Reviewerly/exHarmony huggingface dataset:
Knowledge Discovery from Healthcare Electronic Records for Sustainable Environment

The medical history of a patient is an essential piece of information in healthcare agencies, which keep records of patients. Due to the fact that each person may have different medical complications, healthcare data remain sparse, high-dimensional and possibly inconsistent. The knowledge discovery from such data is not easily manageable for patient behaviors. It becomes a challenge for both physicians and healthcare agencies to discover knowledge from many healthcare electronic records. Data mining, as evidenced from the existing published literature, has proven its effectiveness in transforming large data collections into meaningful information and knowledge. This paper proposes an overview of the data mining techniques used for knowledge discovery in medical records. Furthermore, based on real healthcare data, this paper also demonstrates a case study of discovering knowledge with the help of three data mining techniques: (1) association analysis; (2) sequential pattern mining; (3) clustering. Particularly, association analysis is used to extract frequent correlations among examinations done by patients with a specific disease, sequential pattern mining allows extracting frequent patterns of medical events and clustering is used to find groups of similar patients. The discovered knowledge may enrich healthcare guidelines, improve their processes and detect anomalous patients’ behavior with respect to the medical guidelines.
Dynamic Optimization in Fast-Changing Environments via Offline Evolutionary Search

Dynamic optimization, for which the objective functions change over time, has attracted intensive investigations due to the inherent uncertainty associated with many real-world problems. For its robustness with respect to noise, evolutionary algorithms (EAs) have been expected to have great potential for dynamic optimization. Many dynamic optimization methods, such as diversity-driven methods, memory methods, and prediction methods have been proposed based on EAs to deal with environmental changes. However, they face difficulties in adapting to fast changes in dynamic optimization as EAs normally need quite a few fitness evaluations to find a near-optimum solution. To address this issue, this article proposes a new framework of applying EAs in the context of dynamic optimization to deal with fast changing environments. We suggest that instead of online evolving (searching) solutions for the ever-changing objective function, EAs are more suitable for acquiring an archive of solutions in an offline way, which could be adopted to construct a system to provide high-quality solutions efficiently in a dynamic environment. To be specific, we formulate the offline search as a static set-oriented optimization problem. Then, a set of solutions is obtained by an EA for this set-oriented optimization problem. After this, the obtained solution set is adopted to do fast adaptation to the corresponding dynamic optimization problem. The general framework is instantiated for continuous dynamic-constrained optimization problems, and the empirical results show the potential of the proposed framework. The superiority of the framework is also verified on a dynamic vehicle routing problem with changing demands.
Automatic Modulation Classification Based on Improved R-Transformer

Cognitive radio technology is an essential branch in wireless communication, and automatic modulation classification (AMC) is an essential component of intelligent communication systems. As the spectrum traffic becoming congested, the fast modulation classification becomes challenging. While modulation classification has been a well-studied problem, achieving high accuracy from a small number of samples is challenging. This paper builds on previous work by using an R-transformer-based network to identify a complex open-source RadioML dataset. We obtain several network structures suitable for AMC. The network consisting of CNN and attention mechanism can rely on minor parameters and minimal model size to obtain the best performance. Still, the disadvantage is the requirement of the input sequence length. In contrast, an improved-RT model can solve this problem. Compared to existing results, networks proposed in this paper can achieve better accuracy with very few required parameters.
Machine Learning for Load Balancing in Cloud Datacenters

In the cloud datacenter, the resource utilization of different virtual machine (VM) and physical machine (PM) varies with time and it may lead to SLO violation and then degrade the application performance. In order to minimize the probability of SLO violation, load balancing is used to dynamically migrate VMs from overloaded PMs to underloaded PMs. Previous load balancing methods fail to achieve long term load balance. To address this problem, in this paper, we propose different load balancing methods and evaluate their performance on several metrics. We use the Fast Fourier Transform (FFT) method, an improved FFT method considering more frequencies in FFT and the long short term memory (LSTM) machine learning model to predict the resource utilization of VM and PM in the future. LSTM can always achieve the best prediction performance in the prediction. Taking advantage of the ML technique, we then propose a heuristic based method and a reinforcement learning (RL) based method relying on ML workload prediction to generate the VM migration plan in the datacenter. We conduct experiments in both trace-driven simulation (based on Google cluster trace, PlanetLab trace, Worldcup trace) and real implementation in terms of the SLO violation rate, the number of migrations and overhead. The experimental results show that the workload prediction helps reduce the SLO violation rate and/or the number of migrations, which improves the load balance performance in a datacenter. Also, the RL based VM migration method outperforms the heuristic based method in a heavily loaded system but does not show obvious advantages in a lightly loaded system.
SPoTKD: A Protocol for Symmetric Key Distribution Over Public Channels Using Self-Powered Timekeeping Devices

