Quasiparticle self-consistent GWGW band structures of Mg-IV-N2_2 compounds: the role of semicore dd states

We present improved band structure calculations of the Mg-IV-N$_2$ compounds in the quasiparticle self-consistent $GW$ approximation. Compared to previous calculations (Phys. Rev. B 94, 125201 (2016)) we here include the effects of the Ge-3$d$ and Sn-4$d$ semicore states and find that these tend to reduce the band gap significantly. This places the band gap of MgSnN$_2$ in the difficult to reach green region of the visible spectrum. The stability of the materials with respect to competing binary compounds is also evaluated and details of the valence band maximum manifold splitting and effective masses are provided

Quasiparticle self-consistent GWGW electronic band structures of Be-IV-N2_2 compounds

The electronic band structures of BeSiN$_2$ and BeGeN$_2$ compounds are calculated using the quasiparticle self-consistent $GW$ method. The lattice parameters are calculated for the wurtzite based crystal structure commonly found in other II-IV-N$_2$ compounds with the $Pbn$2$_1$ space group. They are determined both in the local density approximation (LDA) and generalized gradient approximation (GGA), which provide lower and upper limits. At the GGA lattice constants, which gives lattice constants closer to the experimental ones, BeSiN$_2$ is found to have an indirect band gap of 6.88 eV and its direct gap at $\Gamma$ is 7.77 eV, while in BeGeN$_2$ the gap is direct at $\Gamma$ and equals 5.03 eV. To explain the indirect gap in BeSiN$_2$, comparisons are made with the parent III-N compound w-BN band structure. The effective mass parameters are also evaluated and found to decrease from BeSiN$_2$ to BeGeN$_2$

N-Version Obfuscation: Impeding Software Tampering Replication with Program Diversity

Tamper-resistance is a fundamental software security research area. Many approaches have been proposed to thwart specific procedures of tampering, e.g., obfuscation and self-checksumming. However, to our best knowledge, none of them can achieve theoretically tamper-resistance. Our idea is to impede the replication of tampering via program diversification, and thus increasing the complexity to break the whole software system. To this end, we propose to deliver same featured, but functionally nonequivalent software copies to different machines. We formally define the problem as N-version obfuscation, and provide a viable means to solve the problem. Our evaluation result shows that the time required for breaking a software system is linearly increased with the number of software versions, which is O(n) complexity

A Survey of Point-of-interest Recommendation in Location-based Social Networks

Point-of-interest (POI) recommendation that suggests new places for users to visit arises with the popularity of location-based social networks (LBSNs). Due to the importance of POI recommendation in LBSNs, it has attracted much academic and industrial interest. In this paper, we offer a systematic review of this field, summarizing the contributions of individual efforts and exploring their relations. We discuss the new properties and challenges in POI recommendation, compared with traditional recommendation problems, e.g., movie recommendation. Then, we present a comprehensive review in three aspects: influential factors for POI recommendation, methodologies employed for POI recommendation, and different tasks in POI recommendation. Specifically, we propose three taxonomies to classify POI recommendation systems. First, we categorize the systems by the influential factors check-in characteristics, including the geographical information, social relationship, temporal influence, and content indications. Second, we categorize the systems by the methodology, including systems modeled by fused methods and joint methods. Third, we categorize the systems as general POI recommendation and successive POI recommendation by subtle differences in the recommendation task whether to be bias to the recent check-in. For each category, we summarize the contributions and system features, and highlight the representative work. Moreover, we discuss the available data sets and the popular metrics. Finally, we point out the possible future directions in this area and conclude this survey

GT-SEER: Geo-Temporal SEquential Embedding Rank for Point-of-interest Recommendation

Point-of-interest (POI) recommendation is an important application in location-based social networks (LBSNs), which learns the user preference and mobility pattern from check-in sequences to recommend POIs. However, previous POI recommendation systems model check-in sequences based on either tensor factorization or Markov chain model, which cannot capture contextual check-in information in sequences. The contextual check-in information implies the complementary functions among POIs that compose an individual's daily check-in sequence. In this paper, we exploit the embedding learning technique to capture the contextual check-in information and further propose the \textit{{\textbf{SE}}}quential \textit{{\textbf{E}}}mbedding \textit{{\textbf{R}}}ank (\textit{SEER}) model for POI recommendation. In particular, the \textit{SEER} model learns user preferences via a pairwise ranking model under the sequential constraint modeled by the POI embedding learning method. Furthermore, we incorporate two important factors, i.e., temporal influence and geographical influence, into the \textit{SEER} model to enhance the POI recommendation system. Due to the temporal variance of sequences on different days, we propose a temporal POI embedding model and incorporate the temporal POI representations into a temporal preference ranking model to establish the \textit{T}emporal \textit{SEER} (\textit{T-SEER}) model. In addition, We incorporate the geographical influence into the \textit{T-SEER} model and develop the \textit{\textbf{Geo-Temporal}} \textit{{\textbf{SEER}}} (\textit{GT-SEER}) model

PersisDroid: Android Performance Diagnosis via Anatomizing Asynchronous Executions

