OpenRaiser/Intern-Atlas huggingface dataset:
Experience Replay Optimization
Venue: arXiv cs.LG 2019
Year: 2019

Experience replay enables reinforcement learning agents to memorize and reuse past experiences, just as humans replay memories for the situation at hand. Contemporary off-policy algorithms either replay past experiences uniformly or utilize a rule-based replay strategy, which may be sub-optimal. In this work, we consider learning a replay policy to optimize the cumulative reward. Replay learning is challenging because the replay memory is noisy and large, and the cumulative reward is unstable. To address these issues, we propose a novel experience replay optimization (ERO) framework which alternately updates two policies: the agent policy, and the replay policy. The agent is updated to maximize the cumulative reward based on the replayed data, while the replay policy is updated to provide the agent with the most useful experiences. The conducted experiments on various continuous control tasks demonstrate the effectiveness of ERO, empirically showing promise in experience replay learning to improve the performance of off-policy reinforcement learning algorithms.
Hierarchical Document Encoder for Parallel Corpus Mining
Venue: arXiv cs.CL 2019
Year: 2019

We explore using multilingual document embeddings for nearest neighbor mining of parallel data. Three document-level representations are investigated: (i) document embeddings generated by simply averaging multilingual sentence embeddings; (ii) a neural bag-of-words (BoW) document encoding model; (iii) a hierarchical multilingual document encoder (HiDE) that builds on our sentence-level model. The results show document embeddings derived from sentence-level averaging are surprisingly effective for clean datasets, but suggest models trained hierarchically at the document-level are more effective on noisy data. Analysis experiments demonstrate our hierarchical models are very robust to variations in the underlying sentence embedding quality. Using document embeddings trained with HiDE achieves state-of-the-art performance on United Nations (UN) parallel document mining, 94.9% P@1 for en-fr and 97.3% P@1 for en-es.
GAN-Knowledge Distillation for one-stage Object Detection
Venue: arXiv cs.CV 2019
Year: 2019

Convolutional neural networks have a significant improvement in the accuracy of Object detection. As convolutional neural networks become deeper, the accuracy of detection is also obviously improved, and more floating-point calculations are needed. Many researchers use the knowledge distillation method to improve the accuracy of student networks by transferring knowledge from a deeper and larger teachers network to a small student network, in object detection. Most methods of knowledge distillation need to designed complex cost functions and they are aimed at the two-stage object detection algorithm. This paper proposes a clean and effective knowledge distillation method for the one-stage object detection. The feature maps generated by teacher network and student network are used as true samples and fake samples respectively, and generate adversarial training for both to improve the performance of the student network in one-stage object detection.
Accelerating Mini-batch SARAH by Step Size Rules
Venue: arXiv cs.LG 2019
Year: 2019

StochAstic Recursive grAdient algoritHm (SARAH), originally proposed for convex optimization and also proven to be effective for general nonconvex optimization, has received great attention due to its simple recursive framework for updating stochastic gradient estimates. The performance of SARAH significantly depends on the choice of step size sequence. However, SARAH and its variants often employ a best-tuned step size by mentor, which is time consuming in practice. Motivated by this gap, we proposed a variant of the Barzilai-Borwein (BB) method, referred to as the Random Barzilai-Borwein (RBB) method, to calculate step size for SARAH in the mini-batch setting, thereby leading to a new SARAH method: MB-SARAH-RBB. We prove that MB-SARAH-RBB converges linearly in expectation for strongly convex objective functions. We analyze the complexity of MB-SARAH-RBB and show that it is better than the original method. Numerical experiments on standard data sets indicate that MB-SARAH-RBB outperforms or matches state-of-the-art algorithms.
Multiple-Identity Image Attacks Against Face-based Identity Verification
Venue: arXiv cs.CV 2019
Year: 2019

Facial verification systems are vulnerable to poisoning attacks that make use of multiple-identity images (MIIs)---face images stored in a database that resemble multiple persons, such that novel images of any of the constituent persons are verified as matching the identity of the MII. Research on this mode of attack has focused on defence by detection, with no explanation as to why the vulnerability exists. New quantitative results are presented that support an explanation in terms of the geometry of the representations spaces used by the verification systems. In the spherical geometry of those spaces, the angular distance distributions of matching and non-matching pairs of face representations are only modestly separated, approximately centred at 90 and 40-60 degrees, respectively. This is sufficient for open-set verification on normal data but provides an opportunity for MII attacks. Our analysis considers ideal MII algorithms, demonstrating that, if realisable, they would deliver faces roughly 45 degrees from their constituent faces, thus classed as matching them. We study the performance of three methods for MII generation---gallery search, image space morphing, and representation space inversion---and show that the latter two realise the ideal well enough to produce effective attacks, while the former could succeed but only with an implausibly large gallery to search. Gallery search and inversion MIIs depend on having access to a facial comparator, for optimisation, but our results show that these attacks can still be effective when attacking disparate comparators, thus securing a deployed comparator is an insufficient defence.
Improving Zero-shot Translation with Language-Independent Constraints
Venue: arXiv cs.CL 2019
Year: 2019

