Reviewerly/exHarmony huggingface dataset:
A3DQN: Adaptive Anderson Acceleration for Deep Q-Networks

Reinforcement learning (RL) has been used for an agent to learn efficient decision-making strategies through its interactions with an environment. However, slow convergence and sample inefficiency of RL algorithms make them impractical for complex real-world problems. In this paper, we present an acceleration scheme, called Anderson acceleration (AA), for RL, where the value function in the next iteration is calculated using a linear combination of value functions in the previous iterations. Since the original AA method suffers from instability, we consider adaptive Anderson acceleration (A3) as a stabilized variant of AA, which contains both adaptive regularization to handle instability and safeguarding to enhance performance. We first apply A3 to value iteration for Q-functions and show its convergence property. To extend the idea of A3 to model-free deep RL, we devise a simple variant of deep Q-networks (DQN). Our experiments on the Atari 2600 benchmark demonstrate that the proposed method outperforms double DQN in terms of both final performance and learning speed.
Double-Adversarial Activation Anomaly Detection: Adversarial Autoencoders are Anomaly Generators

Anomaly detection is a challenging task for machine learning algorithms due to the inherent class imbalance. It is costly and time-demanding to manually analyse the observed data, thus usually only few known anomalies if any are available. Inspired by generative models and the analysis of the hidden activations of neural networks, we introduce a novel unsupervised anomaly detection method called DA3D. Here, we use adversarial autoencoders to generate anomalous counterexamples based on the normal data only. These artificial anomalies used during training allow the detection of real, yet unseen anomalies. With our novel generative approach, we transform the unsupervised task of anomaly detection to a supervised one, which is more tractable by machine learning and especially deep learning methods. DA3D surpasses the performance of state-of-the-art anomaly detection methods in a purely data-driven way, where no domain knowledge is required.
A Novel Approach of Resource Scheduling Algorithm to Improve QoS in Green Cloud Computing

The employments of approaches in Green Cloud Computing are emerald use, green design, green manufacturing, and green removal. Green Cloud Computing is, for the most part, used to secure condition, upgrade energy utilization and keeps Green condition. Expanding vitality effectiveness and lessening the utilization of risky materials. Reduce the essentialness usage from green processing frameworks changed over into lesser carbon dioxide surges, starting from a decline in the non-sustainable power basis used in power plant life and shipping. Green enrolling can urge us to ensure, secure spot and sound condition all over on the planet. The proposed algorithm of Green Resource Usage Distributor Scheduling (GRUD-S) is implemented; surely this is to improve the average supply consumption and uniformly to reduce power consumption. And this algorithm is also used to calculate the resource cost based on the completion of job requests among the nodes. Based on the temperature interpreter value the allocation of VM to the Multi-core Physical Machines and comparatively low temperature than single-core machines. This paper will help us with taking a couple of exercises right now under in the field of PCs/devices industry and better ways to deal with save huge proportions of imperativeness which is wasted in huge extension.
Reduced-Dimension Filtering in Triplet Markov Models

This article presents an optimal reduced-dimension Kalman filter for a family of triplet Markov models (TMMs). The problem is to estimate the state vector in the case when the auxiliary process in the TMM can be eliminated. Sufficient conditions for this elimination to be feasible are established and we give a selection of illustrative real-life TMM examples, where these conditions are satisfied. We subsequently show that the original TMM boils down to a pairwise Markov model (PMM) of second order. Then, we derive a new optimal Kalman filter applicable to any linear PMM of second order. Our numerical results confirm that the proposed estimator can provide substantial complexity reduction with either no or minor accuracy loss, depending on the use of model approximation.
A Lazy Approach to Long-Horizon Gradient-Based Meta-Learning

Gradient-based meta-learning first trains task-specific models by an inner loop and then backpropagates meta-gradients through the loop to update the meta-model. To avoid high-order gradients, existing methods either take a small number of inner steps or approximate the meta-updates for the situations that the meta-model and task models lie in the same space. To enable long inner horizons for more general meta-learning problems, we instead propose an intuitive teacher-student strategy. The key idea is to employ a student network to adequately explore the search space of task-specific models, followed by a teacher’s "leap" toward the regions probed by the student. The teacher not only arrives at a high-quality model but also defines a lightweight computational graph for the meta-gradients. Our approach is generic; it performs well when applied to four meta-learning algorithms over three tasks: few-shot learning, long-tailed object recognition, and adversarial blackbox attack.
Context-Aware Multi-Task Learning for Traffic Scene Recognition in Autonomous Vehicles