In this paper, we propose a novel class of symmetric key distribution protocol that leverages basic security primitives offered by low-cost, hardware chipsets containing millions of synchronized self-powered timers. The keys are derived from the temporal dynamics of a physical, micro-scale time-keeping device which makes the keys immune to any potential side-channel attacks, malicious tampering, or snooping. Using the behavioral model of the self-powered timers, we first show that the derived key-strings can pass the randomness test as defined by the National Institute of Standards and Technology (NIST) suite. The key-strings are then used in two SPoTKD (Self-Powered Timer Key Distribution) protocols that exploit the timer&#x2019;s dynamics as one-way functions: (a) protocol 1 facilitates secure communications between a user and a remote Server; and (b) protocol 2 facilitates secure communications between two users. In this paper, we investigate the security of these protocols under standard model and against different adversarial attacks. Using Monte-Carlo simulations, we also investigate the robustness of these protocols in the presence of real-world operating conditions and propose error-correcting SPoTKD protocols to mitigate these noise-related artifacts.
Coding for Scalable Blockchains via Dynamic Distributed Storage

Blockchains store transaction data in the form of a distributed ledger where each node in the network stores a current copy of the sequence of transactions as a hash chain. This requirement of storing the entire ledger incurs a high storage cost that grows undesirably large for high transaction rates and large networks. In this work we use secret key sharing, private key encryption, and distributed storage to design a coding scheme such that each node stores only a part of each transaction, thereby reducing the cold storage cost to a fraction of its original cost. In addition, the storage code ensures the security of the storage from active adversaries that may aim to corrupt prior transactions by altering copies of the ledger. We further employ a dynamic zone allocation algorithm that spreads the node allocation and data distribution across transactions. Under this coding scheme we show that we can also improve the integrity of the transaction data in the network over current schemes.
Autonomic Resource Management in a Cloud-Based Infrastructure Environment

With the advent of cloud platform giants like AWS and Google, small- and mid-scale industries are continuously shifting their infrastructure over these platforms. Since hardware and service management of these platforms are induced with high accuracy and availability, these giants earned a huge reputation in the IT industry. As computing infrastructure is expanding exponentially, therefore, the resource management is getting complex and challenging day by day. Cloud environment is highly uncertain in terms of resource dispersion and availability. Administrators often experience challenges allocating available resources due to the factors such as sudden failure, dynamism, and heterogeneity. As a matter of fact, the existing frameworks and their resource management mechanisms have been facing multilateral issues and challenges to handle such flexible and unpredictable environments. In such a scenario, a question arises that how to handle and alleviate such issues and challenges efficiently and effectively through effective deployment and management of the computing resources over cloud? Dynamic resource management handles the issues related to deploying, configuring, provisioning, reprovisioning, and releasing related issues by understanding the dynamically scalable on-demand need of the users and their related applications. This chapter presents the broad overview of methodologies and mechanisms of autonomic cloud computing environment. Autonomic computing executes exclusively dynamic allocation resources in such a manner that it reconfigures itself with the availability of resources in real time. This chapter also provides a straightforward approach to the domain researchers in analyzing the paramount characteristics of autonomic resources management and aids research scholars in identifying and characterizing the primary techniques for the application corresponding to the environment in future research perspectives.
Weakly Supervised Instance Segmentation for Videos with Temporal Mask Consistency

Weakly supervised instance segmentation reduces the cost of annotations required to train models. However, existing approaches which rely only on image-level class labels predominantly suffer from errors due to (a) partial segmentation of objects and (b) missing object predictions. We show that these issues can be better addressed by training with weakly labeled videos instead of images. In videos, motion and temporal consistency of predictions across frames provide complementary signals which can help segmentation. We are the first to explore the use of these video signals to tackle weakly supervised instance segmentation. We propose two ways to leverage this information in our model. First, we adapt inter-pixel relation network (IRN) [1] to effectively incorporate motion information during training. Second, we introduce a new MaskConsist module, which addresses the problem of missing object instances by transferring stable predictions between neighboring frames during training. We demonstrate that both approaches together improve the instance segmentation metric AP <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50</inf> on video frames of two datasets: Youtube-VIS and Cityscapes by 5% and 3% respectively.
Dynamic Parameters-Based Reversible Data Transform (RDT) Algorithm in Recommendation System