An important concern in training multilingual neural machine translation (NMT) is to translate between language pairs unseen during training, i.e zero-shot translation. Improving this ability kills two birds with one stone by providing an alternative to pivot translation which also allows us to better understand how the model captures information between languages. In this work, we carried out an investigation on this capability of the multilingual NMT models. First, we intentionally create an encoder architecture which is independent with respect to the source language. Such experiments shed light on the ability of NMT encoders to learn multilingual representations, in general. Based on such proof of concept, we were able to design regularization methods into the standard Transformer model, so that the whole architecture becomes more robust in zero-shot conditions. We investigated the behaviour of such models on the standard IWSLT 2017 multilingual dataset. We achieved an average improvement of 2.23 BLEU points across 12 language pairs compared to the zero-shot performance of a state-of-the-art multilingual system. Additionally, we carry out further experiments in which the effect is confirmed even for language pairs with multiple intermediate pivots.
A New Framework for software Library Investment Metrics
Venue: arXiv cs.SE 2019
Year: 2019

Software quality is considered as one of the most important challenges in software engineering. It has many dimensions which differ from users' point of view that depend on their requirements. Therefore, those dimensions lead to difficulty in measuring and defining the software quality properly. Software quality measurement is the main core of the software quality. Thus, it is necessary to study and develop the software measurements to meet the better quality. The use of libraries increases software quality more than that of using generic programming because these libraries are prepared and tested in advance. In addition, these libraries reduce the effort that is spent in designing, testing, and maintenance processes. In this research, we presented a new model to calculate the saved effort that results from using libraries instead of generic programming in the coding, testing, and productivity processes. The proposed model consists of three metrics that are Library Investment Ratio, Library Investment Level, and Program Simplicity. An empirical analyzes has been applied into ten projects to compare the results of the model with Reuse Percent. The results show that the model has better indication of the improvement of software quality and productivity rather than Reuse Percent.
Companion Surface of Danger Cylinder and its Role in Solution Variation of P3P Problem
Venue: arXiv cs.CV 2019
Year: 2019

Traditionally the danger cylinder is intimately related to the solution stability in P3P problem. In this work, we show that the danger cylinder is also closely related to the multiple-solution phenomenon. More specifically, we show when the optical center lies on the danger cylinder, of the 3 possible P3P solutions, i.e., one double solution, and two other solutions, the optical center of the double solution still lies on the danger cylinder, but the optical centers of the other two solutions no longer lie on the danger cylinder. And when the optical center moves on the danger cylinder, accordingly the optical centers of the two other solutions of the corresponding P3P problem form a new surface, characterized by a polynomial equation of degree 12 in the optical center coordinates, called the Companion Surface of Danger Cylinder (CSDC). That means the danger cylinder always has a companion surface. For the significance of CSDC, we show that when the optical center passes through the CSDC, the number of solutions of P3P problem must change by 2. That means CSDC acts as a delimitating surface of the P3P solution space. These new findings shed some new lights on the P3P multi-solution phenomenon, an important issue in PnP study.
SoK of Used Cryptography in Blockchain
Venue: arXiv cs.CR 2019
Year: 2019

The underlying fundaments of blockchain are cryptography and cryptographic concepts that provide reliable and secure decentralized solutions. Although many recent papers study the use-cases of blockchain in different industrial areas, such as finance, health care, legal relations, IoT, information security, and consensus building systems, only few studies scrutinize the cryptographic concepts used in blockchain. To the best of our knowledge, there is no Systematization of Knowledge (SoK) that gives a complete picture of the existing cryptographic concepts which have been deployed or have the potential to be deployed in blockchain. In this paper, we thoroughly review and systematize all cryptographic concepts which are already used in blockchain. Additionally, we give a list of cryptographic concepts which have not yet been applied but have big potentials to improve the current blockchain solutions. We also include possible instantiations of these cryptographic concepts in the blockchain domain. Last but not least, we explicitly postulate 21 challenging problems that cryptographers interested in blockchain can work on.
Experiencing Extreme Height for The First Time: The Influence of Height, Self-Judgment of Fear and a Moving Structural Beam on the Heart Rate and Postural Sway During the Quiet Stance
Venue: arXiv cs.HC 2019
Year: 2019

