Reviewerly/exHarmony huggingface dataset:
Measuring the short text similarity based on semantic and syntactic information

Determining the similarity between short texts plays an important role in natural language processing applications such as search, query suggestion and automatic summary, which has attracted widespread attention. Unlike traditional long texts, short texts present the characteristics of short length, weak signal, and high ambiguity. Researchers have proposed many methods, from simple vector space models to more sophisticated distributed semantics. However, these methods only consider the literal meaning of words, ignoring the impact of word ambiguity and the semantic information contained in the structure of the short text. Additionally, words on their own are often insufficient for expressing semantics, as many terms are composed of multiple words. In this paper, we propose a method based on semantic and syntactic information for short text similarity calculations by using knowledge and corpora to express the meaning of the term to solve polysemy, and using a constituency parse tree to capture the syntactic structure of short texts. Additionally, the proposed method uses terms as semantic units. Experimental results on ground-truth datasets demonstrate that the proposed method outperforms baseline methods.
Verifiable and Forward-secure Encrypted Search Using Blockchain Techniques

Dynamic Symmetric Searchable Encryption (SSE) is a practical cryptographic primitive that enables data owners to search and update encrypted data hosted on untrusted servers. Recently, there is a growing interest to design dynamic SSE schemes with forward security. That is, the server cannot learn the association between the updated data and any query made in the past. However, due to the complexity of update operations, this security property introduces a great challenge of designing verifiable SSE schemes. It is difficult to verify the correctness of updated search results while preserving forward privacy. In this work, we explore how blockchain techniques can help us achieve a verifiable and dynamic SSE construction with forward security. First, we propose a new dynamic SSE scheme based on blockchain techniques, and apply it as the underlying building blocks to preserve forward-secure updates. Second, we resort to the emerging smart contract technique to customize a verification scheme, making updated results easily verifiable. Based on this new primitive, the robustness of the encrypted search service is ensured and forward security is preserved for update operations. Finally, we implement the prototype in Python and Solidity, and conduct performance evaluations on Ethereum. The extensive security analysis and performance evaluations on the real-world dataset demonstrate that our blockchain-assisted SSE scheme is secure and feasible.
Improving convolutional neural network for text classification by recursive data pruning

In spite of the state-of-the-art performance of deep neural networks, shallow neural networks are still the choice in applications with limited computing and memory resources. Convolutional neural network (CNN), in particular the one-convolutional-layer CNN, is a widely-used shallow neural network in natural language processing tasks such as text classification. However, it was found that CNNs may misfit to task-irrelevant words in dataset, which in turn leads to unsatisfactory performance. To alleviate this problem, attention mechanism can be integrated into CNN, but this takes up the limited resources. In this paper, we propose to address the misfitting problem from a novel angle - pruning task-irrelevant words from the dataset. The proposed method evaluates the performance of each convolutional filter based on its discriminative power of the feature generated at the pooling layer, and prunes words captured by the poorly-performed filters. Experiment results show that our proposed model significantly outperforms the CNN baseline model. Moreover, our proposed model produces performance similar to or better than the benchmark models (attention integrated CNNs) while demanding less parameters and FLOPs, and is therefore a choice model for resource limited scenarios, such as mobile applications.
An investigation on the attenuation effect of air pollution on regional solar radiation

Due to the continuous increase of environmental pollution in recent years, the high concentration of particulate matters in air has greatly reduced the amount of solar radiation that can reach the earth, and this reduction has a direct effect on the use of solar energy in buildings. To quantify this attenuation effect, historical meteorological data collected from five regions, namely, Beijing, Tianjin, Jinan, Xi'an and Zhengzhou, in China from 2014 to 2016 were used to investigate the correlation between clearness index (reflecting available radiation) and air quality index (reflecting pollution level). The analysis results have revealed that higher air quality index would result in lower clearness index, and the sunny days gave higher decreasing rate than cloudy days. For all five regions, their monthly clearness index attenuation showed higher values in winter than that in summer. The monthly solar radiation attenuation, however, showed an opposite trend, due to higher solar altitude in summer. Additionally, different regions had different annual solar radiation attenuation ratio, with Tianjin giving the highest of 6.56% (651.17 MJ/m2), followed by Beijing (3.92%, 410.08 MJ/m2), Xi'an (4.94%, 510.42 MJ/m2), Zhengzhou (3.99%, 427.64 MJ/m2) and Jinan (2.69%, 284.66 MJ/m2).
Highlight Every Step: Knowledge Distillation via Collaborative Teaching

