OpenRaiser/Intern-Atlas huggingface dataset:
How did Donald Trump Surprisingly Win the 2016 United States Presidential Election? an Information-Theoretic Perspective (Clean Sensing for Big Data Analytics:Optimal Strategies,Estimation Error Bounds Tighter than the Cramér-Rao Bound)
Venue: arXiv cs.IT 2018
Year: 2018

Donald Trump was lagging behind in nearly all opinion polls leading up to the 2016 US presidential election, but he surprisingly won the election. This raises the following important questions: 1) why most opinion polls were not accurate in 2016? and 2) how to improve the accuracies of opinion polls? In this paper, we study the inaccuracies of opinion polls in the 2016 election through the lens of information theory. We first propose a general framework of parameter estimation, called clean sensing (polling), which performs optimal parameter estimation with sensing cost constraints, from heterogeneous and potentially distorted data sources. We then cast the opinion polling as a problem of parameter estimation from potentially distorted heterogeneous data sources, and derive the optimal polling strategy using heterogenous and possibly distorted data under cost constraints. Our results show that a larger number of data samples do not necessarily lead to better polling accuracy, which give a possible explanation of the inaccuracies of opinion polls in 2016. The optimal sensing strategy should instead optimally allocate sensing resources over heterogenous data sources according to several factors including data quality, and, moreover, for a particular data source, it should strike an optimal balance between the quality of data samples, and the quantity of data samples. As a byproduct of this research, in a general setting, we derive a group of new lower bounds on the mean-squared errors of general unbiased and biased parameter estimators. These new lower bounds can be tighter than the classical Cramér-Rao bound (CRB) and Chapman-Robbins bound. Our derivations are via studying the Lagrange dual problems of certain convex programs. The classical Cramér-Rao bound and Chapman-Robbins bound follow naturally from our results for special cases of these convex programs.
Image Super-Resolution via RL-CSC: When Residual Learning Meets Convolutional Sparse Coding
Venue: arXiv cs.CV 2018
Year: 2018

We propose a simple yet effective model for Single Image Super-Resolution (SISR), by combining the merits of Residual Learning and Convolutional Sparse Coding (RL-CSC). Our model is inspired by the Learned Iterative Shrinkage-Threshold Algorithm (LISTA). We extend LISTA to its convolutional version and build the main part of our model by strictly following the convolutional form, which improves the network's interpretability. Specifically, the convolutional sparse codings of input feature maps are learned in a recursive manner, and high-frequency information can be recovered from these CSCs. More importantly, residual learning is applied to alleviate the training difficulty when the network goes deeper. Extensive experiments on benchmark datasets demonstrate the effectiveness of our method. RL-CSC (30 layers) outperforms several recent state-of-the-arts, e.g., DRRN (52 layers) and MemNet (80 layers) in both accuracy and visual qualities. Codes and more results are available at https://github.com/axzml/RL-CSC.
Gray-box Adversarial Testing for Control Systems with Machine Learning Component
Venue: arXiv cs.LG 2018
Year: 2018

Neural Networks (NN) have been proposed in the past as an effective means for both modeling and control of systems with very complex dynamics. However, despite the extensive research, NN-based controllers have not been adopted by the industry for safety critical systems. The primary reason is that systems with learning based controllers are notoriously hard to test and verify. Even harder is the analysis of such systems against system-level specifications. In this paper, we provide a gradient based method for searching the input space of a closed-loop control system in order to find adversarial samples against some system-level requirements. Our experimental results show that combined with randomized search, our method outperforms Simulated Annealing optimization.
Convex Relaxations of Convolutional Neural Nets
Venue: arXiv cs.LG 2018
Year: 2018

We propose convex relaxations for convolutional neural nets with one hidden layer where the output weights are fixed. For convex activation functions such as rectified linear units, the relaxations are convex second order cone programs which can be solved very efficiently. We prove that the relaxation recovers the global minimum under a planted model assumption, given sufficiently many training samples from a Gaussian distribution. We also identify a phase transition phenomenon in recovering the global minimum for the relaxation.
Sentence-Level Sentiment Analysis of Financial News Using Distributed Text Representations and Multi-Instance Learning
Venue: arXiv cs.IR 2018
Year: 2018

Researchers and financial professionals require robust computerized tools that allow users to rapidly operationalize and assess the semantic textual content in financial news. However, existing methods commonly work at the document-level while deeper insights into the actual structure and the sentiment of individual sentences remain blurred. As a result, investors are required to apply the utmost attention and detailed, domain-specific knowledge in order to assess the information on a fine-grained basis. To facilitate this manual process, this paper proposes the use of distributed text representations and multi-instance learning to transfer information from the document-level to the sentence-level. Compared to alternative approaches, this method features superior predictive performance while preserving context and interpretability. Our analysis of a manually-labeled dataset yields a predictive accuracy of up to 69.90%, exceeding the performance of alternative approaches by at least 3.80 percentage points. Accordingly, this study not only benefits investors with regard to their financial decision-making, but also helps companies to communicate their messages as intended.
Deep Representation Learning for Clustering of Health Tweets
Venue: arXiv cs.CL 2018
Year: 2018

