OpenRaiser/Intern-Atlas huggingface dataset:
Deep neural network-based classification model for Sentiment Analysis
Venue: arXiv cs.CL 2019
Year: 2019

The growing prosperity of social networks has brought great challenges to the sentimental tendency mining of users. As more and more researchers pay attention to the sentimental tendency of online users, rich research results have been obtained based on the sentiment classification of explicit texts. However, research on the implicit sentiment of users is still in its infancy. Aiming at the difficulty of implicit sentiment classification, a research on implicit sentiment classification model based on deep neural network is carried out. Classification models based on DNN, LSTM, Bi-LSTM and CNN were established to judge the tendency of the user's implicit sentiment text. Based on the Bi-LSTM model, the classification model of word-level attention mechanism is studied. The experimental results on the public dataset show that the established LSTM series classification model and CNN classification model can achieve good sentiment classification effect, and the classification effect is significantly better than the DNN model. The Bi-LSTM based attention mechanism classification model obtained the optimal R value in the positive category identification.
Evaluation of a Flow-Based Hypergraph Bipartitioning Algorithm
Venue: arXiv cs.DS 2019
Year: 2019

In this paper, we propose HyperFlowCutter, an algorithm for balanced hypergraph bipartitioning. It is based on minimum S-T hyperedge cuts and maximum flows. It computes a sequence of bipartitions that optimize cut size and balance in the Pareto sense, being able to trade one for the other. HyperFlowCutter builds on the FlowCutter algorithm for partitioning graphs. We propose additional features, such as handling disconnected hypergraphs, novel methods for obtaining starting S,T pairs as well as an approach to refine a given partition with HyperFlowCutter. Our main contribution is ReBaHFC, a new algorithm which obtains an initial partition with the fast multilevel hypergraph partitioner PaToH and then improves it using HyperFlowCutter as a refinement algorithm. ReBaHFC is able to significantly improve the solution quality of PaToH at little additional running time. The solution quality is only marginally worse than that of the best-performing hypergraph partitioners KaHyPar and hMETIS, while being one order of magnitude faster. Thus ReBaHFC offers a new time-quality trade-off in the current spectrum of hypergraph partitioners. For the special case of perfectly balanced bipartitioning, only the much slower plain HyperFlowCutter yields slightly better solutions than ReBaHFC, while only PaToH is faster than ReBaHFC.
Neural Image Captioning
Venue: arXiv cs.CL 2019
Year: 2019

In recent years, the biggest advances in major Computer Vision tasks, such as object recognition, handwritten-digit identification, facial recognition, and many others., have all come through the use of Convolutional Neural Networks (CNNs). Similarly, in the domain of Natural Language Processing, Recurrent Neural Networks (RNNs), and Long Short Term Memory networks (LSTMs) in particular, have been crucial to some of the biggest breakthroughs in performance for tasks such as machine translation, part-of-speech tagging, sentiment analysis, and many others. These individual advances have greatly benefited tasks even at the intersection of NLP and Computer Vision, and inspired by this success, we studied some existing neural image captioning models that have proven to work well. In this work, we study some existing captioning models that provide near state-of-the-art performances, and try to enhance one such model. We also present a simple image captioning model that makes use of a CNN, an LSTM, and the beam search1 algorithm, and study its performance based on various qualitative and quantitative metrics.
EVA: Generating Emotional Behavior of Virtual Agents using Expressive Features of Gait and Gaze
Venue: arXiv cs.HC 2019
Year: 2019

We present a novel, real-time algorithm, EVA, for generating virtual agents with various perceived emotions. Our approach is based on using Expressive Features of gaze and gait to convey emotions corresponding to happy, sad, angry, or neutral. We precompute a data-driven mapping between gaits and their perceived emotions. EVA uses this gait emotion association at runtime to generate appropriate walking styles in terms of gaits and gaze. Using the EVA algorithm, we can simulate gaits and gazing behaviors of hundreds of virtual agents in real-time with known emotional characteristics. We have evaluated the benefits in different multi-agent VR simulation environments. Our studies suggest that the use of expressive features corresponding to gait and gaze can considerably increase the sense of presence in scenarios with multiple virtual agents.
On Privacy Risks of Public WiFi Captive Portals
Venue: arXiv cs.CR 2019
Year: 2019

