Miracle-dz/UniFAR-FTUs huggingface dataset:
Part 15++: An Enhanced ID-based Approach for Etiquette and Enforcement Management in Unlicensed Band

The open, simple and well-conceived global rules for the unlicensed bands operating in the industrial, scientific and medical (ISM) band has enabled tremendous product and service innovation. However, the argument can be made that the unfettered use of unlicensed bands could lead to certain "tragedies of the commons" and that changes to these rules might improve overall operation. We seek to assess whether simple additions or alterations to these rules might further the efficiency of these bands while not constraining or distorting this successful model. The current approach by spectrum regulators for mitigating harmful interference is through implementing conservative strategies, which cap the technical parameters of the devices. However, if these devices can be somehow informed about an impending interference, then it might be mitigated, improving the overall system efficiency. Moreover, there is a growing interest by other technologies to share this band, which could exacerbate potential interference issues. This calls for leaving behind some of the conservative operational aspects of Part 15 and allowing these devices to work in a more self-aware and self-governing environment1. Through our proposal for a Part 15++, we envision that unlicensed devices are able to avoid interference through a self-monitoring approach enabled by an identification (ID) mechanism. With denser and broader use of these bands, we believe that creating a common identification method might allow for a more efficient coexistence among heterogeneous technologies. The common ID structure could help a device self-monitor by providing information on: who is operating in a given space-time-frequency; who is causing observed interference; what are the alternatives to avoid that interference; and whether rule violations are occurring. This paper further investigates the benefit and cost of extending granular identification requirements in exchange for relaxation of the already successful Part 15 rules. Eventually, we aim at providing more freedom to the unlicensed band while keeping efficiency in check with enforcement as a byproduct of our initiative. At the end of the day, we need to decide if we are willing to tradeoff the conservative yet successful Part 15 restrictions for a more coordinated approach.
Semisupervised graph-based hyperspectral images classification using low-rank representation graph with considering the local structure of data

Abstract. Because of limited labeled samples, semisupervised learning (SSL) methods have attracted much attention for classification of hyperspectral images (HSIs). Graph-based methods that treat data samples as nodes in a graph are very popular classes of SSL in the HSI data analysis. However, constructing a graph that can well capture the essential data structure is critical for these classes of SSL methods. A graph construction method based on low-rank representation (LRR) is proposed. Since LRR only captures the global structure of data, it cannot provide an informative graph for graph-based SSL tasks. To increase the effectiveness of the LRR-based graph, the local structure information is incorporated into the objective function of LRR as an additional penalty term. The proposed low-rank and local linear graph (LRLLG) takes the global and local structure into account, hence it provides a more generative and discriminative graph. Experimental results on two well-known data sets demonstrate that LRLLG outperforms the traditional graph construction methods in label propagation and graph-based SSL methods for HSIs.
A Contextual Random Walk Model for Automated Playlist Generation

In this paper, we propose new methods for generating playlists with a single graph, which represents multiple types of relations in a playlist. Although current users are familiar with online music services, they have difficulty in deciding which tracks to listen to because there are millions of tracks available on such services. Automated playlist generation is one of the best solutions to solving this costly task of finding interesting tracks from the enormous tracks. Accordingly, one playlist-generation task, namely, hit rate, in which several tracks are given as a user query, is focused on in this study. There are four types of context objects (playlists, tracks, artists, and users) in the basic information on playlists, and three types of relations (playlists contain tracks and artists, users create playlists and artists play and/or sing tracks) in playlists. First, different types of relations in playlists are combined, and a single graph linking different context objects is generated. Next, a random walk is applied to the graph, and the expected values of track nodes are calculated on the basis of the transition probabilities of nodes in the graph. Finally, tracks are recommended in order of the expected values. The results of an experimental evaluation of the proposed methods in comparison with conventional methods revealed that one of the proposed methods (RW-hybrid) improved effectiveness by up to 21%. Moreover, this method reduces execution time as much as the fastest existing methods.
Taint-assisted IAT Reconstruction against Position Obfuscation

