Miracle-dz/UniFAR-FTUs huggingface dataset:
Subgroup Discovery Method Based on User Behavior Analysis

Subgroup discovery and analytics is an important tool for descriptive data mining. Most methods about subgroup discovery are based on the graph structure, ignoring the hidden mutual influence between nodes. In this paper, we consider the above issues, re-think the impact of implicit relationships between nodes, base on the network structure partition method, make the interaction behavior between user nodes reflected in the graph structure. A method of subgroup discovery based on behavior interaction (abbreviated as SDBI) is proposed. First, extract some features of data by using the method named "Initial classification of prominent data ", and then extract the remaining data in order according to the importance of the data, and construct the selected data node as a weighted complete digraph. Afterwards, the SDBI were used to divide subgroups. The results prove that the proposed method can effectively consider the problem of data non-uniformity and accurately divide the results.
Truly Batch Apprenticeship Learning with Deep Successor Features

We introduce a novel apprenticeship learning algorithm to learn an expert's underlying reward structure in off-policy model-free batch settings. Unlike existing methods that require hand-crafted features, on-policy evaluation, further data acquisition for evaluation policies or the knowledge of model dynamics, our algorithm requires only batch data (demonstrations) of the observed expert behavior. Such settings are common in many real-world tasks---health care, finance, or industrial process control---where accurate simulators do not exist and additional data acquisition is costly. We develop a transition-regularized imitation learning model to learn a rich feature representation and a near-expert initial policy that makes the subsequent batch inverse reinforcement learning process viable. We also introduce deep successor feature networks that perform off-policy evaluation to estimate feature expectations of candidate policies. Under the batch setting, our method achieves superior results on control benchmarks as well as a real clinical task of sepsis management in the Intensive Care Unit.
Hierarchical POMDP Framework for a Robot-Assisted ASD Diagnostic Protocol

Since the diagnosis of autism spectrum disorder (ASD) relies heavily on behavioral observations by experienced clinician, we seek to investigate whether parts of this job can be autonomously performed by a humanoid robot using only sensors available on-board. To that end, we developed a robot-assisted ASD diagnostic protocol. In this work we propose the Partially observable Markov decision process (POMDP) framework for such protocol which enables the robot to infer information about the state of the child based on observations of child's behavior. We extend our previous work by developing a protocol POMDP model which uses tasks of the protocol as actions. We devise a method to interface protocol and task models by using belief at the end of a task to generate observations for the protocol POMDP, resulting in a hierarchical POMDP framework. We evaluate our approach through an exploratory study with fifteen children (seven typically developing and eight with ASD).
Reducing T-count with the ZX-calculus

Reducing the number of non-Clifford quantum gates present in a circuit is an important task for efficiently implementing quantum computations, especially in the fault-tolerant regime. We present a new method for reducing the number of T-gates in a quantum circuit based on the ZX-calculus, which matches or beats previous approaches to T-count reduction on the majority of our benchmark circuits in the ancilla-free case, in some cases yielding up to 50% improvement. Our method begins by representing the quantum circuit as a ZX-diagram, a tensor network-like structure that can be transformed and simplified according to the rules of the ZX-calculus. We then show that a recently-proposed simplification strategy can be extended to reduce T-count using a new technique called phase teleportation. This technique allows nonClifford phases to combine and cancel by propagating non-locally through a generic quantum circuit. Phase teleportation does not change the number or location of non-phase gates and the method also applies to arbitrary non-Clifford phase gates as well as gates with unknown phase parameters in parametrised circuits. Furthermore, the simplification strategy we use is powerful enough to validate equality of many circuits. In particular, we use it to show that our optimised circuits are indeed equal to the original ones. We have implemented the routines of this paper in the open-source library PyZX.
The Long-Term Impact of Labor Market Interruptions: How Crucial is Timing?

Abstract In this day of two earner and single adult families many women and a small but growing minority of men face the decision whether and when to drop out of the labor force for a time, most often in order to take care of young children or in some cases of elderly family members. In addition, both women and men face the risk of occasional interruptions in their labor force participation when they are unable to find a job. In this study we use the NLSY79 data to investigate the long run effects on earnings of dropping out of the labor force and/or of being unemployed during the first 15 years of the careers of men and women firmly attached to the labor force, with particular attention to the importance of the timing of these interruptions. After controlling for numerous relevant factors, we find no significant negative impact on wage growth associated with time out of the labor force either early on or later, but do find that unemployment during the second half of the period has such effects both for men and for women.
Apple Leaf Disease Identification through Region-of-Interest-Aware Deep Convolutional Neural Network

