Reviewerly/exHarmony huggingface dataset:
Multi-class sentiment analysis on twitter: Classification performance and challenges

Sentiment analysis refers to the automatic collection, aggregation, and classification of data collected online into different emotion classes. While most of the work related to sentiment analysis of texts focuses on the binary and ternary classification of these data, the task of multi-class classification has received less attention. Multi-class classification has always been a challenging task given the complexity of natural languages and the difficulty of understanding and mathematically "quantifying" how humans express their feelings. In this paper, we study the task of multi-class classification of online posts of Twitter users, and show how far it is possible to go with the classification, and the limitations and difficulties of this task. The proposed approach of multi-class classification achieves an accuracy of 60.2% for 7 different sentiment classes which, compared to an accuracy of 81.3% for binary classification, emphasizes the effect of having multiple classes on the classification performance. Nonetheless, we propose a novel model to represent the different sentiments and show how this model helps to understand how sentiments are related. The model is then used to analyze the challenges that multi-class classification presents and to highlight possible future enhancements to multi-class classification accuracy.
Efficient resource provisioning for elastic Cloud services based on machine learning techniques

Automated resource provisioning techniques enable the implementation of elastic services, by adapting the available resources to the service demand. This is essential for reducing power consumption and guaranteeing QoS and SLA fulfillment, especially for those services with strict QoS requirements in terms of latency or response time, such as web servers with high traffic load, data stream processing, or real-time big data analytics. Elasticity is often implemented in cloud platforms and virtualized data-centers by means of auto-scaling mechanisms. These make automated resource provisioning decisions based on the value of specific infrastructure and/or service performance metrics. This paper presents and evaluates a novel predictive auto-scaling mechanism based on machine learning techniques for time series forecasting and queuing theory. The new mechanism aims to accurately predict the processing load of a distributed server and estimate the appropriate number of resources that must be provisioned in order to optimize the service response time and fulfill the SLA contracted by the user, while attenuating resource over-provisioning in order to reduce energy consumption and infrastructure costs. The results show that the proposed model obtains a better forecasting accuracy than other classical models, and makes a resource allocation closer to the optimal case.
OAG

Linking entities from different sources is a fundamental task in building open knowledge graphs. Despite much research conducted in related fields, the challenges of linkinglarge-scale heterogeneous entity graphs are far from resolved. Employing two billion-scale academic entity graphs (Microsoft Academic Graph and AMiner) as sources for our study, we propose a unified framework --- LinKG --- to address the problem of building a large-scale linked entity graph. LinKG is coupled with three linking modules, each of which addresses one category of entities. To link word-sequence-based entities (e.g., venues), we present a long short-term memory network-based method for capturing the dependencies. To link large-scale entities (e.g., papers), we leverage locality-sensitive hashing and convolutional neural networks for scalable and precise linking. To link entities with ambiguity (e.g., authors), we propose heterogeneous graph attention networks to model different types of entities. Our extensive experiments and systematical analysis demonstrate that LinKG can achieve linking accuracy with an F1-score of 0.9510, significantly outperforming the state-of-the-art. LinKG has been deployed to Microsoft Academic Search and AMiner to integrate the two large graphs. We have published the linked results---the Open Academic Graph (OAG)\footnote\urlhttps://www.openacademic.ai/oag/ , making it the largest publicly available heterogeneous academic graph to date.
The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers

Solving mathematical word problems (MWPs) automatically is challenging, primarily due to the semantic gap between human-readable words and machine-understandable logics. Despite the long history dated back to the 1960s, MWPs have regained intensive attention in the past few years with the advancement of Artificial Intelligence (AI). Solving MWPs successfully is considered as a milestone towards general AI. Many systems have claimed promising results in self-crafted and small-scale datasets. However, when applied on large and diverse datasets, none of the proposed methods in the literature achieves high precision, revealing that current MWP solvers still have much room for improvement. This motivated us to present a comprehensive survey to deliver a clear and complete picture of automatic math problem solvers. In this survey, we emphasize on algebraic word problems, summarize their extracted features and proposed techniques to bridge the semantic gap, and compare their performance in the publicly accessible datasets. We also cover automatic solvers for other types of math problems such as geometric problems that require the understanding of diagrams. Finally, we identify several emerging research directions for the readers with interests in MWPs.
Embedding Uncertain Knowledge Graphs