High storage and computational costs obstruct deep neural networks to be deployed on resource-constrained devices. Knowledge distillation (KD) aims to train a compact student network by transferring knowledge from a larger pretrained teacher model. However, most existing methods on KD ignore the valuable information among the training process associated with training results. In this article, we provide a new collaborative teaching KD (CTKD) strategy which employs two special teachers. Specifically, one teacher trained from scratch (i.e., scratch teacher) assists the student step by step using its temporary outputs. It forces the student to approach the optimal path toward the final logits with high accuracy. The other pretrained teacher (i.e., expert teacher) guides the student to focus on a critical region that is more useful for the task. The combination of the knowledge from two special teachers can significantly improve the performance of the student network in KD. The results of experiments on CIFAR-10, CIFAR-100, SVHN, Tiny ImageNet, and ImageNet datasets verify that the proposed KD method is efficient and achieves state-of-the-art performance.
Trust Management as a Service: Enabling Trusted Execution in the Face of Byzantine Stakeholders

Trust is arguably the most important challenge for critical services both deployed as well as accessed remotely over the network. These systems are exposed to a wide diversity of threats, ranging from bugs to exploits, active attacks, rogue operators, or simply careless administrators. To protect such applications, one needs to guarantee that they are properly configured and securely provisioned with the "secrets" (e.g., encryption keys) necessary to preserve not only the confidentiality, integrity and freshness of their data but also their code. Furthermore, these secrets should not be kept under the control of a single stakeholder—which might be compromised and would represent a single point of failure—and they must be protected across software versions in the sense that attackers cannot get access to them via malicious updates. Traditional approaches for solving these challenges often use ad hoc techniques and ultimately rely on a hardware security module (HSM) as root of trust. We propose a more powerful and generic approach to trust management that instead relies on trusted execution environments (TEEs) and a set of stakeholders as root of trust. Our system, PALÆMON, can operate as a managed service deployed in an untrusted environment, i.e., one can delegate its operations to an untrusted cloud provider with the guarantee that data will remain confidential despite not trusting any individual human (even with root access) nor system software. PALÆMON addresses in a secure, efficient and cost-effective way five main challenges faced when developing trusted networked applications and services. Our evaluation on a range of benchmarks and real applications shows that PALÆMON performs efficiently and can protect secrets of services without any change to their source code.
Chinese Clinical Named Entity Recognition in Electronic Medical Records: Development of a Lattice Long Short-Term Memory Model With Contextualized Character Representations

Background Clinical named entity recognition (CNER), whose goal is to automatically identify clinical entities in electronic medical records (EMRs), is an important research direction of clinical text data mining and information extraction. The promotion of CNER can provide support for clinical decision making and medical knowledge base construction, which could then improve overall medical quality. Compared with English CNER, and due to the complexity of Chinese word segmentation and grammar, Chinese CNER was implemented later and is more challenging. Objective With the development of distributed representation and deep learning, a series of models have been applied in Chinese CNER. Different from the English version, Chinese CNER is mainly divided into character-based and word-based methods that cannot make comprehensive use of EMR information and cannot solve the problem of ambiguity in word representation. Methods In this paper, we propose a lattice long short-term memory (LSTM) model combined with a variant contextualized character representation and a conditional random field (CRF) layer for Chinese CNER: the Embeddings from Language Models (ELMo)-lattice-LSTM-CRF model. The lattice LSTM model can effectively utilize the information from characters and words in Chinese EMRs; in addition, the variant ELMo model uses Chinese characters as input instead of the character-encoding layer of the ELMo model, so as to learn domain-specific contextualized character embeddings. Results We evaluated our method using two Chinese CNER datasets from the China Conference on Knowledge Graph and Semantic Computing (CCKS): the CCKS-2017 CNER dataset and the CCKS-2019 CNER dataset. We obtained F1 scores of 90.13% and 85.02% on the test sets of these two datasets, respectively. Conclusions Our results show that our proposed method is effective in Chinese CNER. In addition, the results of our experiments show that variant contextualized character representations can significantly improve the performance of the model.
Rusty: Runtime Interference-Aware Predictive Monitoring for Modern Multi-Tenant Systems

