Reviewerly/exHarmony huggingface dataset:
Smart Contracts for Spectrum Sensing as a Service

Mobile network operators (MNO) can opportunistically use the licensed bands of the primary users (PU) provided that they monitor the spectrum and stop their transmission upon detection of the PU. As deploying spectrum sensors may be prohibitively expensive, the MNO can buy spectrum sensing service from sensing helpers in its proximity. However, such a trade requires a framework with three key functions: helper selection, faulty or malicious helper identification, and payment to honest helpers. Here, we introduce Spass which provides these functions and facilitates a fair exchange between the entities without a trusted third party via smart contracts (SC) running on a blockchain network. While payments via SCs seem conceptually simple, realizing it is difficult due to the cost of using SC functions which might be prohibitive as write/computation operations on the SCs might have a cost, e.g., in Ethereum. Considering our design goals and SC-related overhead, we derive the optimal Spass parameters maximizing the MNO's profit. Moreover, we propose a K-means clustering approach to identify independent malicious helpers, and using both lossless and lossy compression on the helpers' sensing report to decrease the cost of write operations. Via simulations, we show under which conditions Spass-powered service leads to a profitable business for an MNO.
Learning Multilingual Meta-Embeddings for Code-Switching Named Entity Recognition

In this paper, we propose Multilingual Meta-Embeddings (MME), an effective method to learn multilingual representations by leveraging monolingual pre-trained embeddings.MME learns to utilize information from these embeddings via a self-attention mechanism without explicit language identification.We evaluate the proposed embedding method on the code-switching English-Spanish Named Entity Recognition dataset in a multilingual and cross-lingual setting.The experimental results show that our proposed method achieves state-of-the-art performance on the multilingual setting, and it has the ability to generalize to an unseen language task.
Multisensor Poisson Multi-Bernoulli Filter for Joint Target–Sensor State Tracking

In a typical multitarget tracking (MTT) scenario, the sensor state is either assumed known, or tracking is performed in the sensor's (relative) coordinate frame. This assumption does not hold when the sensor, e.g., an automotive radar, is mounted on a vehicle, and the target state should be represented in a global (absolute) coordinate frame. Then it is important to consider the uncertain location of the vehicle on which the sensor is mounted for MTT. In this paper, we present a multisensor low complexity Poisson multi-Bernoulli MTT filter, which jointly tracks the uncertain vehicle state and target states. Measurements collected by different sensors mounted on multiple vehicles with varying location uncertainty are incorporated sequentially based on the arrival of new sensor measurements. In doing so, targets observed from a sensor mounted on a well-localized vehicle reduce the state uncertainty of other poorly localized vehicles, provided that a common non-empty subset of targets is observed. A low complexity filter is obtained by approximations of the joint sensor-target state density minimizing the Kullback-Leibler divergence (KLD). Results from synthetic as well as experimental measurement data, collected in a vehicle driving scenario, demonstrate the performance benefits of joint vehicle-target state tracking.
Wasserstein Generative Learning with Kinematic Constraints for Probabilistic Interactive Driving Behavior Prediction

Since prediction plays a significant role in enhancing the performance of decision making and planning procedures, the requirement of advanced methods of prediction becomes urgent. Although many literatures propose methods to make prediction on a single agent, there is still a challenging and open problem on how to make prediction for multi-agent systems. In this work, by leveraging the power of statistics and information theory, we propose a novel deep latent variable model based on Wasserstein auto-encoder, which is able to learn a complex probabilistic distribution. Models such as neural networks cannot guarantee the satisfaction of dynamic system constraints directly. Therefore, we also propose a novel generative model structure to enable our approach to satisfy the kinematic constraints automatically. We test our model on both numerical examples and a real-world application to demonstrate its accuracy and efficiency. The results show that the proposed model achieves a better prediction accuracy than the other state-of-the-art methods under common evaluation metrics. Moreover, we introduce statistics to evaluate if the generative model literally learns the interaction patterns between different agents in the environments.
An ensemble method with sentiment features and clustering support

Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) are efficiently applied to natural language processing, especially sentiment analysis. CNN employs filters to capture local dependencies while LSTM designs a cell to memorize long-distance information. However, integrating these advantages into one model is challenging because of overfitting in training. To avoid this problem, we propose a freezing technique to learn sentiment-specific vectors from CNN and LSTM. This technique is efficient for integrating the advantages of various deep learning models. We also observe that semantically clustering documents into groups is more beneficial for ensemble methods. According to the experiments, our method achieves competitive results on the five well-known datasets: Pang & Lee movie reviews, Stanford Twitter Sentiment and Stanford Sentiment Treebank for sentence level, IMDB large movie reviews, and SenTube for document level.
Exploiting reverse target-side contexts for neural machine translation via asynchronous bidirectional decoding