The protection and processing of the sensitive data in recommendation system are the major concern. Existing literature, used homomorphic encryption (HE), Reversible Data Transform (RDT), differential privacy (DP) and many more schemes to protect user information. Existing RDT scheme require prior sharing of the parameters and an alternative mechanism e.g., Shamir Threshold Protocol or Diffie-hellman algorithm are used to protect the sharing parameters. In this paper, we proposed a chaotic based RDT approach for privacy-preserving data mining (PPDM) in recommendation system. Using this approach, RDT parameter values will be generated locally and because of this, prior sharing of the parameter values for the recovery process will not be necessary. This approach can be used as an alternative to the standard-RDT algorithm where bandwidth and memory are considered important factors. Our results on the Iris data set clearly show that the proposed chaotic RDT shows similar results as standard-RDT. Secondly, in this paper, we explore the usage of the RDT algorithm on real app usage records in the mobile app recommendation (MAR) domain. Thirdly, we tested the application of the RDT algorithm for the standard MovieLens dataset to ensure the validity of results because app usage dataset is publicly not available. Our results show that the proposed RDT algorithm can replace HE if an adaptive recommendation approach is used. Similarly, we can safely use the RDT approach to any data including user rating, health data or app usage frequency to ensure user privacy before delivering it to the recommender-server.
Application of ANN for forecasting of PV plant output power – Case study Oryahovo

Nowadays, the prediction of energy production of grid connected photovoltaic (PV) power plant is important for ensuring the quality and continuity of power supply. In this context, this paper analyzes different approaches for prediction of PV power output, using Artificial Neural Network (ANN). The Multi-Layer Perceptron (MLP) ANN method is widespread in the scientific literature for foresting of the solar irradiance and PV power output. The ANN is modeled using five years of solar data, measured or estimated using previous research and the measured power output at each 10 minutes. The accuracy of the optimal configuration is around 8% for the RRMSE.
Self-supervised attention flow for dialogue state tracking

The performance of existing approaches for dialogue state tracking (DST) is often limited by the deficiency of labeled datasets, and inefficient utilization of data is also a practical yet tough problem of the DST task. In this paper, we aim to tackle these challenges in a self-supervised manner by introducing an auxiliary pre-training task that learns to pick up the correct dialogue response from a group of candidates. Moreover, we propose an attention flow mechanism that is augmented with a soft-threshold function in a dynamic way to better understand the user intent and filter out the redundant information. Extensive experiments on the multi-domain dialogue state tracking dataset MultiWOZ 2.1 demonstrate the effectiveness of our proposed method, and we also show that it is able to adapt to zero/few-shot cases under the proposed self-supervised framework.
Research on Application of Computer Technology in the Optimization of Department Information System

Abstract Aiming at the problems of weak financial management foundation, lack or failure of internal financial accounting management, etc., the characteristics of financial management of SMEs are analysed. The article develops a reasonable financial enterprise management system, which can promote the long-term development of the enterprise. The graphical user interface developed based on SWT realizes the main functions of enterprise financial management. Practice shows that the system is not only easy to operate, but also has a friendly interface. It is a flexible, reliable, fully functional, and practical information management system.
TANGO: Commonsense Generalization in Predicting Tool Interactions for Mobile Manipulators

Robots assisting us in factories or homes must learn to make use of objects as tools to perform tasks, e.g., a tray for carrying objects. We consider the problem of learning commonsense knowledge of when a tool may be useful and how its use may be composed with other tools to accomplish a high-level task instructed by a human. We introduce TANGO, a novel neural model for predicting task-specific tool interactions. TANGO is trained using demonstrations obtained from human teachers instructing a virtual robot in a physics simulator. TANGO encodes the world state consisting of objects and symbolic relationships between them using a graph neural network. The model learns to attend over the scene using knowledge of the goal and the action history, finally decoding the symbolic action to execute. Crucially, we address generalization to unseen environments where some known tools are missing, but alternative unseen tools are present. We show that by augmenting the representation of the environment with pre-trained embeddings derived from a knowledge-base, the model can generalize effectively to novel environments. Experimental results show a 60.5-78.9% improvement over the baseline in predicting successful symbolic plans in unseen settings for a simulated mobile manipulator.
Informed Autonomous Exploration of Subterranean Environments

