Miracle-dz/UniFAR-FTUs huggingface dataset:
GraphCAR: Content-aware Multimedia Recommendation with Graph Autoencoder

Precisely recommending relevant multimedia items from massive candidates to a large number of users is an indispensable yet difficult task on many platforms. A promising way is to project users and items into a latent space and recommend items via the inner product of latent factor vectors. However, previous studies paid little attention to the multimedia content itself and couldn't make the best use of preference data like implicit feedback. To fill this gap, we propose a Content-aware Multimedia Recommendation Model with Graph Autoencoder (GraphCAR), combining informative multimedia content with user-item interaction. Specifically, user-item interaction, user attributes and multimedia contents (e.g., images, videos, audios, etc.) are taken as input of the autoencoder to generate the item preference scores for each user. Through extensive experiments on two real-world multimedia Web services: Amazon and Vine, we show that GraphCAR significantly outperforms state-of-the-art techniques of both collaborative filtering and content-based methods.
Interactive Symptom Elicitation for Diagnostic Information Retrieval

Medical information retrieval suffers from a dual problem: users struggle in describing what they are experiencing from a medical perspective and the search engine is struggling in retrieving the information exactly matching what users are experiencing. We demonstrate interactive symptom elicitation for diagnostic information retrieval. Interactive symptom elicitation builds a model from the user's initial description of the symptoms and interactively elicitates new information about symptoms by posing questions of related, but uncertain, symptoms for the user. As a result, the system interactively learns the estimates of symptoms while controlling the uncertainties related to the diagnostic process. The learned model is then used to rank the associated diagnoses that the user might be experiencing. Our preliminary experimental results show that interactive symptom elicitation can significantly improve user's capability to describe their symptoms, increase the confidence of the model, and enable effective diagnostic information retrieval.
K-plet Recurrent Neural Networks for Sequential Recommendation

Recurrent Neural Networks have been successful in learning meaningful representations from sequence data, such as text and speech. However, recurrent neural networks attempt to model only the overall structure of each sequence independently, which is unsuitable for recommendations. In recommendation system, an optimal model should not only capture the global structure, but also the localized relationships. This poses a great challenge in the application of recurrent neural networks to the sequence prediction problem. To tackle this challenge, we incorporate the neighbor sequences into recurrent neural networks to help detect local relationships. Thus we propose a K -plet R ecurrent Neural Network (Kr Network for short) to accommodate multiple sequences jointly, and then introduce two ways to model their interactions between sequences. Experimental results on benchmark datasets show that our proposed architecture Kr Network outperforms state-of-the-art baseline methods in terms of generalization, short-term and long term prediction accuracy.
A Taxonomy of Queries for E-commerce Search

Understanding the search tasks and search behavior of users is necessary for optimizing search engine results. While much work has been done on understanding the users in Web search, little knowledge is available about the search tasks and behavior of users in the E-Commerce (E-Com) search applications. In this paper, we share the first empirical study of the queries and search behavior of users in E-Com search by analyzing search log from a major E-Com search engine. The analysis results show that E-Com queries can be categorized into five categories, each with distinctive search behaviors: (1) Shallow Exploration Queries are short vague queries that a user may use initially in exploring the product space. (2) Targeted Purchase Queries are queries used by users to purchase items that they are generally familiar with, thus without much decision making. (3) Major-Item Shopping Queries are used by users to shop for a major item which is often relatively expensive and thus requires some serious exploration, but typically in a limited scope of choices. (4) Minor-Item Shopping Queries are used by users to shop for minor items that are generally not very expensive, but still require some exploration of choices. (5) Hard-Choice Shopping Queries are used by users who want to deeply explore all the candidate products before finalizing the choice often appropriate when multiple products must be carefully compared with each other. These five categories form a taxonomy for E-Com queries and can shed light on how we may develop customized search technologies for each type of search queries to improve search engine utility.
Towards the Modeling of Flexibility Using Artificial Neural Networks in Energy Management and Smart Grids: Note

This paper presents a novel approach to the representation and communication of the energy flexibility of distributed energy resources. The approach uses artificial neural networks (ANNs) to represent the devices and act as surrogate models. The main benefit of this approach is its potential to represent arbitrary energy flexibilities and the resulting universal applicability in various usage patterns, some of which are presented in detail in this paper. Furthermore, the flexibility represented by an ANN can be conditioned on the state of the corresponding devices and their environment, such that only a small state update needs to be communicated to construct feasible load profiles by a third party. Therefore, in contrast to other approaches, such as support vector data description, new ANNs only need to be constructed once the device configuration changes.
Identifying Users behind Shared Accounts in Online Streaming Services