Based on a unified encoder-decoder framework with attentional mechanism, neural machine translation (NMT) models have attracted much attention and become the mainstream in the community of machine translation. Generally, the NMT decoders produce translation in a left-to-right way. As a result, only left-to-right target-side contexts from the generated translations are exploited, while the right-to-left target-side contexts are completely unexploited for translation. In this paper, we extend the conventional attentional encoder-decoder NMT framework by introducing a backward decoder, in order to explore asynchronous bidirectional decoding for NMT. In the first step after encoding, our backward decoder learns to generate the target-side hidden states in a right-to-left manner. Next, in each timestep of translation prediction, our forward decoder concurrently considers both the source-side and the reverse target-side hidden states via two attention models. Compared with previous models, the innovation in this architecture enables our model to fully exploit contexts from both source side and target side, which improve translation quality altogether. We conducted experiments on NIST Chinese-English, WMT English-German and Finnish-English translation tasks to investigate the effectiveness of our model. Experimental results show that (1) our improved RNN-based NMT model achieves significant improvements over the conventional RNNSearch by 1.44/-3.02, 1.11/-1.01, and 1.23/-1.27 average BLEU and TER points, respectively; and (2) our enhanced Transformer outperforms the standard Transformer by 1.56/-1.49, 1.76/-2.49, and 1.29/-1.33 average BLEU and TER points, respectively. We released our code at https://github.com/DeepLearnXMU/ABD-NMT.
Pay Attention to Them: Deep Reinforcement Learning-Based Cascade Object Detection

This paper proposes a novel and effective approach, namely pay attention to them (PAT), to general object detection, which integrates the bottom-up single-shot convolutional neural networks (CNNs) and a top-down operating strategy. PAT starts by routinely applying a CNN regression detector to the entire input image. It then conducts refinement, which locates a sub-region that probably contains relevant objects through an intelligent agent built with an attentional mechanism and zooms it in to launch the detector again. This refining step is repeated in a cascaded way, where all the bounding boxes produced are scaled according to the original resolution and the sub-marginal and overlapping parts are wiped out to generate the final output. Due to such progressive processing, PAT improves the detection accuracy, especially for the objects of small sizes. Extensive experiments are conducted on the Pascal VOC and MS COCO benchmarks, and the results show that PAT is able to improve the representative baseline detectors, i.e., single shot multibox detector, YOLOv2, and Faster regions with CNN features, with remarkable accuracy gains [about 2%-5% mean Average Precision (mAP)], which demonstrates its competency.
Speaker Recognition Using LPC, MFCC, ZCR Features with ANN and SVM Classifier for Large Input Database

Speaker recognition is a biometric technique which uses individual speakers voice samples as a input for recognition purpose. Main goal of this work is to obtain better accuracy for speaker recognition system for large number of voice database. In this paper, a comparative study is made between various combinations of features for speaker identification system with feedforward artificial neural network(FFANN) and support vector machine for 10 and 20 speakers. Linear predictive coding, Mel frequency cepstral coefficient and zero crossing rate are used as a feature extraction techniques. Each features are tested separately and in combination with FFANN and SVM classifier on Matlab software. For ANN classifier 70% of total database are used for training,15% for validation and remaining 15% data are used for testing, number of hidden layers and number of neurons used are 2 and 80 respectively. It is observed from the result that efficiency of the system is not dropping by increasing number of speakers from 10(320 voice samples) to 20(640 voice samples) when using combination of LPC, MFCC, ZCR features with ANN classifier.
Efficient chameleon hash functions in the enhanced collision resistant model

