Reviewerly/exHarmony huggingface dataset:
Privacy-Preserving Social Media Data Publishing for Personalized Ranking-Based Recommendation

Personalized recommendation is crucial to help users find pertinent information. It often relies on a large collection of user data, in particular users' online activity (e.g., tagging/rating/checking-in) on social media, to mine user preference. However, releasing such user activity data makes users vulnerable to inference attacks, as private data (e.g., gender) can often be inferred from the users' activity data. In this paper, we proposed PrivRank, a customizable and continuous privacy-preserving social media data publishing framework protecting users against inference attacks while enabling personalized ranking-based recommendations. Its key idea is to continuously obfuscate user activity data such that the privacy leakage of user-specified private data is minimized under a given data distortion budget, which bounds the ranking loss incurred from the data obfuscation process in order to preserve the utility of the data for enabling recommendations. An empirical evaluation on both synthetic and real-world datasets shows that our framework can efficiently provide effective and continuous protection of user-specified private data, while still preserving the utility of the obfuscated data for personalized ranking-based recommendation. Compared to state-of-the-art approaches, PrivRank achieves both a better privacy protection and a higher utility in all the ranking-based recommendation use cases we tested.
MatLab vs. Python vs. R

Matlab, Python and R have all been used successfully in teaching college students fundamentals of mathematics & statistics.In today's data driven environment, the study of data through big data analytics is very powerful, especially for the purpose of decision making and using data statistically in this data rich environment.MatLab can be used to teach introductory mathematics such as calculus and statistics.Both Python and R can be used to make decisions involving big data.On the one hand, Python is perfect for teaching introductory statistics in a data rich environment.On the other hand, while R is a little more involved, there are many customizable programs that can make somewhat involved decisions in the context of prepackaged, preprogrammed statistical analysis.
Extractive Arabic Text Summarization Using Modified PageRank Algorithm

This paper proposed an approach for Arabic text summarization. Text summarization is one of the natural language processing's applications which is used for reducing the original text amount and retrieving only the important information from the original text. The Arabic language has a complex morphological structure which makes it very difficult to extract nouns to be used as a feature for summarization process. Therefore, Al-Khalil morphological analyzer is used to solve the problem of nouns extraction. The proposed approach is a graph-based system, which represents the document as a graph where the vertices of the graph are the sentences. A Modified PageRank algorithm is applied with an initial score for each node that is the number of nouns in this sentence. More nouns in the sentence mean more information, so nouns count used here as initial rank for the sentence. Edges between sentences are the cosine similarity between the sentences, to get a final summary that contains sentences with more information and well connected with each other. The process of text summarization consists of three major stages: pre-processing stage, features extraction and graph construction stage, and finally applying the Modified PageRank algorithm and summary extraction. The Modified PageRank algorithm used a different number of iterations to find the number returns the best summary results, and the extracted summary depends on compression ratio, taking into account removing redundancy depending on the overlapping between the sentences. To evaluate the performance of this approach EASC Corpus is used as a standard. LexRank and TextRank algorithms were used under the same circumstances, the proposed approach provides better results when compared with other Arabic text summarization techniques. The proposed approach performs efficiently with the number of iteration 10,000.
Learning Feasibility for Task and Motion Planning in Tabletop Environments

Task and motion planning (TMP) combines discrete search and continuous motion planning. Earlier work has shown that to efficiently find a task-motion plan, the discrete search can leverage information about the continuous geometry. However, incorporating continuous elements into discrete planners presents challenges. We improve the scalability of TMP algorithms in tabletop scenarios with a fixed robot by introducing geometric knowledge into a constraint-based task planner in a robust way. The key idea is to learn a classifier for feasible motions and to use this classifier as a heuristic to order the search for a task-motion plan. The learned heuristic guides the search toward feasible motions and, thus, reduces the total number of motion planning attempts. A critical property of our approach is allowing robust planning in diverse scenes. We train the classifier on minimal exemplar scenes and then use principled approximations to apply the classifier to complex scenarios in a way that minimizes the effect of errors. By combining learning with planning, our heuristic yields order-of-magnitude run time improvements in diverse tabletop scenarios. Even when classification errors are present, properly biasing our heuristic ensures that we will have little computational penalty.
Quaternary gray-code phase unwrapping for binary fringe projection profilometry