Open access WiFi hotspots are widely deployed in many public places, including restaurants, parks, coffee shops, shopping malls, trains, airports, hotels, and libraries. While these hotspots provide an attractive option to stay connected, they may also track user activities and share user/device information with third-parties, through the use of trackers in their captive portal and landing websites. In this paper, we present a comprehensive privacy analysis of 67 unique public WiFi hotspots located in Montreal, Canada, and shed some light on the web tracking and data collection behaviors of these hotspots. Our study reveals the collection of a significant amount of privacy-sensitive personal data through the use of social login (e.g., Facebook and Google) and registration forms, and many instances of tracking activities, sometimes even before the user accepts the hotspot's privacy and terms of service policies. Most hotspots use persistent third-party tracking cookies within their captive portal site; these cookies can be used to follow the user's browsing behavior long after the user leaves the hotspots, e.g., up to 20 years. Additionally, several hotspots explicitly share (sometimes via HTTP) the collected personal and unique device information with many third-party tracking domains.
CloudCoaster: Transient-aware Bursty Datacenter Workload Scheduling
Venue: arXiv cs.DC 2019
Year: 2019

Today's clusters often have to divide resources among a diverse set of jobs. These jobs are heterogeneous both in execution time and in their rate of arrival. Execution time heterogeneity has lead to the development of hybrid schedulers that can schedule both short and long jobs to ensure good task placement. However, arrival rate heterogeneity, or burstiness, remains a problem in existing schedulers. These hybrid schedulers manage resources on statically provisioned cluster, which can quickly be overwhelmed by bursts in the number of arriving jobs. In this paper we propose CloudCoaster, a hybrid scheduler that dynamically resizes the cluster by leveraging cheap transient servers. CloudCoaster schedules jobs in an intelligent way that increases job performance while reducing overall resource cost. We evaluate the effectiveness of CloudCoaster through simulations on real-world traces and compare it against a state-of-art hybrid scheduler. CloudCoaster improves the average queueing delay time of short jobs by 4.8X while maintaining long job performance. In addition, CloudCoaster reduces the short partition budget by over 29.5%.
CARVE: Practical Security-Focused Software Debloating Using Simple Feature Set Mappings
Venue: arXiv cs.CR 2019
Year: 2019

Software debloating is an emerging field of study aimed at improving the security and performance of software by removing excess library code and features that are not needed by the end user (called bloat). Software bloat is pervasive, and several debloating techniques have been proposed to address this problem. While these techniques are effective at reducing bloat, they are not practical for the average user, risk creating unsound programs and introducing vulnerabilities, and are not well suited for debloating complex software such as network protocol implementations. In this paper, we propose CARVE, a simple yet effective security-focused debloating technique that overcomes these limitations. CARVE employs static source code annotation to map software features source code, eliminating the need for advanced software analysis during debloating and reducing the overall level of technical sophistication required by the user. CARVE surpasses existing techniques by introducing debloating with replacement, a technique capable of preserving software interoperability and mitigating the risk of creating an unsound program or introducing a vulnerability. We evaluate CARVE in 12 debloating scenarios and demonstrate security and performance improvements that meet or exceed those of existing techniques.
Lifting Datalog-Based Analyses to Software Product Lines
Venue: arXiv cs.SE 2019
Year: 2019

Applying program analyses to Software Product Lines (SPLs) has been a fundamental research problem at the intersection of Product Line Engineering and software analysis. Different attempts have been made to "lift" particular product-level analyses to run on the entire product line. In this paper, we tackle the class of Datalog-based analyses (e.g., pointer and taint analyses), study the theoretical aspects of lifting Datalog inference, and implement a lifted inference algorithm inside the Soufflé Datalog engine. We evaluate our implementation on a set of benchmark product lines. We show significant savings in processing time and fact database size (billions of times faster on one of the benchmarks) compared to brute-force analysis of each product individually.
An External Knowledge Enhanced Multi-label Charge Prediction Approach with Label Number Learning
Venue: arXiv cs.CL 2019
Year: 2019

Multi-label charge prediction is a task to predict the corresponding accusations for legal cases, and recently becomes a hot topic. However, current studies use rough methods to deal with the label number. These methods manually set parameters to select label numbers, which has an effect in final prediction quality. We propose an external knowledge enhanced multi-label charge prediction approach that has two phases. One is charge label prediction phase with external knowledge from law provisions, the other one is number learning phase with a number learning network (NLN) designed. Our approach enhanced by external knowledge can automatically adjust the threshold to get label number of law cases. It combines the output probabilities of samples and their corresponding label numbers to get final prediction results. In experiments, our approach is connected to some state of-the art deep learning models. By testing on the biggest published Chinese law dataset, we find that our approach has improvements on these models. We future conduct experiments on multi-label samples from the dataset. In items of macro-F1, the improvement of baselines with our approach is 3%-5%; In items of micro-F1, the significant improvement of our approach is 5%-15%. The experiment results show the effectiveness our approach for multi-label charge prediction.
Desiderata for Planning Systems in General-Purpose Service Robots
Venue: arXiv cs.RO 2019
Year: 2019

