OpenRaiser/Intern-Atlas huggingface dataset:
Keyword Search Engine Enriched by Expert System Features
Venue: arXiv cs.IR 2020
Year: 2020

Keyword search engines are essential elements of large information spaces. The largest information space is the Web, and keyword search engines play crucial role there. The advent of keyword search engines has provided a quantum leap in the development of the Web. Since then, the Web has continued to evolve, and keyword search systems have proven inadequate. A new quantum leap in the development of keyword search engines is needed. This quantum leap can be provided with more intellectual keyword search engines. The increased intelligence of such keyword search engines can be achieved through a combination of keyword search engines and expert systems. The paper reveals how it can be done.
Quickest Change Detection with Privacy Constraint
Venue: arXiv cs.IT 2020
Year: 2020

This paper considers Lorden's minimax quickest change detection (QCD) problem with a privacy constraint. The goal is to sanitize a signal to satisfy inference privacy requirements while being able to detect a change quickly. We show that the Generalized Likelihood Ratio (GLR) CuSum achieves asymptotic optimality with a properly designed sanitization channel. We formulate the design of this sanitization channel as an optimization problem, which is however challenging to solve. We propose relaxations to the optimization problem and develop algorithms to obtain a solution. We also consider the privacy-aware QCD problem under a decentralized framework and propose algorithms to solve the relaxed channel design problem under this framework.
GRAC: Self-Guided and Self-Regularized Actor-Critic
Venue: arXiv cs.LG 2020
Year: 2020

Deep reinforcement learning (DRL) algorithms have successfully been demonstrated on a range of challenging decision making and control tasks. One dominant component of recent deep reinforcement learning algorithms is the target network which mitigates the divergence when learning the Q function. However, target networks can slow down the learning process due to delayed function updates. Our main contribution in this work is a self-regularized TD-learning method to address divergence without requiring a target network. Additionally, we propose a self-guided policy improvement method by combining policy-gradient with zero-order optimization to search for actions associated with higher Q-values in a broad neighborhood. This makes learning more robust to local noise in the Q function approximation and guides the updates of our actor network. Taken together, these components define GRAC, a novel self-guided and self-regularized actor critic algorithm. We evaluate GRAC on the suite of OpenAI gym tasks, achieving or outperforming state of the art in every environment tested.
Pretty Good Phone Privacy
Venue: arXiv cs.NI 2020
Year: 2020

To receive service in today's cellular architecture, phones uniquely identify themselves to towers and thus to operators. This is now a cause of major privacy violations, as operators now sell and leak identity and location data of hundreds of millions of mobile users. In this paper, we take an end-to-end perspective on the cellular architecture and find key points of decoupling that enable us to protect user identity and location privacy with no changes to physical infrastructure, no added latency, and no requirement of direct cooperation from existing operators. We describe Pretty Good Phone Privacy (PGPP) and demonstrate how our modified backend stack (NGC) works with real phones to provide ordinary yet privacy-preserving connectivity. We explore inherent privacy and efficiency tradeoffs in a simulation of a large metropolitan region. We show how PGPP maintains today's control overheads while significantly improving user identity and location privacy.
Examining the Impact of Algorithm Awareness on Wikidata's Recommender System Recoin
Venue: arXiv cs.HC 2020
Year: 2020

