Reviewerly/exHarmony huggingface dataset:
E3: A HW/SW Co-design Neuroevolution Platform for Autonomous Learning in Edge Device

The true potential of AI can be realized once we move beyond supervised training using labelled datasets on the cloud to autonomous learning on edge devices. While techniques like Reinforcement Learning are promising for their autonomous learning ability, they exhibit high compute and memory requirements due to gradient computations, making them prohibitive for edge deployment. In this paper, we propose E3, a HW/SW co-designed edge learning system on a FPGA. E3 uses a gradient-free approach called neuro-evolution (NE) to evolve the neural network (NN) topology and weights dynamically. The NNs evolved using NE are highly irregular, and a population of such NNs need to be evaluated quickly in order for the NE algorithm to make progress. To address this, we develop INAX, a specialized accelerator inside E3 for efficient irregular network computation. INAX leverages multiple avenues of parallelism both within and across the evolved NNs. E3 shows averaged 30× speedup than CPU-based solution across a suite of OpenAI environments.
Research on Modeling and Transformation Method of Web Service Composition Based on Petri Net

BPEL (Business Process Execution Language) is a classical Web service composition process language. In this situation, it is also known as WS-BPEL or BPEL4WS. However, BPEL is lack of formal analysis and verification methods. In order to solve this problem, a serial of rules to convert the BPEL to Petri net model are presented in this study, which enable the more intuitive and rigorous mathematical description of service composition process, and also facilitate the analysis and verification to the composite services.
Binarized Embeddings for Fast, Space-Efficient Knowledge Graph Completion

Methods based on vector embeddings of knowledge graphs have been actively pursued as a promising approach to knowledge graph completion.However, existing embedding models generate storage-inefficient representations, particularly when the number of entities and relations, and the dimensionality of the real-valued embedding vectors are large.We present a binarized CANDECOMP/PARAFAC (CP) decomposition algorithm, which we refer to as B-CP, where real-valued parameters are replaced by binary values to reduce model size.Moreover, a fast score computation technique is developed with bitwise operations.We prove that B-CP is fully expressive given a sufficiently large dimensionality of embedding vectors.Experimental results on several benchmark datasets demonstrate that the proposed method successfully reduces model size by more than an order of magnitude while maintaining task performance at the same level as the real-valued CP model.
Hybrid Syntactic Graph Convolutional Networks for Chinese Event Detection

Event Detection (ED) is a task that aims to recognize triggers and identify the event type in sentences. Syntactic information plays a crucial role in event detection model accurately to recognize the triggers and event type. The previous works commonly use word embedding to obtain context representation that cannot fully exploit syntactic information. In this paper, we propose a novel model HSGCN (Hybrid Syntactic Graph Convolutional Networks) for Chinese event detection, which utilizes graph convolutional networks to generate sentence-level feature and exploit a task-specific hybrid representation considering both character-level feature and word-level feature. Our experiments demonstrate that HSGCN model can capture rich syntactic to improve identifying the triggers and event type. Compared with existing models, HSGCN can achieve efficient and accurate results on ACE 2005 and KBPEval2017 datasets. In trigger identification and type classification tasks, HSGCN respectively achieved 70.2\(\%\) and 65.7\(\%\) F-score with average 1.2\(\%\) and 0.9\(\%\) absolute improvement compare to state-of-the-art method.
An Improved Bilinear Pooling Method for Image-Based Action Recognition

Action recognition in still images is a challenging task because of the complexity of human motions and the variance of background in the same action category. And some actions typically occur in fine-grained categories, with little visual differences between these categories. So extracting discriminative features or modeling various semantic parts is essential for image-based action recognition. Many methods apply expensive manual annotations to learn discriminative parts information for action recognition, which may severely hinder potential applications in real life. In recent years, bilinear pooling method has shown its effectiveness for image classification due to its learning distinctive features automatically. Inspired by this model, in this paper, an improved bilinear pooling method is proposed for image-based action recognition. The previous bilinear pooling approaches contain lots of noisy background or harmful feature information, which limit their application for action recognition. In our method, the attention mechanism is introduced into hierarchical bilinear pooling framework with mask aggregation. The proposed model can generate the distinctive and RoI-aware feature information by combining multiple attention mask maps from the channel and spatial-wise attention features. Specifically, our method makes the network to pay more attention to discriminative region of the vital objects in an image. We verify our model on the two challenging datasets: 1) Stanford 40 action dataset and 2) our action dataset that includes 60 categories. Experimental results demonstrate the effectiveness of our approach, which is superior to the traditional and state-of-the-art methods.
Height Estimation of Children under Five Years using Depth Images

