The ability to compose code in a modular fashion is important to the construction of large programs. In the logic programming setting, it is desirable that such capabilities be realized through logic-based devices. We describe an approach for doing this here. In our scheme a module corresponds to a block of code whose external view is mediated by a signature. Thus, signatures impose a form of hiding that is explained logically via existential quantifications over predicate, function and constant names. Modules interact through the mechanism of accumulation that translates into conjoining the clauses in them while respecting the scopes of existential quantifiers introduced by signatures. We show that this simple device for statically structuring name spaces suffices for realizing features related to code scoping for which the dynamic control of predicate definitions was earlier considered necessary. The module capabilities we present have previously been implemented via the compile-time inlining of accumulated modules. This approach does not support separate compilation. We redress this situation by showing how each distinct module can be compiled separately and inlining can be realized by a later, complementary and equally efficient linking phase.
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Stochastic dual dynamic programming is a cutting plane type algorithm for multi-stage stochastic optimization originated about 30 years ago. In spite of its popularity in practice, there does not exist any analysis on the convergence rates of this method. In this paper, we first establish the number of iterations, i.e., iteration complexity, required by a basic dynamic cutting plane method for solving relatively simple multi-stage optimization problems, by introducing novel mathematical tools including the saturation of search points. We then refine these basic tools and establish the iteration complexity for both deterministic and stochastic dual dynamic programming methods for solving more general multi-stage stochastic optimization problems under the standard stage-wise independence assumption. Our results indicate that the complexity of some deterministic variants of these methods mildly increases with the number of stages $T$, in fact linearly dependent on $T$ for discounted problems. Therefore, they are efficient for strategic decision making which involves a large number of stages, but with a relatively small number of decision variables in each stage. Without explicitly discretizing the state and action spaces, these methods might also be pertinent to the related reinforcement learning and stochastic control areas.
Synthesizing large logic programs through symbolic Inductive Logic Programming (ILP) typically requires intermediate definitions. However, cluttering the hypothesis space with intensional predicates typically degrades performance. In contrast, gradient descent provides an efficient way to find solutions within such high- dimensional spaces. Neuro-symbolic ILP approaches have not fully exploited this so far. We propose extending the {\delta}ILP approach to inductive synthesis with large-scale predicate invention, thus allowing us to exploit the efficacy of high-dimensional gradient descent. We show that large-scale predicate invention benefits differentiable inductive synthesis through gradient descent and allows one to learn solutions for tasks beyond the capabilities of existing neuro-symbolic ILP systems. Furthermore, we achieve these results without specifying the precise structure of the solution within the language bias.
The massive deployment of low-end wireless Internet of things (IoT) devices opens the challenge of finding de-centralized and lightweight alternatives for secret key distribution. A possible solution, coming from the physical layer, is the secret key generation (SKG) from channel state information (CSI) during the channel's coherence time. This work acknowledges the fact that the CSI consists of deterministic (predictable) and stochastic (unpredictable) components, loosely captured through the terms large-scale and small-scale fading, respectively. Hence, keys must be generated using only the random and unpredictable part. To detrend CSI measurements from deterministic components, a simple and lightweight approach based on Kalman filters is proposed and is evaluated using an implementation of the complete SKG protocol (including privacy amplification that is typically missing in many published works). In our study we use a massive multiple input multiple output (mMIMO) orthogonal frequency division multiplexing outdoor measured CSI dataset. The threat model assumes a passive eavesdropper in the vicinity (at 1 meter distance or less) from one of the legitimate nodes and the Kalman filter is parameterized to maximize the achievable key rate.
