Reviewerly/exHarmony huggingface dataset:
FeatherNet

Convolutional Neural Network (ConvNet or CNN) algorithms are characterized by a large number of model parameters and high computational complexity. These two requirements have made it challenging for implementations on resource-limited FPGAs. The challenges are magnified when considering designs for low-end FPGAs. While previous work has demonstrated successful ConvNet implementations with high-end FPGAs, this article presents a ConvNet accelerator design that enables the implementation of complex deep ConvNet architectures on resource-constrained FPGA platforms aimed at the IoT market. We call the design “FeatherNet” for its light resource utilization. The implementations are VHDL-based providing flexibility in design optimizations. As part of the design process, new methods are introduced to address several design challenges. The first method is a novel stride-aware graph-based method targeted at ConvNets that aims at achieving efficient signal processing with reduced resource utilization. The second method addresses the challenge of determining the minimal precision arithmetic needed while preserving high accuracy. For this challenge, we propose variable-width dynamic fixed-point representations combined with a layer-by-layer design-space pruning heuristic across the different layers of the deep ConvNet model. The third method aims at achieving a modular design that can support different types of ConvNet layers while ensuring low resource utilization. For this challenge, we propose the modules to be relatively small and composed of computational filters that can be interconnected to build an entire accelerator design. These model elements can be easily configured through HDL parameters (e.g., layer type, mask size, stride, etc.) to meet the needs of specific ConvNet implementations and thus they can be reused to implement a wide variety of ConvNet architectures. The fourth method addresses the challenge of design portability between two different FPGA vendor platforms, namely, Intel/Altera and Xilinx. For this challenge, we propose to instantiate the device-specific hardware blocks needed in each computational filter, rather than relying on the synthesis tools to infer these blocks, while keeping track of the similarities and differences between the two platforms. We believe that the solutions to these design challenges further advance knowledge as they can benefit designers and other researchers using similar devices or facing similar challenges. Our results demonstrated the success of addressing the design challenges and achieving low (30%) resource utilization for the low-end FPGA platforms: Zedboard and Cyclone V. The design overcame the limitation of designs targeted for high-end platforms and that cannot fit on low-end IoT platforms. Furthermore, our design showed superior performance results (measured in terms of [Frame/s/W] per Dollar) compared to high-end optimized designs.
Learning Improved Semantic Representations with Tree-Structured LSTM for Hashtag Recommendation: An Experimental Study

A hashtag is a type of metadata tag used on social networks, such as Twitter and other microblogging services. Hashtags indicate the core idea of a microblog post and can help people to search for specific themes or content. However, not everyone tags their posts themselves. Therefore, the task of hashtag recommendation has received significant attention in recent years. To solve the task, a key problem is how to effectively represent the text of a microblog post in a way that its representation can be utilized for hashtag recommendation. We study two major kinds of text representation methods for hashtag recommendation, including shallow textual features and deep textual features learned by deep neural models. Most existing work tries to use deep neural networks to learn microblog post representation based on the semantic combination of words. In this paper, we propose to adopt Tree-LSTM to improve the representation by combining the syntactic structure and the semantic information of words. We conduct extensive experiments on two real world datasets. The experimental results show that deep neural models generally perform better than traditional methods. Specially, Tree-LSTM achieves significantly better results on hashtag recommendation than standard LSTM, with a 30% increase in F1-score, which indicates that it is promising to utilize syntactic structure in the task of hashtag recommendation.
Markerless Racket Pose Detection and Stroke Classification Based on Stereo Vision for Table Tennis Robots

For table tennis robots, it is a significant challenge to understand the opponent's movements and return the ball accordingly with high performance. One has to cope with various ball speeds and spins resulting from different stroke types. In this paper, we propose a real-time 3D racket pose detection method and classify racket movements into five stroke categories with a neural network. By using two monocular cameras, we can extract the racket's contours and choose some special points as feature points in image coordinates. With the 3D geometrical information of a racket, a wide baseline stereo matching method is proposed to find the corresponding feature points and compute the 3D position and orientation of the racket by triangulation and plane fitting. Then, a Kalman filter is adopted to track the racket pose, and a neural network with two hidden layers is used to classify the pose movements. We conduct two experiments to evaluate the accuracy of racket pose detection and classification, in which the average error in position and orientation is around 7.8 mm and 7.2° by comparing with the ground truth from a KUKA robot. The classification accuracy is 98%, the same as the human pose estimation method with Convolutional Pose Machines (CPMs).
Unsupervised Learning of Paragraph Embeddings for Context-Aware Recommendation

