OpenRaiser/Intern-Atlas huggingface dataset:
Jo: The Smart Journal
Venue: arXiv cs.HC 2019
Year: 2019

We introduce Jo, a mobile application that attempts to improve user's well-being. Jo is a journaling application--users log their important moments via short texts and optionally an attached photo. Unlike a static journal, Jo analyzes these moments and helps users take action towards increased well-being. For example, Jo annotates each moment with a set of values (e.g., family, socialization, mindfulness), thereby giving the user insights about the balance in their lives. In addition, Jo helps the user create reminders that enable them to create additional happy moments. We describe the results of fielding Jo in a study of 39 participants. The results illustrate the promise of a journaling application that provides personalized feedback, and points at further research.
A Study on the Prevalence of Human Values in Software Engineering Publications, 2015-2018
Venue: arXiv cs.SE 2019
Year: 2019

Failure to account for human values in software (e.g., equality and fairness) can result in user dissatisfaction and negative socio-economic impact. Engineering these values in software, however, requires technical and methodological support throughout the development life cycle. This paper investigates to what extent software engineering (SE) research has considered human values. We investigate the prevalence of human values in recent (2015 - 2018) publications at some of the top-tier SE conferences and journals. We classify SE publications, based on their relevance to different values, against a widely used value structure adopted from social sciences. Our results show that: (a) only a small proportion of the publications directly consider values, classified as relevant publications; (b) for the majority of the values, very few or no relevant publications were found; and (c) the prevalence of the relevant publications was higher in SE conferences compared to SE journals. This paper shares these and other insights that motivate research on human values in software engineering.
A Strong Feature Representation for Siamese Network Tracker
Venue: arXiv cs.CV 2019
Year: 2019

Object tracking has important application in assistive technologies for personalized monitoring. Recent trackers choosing AlexNet as their backbone to extract features have gained great success. However, AlexNet is too shallow to form a strong feature representation, the tracker based on the Siamese network have an accuracy gap compared with state-of-the-art algorithms. To solve this problem, this paper proposes a tracker called SiamPF. Firstly, the modified pre-trained VGG16 network is fine-tuned as the backbone. Secondly, an AlexNet-like branch is added after the third convolutional layer and merged with the response map of the backbone network to form a preliminary strong feature representation. And then, a channel attention block is designed to adaptively select the contribution features. Finally, the APCE is modified to process the response map to reduce interference and focus the tracker on the target. Our SiamPF only used ILSVRC2015-VID for training, but it achieved excellent performance on OTB-2013 / OTB-2015 / VOT2015 / VOT2017, while maintaining the real-time performance of 41FPS on the GTX 1080Ti.
Formal verification of trading in financial markets
Venue: arXiv cs.LO 2019
Year: 2019

We introduce a formal framework for analyzing trades in financial markets. An exchange is where multiple buyers and sellers participate to trade. These days, all big exchanges use computer algorithms that implement double sided auctions to match buy and sell requests and these algorithms must abide by certain regulatory guidelines. For example, market regulators enforce that a matching produced by exchanges should be \emph{fair}, \emph{uniform} and \emph{individual rational}. To verify these properties of trades, we first formally define these notions in a theorem prover and then give formal proofs of relevant results on matchings. Finally, we use this framework to verify properties of two important classes of double sided auctions. All the definitions and results presented in this paper are completely formalised in the Coq proof assistant without adding any additional axioms to it.
Global AI Ethics: A Review of the Social Impacts and Ethical Implications of Artificial Intelligence
Venue: arXiv cs.CY 2019
Year: 2019

The ethical implications and social impacts of artificial intelligence have become topics of compelling interest to industry, researchers in academia, and the public. However, current analyses of AI in a global context are biased toward perspectives held in the U.S., and limited by a lack of research, especially outside the U.S. and Western Europe. This article summarizes the key findings of a literature review of recent social science scholarship on the social impacts of AI and related technologies in five global regions. Our team of social science researchers reviewed more than 800 academic journal articles and monographs in over a dozen languages. Our review of the literature suggests that AI is likely to have markedly different social impacts depending on geographical setting. Likewise, perceptions and understandings of AI are likely to be profoundly shaped by local cultural and social context. Recent research in U.S. settings demonstrates that AI-driven technologies have a pattern of entrenching social divides and exacerbating social inequality, particularly among historically-marginalized groups. Our literature review indicates that this pattern exists on a global scale, and suggests that low- and middle-income countries may be more vulnerable to the negative social impacts of AI and less likely to benefit from the attendant gains. We call for rigorous ethnographic research to better understand the social impacts of AI around the world. Global, on-the-ground research is particularly critical to identify AI systems that may amplify social inequality in order to mitigate potential harms. Deeper understanding of the social impacts of AI in diverse social settings is a necessary precursor to the development, implementation, and monitoring of responsible and beneficial AI technologies, and forms the basis for meaningful regulation of these technologies.
Memristive Devices for Computation-In-Memory
Venue: arXiv cs.ET 2019
Year: 2019