Object Storage Systems (OSS) inside a cloud promise scalability, durability, availability, and concurrency. However, open-source OSS does not have a specific approach to letting users and administrators search based on the data, which is contained inside the object storage, without involving the entire cloud infrastructure. Therefore, in this paper, we propose Sherlock, a novel Content-Based Searching (CoBS) architecture to extract additional information from images and documents. Here, we store the additional information in an Elasticsearch-enabled database, which helps us to search for our desired data based on its contents. This approach works in two sequential stages. First, the data will be uploaded to a classifier that will determine the data type and send it to the specific model for the data. Here, the images that are being uploaded are sent to our trained model for object detection, and the documents are sent for keyword extraction. Next, the extracted information is sent to Elasticsearch, which enables searching based on the contents. Because the precision of the models is so fundamental to the search's correctness, we train our models with comprehensive datasets (Microsoft COCO Dataset for multimedia data and SemEval2017 Dataset for document data). Furthermore, we put our designed architecture to the test with a real-world implementation of an open-source OSS called OpenStack Swift. We upload images into the dataset of our implementation in various segments to find out the efficacy of our proposed model in real-life Swift object storage.
In this paper, we study terahertz (THz) simultaneous wireless information and power transfer (SWIPT) systems. Since coherent information detection is challenging at THz frequencies and Schottky diodes are not usable for THz energy harvesting (EH), we employ unipolar amplitude shift keying (ASK) modulation at the transmitter (TX) and a resonant tunnelling diode (RTD)- based EH circuit at the receiver (RX) to extract both information and power from the received signal. However, the electrical properties of Schottky diodes and RTDs are different, and unlike EH receivers based on a single Schottky diode, an accurate closed-form EH model for RTD-based RXs is not available, yet. In this paper, we model the dependency of the instantaneous RX output power on the instantaneous received power by a non-linear piecewise function, whose parameters are adjusted to fit circuit simulation results. We formulate an optimization problem to maximize the mutual information between the TX and RX signals subject to constraints on the peak amplitude of the transmitted signal and the required average harvested power at the RX. Furthermore, we determine a feasibility condition for the formulated problem, and for high and low required average harvested powers, we derive the achievable information rate numerically and in closed form, respectively. Our simulation results highlight a tradeoff between the information rate and the average harvested power. Finally, we show that this tradeoff is determined by the peak amplitude of the transmitted signal and the maximum instantaneous harvested power for low and high received signal powers, respectively.
Graphics Processing Units (GPU) offer tremendous computational power by following a throughput oriented computing paradigm where many thousand computational units operate in parallel. Programming this massively parallel hardware is challenging. Programmers must correctly and efficiently coordinate thousands of threads and their accesses to various shared memory spaces. Existing mainstream GPU programming languages, such as CUDA and OpenCL, are based on C/C++ inheriting their fundamentally unsafe ways to access memory via raw pointers. This facilitates easy to make, but hard to detect bugs such as data races and deadlocks. In this paper, we present Descend: a safe GPU systems programming language. In the spirit of Rust, Descend's type system enforces safe CPU and GPU memory management by tracking Ownership and Lifetimes. Descend introduces a new holistic GPU programming model where computations are hierarchically scheduled over the GPU's execution resources: grid, blocks, and threads. Descend's extended Borrow checking ensures that execution resources safely access memory regions without introducing data races. For this, we introduced views describing safe parallel access patterns of memory regions. We discuss the memory safety guarantees offered by Descend's type system and evaluate our implementation of Descend using a number of benchmarks, showing that no significant runtime overhead is introduced compared to manually written CUDA programs lacking Descend's safety guarantees.
Refactorings are transformations to improve the code design without changing overall functionality and observable behavior. During the refactoring process of smelly test code, practitioners may struggle to identify refactoring candidates and define and apply corrective strategies. This paper reports on an empirical study aimed at understanding how test smells and test refactorings are discussed on the Stack Exchange network. Developers commonly count on Stack Exchange to pick the brains of the wise, i.e., to `look up' how others are completing similar tasks. Therefore, in light of data from the Stack Exchange discussion topics, we could examine how developers understand and perceive test smells, the corrective actions they take to handle them, and the challenges they face when refactoring test code aiming to fix test smells. We observed that developers are interested in others' perceptions and hands-on experience handling test code issues. Besides, there is a clear indication that developers often ask whether test smells or anti-patterns are either good or bad testing practices than code-based refactoring recommendations.