The global infrastructure of the Web, designed as an open and transparent system, has a significant impact on our society. However, algorithmic systems of corporate entities that neglect those principles increasingly populated the Web. Typical representatives of these algorithmic systems are recommender systems that influence our society both on a scale of global politics and during mundane shopping decisions. Recently, such recommender systems have come under critique for how they may strengthen existing or even generate new kinds of biases. To this end, designers and engineers are increasingly urged to make the functioning and purpose of recommender systems more transparent. Our research relates to the discourse of algorithm awareness, that reconsiders the role of algorithm visibility in interface design. We conducted online experiments with 105 participants using MTurk for the recommender system Recoin, a gadget for Wikidata. In these experiments, we presented users with one of a set of three different designs of Recoin's user interface, each of them exhibiting a varying degree of explainability and interactivity. Our findings include a positive correlation between comprehension of and trust in an algorithmic system in our interactive redesign. However, our results are not conclusive yet, and suggest that the measures of comprehension, fairness, accuracy and trust are not yet exhaustive for the empirical study of algorithm awareness. Our qualitative insights provide a first indication for further measures. Our study participants, for example, were less concerned with the details of understanding an algorithmic calculation than with who or what is judging the result of the algorithm.
CommunityClick: Capturing and Reporting Community Feedback from Town Halls to Improve Inclusivity
Venue: arXiv cs.HC 2020
Year: 2020

Local governments still depend on traditional town halls for community consultation, despite problems such as a lack of inclusive participation for attendees and difficulty for civic organizers to capture attendees' feedback in reports. Building on a formative study with 66 town hall attendees and 20 organizers, we designed and developed CommunityClick, a communitysourcing system that captures attendees' feedback in an inclusive manner and enables organizers to author more comprehensive reports. During the meeting, in addition to recording meeting audio to capture vocal attendees' feedback, we modify iClickers to give voice to reticent attendees by allowing them to provide real-time feedback beyond a binary signal. This information then automatically feeds into a meeting transcript augmented with attendees' feedback and organizers' tags. The augmented transcript along with a feedback-weighted summary of the transcript generated from text analysis methods is incorporated into an interactive authoring tool for organizers to write reports. From a field experiment at a town hall meeting, we demonstrate how CommunityClick can improve inclusivity by providing multiple avenues for attendees to share opinions. Additionally, interviews with eight expert organizers demonstrate CommunityClick's utility in creating more comprehensive and accurate reports to inform critical civic decision-making. We discuss the possibility of integrating CommunityClick with town hall meetings in the future as well as expanding to other domains.
Load Driven Branch Predictor (LDBP)
Venue: arXiv cs.AR 2020
Year: 2020

Branch instructions dependent on hard-to-predict load data are the leading branch misprediction contributors. Current state-of-the-art history-based branch predictors have poor prediction accuracy for these branches. Prior research backs this observation by showing that increasing the size of a 256-KBit history-based branch predictor to its 1-MBit variant has just a 10% reduction in branch mispredictions. We present the novel Load Driven Branch Predictor(LDBP) specifically targeting hard-to-predict branches dependent on a load instruction. Though random load data determines the outcome for these branches, the load address for most of these data has a predictable pattern. This is an observable template in data structures like arrays and maps. Our predictor model exploits this behavior to trigger future loads associated with branches ahead of time and use its data to predict the branch's outcome. The predictable loads are tracked, and the precomputed outcomes of the branch instruction are buffered for making predictions. Our experimental results show that compared to a standalone 256-Kbit IMLI predictor, when LDBP is augmented with a 150-Kbit IMLI, it reduces the average branch mispredictions by 20% and improves average IPC by 13.1% for benchmarks from SPEC CINT2006 and GAP benchmark suite.
Is there a role for statistics in artificial intelligence?
Venue: arXiv cs.CY 2020
Year: 2020

The research on and application of artificial intelligence (AI) has triggered a comprehensive scientific, economic, social and political discussion. Here we argue that statistics, as an interdisciplinary scientific field, plays a substantial role both for the theoretical and practical understanding of AI and for its future development. Statistics might even be considered a core element of AI. With its specialist knowledge of data evaluation, starting with the precise formulation of the research question and passing through a study design stage on to analysis and interpretation of the results, statistics is a natural partner for other disciplines in teaching, research and practice. This paper aims at contributing to the current discussion by highlighting the relevance of statistical methodology in the context of AI development. In particular, we discuss contributions of statistics to the field of artificial intelligence concerning methodological development, planning and design of studies, assessment of data quality and data collection, differentiation of causality and associations and assessment of uncertainty in results. Moreover, the paper also deals with the equally necessary and meaningful extension of curricula in schools and universities.
Closed-loop spiking control on a neuromorphic processor implemented on the iCub
Venue: arXiv cs.ET 2020
Year: 2020

