Reviewerly/exHarmony huggingface dataset:
Homomorphic sensing of subspace arrangements

Homomorphic sensing is a recent algebraic-geometric framework that studies the unique recovery of points in a linear subspace from their images under a given collection of linear maps. It has been successful in interpreting such a recovery in the case of permutations composed by coordinate projections, an important instance in applications known as unlabeled sensing, which models data that are out of order and have missing values. In this paper, we provide tighter and simpler conditions that guarantee the unique recovery for the single-subspace case, extend the result to the case of a subspace arrangement, and show that the unique recovery in a single subspace is locally stable under noise. We specialize our results to several examples of homomorphic sensing such as real phase retrieval and unlabeled sensing. In so doing, in a unified way, we obtain conditions that guarantee the unique recovery for those examples, typically known via diverse techniques in the literature, as well as novel conditions for sparse and unsigned versions of unlabeled sensing. Similarly, our noise result also implies that the unique recovery in unlabeled sensing is locally stable.
A Dynamic Time-Evolution Control Method to Improve the Performance of Optoelectronic Coherent Ising Machine

A simple dynamic time-evolution control method is proposed and experimentally demonstrated in an opto-electronic-oscillator based coherent Ising machine. The experimental results show significant performance improvement can be realized using this method at different parameter settings.
Distributed Weighted Least Squares Estimator Without Prior Distribution Knowledge

This paper concerns with a distributed state estimation problem, where all sensor nodes are required to achieve a consensus estimation. The weighted least squares (WLS) estimator is a promising way to tackle this problem since it does not need the prior distribution knowledge with respect to the estimated quantity and noise terms. To this end, the equivalent relation between the information filter and the WLS estimator is explored first. Following this, an optimization problem coupled with a consensus constraint is established. Finally, the consensus-based distributed WLS problem is handled by the alternating direction method of multiplier. The convergence and consensus estimations between nodes are tested by numerical simulations and theoretical analyses.
Automatic, Illumination-Invariant and Real-Time Green-Screen Keying Using Deeply Guided Linear Models

The conventional green screen keying method requires users’ interaction to guide the whole process and usually assumes a well-controlled illumination environment. In the era of “we-media”, millions of short videos are shared online every day, and most of them are produced by amateurs in relatively poor conditions. As a result, a fully automatic, real-time, and illumination-robust keying method would be very helpful and commercially promising in this era. In this paper, we propose a linear model guided by deep learning prediction to solve this problem. The simple, yet effective algorithm inherits the robustness of the deep-learning-based segmentation method, as well as the high matting quality of energy-minimization-based matting algorithms. Furthermore, thanks to the introduction of linear models, the proposed minimization problem is much less complex, and thus, real-time green screen keying is achieved. In the experiment, our algorithm achieved comparable keying performance to the manual keying software and deep-learning-based methods while beating other shallow matting algorithms in terms of accuracy. As for the matting speed and robustness, which are critical for a practical matting system, the proposed method significantly outperformed all the compared methods and showed superiority over all the off-the-self approaches.
Towards Straggler-Tolerant and Accuracy-Aware Distributed DNN Training in Clouds

This study investigated how straggler mitigation affects accuracy during distributed training. While distributed training is one promising way to shorten training time, we cannot obtain a maximum performance improvement if some workers become stragglers due to a slowdown. We can avoid the performance impact from stragglers by excluding them from training. However, we face another problem of decreasing accuracy during training because gradients computed by excluded stragglers are not reflected in the training. To achieve both straggler tolerance and accuracy awareness, we should implement distributed training so that excluded stragglers return to the training once they recover from slowdown. Although some implementations supporting auto-scaling have been created, they have not evaluated the training accuracy during scale-in to exclude stragglers from training and scale-out to return them to training. Therefore, we conducted experiments to see how excluding stragglers causes accuracy degradation. We also discussed how the exclusion time and training data assignment can have an impact on accuracy during training.
Design of Immersive and Interactive Application Based on Augmented Reality and Machine Learning