In order to achieve a higher speed for fringe projection profilometry, we propose a quaternary gray-code phase unwrapping method using only two defocused binary patterns. We present a weighted optimization algorithm for the design of special binary code patterns. The designed binary patterns are projected on the object with a defocused projector, and interestingly become quaternary patterns which are more expressive gray codes. After the distorted code patterns are captured by a camera, they will undergo our proposed normalization-denoising-clustering process to recover the ideal gray codes. Besides, continuity/geometry constraints are integrated with this phase unwrapping method to further reduce the number of required patterns. The integrated method can recover a high-quality 3D shape with only five binary patterns including three patterns for three-step phase shifting algorithms and two for our phase unwrapping method. The effectiveness of the proposed method is demonstrated by both simulations and experiments.
Intel® RealSense™ SR300 Coded Light Depth Camera

Intel® RealSense™ SR300 is a depth camera capable of providing a VGA-size depth map at 60 fps and 0.125mm depth resolution. In addition, it outputs an infrared VGA-resolution image and a 1080p color texture image at 30 fps. SR300 form-factor enables it to be integrated into small consumer products and as a front facing camera in laptops and Ultrabooks™. The SR300 depth camera is based on a coded-light technology where triangulation between projected patterns and images captured by a dedicated sensor is used to produce the depth map. Each projected line is coded by a special temporal optical code, that enables a dense depth map reconstruction from its reflection. The solid mechanical assembly of the camera allows it to stay calibrated throughout temperature and pressure changes, drops, and hits. In addition, active dynamic control maintains a calibrated depth output. An extended API LibRS released with the camera allows developers to integrate the camera in various applications. Algorithms for 3D scanning, facial analysis, hand gesture recognition, and tracking are within reach for applications using the SR300. In this paper, we describe the underlying technology, hardware, and algorithms of the SR300, as well as its calibration procedure, and outline some use cases. We believe that this paper will provide a full case study of a mass-produced depth sensing product and technology.
Better Automatic Evaluation of Open-Domain Dialogue Systems with Contextualized Embeddings

Despite advances in open-domain dialogue systems, automatic evaluation of such systems is still a challenging problem. Traditional reference-based metrics such as BLEU are ineffective because there could be many valid responses for a given context that share no common words with reference responses. A recent work proposed Referenced metric and Unreferenced metric Blended Evaluation Routine (RUBER) to combine a learning-based metric, which predicts relatedness between a generated response and a given query, with reference-based metric; it showed high correlation with human judgments. In this paper, we explore using contextualized word embeddings to compute more accurate relatedness scores, thus better evaluation metrics. Experiments show that our evaluation metrics outperform RUBER, which is trained on static embeddings.
Object detection based on an adaptive attention mechanism

Object detection is an important component of computer vision. Most of the recent successful object detection methods are based on convolutional neural networks (CNNs). To improve the performance of these networks, researchers have designed many different architectures. They found that the CNN performance benefits from carefully increasing the depth and width of their structures with respect to the spatial dimension. Some researchers have exploited the cardinality dimension. Others have found that skip and dense connections were also of benefit to performance. Recently, attention mechanisms on the channel dimension have gained popularity with researchers. Global average pooling is used in SENet to generate the input feature vector of the channel-wise attention unit. In this work, we argue that channel-wise attention can benefit from both global average pooling and global max pooling. We designed three novel attention units, namely, an adaptive channel-wise attention unit, an adaptive spatial-wise attention unit and an adaptive domain attention unit, to improve the performance of a CNN. Instead of concatenating the output of the two attention vectors generated by the two channel-wise attention sub-units, we weight the two attention vectors based on the output data of the two channel-wise attention sub-units. We integrated the proposed mechanism with the YOLOv3 and MobileNetv2 framework and tested the proposed network on the KITTI and Pascal VOC datasets. The experimental results show that YOLOv3 with the proposed attention mechanism outperforms the original YOLOv3 by mAP values of 2.9 and 1.2% on the KITTI and Pascal VOC datasets, respectively. MobileNetv2 with the proposed attention mechanism outperforms the original MobileNetv2 by a mAP value of 1.7% on the Pascal VOC dataset.
Zero-Bias Deep Learning for Accurate Identification of Internet-of-Things (IoT) Devices

The Internet of Things (IoT) provides applications and services that would otherwise not be possible. However, the open nature of IoT make it vulnerable to cybersecurity threats. Especially, identity spoofing attacks, where an adversary passively listens to existing radio communications and then mimic the identity of legitimate devices to conduct malicious activities. Existing solutions employ cryptographic signatures to verify the trustworthiness of received information. In prevalent IoT, secret keys for cryptography can potentially be disclosed and disable the verification mechanism. Non-cryptographic device verification is needed to ensure trustworthy IoT. In this paper, we propose an enhanced deep learning framework for IoT device identification using physical layer signals. Specifically, we enable our framework to report unseen IoT devices and introduce the zero-bias layer to deep neural networks to increase robustness and interpretability. We have evaluated the effectiveness of the proposed framework using real data from ADS-B (Automatic Dependent Surveillance-Broadcast), an application of IoT in aviation. The proposed framework has the potential to be applied to accurate identification of IoT devices in a variety of IoT applications and services. Codes and data are available in IEEE Dataport.
Digital technologies and collective intelligence for healthcare ecosystem: Optimizing Internet of Things adoption for pandemic management