Malnutrition is a global health crisis and is a leading cause of death among children under 5 years. Detecting malnutrition requires anthropometric measurements of weight, height, and middle-upper arm circumference. However, measuring them accurately is a challenge, especially in the global south, due to limited resources. In this work, we propose a CNN-based approach to estimate the height of standing children under 5 years from depth images collected using a smartphone. According to the SMART Methodology Manual, the acceptable accuracy for height is less than 1.4 cm. On training our deep learning model on 87131 depth images, our model achieved a mean absolute error of 1.64% on 57064 test images. For 70.3% test images, we estimated height accurately within the acceptable 1.4 cm range. Thus, our proposed solution can accurately detect stunting (low height-for-age) in standing children below 5 years of age.
Efficient Hyperparameters optimization Through Model-based Reinforcement Learning and Meta-Learning

Hyperparameter optimization (HPO) plays a vital role in the performance of machine learning algorithms. When the algorithm is complex or the dataset is large, the computational cost of algorithm evaluation is very high, which is a major challenge for HPO. In this paper, we propose a reinforcement learning optimization method for efficient hyperparameter tuning. In particular, an RL agent selects hyperparameters sequentially, thus the search space is greatly reduced. The k-fold cross-validation result is used as a reward signal to update the agent. To speed up the training of the agent, we employ a predictive model to evaluate an algorithm with the selected hyperparameters. However, model inaccuracy is further exacerbated by long-horizon rollout, resulting in collapse performance. We employ a straightforward method to discipline the model rollout in short-horizon. After the short-horizon model rollout, the model is trained again with new samples. The model training and the model rollout are alternately repeated. To fast adapt to new tasks, meta-learning is used to train the predictive model on a variety of learning tasks, such that it can solve new learning tasks using only a small number of training samples. We evaluate our approach on 25 datasets and the experimental results demonstrate that with the limited resources, our method can significantly improve the efficiency and outperforms state-of-the-art Bayesian methods and evolution method.
Knowledge Discovery in Surveys using Machine Learning: A Case Study of Women in Entrepreneurship in UAE

Knowledge Discovery plays a very important role in analyzing data and getting insights from them to drive better business decisions. Entrepreneurship in a Knowledge based economy contributes greatly to the development of a country's economy. In this paper, we analyze surveys that were conducted on women in entrepreneurship in UAE. Relevant insights are extracted from the data that can help us to better understand the current landscape of women in entrepreneurship and predict the future as well. The features are analyzed using machine learning to drive better business decisions in the future.
Lightweight semantic segmentation algorithm based on MobileNetV3 network

With the popularization of intelligent terminals, more and more image segmentation tasks need to be carried out on mobile terminals. However, due to the limitation of mobile terminal computing and storage capacity, many classical semantic segmentation network models are too large to be applicable to mobile terminals. Therefore, this paper proposes a lightweight semantic segmentation algorithm based on the improved MobileNetV3 network. First, the leaky-ReLU6 activation function presented in this article was applied to the first seven layers of the MobileN etV3 network, and then the improved MobileNetV3 was used as an encoder for feature extraction. Then, the decoder part uses bilinear interpolation and depth separable convolution. Finally, the jump connection structure is retained to fuse the features of the lower layer and the higher layer. By comparing the network proposed in this paper with classic networks such as FCN, SegNet, UNet and DeepLabV3+, it is found that the network designed in this paper can effectively guarantee the segmentation accuracy, greatly reduce the number of parameters and shorten the running time of the network. It can be better applied to the mobile terminal with tight computing resources and space resources.
Called function identification of IoT devices by network traffic analysis

IoT devices are currently used in various situations, with the number of applications steadily increasing over the past few years. These IoT devices are connected to outside networks, and as such are often targets of hacking attempts and associated with private user information leaks. The end user is often unaware of how these devices operate, and as a result is unable to notice any unauthorized communication. In response, we aim to create a system to visualize the activity of an IoT device (IoT activity monitor). To create this system, we analyze the network traffic of an IoT device and propose a way to estimate which function it uses. Using Wire-Shark, a packet capture software, we evaluate the data transmitted from the Amazon Echo Spot, Amazon Echo Dot and Amazon Echo Flex, which are smart speakers. We confirmed that we can estimate 8~10 kinds of called functions with 76.1%, 89.8% and 85.2% accuracy for each smart speaker respectively.
Effective Security Mechanisms for Big Data Using Block Chain Technology