The sparsity of data is one of the main reasons restricting the performance of recommender systems. In order to solve the sparsity problem, some recommender systems use auxiliary information, especially text information, as a supplement to increase the prediction accuracy of the ratings. However, the two mainstream approaches based on text analysis have some limitations. The bag-of-words-based model is one of them, being difficult to use the contextual information of the paragraph effectively so that only the shallow understanding of paragraph can be parsed. Another model based on deep learning can extract the contextual information of the paragraph, but it also increases the complexity of the model. This paper proposes a novel context-aware recommendation model named paragraph vector matrix factorization (P2VMF) which integrates the unsupervised learning of paragraph embeddings into probabilistic matrix factorization (PMF). Therefore, P2VMF can capture the semantic information of the paragraph and can improve the prediction accuracy of the ratings. Our extensive experiments on real-world datasets show that the performance of the P2VMF model is preferable as compared with those multiple recommendation models in the situation, where the ratings are quite sparse. And we also verified that the P2V part of the model can well express the semantics in the form of vectors.
The 2nd International Workshop on Narrative Extraction from Text: Text2Story 2019

Building upon the success of the first edition, we organize the second edition of the Text2Story Workshop on Narrative Extraction from Texts in conjunction with the 41st European Conference on Information Retrieval (ECIR 2019) on April 14, 2019. Our objective is to further consolidate the efforts of the community and reflect upon the progress made since the last edition. Although the understanding of natural language has improved over the last couple of years – with research works emerging on the grounds of information extraction and text mining – the problem of constructing consistent narrative structures is yet to be solved. It is expected that the state-of-the-art has been advancing in pursuit of methods that automatically identify, interpret and relate the different elements of narratives which are often spread among different sources. In the second edition of the workshop, we foster the discussion of recent advances in the link between Information Retrieval (IR) and formal narrative representations from text.
An Empirical Analysis of the Effect of Agile Teams on Software Productivity

Software productivity is a multidimensional concept that is categorized as an indication of project success. For software teams, productivity is a critical aspect that needs to be considered to ensure successful software development. Several factors can affect an agile software team's during the software evolution process. However, the literature review shows that there is a lack of empirical evidence to identify these critical factors and its effect on productivity in agile software development in terms of agile teams. The aim of this work is to distinguish these factors and to specify how agile teams can have a productive impact on the software. To accomplish this, we reviewed the relevant literature and administrated an online survey, which was administrated to 52 agile software development companies in Pakistan. The most commonly identified factors are team member and leader role, inter-team relationship, handling requirements, team velocity, conformance quality and team vision, as well as a number of host of sub-factors. The researcher used statistical techniques, such as Spearman's correlation, mean and standard deviation, to examine the results. The results showed that productivity correlates positively and significantly with most of the identified factors, except those with a negative correlation. The outcome revealed that these significant factors are critical factors to consider when examining team productivity in agile software development. The resulting quantitative analysis is expected to provide further insight into the dynamics of agile teamwork and establish a basis for further quantitative and qualitative modelling.
Distributed representation of knowledge graphs with subgraph-aware proximity

The distributed representation of Knowledge graphs (KGs) aims to embed the original KG into a low-dimensional embedding vector space, so as to facilitate the completion of KGs as well as the application of KGs in other AI fields. Most existing models preserve certain proximity property of KGs in the embedding space, such as the first/second-order proximity and the sequence-aware higher-order proximity. However, the ubiquitous similarity relationship between different sequences has rarely been discussed. In this paper, we propose a unified framework to preserve the subgraph-aware proximity in the embedding space, holding that the sequences within a subgraph generally imply a similar pattern. Especially, according to the composition and structure of KG sequences, we provide three methods for computing the embeddings of KG sequences: 1) Simply adding the involved relations of the KG sequences in a relation subgraph; 2) Recurrent neural network for the KG sequences in a complete subgraph; 3) Dilated recurrent neural network to match the special structure of the KG sequences in a complete subgraph. Empirically, we evaluate the proposed framework on the KG completion tasks of link prediction and entity classification. The results show that our framework outperforms the baselines by preserving the subgraph-aware proximity. Especially, exploring the special structure of KG sequences can further improve the performance.
Exploiting Filtering approach with Web Scrapping for Smart Online Shopping : Penny Wise: A wise Tool for Online Shopping