Despite neuromorphic engineering promises the deployment of low latency, adaptive and low power systems that can lead to the design of truly autonomous artificial agents, the development of a fully neuromorphic artificial agent is still missing. While neuromorphic sensing and perception, as well as decision-making systems, are now mature, the control and actuation part is lagging behind. In this paper, we present a closed-loop motor controller implemented on mixed-signal analog-digital neuromorphic hardware using a spiking neural network. The network performs a proportional control action by encoding target, feedback, and error signals using a spiking relational network. It continuously calculates the error through a connectivity pattern, which relates the three variables by means of feed-forward connections. Recurrent connections within each population are used to speed up the convergence, decrease the effect of mismatch and improve selectivity. The neuromorphic motor controller is interfaced with the iCub robot simulator. We tested our spiking P controller in a single joint control task, specifically for the robot head yaw. The spiking controller sends the target positions, reads the motor state from its encoder, and sends back the motor commands to the joint. The performance of the spiking controller is tested in a step response experiment and in a target pursuit task. In this work, we optimize the network structure to make it more robust to noisy inputs and device mismatch, which leads to better control performances.
Intimate Partner Violence and Injury Prediction From Radiology Reports
Venue: arXiv cs.CY 2020
Year: 2020

Intimate partner violence (IPV) is an urgent, prevalent, and under-detected public health issue. We present machine learning models to assess patients for IPV and injury. We train the predictive algorithms on radiology reports with 1) IPV labels based on entry to a violence prevention program and 2) injury labels provided by emergency radiology fellowship-trained physicians. Our dataset includes 34,642 radiology reports and 1479 patients of IPV victims and control patients. Our best model predicts IPV a median of 3.08 years before violence prevention program entry with a sensitivity of 64% and a specificity of 95%. We conduct error analysis to determine for which patients our model has especially high or low performance and discuss next steps for a deployed clinical risk model.
FlexWatts: A Power- and Workload-Aware Hybrid Power Delivery Network for Energy-Efficient Microprocessors
Venue: arXiv cs.AR 2020
Year: 2020

Modern client processors typically use one of three commonly-used power delivery network (PDN): 1) motherboard voltage regulators (MBVR), 2) integrated voltage regulators (IVR), and 3) low dropout voltage regulators (LDO). We observe that the energy-efficiency of each of these PDNs varies with the processor power (e.g., thermal design power (TDP) and dynamic power-state) and workload characteristics. This leads to energy inefficiency and performance loss, as modern client processors operate across a wide spectrum of power consumption and execute a wide variety of workloads. We propose FlexWatts, a hybrid adaptive PDN for modern client processors whose goal is to provide high energy-efficiency across the processor's wide range of power consumption and workloads by dynamically allocating PDNs to processor domains. FlexWatts is based on three key ideas. First, it combines IVRs and LDOs in a novel way to share multiple on-chip and off-chip resources. This hybrid PDN is allocated for processor domains with a wide power consumption range and it dynamically switches between two modes: IVR-Mode and LDO-Mode, depending on the power consumption. Second, for all other processor domains, FlexWatts statically allocates off-chip VRs. Third, FlexWatts introduces a prediction algorithm that switches the hybrid PDN to the mode that is the most beneficial. To evaluate the tradeoffs of PDNs, we develop and open-source PDNspot, the first validated architectural PDN model that enables quantitative analysis of PDN metrics. Using PDNspot, we evaluate FlexWatts on a wide variety of SPEC CPU2006, 3DMark06, and battery life workloads against IVR, the state-of-the-art PDN in modern client processors. For a 4W TDP processor, FlexWatts improves the average performance of the SPEC CPU2006 and 3DMark06 workloads by 22% and 25%, respectively. FlexWatts has comparable cost and area overhead to IVR.
Amazon Fake Reviews
Venue: arXiv cs.CY 2020
Year: 2020