Twitter has been a prominent social media platform for mining population-level health data and accurate clustering of health-related tweets into topics is important for extracting relevant health insights. In this work, we propose deep convolutional autoencoders for learning compact representations of health-related tweets, further to be employed in clustering. We compare our method to several conventional tweet representation methods including bag-of-words, term frequency-inverse document frequency, Latent Dirichlet Allocation and Non-negative Matrix Factorization with 3 different clustering algorithms. Our results show that the clustering performance using proposed representation learning scheme significantly outperforms that of conventional methods for all experiments of different number of clusters. In addition, we propose a constraint on the learned representations during the neural network training in order to further enhance the clustering performance. All in all, this study introduces utilization of deep neural network-based architectures, i.e., deep convolutional autoencoders, for learning informative representations of health-related tweets.
Honey Authentication with Machine Learning Augmented Bright-Field Microscopy
Venue: arXiv cs.LG 2018
Year: 2018

Honey has been collected and used by humankind as both a food and medicine for thousands of years. However, in the modern economy, honey has become subject to mislabelling and adulteration making it the third most faked food product in the world. The international scale of fraudulent honey has had both economic and environmental ramifications. In this paper, we propose a novel method of identifying fraudulent honey using machine learning augmented microscopy.
Understanding What Drives Bitcoin Trading Activities
Venue: arXiv cs.CY 2018
Year: 2018

Cryptocurrencies have gained tremendous popularity over the past few years. The purpose of this study is to try to understand the factors that are driving cryptocurrency-related trading activities. Focusing on the well-established cryptocurrency called Bitcoin, we find that online search popularity and the volume of trade in unrelated stock markets positively and negatively, respectively, influence Bitcoin trading volume. We also find no statistical evidence that the underlying sentiment behind relevant financial news influence Bitcoin trading volume. We believe these results might be of great value to investors interested in cryptocurrencies and might instigate further research on this topic.
On-line Remote EKG as a Web Service
Venue: arXiv cs.CY 2018
Year: 2018

A 3-leads, non-diagnostic EKG has a role in emergency rescue and homecare. In this paper we introduce the design and a prototype of a service, provided to a doctor and a patient, for the on-line remote visualization of patient's 3-leads EKG. The architecture is based on the HTTP protocol, using commercial off-the-shelf devices to implement the sensor on patient's side, a browser on a laptop PC on the doctor's side as viewer, and a cloud container to connect the two using Websockets. A prototype is built to evaluate signal latency, power consumption of the patient side device, and the quality of the rendering. After some experiments, latency is measured below $1sec$, and power consumption is estimated in the 2A*3.3V range; visualization is comparable to commercial, non-diagnostic products. The prototype patient device is portable, and can be operated using rechargeable battery packs. Its cost is below 100\$, and all the required equipment is commercially available. The architecture is ready for on field evaluation, and we indicate how to improve power consumption while reducing cost.
The Impact of Countdown Clocks on Subway Ridership in New York City
Venue: arXiv cs.CY 2018
Year: 2018

Protecting the passengers' safety and increasing ridership are two never ending pursuits of public transit agencies. One of the proposed methods to achieve both goals for subway service is to implement real time train arriving countdown clocks in subway stations. Metropolitan Transportation Authority (MTA) of New York City (NYC) chose to install such countdown clocks in their stations starting from 2007 on a selection of subway lines. Due to the recent development of Bluetooth Beacon technology, the MTA could now install countdown clocks and train trackers in a non intrusive manner with much faster speed. As a result, the MTA is aiming to install countdown clocks in every subway station on every line. However, with such an aggressive plan, the impact of countdown clocks on subway ridership has not been fully studied. This paper proposes using Panel Regression methods, specifically, Random Effect (RE) model and Fixed Effect (FE) model to quantify the impact of countdown clocks on subway ridership. Machine Learning methods, namely Random Forest (RF) with AdaBoost and Decision Tree (DT) Regression, are also used as alternative data driven approaches for the FE and RE model. The results show that for the G line service, which runs between Brooklyn and Queens, the introduction of countdown clocks could increase weekly ridership by about 1783 per station. The study also found that the machine learning methods provide better accuracy in predicting the ridership than RE and FE models.
Cooperation of Multiple Autonomous Robots and Analysis of their Swarm Behavior
Venue: arXiv cs.RO 2018
Year: 2018

