Miracle-dz/UniFAR-FTUs huggingface dataset:
Content Explorer: Recommending Novel Entities for a Document Writer

Background research is an essential part of document writing. Search engines are great for retrieving information once we know what to look for. However, the bigger challenge is often identifying topics for further research. Automated tools could help significantly in this discovery process and increase the productivity of the writer. In this paper, we formulate the problem of recommending topics to a writer. We consider this as a supervised learning problem and run a user study to validate this approach. We propose an evaluation metric and perform an empirical comparison of state-of-the-art models for extreme multi-label classification on a large data set. We demonstrate how a simple modification of the cross-entropy loss function leads to improved results of the deep learning models.
Deep Video Stabilization Using Adversarial Networks

Video stabilization is necessary for many hand‐held shot videos. In the past decades, although various video stabilization methods were proposed based on the smoothing of 2D, 2.5D or 3D camera paths, hardly have there been any deep learning methods to solve this problem. Instead of explicitly estimating and smoothing the camera path, we present a novel online deep learning framework to learn the stabilization transformation for each unsteady frame, given historical steady frames. Our network is composed of a generative network with spatial transformer networks embedded in different layers, and generates a stable frame for the incoming unstable frame by computing an appropriate affine transformation. We also introduce an adversarial network to determine the stability of apiece of video. The network is trained directly using the pair of steady and unsteady videos. Experiments show that our method can produce similar results as traditional methods, moreover, it is capable of handling challenging unsteady video of low quality, where traditional methods fail, such as video with heavy noise or multiple exposures. Our method runs in real time, which is much faster than traditional methods.
Fraud Phone Calls Analysis Based on Label Propagation Community Detection Algorithm

With the continuous development of communications industry, the majority of users gradually enjoy a variety of communications services. In the meantime, however, more and more fraud phones appears in the user's daily life. Although there are currently many interception schemes for fraud phones, they all belong to passive interception and rely on intelligent terminals. Therefore, this paper proposes a fraud phone calls analysis method based on label propagation community detection algorithm (LPA). Call content data are transformed into complex network. The LPA algorithm is used to generate fraud communities on this complex network. Detail analysis is also carried out for extracting the detail of communities. Results show that the methods can help to quickly identity the fraud phone calls.
Word Relation Autoencoder for Unseen Hypernym Extraction Using Word Embeddings

Lexicon relation extraction given distributional representation of words is an important topic in NLP. We observe that the state-of-the-art projection-based methods cannot be generalized to handle unseen hypernyms. We propose to analyze it in the perspective of pollution and construct the corresponding indicator to measure it. We propose a word relation autoencoder (WRAE) model to address the challenge. Experiments on several hypernym-like lexicon datasets show that our model outperforms the competitors significantly.
Bug synthesis: challenging bug-finding tools with deep faults

In spite of decades of research in bug detection tools, there is a surprising dearth of ground-truth corpora that can be used to evaluate the efficacy of such tools. Recently, systems such as LAVA and EvilCoder have been proposed to automatically inject bugs into software to quickly generate large bug corpora, but the bugs created so far differ from naturally occurring bugs in a number of ways. In this work, we propose a new automated bug injection system, Apocalypse, that uses formal techniques—symbolic execution, constraint-based program synthesis and model counting—to automatically inject fair (can potentially be discovered by current bug-detection tools), deep (requiring a long sequence of dependencies to be satisfied to fire), uncorrelated (each bug behaving independent of others), reproducible (a trigger input being available) and rare (can be triggered by only a few program inputs) bugs in large software code bases. In our evaluation, we inject bugs into thirty Coreutils programs as well as the TCAS test suite. We find that bugs synthesized by Apocalypse are highly realistic under a variety of metrics, that they do not favor a particular bug-finding strategy (unlike bugs produced by LAVA), and that they are more difficult to find than manually injected bugs, requiring up around 240× more tests to discover with a state-of-the-art symbolic execution tool.
Predicting Delay Discounting from Social Media Likes with Unsupervised Feature Learning

Delay discounting, a behavioral measure of impulsivity, is often used to quantify the human tendency to choose a smaller, sooner reward (e.g., $1 today) over a larger, later reward ($2 tomorrow). Delay discounting and its relation to human decision making is a hot topic in economics and behavior science since pitting the demands of long-term goals against short-term desires is among the most difficult tasks in human decision making. Previously, small-scale studies based on questionnaires were used to analyze an individual's delay discounting rate (DDR) and its relation to his/her real-world behavior such as substance abuse, pathological gambling and poor academic performance. In this research, we employ large-scale social media analytics to study DDR and its relation to people's social media behavior (e.g., their Likes on Facebook). We also build computational models to automatically infer DDR from Social Media Likes. Since the predicting feature space is very large and the size of the delay discounting ground truth dataset is relatively small, we focus on studying the impact of different unsupervised feature learning methods on predicting performance. Our results demonstrate the significant role unsupervised feature learning plays in this task.
Evaluation and Comparison of Resonant Vibration in Switched Reluctance Motor with Different Turn-on and Turn-off Angles

