Reviewerly/exHarmony huggingface dataset:
Beamer: Stage-Aware Coflow Scheduling to Accelerate Hyper-Parameter Tuning in Deep Learning Clusters

Training a neural network requires retraining the same model many times to search for the configuration of hyper-parameters with the best training result. It is common to launch multiple training jobs and evaluate them in stages. At the completion of each stage, jobs with unpromising configurations will be terminated and jobs with new configurations will start. Each job typically performs distributed training across multiple GPUs, and GPUs periodically synchronize their models over the network. However, model synchronizations of running jobs cause severe network congestion, significantly increasing the stage completion time (SCT) and thus the time to successfully search for the desired configuration. Existing flow schedulers are ineffective to reduce SCT since they are agnostic to training stages. In this paper, we propose a stage-aware coflow scheduling method to minimize the average SCT. In this method, an algorithm is designed to order coflows by considering stage information and then coflows are scheduled according to the order. Mathematical analysis shows that the method achieves the average SCT within 20/3 of the optimal. We implement the method in a real system called Beamer. Extensive testbed experiments and simulations show that Beamer significantly outperforms advanced network designs, such as Sincronia, FIFO-LM, and per-flow fair sharing.
Robust face recognition for occluded real‐world images using constrained probabilistic sparse network

Aiming at the occluded real-world face images across illumination, pose, expression, and resolution variations, a robust face recognition for occluded real-world images using constrained probabilistic sparse network is presented. A constrained probabilistic sparse representation network is constructed to obtain the features of all the training images from a global perspective, and the new network nodes are generated through the random combination of the training images. In the probabilistic sparse representation network, the probabilities of each class of the sparse subspace that the occluded test images individually belong to are defined and calculated. The final classifications of the test images are determined by the joint maximum probability of the network nodes. Meanwhile the second-order gradient constraint is the first introduced in the probabilistic sparse representation network. It is found that the constraint uses the adjacent pixels of the face images to obtain the local texture similarity, and further use the local texture similarity to distinguish the occlusion and non-occlusion parts. Thus the constraint can reduce the influence of the occlusion part on face recognition. Extensive experiments with the 12 existing methods on the five face databases demonstrate that the recognition rate of the proposed method is the best than the non-deep learning methods compared, and the proposed method can obtain nearly the same recognition rate with an advantage of a very less time consumption compared to the state-of-the-art deep learning methods.
Drone to Obstacle Distance Estimation Using YOLO V3 Network and Mathematical Principles

Abstract Now a days, drones are very commonly used in various real time applications. Moving towards autonomy, these drones rely on obstacle detection sensors and various collision avoidance algorithms programmed into it. Development of fully autonomous drones provide the fundamental benefits of being able to operate in hazardous environments without a human pilot. Among the various sensors, monocular cameras provide a rich source of information and are one of the main sensing mechanisms in low flying drones. These drones can be used for rescue and search operations, traffic monitoring, infrastructure, and pipeline inspection, and in construction sites. In this paper, we propose an onboard obstacle detection model using deep learning techniques, combined with a mathematical approach to calculate the distance between the detected obstacle and the drone. This when implemented does not need any additional sensor or Global Positioning Systems (GPS) other than the vision sensor.
Analysis of Anomaly Detection and Identification Methods in 5G Traffic

Anomaly detection is an important task in 5G technology and many other areas of human life. In this paper, statistical methods of data analysis, such as survival analysis, time series analysis (fractal), classification method (decision trees), cluster analysis (DBSCAN), entropy method were chosen to detect anomalies. A description of the selected methods is given. Simulation modeling of these methods on the example of telecommunication network traffic was performed. As anomalies were chosen DDoS attacks, UDP-flood, TCP SYN, ARP attacks and HTTP flood. A comparative analysis of the performance of these methods for the identification of anomalies (attacks) on the probability of detecting anomalies, the probability of false positive detection, the running time of each method to detect the anomaly. Experimental results showed that the decision tree method is the best in all comparison parameters. The entropy analysis method is slightly slower, and it gives slightly more false positives. This is followed by the cluster analysis method, which is slightly worse at identifying anomalies. Then the fractal analysis method showed a lower probability of identifying anomalies, a higher probability of false positives and a longer running time. The worst was the survival analysis method.
A Statistical Model to Assess the Team’s Productivity in Agile Software Teams