General-purpose service robots are expected to undertake a broad range of tasks at the request of users. Knowledge representation and planning systems are essential to flexible autonomous robots, but the field lacks a unified perspective on which features are essential for general-purpose service robots. Progress towards planning and reasoning for general-purpose service robots is hindered by differing assumptions about users, the environment, and the overall robot system. In this position paper, we propose desiderata for planning and reasoning systems to promote general-purpose service robots. Each proposed item draws on our experience with research on service robots in the office and home and on the demands of these environments. Our desiderata emphasize support for natural human-interfaces as well as for robust fallback methods when interactions with humans and the environment fail. We highlight relevant work towards these goals.
Application of Flexible Numerology to Blockage Mitigation in 5G-mmWave Networks
Venue: arXiv cs.IT 2019
Year: 2019

The 5G New Radio (NR) standard for wireless communications supports the millimetre-wave (mmWave) spectrum to yield unprecedented improvement of the access network capacity. However, intermittent blockages in the mmWave signal may degrade the system performance and lead to the under-utilisation of the allocated resources. To circumvent this problem, the transmission slot-time shall be adjusted according to the blockage condition, avoiding the resource under-utilisation. In this paper, we propose that the 5G NR flexible numerology should be applied to adapt the slot-time in order to mitigate the blockage effects. We validate this claim by analysing the expected data rate of a mmWave system, under a range of blockage scenarios. We show that different blockage scenarios may require different numerologies to produce best performance, and that the correct choice of numerology may improve this performance by as much as hundreds of Mbps. Our results carry insights important for the design of blockage-aware scheduling mechanisms for 5G.
Detecting Cyber-Related Discussions in Online Social Platforms
Venue: arXiv cs.IR 2019
Year: 2019

As the use of social platforms continues to evolve, in areas such as cyber-security and defence, it has become imperative to develop adaptive methods for tracking, identifying and investigating cyber-related activities on these platforms. This paper introduces a new approach for detecting cyber-related discussions in online social platforms using a candidate set of terms that are representative of the cyber domain. The objective of this paper is to create a cyber lexicon with cyber-related terms that is applicable to the automatic detection of cyber activities across various online platforms. The method presented in this paper applies natural language processing techniques to representative data from multiple social platform types such as Reddit, Stack overflow, twitter and cyberwar news to extract candidate terms for a generic cyber lexicon. In selecting the candidate terms, we introduce the APMIS Aggregated Pointwise Mutual Information Score in comparison with the Term Frequency-Term Degree Ratio (FDR Score) and Term Frequency-Inverse Document Frequency Score (TF-IDF Score). These scoring mechanisms are robust to account for term frequency, term relevance and mutual dependence between terms. Finally, we evaluate the performance of the cyber lexicon by measuring its precision of in classifying discussions as 'Cyber-Related' or 'Non-Cyber-Related'.
Semantic-Effectiveness Filtering and Control for Post-5G Wireless Connectivity
Venue: arXiv cs.NI 2019
Year: 2019

The traditional role of a communication engineer is to address the technical problem of transporting bits reliably over a noisy channel. With the emergence of 5G, and the availability of a variety of competing and coexisting wireless systems, wireless connectivity is becoming a commodity. This article argues that communication engineers in the post-5G era should extend the scope of their activity in terms of design objectives and constraints beyond connectivity to encompass the semantics of the transferred bits within the given applications and use cases. To provide a platform for semantic-aware connectivity solutions, this paper introduces the concept of a semantic-effectiveness (SE) plane as a core part of future communication architectures. The SE plane augments the protocol stack by providing standardized interfaces that enable information filtering and direct control of functionalities at all layers of the protocol stack. The advantages of the SE plane are described in the perspective of recent developments in 5G, and illustrated through a number of example applications. The introduction of a SE plane may help replacing the current "next-G paradigm" in wireless evolution with a framework based on continuous improvements and extensions of the systems and standards.
The Geometry of Community Detection via the MMSE Matrix
Venue: arXiv cs.IT 2019
Year: 2019

The information-theoretic limits of community detection have been studied extensively for network models with high levels of symmetry or homogeneity. The contribution of this paper is to study a broader class of network models that allow for variability in the sizes and behaviors of the different communities, and thus better reflect the behaviors observed in real-world networks. Our results show that the ability to detect communities can be described succinctly in terms of a matrix of effective signal-to-noise ratios that provides a geometrical representation of the relationships between the different communities. This characterization follows from a matrix version of the I-MMSE relationship and generalizes the concept of an effective scalar signal-to-noise ratio introduced in previous work. We provide explicit formulas for the asymptotic per-node mutual information and upper bounds on the minimum mean-squared error. The theoretical results are supported by numerical simulations.
Security Implications Of Compiler Optimizations On Cryptography -- A Review
Venue: arXiv cs.CR 2019
Year: 2019

