Miracle-dz/UniFAR-FTUs huggingface dataset:
On discovering relevant features for tongue colored image analysis

Artificial Intelligent Systems are increasingly used to support early diagnosis of multiple relevant diseases. The spread of these systems is boosted by the application of machine learning techniques on datasets (also in the form of videos and images) obtained from different information sources. A key role is played by artificial vision systems that are in charge of reasoning on data acquired from different devices, including smartphones. The facility to disseminate and share information let to the globalization of medical protocols previously used just in some world's areas. This is the case of tongue inspection, widely used in Traditional Chinese Medicine (TCM) to perform a diagnosis, which allows physicians to obtain useful indications on the state of internal organs by observing the color and the consistency of patient's tongue. The current interest in tongue's image analysis is also motivated by the possibility of performing a first self-analysis on a possible disease suggesting further medical investigation. The paper is a non-exhaustive overview of the features most frequently used in artificial vision systems contextualized to tongue analysis. It highlights shortcomings in some of the existing studies and provides insights for future research. Our work aims to provide a unifying view that can support the researchers working on Tongue Colored Image Analysis.
Network Embedding and Change Modeling in Dynamic Heterogeneous Networks

Network embedding learns the vector representations of nodes. Most real world networks are heterogeneous and evolve over time. There are, however, no network embedding approaches designed for dynamic heterogeneous networks so far. Addressing this research gap is beneficial for analyzing and mining real world networks. We develop a novel representation learning method, change2vec, which considers a dynamic heterogeneous network as snapshots of networks with different time stamps. Instead of processing the whole network at each time stamp, change2vec models changes between two consecutive static networks by capturing newly-added and deleted nodes with their neighbour nodes as well as newly-formed or deleted edges that caused core structural changes known as triad closure or open processes. Change2vec leverages metapath based node embedding and change modeling to preserve both heterogeneous and dynamic features of a network. Experimental results show that change2vec outperforms two state-of-the-art methods in terms of clustering performance and efficiency.
ECOM'19: The SIGIR 2019 Workshop on eCommerce

eCommerce Information Retrieval is receiving increasing attention in the academic literature, and is an essential component of some of the largest web sites (such as eBay, Amazon, Airbnb, Alibaba, Taobao, Target, Facebook, Home Depot, and others). These kinds of organisations clearly value the importance of research into Information Retrieval. The purpose of this workshop is to bring together researchers and practitioners of eCommerce IR to discuss topics unique to it, to set a research agenda, to examine how to build data sets, and how evaluate algorithms for research into this fascinating topic. eCommerce IR is ripe for research and has a unique set of problems. For example, in eCommerce search there may be no hypertext links between documents (products); there is a click stream, but more importantly, there is often a buy stream. eCommerce problems are wide in scope and range from user interaction modalities through to dynamic updates of a rapidly changing collection on auction sites, and the experienceness of some products (such as Airbnb bookings). This workshop is a follow up to very successful workshops held at SIGIR 2017 and SIGIR 2018. This year we will be running a data challenge (sponsored by eBay) which will allow us to follow up on multiple aspects that were discussed in the previous workshops (in particular, deterministic rank orders and how to evaluate these).
Virtual private ledgers: embedding private distributed ledgers over a public blockchain by cryptography

Distributed ledgers allow us to replicate databases of records across mutually untrusted parties. The best known example of distributed ledger is perhaps the Bitcoin blockchain, which maintains a consistent history of financial transactions organized as a hashed chain of blocks. Distributed ledgers can be public, i.e., accessible by everyone, or private, i.e., accessible only by a given consortium of parties. In this paper, we explore the technological possibilities of applying Identity-Based Encryption and Attribute-Based Encryption to distributed ledgers. We introduce the novel concept of Virtual Private Ledger. A Virtual Private Ledger is a private distributed ledger embedded in a public cryptocurrency ledger by means of cryptography. A Virtual Private Ledger provides for the same confidentiality and integrity of a private distributed ledger, but without its high operational costs. In particular, nodes that maintain the ledger do not have to be always online to trust the order and the integrity of the records. We analytically show that Virtual Private Ledgers can be implemented over many existing cryptocurrency ledgers like Ethereum, EOS.IO, IOTA, XRP. Different cryptocurrencies lead to different trade-offs between the Virtual Private Ledger max record size, cost, validation time, and max consortium members.
Sequence and Time Aware Neighborhood for Session-based Recommendations: STAN