Agile methods promise to achieve high productivity and provide high-quality software. Agile software development is the most important approach that has spread through the world of software development over the past decade. Software team productivity measurement is essential in agile teams to increase the performance of software development. Due to the prevalence of agile methodologies and increasing competition of software development companies, software team productivity has become one of the crucial challenges for agile software companies and teams. Awareness of the level of team productivity can help them to achieve better estimation results on the time and cost of the projects. However, to measure software productivity, there is no definitive solution or approach whether in traditional and agile software development teams that lead to the occurrence of many problems in achieving a reliable definition of software productivity. Hence, this study aims to propose a statistical model to assess the team’s productivity in agile teams. A survey was conducted with forty software companies and measured the impact of six factors of the team on productivity in these companies. The results show that team effectiveness factors including inter-team relationship, quality conformance by the team, team vision, team leader, and requirements handled by the team had a significant impact on the team’s productivity. Moreover, the results also state that inter-team relations affect the most on software teams’ productivity. Finally, the model fit test showed that 80% of productivity depends on team effectiveness factors.
A Remote Calibration Device Using Edge Intelligence

Power system facility calibration is a compulsory task that requires in-site operations. In this work, we propose a remote calibration device that incorporates edge intelligence so that the required calibration can be accomplished with little human intervention. Our device entails a wireless serial port module, a Bluetooth module, a video acquisition module, a text recognition module, and a message transmission module. First, the wireless serial port is used to communicate with edge node, the Bluetooth is used to search for nearby Bluetooth devices to obtain their state information and the video is used to monitor the calibration process in the calibration lab. Second, to improve the intelligence, we propose a smart meter reading method in our device that is based on artificial intelligence to obtain information about calibration meters. We use a mini camera to capture images of calibration meters, then we adopt the Efficient and Accurate Scene Text Detector (EAST) to complete text detection, finally we built the Convolutional Recurrent Neural Network (CRNN) to complete the recognition of the meter data. Finally, the message transmission module is used to transmit the recognized data to the database through Extensible Messaging and Presence Protocol (XMPP). Our device solves the problem that some calibration meters cannot return information, thereby improving the remote calibration intelligence.
Graph Based Gaussian Processes on Restricted Domains

Abstract In nonparametric regression, it is common for the inputs to fall in a restricted subset of Euclidean space. Typical kernel-based methods that do not take into account the intrinsic geometry of the domain across which observations are collected may produce sub-optimal results. In this article, we focus on solving this problem in the context of Gaussian process (GP) models, proposing a new class of Graph Laplacian based GPs (GL-GPs), which learn a covariance that respects the geometry of the input domain. As the heat kernel is intractable computationally, we approximate the covariance using finitely-many eigenpairs of the Graph Laplacian (GL). The GL is constructed from a kernel which depends only on the Euclidean coordinates of the inputs. Hence, we can benefit from the full knowledge about the kernel to extend the covariance structure to newly arriving samples by a Nyström type extension. We provide substantial theoretical support for the GL-GP methodology, and illustrate performance gains in various applications.
MOOC Vocal Music Blended Teaching Mode and Digital Resource Platform Design in the Information Age

Due to the application of computer technology and network technology, the learning speed is getting faster and faster. From the era of digital processing to microcomputers, then to networking, blended teaching mode will surely become the mainstream mode. Educational resources must meet the digital, networked, mobile, and convenient living habits and teaching needs of the information age. Based on the principles and methods of information technology, this paper designs the MOOC vocal blended teaching process consisting of “pre-class online learning, in-class knowledge explanation and after-class review consolidation”; this paper designs the MOOC digital resource management platform composed of “infrastructure layer, support platform layer, software functional layer and user access layer”, to provide the complete solution for the MOOC vocal blended teaching mode in the information age.
Mixed Reality System for Teaching and Learning: A Systematic Literature Review