This paper investigates the influence of turn-on and turn-off angles on radial vibration, especially vibration caused by resonance, i.e. resonant vibration. It is well-known that current harmonics may cause resonance. And the frequency of current harmonic is highly dependent on motor speed. Thus based on the natural frequencies obtained by vibration mode analysis, the possible motor speeds where resonance vibration may occur are firstly derived. Then the harmonics of phase current, radial force and vibration are compared with different turn-on and turn-off angles through simulation model, which is built up by finite element analysis (FEA) and transfer function. Simulation results show that with the advance of turn-on angle or with the delay of turn-off angle, the harmonics of current, radial force and vibration can be reduced. Finally, experiments are conducted to verify the simulation results and conclusions.
Muscle Thickness and Curvature Influence Atrial Conduction Velocities

Electroanatomical mapping is currently used to provide clinicians with information about the electrophysiological state of the heart and to guide interventions like ablation. These maps can be used to identify ectopic triggers of an arrhythmia such as atrial fibrillation (AF) or changes in the conduction velocity (CV) that have been associated with poor cell to cell coupling or fibrosis. Unfortunately, many factors are known to affect CV, including membrane excitability, pacing rate, wavefront curvature, and bath loading, making interpretation challenging. In this work, we show how endocardial conduction velocities are also affected by the geometrical factors of muscle thickness and wall curvature. Using an idealized three-dimensional strand, we show that transverse conductivities and boundary conditions can slow down or speed up signal propagation, depending on the curvature of the muscle tissue. In fact, a planar wavefront that is parallel to a straight line normal to the mid-surface does not remain normal to the mid-surface in a curved domain. We further demonstrate that the conclusions drawn from the idealized test case can be used to explain spatial changes in conduction velocities in a patient-specific reconstruction of the left atrial posterior wall. The simulations suggest that the widespread assumption of treating atrial muscle as a two-dimensional manifold for electrophysiological simulations will not accurately represent the endocardial conduction velocities in regions of the heart thicker than 0.5 mm with significant wall curvature.
Simple learning method to guarantee operational range of optical monitors

It is necessary to guarantee the operational range of machine learning (ML)-based optical physicallayer monitors (OPMs). To declare high-level monitoring objectives and obtain their values from OPMs, finding a methodology to accurately estimate the value of a target quantity and ensure their operational range is necessary. We introduce a deep neural network (DNN) with a digital coherent receiver to ML-based OPMs to deal with the abundance of training data needed for convergence and the preprocessing of input data by human engineers needed for feature (representation) extraction. However, guaranteeing the operational range of trained models on DNN-based OPMs was left for another investigation. To address this issue with DNN-based OPMs, we propose an "operational range expander," a simple treatment of the link between pre-processing training datasets and their specified operational range. We assess the operational range expander by performing simulation and experimentation using a DNNbased optical signal-to-noise ratio (OSNR) estimator. We select a laser frequency offset between a signal and a local oscillator in digital coherent receivers as an example quantity for a practical operational range expander in this study. This is because the OPMs need to work before digitally compensating frequency offset despite the difficulty in fully controlling frequency offset in practical situations. We evaluate bias errors and standard deviations of OSNR estimation from different frequency offsets ranging from -3.5 to 3.5 GHz and confirm that the provided operational range expander specified the operational range of DNNbased OSNR estimators through their training phase.
Soft-Handover Algorithm for Hybrid Industrial Wireless Sensor and Actuator Networks

Wireless communication technologies play an important role in today's society and, for that reason, wireless systems are almost ubiquitous. The keys of the success of wireless communications are, among others: reduced cost of wiring, implementation and maintenance or greater flexibility. Given these advantages, there is a growing interest in expanding the wired networks used in the most critical industrial applications to a wireless domain, fulfilling the real-time requirements of these applications. In addition, the incorporation of mobile devices to hybrid networks allows to expand the application area and obtain even more benefits from using wireless solutions. Nevertheless, guaranteeing the robustness and determinism required by factory automation applications where several Access Points (AP) are necessary to cover the entire wireless area is not an easy task. Therefore, in this paper, a novel soft-handover algorithm is presented. This algorithm guarantees an uninterrupted communication during the handover process required by the wireless devices together with a dynamic management of the resources within the hybrid (wired/wireless) scheme. To make this possible, the handover algorithm is combined with a slotted Medium Access Control (MAC) based on a superframe structure with a contention-free phase and a contention phase, where the contention-free phase is divided into uplink and downlink slots. The performance of the algorithm has been evaluated through OMNeT++ simulations.
Covert Online Ethnography and Machine Learning for Detecting Individuals at Risk of Being Drawn into Online Sex Work