Falling from elevated surfaces is the main cause of death and injury at construction sites. Based on the Bureau of Labor Statistics (BLS) reports, an average of nearly three workers per day suffer fatal injuries from falling. Studies show that postural instability is the foremost cause of this disproportional falling rate. To study what affects the postural stability of construction workers, we conducted a series of experiments in the virtual reality (VR). Twelve healthy adults, all students at the University of Nebraska were recruited for this study. During each trial, participants heart rates and postural sways were measured as the dependent factors. The independent factors included a moving structural beam (MB) coming directly at the participants, the presence of VR, height, the participants self-judgment of fear, and their level of acrophobia. The former was designed in an attempt to simulate some part of the steel erection procedure, which is one of the key tasks of ironworkers. The results of this study indicate that height increase the postural sway. Self-judged fear significantly was found to decrease postural sway, more specifically the normalized total excursion of the center of pressure (TE), both in the presence and absence of height. Also, participants heart rates significantly increase once they are confronted by a moving beam in the virtual environment (VE), even though they are informed that the beam will not hit them. The findings of this study can be useful for training novice ironworkers that will be subjected to height and steel erection for the first time.
Citizens' Emotion on GST: A Spatio-Temporal Analysis over Twitter Data
Venue: arXiv cs.IR 2019
Year: 2019

People might not be close-at-hand but they still are - by virtue of the social network. The social network has transformed lives in many ways. People can express their views, opinions and life experiences on various platforms be it Twitter, Facebook or any other medium there is. Such events constitute of reviewing a product or service, conveying views on political banters, predicting share prices or giving feedback on the government policies like Demonetization or GST. These social platforms can be used to investigate the insights of the emotional curve that the general public is generating. This kind of analysis can help make a product better, predict the future prospects and also to implement the public policies in a better way. Such kind of research on sentiment analysis is increasing rapidly. In this research paper, we have performed temporal analysis and spatial analysis on 1,42,508 and 58,613 tweets respectively and these tweets were posted during the post-GST implementation period from July 04, 2017 to July 25, 2017. The tweets were collected using the Twitter streaming API. A well-known lexicon, National Research Council Canada (NRC) emotion Lexicon is used for opinion mining that exhibits a blend of eight basic emotions i.e. joy, trust, anticipation, surprise, fear sadness, anger, disgust and two sentiments i.e. positive and negative for 6,554 words.
A New Statistical Approach for Comparing Algorithms for Lexicon Based Sentiment Analysis
Venue: arXiv cs.CL 2019
Year: 2019

Lexicon based sentiment analysis usually relies on the identification of various words to which a numerical value corresponding to sentiment can be assigned. In principle, classifiers can be obtained from these algorithms by comparison with human annotation, which is considered the gold standard. In practise this is difficult in languages such as Portuguese where there is a paucity of human annotated texts. Thus in order to compare algorithms, a next best step is to directly compare different algorithms with each other without referring to human annotation. In this paper we develop methods for a statistical comparison of algorithms which does not rely on human annotation or on known class labels. We will motivate the use of marginal homogeneity tests, as well as log linear models within the framework of maximum likelihood estimation We will also show how some uncertainties present in lexicon based sentiment analysis may be similar to those which occur in human annotated tweets. We will also show how the variability in the output of different algorithms is lexicon dependent, and quantify this variability in the output within the framework of log linear models.
Generic Ontology Design Patterns at Work
Venue: arXiv cs.AI 2019
Year: 2019

Generic Ontology Design Patterns, GODPs, are defined in Generic DOL, an extension of DOL, the Distributed Ontology, Model and Specification Language, and implemented using Heterogeneous Tool Set. Parameters such as classes, properties, individuals, or whole ontologies may be instantiated with arguments in a host ontology. The potential of Generic DOL is illustrated with GODPs for an example from the literature, namely the Role design pattern. We also discuss how larger GODPs may be composed by instantiating smaller GODPs.
Multi-Category Fairness in Sponsored Search Auctions
Venue: arXiv cs.GT 2019
Year: 2019

Fairness in advertising is a topic of particular concern motivated by theoretical and empirical observations in both the computer science and economics literature. We examine the problem of fairness in advertising for general purpose platforms that service advertisers from many different categories. First, we propose inter-category and intra-category fairness desiderata that take inspiration from individual fairness and envy-freeness. Second, we investigate the "platform utility" (a proxy for the quality of the allocation) achievable by mechanisms satisfying these desiderata. More specifically, we compare the utility of fair mechanisms against the unfair optimal, and we show by construction that our fairness desiderata are compatible with utility. That is, we construct a family of fair mechanisms with high utility that perform close to optimally within a class of fair mechanisms. Our mechanisms also enjoy nice implementation properties including metric-obliviousness, which allows the platform to produce fair allocations without needing to know the specifics of the fairness requirements.
A Deep Reinforcement Learning Approach for Global Routing
Venue: arXiv cs.LG 2019
Year: 2019