Traffic scene recognition, which requires various visual classification tasks, is a critical ingredient in autonomous vehicles. However, most existing approaches treat each relevant task independently from one another, never considering the entire system as a whole. Because of this, they are limited to utilizing a task-specific set of features for all possible tasks of inference-time, which ignores the capability to leverage common task-invariant contextual knowledge for the task at hand. To address this problem, we propose an algorithm to jointly learn the task-specific and shared representations by adopting a multi-task learning network. Specifically, we present a lower bound for the mutual information constraint between shared feature embedding and input that is considered to be able to extract common contextual information across tasks while preserving essential information of each task jointly. The learned representations capture richer contextual information without additional task-specific network. Extensive experiments on the large-scale dataset HSD demonstrate the effectiveness and superiority of our network over state-of-the-art methods.
Hirschman–Widder densities

Hirschman and Widder introduced a class of Pólya frequency functions given by linear combinations of one-sided exponential functions. The members of this class are probability densities, and the class is closed under convolution but not under pointwise multiplication. We show that, generically, a polynomial function of such a density is a Pólya frequency function only if the polynomial is a homothety, and also identify a subclass for which each positive-integer power is a Pólya frequency function. We further demonstrate connections between the Maclaurin coefficients, the moments of these densities, and the recovery of the density from finitely many moments, via Schur polynomials.
FlowCaps: Optical Flow Estimation with Capsule Networks For Action Recognition

Capsule networks (CapsNets) have recently shown promise to excel in most computer vision tasks, especially pertaining to scene understanding. In this paper, we explore CapsNet's capabilities in optical flow estimation, a task at which convolutional neural networks (CNNs) have already outperformed other approaches. We propose a CapsNet-based architecture, termed FlowCaps, which attempts to a) achieve better correspondence matching via finer-grained, motion-specific, and more-interpretable encoding crucial for optical flow estimation, b) perform better-generalizable optical flow estimation, c) utilize lesser ground truth data, and d) significantly reduce the computational complexity in achieving good performance, in comparison to its CNN-counterparts.
Designing and using digital platforms for 21st century learning

This paper is written in response to the original article "Designing for 21stcentury learning online: A heuristic method to enable educator learning support roles" (Nacu et al. 2018). The article presents a guide for teacher interactions with students in networked technologies. It also serves as a guide for network designers who are concerned about the quality of online learning, and want more equitable access to systems that inspire young people "to pursue their interests and take ownership of their learning" (Nacu et al. 2018, p. 1029). During the Covid-19 pandemic, network platforms assumed a central role in the educational process. The article is a timely reminder of the importance of designing and implementing platforms that will address twenty-first century learning goals. This response to the Nacu et al. article reviews their research from a "theory of change" perspective. The authors in the original article present a heuristic that expands the online roles that educators currently use to support young people in building their knowledge. To realize the full potential of the heuristic, a more holistic approach is required, one that repositions online learning as a knowledge-building environment. A theory of change could identify the critical variables needed to help teachers and designers transition to a more comprehensive understanding of online learning. Unfortunately, heuristics by themselves do not prepare network designers or teachers to do this work. Future research can embed the heuristic in a broader, deeper effort to prepare teachers and designers to enact student-centered online learning environments.
Deep Learning based Emotion Recognition IoT System

Recently, IoT systems are wildly used in various fields, and received the most attention in the research field of healthcare system. This study aims to realize an emotion recognition IoT system for helping check the people feeling and health status. In edge side, the designed system applies a motion sensor, and a RGB camera which were equipped on a compact edge device, Raspberry Pi, for detecting human motion and take the people image. When motion sensor detect motion, the RGB camera is started and image of the people is taken. Then, the face area in the image are cropped from the people image by Haar feature-based cascade classifiers. When the face is detected, deep learning model of MobileNet is applied for human identification, and the face image is sent to server for emotion recognition by deep learning. In terms of human identification models, MobileNet, which was equipped on the Raspberry Pi, has been trained previously on a GPU machine. Emotion recognition deep learning model is also trained by GPU machine and equipped on a server machine. The MobileNet is also selected as emotion recognition deep learning model, according the compassion of nine state-of-the-art deep learning models. In the experimental results, the motion sensor and RGB camera work well, and the human identification accuracy almost achieves 100%. Furthermore, face images are transferred from edge to cloud correctly, and emotion recognition also achieves a better accuracy. The experimental results also proved the effectiveness of proposed system.
A distortion-free watermarking approach for verifying integrity of relational databases