Online streaming services are prevalent. Major service providers, such as Netflix (for movies) and Spotify (for music), usually have a large customer base. More often than not, users may share an account. This has attracted increasing attention recently, as account sharing not only compromises the service provider's financial interests but also impairs the performance of recommendation systems and consequently the quality of service provided to the users. To address this issue, this paper focuses on the problem of user identification in shared accounts. Our goal is three-fold: (1) Given an account, along with its historical session logs, we identify a set of users who share such account; (2) Given a new session issued by an account, we find the corresponding user among the identified users of such account; (3) We aim to boost the performance of item recommendation by user identification. While the mapping between users and accounts is unknown, we propose an unsupervised learning-based framework, Session-based Heterogeneous graph Embedding for User Identification (SHE-UI), to differentiate and model the preferences of users in an account, and to group sessions by these users. In SHE-UI, a heterogeneous graph is constructed to represent items such as songs and their available metadata such as artists, genres, and albums. An item-based session embedding technique is proposed using a normalized random walk in the heterogeneous graph. Our experiments conducted on two large-scale music streaming datasets, Last.fm and KKBOX, show that SHE-UI not only accurately identifies users, but also significantly improves the performance of item recommendation over the state-of-the-art methods.
A single-chip IPSEC cryptographic processor

The need for securing the Internet has become a fundamental issue over the last decade and the Internet Protocol Security (IPSec) standard, which incorporates cryptographic algorithms, has been developed as one solution to this problem. Typically, hardware implementations of cryptographic algorithms provide physical security and high speeds. In this paper a novel single-chip hardware IPSec cryptographic design is described, which comprises the Rijndael encryption algorithm and HMAC-SHA-1 authentication algorithm. In particular, the design supports the cryptographic requirements of the IP Authentication Header (AH) and Encapsulation Security Payload (ESP) and any combination of these two protocols. Indeed, it is capable of supporting any application requiring authentication and/or encryption, such as wireless local area networks (WLANs) the Secure Socket Layer (SSL) protocol, virtual private networks (VPNs) and firewalls. The IPSec cryptographic design can provide both the necessary security and performance for phone line modems, T1 wireless and 10 Mbit/s Ethernet networks.
Extracting the Full Potential of Voltage

Smart meters allow us to track a variety of electrical parameters including voltage. Even though mains voltage is assumed to remain constant, it does vary, albeit within a small range, so the variation is mostly ignored. But we show how the small variations in voltage sensed at different points in the electrical system and the relative variations in voltage values at a certain location can be exploited for better disaggregation of appliance loads, differentiating between appliances, even of the same type, increasing the scope of disaggregation for performing tasks like sub-metering and fault localization. We believe that our solution is a highly practical and simpler alternative to the more involved techniques that require sophisticated algorithms and/or hardware. To top it all, the proposed technique is also general-purpose and cost-effective.
Multihop Attention Networks for Question Answer Matching

Attention based neural network models have been successfully applied in answer selection, which is an important subtask of question answering (QA). These models often represent a question by a single vector and find its corresponding matches by attending to candidate answers. However, questions and answers might be related to each other in complicated ways which cannot be captured by single-vector representations. In this paper, we propose Multihop Attention Networks (MAN) which aim to uncover these complex relations for ranking question and answer pairs. Unlike previous models, we do not collapse the question into a single vector, instead we use multiple vectors which focus on different parts of the question for its overall semantic representation and apply multiple steps of attention to learn representations for the candidate answers. For each attention step, in addition to common attention mechanisms, we adopt sequential attention which utilizes context information for computing context-aware attention weights. Via extensive experiments, we show that MAN outperforms state-of-the-art approaches on popular benchmark QA datasets. Empirical studies confirm the effectiveness of sequential attention over other attention mechanisms.
Ranking Documents by Answer-Passage Quality

Evidence derived from passages that closely represent likely answers to a posed query can be useful input to the ranking process. Based on a novel use of Community Question Answering data, we present an approach for the creation of such passages. A general framework for extracting answer passages and estimating their quality is proposed, and this evidence is integrated into ranking models. Our experiments on two web collections show that such quality estimates from answer passages provide a strong indication of document relevance and compare favorably to previous passage-based methods. Combining such evidence can significantly improve over a set of state-of-the-art ranking models, including Quality-Biased Ranking, External Expansion, and a combination of both. A final ranking model that incorporates all quality estimates achieves further improvements on both collections.
Beyond Pooling