Chameleon hash functions are collision resistant when only the hashing keys of the functions are known. In particular, without the knowledge of the secret information, the chameleon hash function is merely like a regular cryptographic hash function, where it is hard to find collisions. However anyone who has trapdoor keys can efficiently generate pre-images for the chameleon hash function. In some applications, such as redactable blockchains, unfortunately the existing properties do not suffice and we need more features. Actually, it is required that without knowing the trapdoor keys, nobody can compute collisions, even if he can see collisions for arbitrary hash functions. In 2017, Ateniese et al. introduced the notion of chameleon hash functions in the enhanced collision resistant model and proposed a construction in the standard model satifying the features. To date, efficient constructions of this kind of chameleon hash functions remain as an open research problem. In this paper, we answer this problem affirmatively by presenting efficient constructions of the chameleon hash function satisfying the enhanced collision resistance. The contributions of this work are twofold. First, we show the weakness of previous work. Then, we proceed with proposing new schemes with more efficiency. Technically, we present a new chameleon hash function in the basic model and based on simple assumptions. This chameleon hash function is well compatible with Groth-Sahai proof systems and the Cramer-Shoup encryption schemes, and can be used as a stepping stone to construct an efficient chameleon hash function in the enhanced collision resistant model. Moreover, we show our basic chameleon hash can be combined with optimal ZK-SNARKs of Groth and Maller that leads to shorter sizes for chameleon hash function in the enhanced collision resistant model.
py2DIC: A New Free and Open Source Software for Displacement and Strain Measurements in the Field of Experimental Mechanics

Thanks to the advances in computer power, memory storage and the availability of low-cost and high resolution digital cameras, Digital Image Correlation (DIC) is currently one of the most used optical and non-contact techniques for measuring material deformations. A free and open source 2D DIC software, named py2DIC, was developed at the Geodesy and Geomatics Division of the Sapienza University of Rome. Implemented in Python, the software is based on the template matching method and computes the 2D displacements and strains of samples subjected to mechanical loading. In this work, the potentialities of py2DIC were evaluated by processing two different sets of experimental data and comparing the results with other three well known DIC software packages Ncorr, Vic-2D and DICe. Moreover, an accuracy assessment was performed comparing the results with the values independently measured by a strain gauge fixed on one of the samples. The results demonstrate the possibility of successfully characterizing the deformation mechanism of the investigated materials, highlighting the pros and cons of each software package.
Artificial Intelligence and Music: Open Questions of Copyright Law and Engineering Praxis

The application of artificial intelligence (AI) to music stretches back many decades, and presents numerous unique opportunities for a variety of uses, such as the recommendation of recorded music from massive commercial archives, or the (semi-)automated creation of music. Due to unparalleled access to music data and effective learning algorithms running on high-powered computational hardware, AI is now producing surprising outcomes in a domain fully entrenched in human creativity—not to mention a revenue source around the globe. These developments call for a close inspection of what is occurring, and consideration of how it is changing and can change our relationship with music for better and for worse. This article looks at AI applied to music from two perspectives: copyright law and engineering praxis. It grounds its discussion in the development and use of a specific application of AI in music creation, which raises further and unanticipated questions. Most of the questions collected in this article are open as their answers are not yet clear at this time, but they are nonetheless important to consider as AI technologies develop and are applied more widely to music, not to mention other domains centred on human creativity.
Visual Cause Analytics for Traffic Congestion

Urban traffic congestion has become an important issue not only affecting our daily lives, but also limiting economic development. The primary cause of urban traffic congestion is that the number of vehicles is higher than the permissible limit of the road. Previous studies have focused on dispersing traffic volume by detecting urban traffic congestion zones and predicting future trends. However, to solve the fundamental problem, it is necessary to discover the cause of traffic congestion. Nevertheless, it is difficult to find a research which presents an approach to identify the causes of traffic congestion. In this paper, we propose a technique to analyze the cause of traffic congestion based on the traffic flow theory. We extract vehicle flows from traffic data, such as GPS trajectory and Vehicle Detector data. We detect vehicle flow changes utilizing the entropy from the information theory. Then, we build cumulative vehicle count curves (N-curve) that can quantify the flow of the vehicles in the traffic congestion area. The N-curves are classified into four different traffic congestion patterns by a convolutional neural network. Analyzing the causes and influence of traffic congestion is difficult and requires considerable experience and knowledge. Therefore, we present a visual analytics system that can efficiently perform a series of processes to analyze the cause and influence of traffic congestion. Through case studies, we have evaluated that our system can classify the causes of traffic congestion and can be used efficiently in road planning.
Hypernetwork Functional Image Representation

Motivated by the human way of memorizing images we introduce their functional representation, where an image is represented by a neural network. For this purpose, we construct a hypernetwork which takes an image and returns weights to the target network, which maps point from the plane (representing positions of the pixel) into its corresponding color in the image. Since the obtained representation is continuous, one can easily inspect the image at various resolutions and perform on it arbitrary continuous operations. Moreover, by inspecting interpolations we show that such representation has some properties characteristic to generative models. To evaluate the proposed mechanism experimentally, we apply it to image super-resolution problem. Despite using a single model for various scaling factors, we obtained results comparable to existing super-resolution methods.
Classifying Attention Types with Thermal Imaging and Eye Tracking