In this paper, we extended previous studies of cooperating autonomous robots to include situations when environmental changes and changes in the number of robots in the swarm can affect the efficiency to execute tasks assigned to the swarm of robots. We have presented a novel approach based on partition of the robot behavior. The sub-diagrams describing sub-routs allowed us to model advanced interactions between autonomous robots using limited number of state combinations avoiding combinatorial explosion of reachability. We identified the systems for which we can ensure the correctness of robots interactions. New techniques were presented to verify and analyze combined robots' behavior. The partitioned diagrams allowed us to model advanced interactions between autonomous robots and detect irregularities such as deadlocks, lack of termination etc. The techniques were presented to verify and analyze combined robots' behavior using model checking approach. The described system, Dedan verifier, is still under development. In the near future, timed and probabilistic verification are planned.
Prediction of Success or Failure for Final Examination using Nearest Neighbor Method to the Trend of Weekly Online Testing
Venue: arXiv cs.CY 2018
Year: 2018

Using the trends of estimated abilities in terms of item response theory for online testing, we can predict the success/failure status for the final examination to each student at early stages in courses. In prediction, we applied the newly developed nearest neighbor method for determining the similarity of learning skill in the trends of estimated abilities, resulting a better prediction accuracy for success or failure. This paper shows that the use of the learning analytics incorporating the trends for abilities is effective. ROC curve and recall precision curve are informative to assist the proposed method.
A taxonomy of circular economy indicators
Venue: arXiv cs.CY 2018
Year: 2018

Implementing circular economy (CE) principles is increasingly recommended as a convenient solution to meet the goals of sustainable development. New tools are required to support practitioners, decision-makers and policy-makers towards more CE practices, as well as to monitor the effects of CE adoption. Worldwide, academics, industrialists and politicians all agree on the need to use CE-related measuring instruments to manage this transition at different systemic levels. In this context, a wide range of circularity indicators (C-indicators) has been developed in recent years. Yet, as there is not one single definition of the CE concept, it is of the utmost importance to know what the available indicators measure in order to use them properly. Indeed, through a systematic literature review-considering both academic and grey literature-55 sets of C-indicators, developed by scholars, consulting companies and governmental agencies, have been identified, encompassing different purposes, scopes, and potential usages. Inspired by existing taxonomies of eco-design tools and sustainability indicators, and in line with the CE characteristics, a classification of indicators aiming to assess, improve, monitor and communicate on the CE performance is proposed and discussed. In the developed taxonomy including 10 categories, C-indicators are differentiated regarding criteria such as the levels of CE implementation (e.g. micro, meso, macro), the CE loops (maintain, reuse, remanufacture, recycle), the performance (intrinsic, impacts), the perspective of circularity (actual, potential) they are taking into account, or their degree of transversality (generic, sector-specific). In addition, the database inventorying the 55 sets of C-indicators is linked to an Excel-based query tool to facilitate the selection of appropriate indicators according to the specific user's needs and requirements. This study enriches the literature by giving a first need-driven taxonomy of C-indicators, which is experienced on several use cases. It provides a synthesis and clarification to the emerging and must-needed research theme of C-indicators, and sheds some light on remaining key challenges like their effective uptake by industry. Eventually, limitations, improvement areas, as well as implications of the proposed taxonomy are intently addressed to guide future research on C-indicators and CE implementation.
Performance modelling of access control mechanisms for local and vehicular wireless networks
Venue: arXiv cs.NI 2018
Year: 2018

Carrier sense multiple access collision avoidance (CSMA/CA) is the basic scheme upon which access to the shared medium is regulated in many wireless networks. With CSMA/CA a station willing to start a transmission has first to find the channel free for a given duration otherwise it will go into \emph{backoff}, i.e. refraining for transmitting for a randomly chosen delay. Performance analysis of a wireless network employing CSMA/CA regulation is not an easy task: except for simple network configuration analytical solution of key performance indicators (KPI) cannot be obtained hence one has to resort to formal modelling tools. In this paper we present a performance modelling study targeting different kind of CSMA/CA based wireless networks, namely: the IEEE 802.11 Wireless Local Area Networks (WLANs) and the 802.11p Vehicular Ad Hoc Networks (VANETs), which extends 802.11 with priorities over packets. The modelling framework we introduce allows for considering: i) an arbitrarily large number of stations, ii) different traffic conditions (saturated/non-saturated), iii) different hypothesis concerning the shared channel (ideal/non-ideal). We apply statistical model checking to assess KPIs of different network configurations.
On-Demand Video Dispatch Networks: A Scalable End-to-End Learning Approach
Venue: arXiv cs.NI 2018
Year: 2018