Windows Application Programming Interface (API) is an important data source for analysts to effectively understand the functions of malware. Due to this, malware authors are likely to hide the imported APIs in their malware by taking advantage of various obfuscation techniques. In this paper, we first build a formal model of the Import Address Table (IAT) reconstruction procedure to keep our description independent of specific implementations and then formally point out that the current IAT reconstruction is vulnerable to position obfuscation techniques, which are anti-analysis techniques obfuscating the positions of loaded APIs or Dynamic Link Libraries (DLLs). Next, we introduce an approach for API name resolution, which is an essential step in IAT reconstruction, on the basis of taint analysis to defeat position obfuscation techniques. The key idea of our approach is that we first define taint tags, each of which has a unique value for each API, apply the taint of the API to each of its instructions, track the movement of the API instructions by propagating the tags, and then resolve API names from the propagated tags for IAT reconstruction after acquiring a memory dump of the process under analysis. Finally, we experimentally demonstrate that a system in which our proposed API name resolution has been implemented enables us to correctly identify imported APIs even when malware authors apply various position obfuscation techniques to their malware.
Uncovering the Perception of Urban Outdoor Areas Expressed in Social Media

Learning about people's perception that emerges from urban areas has been an interesting multidisciplinary research goal because it has a great potential to ease the hard task of understanding intrinsic characteristics of urban areas. To this end, we propose an approach that explores spatial and semantic aspects in free-text messages shared on location-based social networks (LBSNs) for uncovering and mapping the perception reflected regarding urban outdoor areas. Studying outdoor areas of Chicago, we show that LBSN data carry valuable information about places and could also be used to extract urban perception, helping to better understand urban areas from many aspects. We demonstrate, through a survey with volunteers, that our approach has the potential to correctly capture the opinion considered by the users regarding the reflected perception of those areas, indicating that it could be a feasible alternative for the task under study.
Detection of Smoking Events from Confounding Activities of Daily Living

Although smoking prevalence is declining in many countries, smoking related health problems still leads the preventable causes of death in the world. Several smoking intervention mechanisms have been introduced to help smoking cessation such as counselling program, motivational interview and pharmacotherapy. However, these methods lack providing real time personalized intervention messages to the smoking addicted users. The challenge is to develop an automated smoking behavior detection. We address this challenge by proposing a non-invasive sensor based automated framework for smoking behavior detection. We used a wristband based accelerometer and gyroscope sensors to detect smoking activities, differentiating with the closely confounding activities. We extract several features using learning algorithms and the empirical results with our participants show good accuracy in detecting the smoking activity in terms of precision, recall, and Flscore.
Transforming Geo-Referenced Data in Contextual Information for Context-Aware Recommender Systems

A recommender system can be defined as an information filtering technology which can be used to output a ranking of items (e.g. products, places, etc) that are likely to be of interest to a user. Context-aware recommender systems makes recommendations by incorporating contextual information into the recommendation process. However, there is a lack of automatic methods to obtain contextual information for such systems. In this work, we have proposed to apply clustering techniques to transform geo-referenced data (i.e. latitude and longitude) in contextual information (i.e. regions) to feed the contextual systems. We have evaluated our proposal in the Yelp dataset, which showed evidences that our contextual information can provide better recommendations.
Task Recommendation for Group Users in Public IoT Environments

There are an increasing number of public Internet of Things (IoT) devices installed in urban environments, with which users can perform a wide variety of tasks. Owing to the nature of public spaces, such IoT devices must support groups of users rather than just individuals. However, because the type and quality of IoT devices in public environments varies, it may be difficult for groups of users to recognize the opportunities to perform tasks. Moreover, group users are often new to a certain public place, and have not previously performed tasks in IoT-enriched public spaces. In this paper, we propose a two-phase task recommendation approach for groups of IoT users in public environments. In the first phase, we employ a random walk with restart (RWR) algorithm to overcome the problem of sparse historical data for the performance of user tasks in public IoT environments. The second phase predicts a set of operations (IoT device functionalities) that are most appropriate for each candidate task. In this phase, to more effectively predict IoT operations for a user task we consider the contextual semantics of users via a classification model. We evaluate our approach using real-world datasets collected from practical IoT testbed environments. In addition, we show that an appropriate set of task operations can be predicted effectively by considering task types and contextual semantics.
Survey of neural network‐based models for short‐term traffic state prediction

Traffic state prediction is a key component in intelligent transport systems (ITS) and has attracted much attention over the last few decades. Advances in computational power and availability of a large amount of data have paved the way to employ advanced neural network (NN) models for ITS, including deep architectures. There have been various NN‐based approaches proposed for short‐term traffic state prediction that are surveyed in this article, where the existing NN models are classified and their application to this area is reviewed. An in‐depth discussion is provided to demonstrate how different types of NNs have been used for different aspects of short‐term traffic state prediction. Finally, possible further research directions are suggested for additional applications of NN models, especially using deep architectures, to address the dynamic nature in complex transportation networks.
Learning Adaptive Graph Protection Strategy on Dynamic Networks via Reinforcement Learning