How can we identify individuals at risk of being drawn into online sex work? The spread of online communication removes transaction costs and enables a greater number of people to be involved in illicit activities, including online sex trade. As a result, social media platforms often work as springboard for criminal careers posing a significant risk to the economy, public health and trust. Detecting deviant behaviors online is limited by the poor availability of ground-truth data and machine learning tools. Unlike prior work which focuses exclusively on either qualitative or quantitative methods, in this paper we combine covert online ethnography with semi-supervised learning methodologies, using data from a popular European adult forum. We obtained risk assessment results of 78 users using covert online ethnography, and set out to build a machine learning model that can predict the risk factor in other 28,832 users. Results show that a combination-based approach in which all features are used yields the most accurate results.
Bolidophyceae, a Sister Picoplanktonic Group of Diatoms – A Review

Pico- and nano-phytoplankton (respectively, 0.2-2 and 2-20 µm in cell size) play a key role in many marine ecosystems. In this size range, Bolidophyceae is a group of eukaryotes that contains species with cells surrounded by 5 or 8 silica plates (Parmales) as well as naked flagellated species (formerly Bolidomonadales). Bolidophyceae share a common ancestor with diatoms, one of the most successful groups of phytoplankton. This review summarizes the current information on taxonomy, phylogeny, ecology and physiology obtained by recent studies using a range of approaches including metabarcoding. Despite their rather small contribution to the phytoplankton communities (on average less than 0.1%), Bolidophyceae are very widespread throughout marine systems from the tropics to the pole. This review concludes by discussing similarities and differences between Bolidophyceae and diatoms.
Put It Back: Entity Typing with Language Model Enhancement

Entity typing aims to classify semantic types of an entity mention in a specific context. Most existing models obtain training data using distant supervision, and inevitably suffer from the problem of noisy labels. To address this issue, we propose entity typing with language model enhancement. It utilizes a language model to measure the compatibility between context sentences and labels, and thereby automatically focuses more on context-dependent labels. Experiments on benchmark datasets demonstrate that our method is capable of enhancing the entity typing model with information from the language model, and significantly outperforms the state-of-the-art baseline. Code and data for this paper can be found from https://github.com/thunlp/LME.
Impact of the Fan Design and Rotational Direction on the Thermal Characteristics of Induction Motors

Low manufacturing cost and high reliability make the induction machine suitable for different constant and variable speed applications. For machines operating in hazardous environments, the fully enclosed type is used, with limited access to the stator and rotor. Indirect air cooling method is usually employed with fins casing and an attached fan. Thus, the fan design is crucial as being the only resource to remove heat away from the machine. A poor thermal management of the electrical machine can lead to a machine failure and reduce the machine lifespan. Some machines are operating for bi-directional operation, which means the performance of the machine should be reviewed under both conditions. This paper focus on the impact of the fan cooling on the thermal profile of a 5.5 kW induction machine under bidirectional operation. Comparative studies using CFD simulations have been carried out to identify the effect of different fan types when operating at particular conditions. The performed work provides an understanding of the air flow under bi-directional operation, which notes the importance of the fan design and rotational direction in induction machines.
Nowcasting Air Quality by Fusing Insights from Meteorological Data, Satellite Imagery and Social Media Images Using Deep Learning

Peatland fire and haze events in Southeast Asia are disasters with trans-boundary implications, having increased in recent years along with rapid deforestation, land clearing and severe dry seasons. Aerosols are emitted in high concentrations from the fires, which degrade air quality and reduce visibility, in turn causing economic, social, health, and environmental problems. During haze events, it is critical for public authorities to have timely information about affected populations. Currently, Indonesian disaster management authorities manage forest and peatland fire and haze events based on satellite data and sensors. They are looking for more real-time information in order to better protect vulnerable populations and environment. This paper explores information on visibility extracted from photos shared on social media to improve forecasting performance for haze severity. Our results show that visibility information can improve forecast accuracy over a baseline approach with common features, namely data from satellites and ground air quality sensors. Furthermore, by using social media photos, our model adds a near real-time property to the forecast model, with potential to improve disaster management and mitigation,
Biomechanical Signal Analysis for Evaluation of Gait in Parkinson's Disease

