Find the Conversation Killers: a Predictive Study of Thread-ending Posts

How to improve the quality of conversations in online communities has attracted considerable attention recently. Having engaged, urbane, and reactive online conversations has a critical effect on the social life of Internet users. In this study, we are particularly interested in identifying a post in a multi-party conversation that is unlikely to be further replied to, which therefore kills that thread of the conversation. For this purpose, we propose a deep learning model called the ConverNet. ConverNet is attractive due to its capability of modeling the internal structure of a long conversation and its appropriate encoding of the contextual information of the conversation, through effective integration of attention mechanisms. Empirical experiments on real-world datasets demonstrate the effectiveness of the proposal model. For the widely concerned topic, our analysis also offers implications for improving the quality and user experience of online conversations.Comment: Accepted by WWW 2018 (The Web Conference, 2018

A Comparative Analysis of Inconel 718 Made by Additive Manufacturing and Suction Casting: Microstructure Evolution in Homogenization

Homogenization is one of the critical stages in the post-heat treatment of additive manufacturing (AM) component to achieve uniform microstructure. During homogenization, grain coarsening could be an issue to reserve strength, which requires careful design of both time and temperature. Therefore, a proper design of homogenization becomes particularly important for AM design, for which work hardening is usually no longer an option. In this work, we discovered an intriguing phenomenon during homogenization of suction-cast and AM Inconel 718 superalloys. Through both short and long-term isothermal heat treatments at 1180{\deg}C, we observed an abnormal grain growth in the suction-cast alloy but continuous recrystallization in the alloy made by laser powder bed fusion (LPBF). The grain size of AM samples keeps as small as 130 {\mu}m and is even slightly reduced after homogenization for 12 hours. The homogeneity of Nb in the AM alloys is identified as the critical factor for NbC formation, which further influences the recrystallization kinetics at 1180{\deg}C. Multi-type dislocation behaviors are studied to elucidate the grain refinement observed in homogenized alloys after LPBF. This work provides a new pathway on microstructure engineering of AM alloys for improved mechanical performance superior to traditionally manufactured ones.Comment: 28 pages, 8 figures, 3 table

Short Text Topic Modeling Techniques, Applications, and Performance: A Survey

Analyzing short texts infers discriminative and coherent latent topics that is a critical and fundamental task since many real-world applications require semantic understanding of short texts. Traditional long text topic modeling algorithms (e.g., PLSA and LDA) based on word co-occurrences cannot solve this problem very well since only very limited word co-occurrence information is available in short texts. Therefore, short text topic modeling has already attracted much attention from the machine learning research community in recent years, which aims at overcoming the problem of sparseness in short texts. In this survey, we conduct a comprehensive review of various short text topic modeling techniques proposed in the literature. We present three categories of methods based on Dirichlet multinomial mixture, global word co-occurrences, and self-aggregation, with example of representative approaches in each category and analysis of their performance on various tasks. We develop the first comprehensive open-source library, called STTM, for use in Java that integrates all surveyed algorithms within a unified interface, benchmark datasets, to facilitate the expansion of new methods in this research field. Finally, we evaluate these state-of-the-art methods on many real-world datasets and compare their performance against one another and versus long text topic modeling algorithm.Comment: arXiv admin note: text overlap with arXiv:1808.02215 by other author

Cloud-based Privacy Preserving Image Storage, Sharing and Search

High-resolution cameras produce huge volume of high quality images everyday. It is extremely challenging to store, share and especially search those huge images, for which increasing number of cloud services are presented to support such functionalities. However, images tend to contain rich sensitive information (\eg, people, location and event), and people's privacy concerns hinder their readily participation into the services provided by untrusted third parties. In this work, we introduce PIC: a Privacy-preserving large-scale Image search system on Cloud. Our system enables efficient yet secure content-based image search with fine-grained access control, and it also provides privacy-preserving image storage and sharing among users. Users can specify who can/cannot search on their images when using the system, and they can search on others' images if they satisfy the condition specified by the image owners. Majority of the computationally intensive jobs are outsourced to the cloud side, and users only need to submit the query and receive the result throughout the entire image search. Specially, to deal with massive images, we design our system suitable for distributed and parallel computation and introduce several optimizations to further expedite the search process. We implement a prototype of PIC including both cloud side and client side. The cloud side is a cluster of computers with distributed file system (Hadoop HDFS) and MapReduce architecture (Hadoop MapReduce). The client side is built for both Windows OS laptops and Android phones. We evaluate the prototype system with large sets of real-life photos. Our security analysis and evaluation results show that PIC successfully protect the image privacy at a low cost of computation and communication.Comment: 15 pages, 12 figure

Triggercast: Enabling Wireless Collisions Constructive

It is generally considered that concurrent transmissions should be avoided in order to reduce collisions in wireless sensor networks. Constructive interference (CI) envisions concurrent transmissions to positively interfere at the receiver. CI potentially allows orders of magnitude reductions in energy consumptions and improvements on link quality. In this paper, we theoretically introduce a sufficient condition to construct CI with IEEE 802.15.4 radio for the first time. Moreover, we propose Triggercast, a distributed middleware, and show it is feasible to generate CI in TMote Sky sensor nodes. To synchronize transmissions of multiple senders at the chip level, Triggercast effectively compensates propagation and radio processing delays, and has $95^{th}$ percentile synchronization errors of at most 250ns. Triggercast also intelligently decides which co-senders to participate in simultaneous transmissions, and aligns their transmission time to maximize the overall link PRR, under the condition of maximal system robustness. Extensive experiments in real testbeds reveal that Triggercast significantly improves PRR from 5% to 70% with 7 concurrent senders. We also demonstrate that Triggercast provides on average $1.3\times$ PRR performance gains, when integrated with existing data forwarding protocols.Comment: 10 pages, 18 figure