Big data is referred as vast numbers of heterogeneous sources of data. The age of big data providing immense volumes of data points offers greater knowledge, such as groundbreaking research, successful management choices and greater consumer value. The details should be treated effectively in order to accomplish these objectives. The data can be in any form like structured information, unstructured information, and semi-structured records. The data can be accessed from several sources such as sensors, IOT, contact network to network, mobile to mobile communication, etc. Information security is almost as broad as big data. Information Security is pursuing essential sensitive data standards. Details relating to hospitals, client data, biometric data, financial data, confidential information, etc. can be sensitive data. Therefore, hackers can target and get confidential data quickly. If any improper access to confidential information may lead to data loss, the trust of businesses can be undermined by modification or misuse. To ensure the confidentiality of sensitive data, cryptographic methods are used. Data collection, data search and data processing should be protected by a security framework. Therefore, to deal with these systems, a new effective scheme is proposed. This study focuses on blockchain technologies to ensure critical data protection. Blockchain is a distributed database that is a collection of cryptography-generated data blocks. It contains a peer-to - peer network, distributed ledger, consensus system and infrastructure for smart contracts. The present work is proposed which involves to achieve stable data transfer & data mining of big data, a new encryption strategy blends with block chain technology. This study would lead to a specific protection framework for the transfer and retrieval of all confidential data.
Pay-as-you-go consequence-based reasoning for the description logic <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:mi mathvariant="script">SROIQ</mml:mi></mml:math>

Consequence-based (CB) reasoners combine ideas from resolution and (hyper)tableau calculi for solving key reasoning problems in Description Logics (DLs), such as ontology classification. Existing CB reasoners, however, are only capable of handling DLs without nominals (such as ALCHIQ), or DLs without disjunction (such as Horn-ALCHOIQ). In this paper, we present a consequence-based calculus for concept subsumption and classification in the DL ALCHOIQ+, which extends ALC with role hierarchies, inverse roles, number restrictions, and nominals; to the best of our knowledge, ours is the first CB calculus for an NExpTime-complete DL. By using standard transformations, our calculus extends to SROIQ, which covers all of OWL 2 DL except for datatypes. A key feature of our calculus is its pay-as-you-go behaviour: our calculus is worst-case optimal for all the well-known proper fragments of ALCHOIQ+. Furthermore, our calculus can be applied to DL reasoning problems other than subsumption and ontology classification, such as instance retrieval and realisation. We have implemented our calculus as an extension of Sequoia, a CB reasoner which previously supported ontology classification in SRIQ. We have performed an empirical evaluation of our implementation, which shows that Sequoia offers competitive performance. Although there still remains plenty of room for further optimisation, the calculus presented in this paper and its implementation provide an important addition to the repertoire of reasoning techniques and practical systems for expressive DLs.
Illegal Trash Thrower Detection Based on HOGSVM for a Real-Time Monitoring System

Some people are too ignorant of their surroundings. Most of them, usually, throw trash illegally in the streets or abandoned areas. In this paper, to get rid of such a problem, we have proposed a real-time monitoring mechanism to find the illegal trash throwing person for an intelligent surveillance system. The proposed mechanism of finding people and trash from the video frame can be done using subsequently applying the following several steps. At first, we have applied the shadow removal technique and then applied the background subtraction procedure. To reduce the noise levels, we have implemented labeling & filtering operations. For extracting feature vectors, a histogram of oriented gradients (HOG) is used. Finally, by applying a support vector machine classifier (SVM), we have identified both humans and trash from the video frames. The key contribution of this proposed framework is to gain better performance by applying the shadow removal technique before the background subtraction procedure. Besides, the temporary bounding box concept has been taken into consideration during the trash detection phase.
Threshold Garbled Circuits and Ad Hoc Secure Computation