Mixed reality is becoming more popular, and a lot of technology companies trying to develop and enhance mixed reality technology and using it not just in manufactures or industries but lately the technology is used for teaching and learning, either in school or for training purposes. While traditional teaching and learning processes are not effective anymore and most of the time, the learning objectives are not reached. This paper has an objective to point out the benefits of using mixed reality for teaching and learning. 99 articles have been collected and reviewed carefully as a quality assessment process, there are 41 articles has been selected for further review and top 5 benefits will be discussed. This review informs educators and scholars that there are common benefits, and the most popular benefit is better learning engagement.
Task-Oriented User Evaluation on Critiquing-Based Recommendation Chatbots

Dialogue-based conversational recommender systems (DCRSs) have become a new trend in recommender systems (RSs), allowing users to communicate with the system in natural language to facilitate feedback provision and product exploration. However, little work has been done to empirically study user perception of and interaction with such systems and, more importantly, how to best support users in providing feedback on the recommendation they receive. In this article, we aim to develop effective <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">critiquing</i> mechanisms for DCRS to improve its feedback elicitation process (i.e., allowing users to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">critique</i> the current recommendation during the dialogue). Specifically, we have implemented three prototype systems featuring three different critiquing techniques, respectively, i.e., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">user-initiated critiquing, progressive system-suggested critiquing</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cascading system-suggested critiquing</i> . We have then conducted two task-oriented user studies involving 292 subjects to evaluate the three prototypes. In particular, we consider two typical types of user tasks in RSs: basic recommendation task (BRT, i.e., looking for items according to the user’s preferences), and exploration-oriented task (EOT, i.e., exploring different types of items). Results show that EOT stimulates more user interaction, while BRT results in higher user satisfaction. Moreover, when users perform EOT, the type of critiquing techniques is more likely to influence user perception and moderate the relationships between certain interaction metrics and users’ perceived serendipity. The findings suggest effective critiquing techniques to enhance the interaction between users and the recommendation chatbot when the system makes recommendations for different purposes.
Free-Roam Virtual Reality: A New Avenue for Gaming

Recent coronavirus pandemic has restricted people’s movement. This has resulted in people spending more time indoors, and quite often, in pursuits like streaming and/or gaming. Traditional gaming often requires the users to sit for hours in front of a screen. This makes the practice unhealthy, and at the same time, makes it relatively less social. Free-roam gaming is a new form of gaming that utilizes virtual reality (VR) techniques to create an environment in which the user feels present. In free-roam gaming, a camera captures users’ movements and converts into gaming actions in the game. Generally such free-roam games are conducted in large spaces to allow for free movement, and thus, allow a group of users to play the same simultaneously. This chapter explores the free-roam VR games via a case study of Zero Latency and the potential of such VR games in dealing with the isolation caused by the coronavirus pandemic.
Network Embedding-Based Approach for Detecting Collusive Spamming Groups on E-Commerce Platforms

Information security is one of the key issues in e-commerce Internet of Things (IoT) platform research. The collusive spamming groups on e-commerce platforms can write a large number of fake reviews over a period of time for the evaluated products, which seriously affect the purchase decision behaviors of consumers and destroy the fair competition environment among merchants. To address this problem, we propose a network embedding based approach to detect collusive spamming groups. First, we use the idea of a meta-graph to construct a heterogeneous information network based on the user review dataset. Second, we exploit the modified DeepWalk algorithm to learn the low-dimensional vector representations of user nodes in the heterogeneous information network and employ the clustering methods to obtain candidate spamming groups. Finally, we leverage an indicator weighting strategy to calculate the spamming score of each candidate group, and the top-k groups with high spamming scores are considered to be the collusive spamming groups. The experimental results on two real-world review datasets show that the overall detection performance of the proposed approach is much better than that of baseline methods.
A Parallel Corpora for bi-directional Neural Machine Translation for Low Resourced Ethiopian Languages

In this paper, we described an effort towards the development of parallel corpora for English and Ethiopian Languages, such as Wolaita, Gamo, Gofa, and Dawuro neural machine translation. The corpus is collected from the religious domain and to check the usability of the collected parallel corpora a bi-directional Neural Machine Translation experiments were conducted. The neural machine translation shows good results as a baseline experiment of BLEU score of 13.8 in Wolaita-English and 8.2 English-Wolaita machine translation. The Wolaita-English translation shows a better result than the other pairs of Ethiopian languages and the result of neural machine translation performs well when the amount of dataset increases, thus the amount of dataset has a great impact on the performance. Besides these, the morphological richness of Ethiopian language contributed to the low performance of neural machine translation when the Ethiopian language is used as the target language. Further, we are working on minimizing the effect of morphological richness through different morphological processing techniques in the translation of Ethiopian languages.
Simultaneous measurement of elastic constants from dynamic mechanical analysis with digital image correlation