In view of the current situation of information expression in passive two-dimensional display space, from the perspective of improving users’ willingness to use space, Augmented Reality technology is integrated with virtual space containing rich information, supplemented by Machine Learning to construct an immersive environment with interactive function. Under the integrated development environment of Unity3D, Vuforia, ARCore and Arm NN are adopted to integrate virtual information with markers, and applications with functions of image recognition, plane recognition, object recognition and command interaction are designed. The design scheme is introduced, and the realization processes are discussed in detail. Test shows that the applications provide rich information with three-dimensional virtual-fusion display and natural way of interacting, which bring space immersion and good interactive experience to users. The immersive and interactive space improves the interactive experience and strengthens the effect of information transmission, which could be further applied to other fields.
Spam E-mail Identification Using Blockchain Technology

Over the past few years, there has been an alarming rise in the cyber attacks which are being carried out by Social Engineering Technique. Email is widely used for communication purposes and thus Spam Email Attacks are found to be the most common social engineering technique used by attackers to intrude into the system and perform malicious operations. Almost 85% of the overall email traffic is found to be spam (122.3 billion spam e-mails transmitted per day), which causes severe damage like, data loss, account compromise, ransomware attack, malware infection into the organization/personal systems. Various Artificial Intelligence-based methods (based on reviewing the content of an email) are created to identify spam emails, still, the count of hacks and loss due to spam emails is increasing on daily basis. On the other hand, blockchain being one of the cutting edges and disruptive technology has gained attention in the past few years. In this literature, authors have proposed a blockchain-based system to counter, prevent and identify spam emails. Authors have integrated the wallet-to-wallet transaction in Ethereum, with an existing email system to identify spam and legitimate email. The proposed framework is not based on verifying the content of an email at mailing servers instead the server verifies or check if or not the Cryptocurrency is paid, and thus the proposed framework is supposed to have small workloads and better performance throughput in terms of sending and receiving emails. The authors were able to create a Proof of Concept of the proposed methodology. All the endpoints created to achieve the same were executed in less than 1.5 sec of Elapse Time Average. The proposed framework will act as a Single Source of truth in identifying the Spam E-Mail.
How Frustrated Are You? User Perception About Different Videoconference Quality Degradations

During the COVID-19 crisis in 2020, most of the usual activities, such as attending business meetings, classes at schools, and universities, as well as social gatherings with friends, colleagues, and family, took place on various online platforms. This migration has led to a globally significant increase in the number of users of videoconferencing applications and, thus, a significant increase in the share of video traffic in total Internet traffic. Encouraged by such a situation, we became curious and motivated to explore user experiences and practices when using these online meeting platforms. This paper brings the first results of a survey conducted on a sample of 541 respondents. There are three main result categories which we discussed in the paper: (a) characteristics of a user environment from where the conferences are most often accessed, (b) description of a typical online meeting use-case scenario, and (c) user perception about different quality degradations, which may occur during the meeting. One of the interesting findings we unraveled is that user quality of experience for videoconferencing service varies depending on their role in the meeting.
Exploiting Physical Presence Sensing to Secure Voice Assistant Systems

Voice Assistant System (VAS) provides a convenient way for users to interact with smart-home devices via a voice interface. However, it raises unique security issues, including voice replay and injection attacks, where attackers remotely and maliciously control the smart-home devices via a voice interface. In this paper, we consider a typical smart-home scenario in which a VAS device and a compromised speaker device are placed in close physical proximity. The attacker can remotely play malicious voice commands through the speaker device to manipulate the VAS device for malicious purposes. We propose a defense system on the VAS device to secure the VAS device against both voice replay and injection attacks, without any additional devices and without any extra user effort. Specifically, our system aims to collect voice data and wireless data continuously from the VAS device and then extracts the Mel-Cepstral Frequency Coefficients (MFCC) features from voice and wireless data. We consider that both voice and wireless data are affected by the same present users’ physical activities, and the correlation can be used to detect the attacks. Finally, our system applies a deep learning model that learns from previous time-series data and analyzes real-time data to infer whether the real-time voice command is generated from a user or the speaker device. We have tested our system in certain real-world smart-home scenarios. Our experiments showed that the proposed system has a probability between 76.4% to 89.1% to successfully detect the voice replay and injection attacks in the considered scenarios.
Online quantum time series processing with random oscillator networks