Often, there are suspicious Amazon reviews that seem to be excessively positive or have been created through a repeating algorithm. I moved to detect fake reviews on Amazon through semantic analysis in conjunction with meta data such as time, word choice, and the user who posted. I first came up with several instances that may indicate a review isn't genuine and constructed what the algorithm would look like. Then I coded the algorithm and tested the accuracy of it using statistical analysis and analyzed it based on the six qualities of code.
Introspective Learning by Distilling Knowledge from Online Self-explanation
Venue: arXiv cs.CV 2020
Year: 2020

In recent years, many explanation methods have been proposed to explain individual classifications of deep neural networks. However, how to leverage the created explanations to improve the learning process has been less explored. As the privileged information, the explanations of a model can be used to guide the learning process of the model itself. In the community, another intensively investigated privileged information used to guide the training of a model is the knowledge from a powerful teacher model. The goal of this work is to leverage the self-explanation to improve the learning process by borrowing ideas from knowledge distillation. We start by investigating the effective components of the knowledge transferred from the teacher network to the student network. Our investigation reveals that both the responses in non-ground-truth classes and class-similarity information in teacher's outputs contribute to the success of the knowledge distillation. Motivated by the conclusion, we propose an implementation of introspective learning by distilling knowledge from online self-explanations. The models trained with the introspective learning procedure outperform the ones trained with the standard learning procedure, as well as the ones trained with different regularization methods. When compared to the models learned from peer networks or teacher networks, our models also show competitive performance and requires neither peers nor teachers.
Hierarchical Coding for Cloud Storage: Topology-Adaptivity, Scalability, and Flexibility
Venue: arXiv cs.IT 2020
Year: 2020

In order to accommodate the ever-growing data from various, possibly independent, sources and the dynamic nature of data usage rates in practical applications, modern cloud data storage systems are required to be scalable, flexible, and heterogeneous. The recent rise of the blockchain technology is also moving various information systems towards decentralization to achieve high privacy at low costs. While codes with hierarchical locality have been intensively studied in the context of centralized cloud storage due to their effectiveness in reducing the average reading time, those for decentralized storage networks (DSNs) have not yet been discussed. In this paper, we propose a joint coding scheme where each node receives extra protection through the cooperation with nodes in its neighborhood in a heterogeneous DSN with any given topology. This work extends and subsumes our prior work on coding for centralized cloud storage. In particular, our proposed construction not only preserves desirable properties such as scalability and flexibility, which are critical in dynamic networks, but also adapts to arbitrary topologies, a property that is essential in DSNs but has been overlooked in existing works.
CLEVR Parser: A Graph Parser Library for Geometric Learning on Language Grounded Image Scenes
Venue: arXiv cs.CL 2020
Year: 2020

The CLEVR dataset has been used extensively in language grounded visual reasoning in Machine Learning (ML) and Natural Language Processing (NLP) domains. We present a graph parser library for CLEVR, that provides functionalities for object-centric attributes and relationships extraction, and construction of structural graph representations for dual modalities. Structural order-invariant representations enable geometric learning and can aid in downstream tasks like language grounding to vision, robotics, compositionality, interpretability, and computational grammar construction. We provide three extensible main components - parser, embedder, and visualizer that can be tailored to suit specific learning setups. We also provide out-of-the-box functionality for seamless integration with popular deep graph neural network (GNN) libraries. Additionally, we discuss downstream usage and applications of the library, and how it accelerates research for the NLP research community.
Optimizing Away JavaScript Obfuscation
Venue: arXiv cs.CR 2020
Year: 2020