Modern micro-service and container-based cloud-native applications have leveraged multi-tenancy as a first class system design concern. The increasing number of co-located services/workloads into server facilities stresses resource availability and system capability in an unconventional and unpredictable manner. To efficiently manage resources in such dynamic environments, run-time observability and forecasting are required to capture workload sensitivities under differing interference effects, according to applied co-location scenarios. While several research efforts have emerged on interference-aware performance modelling, they are usually applied at a very coarse-grained manner e.g., estimating the overall performance degradation of an application, thus failing to effectively quantify, predict or provide educated insights on the impact of continuous runtime interference on per-resource allocations. In this paper, we present Rusty, a predictive monitoring system that leverages the power of Long Short-Term Memory networks to enable fast and accurate runtime forecasting of key performance metrics and resource stresses of cloud-native applications under interference. We evaluate Rusty under a diverse set of interference scenarios for a plethora of representative cloud workloads, showing that Rusty i) achieves extremely high prediction accuracy, average R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> value of 0.98, ii) enables very deep prediction horizons retaining high accuracy, e.g., R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of around 0.99 for a horizon of 1 sec ahead and around 0.94 for an horizon of 5 sec ahead, while iii) satisfying, at the same time, the strict latency constraints required to make Rusty practical for continuous predictive monitoring at runtime.
Semi-supervised classification via simultaneous label and discriminant embedding estimation

Graph-based semi-supervised learning has recently proved to be a powerful paradigm for processing and mining large datasets. The main advantage relies on the fact that these methods can be useful in propagating a small set of known labels to a large set of unlabeled data. However, the lack of labeled data in real applications may affect the semi-supervised learning's performance. This paper introduces a new semi-supervised framework for simultaneous linear feature extraction and label propagation. The proposed method simultaneously estimates a discriminant transformation, as well as the unknown label, by exploiting both labeled and unlabeled data. The learning model's unknowns are to be estimated by integrating two types of graph-based smoothness constraints. The resulting semi-supervised model is expected to learn more discriminative information. Experiments are conducted on nine public image datasets. These experimental results show that the proposed method's performance can be better than that of many state-of-the-art graph-based semi-supervised algorithms.
S-ADDOPT: Decentralized Stochastic First-Order Optimization Over Directed Graphs

In this letter, we study decentralized stochastic optimization to minimize a sum of smooth and strongly convex cost functions when the functions are distributed over a directed network of nodes. In contrast to the existing work, we use gradient tracking to improve certain aspects of the resulting algorithm. In particular, we propose the S-ADDOPT algorithm that assumes a stochastic first-order oracle at each node and show that for a constant step-size α, each node converges linearly inside an error ball around the optimal solution, the size of which is controlled by α. For decaying step-sizes O(1/k), we show that S-ADDOPT reaches the exact solution sublinearly at O(1/k) and its convergence is asymptotically network independent. Thus the asymptotic behavior of S-ADDOPT is comparable to the centralized stochastic gradient descent. Numerical experiments over both strongly convex and nonconvex problems illustrate the convergence behavior and the performance comparison of the proposed algorithm.
Design of Smart Unstaffed Retail Shop Based on IoT and Artificial Intelligence

Unstaffed retail shops have emerged recently and been noticeably changing our shopping styles. In terms of these shops, the design of vending machine is critical to user shopping experience. The conventional design typically uses weighing sensors incapable of sensing what the customer is taking. In the present study, a smart unstaffed retail shop scheme is proposed based on artificial intelligence and the internet of things, as an attempt to enhance the user shopping experience remarkably. To analyze multiple target features of commodities, the SSD ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$300\times 300$ </tex-math></inline-formula> ) algorithm is employed; the recognition accuracy is further enhanced by adding sub-prediction structure. Using the data set of 18, 000 images in different practical scenarios containing 20 different type of stock keeping units, the comparison experimental results reveal that the proposed SSD ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$300\times 300$ </tex-math></inline-formula> ) model outperforms than the original SSD ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$300\times 300$ </tex-math></inline-formula> ) in goods detection, the mean average precision of the developed method reaches 96.1% on the test dataset, revealing that the system can make up for the deficiency of conventional unmanned container. The practical test shows that the system can meet the requirements of new retail, which greatly increases the customer flow and transaction volume.
Side-Channel Attack in Internet of Things: A Survey