Due to high availability and easy accessibility of information, it has become quite difficult to assure security of data. Even though watermarking seems to be an effective solution to protect data, it is still challenging to be used with relational databases. Moreover, inserting a watermark in database may lead to distortion. As a result, the contents of database can no longer remain useful. Our proposed distortion-free watermarking approach ensures that integrity of database can be preserved by generating an image watermark from its contents. This image is registered with Certification Authority (CA) before the database is distributed for use. In case, the owner suspects any kind of tampering in the database, an image watermark is generated and compared with the registered image watermark. If both do not match, it can be concluded that the integrity of database has been compromised. Experiments are conducted on Forest Cover Type data set to localize tampering to the finest granularity. Results show that our approach can detect all types of attack with 100% accuracy.
A Human–Machine Interaction Scheme Based on Background Knowledge in 6G-Enabled IoT Environment

6G-Enabled Internet of Things (IoT) is about to open a new era of Internet of Everything (IoE). It creates favorable conditions for new application services. The human-machine dialogue system, one of the most important forms of human–machine interaction, is expected to replace mobile applications in the future. This article proposes a dialogue generation scheme named background knowledge-aware dialogue generation model with pretrained encoders (BKADGPE). Dialogue generation, which takes the context as input and response as output, is a sequence-to-sequence (Seq2Seq) task. Instead of only generating the response based on the previous sequence of utterances, background knowledge-aware dialogue generation is also relying on background knowledge documents. This is because people often communicate based on their background knowledge. This article divides it into two tasks: 1) a knowledge selection task and 2) a response generation task. One of the latest language pretraining models, a lite bidirectional encoder representations from transformers (ALBERT), is applied as the encoder. In the knowledge selection task, ALBERT adds the linear layer and softmax layer to predict the content-related knowledge span. In the response generation task, the ALBERT after fine-tuning through the knowledge selection task adds the left-context-only transformer with a copy mechanism to incorporate background knowledge span into the generated response. Empirical studies on the HOLL-E dataset show that the result of BKADGPE is better than the related works.
Bayesian Post-Model-Selection Estimation

Estimation after model selection refers to the problem where the exact observation model is unknown and is assumed to belong to a set of candidate models. Thus, a data-based model-selection stage is performed prior to the parameter estimation stage, which affects the performance of the subsequent estimation. In this letter, we investigate post-model-selection Bayesian parameter estimation of a random vector with an unknown deterministic support set, where this support set represents the model. First, we present different estimators, including the oracle minimum mean-squared-error (MMSE), the coherent MMSE, the selected MMSE, and the full model MMSE. Then, we develop the selective Bayesian Cramer-Rao bound (BCRB) and selective tighter BCRB, which are lower bounds on the mean-squared-error (MSE) for any coherent estimator.
Leukemia Diagnosis Based on Machine Learning Algorithms

Leukemia is brought about by the quick generation of unusual white platelets. The high number of strange white platelets are not ready to battle contamination, and they impede the capacity of the bone marrow to create red platelets and platelets. Machine Learning techniques are widely used in the diagnosis and classification of different leukemia types in the patients. In this paper, we have described the different machine learning algorithms like Support Vector Machines, k-Nearest Neighbour, Neural Networks, Naïve Bayes and Deep Learning algorithms which are used to classify leukemia into its sub-types and presented a comparative study of these algorithms.
Dijkstra monads forever: termination-sensitive specifications for interaction trees

This paper extends the Dijkstra monad framework, designed for writing specifications over effectful programs using monadic effects, to handle termination sensitive specifications over interactive programs. We achieve this by introducing base specification monads for non-terminating programs with uninterpreted events. We model such programs using interaction trees, a coinductive datatype for representing programs with algebraic effects in Coq, which we further develop by adding trace semantics. We show that this approach subsumes typical, simple proof principles. The framework is implemented as an extension of the Interaction Trees Coq library.
Two quantum private query protocols based on Bell states and single photons

This paper proposes two quantum privacy query protocols. In the single vector sub-privacy query of protocol 1, it is proposed for the first time to distinguish different measurement results by publishing 2 bits of classic information. In the protocol, the database Bob selects the Z base and the Bell base to measure the particles sent by Alice according to the key value of the encrypted database and publishes 2 bits of classic information based on the measurement results. The user Alice infers the key value of the encrypted database through these two pieces of classic information. In the two-way quantum privacy query of protocol 2, the database Bob measures the particles sent back by Alice, converts the measurement result into a key value of length 3, and prepares a particle that is the same as the measurement result and sends it back to Alice. Alice returns the measurement of particles can infer a set of data encryption keys, and the length of each key is 3 bits. Users in the two protocols proposed in the article can obtain the query key with a probability of 1/4. As the key length increases, the number of query bits in protocol 2 decreases, but the probability of execution failure increases. But protocol 2 has a higher quantum efficiency. Both protocols can guarantee the privacy of users and databases.
A data-sharing scheme that supports multi-keyword search for electronic medical records

