Miracle-dz/UniFAR-FTUs huggingface dataset:
Causal Deep Information Bottleneck

Estimating causal effects in the presence of latent confounding is a frequently occurring problem in several tasks. In real world applications such as medicine, accounting for the effects of latent confounding is even more challenging as a result of high-dimensional and noisy data. In this work, we propose estimating the causal effect from the perspective of the information bottleneck principle by explicitly identifying a low-dimensional representation of latent confounding. In doing so, we prove theoretically that the proposed model can be used to recover the average causal effect. Experiments on both synthetic data and existing causal benchmarks illustrate that our method achieves state-of-the-art performance in terms of prediction accuracy and sample efficiency, without sacrificing interpretability.
Exploring Robust Property Preservation for Secure Compilation

Good programming languages provide helpful abstractions for writing more secure code (types, modules, interfaces, procedures, structured control flow), but the security properties from the source language are generally not preserved when compiling a program and linking it with adversarial low-level code (e.g., a library or a legacy application). Linked low-level code that is malicious or compromised can for instance read and write the compiled program’s data and code, jump to arbitrary instructions, or smash the stack, blatantly violating any source-level abstraction. By contrast, a secure compilation chain protects source-level abstractions all the way down, ensuring that even an adversarial target-level context cannot break the security properties of a compiled program any more than some source-level context could. However, the precise class of security properties one chooses to preserve crucially impacts not only the supported security goals and the strength of the attacker model, but also the kind of protections the compilation chain has to introduce and the kind of proof techniques one can use to make sure that the protections are watertight. Since efficiently achieving and proving secure compilation at scale are challenging open problems, designers of secure compilation chains have to strike a pragmatic balance between security and efficiency that matches their application domain. To inform this difficult design decision, we thoroughly explore a large space of formal secure compilation criteria based on the preservation of properties that are robustly satisfied against arbitrary adversarial contexts. We study robustly preserving various classes of trace properties such as safety, of hyperproperties such as noninterference, and of relational hyperproperties such as trace equivalence. For each of the studied classes we propose an equivalent "property-free" characterization of secure compilation that is generally better tailored for proofs. We, moreover, order the secure compilation criteria by their relative strength, discover a collapse between preserving hyperliveness and preserving all hyperproperties, and prove several separation results. Finally, we show that even the strongest of our secure compilation criteria, the robust preservation of all relational hyperproperties, is achievable for a simple translation from a statically typed to a dynamically typed language. We prove this using a universal embedding, a context back-translation technique previously developed for fully abstract compilation. We also illustrate that for proving the robust preservation of most relational safety properties including safety, noninterference, and sometimes trace equivalence, a less powerful but more generic technique can back-translate a finite set of finite execution prefixes into a source context.
Minimizing Age-of-Information with Throughput Constraints in Multi-Path Network Communication

Age-of-information ( AoI ) is a newly proposed time-critical networking performance metric. In this paper, we consider multi-path network communication problems for fulfilling a single-unicast periodic transmission task. The task generates certain amount of data at a sender periodically, and requires the sender to stream those data to a receiver using multiple paths over a multi-hop network. Our objective is to minimize either the peak AoI or the average AoI , by jointly optimizing the multi-path routing strategy and the task activation length. To our best knowledge, (i) as compared to existing AoI studies, we are the first to minimize the task-level AoI with the multi-path routing optimization involved, and (ii) as compared to existing time-critical multi-path communication studies, we minimize the AoI with the task activation length to be a decision variable, while they minimize the maximum delay given a fixed task activation length. In this paper, first, comparing the newly proposed AoI with the common-studied maximum delay, we show that the maximum-delay-optimal solution can achieve a suboptimal AoI , and we characterize near-tight optimality gaps of this suboptimal AoI . Second, we give the fundamental structures of our problems, by proving that the peak-AoI minimization can differ from the average-AoI minimization, but both of them are NP-hard in the weak sense, with a pseudo-polynomial time algorithm which can solve them optimally designed. Third, we develop an approximation framework, which can leverage an arbitrary approximation algorithm of the maximum-delay-minimization problem to solve our AoI -minimization problems approximately. Our framework has a constant approximation ratio, and a time complexity that is the same as that of the used approximation algorithm. Finally, we conduct extensive random simulations to empirically verify our proposed results and evaluate our designed algorithms.
Combining Online News Articles and Web Search to Predict the Fluctuation of Real Estate Market in Big Data Context