Recent advances in sequence-aware approaches for session-based recommendation, such as those based on recurrent neural networks, highlight the importance of leveraging sequential information from a session while making recommendations. Further, a session based k-nearest-neighbors approach (SKNN) has proven to be a strong baseline for session-based recommendations. However, SKNN does not take into account the readily available sequential and temporal information from sessions. In this work, we propose Sequence and Time Aware Neighborhood (STAN), with vanilla SKNN as its special case. STAN takes into account the following factors for making recommendations: i) position of an item in the current session, ii) recency of a past session w.r. t. to the current session, and iii) position of a recommendable item in a neighboring session. The importance of above factors for a specific application can be adjusted via controllable decay factors. Despite being simple, intuitive and easy to implement, empirical evaluation on three real-world datasets shows that STAN significantly improves over SKNN, and is even comparable to the recently proposed state-of-the-art deep learning approaches. Our results suggest that STAN can be considered as a strong baseline for evaluating session-based recommendation algorithms in future.
Analyzing and Predicting News Popularity in an Instant Messaging Service

With widespread use of mobile devices, instant messaging (IM) services have recently attracted a great deal of attention by millions of users. This has motivated news agencies to share their contents via such platforms in addition to their websites and popular social media. As a result, thousands of users nowadays follow the news agencies through their verified channels in IM services. However, user interactions with such platforms is relatively unstudied. In this paper, we provide an initial study to analyze and predict news popularity in an instant messaging service. To this aim, we focus on Telegram, a popular IM service with 200 million monthly active users. We explore the differences between news popularity analysis in Telegram and typical social media, such as Twitter, and highlight its unique characteristics. We perform our analysis on the data we collected from four diverse news agencies. Following our analysis, we study the task of news popularity prediction in Telegram and show that the performance of the prediction models can be substantially improved by learning from the data of multiple news agencies using multi-task learning. To foster research in this area, we have made the collected data publicly available.
Broadband wireless communication systems: Channel modeling and system performance analysis

In this paper, we present a wideband one-ring multiple-input multiple-output (MIMO) channel model for non-isotropic scattering environments. The model is designed in such a way that the delay power spectral density (PSD) of the resulting reference channel model is identical to a given delay PSD. Furthermore, we present an efficient deterministic channel simulation model obtained by using the principle of deterministic channel modeling. The statistical properties of both the reference model and the simulation model are also studied. Analytical expressions will be presented for the temporal autocorrelation function (ACF), the two-dimensional (2-D) space cross-correlation function (CCF), and the frequency correlation function (FCF). We show that the statistical properties of the deterministic simulation model can be brought into astonishingly good agreement with those of the reference model. The resulting deterministic simulation model enables the performance evaluation of wideband MIMO communication systems by simulation, which is shown exemplarily by studying the system performance of a space-time coded MIMO orthogonal frequency division multiplexing (OFDM) system.
Indexing large updatable datasets in multi-version database management systems

Database Management Systems (DBMS) need to handle large updatable datasets in on-line transaction processing (OLTP) workloads. Most modern DBMS provide snapshots of data in multi-version concurrency control (MVCC) transaction management scheme. Each transaction operates on a snapshot of the database, which is calculated from a set of tuple versions. High parallelism and resource-efficient append-only data placement on secondary storage is enabled. One major issue in indexing tuple versions on modern hardware technologies is the high write amplification for tree-indexes. Partitioned B-Trees (PBT) [5] is based on the structure of the ubiquitous B+-Tree [8]. They achieve a near optimal write amplification and beneficial sequential writes on secondary storage. Yet they have not been implemented in a MVCC enabled DBMS to date. In this paper we present the implementation of PBTs in PostgreSQL extended with SIAS. Compared to PostgreSQL's B+-Trees PBTs have 50% better transaction throughput under TPC-C and a 30% improvement to standard PostgreSQL with Heap-Only Tuples.
Event Tracker: A Text Analytics Platform for Use During Disasters

Emergency management organisations currently rely on a wide range of disparate tools and technologies to support the monitoring and management of events during crisis situations. This has a number of disadvantages, in terms of training time for new staff members, reliance on external services, and a lack of integration (and hence poor transfer of information) between those services. On the other hand, Event Tracker is a new solution that aims to provide a unified view of an event, integrating information from emergency response officers, the public (via social media) and also volunteers from around the world. In particular, Event Tracker provides a series of novel functionalities to realise this unified view of the event, namely: real-time identification of critical information, automatic grouping of content by the information needs of response officers, as well as real-time volunteers management and communication. This is supported by an efficient and scalable back-end infrastructure designed to ingest and process high-volumes of real-time streaming data with low latency.
Ensembles of Recurrent Networks for Classifying the Relationship of Fake News Titles