Dynamic mechanical analysis (DMA) method is used for measuring viscoelastic properties of materials. The governing equations used for calculating modulus neglect shear deflection component and use an assumption of constant Poisson's ratio in absence of methods that can conduct full field deformation measurement. The present work is focused on using an in-situ imaging method to obtain the full field displacement during the specimen deformation. Digital image correlation is used to calculate the values of Poisson's ratio over temperature and strain to implement a correction factor in the complex moduli calculations. The method developed in this work will provide accurate measurements of viscoelastic properties of polymers, especially for materials whose properties are very sensitive to Poisson's ratio values and temperature variations. • In-situ imaging and Digital image correlation are used to obtain Poisson's ratio. • The complex and bulk moduli are calculated with respect to varying Poisson's ratio. • The method developed can help in accurately characterizing soft materials.
Best-Practice Aspects of Quantum-Computer Calculations: A Case Study of the Hydrogen Molecule

Quantum computers are reaching one crucial milestone after another. Motivated by their progress in quantum chemistry, we performed an extensive series of simulations of quantum-computer runs that were aimed at inspecting the best-practice aspects of these calculations. In order to compare the performance of different setups, the ground-state energy of the hydrogen molecule was chosen as a benchmark for which the exact solution exists in the literature. Applying the variational quantum eigensolver (VQE) to a qubit Hamiltonian obtained by the Bravyi-Kitaev transformation, we analyzed the impact of various computational technicalities. These included (i) the choice of the optimization methods, (ii) the architecture of the quantum circuits, as well as (iii) the different types of noise when simulating real quantum processors. On these, we eventually performed a series of experimental runs as a complement to our simulations. The simultaneous perturbation stochastic approximation (SPSA) and constrained optimization by linear approximation (COBYLA) optimization methods clearly outperformed the Nelder-Mead and Powell methods. The results obtained when using the Ry variational form were better than those obtained when the RyRz form was used. The choice of an optimum entangling layer was sensitively interlinked with the choice of the optimization method. The circular entangling layer was found to worsen the performance of the COBYLA method, while the full-entangling layer improved it. All four optimization methods sometimes led to an energy that corresponded to an excited state rather than the ground state. We also show that a similarity analysis of measured probabilities can provide a useful insight.
Hybrid lightning protocol: An approach for blockchain scalability issue

Blockchain is the latest disruptive innovation. It is the underlying technology of cryptocurrency. However, the facts show that most cryptocurrencies are stuck in big issues such as scalability issues, resulting in longer transaction times. A two-layer solution was offered in previous studies to solve the scalability problem, but many issues emerged from this idea. Some of the 2 Layer proposals use the Proof of Work (PoW) protocol model, and some other studies use the Proof of Stake (PoS) protocol model. In this study, the authors propose the Hybrid Lightning Protocol to solve the scalability problem of Blockchain networks by combining the Proof of Work (PoW) and Proof of Stake (PoS) protocols without sharding. The author analyzes and designs the development of the Blockchain protocol based on previous research. This research is a Blockchain protocol model that can achieve a maximum throughput of around 1,668,000 transactions/second on a 2 Gbps Fiber network, which can reduce cryptocurrency volatility and shorten transaction times by solving scalability problems.
Effective Text Augmentation Strategy for NLP Models

Data augmentation effectively increases variance in training data resulting in increased accuracy and generalization in deep learning tasks. Augmentation of text data requires careful implementation so as to avoid text attacks. This paper presents a novel strategy for augmentation of text data in a meaningful way leading to improved accuracy, as well as provides a baseline model for comparison. The proposed strategy uses a mixed of pre- and post-augmentation utilizing four operations—random swap, random deletion, back-translation, random synonym insertion, in two different settings on a classification model based on recurrent neural networks. Experimental results on Apple Twitter Sentiment Dataset reveal that the proposed method achieves an increased accuracy of 3.29%, which is a significant improvement on datasets with limited training data.