The real estate price is of paramount importance in both economic and social fields. It is a key indicator of the operation of real estate market and its prediction is essential in the decision-making process of both average people and official governments. Past researchers on this topic have already proposed several prediction methods including linear regression models, nonlinear regression models and machine learning models. Nevertheless, those models have generally neglected the impact of human behavior, which we believe is a significant factor of the real estate price prediction. What’s more, past studies have shown that news sentiments could improve the prediction performance of real estate price. Search engine query data were studied to reflect web users’ behavior by analyzing the frequency of words searched by online users. Researchers have already used the news sentiments and query data for prediction, respectively. But none have combined them together as an integrated model. In this paper, we propose an integrated method that throws new light on the prediction of real estate price in China by integrating these two factors into the forecasting model. In our method, we extract sentiment series from both news data and search engine query data by adding weights to original sentiment series that are produced by news data alone. Then both the weighted series and original ones are used as inputs of several well-acknowledged data mining models, including SVR, RBFNN and BPNN, to produce prediction results. To validate the integrated model, we apply it to four representative cities in China respectively, and compare the results produced by the integrated model using weighted inputs with non-integrated ones using original inputs. The results show that for every one of the four cities, the integrated model generally leads to lower prediction errors than the non-integrated ones. This not only validates the model’s accuracy and universality, but also proves the hypothesis that human searching behavior as a strong impact in typical Chinese cities’ real estate market and can enhance the prediction accuracy of real estate prices.
A Simple but Hard-to-Beat Baseline for Session-based Recommendations

Convolutional Neural Networks (CNNs) models have been recently introduced in the domain of top-$N$ session-based recommendations. An ordered collection of past items the user has interacted with in a session (or sequence) are embedded into a 2-dimensional latent matrix, and treated as an image. The convolution and pooling operations are then applied to the mapped item embeddings. In this paper, we first examine the typical session-based CNN recommender and show that both the generative model and network architecture are suboptimal when modeling long-range dependencies in the item sequence. To address the issues, we propose a simple, but very effective generative model that is capable of learning high-level representation from both short- and long-range dependencies. The network architecture of the proposed model is formed of a stack of holed convolutional layers, which can efficiently increase the receptive fields without relying on the pooling operation. Another contribution is the effective use of residual block structure in recommender systems, which can ease the optimization for much deeper networks. The proposed generative model attains state-of-the-art accuracy with less training time in the session-based recommendation task. It accordingly can be used as a powerful session-based recommendation baseline to beat in future, especially when there are long sequences of user feedback.
NEUZZ: Efficient Fuzzing with Neural Program Learning

Fuzzing has become the de facto standard technique for finding software vulnerabilities. However, even the state-of-the-art fuzzers are not very efficient at finding hard-to-trigger software bugs. Coverage-guided evolutionary fuzzers, while fast and scalable, often get stuck at fruitless sequences of random mutations. By contrast, more sys-tematic techniques like symbolic and concolic execution incur significant performance overhead and struggle to scale to larger programs. We design, implement, and evaluate N EUZZ , an efficient fuzzer that guides the fuzzing input generation process using deep neural networks. N EUZZ efficiently learns a differentiable neural approximation of the target program logic. The differentiability of the surrogate neural program, unlike the original target program, allows us to use efficient optimization techniques like gradient descent to identify promising mutations that are more likely to trigger hard-to-reach code in the target program. We evaluate N EUZZ on 10 popular real-world programs and demonstrate that N EUZZ consistently outperforms AFL, a state-of-the-art evolutionary fuzzer, both at finding new bugs and achieving higher edge coverage. In total, N EUZZ found 36 previously unknown bugs that AFL failed to find and achieved, on average, 70 × more edge coverage than AFL. Our results also demonstrate that N EUZZ can achieve average 9 × more edge coverage while taking 16 × less training time than other learning-enabled fuzzers.
A Comparison of Structural and Behavioral Community Detection Algorithms

Community detection in large social, biological or information network is an important problem since it helps uncover hidden structural properties in the network. For detecting communities, one typically defines or chooses an objective function such that a higher value corresponds to clusters densely connected inside and sparsely connected to nodes outside its cluster. It is shown that such communities often have things in common among its members. There have been a multitude of comparative studies of different objective functions and/or different methodologies for optimization. But to the best of author’s knowledge, there have not been any comparative study of community detection methodologies that rely only on structural data with methodologies that rely entirely on behavioral data. For this purpose, node pair similarity is modelled in terms of behaviour data and well known clustering methodologies along with other structural methods are executed on a real world network. The results are then objectively evaluated using 6 well defined quality functions, i.e., modularity, density, separability, clustering coefficient, like-mindedness and like-mindedness ratio. We also provide a new behavioral property based hierarchical clustering algorithm which attempts to maximize one of the behavioral quality functions (like-mindedness).
Deep Semantic Matching for Optical Flow