CMOS technology and its continuous scaling have made electronics and computers accessible and affordable for almost everyone on the globe; in addition, they have enabled the solutions of a wide range of societal problems and applications. Today, however, both the technology and the computer architectures are facing severe challenges/walls making them incapable of providing the demanded computing power with tight constraints. This motivates the need for the exploration of novel architectures based on new device technologies; not only to sustain the financial benefit of technology scaling, but also to develop solutions for extremely demanding emerging applications. This paper presents two computation-in-memory based accelerators making use of emerging memristive devices; they are Memristive Vector Processor and RRAM Automata Processor. The preliminary results of these two accelerators show significant improvement in terms of latency, energy and area as compared to today's architectures and design.
Asynchronous Snapshots of Actor Systems for Latency-Sensitive Applications
Venue: arXiv cs.PL 2019
Year: 2019

The actor model is popular for many types of server applications. Efficient snapshotting of applications is crucial in the deployment of pre-initialized applications or moving running applications to different machines, e.g for debugging purposes. A key issue is that snapshotting blocks all other operations. In modern latency-sensitive applications, stopping the application to persist its state needs to be avoided, because users may not tolerate the increased request latency. In order to minimize the impact of snapshotting on request latency, our approach persists the application's state asynchronously by capturing partial heaps, completing snapshots step by step. Additionally, our solution is transparent and supports arbitrary object graphs. We prototyped our snapshotting approach on top of the Truffle/Graal platform and evaluated it with the Savina benchmarks and the Acme Air microservice application. When performing a snapshot every thousand Acme Air requests, the number of slow requests ( 0.007% of all requests) with latency above 100ms increases by 5.43%. Our Savina microbenchmark results detail how different utilization patterns impact snapshotting cost. To the best of our knowledge, this is the first system that enables asynchronous snapshotting of actor applications, i.e. without stop-the-world synchronization, and thereby minimizes the impact on latency. We thus believe it enables new deployment and debugging options for actor systems.
Channel Impulse Response-based Physical Layer Authentication in a Diffusion-based Molecular Communication System
Venue: arXiv cs.CR 2019
Year: 2019

Consider impersonation attack by an active malicious nano node (Eve) on a diffusion based molecular communication (DbMC) system---Eve transmits during the idle slots to deceive the nano receiver (Bob) that she is indeed the legitimate nano transmitter (Alice). To this end, this work exploits the 3-dimensional (3D) channel impulse response (CIR) with $L$ taps as device fingerprint for authentication of the nano transmitter during each slot. Specifically, Bob utilizes the Alice's CIR as ground truth to construct a binary hypothesis test to systematically accept/reject the data received in each slot. Simulation results highlight the great challenge posed by impersonation attack--i.e., it is not possible to simultaneously minimize the two error probabilities. In other words, one needs to tolerate on one error type in order to minimize the other error type.
Robust and fast generation of top and side grasps for unknown objects
Venue: arXiv cs.RO 2019
Year: 2019

In this work, we present a geometry-based grasping algorithm that is capable of efficiently generating both top and side grasps for unknown objects, using a single view RGB-D camera, and of selecting the most promising one. We demonstrate the effectiveness of our approach on a picking scenario on a real robot platform. Our approach has shown to be more reliable than another recent geometry-based method considered as baseline [7] in terms of grasp stability, by increasing the successful grasp attempts by a factor of six.
Metric Dimension Parameterized by Treewidth
Venue: arXiv cs.CC 2019
Year: 2019

A resolving set $S$ of a graph $G$ is a subset of its vertices such that no two vertices of $G$ have the same distance vector to $S$. The Metric Dimension problem asks for a resolving set of minimum size, and in its decision form, a resolving set of size at most some specified integer. This problem is NP-complete, and remains so in very restricted classes of graphs. It is also W[2]-complete with respect to the size of the solution. Metric Dimension has proven elusive on graphs of bounded treewidth. On the algorithmic side, a polytime algorithm is known for trees, and even for outerplanar graphs, but the general case of treewidth at most two is open. On the complexity side, no parameterized hardness is known. This has led several papers on the topic to ask for the parameterized complexity of Metric Dimension with respect to treewidth. We provide a first answer to the question. We show that Metric Dimension parameterized by the treewidth of the input graph is W[1]-hard. More refinedly we prove that, unless the Exponential Time Hypothesis fails, there is no algorithm solving Metric Dimension in time $f(\text{pw})n^{o(\text{pw})}$ on $n$-vertex graphs of constant degree, with $\text{pw}$ the pathwidth of the input graph, and $f$ any computable function. This is in stark contrast with an FPT algorithm of Belmonte et al. [SIAM J. Discrete Math. '17] with respect to the combined parameter $\text{tl}+Δ$, where $\text{tl}$ is the tree-length and $Δ$ the maximum-degree of the input graph.
Understanding Neural Machine Translation by Simplification: The Case of Encoder-free Models
Venue: arXiv cs.CL 2019
Year: 2019