To ensure security for data exchange is a challenges task in Internet of Things (IoT). Thus, research on side-channel attack is a major issue in this domain. Side-channel attack is based on side-channel information. This attack is of either ciphertext only attack or plaintext only attack or chosen plaintext attack. Moreover, since this attack is cheap to perform, it requires little computing power and is relatively easy to perform. So, this attack is growing day by day. Therefore, security is not an easy task to establish in a given system. The motive behind this paper is to present a comprehensive survey on different types of IoT attack with special focus on side-channel attack. In addition, a list of research issues and open challenges are also highlighted in the paper.
Sentiment Identification in COVID-19 Specific Tweets

In 2020, our world has been hit by a global pandemic of COVID-19, belonging to the family of Coronavirus.Due to the rapid increase in the infection and the death rate, people have started to develop mixed feelings regarding this situation.Therefore, in this study, our sole focus is to analyze the emotions expressed by people using social media such as Twitter etc. Accumulating and studying the concerning tweets will provide aid to elicitate the real emotions during this hard time.The goal of this study is to present a domain-specific approach to understand sentiments manifested within people around the globe regarding this situation.In order to attain this, coronaspecific tweets are acquired from twitter platform.After gathering the tweets, they are labelled and a model is developed which is effective for detecting the actual sentiment behind a tweet related to COVID-19.The substantial assessments are performed in bi -class and multi-class setting over n-gram feature set along with cross-dataset evaluation of different machine learning techniques in order to develop the model.Our experiments reveal that the proposed model performs well in perceiving the perception of people about COVID-19 with a maximum accuracy of about 93%.
Optimizing Temporal Convolutional Network Inference on FPGA-Based Accelerators

Convolutional Neural Networks (CNNs) are extensively used in a wide range of applications, commonly including computer vision tasks like image and video classification, recognition and segmentation. Recent research results demonstrate that multi-layer (deep) network involving mono-dimensional convolutions and dilation can be effectively used in time series and sequences classification and segmentation, as well as in tasks involving sequence modeling. These structures, commonly referred to as Temporal Convolutional Networks (TCNs), represent an extremely promising alternative to recurrent architectures, commonly used across a broad range of sequence modeling tasks. While FPGA based inference accelerators for classic CNNs are widespread, literature is lacking in a quantitative evaluation of their usability on inference for TCN models. In this paper we present such an evaluation, considering a CNN accelerator with specific features supporting TCN kernels as a reference and a set of state-of-the-art TCNs as a benchmark. Experimental results show that, during TCN execution, operational intensity can be critical for the overall performance. We propose a convolution scheduling based on batch processing that can boost efficiency up to 96% of theoretical peak performance. Overall we can achieve up to 111,8 GOPS/s and a power efficiency of 33,8 GOPS/s/W on an Ultrascale+ ZU3EG (up to 10× speedup and 3× power efficiency improvement with respect to pure software implementation).
Reference-point-based multi-objective optimization algorithm with opposition-based voting scheme for multi-label feature selection

Multi-label classification is a machine learning task to construct a model for assigning an entity in the dataset to two or more class labels. In order to improve the performance of multi-label classification, a multi-objective feature selection algorithm has been proposed in this paper. Feature selection as a preprocessing task for Multi-label classification problems aims to choose a subset of relevant features. Selecting a small number of high-quality features decreases the computational cost and at the same time maximizes the classification performance. However extreme decreasing the number of features causes the failure of classification. As a result, feature selection has two conflicting objectives, namely, minimizing the classification error and minimizing the number of selected features. This paper proposes a multi-objective optimization algorithm to tackle the multi-label feature selection. The task is to find a set of solutions (a subset of features) in a sophisticated large-scale search space using a reference-based multi-objective optimization method. The proposed algorithm utilizes an opposition-based binary operator to generate more diverse solutions. Injection of extreme point of the Pareto-front is another component of the algorithm which aims to find feature subsets with less classification error. The proposed method is compared with two other existing methods on eight multi-label benchmark datasets. The experimental results show that the proposed method outperforms existing algorithms in terms of various multi-objective evaluation measures, such as Hyper-volume indicator, Pure diversity, Two-set coverage, and Pareto-front proportional contribution. The proposed method leads to get a set of well-distributed trade-off solutions which reach less classification error in comparing with competitors, even with the fewer number of features.