We tackle the problem of estimating optical flow from a monocular camera in the context of autonomous driving. We build on the observation that the scene is typically composed of a static background, as well as a relatively small number of traffic participants which move rigidly in 3D. We propose to estimate the traffic participants using instance-level segmentation. For each traffic participant, we use the epipolar constraints that govern each independent motion for faster and more accurate estimation. Our second contribution is a new convolutional net that learns to perform flow matching, and is able to estimate the uncertainty of its matches. This is a core element of our flow estimation pipeline. We demonstrate the effectiveness of our approach in the challenging KITTI 2015 flow benchmark, and show that our approach outperforms published approaches by a large margin.
An evaluation of dynamic electronic catalog models in relational database systems

Electronic catalogs are electronic representations about products and services in the electronic commerce environment and require diverse and flexible schemas. Although relational database systems seem to be an obvious choice for their storage, traditional designs of relational schemas do not support electronic catalogs in the most effective ways. Therefore, new models for managing diverse and flexible schemas in relational databases are required for such systems. Proposed in this paper are several models for electronic catalogs using relational tables, and an experimental evaluation of their efficiency. The results of this study can be put to practical use and are, in fact, being applied in the design of a commercial software product.
Learning Grasp Configurations for Novel Objects from Prior Examples

We present a new approach to learning grasp configurations for a novel object from known example objects. We assume the novel object and the example object belong to the same category in which objects share the same topology and have a similar shape. Both the novel and the example objects are segmented into the same semantic parts. We learn a grasp space for each part of the example object using a combination of optimization and learning algorithm. We perform shape warping between the corresponding parts of the example object and the novel object, and use them to compute the corresponding grasps. Finally, we assemble the individual parts and the associated grasps of the novel object, and use local replanning to adjust grasp configurations to satisfy the stability and physical constraints (e.g., that they are penetrationfree). Our algorithm can automatically handle a wide range of object categories and a variety of robotic hand grasps.
Multi-Task Unsupervised Contextual Learning for Behavioral Annotation

Unsupervised learning has been an attractive method for easily deriving meaningful data representations from vast amounts of unlabeled data. These representations or, in the context of deep learning, embeddings, often yield superior results in many tasks, whether used directly or as features in subsequent training stages. However, the quality of the embeddings is highly dependent on the assumed knowledge in the unlabeled data and how the system extracts information without supervision. Domain portability is also very limited in unsupervised learning, often requiring re-training on other large-scale corpora to achieve robustness. In this work we present a paradigm for unsupervised contextual learning for behavioral interactions which addresses unsupervised domain adaption. We introduce a multitask objective into unsupervised learning and show that embeddings generated through this process increases performance of behavior related tasks.
Delay and Power Consumption Tradeoff in Cache-enabled Dense Small Cell Networks: A Generalized Benders Decomposition Approach

Enabling caching capabilities in dense small cell networks (DSCNs) has a direct impact on file delivery delay and power consumption. Most existing work studied these two performance metrics separately in cache-enabled DSCNs. However, file delivery delay and power consumption are coupled with each other and cannot be minimized simultaneously. In this paper, we investigate the optimal tradoff between these two performance metrics. Firstly, we formulate the joint file delivery delay and power consumption optimization (JDPO) problem where power control, user association and file placement are jointly considered. Then we convert it to a form that can be handled by Generalized Benders Decomposition (GDB). with GDB, we decompose the converted JDPO problem into two smaller problems, i.e., primal problem related to power control and master problem related to user association and file placement. An iterative algorithm is proposed and proved to be $\epsilon$-optimal, in which the primal problem and master problem are solved iteratively to approach the optimal solution. To further reduce the complexity of the master problem, an accelerated algorithm based on semi-definite relaxation is proposed. Finally, the simulation results demonstrate that the proposed algorithm can approach the optimal tradeoff between file delivery delay and power consumption.
The cognitive complexities confronting developers using object technology