In this paper, we try to understand neural machine translation (NMT) via simplifying NMT architectures and training encoder-free NMT models. In an encoder-free model, the sums of word embeddings and positional embeddings represent the source. The decoder is a standard Transformer or recurrent neural network that directly attends to embeddings via attention mechanisms. Experimental results show (1) that the attention mechanism in encoder-free models acts as a strong feature extractor, (2) that the word embeddings in encoder-free models are competitive to those in conventional models, (3) that non-contextualized source representations lead to a big performance drop, and (4) that encoder-free models have different effects on alignment quality for German-English and Chinese-English.
Multi-Task Regression-based Learning for Autonomous Unmanned Aerial Vehicle Flight Control within Unstructured Outdoor Environments
Venue: arXiv cs.RO 2019
Year: 2019

Increased growth in the global Unmanned Aerial Vehicles (UAV) (drone) industry has expanded possibilities for fully autonomous UAV applications. A particular application which has in part motivated this research is the use of UAV in wide area search and surveillance operations in unstructured outdoor environments. The critical issue with such environments is the lack of structured features that could aid in autonomous flight, such as road lines or paths. In this paper, we propose an End-to-End Multi-Task Regression-based Learning approach capable of defining flight commands for navigation and exploration under the forest canopy, regardless of the presence of trails or additional sensors (i.e. GPS). Training and testing are performed using a software in the loop pipeline which allows for a detailed evaluation against state-of-the-art pose estimation techniques. Our extensive experiments demonstrate that our approach excels in performing dense exploration within the required search perimeter, is capable of covering wider search regions, generalises to previously unseen and unexplored environments and outperforms contemporary state-of-the-art techniques.
SentiMATE: Learning to play Chess through Natural Language Processing
Venue: arXiv cs.LG 2019
Year: 2019

We present SentiMATE, a novel end-to-end Deep Learning model for Chess, employing Natural Language Processing that aims to learn an effective evaluation function assessing move quality. This function is pre-trained on the sentiment of commentary associated with the training moves and is used to guide and optimize the agent's game-playing decision making. The contributions of this research are three-fold: we build and put forward both a classifier which extracts commentary describing the quality of Chess moves in vast commentary datasets, and a Sentiment Analysis model trained on Chess commentary to accurately predict the quality of said moves, to then use those predictions to evaluate the optimal next move of a Chess agent. Both classifiers achieve over 90 % classification accuracy. Lastly, we present a Chess engine, SentiMATE, which evaluates Chess moves based on a pre-trained sentiment evaluation function. Our results exhibit strong evidence to support our initial hypothesis - "Can Natural Language Processing be used to train a novel and sample efficient evaluation function in Chess Engines?" - as we integrate our evaluation function into modern Chess engines and play against agents with traditional Chess move evaluation functions, beating both random agents and a DeepChess implementation at a level-one search depth - representing the number of moves a traditional Chess agent (employing the alpha-beta search algorithm) looks ahead in order to evaluate a given chess state.
Liger: Combining Interaction Paradigms for Visual Analysis
Venue: arXiv cs.HC 2019
Year: 2019

Visualization tools usually leverage a single interaction paradigm (e.g., manual view specification, visualization by demonstration, etc.), which fosters the process of visualization construction. A large body of work has investigated the effectiveness of individual interaction paradigms, building an understanding of advantages and disadvantages of each in isolation. However, how can we leverage the benefits of multiple interaction paradigms by combining them into a single tool? We currently lack a holistic view of how interaction paradigms that use the same input modality (e.g., mouse) can be combined into a single tool and how people use such tools. To investigate opportunities and challenges in combining paradigms, we first created a multi-paradigm prototype (Liger) that combines two mouse-based interaction paradigms (manual view specification and visualization by demonstration) in a unified tool. We then conducted an exploratory study with Liger, providing initial evidence that people 1) use both paradigms interchangeably, 2) seamlessly switch between paradigms based on the operation at hand, and 3) choose to successfully complete a single operation using a combination of both paradigms.
Cluster deletion revisited
Venue: arXiv cs.DS 2019
Year: 2019