Graph protection strategies aim to suppress the epidemic propagation in a network by allocating protection resources to maximize the ratio of surviving node. Research on this topic has been active and promising due to its wide-range applications. However, most of the recent developments are simulated by assuming that the network structure remains static during epidemics. Moreover, the existing protection schemes are limited to the simplified pre-emptive and post-emptive schemes. The pre-emptive scheme protects the most critical nodes of networks prior to epidemic spreading, behaving as a prevention mechanism. In post-emptive schemes, the protections are allocated in the presence of epidemics, when the attacks have already spread over the network, simulating a late curative response. Given a limited k resource budget, both of those schemes spend the whole resources in a single chance. In this paper, we introduce a novel adaptive protection scheme by gradually protecting nodes in response to the incoming attacks. We consider the adaptive scheme in a more challenging network structure, the dynamic networks. We propose the n-step fitted Q-learning for training the model under reinforcement approach. We further incorporate graph embedding as a feature-based representation of the network state. We also demonstrate the potential of our proposal as a non-deterministic approach for this graph protection problem. Experimental results show that our proposed model effectively restrain epidemic propagation in real-world network datasets.
What Influences People to Broaden Their Horizons?

Personalization, including both self-selected and pre-selected, is inevitable when tremendous amounts of media content are available. Since personalization, which is believed to encourage people to consume fewer diverse contents, can lead to fragmentation and polarization in society, exposing people to more diverse contents is important. In this paper, we investigate using web TV data what behavioral and environmental features influence users to consume more diverse contents. We tackle this problem by building a classification model that predicts whether users will consume contents that are comprised of more diverse sources and features. We measure the change of the consumed content diversity of users with the method in our previous work and design six features based on our basic research and previous studies. The result of our experiment shows that our model predicts with high accuracy the change of the consumed content diversity of users, which means that the increase of the diversity of the consumed content of users is explained well with our six features. Our analysis of the model shows that consuming contents across multiple genres and inadvertent exposure to various contents positively affect users and encourage them broaden their horizons. The former has especially strong influence. In addition, we demonstrate that active-seeking behaviors of users and the amount of content in their most interested areas negatively affect the increase of their consumed content diversity. Our findings in this study are expected to be applied to web media to moderate the effect of selective exposure and reduce fragmentation and polarization in society.
Spatial Action Decomposition Learning Applied to RTS Combat Games

Learning good policies for multi-agent systems is a complex task. Existing methods are often limited to a small number of agents, as learning becomes intractable when the agent number increases considerably. In this paper we describe Spatial Action Decomposition Learning that tries to overcome inefﬁciencies of standard multi-agent Q -learning methods by exploiting existing spatial action correlations. We apply our method to real-time strategy (RTS) game combat scenarios and show that Spatial Action Decomposition Learning based systems can outperform handcrafted scripts and policies optimized by independent Q -learning.
Evolving NPC Behaviours in A-life with Player Proxies

Game development costs are on the rise as players expect massive open-world games populated with richly interactive non-playable characters (NPC). Procedural content generation has the potential to reduce the development costs as well as make the content more player-specific. Recent work on evolving artificial intelligence for NPCs focused on combat between NPCs and the player. In this paper we propose to evolve an ecosystem of NPCs for a broader class of games. To allow players’ actions and playstyles to inform the evolution, we propose to model actual players and create AIcontrolled proxies to evolve NPCs against. The approach removes the time pressure from the evolution and allows for traditional quality assurance methods while keeping the evolution player-informed.
InNet: Learning to Detect Shadows with Injection Network

Shadows bring great challenges but also play essential roles in image understanding. Most recent shadow detection methods are based on patches, then further reasoning method is required for the obtaining of a completed shadow detection result for an image. In this paper, an injection network is proposed to detect shadow regions for the whole image directly. In order to maintain as many as details, the skip structure is applied to directly inject the details from convolutional layers to de-convolutional layers. Meanwhile, a weighted loss function is proposed for the network training. With this adapted loss function, the network becomes more sensitive to errors of shadow regions. Thus the proposed network can focus on the learning of robust shadow features. Furthermore, a shadow refinement method is proposed to optimize the boundary region of shadows. In the experiments, the proposed methods are extensively evaluated on two popular datasets and shown better performance on shadow detection compared with current methods.
Reddit Weight Loss Communities: Do They Have What It Takes for Effective Health Interventions?

