Reviewerly/exHarmony huggingface dataset:
One Engine To Serve 'em All: Inferring Taint Rules Without Architectural Semantics

Dynamic binary taint analysis has wide applications in the security analysis of commercial-off-the-shelf (COTS) binaries.One of the key challenges in dynamic binary analysis is to specify the taint rules that capture how taint information propagates for each instruction on an architecture.Most of the existing solutions specify taint rules using a deductive approach by summarizing the rules manually after analyzing the instruction semantics.Intuitively, taint propagation reflects on how an instruction input affects its output, and thus can be observed from instruction executions.In this work, we propose an inductive method for taint propagation and develop a universal taint tracking engine that is architecture-agnostic.Our taint engine, TAINTINDUCE, can learn taint rules with minimal architectural knowledge by observing the execution behavior of instructions.To measure its correctness and guide taint rule generation, we define the precise notion of soundness for bit-level taint tracking in this novel setup.In our evaluation, we show that TAINTINDUCE automatically learns rules for 4 widely used architectures: x86, x64, AArch64, and MIPS-I.It can detect vulnerabilities for 24 CVEs in 15 applications on both Linux and Windows over millions of instructions and is comparable with other mature existing tools (TEMU [51], libdft [32], Triton [42]).TAINTINDUCE can be used as a stand-alone taint engine or be used to complement existing taint engines for unhandled instructions.Further, it can be used as a cross-referencing tool to uncover bugs in taint engines, emulation implementations and ISA documentations.
Providing Information through Smart Platforms: An Applied Study on Academic Libraries in Saudi Universities

Information services using smart platforms have become one of the demands of this era since the much of information centres' users prefer using them for their speed and simplicity, capacity and efficiency in collecting vast amounts of information that meets the user's needs. Smartphones have spread out globally in a dramatic form, which means that libraries and information centres are now facing tremendous challenges to meet the needs of its users.&#x0D; This study aims at highlighting the efforts of the Library Information Services Specialists' of providing information services by the use of smart platforms that have been applied in Saudi Academic Libraries and Centres, which have delivered the said services. The main requirements for offering information services via smart platforms in the academic libraries and Information Centers were considered the subject of the study (Saudi academic Library and Resource centres at Saudi Universities). However, the significance of the study relies on empowering the researchers and students to have the means of free access to the research, studies, and all sources of digital information via their smartphones.&#x0D; The study consists of two parts, applied and theoretical fields in its methodology. The implementation of the applied research involved conducting a survey among the information services providers at the Saudi academic Libraries and Resource centres at Saudi Universities. Several users of the said libraries have conducted well-planned interviews with 156 information professionals and researchers to identify their views, insights, and opinions on the benefits of offering information services via smart platforms. The theoretical part took from the resources’, references and websites that discussed the aforementioned topic. Also addressed were its obstacles facing the said services in the era of information technology as the study sought to find out the answer.
Automatic Digital Modulation Classification Based on Curriculum Learning

Neural network shows great potential in modulation classification because of its excellent accuracy and achievability but overfitting and memorizing data noise often happen in previous researches on automatic digital modulation classifier. To solve this problem, we utilize two neural networks, namely MentorNet and StudentNet, to construct an automatic modulation classifier, which possesses great performance on the test set with −18–20 dB signal-to-noise ratio (SNR). The MentorNet supervises the training of StudentNet according to curriculum learning, and deals with the overfitting problem in StudentNet. The proposed classifier is verified in several test sets containing additive white Gaussian noise (AWGN), Rayleigh fading, carrier frequency offset and phase offset. Experimental results reveal that the overall accuracy of this classifier for common eleven modulation types was up to 99.3% while the inter-class accuracy could be up to 100%, which was much higher than many other classifiers. Besides, in the presence of interferences, the overall accuracy of this novel classifier still could reach 90% at 10 dB SNR indicting its excellent robustness, which makes it suitable for applications like military electronic warfare.
Exploring the applicability of low-shot learning in mining software repositories

Despite the well-documented and numerous recent successes of deep learning, the application of standard deep architectures to many classification problems within empirical software engineering remains problematic due to the large volumes of labeled data required for training. Here we make the argument that, for some problems, this hurdle can be overcome by taking advantage of low-shot learning in combination with simpler deep architectures that reduce the total number of parameters that need to be learned. We apply low-shot learning to the task of classifying UML class and sequence diagrams from Github, and demonstrate that surprisingly good performance can be achieved by using only tens or hundreds of examples for each category when paired with an appropriate architecture. Using a large, off-the-shelf architecture, on the other hand, doesn't perform beyond random guessing even when trained on thousands of samples. Our findings suggest that identifying problems within empirical software engineering that lend themselves to low-shot learning could accelerate the adoption of deep learning algorithms within the empirical software engineering community.
Knowledge Embedding with Geospatial Distance Restriction for Geographic Knowledge Graph Completion