Embedding models for deterministic Knowledge Graphs (KG) have been extensively studied, with the purpose of capturing latent semantic relations between entities and incorporating the structured knowledge they contain into machine learning. However, there are many KGs that model uncertain knowledge, which typically model the inherent uncertainty of relations facts with a confidence score, and embedding such uncertain knowledge represents an unresolved challenge. The capturing of uncertain knowledge will benefit many knowledge-driven applications such as question answering and semantic search by providing more natural characterization of the knowledge. In this paper, we propose a novel uncertain KG embedding model UKGE, which aims to preserve both structural and uncertainty information of relation facts in the embedding space. Unlike previous models that characterize relation facts with binary classification techniques, UKGE learns embeddings according to the confidence scores of uncertain relation facts. To further enhance the precision of UKGE, we also introduce probabilistic soft logic to infer confidence scores for unseen relation facts during training. We propose and evaluate two variants of UKGE based on different confidence score modeling strategies. Experiments are conducted on three real-world uncertain KGs via three tasks, i.e. confidence prediction, relation fact ranking, and relation fact classification. UKGE shows effectiveness in capturing uncertain knowledge by achieving promising results, and it consistently outperforms baselines on these tasks.
DNNVM: End-to-End Compiler Leveraging Heterogeneous Optimizations on FPGA-Based CNN Accelerators

state-of-the-art method for several artificial intelligence domains in recent years. The increasingly complex CNN models are both computation-bound and I/O-bound. Fieldprogrammable gate array-based accelerators driven by custom instruction set architecture (ISA) achieve a balance between generality and efficiency, but there is much on them left to be optimized. We propose the full-stack compiler deep neural network virtual machine (DNNVM), which is an integration of optimizers for graphs, loops and data layouts, an assembler, a runtime supporter, and a validation environment. The DNNVM works in the context of deep learning frameworks and transforms CNN models into the directed acyclic graph: XGraph. Based on XGraph, we transform the optimization challenges for both data layout and pipeline into graph-level problems. DNNVM enumerates all potentially profitable fusion opportunities by a heuristic subgraph isomorphism algorithm to leverage pipeline and data layout optimizations, and searches for the best choice of execution strategies of the whole computing graph. On the Xilinx ZU2@330 MHz and ZU9@330 MHz, we achieve equivalently state-of-the-art performance on our benchmarks by naïve implementations without optimizations, and the throughput is further improved up to 1.26× by leveraging heterogeneous optimizations in DNNVM. Finally, with ZU9@330 MHz, we achieve state-of-the-art performance for VGG and ResNet50. We achieve a throughput of 2.82 TOPs/s and an energy efficiency of 123.7 GOPs/s/W for VGG. Additionally, we achieve 1.38 TOPs/s for ResNet50 and 1.41 TOPs/s for GoogleNet.
Outlier Aware Network Embedding for Attributed Networks

Attributed network embedding has received much interest from the research community as most of the networks come with some content in each node, which is also known as node attributes. Existing attributed network approaches work well when the network is consistent in structure and attributes, and nodes behave as expected. But real world networks often have anomalous nodes. Typically these outliers, being relatively unexplainable, affect the embeddings of other nodes in the network. Thus all the downstream network mining tasks fail miserably in the presence of such outliers. Hence an integrated approach to detect anomalies and reduce their overall effect on the network embedding is required.Towards this end, we propose an unsupervised outlier aware network embedding algorithm (ONE) for attributed networks, which minimizes the effect of the outlier nodes, and hence generates robust network embeddings. We align and jointly optimize the loss functions coming from structure and attributes of the network. To the best of our knowledge, this is the first generic network embedding approach which incorporates the effect of outliers for an attributed network without any supervision. We experimented on publicly available real networks and manually planted different types of outliers to check the performance of the proposed algorithm. Results demonstrate the superiority of our approach to detect the network outliers compared to the state-of-the-art approaches. We also consider different downstream machine learning applications on networks to show the efficiency of ONE as a generic network embedding technique. The source code is made available at https://github.com/sambaranban/ONE.
Depthwise Convolution Is All You Need for Learning Multiple Visual Domains

There is a growing interest in designing models that can deal with images from different visual domains. If there exists a universal structure in different visual domains that can be captured via a common parameterization, then we can use a single model for all domains rather than one model per domain. A model aware of the relationships between different domains can also be trained to work on new domains with less resources. However, to identify the reusable structure in a model is not easy. In this paper, we propose a multi-domain learning architecture based on depthwise separable convolution. The proposed approach is based on the assumption that images from different domains share cross-channel correlations but have domain-specific spatial correlations. The proposed model is compact and has minimal overhead when being applied to new domains. Additionally, we introduce a gating mechanism to promote soft sharing between different domains. We evaluate our approach on Visual Decathlon Challenge, a benchmark for testing the ability of multi-domain models. The experiments show that our approach can achieve the highest score while only requiring 50% of the parameters compared with the state-of-the-art approaches.
Bernstain-search differential evolution algorithm for numerical function optimization