Online social networks are an important tool for people to share information and have been extensively used for people to achieve beneficial changes in health. Obesity is a major public health concern that affects about one third of the world's population. In order to alleviate this problem, health professionals are focusing on health interventions, which can be performed online. In this study we analyze three distinct online communities about weight and diet in Reddit. We model our data as 3 directed and weighted graphs of the posts and comments and evaluate the interaction between users of each community. We also analyze specific characteristics of each community, the habits of daily activity of the users and the formation of implicit bonds of friendship through the formation of communities. Our main results show that Reddit is a content-centered social network, in which what matters is what is posted and not who posts. In addition, users tend to create implicit friendship relationships through denser regions of interactions. Our results show that, contrary to expectations, the three communities present the same behavior pattern in a general point of view, which facilitates the development of non-directed online weight loss intervention strategies.
Construction of compressive measurement matrix based on convolution of fractional chaos and cyclic matrix

Abstract. A method is proposed to construct an improved measurement matrix—chaotic cyclic convolution measurement matrix (CCCMM). It is constructed by convoluting the fractional order Lorenz chaotic sequences and the cyclic matrix; since cyclic matrix is easy to implement on hardware, accurate reconstructed signal can be obtained by using fractional order chaos with pseudorandomness and convoluting them makes computing results smooth. Meanwhile, CCCMM is proved to have the high probability to satisfy the restricted isometry property. Then, the one-dimensional signals and two-dimensional images are simulated by the CCCMM and other methods, and the results show that the CCCMM is more superior in evaluating recovered signals with parameters and the visual effect of the restored images.
Learning to perform joint image super-resolution and rain removal via a single-convolutional neural network

Abstract. In outdoor low-level vision systems, not only is the resolution of the imaging system important, but rain corrupts the visibility of outdoor scenes and may cause computer vision systems to fail. We present a deep convolutional neural network (CNN) architecture for simultaneously performing single-image super-resolution and rain removal. Instead of learning an end-to-end mapping between the low-resolution rainy images and high-resolution clean images in the original image space, we train our network in the detail space, i.e., the space obtained by high-pass filtering the original image. The proposed CNN has a lightweight structure, yet it outperforms super-resolution and rain removal consecutively by a significantly large margin (>1 dB on average).
PSM-Flow: Probabilistic Subgraph Mining for Discovering Reusable Fragments in Workflows

Scientific workflows define computational processes needed for carrying out scientific experiments. Existing workflow repositories contain hundreds of scientific workflows, where scientists can find materials and knowledge to facilitate workflow design for running related experiments. Identifying reusable fragments in growing workflow repositories has become increasingly important. In this paper, we present PSM-Flow, a probabilistic subgraph mining algorithm designed to discover commonly occurring fragments in a workflow corpus using a modified version of the Latent Dirichlet Allocation algorithm. The proposed model encodes the geodesic distance between workflow steps into the model for implicitly modeling fragments. PSM-Flow captures variations of frequent fragments while maintaining its space complexity bounded polynomially, as it requires no candidate generation. We applied PSM-Flow to three real-world scientific workflow datasets containing more than 750 workflows for neuroimaging analysis. Our results show that PSM-Flow outperforms three state of the art frequent subgraph mining techniques. We also discuss other potential future improvements of the proposed method.
Conditional Relationship Extraction for Diseases and Symptoms by a Web Search-Based Approach

This paper studies the strategies of automatically extracting the conditional relationships between diseases and symptoms from a Chinese encyclopedia site and the disease-related web pages searched from the Internet. At first, the seed symptoms of a disease are extracted from an online medical encyclopedia automatically. These seed symptoms are utilized as query keywords to automatically find more symptoms of a disease from the unstructured documents of the disease-related search results. Next, a jointly learning method is used to construct the embedded representations of the conditional terms and pattern terms. Besides, the semantic similarity matrix of conditional terms is computed through the co-clustering algorithm to discover the representative conditional terms of the clusters. The result of experiments shows that the proposed method, which discovers the semantically related symptoms of a disease associated with conditionals, achieves high accuracy. Besides, many unusually known symptoms considered by the medical experts are discovered, which may be noticeable symptoms needing further verification in the future.