When implementing secure software, developers must ensure certain requirements, such as the erasure of secret data after its use and execution in real time. Such requirements are not explicitly captured by the C language and could potentially be violated by compiler optimizations. As a result, developers typically use indirect methods to hide their code's semantics from the compiler and avoid unwanted optimizations. However, such workarounds are not permanent solutions, as increasingly efficient compiler optimization causes code that was considered secure in the past now vulnerable. This paper is a literature review of (1) the security complications caused by compiler optimizations, (2) approaches used by developers to mitigate optimization problems, and (3) recent academic efforts towards enabling security engineers to communicate implicit security requirements to the compiler. In addition, we present a short study of six cryptographic libraries and how they approach the issue of ensuring security requirements. With this paper, we highlight the need for software developers and compiler designers to work together in order to design efficient systems for writing secure software.
Early Detection for Optimal-Latency Communications in Multi-Hop Links
Venue: arXiv cs.IT 2019
Year: 2019

Modern wireless machine-to-machine-type communications aim to provide both ultra reliability and low latency, stringent requirements that appear to be mutually exclusive. From the noisy channel coding theorem, we know that reliable communications mandate transmission rates that are lower than the channel capacity. To guarantee arbitrarily-low error probability, this implies the use of messages whose lengths tend to infinity. However, long messages are not suitable for low-latency communications. In this paper, we propose an early-detection scheme for wireless communications under a finite-blocklength regime that employs a sequential-test technique to reduce latency while maintaining reliability. We prove that our scheme leads to an average detection time smaller than the symbol duration. Furthermore, in multi-hop low-traffic or continuous-transmission links, we show that our scheme can reliably detect symbols before the end of their transmission, significantly reducing the latency, while keeping the error probability below a predefined threshold.
A Road-map Towards Explainable Question Answering A Solution for Information Pollution
Venue: arXiv cs.IR 2019
Year: 2019

The increasing rate of information pollution on the Web requires novel solutions to tackle that. Question Answering (QA) interfaces are simplified and user-friendly interfaces to access information on the Web. However, similar to other AI applications, they are black boxes which do not manifest the details of the learning or reasoning steps for augmenting an answer. The Explainable Question Answering (XQA) system can alleviate the pain of information pollution where it provides transparency to the underlying computational model and exposes an interface enabling the end-user to access and validate provenance, validity, context, circulation, interpretation, and feedbacks of information. This position paper sheds light on the core concepts, expectations, and challenges in favor of the following questions (i) What is an XQA system?, (ii) Why do we need XQA?, (iii) When do we need XQA? (iv) How to represent the explanations? (iv) How to evaluate XQA systems?
Low-power and Reliable Solid-state Drive with Inverted Limited Weight Coding
Venue: arXiv cs.IT 2019
Year: 2019

In this work, we propose a novel coding scheme which based on the characteristics of NAND flash cells, generates codewords that reduce the energy consumption and improve the reliability of solid-state drives. This novel coding scheme, namely Inverted Limited Weight Coding (ILWC), favors a greater number of '1's appearing in its generated codewords at the cost of added information redundancy, as a form of flag bits. This increase in the number of bits valued as logical '1', in the generated codewords, will increase the number of cells that have lower threshold voltages. Through cells with lower threshold voltages, ILWC fruitfully reduces the SSD's program operation energy consumption. Moreover, it increases the SSD's data retention rate and reliability by decreasing the threshold voltage of the cells. The evaluation of our proposed coding method on three different SSDs, indicates more than 20% reduction in the SSD's program operation energy consumption. In addition, ILWC improves the cells' data retention rate by decreasing their intrinsic electric field by more than 18%. Moreover, the SSD's cell-to-cell coupling noise is diminished with the help of 35% reduction in the worst-case threshold voltage shift in a cell's adjacent cells. All this leads to 5.3% reduction in the MLC's cell error rate. In addition, ILWC achieves 37.5% improvement in the performance of the SSD program operation.
Evaluating the distribution learning capabilities of GANs
Venue: arXiv cs.LG 2019
Year: 2019

We evaluate the distribution learning capabilities of generative adversarial networks by testing them on synthetic datasets. The datasets include common distributions of points in $R^n$ space and images containing polygons of various shapes and sizes. We find that by and large GANs fail to faithfully recreate point datasets which contain discontinous support or sharp bends with noise. Additionally, on image datasets, we find that GANs do not seem to learn to count the number of objects of the same kind in an image. We also highlight the apparent tension between generalization and learning in GANs.