The standard Differential Evolution Algorithm (sDE) is a stochactic search method commonly used in evolutionary computing. The problem solving success of sDE is highly sensitive to the genetic operators used and the initial values of the parameters of these operators. Since a universal Differential Evolution Algorithm (uDE) is not sensitive to the structure and parameter values of the genetic operators used, it is parameter-free in practice and easier to control than sDE. uDE does not need a trial-and-error process when selecting the genetic operators and initial values of intrinsic parameter of related genetic operators to solve the problem, unlike the sDE. Therefore, the use and adaptation of a uDE to solve different types of numerical engineering problems is easy and time-consuming compared to sDE. In this paper, a new uDE, Bernstain-Search Differential Evolution Algorithm (BSD), is introduced. BSD is new and easily controllable, simple structured, non-recursive, highly efficient, fast and practically parameter-free uDE. BSD have a too feasible random crossover and mutation process and does not have a control-parameter setting process, contrary to sDE and its improved variants. In this paper, 30 benchmark problems of CEC’2014, 60 classic benchmark problems, image evolution problems for 12 test images and one Triangulated Irregular Network (TIN) refinement problem were used in the experiments performed to investigate the problem solving success of BSD, statistically. Four tested methods (i.e., ABC, JADE, CUCKOO, WDE) were used in the conducted experiments. Problem solving successes of BSD and tested methods were statistically compared by using Wilcoxon Signed Rank Test piecewisely. Results obtained from the performed tests showed that in general, problem solving success of BSD is statistically better than the tested methods that have been used in this paper.
Application of Artificial Intelligence in Automation of Supply Chain Management

A well-functioning supply chain is a key to success for every business entity. Having an accurate projection on inventory offers a substantial competitive advantage. There are many internal factors like product introductions, distribution network expansion; and external factors such as weather, extreme seasonality, and changes in customer perception or media coverage that affects the performance of the supply chain. In recent years Artificial Intelligence (AI) has been proved to become an extension of our brain, expanding our cognitive abilities to levels that we never thought would be possible. Though many believe AI will replace humans, it is not true, rather it will help us to unleash our true strategic and creative potential. AI consists of a set of computational technologies developed to sense, learn, reason, and act appropriately. With the technological advancement in mobile computing, the capacity to store huge data on the internet, cloud-based machine learning and information processing algorithms etc. AI has been integrated into many sectors of business and been proved to reduce costs, increase revenue, and enhance asset utilization. AI is helping businesses to get almost 100% accurate projection and forecast the customer demand, optimizing their R&amp;D and increase manufacturing with lower cost and higher quality, helping them in the promotion (identifying target customers, demography, defining the price, and designing the right message, etc.) and providing their customers a better experience. These four areas of value creation are extremely important for gaining competitive advantage. Supply-chain leaders use AI-powered technologies to a) make efficient designs to eliminate waste b) real-time monitoring and error-free production and c) facilitate lower process cycle times. These processes are crucial in bringing Innovation faster to the market.
Deep Video‐Based Performance Cloning

Abstract We present a new video‐based performance cloning technique. After training a deep generative network using a reference video capturing the appearance and dynamics of a target actor, we are able to generate videos where this actor reenacts other performances. All of the training data and the driving performances are provided as ordinary video segments, without motion capture or depth information. Our generative model is realized as a deep neural network with two branches, both of which train the same space‐time conditional generator, using shared weights. One branch, responsible for learning to generate the appearance of the target actor in various poses, uses paired training data, self‐generated from the reference video. The second branch uses unpaired data to improve generation of temporally coherent video renditions of unseen pose sequences. Through data augmentation, our network is able to synthesize images of the target actor in poses never captured by the reference video. We demonstrate a variety of promising results, where our method is able to generate temporally coherent videos, for challenging scenarios where the reference and driving videos consist of very different dance performances.
An Attention-Based BiLSTM-CRF Model for Chinese Clinic Named Entity Recognition