Abstract Traditional approaches for the identification of leaf diseases involve the use of handcrafted features such as colors and textures for feature extraction. Therefore, these approaches may have limitations in extracting abundant and discriminative features. Although deep learning approaches have been recently introduced to overcome the shortcomings of traditional approaches, existing deep learning models such as VGG and ResNet have been used in these approaches. This indicates that the approach can be further improved to increase the discriminative power because the spatial attention mechanism to predict the background and spot areas (i.e., local areas with leaf diseases) has not been considered. Therefore, a new deep learning architecture, which is hereafter referred to as region-of-interest-aware deep convolutional neural network (ROI-aware DCNN), is proposed to make deep features more discriminative and increase classification performance. The primary idea is that leaf disease symptoms appear in leaf area, whereas the background region does not contain useful information regarding leaf diseases. To realize this, two subnetworks are designed. One subnetwork is the ROI subnetwork to provide more discriminative features from the background, leaf areas, and spot areas in the feature map. The other subnetwork is the classification subnetwork to increase the classification accuracy. To train the ROI-aware DCNN, the ROI subnetwork is first learned with a new image set containing the ground truth images where the background, leaf area, and spot area are divided. Subsequently, the entire network is trained in an end-to-end manner to connect the ROI subnetwork with the classification subnetwork through a concatenation layer. The experimental results confirm that the proposed ROI-aware DCNN can increase the discriminative power by predicting the areas in the feature map that are more important for leaf diseases identification. The results prove that the proposed method surpasses conventional state-of-the-art methods such as VGG, ResNet, SqueezeNet, bilinear model, and multiscale-based deep feature extraction and pooling.
Connecting Language and Knowledge with Heterogeneous Representations for Neural Relation Extraction

Knowledge Bases (KBs) require constant updating to reflect changes to the world they represent. For general purpose KBs, this is often done through Relation Extraction (RE), the task of predicting KB relations expressed in text mentioning entities known to the KB. One way to improve RE is to use KB Embeddings (KBE) for link prediction. However, despite clear connections between RE and KBE, little has been done toward properly unifying these models systematically. We help close the gap with a framework that unifies the learning of RE and KBE models leading to significant improvements over the state-of-the-art in RE. The code is available at https://github.com/billy-inn/HRERE.
Using RGB Image as Visual Input for Mapless Robot Navigation

Robot navigation in mapless environment is one of the essential problems and challenges in mobile robots. Deep reinforcement learning is a promising technique to tackle the task of mapless navigation. Since reinforcement learning requires a lot of explorations, it is usually necessary to train the agent in the simulator and then migrate to the real environment. The big reality gap makes RGB image, the most common visual sensor, rarely used. In this paper we present a learning-based mapless motion planner by taking RGB images as visual inputs. Many parameters in end-to-end navigation network taking RGB images as visual input are used to extract visual features. Therefore, we decouple visual features extracted module from the reinforcement learning network to reduce the need of interactions between agent and environment. We use Variational Autoencoder (VAE) to encode the image, and input the obtained latent vector as low-dimensional visual features into the network together with the target and motion information, so that the sampling efficiency of the agent is greatly improved. We built simulation environment as robot navigation environment for algorithm comparison. In the test environment, the proposed method was compared with the end-to-end network, which proved its effectiveness and efficiency. The source code is available: this https URL.
Bayesian Networks-Based Data Publishing Method Using Smooth Sensitivity

The issue of privacy protection in data publishing is an important topic in the field of information security. How to prevent the disclosure of sensitive information has become a hot topic of research. Due to the large data volume and the high correlation, high-dimensional data leads to poor data utility when data is published by differential privacy. Although the current high-dimensional data publishing methods can solve the problem of high-dimensional data publishing, the published synthetic data is of poor utility. In order to improve the utility of the published synthetic data, this paper proposes a Bayesian network-based data publishing method, which makes use of the concept of smooth sensitivity, to analyze the actual data set, which can reduce the added noise while achieving differential privacy and improve the utility of published data. The experiments are performed on real datasets and compared with the PrivBayes method to verify that the proposed method has advantages in data utility.
Ship-SIBISCaS: A First Step towards the Identification of Potential Maritime Law Infringements by means of LSA-Based Image