We design a dispatch system to improve the peak service quality of video on demand (VOD). Our system predicts the hot videos during the peak hours of the next day based on the historical requests, and dispatches to the content delivery networks (CDNs) at the previous off-peak time. In order to scale to billions of videos, we build the system with two neural networks, one for video clustering and the other for dispatch policy developing. The clustering network employs autoencoder layers and reduces the video number to a fixed value. The policy network employs fully connected layers and ranks the clustered videos with dispatch probabilities. The two networks are coupled with weight-sharing temporal layers, which analyze the video request sequences with convolutional and recurrent modules. Therefore, the clustering and dispatch tasks are trained in an end-to-end mechanism. The real-world results show that our approach achieves an average prediction accuracy of 17%, compared with 3% from the present baseline method, for the same amount of dispatches.
An open-source sensor platform for analysis of group dynamics
Venue: arXiv cs.DC 2018
Year: 2018

The collaboration of several people in groups is becoming more and more important nowadays. Teamwork is often used for decision-making processes and for solving complex problems. Research in this area focuses on the quantification and analysis of behavior within and between groups. By using wearable electronic devices, such as badges, this quantification can be performed automatically and with high accuracy. The goal of this work is the design and implementation of a new firmware for the badges of the Rhythm project - an open-source project of the Human Dynamics Group of the MIT Media Lab. The firmware is characterized by a modular and extensible architecture and combines different techniques. These include a filesystem, which efficiently stores sequentially generated data based on a virtual memory abstraction, a serialization library, which enables a platform-independent exchange of structured data, and a time synchronization technique, which compensates frequency deviations of the oscillator. In addition, an automated test environment was designed for the verification of individual functional software components of the application. As a result of detailed analysis and special measures, the power consumption could be significantly reduced compared to the previous implementation. Due to the hierarchical and modular structure and the high degree of abstraction, the developed techniques can also be integrated into other projects and platforms.
Adaptive virtual organisms: A compositional model for complex hardware-software binding
Venue: arXiv cs.DC 2018
Year: 2018

The relation between a structure and the function running on that structure is of central interest in many fields, including computer science, biology (organ vs. function), psychology (body vs. mind), architecture (designs vs. functionality), etc. Our paper addresses this question with reference to computer science recent hardware and software advances, particularly in areas as robotics, AI-hardware, self-adaptive systems, IoT, CPS, etc. At the modelling, conceptual level, our main contribution is the introduction of the concept of "virtual organism" (VO), to populate the intermediary level between rigid, slightly reconfigurable, hardware agents and abstract, intelligent, adaptive software agents. A virtual organism has a structure, resembling the hardware capabilities, and it runs low-level functions, implementing the software requirements. The model is compositional in space (allowing the virtual organisms to aggregate into larger organisms) and in time (allowing the virtual organisms to get composed functionalities). Technically, the virtual organisms studied here are in 2D and their structures are described by regular 2D pattens; adding the time dimension, we conclude the VO model is in 3D. By reconfiguration, an organism may change its structure to another structure belonging to the same 2D pattern. We illustrate the VO concept with three increasingly more complex VOs: (1) a tree collector; (2) a feeding cell; and (3) a collection of connected feeding cells. We implemented a simulator for tree collector organisms and the quantitative results show reconfigurable structures are better suited than fixed structures in dynamically changing environments. We briefly show how Agapia - a structured parallel, interactive programming language where dataflow and control flow structures can be freely mixed - may be used for getting quick implementations for VO's simulation.
Application-Specific System Processor for the SHA-1 Hash Algorithm
Venue: arXiv cs.DC 2018
Year: 2018

This work proposes an Application-Specific System Processor (ASSP) hardware for the Secure Hash Algorithm 1 (SHA-1) algorithm. The proposed hardware was implemented in a Field Programmable Gate Array (FPGA) Xilinx Virtex 6 xc6vlx240t-1ff1156. The throughput and the occupied area were analyzed for several implementations in parallel instances of the hash algorithm. The results showed that the hardware proposed for the SHA-1 achieved a throughput of 0.644 Gbps for a single instance and slightly more than 28 Gbps for 48 instances in a single FPGA. Various applications such as password recovery, password validation, and high volume data integrity checking can be performed efficiently and quickly with an ASSP for SHA1.
Using machine learning to create high-efficiency freeform illumination design tools
Venue: arXiv cs.LG 2018
Year: 2018

We present a method for improving the efficiency and user experience of freeform illumination design with machine learning. By utilizing orthogonal polynomials to interface with artificial neural networks, we are able to generalize relationships between freeform surface shapes and design parameters. Then, by training the network to generalize the relationship between high-level design goals and final performance, we were able to transform what is traditionally a difficult and computationally intensive problem into a compact, user friendly form. The potential of the proposed method is demonstrated through the design of uniform square patterns from off-axis positions and rectangular patterns of tuneable aspect ratios and distances from the target.