A Geographic Knowledge Graph (GeoKG) links geographic relation triplets into a large-scale semantic network utilizing the semantic of geo-entities and geo-relations. Unfortunately, the sparsity of geo-related information distribution on the web leads to a situation where information extraction systems can hardly detect enough references of geographic information in the massive web resource to be able to build relatively complete GeoKGs. This incompleteness, due to missing geo-entities or geo-relations in GeoKG fact triplets, seriously impacts the performance of GeoKG applications. In this paper, a method with geospatial distance restriction is presented to optimize knowledge embedding for GeoKG completion. This method aims to encode both the semantic information and geospatial distance restriction of geo-entities and geo-relations into a continuous, low-dimensional vector space. Then, the missing facts of the GeoKG can be supplemented through vector operations. Specifically, the geospatial distance restriction is realized as the weights of the objective functions of current translation knowledge embedding models. These optimized models output the optimized representations of geo-entities and geo-relations for the GeoKG’s completion. The effects of the presented method are validated with a real GeoKG. Compared with the results of the original models, the presented method improves the metric Hits@10(Filter) by an average of 6.41% for geo-entity prediction, and the Hits@1(Filter) by an average of 31.92%, for geo-relation prediction. Furthermore, the capacity of the proposed method to predict the locations of unknown entities is validated. The results show the geospatial distance restriction reduced the average error distance of prediction by between 54.43% and 57.24%. All the results support the geospatial distance restriction hiding in the GeoKG contributing to refining the embedding representations of geo-entities and geo-relations, which plays a crucial role in improving the quality of GeoKG completion.
In Aggregate: Trends, Needs, and Opportunities from Research Data Management Surveys

Preliminary data presented at: In Aggregate: Trends, Needs, and Opportunities from Faculty Research Data Management Surveys [presentation]. IASSIST Annual Conference, May 23–26, 2017, Lawrence, KS. A popular starting point for libraries engaging in research data management (RDM) services is a needs assessment (NA); a preliminary count identified more than 50 published NA case studies. However, no overarching analysis has yet been conducted. The authors compared assessments to characterize the case study institution types; establish the target population assessed; discover cross-institutional trends both in the topics covered and the issues identified; and determine remaining gaps in the literature. Thirty-seven studies conducted in the United States were included. Twenty-five were at public, doctoral, highest-research institutions. The most frequently assessed respondents were faculty (n = 3,847). The most frequent topics involved storing, sharing, and maintaining long-term access to data. Gaps include assessing students, staff, and nonfaculty researcher needs; determining needs at various sized and degree-granting institutions; and investigating RDM needs for non-STEM disciplines.
Off-The-Shelf Artificial Intelligence Technologies for Sentiment and Emotion Analysis: A Tutorial on Using IBM Natural Language Processing

Artificial intelligence (AI) rests on the premise that machines can behave in a human-like way and potentially solve complex analytics problems.In recent years, we have seen several off-the-shelf AI technologies that claim to be ready to use.In this paper, we illustrate how one can use one such technology, called IBM Natural Language Understanding (NLU), to solve a data-analytics problem.First, we provide a detailed step-by-step tutorial on how to use NLU.Next, we introduce our case study in which we investigated the implications of Starbucks' pledge to hire refugees.In this context, we used NLU to assign sentiment and emotion scores to social-media posts related to Starbucks made before and after the pledge.We found that consumers' sentiment towards Starbucks became more positive after the pledge whereas investors' sentiment became more negative.Interestingly, we found no significant relationship between consumers' and investors' sentiments.With help from NLU, we also found that consumers' sentiments lacked consensus in that their social media posts contained a great deal of mixed emotions.As part of our case study, we found that NLU correctly classified the polarity of sentiments 72.64 percent of the time, an accuracy value much higher than the 49.77 percent that the traditional bag-of-words approach achieved.Besides illustrating how practitioners/researchers can use off-the-shelf AI technologies in practice, we believe the results from our case study provide value to organizations interested in implementing corporate social responsibility policies.
Detail preservation and feature refinement for object detection