UBAR: Towards Fully End-to-End Task-Oriented Dialog Systems with GPT-2

This paper presents our task-oriented dialog system UBAR which models task-oriented dialogs on a dialog session level. Specifically, UBAR is acquired by fine-tuning the large pre-trained unidirectional language model GPT-2 on the sequence of the entire dialog session which is composed of user utterance, belief state, database result, system act, and system response of every dialog turn. Additionally, UBAR is evaluated in a more realistic setting, where its dialog context has access to user utterances and all content it generated such as belief states, system acts, and system responses. Experimental results on the MultiWOZ datasets show that UBAR achieves state-of-the-art performances in multiple settings, improving the combined score of response generation, policy optimization, and end-to-end modeling by 4.7, 3.5, and 9.4 points respectively. Thorough analyses demonstrate that the session-level training sequence formulation and the generated dialog context are essential for UBAR to operate as a fully end-to-end task-oriented dialog system in real life. We also examine the transfer ability of UBAR to new domains with limited data and provide visualization and a case study to illustrate the advantages of UBAR in modeling on a dialog session level.Comment: Accepted by AAAI 202

Enable Portrait Privacy Protection in Photo Capturing and Sharing

The wide adoption of wearable smart devices with onboard cameras greatly increases people's concern on privacy infringement. Here we explore the possibility of easing persons from photos captured by smart devices according to their privacy protection requirements. To make this work, we need to address two challenges: 1) how to let users explicitly express their privacy protection intention, and 2) how to associate the privacy requirements with persons in captured photos accurately and efficiently. Furthermore, the association process itself should not cause portrait information leakage and should be accomplished in a privacy-preserving way. In this work, we design, develop, and evaluate a protocol, that enables a user to flexibly express her privacy requirement and empowers the photo service provider (or image taker) to exert the privacy protection policy.Leveraging the visual distinguishability of people in the field-of-view and the dimension-order-independent property of vector similarity measurement, we achieves high accuracy and low overhead. We implement a prototype system, and our evaluation results on both the trace-driven and real-life experiments confirm the feasibility and efficiency of our system.Comment: 9 pages, 8 figure

ACTION: Breaking the Privacy Barrier for RFID Systems

Abstract—In order to protect privacy, Radio Frequency Identification (RFID) systems employ Privacy-Preserving Authentication (PPA) to allow valid readers to explicitly authenticate their dominated tags without leaking private information. Typically, an RF tag sends an encrypted message to the reader, then the reader searches for the key that can decrypt the cipher to identify the tag. Due to the large-scale deployment of today’s RFID systems, the key search scheme for any PPA requires a short response time. Previous designs construct balance-tree based key management structures to accelerate the search speed to O(logN), where N is the number of tags. Being efficient, such approaches are vulnerable to compromising attacks. By capturing a small number of tags, compromising attackers are able to identify other tags that have not been corrupted. To address this issue, we propose an Anti-Compromising authenticaTION protocol, ACTION, which employs a novel sparse tree architecture, such that the key of every tag is independent from one another. The advantages of this design include: 1) resilience to the compromising attack, 2) reduction of key storage for tags from O(logN) to O(1), which is significant for resource critical tag devices, and 3) high search efficiency, which is O(logN), as good as the best in the previous designs. Keywords-RFID; privacy; authentication; compromising I

Topological phase transition induced extreme magnetoresistance in TaSb2_{2}

We report extremely large positive magnetoresistance of 1.72 million percent in single crystal TaSb$_{2}$ at moderate conditions of 1.5 K and 15 T. The quadratic growth of magnetoresistance (MR $\propto\,B^{1.96}$) is not saturating up to 15 T, a manifestation of nearly perfect compensation with $<0.1\%$ mismatch between electron and hole pockets in this semimetal. The compensation mechanism is confirmed by temperature-dependent MR, Hall and thermoelectric coefficients of Nernst and Seebeck, revealing two pronounced Fermi surface reconstruction processes without spontaneous symmetry breaking, \textit{i.e.} Lifshitz transitions, at around 20 K and 60 K, respectively. Using quantum oscillations of magnetoresistance and magnetic susceptibility, supported by density-functional theory calculations, we determined that the main hole Fermi surface of TaSb$_{2}$ forms a unique shoulder structure along the $F-L$ line. The flat band top of this shoulder pocket is just a few meV above the Fermi level, leading to the observed topological phase transition at 20 K when the shoulder pocket disappears. Further increase in temperature pushes the Fermi level to the band top of the main hole pocket, induced the second Lifshitz transition at 60 K when hole pocket vanishes completely.Comment: 4 figure

A new high-throughput method using additive manufacturing for alloy design and heat treatment optimization

Many alloys made by Additive Manufacturing (AM) require careful design of post-heat treatment as an indispensable step of microstructure engineering to further enhance the performance. We developed a high-throughput approach by fabricating a long-bar sample heat-treated under a monitored gradient temperature zone for phase transformation study to accelerate the post-heat treatment development of AM alloys. This approach has been proven efficient in determining the aging temperature with peak hardness. We observed that the precipitation strengthening is predominant for the studied superalloy by laser powder bed fusion, and the grain size variation is insensitive on temperature between 605 and 825 Celcius. This new approach can be applied to post-heat treatment optimization of other materials made by AM, and further assist new alloy development.Comment: 13 page, 6 figure