Many managers are adopting object technology initiatives to develop high-quality products more efficiently. Consequently, software practices are changing and there are repercussions across corporate and academic training. As new practices promise to yield increasing benefits in the software development cycle, the complexity is becoming proportionately greater. Technology has come so far, so fast, and with such success, that it is easy to forget how difficult computing actually is. Things appear easy, when looked at superficially, because products are so well designed, and cognitive complexities so well hidden, that preschoolers can be computer literate and the public at large can consider themselves computer savvy. Moreover, the toughness of new problems, because of their misleading simplicity, often fools the scientists themselves. When Smalltalk-80 appeared, object technology was touted as intuitively obvious; but experiences over the past 20 years have proven otherwise. Object technology is a cognitive minefield. This research examines the notion that object technologists, like the workers in artificial intelligence, have underestimated the complexities associated with the analysis, design, and coding of software from “virtual things” (i.e. objects). One manifestation is that the information systems community making up the software infrastructure in many organizations is resisting, misapplying, or only very slowly understanding objects. Another manifestation is that the academic community is perplexed by the way that objects are changing the view of software and how, correspondingly, the computer science curriculum must be adapted. Both of these manifestations are documented herein. Going beyond merely documenting the cognitive complexities of object technology, this research identifies those atomic-level constituents responsible for the shift from simple to complex with respect to analysis and design. This study concludes by proposing a pedagogical model that addresses these constituents and could thereby reduce the learning curve for retraining practitioners and restore clarity to the computer science curriculum. We hope this research contributes to the wider acceptance of object technology.
Duchy: Achieving Both SSD Durability and Controllable SMR Cleaning Overhead in Hybrid Storage Systems

Integrating solid-state drives (SSDs) and shingled magnetic recording (SMR) drives can build cost-effective hybrid storage systems. However, both SSD and SMR drives endure inherent defects that are mutually exclusive. The write endurance of SSD is limited while SMR drives should prevent from writes due to the cleaning-caused performance degradation. In this paper, we propose Duchy, an endurable SSD caching scheme that simultaneously respects SMR constraints. Duchy filters ineffectual write traffic out of SSD without exacerbating the performance degradation of SMR drives. Meanwhile, Duchy leverages SSD to regulate the written zones in SMR drives to achieve controllable cleaning duration. Our experimental results indicate that compared with legacy SSD caching designs, only Duchy can achieve both system performance improvement and SSD write traffic reduction.
Cost/Performance in Modern Data Stores: How Data Caching Systems Succeed

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Data in traditional "caching" data systems resides on secondary storage, and is read into main memory only when operated on. This limits system performance. Main memory data stores with data always in main memory are much faster. But this performance comes at a cost. In this paper, we analyze the costs of both in-memory operations and secondary storage operations where data is not "in cache". We study the performance impact of cache misses on caching system performance. The analysis considers both execution and storage costs. Based on our analysis, we derive cost/performance results for a data caching system [Deuteronomy and its Bw-tree] and a main memory system [MassTree] to understand where each demonstrates the best cost per operation, what is driving the cost differences, and the scale of the differences. This analysis (1) provides insight into why data caching systems continue to dominate the market; (2) points to higher performance that does not rely on simply increasing main memory cache size; and (3) suggests a path to lower costs and hence better cost/performance.
Digital transformation model: analytic approach on participatory governance & community engagement in India

Governments around the globe are more and more aiming at digital and participatory governance to become more integrative and responsive for citizen-centric superior service delivery. Reconstruction of the technical and structural framework is also going along right from the country level to local level. The citizen engagement models are most preferred and prevailing as government functions are shifting more in the direction to implement the open by default and digital governance principles. It focuses on intensifying the accountability, transparency and participatory models in formulating government policies and in response building trust between the people and the government. The Government of India launched MyGov (www.mygov.in)†, its citizen engagement platform in July 2014, which strives to promote proactive citizen participation in India's path-breaking transformation model for governance and socio-economic growth. MyGov has provided citizens a new aspect of democracy. It encourages crowdsourcing of ideas from communities, capacitating citizens to turn up with solutions and become a part of integrative and participatory governance framework by sharing their opinions on government decisions, policies, functions and various other aspects of governance. It has facilitated government to build a participative egalitarianism by optimal utilization of technology to stretch out to communities at large and trigger them to take part in collaborative governance through ventures and ideas and innovations for nation-building. In this study, sentiment analysis and text mining approaches have been used by analyzing the elemental themes, topics, comments to discover the impact of the different contents existing on the platform and the overall reaction of citizens towards a transformative and participative governance model.