The biomechanical signals acquisition through wireless sensor networks and the information processing for healthcare in patients with Parkinson's disease (PD) have an important challenge. As well as other biomechanical signs, patients with PD usually present slow movements, difficult to initiate, vary or interrupt which reflect in gait alterations. The patient should walk at least 10 meters, then turn around and return to the starting point. These movement requirements can affect the wireless communication quality. Currently there are many scales for the assessment of patients with PD, but in recent research, the scale “Movement Disorder Society - Unified Parkinson's Disease-Rating Rating Scale” (MDS-UPDRS) has gained great notoriety. However, evaluation is in a subjective way and depends a lot on the patient's momentary status and the results shown are qualitative only, and the subtle differences not detected. This paper presents results with wireless sensors networks Bluetooth and XBee, respectively, as well as the first stage of a diffuse model to analyse, evaluate and classify the gait according to the parameters established by the MDS-UPDRS, with multi-axial signals from inertial measurement units. The model presented good results for evaluation and classification, always backed-up by the help of medical experts.
Generating Optimal Plans for Boolean Expressions

We present an algorithm that produces optimal plans to evaluate arbitrary Boolean expressions possibly containing conjunctions and disjunctions. The complexity of our algorithm is O(n3n), where n is the number of simple predicates in the Boolean expression. This complexity is far lower than that of Reinwald and Soland's algorithm (O(22n)). This lower complexity allows us to optimize Boolean expressions with up to 16 predicates in a reasonable time. Further, opposed to many existing approaches, our algorithm fulfills all requirements necessary in the context of main memory database systems. We then use this algorithm to (1) determine the optimization potential inherent in Boolean expressions and (2) evaluate the plan quality of two heuristics proposed in the literature.
Building a Benchmark for Evaluating Link Prediction Methods

Although many new methods that aim to improve the performance of link prediction have been proposed in recent years, there is still no widely accepted benchmark for evaluating and comparing these link prediction methods. In this paper, we propose LPBenchmark, a solution towards a fair and effective benchmark for link prediction. LPBenchmark offers a suite of well-selected datasets covering major research fields in link prediction without redundancy. These datasets are selected from widely adopted open access collections of datasets via performing AHC(Adapted Hierarchical Clustering) and DNFS(Deepest Node First Selection) Algorithm. LPBenchmark measures the difficulty of each selected dataset through OSR(Optimal Subset Regression) Algorithm, which makes it possible to fairly compare the experiment performance of two methods operated on different datasets. Moreover, LPBenchmark includes three APIs, allowing researchers to obtain the largest connected components of a dataset, modify a dataset based on node degree and construct subgraphs based on node clustering coefficients. After presenting all the characteristics and functionalities of LPBenchmark, we conduct a comprehensive evaluation on several classic and newly proposed link prediction methods by using LPBenchmark. Results show that LPBenchmark is not only capable of fairly comparing each method's overall performance, but also can reveal each method's advantages and limitations on different types of networks.
On the Optimality of Opportunistic Routing Protocols for Underwater Sensor Networks

In the last decade, underwater wireless sensor networks (UWSNs) have attracted a lot of attention from the research community thanks to their wide range of applications that include seabed mining, military and environmental monitoring. With respect to terrestrial networks, UWSNs pose new research challenges such as the three-dimensional node deployment and the use of acoustic signals. Despite the large number of routing protocols that have been developed for UWSNs, there are very few analytical results that study their optimal configurations given the system's parameters (density of the nodes, frequency of transmission, etc.). In this paper, we make one of the first steps to cover this gap. We study an abstraction of an opportunistic routing protocol and derive its optimal working conditions based on the network characteristics. Specifically, we prove that using a depth threshold, i.e., the minimum length of one transmission hop to the surface, is crucial for the optimality of opportunistic protocols and we give a numerical method to compute it. Moreover, we show that there is a critical depth threshold above which no packet can be transmitted successfully to the surface sinks in large networks, which further highlights the importance of properly configuring the routing protocol. We discuss the implications of our results and validate them by means of stochastic simulations on NS3.
Grounding Semantic Roles in Images

We address the task of visual semantic role labeling (vSRL), the identification of the participants of a situation or event in a visual scene, and their labeling with their semantic relations to the event or situation. We render candidate participants as image regions of objects, and train a model which learns to ground roles in the regions which depict the corresponding participant. Experimental results demonstrate that we can train a vSRL model without reliance on prohibitive image-based role annotations, by utilizing noisy data which we extract automatically from image captions using a linguistic SRL system. Furthermore, our model induces frame—semantic visual representations, and their comparison to previous work on supervised visual verb sense disambiguation yields overall better results.