Dynamic Sampling is a novel, non-uniform, statistical sampling strategy in which documents are selected for relevance assessment based on the results of prior assessments. Unlike static and dynamic pooling methods that are commonly used to compile relevance assessments for the creation of information retrieval test collections, Dynamic Sampling yields a statistical sample from which substantially unbiased estimates of effectiveness measures may be derived. In contrast to static sampling strategies, which make no use of relevance assessments, Dynamic Sampling is able to select documents from a much larger universe, yielding superior test collections for a given budget of relevance assessments. These assertions are supported by simulation studies using secondary data from the TREC 2017 Common Core Track.
Relational or Non-Relational?: A Comparative Evaluation of Database Solutions for Energy Consumption Data

The significance of energy consumption analysis has been growing almost exponentially over the course of the last decade. In order to get analysis results of high accuracy, many algorithms require large volumes of available input data. Different kinds of databases are available today and offer a common interface to store and retrieve such time series data. Relational databases, such as MySQL, are well-established solutions for the storage of key-value pairs (e.g., timestamp of collection and sampled value). However, the constant growth of consumption data poses a serious challenge for the scalability of relational databases, and has led to the emergence of non-relational (NoSQL) database solutions. In this work, we compare a relational and a non-relational database with regard to their performance when used to store energy consumption time series. The insights gained shall serve as a recommendation for the informed database choice for energy data.
COMBINING AUDIO AND SYMBOLIC DESCRIPTORS FOR AUDIO MUSIC SIMILARITY AND RETRIEVAL

At ISMIR 2007 we propose a new approach for an extended feature set for Audio Music Information Retrieval: We transcribe audio data into a symbolic form using a transcription system, extract symbolic descriptors from that representation and combine them with multiple audio features. In the ISMIR paper we show that by this method we are able to further improve music genre classification. At MIREX 2007 we submit the same approach to the Audio Music Similarity and Retrieval task in order to evaluate if the novel approach joining two traditionally separated MIR domains is also useful for this task.
Outlier Detection Methods--- An Analysis

An outlier is an extreme observation that is considerably dissimilar from the rest of the objects. The detection of outlier is helpful in many applications such as data cleaning, network intrusion, credit card fraud detection, telecom fraud detection, customer segmentation, medical analysis etc. Outliers behave very differently from the rest of the observations in the dataset. Outliers are mostly removed to improve the accuracy of the predictions. But, the presence of an outlier can have certain meaning also. In our work we compare detection of outlier techniques based on statistical method, density based method, distance based method and deviation based.
Automated Comparative Table Generation for Facilitating Human Intervention in Multi-Entity Resolution

Entity resolution (ER), the process of identifying entities that refer to the same real-world object, has long been studied in the knowledge graph (KG) community, among many others. Humans, as a valuable source of background knowledge, are increasingly getting involved in this loop by crowdsourcing and active learning, where presenting condensed and easily-compared information is vital to help human intervene in an ER task. However, current methods for single entity or pairwise summarization cannot well support humans to observe and compare multiple entities simultaneously, which impairs the efficiency and accuracy of human intervention. In this paper, we propose an automated approach to select a few important properties and values for a set of entities, and assemble them by a comparative table. We formulate several optimization problems for generating an optimal comparative table according to intuitive goodness measures and various constraints. Our experiments on real-world datasets, comparison with related work and user study demonstrate the superior efficiency, precision and user satisfaction of our approach in multi-entity resolution (MER).
Convolution-based Memory Network for Aspect-based Sentiment Analysis

Memory networks have shown expressive performance on aspect based sentiment analysis. However, ordinary memory networks only capture word-level information and lack the capacity for modeling complicated expressions which consist of multiple words. Targeting this problem, we propose a novel convolutional memory network which incorporates an attention mechanism. This model sequentially computes the weights of multiple memory units corresponding to multi-words. This model may capture both words and multi-words expressions in sentences for aspect-based sentiment analysis. Experimental results show that the proposed model outperforms the state-of-the-art baselines.
POSTER: On Compressing PKI Certificates for Resource Limited Internet of Things Devices