Garbled Circuits (GCs) represent fundamental and powerful tools in cryptography, and many variants of GCs have been considered since their introduction. An important property of the garbled circuits is that they can be evaluated securely if and only if exactly 1 key for each input wire is obtained: no less and no more. In this work we study the case when: 1) some of the wire-keys are missing, but we are still interested in computing the output of the garbled circuit and 2) the evaluator of the GC might have both keys for a constant number of wires. We start to study this question in terms of non-interactive multi-party computation (NIMPC) which is strongly connected with GCs. In this notion there is a fixed number of parties (n) that can get correlated information from a trusted setup. Then these parties can send an encoding of their input to an evaluator, which can compute the output of the function. Similarly to the notion of ad hoc secure computation proposed by Beimel et al. [ITCS 2016], we consider the case when less than n parties participate in the online phase, and in addition we let these parties colluding with the evaluator. We refer to this notion as Threshold NIMPC. In addition, we show that when the number of parties participating in the online phase is a fixed threshold $$l\le n$$ then it is possible to securely evaluate any $$l$$ -input function. We build our result on top of a new secret-sharing scheme (which can be of independent interest) and on the results proposed by Benhamouda, Krawczyk and Rabin [Crypto 2017]. Our protocol can be used to compute any function in $$NC^1$$ in the information-theoretic setting and any function in P assuming one-way functions. As a second (and main) contribution, we consider a slightly different notion of security in which the number of parties that can participate in the online phase is not specified, and can be any number c above the threshold $$l$$ (in this case the evaluator cannot collude with the other parties). We solve an open question left open by Beimel, Ishai and Kushilevitz [Eurocrypt 2017] showing how to build a secure protocol for the case when c is constant, under the Learning with Errors assumption.
3D Ensemble-Based Online Oceanic Flow Field Estimation for Underwater Glider Path Planning

Estimating ocean flow fields in 3D is a critical step in enabling the reliable operation of underwater gliders and other small, low-powered autonomous marine vehicles. Existing methods produce depth-averaged 2D layers arranged at discrete vertical intervals, but this type of estimation can lead to severe navigation errors. Based on the observation that real-world ocean currents exhibit relatively low vertical velocity components, we propose an accurate 3D estimator that extends our previous work in estimating 2D flow fields as a linear combination of basis flows. The proposed algorithm uses data from ensemble forecasting to build a set of 3D basis flows, and then iteratively updates basis coefficients using point measurements of underwater currents. We report results from experiments using actual ensemble forecasts and synthetic measurements to compare the performance of our method to the direct 3D extension of the previous work. These results show that our method produces estimates with dramatically lower error metrics, with and without measurement noise.
Saving Electronic Health Record (EHR) in private and secure mHealth system with blockchain Smart contracts

Electronic healthcare records (EHRs) have become popular because they facilitate sharing of information, and they take small space compared to paper chart systems. Significant improvements continue to be seen in the EHR field because of technological advancement. The current study introduces Health Record Chain (HRC), a mobile application that facilitates communication between patients and caregivers. The technology eliminates the need for physical examination, which encourages social distancing, especially during the COVID-19 pandemic. However, it raises concerns about user’s privacy and adherence to relevant regulations, For instance, Health Insurance Portability and Accountability Act and General Data Protection Regulation GDPR. The suggested system integrates blockchains to control access to user data, ensuring privacy and confidentiality.
Research on Tibetan-Chinese neural network machine translation with few samples

Abstract Machine translation is an important task in natural language processing, and the study of Tibetan-Chinese neural machine translation is of profound significance in promoting Tibetan-Chinese scientific and cultural exchanges and the development of education and culture. In this paper, we investigate the performance of these techniques and methods on Tibetan-Chinese NMT with few samples by using deactivated word lists, data augmentation (back translation), pre-training models (ELMO), and attention mechanisms for the techniques and methods widely used in NMT, using seq2seq and Transformer models as the baseline, and finally, the BLEU value of Tibetan-Chinese NMT is increased from the initial 5.53 to 19.03.
A new temporal locality-based workload prediction approach for SaaS services in a cloud environment

As the paradigm shift toward Software as a Service (SaaS) continues to gain the interest of companies and the scientific community, performances must be optimal. Indeed, cloud providers must provide an optimal quality of service (QoS) for their users in order to survive in such a competitive cloud market. Workload forecasting techniques have been proposed in order to improve capacity planning, ensure efficient management of resources and, hence, maintain SLA contracts with end users. In this context, we propose a new approach to predict the number of requests arriving at a SaaS service in order to prepare the virtualized resources necessary to respond to user requests. The method will be implemented in order to simultaneously achieve a twofold benefit: obtain precise forecast results while optimizing response time. In this regard, we have chosen to control the computation time by dynamizing the size of the sliding window associated to the recent history to be analyzed, since the larger the size of the entry in the prediction model, the more the algorithmic complexity increases. Then, the prediction will be established based on the temporal locality principle and the dynamic assignment of weights to different data points in recent history. Moreover, the proposed method can be extended to cover other uses in prediction. Experiments were carried out to assess the performance of the proposed method using two real workload traces and compared to state-of-the-art methods. The proposed method offers a compromise between the execution time and the accuracy of the prediction.