Maritime safety and security are being constantly jeopardized. Therefore, identifying maritime flow irregularities (semi-)automatically may be crucial to ensure maritime security in the future. This paper presents a Ship Semantic Information-Based, Image Similarity Calculation System (Ship-SIBISCaS), which constitutes a first step towards the automatic identification of this kind of maritime irregularities. In particular, the main goal of Ship-SIBISCaS is to automatically identify the type of ship depicted in a given image (such as abandoned, cargo, container, hospital, passenger, pirate, submersible, three-decker, or warship) and, thus, classify it accordingly. This classification is achieved in Ship-SIBISCaS by finding out the similarity of the ship image and/or description with other ship images and descriptions included in its knowledge base. This similarity is calculated by means of an LSA algorithm implementation that is run on a parallel architecture consisting of CPUs and GPUs (i.e., a heterogeneous system). This implementation of the LSA algorithm has been trained with a collection of texts, extracted from Wikipedia, that associate some semantic information to ImageNet ship images. Thanks to its parallel architecture, the indexing process of this image retrieval system has been accelerated 10 times (for the 1261 ships included in ImageNet). The range of the precision of the image similarity method is 46% to 92% with 100% recall (that is, a 100% coverage of the database).
Efficient VM Selection Heuristics for Dynamic VM Consolidation in Cloud Datacenters

Dynamic Consolidation of Virtual Machines (VMs) in a cloud data center requires live migration of VMs from over-utilized hosts. A primary but relatively an ignored part of consolidation process is the efficient selection of Virtual Machines from an over-utilized host for migration. Two VM selection policies, Threshold Based Selection (TBS) and Capacity Based Selection (CBS), have been proposed in this paper. These policies are based on the idea of simultaneously minimizing multiple factors that contribute to the degradation of the quality of service due to consolidation. Three degrading factors considered in the policies are, the time duration of VM migrations, time duration hosts remain over-utilized and the total number of migrations required for consolidation. Cost functions, involving these degrading factors, have been provided which formed the bases for TBS and CBS. TBS is an efficient VM selection mechanism that focuses more on the time duration of VM migrations and the total number of migrations required for the consolidation process as a trade-off between the three degrading factors. On the other hand, CBS is another efficient mechanism with more emphasis on reducing the time duration for which hosts remain over-utilized. Experiment results obtained by using Cloudsim simulating toolkit have shown that our proposed policies outperformed conventional VM selection policies like MMT, MU, and RC on indicators such as energy consumption, SLA violations, and overall performance efficiency.
Anxious Depression Prediction in Real-time Social Data

Mental well-being and social media have been closely related domains of study. In this research a novel model, AD prediction model, for anxious depression prediction in real-time tweets is proposed. This mixed anxiety-depressive disorder is a predominantly associated with erratic thought process, restlessness and sleeplessness. Based on the linguistic cues and user posting patterns, the feature set is defined using a 5-tuple vector . An anxiety-related lexicon is built to detect the presence of anxiety indicators. Time and frequency of tweet is analyzed for irregularities and opinion polarity analytics is done to find inconsistencies in posting behaviour. The model is trained using three classifiers (multinomial naive bayes, gradient boosting, and random forest) and majority voting using an ensemble voting classifier is done. Preliminary results are evaluated for tweets of sampled 100 users and the proposed model achieves a classification accuracy of 85.09%.
Risk Assessment of Blockchain Technology

Recently, blockchain has attracted a great interest, and today it is considered not only as an innovative technology, but as a potential revolution for the world of business. The motivation of this enthusiasm is due to its capability of enabling new forms of records-keeping, transactions, and interactions between decentralized and mistrusting entities. Indeed, blockchain is driving companies to huge investments and strategic acquisitions, and it is raising innovation and research across industries and academia. However, besides the opportunities offered, this technology may expose institutions and interacting parties to new risks that have to be discovered, understood, and, where possible, eliminated or at least reduced. In this paper we identify the main threats of blockchain and assess their related impact. Then, applying a NIST-compliant approach, we perform a qualitative risk assessment. Finally, we review the possible countermeasures, where existing, for each threat analyzed, and discuss open challenges and future directions.
Improving Indoor Fingerprinting Positioning With Affinity Propagation Clustering and Weighted Centroid Fingerprint

Nowadays, research and development of various indoor positioning systems (IPS) have been increasing owing to flourishing social and commercial interest in location-based services (LBSs). Among LBS technologies, we used the Bluetooth low energy beacon in our system, which consumes less energy and is embedded in many current smartphones and tablets. In particular, the fingerprinting method has become a prime choice in the design of IPS owing to its good location estimation and the fact that a line-of-sight from access points is not required. We propose an improved two-step fingerprinting localization using multiple fingerprint features to enhance the localization accuracy. The proposed system uses a propagation model to convert RSS of beacons to distance and estimate the weighted centroid (WC) of nearby beacons. The estimated WCs along with signal strength and rank of the nearby beacons are stored in the server database for localization instead of RSS from all the deployed beacons. First, the proposed system makes use of diverse fingerprinting features to increase localization accuracy that also reduces both the physical size of the database and the amount of data communication with the server in the execution phase; second, affinity propagation clustering minimizes the searching space of RPs and reduces the computational cost; third, exponential averaging is introduced to smooth the noisy RSS. The experimental results obtained by real field deployment show that the proposed method significantly improves the performance of the positioning system in both the positioning accuracy and radio-map database size.
Autonomous Mobile Scanning Systems for the Digitization of Buildings: A Review