In recent years, significant achievements have been made in object detection thanks to development of deep learning. However, there are still some open problems such as the poor performance in small object detection, especially when the computing resources are limited. In this paper, we propose a single-shot detail-preserving detector with a multi-flow sub-network and a multi-connection module, which is built upon the one-stage strategy to inherit the computational efficiency. Specifically, we first design a detail-preserving backbone network to preserve image details critical for small object detection. Then for feature refinement, we propose a multi-flow sub-network to optimize low-level features for small object detection and a multi-connection module to fuse multi-grained information to enhance feature representation without significant extra computational cost. Extensive experiments on PASCAL VOC and MS COCO demonstrate that our detector achieves state-of-the-art detection accuracies with high computational efficiency. The proposed method with only 300 × 300 input size achieves 82.6% mAP on PASCAL VOC 2007 and 32.9% mAP on MS COCO, both with one Nvidia Titan X GPU.
An Adaptive Multi-Swarm Competition Particle Swarm Optimizer for Large-Scale Optimization

As a powerful tool in optimization, particle swarm optimizers have been widely applied to many different optimization areas and drawn much attention. However, for large-scale optimization problems, the algorithms exhibit poor ability to pursue satisfactory results due to the lack of ability in diversity maintenance. In this paper, an adaptive multi-swarm particle swarm optimizer is proposed, which adaptively divides a swarm into several sub-swarms and a competition mechanism is employed to select exemplars. In this way, on the one hand, the diversity of exemplars increases, which helps the swarm preserve the exploitation ability. On the other hand, the number of sub-swarms adaptively changes from a large value to a small value, which helps the algorithm make a suitable balance between exploitation and exploration. By employing several peer algorithms, we conducted comparisons to validate the proposed algorithm on a large-scale optimization benchmark suite of CEC 2013. The experiments results demonstrate the proposed algorithm is effective and competitive to address large-scale optimization problems.
SGX-Based Secure Indexing System

With the rising popularity of cloud computing technology, many positive effects have been exerted, whereas many security issues arise. The main existing way to ensure data confidentiality is to encrypt the data. However, the encrypted data renders keyword indexing more difficult to achieve, and the way to specify the plain-text keywords for the cipher-text data also reveals the privacy of the data owner to the untrusted service provider. In the meantime, many challenges are imposed on the software-based methods to index encrypted data, and existing hardware-based solutions typically have a large code footprint in a trusted environment. To solve these problems, an SGX-based secure indexing solution was built based on the combination of hardware and software and using Intel's Software Guard Extensions (SGX) technology. The results of the security analysis suggest that our scheme only leaks the access patterns, and our trust code protected by the SGX hardware is very small, thereby minimizing the exposed attack surface. Besides, our experiments verify that our scheme is efficient and practical, both in performance and storage aspects.
Emotion Reinforced Visual Storytelling

Automatic story generation from a sequence of images, i.e., visual storytelling, has attracted extensive attention. The challenges mainly drive from modeling rich visually-inspired human emotions, which results in generating diverse yet realistic stories even from the same sequence of images. Existing works usually adopt sequence-based generative adversarial networks (GAN) by encoding deterministic image content (e.g., concept, attribute), while neglecting probabilistic inference from an image over emotion space. In this paper, we take one step further to create human-level stories by modeling image content with emotions, and generating textual paragraph via emotion reinforced adversarial learning. Firstly, we introduce the concept of emotion engaged in visual storytelling. The emotion feature is a representation of the emotional content of the generated story, which enables our model to capture human emotion. Secondly, stories are generated by recurrent neural network, and further optimized by emotion reinforced adversarial learning with three critics, in which visual relevance, language style, and emotion consistency can be ensured. Our model is able to generate stories based on not only emotions generated by our novel emotion generator, but also customized emotions. The introduction of emotion brings more variety and realistic to visual storytelling. We evaluate the proposed model on the largest visual storytelling dataset (VIST). The superior performance to state-of-the-art methods are shown with extensive experiments.
Jointly-Optimized Searching and Tracking with Random Finite Sets

In this paper, we investigate the problem of joint searching and tracking of multiple mobile targets by a group of mobile agents. The targets appear and disappear at random times inside a surveillance region and their positions are random and unknown. The agents have limited sensing range and receive noisy measurements from the targets. A decision and control problem arises, where the mode of operation (i.e., search or track) as well as the mobility control action for each agent, at each time instance, must be determined so that the collective goal of searching and tracking is achieved. We build our approach upon the theory of random finite sets (RFS) and we use Bayesian multi-object stochastic filtering to simultaneously estimate the time-varying number of targets and their states from a sequence of noisy measurements. We formulate the above problem as a non-linear binary program (NLBP) and show that it can be approximated by a genetic algorithm. Finally, to study the effectiveness and performance of the proposed approach we have conducted extensive simulation experiments.
Guided goal generation for hindsight multi-goal reinforcement learning