This paper proposes a framework based on the collective intelligence principle to understand how the healthcare ecosystem is facing the challenges posed by the COVID-19 by using the Internet of Things (IoT) combined with other digital technologies. The underlying assumption is to consider the Healthcare ecosystem as a collective intelligence system in which the multitude of actors can be coordinated to address the pandemic-specific management challenges. The Italian healthcare ecosystem is analyzed as scenario taking in consideration the ‘genes’ of the collective intelligence: What is being done?, Who is doing it?, Why are they doing it? and How is it being done?. Our analysis introduces policy implications based on a unique decision support system (DSS) to allocate a limited set of IoT devices to a larger group of patients, to balance the alternative needs to improve the conditions of the most severe patients but to maximize the efficiency of device use.
Working Memory Connections for LSTM

Recurrent Neural Networks with Long Short-Term Memory (LSTM) make use of gating mechanisms to mitigate exploding and vanishing gradients when learning long-term dependencies. For this reason, LSTMs and other gated RNNs are widely adopted, being the standard de facto for many sequence modeling tasks. Although the memory cell inside the LSTM contains essential information, it is not allowed to influence the gating mechanism directly. In this work, we improve the gate potential by including information coming from the internal cell state. The proposed modification, named Working Memory Connection, consists in adding a learnable nonlinear projection of the cell content into the network gates. This modification can fit into the classical LSTM gates without any assumption on the underlying task, being particularly effective when dealing with longer sequences. Previous research effort in this direction, which goes back to the early 2000s, could not bring a consistent improvement over vanilla LSTM. As part of this paper, we identify a key issue tied to previous connections that heavily limits their effectiveness, hence preventing a successful integration of the knowledge coming from the internal cell state. We show through extensive experimental evaluation that Working Memory Connections constantly improve the performance of LSTMs on a variety of tasks. Numerical results suggest that the cell state contains useful information that is worth including in the gate structure.
A Text Sentiment Classification Modeling Method Based on Coordinated CNN‐LSTM‐Attention Model

The major challenge that text sentiment classification modeling faces is how to capture the intrinsic semantic, emotional dependence information and the key part of the emotional expression of text. To solve this problem, we proposed a Coordinated CNN-LSTM-Attention(CCLA) model. We learned the vector representations of sentence with CCLA unit. Semantic and emotional information of sentences and their relations are adaptively encoded to vector representations of document. We used softmax regression classifier to identify the sentiment tendencies in the text. Compared with other methods, the CCLA model can well capture the local and long distance semantic and emotional information. Experimental results demonstrated the effectiveness of CCLA model. It shows superior performances over several state-of-the-art baseline methods.
Investigation on Performance of Neural Networks Using Quadratic Relative Error Cost Function

The performance of neural networks with quadratic cost function (MSE cost function) is analyzed in terms of the adjustment rate of weights and its performance in multi-magnitude data processing using a qualitative mathematical method based on mean squared error.However, neural networks using quadratic cost functions exhibit low-weight updating rates and variations in performances in multi-magnitude data processing.This paper investigates the performance of neural networks using a quadratic relative error cost function (REMSE cost function).Two-node-to-one-node models are built to investigate the performance of the REMSE and MSE cost functions in adjustment rate of weights and multi-magnitude data processing.A three-layer neural network is employed to compare the training and prediction performances of the REMSE cost function and MSE cost function.Three LSTM networks are used to evaluate the differences between REMSE, MSE, and Logcosh in actual applications by learning stress and strain of soil.The results indicate that the REMSE cost function can notably accelerate the adjustment rate of weights and improve the performance of the neural network in small magnitude data regression.The applications of the REMSE cost function are also discussed.
Machine learning based on reservoir computing with time-delayed optoelectronic and photonic systems