With the advancement in technology and popularity of e-commerce, the number of online shopping websites have been increased rapidly in the cyber world. This made people's life easy because it is easy to shop through internet. But this also bring effort for people as they spend a lot of time and efforts to search best product deals and offers on e-commerce websites. They have to filter and compare data by themselves. It takes a lot of time and still there are chances of ambiguous results. This paper is based on web crawling and scraping methods applied for identifying best deals from five e-commerce websites. The framework is designed using HTML (Hypertext markup language) and CSS (Cascading style sheet) as front-end and PHP: Hypertext preprocessor language as back-end support. The scrapping scripts are written using python libraries and web crawling works on HTML labels. The novelty in this framework is that we are not storing scrapped data on local database. Instead the results are dynamically fetched and showed every time the user input the query. It will help to improvise the storage and processing ability. Furthermore, the data retrieval process accuracy is 93% with minimum computation and less time.
Integrating heterogeneous sources for predicting question temporal anchors across Yahoo! Answers

Modern Community Question Answering (CQA) web forums provide the possibility to browse their archives using question-like search queries as in Information Retrieval (IR) systems. Although these traditional IR methods have become very successful at fetching semantically related questions, they typically leave unconsidered their temporal relations. That is to say, a group of questions may be asked more often during specific recurring time lines despite being semantically unrelated. In fact, predicting temporal aspects would not only assist these platforms in widening the semantic diversity of their search results, but also in re-stating questions that need to refresh their answers and in producing more dynamic, especially temporally-anchored, displays. In this paper, we devised a new set of time-frame specific categories for CQA questions, which is obtained by fusing two distinct earlier taxonomies (i.e., [29] and [50]). These new categories are then utilized in a large crowd-sourcing based human annotation effort. Accordingly, we present a systematical analysis of its results in terms of complexity and degree of difficulty as it relates to the different question topics1 Incidentally, through a large number of experiments, we investigate the effectiveness of a wider variety of linguistic features compared to what has been done in previous works. We additionally mix evidence/features distilled directly and indirectly from questions by capitalizing on their related web search results. We finally investigate the impact and effectiveness of multi-view learning to boost a large variety of multi-class supervised learners by optimizing a latent layer build on top of two views: one composed of features harvested from questions, and the other from CQA meta data and evidence extracted from web resources (i.e., snippets and Internet archives).
Improving Explainability of Recommendation System by Multi-sided Tensor Factorization

Recently, explainable recommender systems to improve their persuasiveness have attracted attentions. In this regard, some approaches extract information from posts or comments on items and apply them to simple and effective template. These information (e.g., topics and interests), however, are indirectly reflected to the existing recommendation algorithms or models therefore do not directly improve the recommendation accuracy. Moreover, extra resources in deriving information are required. Thereby, we propose a collaborative filtering approach using a tensor which is modeled considering 5Ws aspects and generate explanations by combining factorization results with templates. Quality and explanation of recommendations were evaluated on quantitative/qualitative analyses.
Crowd-Sourced Authentication for Enforcement in Dynamic Spectrum Sharing

The harmful interference caused by rogue radios poses a serious threat to spectrum sharing ecosystems. One approach for mitigating this problem is to adopt an enforcement scheme that can be used by an enforcement entity (e.g., Federal Communications Commission's Enforcement Bureau) to uniquely identify transmitters by authenticating their waveforms. In this approach, the enforcement entity that is authenticating the waveform is not the intended receiver, and hence it has to decode the authentication signal “blindly” with little or no knowledge of the transmission parameters. In real-world scenarios, an enforcement entity may need to cope with additional challenges, including poor signal strength of the received signals and simultaneous co-channel transmissions from multiple transmitters. In this paper, we propose a novel concept that effectively addresses some of these challenges, which we refer to as crowd-sourced blind authentication of co-channel transmitters (CBAT). We also present a concrete instantiation of this concept called frequency offset embedding for CBAT (FREE). Our results show that FREE enables the enforcement entity to blindly authenticate multiple co-channel transmitters with good accuracy by harnessing the power of crowd-sourcing.
A High-Efficiency FPGA-Based Accelerator for Binarized Neural Network

Although the convolutional neural network (CNN) has exhibited outstanding performance in various applications, the deployment of CNN on embedded and mobile devices is limited by the massive computations and memory footprint. To address these challenges, Courbariaux and co-workers put forward binarized neural network (BNN) which quantizes both the weights and activations to [Formula: see text]1. From the perspective of hardware, BNN can greatly simplify the computation and reduce the storage. In this work, we first present the algorithm optimizations to further binarize the first layer and the padding bits of BNN; then we propose a fully binarized CNN accelerator. With the Shuffle–Compute structure and the memory-aware computation schedule scheme, the proposed design can boost the performance for feature maps of different sizes and make full use of the memory bandwidth. To evaluate our design, we implement the accelerator on the Zynq ZC702 board, and the experiments on the SVHN and CIFAR-10 datasets show the state-of-the-art performance efficiency and resource efficiency.
Observation of Human Response to a Robotic Guide Using a Variational Autoencoder