Autonomous exploration is highly challenging in subterranean applications due to the constraints imposed by the nature of the environments (e.g., dead-end branches, unstructured regions, narrow passages and bifurcations). Robots need to constantly balance their exploration objectives with measures to ensure safety. We present an informed exploration approach to address these challenges, which exploits a reachability graph to represent the environment's structure and adaptive navigation to find collision-free motions. Our system makes the inspection task tractable and maximizes the information acquired about the environment while preserving safety. We evaluate our navigation and exploration techniques against several challenging cave scenarios reconstructed using real data. Our experimental results demonstrate that our method enables the robot to make informed decisions and perform exploration more efficiently than existing techniques in confined spaces.
Graph signal recovery using restricted Boltzmann machines

Given: (a) clean training dataset, (b) trained graph machine learning pipeline, and (c) noisy test dataset; how do we make the pipeline robust to such noise? In an attempt to answer this question, we propose a modification to the pipeline that exploits both the content addressability of a restricted Boltzmann machine and the message passing capabilities of a graph neural network. We investigate which of the hidden layers of the pipeline is best suited for our proposed modification. By establishing that a decrease in mutual information indicates an increase in prediction accuracy which is in turn driven by progressive geometric clustering of the samples belonging to the same class, we attempt to explain the behaviour exhibited by the modified pipeline. Empirical results demonstrate that our approach makes the pipeline robust to some real-world noisy scenarios in citation datasets.
Creation of annotated country-level dialectal Arabic resources: An unsupervised approach

Abstract The wide usage of multiple spoken Arabic dialects on social networking sites stimulates increasing interest in Natural Language Processing (NLP) for dialectal Arabic (DA). Arabic dialects represent true linguistic diversity and differ from modern standard Arabic (MSA). In fact, the complexity and variety of these dialects make it insufficient to build one NLP system that is suitable for all of them. In comparison with MSA, the available datasets for various dialects are generally limited in terms of size, genre and scope. In this article, we present a novel approach that automatically develops an annotated country-level dialectal Arabic corpus and builds lists of words that encompass 15 Arabic dialects. The algorithm uses an iterative procedure consisting of two main components: automatic creation of lists for dialectal words and automatic creation of annotated Arabic dialect identification corpus. To our knowledge, our study is the first of its kind to examine and analyse the poor performance of the MSA part-of-speech tagger on dialectal Arabic contents and to exploit that in order to extract the dialectal words. The pointwise mutual information association measure and the geographical frequency of word occurrence online are used to classify dialectal words. The annotated dialectal Arabic corpus (Twt15DA), built using our algorithm, is collected from Twitter and consists of 311,785 tweets containing 3,858,459 words in total. We randomly selected a sample of 75 tweets per country, 1125 tweets in total, and conducted a manual dialect identification task by native speakers. The results show an average inter-annotator agreement score equal to 64%, which reflects satisfactory agreement considering the overlapping features of the 15 Arabic dialects.
SamWalker++: recommendation with informative sampling strategy

Recommendation from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">implicit feedback</i> is a highly challenging task due to the lack of reliable negative feedback data. Existing methods address this challenge by treating all the un-observed data as negative (dislike) but downweight the confidence of these data. However, this treatment causes two problems: (1) Confidence weights of the unobserved data are usually assigned manually, which lack flexibility and may create empirical bias on evaluating user's preference. (2) To handle massive volume of the unobserved feedback data, most of the existing methods rely on stochastic inference and data sampling strategies. However, since a user is only aware of a very small fraction of items in a large dataset, it is difficult for existing samplers to select <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">informative</i> training instances in which the user really dislikes the item rather than does not know it. To address the above two problems, we propose two novel recommendation methods SamWalker and SamWalker++ that support both adaptive confidence assignment and efficient model learning. SamWalker models data confidence with a social network-aware function, which can adaptively specify different weights to different data according to users’ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">social contexts</i> . However, the social network information may not be available in many recommender systems, which hinders application of SamWalker. Thus, we further propose SamWalker++, which does not require any side information and models data confidence with a constructed pseudo-social network. In the pseudo-social network, similar users are connected with specific item nodes or community nodes. This way, the inference of one's data confidence can benefit from the knowledge from other similar users. We also develop fast random-walk-based sampling strategies for our SamWalker and SamWalker++ to adaptively draw informative training instances, which can speed up gradient estimation and reduce sampling variance. Extensive experiments on five real-world datasets demonstrate the superiority of the proposed SamWalker and SamWalker++.