Nowadays, everyone can create and publish news and information anonymously online. However, the credibility of such news and information are not guaranteed. To differentiate fake news from genuine news, one can compare a recent news with earlier posted ones. Identified suspicious news can be debunked to stop the fake news from spreading further. In this paper, we investigate the advantages of recurrent neural networks-based language representations (e.g., BERT, BiLSTM) in order to build ensemble classifiers that can accurately predict if one news title is related to, and, additionally disagrees with an earlier news title. Our experiments, on a dataset of 321k news titles created for the WSDM 2019 challenge, show that the BERT-based models significantly outperform BiLSTM, which in-turn significantly outperforms a simpler embedding-based representation. Furthermore, even the state-of-the-art BERT approach can be enhanced when combined with a simple BM25 feature.
On approximate nesting of multiple social network graphs: a preliminary study

A fundamental problem in Social Network Analysis is how to move from single-layer to multi-layer, which provide a holistic view. User profiles resolution has received considerable attention since it allows to match users on different online social networks (OSNs). However, to the best of our knowledge, no study has focused on nesting operation for merging OSNs graphs. This work is a first step in the direction of defining the data model and the algorithm to perform approximate nesting of multiple OSNs graphs, based on user features. We provide initial experimental evidence based on synthetic data.
Self-tuning storage systems for performance virtualization

The cost of the storage management has been the dominant factor in the Total Cost of Ownership of a computer system. In order to reduce it, a growing trend is to make the storage system self-managing. This thesis investigates two important topics for self-managing storage system design: (1) workload characterization and generation; (2) online QoS enforcement and self-tuning. Understanding workloads is critical for a self-managing storage system design. This thesis presents a synthetic workload generator for TPC-H, an important decision-support commercial workload, by completely characterizing the arrival and access patterns of its queries. The synthesized block-level traces for 22 TPC-H queuries are shown to accurately mimic the behavior of a real trace in terms of response time characteristics for each TPC-H query. Self-tuning is an important aspect of self-management. For a self-tuning storage system, adaptive resource management plays a critical role. One issue is to provide performance virtualization for multiple applications to share a consolidated storage utility in order to meet their Service Level Objecives (SLOs). This thesis presents a 2-level scheduling framework. This framework uses a low-level feedback-driven request scheduler, called AVATAR, that is intended to meet the latency bounds determined by the SLO. The load imposed on AVATAR is regulated by a high-level rate controller, called SARC, to insulate the users from each other. Using extensive UO traces and a detailed storage simulator, it is demonstrated that this 2-level framework can simultaneously meet latency and throughput requirements imposed by an SLO, without requiring extensive knowledge of the underlying storage system. Business continuance requires non-disruptive data migration, which is another important issue in resource management. In such situation, a migration task utilizes spare bandwidth left by client applications, in order to minimize the impact on them. However, a migration task also has its own requirements. Especially, it is desirable to complete a migration task in a specified time period. This thesis presents an opportunistic data migration scheme with an adaptive rate controller. With extensive experiments, it is shown that the scheme is effective and efficient to meet requirements for migration.
Developing Visual Complexity Metrics for Automotive Human-Machine Interfaces

Objective: The purpose of this study is to develop visual complexity metrics based on theoretical bases. Background: With the development of IT technologies, drivers process a large amount of information caused by automotive human-machine interface (HMI), such as a cluster, a head-up display, and a center-fascia. In other words, these systems are becoming more complex and dynamic than traditional driving systems. Especially, these changes can lead to the increase of visual demands. Thus, a concept and tool is required to evaluate the complicated systems. Method: We reviewed prior studies in order to analyze the visual complexity. Based on complexity studies and human perceptual characteristics, the dimensions characterizing the visual complexity were determined and defined. Results: Based on a framework and complexity dimensions, a set of metrics for quantifying the visual complexity was developed. Conclusion: We suggest metrics in terms of perceived visual complexity that can evaluate the in-vehicle displays. Application: This study can provide the theoretical bases in order to evaluate complicated systems. In addition, it can quantitatively measure the visual complexity of In-vehicle information system and be helpful to design in terms of preventing risks, such as human error and distraction.
Semantic Validation of T&E XML Data