Certificate-based Public Key Infrastructure (PKI) schemes are used to authenticate the identity of distinct nodes on the Internet. Using certificates for the Internet of Things (IoT) can allow many privacy sensitive applications to be trusted over the larger Internet architecture. However, since IoT devices are typically resource limited, full sized PKI certificates are not suitable for use in the IoT domain. This work outlines our approach in compressing standards-compliant X.509 certificates so that their sizes are reduced and can be effectively used on IoT nodes. Our scheme combines the use of Concise Binary Object Representation (CBOR) and also a scheme that compresses all data that can be implicitly inferenced within the IoT sub-network. Our scheme shows a certificate compression rate of up to ~30%, which allows effective energy reduction when using X.509-based certificates on IoT platforms.
Semantic Cache System

One of the economical ways to develop a very large scale database is to distribute it among multiple server nodes. Main problem in these types of systems is retrieval of data within significant time; especially when network or server load is high. This task becomes more critical when data is to be retrieved from the database against frequent queries. Cache is used to increase the retrieval performance of mobile computing and distributed database systems. Whenever data is found locally from the cache it is termed as cache hit. Percentage of user posed queries that can be processed (partially or fully) locally from cache is called hit ratio. So, the cache system should be designed in a way that will increase the hit ratio. Improvement in hit ratio ensures efficient reuse of stored data. Due to efficient reuse of stored data, lesser amount of data is required to be retrieved from remote location.
MyCampus: An Agent-Based Environment for Context-Aware Mobile Services

In this paper, we introduce MyCampus, an agent-based environment for context-aware mobile services, which we are in the process of developing and validating on Carnegie Mellon University’s campus. The environment revolves around a growing collection of customizable agents capable of (semi-) automatically discovering and accessing Intranet and Internet services as they assist their users in carrying out different tasks (e.g. planning an evening out, organizing a study group or filtering incoming messages). The power and scalability of the environment directly derives from a set of ontologies for describing contextual attributes, user preferences and web services, making it possible to easily accommodate new task-specific agents and new Web services.
Size matters: the effects of interactive display size on interaction zone expectations

The goal of our research was to understand the effects of display size on interaction zones as it applies to interactive systems. Interaction zone models for interactive displays are often static and do not consider the size of the display in their definition. As the interactive display ecosystem becomes more size diverse, current models for interaction are limited in their applicability. This paper describes the results of an exploratory study in which participants interacted with and discussed expectations with interactive displays ranging from personal to wall-sized. Our approach was open-ended rather than grounded in existing interaction zone models in order to explore potential differences in interaction zones and distances. We found that the existence of different interaction zones and the distance at which these zones are relevant are dependent on display size. In discussion of the results, we explore implications of our findings and offer guidelines for the design of interactive display systems.
Relative timing

Relative Timing is introduced as an informal method for aggressive asynchronous design. It is demonstrated on three example circuits (C-Element, FIFO, and RAPPID Tag Unit), facilitating transformations from speed-independent circuits to burst-mode, relative timed, and pulse-mode circuits. Relative timing enables improved performance, area, power and testability in all three cases.
Challenges in Scheduling Electric Vehicle Charging with Discrete Charging Rates in AC Power Networks

To meet the substantial demand for electrified transportation, a high level of penetration of electric vehicles (EVs) will be expected to incur considerable impacts on the reliability of electricity grid. Hence, there requires an intelligent scheduling mechanism for EV charging for maintaining the electricity grid within the operating limits and absorbing intermittent renewable energy. This note discusses the challenges in designing practical charging algorithms considering common discrete charging rates with minimum power requirements in various charging modes over alternating current (AC) power networks. We present the basic ideas of applying convex relaxation and proper rounding techniques to tackle the combinatorial optimization problem of EV charging.
Pose Flow: Efficient Online Pose Tracking

Multi-person articulated pose tracking in unconstrained videos is an important while challenging problem. In this paper, going along the road of top-down approaches, we propose a decent and efficient pose tracker based on pose flows. First, we design an online optimization framework to build the association of cross-frame poses and form pose flows (PF-Builder). Second, a novel pose flow non-maximum suppression (PF-NMS) is designed to robustly reduce redundant pose flows and re-link temporal disjoint ones. Extensive experiments show that our method significantly outperforms best reported results on two standard Pose Tracking datasets ( [12] and [8]) by 13 mAP 25 MOTA and 6 mAP 3 MOTA respectively. Moreover, in the case of working on detected poses in individual frames, the extra computation of pose tracker is very minor, guaranteeing online 10FPS tracking. Our source codes are made publicly available1.