This paper proposes a robotic-guide system equipped with a haptic device that can deliver kinesthetic feedback to and receive kinesthetic reaction from a follower. In addition, a feature-extraction method from a depth image of a user following the robotic guide based on a variational autoencoder (VAE) model is presented. One of the major roles of a sensory assistive robot is to help visually impaired people to walk through unknown spaces while avoiding obstacles. Haptic sensory information can be used as a directional cue for these people in recognizing the correct direction. We focus on how people react to haptic guidance from the assistive robot because an accurate prediction for human response enables robots to perform a more active role in not interfering with the human movement. In an indoor experiment, we observed the user reaction following our robotic guide in terms of the kinesthetic force that the user received and the depth image taken from the robot. Using the VAE model, the latent variable well represented the feature of the depth image, e.g., brief position information of a user torso. Furthermore, we tracked the precise trajectory of both the user and robotic guide using a motion-capture system.
Reversible Proofs of Sequential Work

Proofs of sequential work (PoSW) are proof systems where a prover, upon receiving a statement $$\chi $$ and a time parameter T computes a proof $$\phi (\chi ,T)$$ which is efficiently and publicly verifiable. The proof can be computed in T sequential steps, but not much less, even by a malicious party having large parallelism. A PoSW thus serves as a proof that T units of time have passed since $$\chi $$ was received. PoSW were introduced by Mahmoody, Moran and Vadhan [MMV11], a simple and practical construction was only recently proposed by Cohen and Pietrzak [CP18]. In this work we construct a new simple PoSW in the random permutation model which is almost as simple and efficient as [CP18] but conceptually very different. Whereas the structure underlying [CP18] is a hash tree, our construction is based on skip lists and has the interesting property that computing the PoSW is a reversible computation. The fact that the construction is reversible can potentially be used for new applications like constructing proofs of replication. We also show how to "embed" the sloth function of Lenstra and Weselowski [LW17] into our PoSW to get a PoSW where one additionally can verify correctness of the output much more efficiently than recomputing it (though recent constructions of "verifiable delay functions" subsume most of the applications this construction was aiming at).
Impact of Parameter Tuning on Machine Learning Based Breast Cancer Classification

Breast cancer is one of the major causes of death among women. Different decision support systems were proposed to assist oncologists in order to accurately diagnose their patients. These decision support systems mainly used Machine Learning (ML) techniques to classify the diagnosis into malign or benign tumor. In this paper, we evaluate and analyze the accuracy of the parameters tuning on the accuracy of three well-known ML techniques: Support Vector Machines, Multi-Layer Perception and Decision trees. We investigate three parameters tuning techniques: Grid Search (GS), Particle Swarm Optimization (PSO) and the default parameters of the Weka Tool over four datasets obtained from the Machine Learning repository. The overall results suggest that using GS and PSO lead to build more accurate classifiers, and therefore can help oncologists to provide more accurate diagnosis.
A semi-parallel framework for greedy information-theoretic feature selection

Feature selection (FS) is a well-studied area that avoids issues related the curse of dimensionality and overfitting. FS is a preprocessing procedure that identifies the feature subset that is both relevant and non-redundant. Although FS has been driven by the exploration of “big data” and the development of high-performance computing, the implementation of scalable information-theoretic FS remains an under-explored topic. In this contribution, we revisit the greedy optimization procedure of information-theoretic filter FS and propose a semi-parallel optimizing paradigm that can provide an equivalent feature set as the greedy FS algorithms in a fraction of the time. We focus on greedy selection algorithms due to their larger computational complexity associated with a rapidly growing number of features. Our framework is benchmarked against twelve datasets, including one extremely large dataset that has more than a million features, and we show our framework can significantly speed up the process of FS while selecting nearly the same features as the state-of-the-art information-theoretic FS methods.
Photon added coherent states of the parity deformed oscillator

Using the parity deformed Heisenberg algebra (RDHA), we first establish associated coherent states (RDCSs) for a pseudo-harmonic oscillator (PHO) system that are defined as eigenstates of a deformed annihilation operator. Such states can be expressed as superposition of an even and odd Wigner cat states.[Formula: see text] The RDCSs minimize a corresponding uncertainty relation, and resolve an identity condition through a positive definite measure which is explicitly derived. We introduce a class of single-mode excited coherent states (PARDCS) of the PHO through “m” times application of deformed creation operators to RDCS. For the states thus constructed, we analyze their statistical properties such as squeezing and sub-Poissonian statistics as well as their uncertainty relations.