Conversational Question Generation (CQG) is a critical task for machines to assist humans in fulfilling their information needs through conversations. The task is generally cast into two different settings: answer-aware and answer-unaware. While the former facilitates the models by exposing the expected answer, the latter is more realistic and receiving growing attentions recently. What-to-ask and how-to-ask are the two main challenges in the answer-unaware setting. To address the first challenge, existing methods mainly select sequential sentences in context as the rationales. We argue that the conversation generated using such naive heuristics may not be natural enough as in reality, the interlocutors often talk about the relevant contents that are not necessarily sequential in context. Additionally, previous methods decide the type of question to be generated (boolean/span-based) implicitly. Modeling the question type explicitly is crucial as the answer, which hints the models to generate a boolean or span-based question, is unavailable. To this end, we present SG-CQG, a two-stage CQG framework. For the what-to-ask stage, a sentence is selected as the rationale from a semantic graph that we construct, and extract the answer span from it. For the how-to-ask stage, a classifier determines the target answer type of the question via two explicit control signals before generating and filtering. In addition, we propose Conv-Distinct, a novel evaluation metric for CQG, to evaluate the diversity of the generated conversation from a context. Compared with the existing answer-unaware CQG models, the proposed SG-CQG achieves state-of-the-art performance.
Long-term user engagement (LTE) optimization in sequential recommender systems (SRS) is shown to be suited by reinforcement learning (RL) which finds a policy to maximize long-term rewards. Meanwhile, RL has its shortcomings, particularly requiring a large number of online samples for exploration, which is risky in real-world applications. One of the appealing ways to avoid the risk is to build a simulator and learn the optimal recommendation policy in the simulator. In LTE optimization, the simulator is to simulate multiple users' daily feedback for given recommendations. However, building a user simulator with no reality-gap, i.e., can predict user's feedback exactly, is unrealistic because the users' reaction patterns are complex and historical logs for each user are limited, which might mislead the simulator-based recommendation policy. In this paper, we present a practical simulator-based recommender policy training approach, Simulation-to-Recommendation (Sim2Rec) to handle the reality-gap problem for LTE optimization. Specifically, Sim2Rec introduces a simulator set to generate various possibilities of user behavior patterns, then trains an environment-parameter extractor to recognize users' behavior patterns in the simulators. Finally, a context-aware policy is trained to make the optimal decisions on all of the variants of the users based on the inferred environment-parameters. The policy is transferable to unseen environments (e.g., the real world) directly as it has learned to recognize all various user behavior patterns and to make the correct decisions based on the inferred environment-parameters. Experiments are conducted in synthetic environments and a real-world large-scale ride-hailing platform, DidiChuxing. The results show that Sim2Rec achieves significant performance improvement, and produces robust recommendations in unseen environments.
Knowledge is a formal way of understanding the world, providing a human-level cognition and intelligence for the next-generation artificial intelligence (AI). One of the representations of knowledge is the structural relations between entities. An effective way to automatically acquire this important knowledge, called Relation Extraction (RE), a sub-task of information extraction, plays a vital role in Natural Language Processing (NLP). Its purpose is to identify semantic relations between entities from natural language text. To date, there are several studies for RE in previous works, which have documented these techniques based on Deep Neural Networks (DNNs) become a prevailing technique in this research. Especially, the supervised and distant supervision methods based on DNNs are the most popular and reliable solutions for RE. This article 1)introduces some general concepts, and further 2)gives a comprehensive overview of DNNs in RE from two points of view: supervised RE, which attempts to improve the standard RE systems, and distant supervision RE, which adopts DNNs to design the sentence encoder and the de-noise method. We further 3)cover some novel methods and describe some recent trends and discuss possible future research directions for this task.
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