Global routing has been a historically challenging problem in electronic circuit design, where the challenge is to connect a large and arbitrary number of circuit components with wires without violating the design rules for the printed circuit boards or integrated circuits. Similar routing problems also exist in the design of complex hydraulic systems, pipe systems and logistic networks. Existing solutions typically consist of greedy algorithms and hard-coded heuristics. As such, existing approaches suffer from a lack of model flexibility and non-optimum solutions. As an alternative approach, this work presents a deep reinforcement learning method for solving the global routing problem in a simulated environment. At the heart of the proposed method is deep reinforcement learning that enables an agent to produce an optimal policy for routing based on the variety of problems it is presented with leveraging the conjoint optimization mechanism of deep reinforcement learning. Conjoint optimization mechanism is explained and demonstrated in details; the best network structure and the parameters of the learned model are explored. Based on the fine-tuned model, routing solutions and rewards are presented and analyzed. The results indicate that the approach can outperform the benchmark method of a sequential A* method, suggesting a promising potential for deep reinforcement learning for global routing and other routing or path planning problems in general. Another major contribution of this work is the development of a global routing problem sets generator with the ability to generate parameterized global routing problem sets with different size and constraints, enabling evaluation of different routing algorithms and the generation of training datasets for future data-driven routing approaches.
An Extensible Framework for Quantifying the Coverage of Defenses Against Untrusted Foundries
Venue: arXiv cs.CR 2019
Year: 2019

The transistors used to construct Integrated Circuits (ICs) continue to shrink. While this shrinkage improves performance and density, it also reduces trust: the price to build leading-edge fabrication facilities has skyrocketed, forcing even nation states to outsource the fabrication of high-performance ICs. Outsourcing fabrication presents a security threat because the black-box nature of a fabricated IC makes comprehensive inspection infeasible. Since prior work shows the feasibility of fabrication-time attackers' evasion of existing post-fabrication defenses, IC designers must be able to protect their physical designs before handing them off to an untrusted foundry. To this end, recent work suggests methods to harden IC layouts against attack. Unfortunately, no tool exists to assess the effectiveness of the proposed defenses---meaning gaps may exist. This paper presents an extensible IC layout security analysis tool called IC Attack Surface (ICAS) that quantifies defensive coverage. For researchers, ICAS identifies gaps for future defenses to target, and enables the quantitative comparison of existing and future defenses. For practitioners, ICAS enables the exploration of the impact of design decisions on an IC's resilience to fabrication-time attack. ICAS takes a set of metrics that encode the challenge of inserting a hardware Trojan into an IC layout, a set of attacks that the defender cares about, and a completed IC layout and reports the number of ways an attacker can add each attack to the design. While the ideal score is zero, practically, our experience is that lower scores correlate with increased attacker effort.
Neural Stored-program Memory
Venue: arXiv cs.NE 2019
Year: 2019

Neural networks powered with external memory simulate computer behaviors. These models, which use the memory to store data for a neural controller, can learn algorithms and other complex tasks. In this paper, we introduce a new memory to store weights for the controller, analogous to the stored-program memory in modern computer architectures. The proposed model, dubbed Neural Stored-program Memory, augments current memory-augmented neural networks, creating differentiable machines that can switch programs through time, adapt to variable contexts and thus resemble the Universal Turing Machine. A wide range of experiments demonstrate that the resulting machines not only excel in classical algorithmic problems, but also have potential for compositional, continual, few-shot learning and question-answering tasks.
Evolving Self-supervised Neural Networks: Autonomous Intelligence from Evolved Self-teaching
Venue: arXiv cs.NE 2019
Year: 2019

This paper presents a technique called evolving self-supervised neural networks - neural networks that can teach themselves, intrinsically motivated, without external supervision or reward. The proposed method presents some sort-of paradigm shift, and differs greatly from both traditional gradient-based learning and evolutionary algorithms in that it combines the metaphor of evolution and learning, more specifically self-learning, together, rather than treating these phenomena alternatively. I simulate a multi-agent system in which neural networks are used to control autonomous foraging agents with little domain knowledge. Experimental results show that only evolved self-supervised agents can demonstrate some sort of intelligent behaviour, but not evolution or self-learning alone. Indications for future work on evolving intelligence are also presented.