy to improve quality in learning with a total of 5 informants, namely homeroom teachers for grade 7, homeroom teachers for grade 8, 9th-grade homeroom teacher, physical education teacher, Craft subject teacher. The instruments used in this study are interviews, observations and documentation. The study results show that teacher digital literacy is very influential in improving the quality of learning. So teachers must have digital literacy skills.
An Intelligent Human Activity Recognizer for Visually Impaired People Using VGG-SVM Model

With the advancement of artificial intelligence and computer vision, assistive devices for visually impaired people is an active research area for the last decade. People with visual disabilities face challenges in the detection and recognition of various human actions which leads to a lack of confidence and constant dependency while performing daily routine activities. The objective of this research is to recognize and classify different human actions based on the position and posture of their body. In this research, captured video is processed for human pose estimation by extraction of 2D body skeleton using OpenPose method. The extracted body points further to the process for feature extraction using pre-trained VGG-19 CNN which classifies the human actions using an SVM classifier. Furthermore, the proposed method integrates with a voice-enabled feature to deliver instructions on classified human activities. The proposed deep learning method is to train and test using three different dataset (a) Weizmann dataset for gesture based human actions, (b) Kinetics dataset for interaction based human actions and (c) CK+ dataset for behaviour based human actions. The accuracy for classification of different human actions reached to 93.08%, 95.03%, and 93.12% and the F1 score reached 93.59%, 95.19% and 93.66%, respectively. These results indicate the significance of proposed method for the assistance of visually impaired people.
CoSCL: Cooperation of Small Continual Learners is Stronger Than a Big One

Continual learning requires incremental compatibility with a sequence of tasks. However, the design of model architecture remains an open question: In general, learning all tasks with a shared set of parameters suffers from severe interference between tasks; while learning each task with a dedicated parameter subspace is limited by scalability. In this work, we theoretically analyze the generalization errors for learning plasticity and memory stability in continual learning, which can be uniformly upper-bounded by (1) discrepancy between task distributions, (2) flatness of loss landscape and (3) cover of parameter space. Then, inspired by the robust biological learning system that processes sequential experiences with multiple parallel compartments, we propose Cooperation of Small Continual Learners (CoSCL) as a general strategy for continual learning. Specifically, we present an architecture with a fixed number of narrower sub-networks to learn all incremental tasks in parallel, which can naturally reduce the two errors through improving the three components of the upper bound. To strengthen this advantage, we encourage to cooperate these sub-networks by penalizing the difference of predictions made by their feature representations. With a fixed parameter budget, CoSCL can improve a variety of representative continual learning approaches by a large margin (e.g., up to 10.64% on CIFAR-100-SC, 9.33% on CIFAR-100-RS, 11.45% on CUB-200-2011 and 6.72% on Tiny-ImageNet) and achieve the new state-of-the-art performance. Our code is available at https://github.com/lywang3081/CoSCL .
Using blockchain to store personal information: a systematic literature review

Blockchain has been gaining significant interest in several domains. However, although this technology is valuable in many cases, blockchain also raises relevant challenges. Data protection is also an increasingly important subject. In particular, after GDPR was published, companies worldwide change the way they process personal data. The objective of this systematic literature review is to examine the usage of blockchain for storing personal data. The review focuses on the advantages and challenges of storing personal data in a blockchain, as well as techniques and technologies for storing personal data using blockchain. We conclude that the main concern is the immutability of blockchain records versus the mandatory change/deletion requirement found in some data protection regulations such as GDPR. However, despite the challenges, blockchain has several advantages for storing personal information.
Estimating Breakpoints in Piecewise Linear Regression Using Machine Learning Methods

Nowadays there are many methods to explain non-linear behavior. However, among them, Piecewise Linear (PWL) regression has received a lot of attention in recent years. Accurate estimation of breakpoints is critical in the PWL model, which compresses nonlinear relationships at breakpoints and exhibits linear behavior between two breakpoints. The aim of this study is to determine the location of the break points in the most useful way by using decision tree regressor and k-means clustering, which are machine learning-based methods. From the experimental results, it was observed that the accuracy of the k-Means clustering method (98%) was higher than the decision tree regressor method (96%).
TextMatcher: Cross-Attentional Neural Network to Compare Image and Text

We study a multimodal-learning problem where, given an image containing a single-line (printed or handwritten) text and a candidate text transcription, the goal is to assess whether the text represented in the image corresponds to the candidate text. This problem, which we dub text matching, is primarily motivated by a real industrial application scenario of automated cheque processing, whose goal is to automatically assess whether the information in a bank cheque (e.g., issue date) match the data that have been entered by the customer while depositing the cheque to an automated teller machine (ATM). The problem finds more general application in several other scenarios too, e.g., personal-identity-document processing in user-registration procedures. We devise a machine-learning model specifically designed for the text-matching problem. The proposed model, termed TextMatcher, compares the two inputs by applying a novel cross-attention mechanism over the embedding representations of image and text, and it is trained in an end-to-end fashion on the desired distribution of errors to be detected. We demonstrate the effectiveness of TextMatcher on the automated-cheque-processing use case, where TextMatcher is shown to generalize well to future unseen dates, unlike existing models designed for related problems. We further assess the performance of TextMatcher on different distributions of errors on the public IAM dataset. Results attest that, compared to a naïve model and existing models for related problems, TextMatcher achieves higher performance on a variety of configurations.
Privacy Protection Management Model for Internet of Things Data

With the rapid development of the Internet of Things, personal information is collected, stored and analyzed through IoT terminal devices, which is benefit for people’s daily life. However, the problems of personal private information leakage are also emerging. In terms of ciphertext data access control of the Internet of Things, it is required to implement fine-grained access control policies for the authorizer. However, the traditional security channel cannot meet the requirements of application security and privacy protection based on resisting key sharing attacks in a “many to many” environment. To solve this problem, this paper proposes a privacy protection management model based on lightweight blockchain, which includes three modules: data storage, sharing and auditing. Data storage module is used to encrypt and store the private data generated by individual users in various IoT devices. The data sharing module proposes a hybrid encryption mechanism, which allows data owners to create data access policies, and uses attribute encryption algorithm to achieve fine-grained access control of private data. The data auditing module proposes a lightweight blockchain architecture to store root of trust and life-cycle behaviors of private data, so as to realize the integrity verification and traceability of circulation trajectory. Finally, the security analysis of the proposed model can resist wireless communication eavesdropping and tampering attacks, attribute inference attack and identity impersonation attack. Simulation experiments are carried out on Fabric and lightweight blockchain systems to compare and analyze the performance consumption of calculating the root of trust, blockchain storage and query. The experimental results show that the performance of the proposed lightweight blockchain is 1666 TPS, which can meet the batch certificate storage requirements in the Internet of Things scenario.
Allocating Teams to Tasks: An Anytime Heuristic Competence-Based Approach

Many practical applications often need to form a team of agents to solve a task since no agent alone has the full set of required competencies to complete the task on time. Here we address the problem of distributing individuals in non-overlapping teams, each team in charge of a specific task. We provide the formalisation of the problem, we encode it as a linear program and show how hard it is to solve it. Given this, we propose an anytime heuristic algorithm that yields feasible team allocations that are good enough solutions. Finally, we report the results of an experimental evaluation over the concrete problem of matching teams of students to internship programs in companies.