Reservoir computing is a powerful machine learning paradigm for online time series processing. It has reached state-of-the-art performance in tasks such as chaotic time series prediction and continuous speech recognition thanks to its unique combination of high computational power and low training cost which sets it aside from alternatives such as traditionally trained recurrent neural networks, and furthermore is amenable to implementations in dedicated hardware, potentially leading to extremely compact and efficient reservoir computers. Recently the use of random quantum systems has been proposed, leveraging the complexity of quantum dynamics for classical time series processing. Extracting the output from a quantum system without disturbing its state too much is problematic however, and can be expected to become a bottleneck in such approaches. Here we propose a reservoir computing inspired approach to online processing of time series consisting of quantum information, sidestepping the measurement problem. We illustrate its power by generalizing two paradigmatic benchmark tasks from classical reservoir computing to quantum information and introducing a task without a classical analogue where a random system is trained to both create and distribute entanglement between systems that never directly interact. Finally, we discuss partial generalizations where only the input or only the output time series is quantum.
Memetic Differential Evolution Using Coordinate Descent

Differential Evolution (DE) is one of the well-established population-based optimization algorithms which has received a lot of attention regarding its potential to solve complex optimization problems. However, DE is capable to explore a huge search space in its early run phase, called exploration phase, its weakness in exploitation avoids local refinement of the promising shrunk region. Therefore, employing a local search can be an efficient strategy to improve the search performance of DE via accelerating of fine tuning phase. This paper purposes an effective Memetic DE algorithm using a well-known single-solution-based optimization method, i.e., Coordinate Descent (CD) algorithm. Local coordinate search is applied on the promising region resulted by top ranked individuals selected from the final population of DE. The proposed method updates the value of each coordinate iteratively by evaluating the sampled points from the local region to improve the resulted candidate solution. Since coordinate search algorithm shrinks the region rapidly, it requires a very small portion of the computational budget to find the optimal coordinates' value. In order to evaluate the proposed Memetic DE, several experiment series are conducted on functions of CEC-2017 benchmark for different number of dimensions (i.e., D=30, 50, and 100). Results clearly indicate that the utilized local coordinate search improves the quality of resulted solution by DE significantly using a very low computational budget, i.e., 20×D.
Two-Stage Genetic Algorithm for Designing Long Short Term Memory (LSTM) Ensembles

Long Short Term Memory (LSTM) is a special kind of Recurrent Neural Networks popularly used in various applications. However, using a single LSTM is often not enough to attain reliable performance on complicated machine learning tasks. This is because LSTM is sensitive to the specifics of the training data. Ensemble learning is a promising approach to improve the performance of LSTMs on complicated tasks. However, it is difficult to design an ensemble of LSTMs. LSTMs that constitute the ensemble should be both accurate and diverse. This paper proposes a new two-phase evolutionary algorithm to design ensembles. The first phase is to evolve best performing LSTMs automatically. A connection weight inheritance approach is used in the first phase to improve the effectiveness and efficiency of the evolutionary process. The second phase is to design ensembles by choosing suitable LSTMs without fixing the ensemble size in advance. We use bagging to train the selected LSTMs to build the ensemble to achieve good diversity among the LSTMs. The proposed approach is evaluated on various classification tasks. The results show the effectiveness of the proposed approach and its significant improvement in performance over many state-of-the-art machine learning models. The results also show the efficiency of the proposed approach in comparison with the baseline algorithm.
Enhancing Teacher-Students’ Digital Competence with Educational Robots

The digital competence of an average teacher is still behind the level needed for teaching in technology-rich classrooms. The teacher-students need skills and knowledge for enhancing their lesson plans and activities with technology. Educational robots can be used to improve teacher-students’ digital competence to support their problem solving, communication, creativity, collaboration, and critical thinking skills. Together with STEAM kits, educational robots provide good opportunities to teach subjects like mathematics, chemistry, biology, and physics. The aim of this case study is to find if a robot-enhanced training course encourages development of teachers-students’ digital competence, and which educational robots or STEAM kits do teacher-students prefer to use in learning activities. We used European Digital Competence Framework for Educators to assess teacher-students’ competence in four areas (digital resources, teaching and learning, empowering learners, facilitating learners’ digital competence) before and after a robot-enhanced training course. Our results indicate that activities with educational robots (hands-on experiments and designing innovative lesson plans) supported and developed teacher-students’ digital competence. As a conclusion we recommend using educational robots or other STEAM kits when developing teachers’ digital competence.
Simpson’s Paradox, Visual Displays, and Causal Diagrams