Mobile scanning systems are being used more and more frequently in industry, construction, and artificial intelligent applications. More particularly, autonomous scanning plays an essential role in the field of the automatic creation of 3D models of building. This paper presents a critical review of current autonomous scanning systems, discussing essential aspects that determine the efficiency and applicability of a scanning system in real environments. Some important issues, such as data redundancy, occlusion, initial assumptions, the complexity of the scanned scene, and autonomy, are analysed in the first part of the document, while the second part discusses other important aspects, such as pre-processing, time requirements, evaluation, and opening detection. A set of representative autonomous systems is then chosen for comparison, and the aforementioned characteristics are shown together in several illustrative tables. Principal gaps, limitations, and future developments are presented in the last section. The paper provides the reader with a general view of the world of autonomous scanning and emphasizes the difficulties and challenges that new autonomous platforms should tackle in the future.
Improved PPP Ambiguity Resolution with the Assistance of Multiple LEO Constellations and Signals

The fusion of low earth orbit (LEO) constellation and Global Navigation Satellite Systems (GNSS) can increase the number of visible satellites and optimize spatial geometry, which is expected to improve the performance of precise point positioning (PPP) ambiguity resolution (AR). In addition, the multi-frequency signals of LEO satellites can bring a variety of observation combinations, which is potential to further improve the efficiency of PPP AR. In this contribution, multi-frequency PPP AR was achieved with the augmentation of different LEO constellations. Three types of LEO constellations were designed with 60, 192, and 288 satellites. Moreover, the corresponding observation data were simulated with the GNSS observations over the ground stations. The LEO constellations were designed to transmit navigation signals on three frequencies: L1, L2, and L5 at 1575.42, 1227.6, and 1176.45 MHz, respectively, which are consistent with the GPS signals. For PPP AR, the uncalibrated phase delay (UPD) products of GNSS and LEO were estimated first. Furthermore, the quality of UPD products was also analyzed. The research findings show that the performance of estimated LEO UPD is comparable to that of GNSS UPD. Based on the UPD products, LEO-augmented multi-GNSS PPP AR can be achieved. Numerous results show that the performance of single-system and multi-GNSS PPP AR can be significantly improved by introducing the LEO constellations. The augmentation performance is more remarkable in the case of increasing LEO satellites. The time to first fix (TTFF) of the GREC fixed solution can be shortened from 7.1 to 4.8, 1.1, and 0.7 min, by introducing observations of 60-, 192-, and 288-LEO constellations, respectively. The positioning accuracy of multi-GNSS fixed solutions is also improved by about 60%, 80%, and 90% with the augmentation of 60-, 192-, and 288-LEO constellations, respectively. Compared to the dual-frequency solutions, the triple-frequency LEO-augmented PPP fixed solution presents a better performance. The TTFF of GREC fixed solutions is shortened to 33 s with the augmentation of 288-LEO constellation under the triple-frequency environment. It is worth indicating that the 288-satellite LEO-only PPP AR was conducted in dual-frequency and triple-frequency modes, respectively. The averaged TTFFs of both modes are 71.8 s and 55.2 s, respectively. It indicates that LEO constellation with 288 satellites is capable of achieving high-precision positioning independently and shows an even better performance than GNSS-only solutions.
Analysis of coverage and area spectrum efficiency of UDN with inter-tier dependence

Stochastic geometry is widely employed to model cellular network. But in most existing works, base stations (BSs) are modelled following a homogeneous Poisson point process (PPP) for one-tier network, or several independent homogeneous PPP for multi-tier network, which ignore the dependence among BSs. In this paper, a three-tier UDN (Ultra dense network) with Macrocell BSs (MBS) for basic coverage, Picocell BSs (PBSs) deployed outside the coverage area of MBSs for compensating coverage holes, and Femtocell BSs (FBSs) surrounding MBSs for capacity improvement modelled by point process with inter-tier dependence is proposed. The tier association probability, the coverage probability and area spectrum efficiency (ASE) are derived. Simulation results validate our derivation, and results show that the proposed network model has 25%–45% performance gain in ASE.