As cloud storage technology develops, data sharing of cloud-based electronic medical records (EMRs) has become a hot topic in the academia and healthcare sectors. To solve the problem of secure search and sharing of EMR in cloud platforms, an EMR data-sharing scheme supporting multi-keyword search is proposed. The proposed scheme combines searchable encryption and proxy re-encryption technologies to perform keyword search and achieve secure sharing of encrypted EMR. At the same time, the scheme uses a traceable pseudo identity to protect the patient’s private information. Our scheme is proven secure based on the modified Bilinear Diffie-Hellman assumption and Quotient Decisional Bilinear Diffie-Hellman assumption under the random oracle model. The performance of our scheme is evaluated through theoretical analysis and numerical simulation.
Estimation of an uncertainty budget and performance measurement for a dual‐wavelength Twyman‐Green interferometer

Abstract In this paper, the uncertainty budget from 10 sources we claim they have direct impact on measurement is estimated. The measurement has been applied to nominal step heights of 1.34 and 1.50 μm using a dual‐wavelength Twyman‐Green interferometer. Experimental results show that the measured step heights match with their nominal values with expanded uncertainty in the range of 5% from the object being measured. The performance of the measurement process over time has been estimated at different confidence levels and the obtained results show that the process is maintained in a state of statistical control. To the best of our knowledge, this is the first report for estimation of uncertainty budget from a synthetic wavelength.
UAV Path Planning for Wildfire Tracking Using Partially Observable Markov Decision Process

Real-time monitoring wildfire spread is of utmost importance to the decision making of the wildfire management. Unmanned aerial vehicles (UAVs) can provide a mobile sensing platform to perform this challenging task. In this paper, a path planning algorithm is designed to enable a group of UAVs to autonomously track wildfire fronts that develop in random directions. The wildfire evolution is generated from a high-fidelity simulation model. Since the full state of the wildfire is unknown, the path planning is formulated as the partially observable Markov decision process (POMDP), which is a dynamic optimization problem and solved approximately by the computationally efficient nominal belief state optimization method. The approach can plan the path autonomously, and the UAVs are able to track any randomly spreading fire fronts through a simple linear model. In addition, the challenges such as collision avoidance and practical constraints of the control variables can be taken into account in the framework of POMDP. Comprehensive simulation results demonstrate the effectiveness of the proposed path planning algorithm.
A method for emergency response alternative decision-making under uncertainty

In this paper, an emergency decision-making method, based on case-based reasoning and cloud model, is proposed to solve the risk decision-making problem in emergency response. Case-based reasoning, by allowing the decision-maker to referring to past decisions, introduces a short-cut to formulate feasible emergency alternatives. Cloud model is used to evaluate and optimise the emergency response alternatives. To evaluate emergency response alternatives, the decision criterion must be determined according to the aim and characteristics of emergency rescue in disasters or accidents. Then, the weight cloud and evaluation cloud of the decision criterion are determined by the Delphi method combined with backward cloud generator, and the synthesised cloud of each alternative is calculated through arithmetic rules of cloud. Finally, a ranking of all response alternatives can be determined, and the best alternative is selected. Case study shows that the method makes the conversion between qualitative description and quantitative indication more effective.
Multimodal Humor Dataset: Predicting Laughter tracks for Sitcoms

A great number of situational comedies (sitcoms) are being regularly made and the task of adding laughter tracks to these is a critical task. Providing an ability to be able to predict whether something will be humorous to the audience is also crucial. In this project, we aim to automate this task. Towards doing so, we annotate an existing sitcom (`Big Bang Theory') and use the laughter cues present to obtain a manual annotation for this show. We provide detailed analysis for the dataset design and further evaluate various state of the art baselines for solving this task. We observe that existing LSTM and BERT based networks on the text alone do not perform as well as joint text and video or only video-based networks. Moreover, it is challenging to ascertain that the words attended to while predicting laughter are indeed humorous. Our dataset and analysis provided through this paper is a valuable resource towards solving this interesting semantic and practical task. As an additional contribution, we have developed a novel model for solving this task that is a multi-modal self-attention based model that outperforms currently prevalent models for solving this task. The project page for our paper is https://delta-lab-iitk.github.io/Multimodal-Humor-Dataset/.