Clinic Named Entity Recognition (CNER) aims to recognize named entities such as body part, disease and symptom from Electronic Health Records (EHRs), which can benefit many intelligent biomedical systems. In recent years, more and more attention has been paid to the end-to-end CNER with recurrent neural networks (RNNs), especially for long short-term memory networks (LSTMs). However, it remains a great challenge for RNNs to capture long range dependencies. Moreover, Chinese presents additional challenges, since it uses logograms instead of alphabets, the ambiguities of Chinese word and has no word boundaries. In this work, we present a BiLSTM-CRF with self-attention mechanism (Att-BiLSTM-CRF) model for Chinese CNER task, which aims to address these problems. Self-attention mechanism can learn long range dependencies by establishing a direct connection between each character. In order to learn more semantic information about Chinese characters, we propose a novel fine-grained character-level representation method. We also introduce part-of-speech (POS) labeling information about our model to capture the semantic information in input sentence. We conduct the experiment by using CCKS-2017 Shared Task 2 dataset to evaluate performance, and the experimental results indicated that our model outperforms other state-of-the-art methods.
A Neural Grammatical Error Correction System Built On Better Pre-training and Sequential Transfer Learning

Grammatical error correction can be viewed as a low-resource sequence-to-sequence task, because publicly available parallel corpora are limited.To tackle this challenge, we first generate erroneous versions of large unannotated corpora using a realistic noising function. The resulting parallel corpora are sub-sequently used to pre-train Transformer models. Then, by sequentially applying transfer learning, we adapt these models to the domain and style of the test set. Combined with a context-aware neural spellchecker, our system achieves competitive results in both restricted and low resource tracks in ACL 2019 BEAShared Task. We release all of our code and materials for reproducibility.
Correction to: Neural Architecture Search

The original version of this chapter was inadvertently published without the author “Thomas Elsken” primary affiliation. The affiliation has now been updated as below.
FoodKG: A Semantics-Driven Knowledge Graph for Food Recommendation

The proliferation of recipes and other food information on the Web presents an opportunity for discovering and organizing diet-related knowledge into a knowledge graph. Currently, there are several ontologies related to food, but they are specialized in specific domains, e.g., from an agricultural, production, or specific health condition point-of-view. There is a lack of a unified knowledge graph that is oriented towards consumers who want to eat healthily, and who need an integrated food suggestion service that encompasses food and recipes that they encounter on a day-to-day basis, along with the provenance of the information they receive. Our resource contribution is a software toolkit that can be used to create a unified food knowledge graph that links the various silos related to food while preserving the provenance information. We describe the construction process of our knowledge graph, the plan for its maintenance, and how this knowledge graph has been utilized in several applications. These applications include a SPARQL-based service that lets a user determine what recipe to make based on ingredients at hand while taking constraints such as allergies into account, as well as a cognitive agent that can perform natural language question answering on the knowledge graph. Resource Website: https://foodkg.github.io
Atomic Multi-Channel Updates with Constant Collateral in Bitcoin-Compatible Payment-Channel Networks

Current cryptocurrencies provide a heavily limited transaction throughput that is clearly insufficient to cater their growing adoption. Payment-channel networks (PCNs) have emerged as an interesting solution to the scalability issue and are currently deployed by popular cryptocurrencies such as Bitcoin and Ethereum. While PCNs do increase the transaction throughput by processing payments off-chain and using the blockchain only as a dispute arbitrator, they unfortunately require high collateral (i.e., they lock coins for a non-constant time along the payment path) and are restricted to payments in a path from sender to receiver. These issues have severe consequences in practice. The high collateral enables denial-of-service attacks that hamper the throughput and utility of the PCN. Moreover, the limited functionality hinders the applicability of current PCNs in many important application scenarios. Unfortunately, current proposals do not solve either of these issues, or they require Turing-complete language support, which severely limit their applicability.
What graph neural networks cannot learn: depth vs width

This paper studies theoretically the capacity limits of graph neural networks (GNN) falling within the message-passing framework. Two main results are presented. First, GNN are shown to be Turing universal under sufficient conditions on their depth, width, node identification, and layer expressiveness. Second, it is discovered that GNN can lose a significant portion of their power when their depth and width is restricted. The proposed impossibility statements stem from a new technique that enables the repurposing of seminal results from theoretical computer science and leads to lower bounds for an array of decision, optimization, and estimation problems involving graphs. Strikingly, several of these problems are deemed impossible unless the product of a GNN's depth and width exceeds (a function of) the graph size; this dependence remains significant even for tasks that appear simple or when considering approximation.