Despite the importance of attention in user performance, current methods for attention classification do not allow to discriminate between different attention types. We propose a novel method that combines thermal imaging and eye tracking to unobtrusively classify four types of attention: sustained, alternating, selective, and divided. We collected a data set in which we stimulate these four attention types in a user study (N = 22) using combinations of audio and visual stimuli while measuring users' facial temperature and eye movement. Using a Logistic Regression on features extracted from both sensing technologies, we can classify the four attention types with high AUC scores up to 75.7% for the user independent-condition independent, 87% for the user-independent-condition dependent, and 77.4% for the user-dependent prediction. Our findings not only demonstrate the potential of thermal imaging and eye tracking for unobtrusive classification of different attention types but also pave the way for novel applications for attentive user interfaces and attention-aware computing.
A survey of adaptive context-aware learning environments

Adaptive context-aware learning environments (ACALEs) can detect the learner’s context and adapt learning materials to match the context. The support for context-awareness and adaptation is essential in these systems so that they can make learning co
Using multi-features to partition users for friends recommendation in location based social network

Friend recommendation is an important feature of social network applications to help people make new friends and expand their social circles. However, the user-location and user-user information in location based social network are both too sparse which contributes to a big challenge for recommendation. In this paper, a new multi-feature SVM based friend recommendation model (MF-SVM) is proposed which regarded as a binary classification problem to tackle this challenge. We extract three features of each user by new methods respectively. The kernel density estimation and information entropy are used to smooth the check-in data and highlight the activity level of users to extract spatial-temporal feature. Then the social feature is extracted by considering the diversity of common friends. After that a new topic model improved by LDA is proposed which both considers user reviews and corresponding service description to extract textual feature. Finally, these features are used to train the SVM and whether the users have a friend link can be predicted by our model. The experiments on real-world datasets demonstrate that the proposed method in this paper outperforms the state-of-art friend recommendation methods under different types of evaluation metrics.
Information Extraction of Cybersecurity Concepts: An LSTM Approach

Extracting cybersecurity entities and the relationships between them from online textual resources such as articles, bulletins, and blogs and converting these resources into more structured and formal representations has important applications in cybersecurity research and is valuable for professional practitioners. Previous works to accomplish this task were mainly based on utilizing feature-based models. Feature-based models are time-consuming and need labor-intensive feature engineering to describe the properties of entities, domain knowledge, entity context, and linguistic characteristics. Therefore, to alleviate the need for feature engineering, we propose the usage of neural network models, specifically the long short-term memory (LSTM) models to accomplish the tasks of Named Entity Recognition (NER) and Relation Extraction (RE). We evaluated the proposed models on two tasks. The first task is performing NER and evaluating the results against the state-of-the-art Conditional Random Fields (CRFs) method. The second task is performing RE using three LSTM models and comparing their results to assess which model is more suitable for the domain of cybersecurity. The proposed models achieved competitive performance with less feature-engineering work. We demonstrate that exploiting neural network models in cybersecurity text mining is effective and practical.
Trust-Based Shard Distribution Scheme for Fault-Tolerant Shard Blockchain Networks

Blockchains guarantee data integrity through consensus of distributed ledgers based on multiple validation nodes called miners.For this reason, any blockchain system can be critically disabled by a malicious attack from a majority of the nodes (e.g., 51% attack).These attacks are more likely to succeed as the number of nodes required for consensus is smaller.Recently, as blockchains are becoming too large (making them difficult to store, send, receive, and manage), sharding is being considered as a technology to help improve the transaction throughput and scalability of blockchains.Sharding distributes block validators to disjoint sets to process transactions in parallel.Therefore, the number of validators of each shard group is smaller, which makes shard-based blockchains more vulnerable to 51% attacks than blockchains that do not use sharding.To solve this problem, this paper proposes a trust-based shard distribution (TBSD) scheme that assigns potential malicious nodes in the network to different shards, preventing malicious nodes from gaining a dominating influence on the consensus of a single shard.TBSD uses a trust-based shard distribution scheme to prevent malicious miners from gathering in on one shard by integration of a trust management system and genetic algorithm (GA).First, the trust of all nodes is computed based on the previous consensus result.Then, a GA is used to compute the shard distribution set to prevent collusion of malicious miners.The performance evaluation shows that the proposed TBSD scheme results in a shard distribution with a higher level of fairness than existing schemes, which provides an improved level of protection against malicious attacks.