Simpson’s paradox has been known for years and can arise in a wide variety of settings. When data exhibit Simpson’s paradox, we are faced with the question of whether to use aggregated data or conditional data. In this article, we present ways to graph data to better understand the paradox and the use of causal diagrams as a tool for deciding whether or not to use conditional data.
Instance Segmentation, Body Part Parsing, and Pose Estimation of Human Figures in Pictorial Maps

In recent years, convolutional neural networks (CNNs) have been applied successfully to recognise persons, their body parts and pose keypoints in photos and videos. The transfer of these techniques to artificially created images is rather unexplored, though challenging since these images are drawn in different styles, body proportions, and levels of abstraction. In this work, we study these problems on the basis of pictorial maps where we identify included human figures with two consecutive CNNs: We first segment individual figures with Mask R-CNN, and then parse their body parts and estimate their poses simultaneously with four different UNet++ versions. We train the CNNs with a mixture of real persons and synthetic figures and compare the results with manually annotated test datasets consisting of pictorial figures. By varying the training datasets and the CNN configurations, we were able to improve the original Mask R-CNN model and we achieved moderately satisfying results with the UNet++ versions. The extracted figures may be used for animation and storytelling and may be relevant for the analysis of historic and contemporary maps.
A joint energy efficiency optimization scheme based on marginal cost and workload prediction in data centers

With the widespread development of cloud computing, the dramatic increase in the number and size of data centers (DCs) has resulted in substantial energy consumption and serious environmental problems. This creates a challenge for the further development of DCs. It is imperative to improve the overall energy efficiency of DCs. According to the statistics, servers and cooling systems are the main energy-consuming components of DCs. Recently, several energy-efficient strategies have been developed to address these problems. However, most of these works only consider the energy optimization of servers or cooling systems separately. Therefore, a Joint Energy Efficiency Optimization Scheme (JEES) is proposed in this paper, where the energy consumed by servers and the cooling system is jointly considered, and coordinately optimized. JEES includes a dynamic online task scheduling algorithm based on marginal cost evaluation, a resource management strategy that integrates the workload prediction technique to manage resources, and a task migration method using marginal cost evaluation. By using the proposed techniques, the total energy consumption of DCs can be reduced. Extensive experiments have been conducted based on real-world workload traces, and the results demonstrate that compared with other techniques, the proposed scheme effectively improves the overall resource utilization and reduces the total energy consumption of DCs.
Search-Free Inference Acceleration for Sparse Convolutional Neural Networks

Sparse convolution neural networks (CNNs) are promising in reducing both memory usage and computational complexity while still preserving high inference accuracy. State-of-the-art sparse CNN accelerators can deliver high throughput by skipping zero weights and/or activations. To operate on only nonzero weights and activations, sparse accelerators typically search pairs of nonzero weights and activations for multiplication-accumulation (MAC) operations. However, the conventional search operation results in a severe limitation in the processing element (PE) array scale because of the enormous demands of internal interconnection and memory bandwidth. In this article, we first provide a design principle to free the search process of sparse CNN accelerations. Specifically, the indexes of the static compressed weights access the dynamic activations directly to avoid the search process for MAC operations. We then develop two search-free inference accelerators, called Swan and Swan-flexible, for sparse CNN accelerations. Swan supports search-free sparse convolution accelerations for interconnection and bandwidth saving. Compared with Swan, Swan-flexible not only has the search-free capability but also comprises a configurable architecture for optimum throughput. We formulate a mathematical optimization problem by combining the configurable characterization with the compressive dataflow to optimize the overall throughput. Evaluations based on a place-and-route process show that the proposed designs, in a compact factor of 4096 PEs, achieve 1.5– <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.7\times $ </tex-math></inline-formula> higher speedup and 6.0– <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$13.6\times $ </tex-math></inline-formula> better energy efficiency than representative accelerator baselines with the same PE array scale.
A Quick Review of ML Algorithms

Machine Learning is considered to be an offset of artificial intelligence that is associated with the design and development of algorithms that will make computers to learn behaviors based on the given data. In today's world, Machine Learning finds application in all streams, be it medical, academics, automobile and so on. Henceforth, this research work has attempted to make the readers understand thoroughly all the different machine learning algorithms ranging from classification till reinforcement learning. To name some of them are Supervised Learning, Unsupervised Learning, Semi-supervised Learning and Reinforcement Learning.