It is anticipated that XML will heavily dominate the next generation of telemetry systems. The syntax of XML-based languages can be constrained by a schema that describes the structure of valid documents. However, the schemas cannot express all dependencies between XML elements and attributes, both within a single document and across multiple documents. This prohibits the XML validation process from being fully automated with standard schema processors. This paper presents an approach that is based on the W3C Semantic Web technologies and allows different vendors and system integrators to independently develop their own semantic validation rules. The rules are equipped with powerful semantics, which allows for specification and validation of complex types of constraints. The approach is not specific to a particular T&E standard and is entirely standards-based.
Annotating semantic roles in a lexicalised grammar environment

Annotating text with abstract information such as semantic roles is costly. In previous efforts, such as PropBank, this process was aided with the help of syntactic trees, manually correcting automatically produced annotations. We argue that when using a lexicalised approach the annotation effort can be made simpler, avoiding the need to explicitly select two entities for each role. Our model is demonstrated by the Groningen Meaning Bank, using Combinatory Categorial Grammar as syntactic formalism, and Discourse Representation Theory as a formal semantic backbone.
Code growth in genetic programming

In this paper we examine how quickly the programs generated using genetic programming grow in size. We found that without a constraint mechanism the programs will grow indefinitely regardless of whether or not the growth acts to improve the programs' solutions. This growth is dominated by non-functional code. If the non-functional code is removed the growth is dominated by functional, but non-executed code. Two methods of controlling the growth: removing non-functional code, and selective pressure applied by penalizing longer programs, are compared. Only the later method appears to be effective in bounding the programs' size.
A Non-Deadlock Time Management Algorithm

HLA (high level architecture) is the standard for modeling and simulation put forward by the American Department of Defense. Time management is an important component of HLA while GALT (greatest available logical time) algorithm is RTI (runtime infrastructure)抯 critical technology in implementing time management. An improper GALT algorithm can easily result in a deadlock so that the whole federation can not advance any more. On the basis of the GALT algorithm introduced by Frederick Kuhl, the principles of deadlock are discussed and some important results are revealed in this paper. If deadlock occurs in a simulation, all federates must have the same GALT and the same output time respectively, and GALT is also equal to output time, and a federate whoselookahead is greater than zero must suspend because of a NMR or NMRA request, other than a TAR, TARA or FQR request. Finally, a GALT algorithm without deadlock is also brought forward in this paper that is calledStature-Measuring, and this algorithm can provide reliable technology support to develop time management services of RTI.
A high-efficiency atomic force microscopy image mode based on Kalman filtering

With the rapid development of the nano-science, an atomic force microscopy (AFM) has been playing an increasingly important role in many fields. Nevertheless, the data utilization rate of the traditional AFM tremendously limits the performance of the system. In this paper, Kalman filtering is applied to replace the traditional data processing mode to improve the system efficiency and image quality. To be specific, with the purpose of utilizing all the data of each scanning point, firstly we use the least square method to match the sample height information in forward an backward scanning processes. Then, for each scanning point, Kalman filtering is applied to deal with all the data recursively to acquire the optimal outcome. Afterwards, we use the optimal outcome to calculate the more accurate height value of samples rather than only utilize a single data of forward scanning to get image. In this way, the system efficiency and the imaging precision of the AFM system are greatly improved. Experiment results demonstrate the effectiveness of the proposed method.
A neuromagnetic source localization using 64 channel SQUID system and MRI

Describes a high-resolution biomagnetic inverse problem using 64 channel SQUID system and MRI. If a head shape is approximated by a sphere, Geselowitz's equation to estimate the biomagnetic field can be simplified. After corresponding the coordinates of SQUID system to the coordinates of MRI, a sphere model and search space are extracted from the MRI data, and the current sources are localized using the improved simplex method. Each current dipole location is combined with the anatomical MRI data. <<ETX>>
Closed testing procedure for multiplicity control. An application on oxidative stress parameters in Hashimoto's Thyroiditis

Closed Testing procedures represent an effective solution to the need to make inferences on multiple aspects at the same time, controlling the Familywise Error Rate (FWER), that is the error rate of the hierarchical family. Closed Testing procedures have a high degree of adaptability to a wide range of experimental situations, both in parametric than in non-parametric ambit. The attention is focused on the Bonferroni-Holm method, frequently used to counteract the problem of multiple comparisons. The present paper aims to show an original application of the Closed Testing procedures for multiplicity control in medical research, with reference to the oxidative stress; in particular the Min-P Bonferrroni-Holm method was applied to the p-value adjustment, related to three parameters (BAP, D-ROMS, AGEs) of oxidative stress in Hashimoto’s thyroidytis.