Typical reinforcement learning (RL) can only perform a single task and thus cannot scale to problems for which an agent needs to perform multiple tasks, such as moving objects to different locations, which is relevant to real-world environments. Hindsight experience replay (HER) based on universal value functions shows promising results in such multi-goal settings by substituting achieved goals for the original goal, frequently giving the agent rewards. However, the achieved goals are limited to the current policy level and lack guidance for learning. We propose a novel guided goal-generation model for multi-goal RL named G-HER. Our method uses a conditional generative recurrent neural network (RNN) to explicitly model the relationship between policy level and goals, enabling the generation of various goals conditions on the different policy levels. Goals generated with a higher policy level provide better guidance for the RL agent, which is equivalent to using knowledge of successful policy in advance to guide the learning of current policy. Our model accelerates the generalization of substitute goals to the whole goal space. The G-HER algorithm is evaluated on several robotic manipulating tasks and demonstrates improved performance and sample efficiency.
Inter-Basket and Intra-Basket Adaptive Attention Network for Next Basket Recommendation

Next basket recommendation with consideration of user sequential shopping behaviors plays a significant role in E-commerce to improve the user experience and service quality. Recently, recurrent neural networks (RNNs), especially attention-based RNN, have been widely adopted in the next basket recommendation. However, existing fixed attention mechanisms are not designed to model the dynamic and diverse characteristics of user appetites. In this paper, we propose an inter-basket and intra-basket adaptive attention network (IIAAN) for the next basket recommendation. Specifically, the inter-basket adaptive attention acts on all historical user baskets to model user's diverse long-term preferences, while the intra-basket adaptive attention is designed to act on item-level in the most recent basket to model user's dynamic and different short-term preferences. Then, we further integrate inter-basket and intra-basket adaptive attentions together to improve recommendation effectiveness. Finally, we evaluate the proposed model IIAAN using three real-world datasets from various E-commerce platforms. Our experimental results show that our model IIAAN significantly outperforms the state-of-the-art approaches for the next basket recommendation.
Neural-based Chinese Idiom Recommendation for Enhancing Elegance in Essay Writing

Although the proper use of idioms can enhance the elegance of writing, the active use of various expressions is a challenge because remembering idioms is difficult. In this study, we address the problem of idiom recommendation by leveraging a neural machine translation framework, in which we suppose that idioms are written with one pseudo target language. Two types of real-life datasets are collected to support this study. Experimental results show that the proposed approach achieves promising performance compared with other baseline methods.
Evaluating Methods for the Identification of Off-Chain Transactions in the Lightning Network

Payment channels and off-chain transactions are used to address blockchain scalability. Those mechanisms rely on the blockchain proper, as the resolution mechanism. They allow for high transaction throughput due to the pure peer-to-peer nature of the transaction exchange that happens directly between the peers, without the involvement of the blockchain transactions. The transactions are not mediated through the blockchain but happen off-chain. The transactions in such overlay networks are not included in the blockchain, they nevertheless leave some data traces in a public ledger. We have used the Bitcoin mainnet and testnet blockchains together with the Lightning network node to explore what can be inferred from the underlying blockchain in the context of Lightning transactions, channel setup, and channel teardown. The main purpose of this study is to identify what methods, transaction signatures, and data can be used to understand the non-visible publicly off-chain transactions. We have proposed heuristics for identifying the setup and teardown transactions, quantified and analyzed the effectiveness of our proposed methods. Using the data from the Bitcoin blockchain, as well as the data from the Lightning network to link related information we have found when parsing the blockchain, we generate network graph representations showing the relationships between the Lightning network channels identified on the blockchain. This study is significant from the personal data and privacy perspectives, as well as from forensics. We have established that at least 75% of all P2WSH transactions are Lightning transactions, and some of the channels can be deduced from the blockchain analysis. The synthesized results demonstrate that our methods are viable for identifying a subset of transactions and that only partial topology of the payment channels can be obtained from the data left in the blockchain.
Neural GRANNy at SemEval-2019 Task 2: A combined approach for better modeling of semantic relationships in semantic frame induction

We describe our solutions for semantic frame and role induction subtasks of SemEval 2019 Task 2. Our approaches got the highest scores, and the solution for the frame induction problem officially took the first place.The main contributions of this paper are related to the semantic frame induction problem.We propose a combined approach that employs two different types of vector representations: dense representations from hidden layers of a masked language model, and sparse representations based on substitutes for the target word in the context.The first one better groups synonyms, the second one is better at disambiguating homonyms.Extending the context to include nearby sentences improves the results in both cases.New Hearst-like patterns for verbs are introduced that prove to be effective for frame induction.Finally, we propose an approach to selecting the number of clusters in agglomerative clustering.