The concept of reservoir computing emerged from a specific machine learning paradigm characterized by a three-layered architecture (input, reservoir, and output), where only the output layer is trained and optimized for a particular task. In recent years, this approach has been successfully implemented using various hardware platforms based on optoelectronic and photonic systems with time-delayed feedback. In this review, we provide a survey of the latest advances in this field, with some perspectives related to the relationship between reservoir computing, nonlinear dynamics, and network theory.
Error bounds for deep ReLU networks using the Kolmogorov–Arnold superposition theorem

We prove a theorem concerning the approximation of multivariate functions by deep ReLU networks, for which the curse of the dimensionality is lessened. Our theorem is based on a constructive proof of the Kolmogorov–Arnold superposition theorem, and on a subset of multivariate continuous functions whose outer superposition functions can be efficiently approximated by deep ReLU networks.
Fine-grained resource provisioning and task scheduling for heterogeneous applications in distributed green clouds

An increasing number of enterprises have adopted cloud computing to manage their important business applications in distributed green cloud ( DGC ) systems for low response time and high cost-effectiveness in recent years. Task scheduling and resource allocation in DGCs have gained more attention in both academia and industry as they are costly to manage because of high energy consumption. Many factors in DGCs, e.g., prices of power grid, and the amount of green energy express strong spatial variations. The dramatic increase of arriving tasks brings a big challenge to minimize the energy cost of a DGC provider in a market where above factors all possess spatial variations. This work adopts a G / G / 1 queuing system to analyze the performance of servers in DGCs. Based on it, a single-objective constrained optimization problem is formulated and solved by a proposed simulated-annealing-based bees algorithm ( SBA ) to find SBA can minimize the energy cost of a DGC provider by optimally allocating tasks of heterogeneous applications among multiple DGCs, and specifying the running speed of each server and the number of powered-on servers in each GC while strictly meeting response time limits of tasks of all applications. Realistic data-based experimental results prove that SBA achieves lower energy cost than several benchmark scheduling methods do.
Learning to Contrast the Counterfactual Samples for Robust Visual Question Answering

In the task of Visual Question Answering (VQA), most state-of-the-art models tend to learn spurious correlations in the training set and achieve poor performance in out-ofdistribution test data.Some methods of generating counterfactual samples have been proposed to alleviate this problem.However, the counterfactual samples generated by most previous methods are simply added to the training data for augmentation and are not fully utilized.Therefore, we introduce a novel selfsupervised contrastive learning mechanism to learn the relationship between original samples, factual samples and counterfactual samples.With the better cross-modal joint embeddings learned from the auxiliary training objective, the reasoning capability and robustness of the VQA model are boosted significantly.We evaluate the effectiveness of our method by surpassing current state-of-the-art models on the VQA-CP dataset, a diagnostic benchmark for assessing the VQA model's robustness.
RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking

In various natural language processing tasks, passage retrieval and passage re-ranking are two key procedures in finding and ranking relevant information.Since both the two procedures contribute to the final performance, it is important to jointly optimize them in order to achieve mutual improvement.In this paper, we propose a novel joint training approach for dense passage retrieval and passage reranking.A major contribution is that we introduce the dynamic listwise distillation, where we design a unified listwise training approach for both the retriever and the re-ranker.During the dynamic distillation, the retriever and the re-ranker can be adaptively improved according to each other's relevance information.We also propose a hybrid data augmentation strategy to construct diverse training instances for listwise training approach.Extensive experiments show the effectiveness of our approach on both MSMARCO and Natural Questions datasets.
Human activity recognition using tools of convolutional neural networks: A state of the art review, data sets, challenges, and future prospects

Human Activity Recognition (HAR) plays a significant role in the everyday life of people because of its ability to learn extensive high-level information about human activity from wearable or stationary devices. A substantial amount of research has been conducted on HAR and numerous approaches based on deep learning have been exploited by the research community to classify human activities. The main goal of this review is to summarize recent works based on a wide range of deep neural networks architecture, namely convolutional neural networks (CNNs) for human activity recognition. The reviewed systems are clustered into four categories depending on the use of input devices like multimodal sensing devices, smartphones, radar, and vision devices. This review describes the performances, strengths, weaknesses, and the used hyperparameters of CNN architectures for each reviewed system with an overview of available public data sources. In addition, a discussion of the current challenges to CNN-based HAR systems is presented. Finally, this review is concluded with some potential future directions that would be of great assistance for the researchers who would like to contribute to this field. We conclude that CNN-based approaches are suitable for effective and accurate human activity recognition system applications despite challenges including availability of data regarding composite or group activities, high computational resource requirements, data privacy concerns, and edge computing limitations. For widespread adaptation, future research should be focused on more efficient edge computing techniques, datasets incorporating contextual information with activities, more explainable methodologies, and more robust systems.