JavaScript is a popular attack vector for releasing malicious payloads on unsuspecting Internet users. Authors of this malicious JavaScript often employ numerous obfuscation techniques in order to prevent the automatic detection by antivirus and hinder manual analysis by professional malware analysts. Consequently, this paper presents SAFE-Deobs, a JavaScript deobfuscation tool that we have built. The aim of SAFE-Deobs is to automatically deobfuscate JavaScript malware such that an analyst can more rapidly determine the malicious script's intent. This is achieved through a number of static analyses, inspired by techniques from compiler theory. We demonstrate the utility of SAFE-Deobs through a case study on real-world JavaScript malware, and show that it is a useful addition to a malware analyst's toolset.
Schedule Sequence Design for Broadcast in Multi-channel Ad Hoc Networks
Venue: arXiv cs.IT 2020
Year: 2020

We consider a single-hop ad hoc network in which each node aims to broadcast packets to its neighboring nodes by using multiple slotted, TDD collision channels. There is no cooperation among the nodes. To ensure successful broadcast, we propose to pre-assign each node a periodic sequence to schedule transmissions and receptions at each time slot. These sequences are referred to as schedule sequences. Since each node starts its transmission schedule independently, there exist relative time offsets among the schedule sequences they use. Our objective is to design schedule sequences such that each node can transmit at least one packet to each of its neighbors successfully within a common period, no matter what the time offsets are. The sequence period should be designed as short as possible. In this paper, we analyze the lower bound on sequence period, and propose a sequence construction method by which the period can achieve the same order as the lower bound. We also consider the random scheme in which each node transmits or receives on a channel at each time slot with a pre-determined probability. The frame length and broadcast completion time under different schemes are compared by numerical studies.
Detecting Malicious URLs of COVID-19 Pandemic using ML technologies
Venue: arXiv cs.CR 2020
Year: 2020

Throughout the COVID-19 outbreak, malicious attacks have become more pervasive and damaging than ever. Malicious intruders have been responsible for most cybercrimes committed recently and are the cause for a growing number of cyber threats, including identity and IP thefts, financial crimes, and cyber-attacks to critical infrastructures. Machine learning (ML) has proven itself as a prominent field of study over the past decade by solving many highly complex and sophisticated real-world problems. This paper proposes an ML-based classification technique to detect the growing number of malicious URLs, due to the COVID-19 pandemic, which is currently considered a threat to IT users. We have used a large volume of Open Source data and preprocessed it using our developed tool to generate feature vectors and we trained the ML model using the apprehensive malicious threat weight. Our ML model has been tested, with and without entropy to forecast the threatening factors of COVID-19 URLs. The empirical evidence proves our methods to be a promising mechanism to mitigate COVID-19 related threats early in the attack lifecycle.
AAA: Adaptive Aggregation of Arbitrary Online Trackers with Theoretical Performance Guarantee
Venue: arXiv cs.CV 2020
Year: 2020

For visual object tracking, it is difficult to realize an almighty online tracker due to the huge variations of target appearance depending on an image sequence. This paper proposes an online tracking method that adaptively aggregates arbitrary multiple online trackers. The performance of the proposed method is theoretically guaranteed to be comparable to that of the best tracker for any image sequence, although the best expert is unknown during tracking. The experimental study on the large variations of benchmark datasets and aggregated trackers demonstrates that the proposed method can achieve state-of-the-art performance. The code is available at https://github.com/songheony/AAA-journal.
Software Engineering Standards for Epidemiological Modeling
Venue: arXiv cs.CY 2020
Year: 2020

There are many normative and technical questions involved in evaluating the quality of software used in epidemiological simulations. In this paper we answer some of these questions and offer practical guidance to practitioners, funders, scientific journals, and consumers of epidemiological research. The heart of our paper is a case study of the Imperial College London (ICL) COVID-19 simulator. We contend that epidemiological simulators should be engineered and evaluated within the framework of safety-critical standards developed by the consensus of the software engineering community for applications such as automotive and aircraft control.