Android applications (apps) grow dramatically in recent years. Apps are user interface (UI) centric typically. Rapid UI responsiveness is key consideration to app developers. However, we still lack a handy tool for profiling app performance so as to diagnose performance problems. This paper presents PersisDroid, a tool specifically designed for this task. The key notion of PersisDroid is that the UI-triggered asynchronous executions also contribute to the UI performance, and hence its performance should be properly captured to facilitate performance diagnosis. However, Android allows tremendous ways to start the asynchronous executions, posing a great challenge to profiling such execution. This paper finds that they can be grouped into six categories. As a result, they can be tracked and profiled according to the specifics of each category with a dynamic instrumentation approach carefully tailored for Android. PersisDroid can then properly profile the asynchronous executions in task granularity, which equips it with low-overhead and high compatibility merits. Most importantly, the profiling data can greatly help the developers in detecting and locating performance anomalies. We code and open-source release PersisDroid. The tool is applied in diagnosing 20 open-source apps, and we find 11 of them contain potential performance problems, which shows its effectiveness in performance diagnosis for Android apps

Almost Optimal Algorithms for Linear Stochastic Bandits with Heavy-Tailed Payoffs

In linear stochastic bandits, it is commonly assumed that payoffs are with sub-Gaussian noises. In this paper, under a weaker assumption on noises, we study the problem of \underline{lin}ear stochastic {\underline b}andits with h{\underline e}avy-{\underline t}ailed payoffs (LinBET), where the distributions have finite moments of order $1+\epsilon$, for some $\epsilon\in (0,1]$. We rigorously analyze the regret lower bound of LinBET as $\Omega(T^{\frac{1}{1+\epsilon}})$, implying that finite moments of order 2 (i.e., finite variances) yield the bound of $\Omega(\sqrt{T})$, with $T$ being the total number of rounds to play bandits. The provided lower bound also indicates that the state-of-the-art algorithms for LinBET are far from optimal. By adopting median of means with a well-designed allocation of decisions and truncation based on historical information, we develop two novel bandit algorithms, where the regret upper bounds match the lower bound up to polylogarithmic factors. To the best of our knowledge, we are the first to solve LinBET optimally in the sense of the polynomial order on $T$. Our proposed algorithms are evaluated based on synthetic datasets, and outperform the state-of-the-art results

HiGRU: Hierarchical Gated Recurrent Units for Utterance-level Emotion Recognition

In this paper, we address three challenges in utterance-level emotion recognition in dialogue systems: (1) the same word can deliver different emotions in different contexts; (2) some emotions are rarely seen in general dialogues; (3) long-range contextual information is hard to be effectively captured. We therefore propose a hierarchical Gated Recurrent Unit (HiGRU) framework with a lower-level GRU to model the word-level inputs and an upper-level GRU to capture the contexts of utterance-level embeddings. Moreover, we promote the framework to two variants, HiGRU with individual features fusion (HiGRU-f) and HiGRU with self-attention and features fusion (HiGRU-sf), so that the word/utterance-level individual inputs and the long-range contextual information can be sufficiently utilized. Experiments on three dialogue emotion datasets, IEMOCAP, Friends, and EmotionPush demonstrate that our proposed HiGRU models attain at least 8.7%, 7.5%, 6.0% improvement over the state-of-the-art methods on each dataset, respectively. Particularly, by utilizing only the textual feature in IEMOCAP, our HiGRU models gain at least 3.8% improvement over the state-of-the-art conversational memory network (CMN) with the trimodal features of text, video, and audio.Comment: NAACL 2019 (10 pages

A Privacy-Preserving QoS Prediction Framework for Web Service Recommendation

QoS-based Web service recommendation has recently gained much attention for providing a promising way to help users find high-quality services. To facilitate such recommendations, existing studies suggest the use of collaborative filtering techniques for personalized QoS prediction. These approaches, by leveraging partially observed QoS values from users, can achieve high accuracy of QoS predictions on the unobserved ones. However, the requirement to collect users' QoS data likely puts user privacy at risk, thus making them unwilling to contribute their usage data to a Web service recommender system. As a result, privacy becomes a critical challenge in developing practical Web service recommender systems. In this paper, we make the first attempt to cope with the privacy concerns for Web service recommendation. Specifically, we propose a simple yet effective privacy-preserving framework by applying data obfuscation techniques, and further develop two representative privacy-preserving QoS prediction approaches under this framework. Evaluation results from a publicly-available QoS dataset of real-world Web services demonstrate the feasibility and effectiveness of our privacy-preserving QoS prediction approaches. We believe our work can serve as a good starting point to inspire more research efforts on privacy-preserving Web service recommendation.Comment: This paper is published in IEEE International Conference on Web Services (ICWS'15

On Secure and Usable Program Obfuscation: A Survey

Program obfuscation is a widely employed approach for software intellectual property protection. However, general obfuscation methods (e.g., lexical obfuscation, control obfuscation) implemented in mainstream obfuscation tools are heuristic and have little security guarantee. Recently in 2013, Garg et al. have achieved a breakthrough in secure program obfuscation with a graded encoding mechanism and they have shown that it can fulfill a compelling security property, i.e., indistinguishability. Nevertheless, the mechanism incurs too much overhead for practical usage. Besides, it focuses on obfuscating computation models (e.g., circuits) rather than real codes. In this paper, we aim to explore secure and usable obfuscation approaches from the literature. Our main finding is that currently we still have no such approaches made secure and usable. The main reason is we do not have adequate evaluation metrics concerning both security and performance. On one hand, existing code-oriented obfuscation approaches generally evaluate the increased obscurity rather than security guarantee. On the other hand, the performance requirement for model-oriented obfuscation approaches is too weak to develop practical program obfuscation solutions