In the Cluster Deletion problem the input is a graph $G$ and an integer $k$, and the goal is to decide whether there is a set of at most $k$ edges whose removal from $G$ results a graph in which every connected component is a clique. In this paper we give an algorithm for Cluster Deletion whose running time is $O^*(1.404^k)$.
An Information Extraction and Knowledge Graph Platform for Accelerating Biochemical Discoveries
Venue: arXiv cs.IR 2019
Year: 2019

Information extraction and data mining in biochemical literature is a daunting task that demands resource-intensive computation and appropriate means to scale knowledge ingestion. Being able to leverage this immense source of technical information helps to drastically reduce costs and time to solution in multiple application fields from food safety to pharmaceutics. We present a scalable document ingestion system that integrates data from databases and publications (in PDF format) in a biochemistry knowledge graph (BCKG). The BCKG is a comprehensive source of knowledge that can be queried to retrieve known biochemical facts and to generate novel insights. After describing the knowledge ingestion framework, we showcase an application of our system in the field of carbohydrate enzymes. The BCKG represents a way to scale knowledge ingestion and automatically exploit prior knowledge to accelerate discovery in biochemical sciences.
Towards Efficient BLE Mesh: Design of an Autonomous Network Joining Algorithm
Venue: arXiv cs.NI 2019
Year: 2019

The Internet of Things (IoT) opens the doors to a digital revolution, but requires a robust protocol to wirelessly interconnect a large number of devices. Bluetooth Low Energy (BLE) Mesh emerges as a suitable candidate, solving the range limitations of original BLE. Nevertheless, the main limitation is shifted to the scatternet architecture: how the considerable number of end systems are interconnected to ensure the network efficiency and scalability. As of today, some timid solutions have emerged, though the most relevant parameters for the best parent selection in BLE mesh network joining procedures have not been identified yet. In this work, we perform a thorough exploration of different device and environmental parameters in a BLE mesh testbed to analyze their impact in the overall network figures of merit, such as end-to-end delay and packet delivery ratio (PDR). According to the inferred relevance, the second part of the work implements and measures the proposed parent selection algorithm. The implementation is based on the open source Fruitymesh protocol and is used as a baseline. The results reflect an enhancement in the network scalability and fairness, accomplishing a delay and PDR improvement of 26% and 10% respectively, and the avoidance of saturated branches of 24%.
Towards Learning Universal, Regional, and Local Hydrological Behaviors via Machine-Learning Applied to Large-Sample Datasets
Venue: arXiv cs.LG 2019
Year: 2019

Regional rainfall-runoff modeling is an old but still mostly out-standing problem in Hydrological Sciences. The problem currently is that traditional hydrological models degrade significantly in performance when calibrated for multiple basins together instead of for a single basin alone. In this paper, we propose a novel, data-driven approach using Long Short-Term Memory networks (LSTMs), and demonstrate that under a 'big data' paradigm, this is not necessarily the case. By training a single LSTM model on 531 basins from the CAMELS data set using meteorological time series data and static catchment attributes, we were able to significantly improve performance compared to a set of several different hydrological benchmark models. Our proposed approach not only significantly outperforms hydrological models that were calibrated regionally but also achieves better performance than hydrological models that were calibrated for each basin individually. Furthermore, we propose an adaption to the standard LSTM architecture, which we call an Entity-Aware-LSTM (EA-LSTM), that allows for learning, and embedding as a feature layer in a deep learning model, catchment similarities. We show that this learned catchment similarity corresponds well with what we would expect from prior hydrological understanding.
Shaped On-Off Keying Using Polar Codes
Venue: arXiv cs.IT 2019
Year: 2019

The probabilistic shaping scheme from Honda and Yamamoto (2013) for polar codes is used to enable power-efficient signaling for on-off keying (OOK). As OOK has a non-symmetric optimal input distribution, shaping approaches that are based on the concatenation of a distribution matcher followed by systematic encoding do not result in optimal signaling. Instead, these approaches represent a time sharing scheme where only a fraction of the codeword symbols is shaped. The proposed scheme uses a polar code for joint distribution matching and forward error correction which enables asymptotically optimal signaling. Numerical simulations show a gain of 1.8 dB compared to uniform transmission at a spectral efficiency of 0.25 bits/channel use for a blocklength of 65,536 bits.
Are Parametric Markov Chains Monotonic?
Venue: arXiv cs.LO 2019
Year: 2019

This paper presents a simple algorithm to check whether reachability probabilities in parametric Markov chains are monotonic in (some of) the parameters. The idea is to construct - only using the graph structure of the Markov chain and local transition probabilities - a pre-order on the states. Our algorithm cheaply checks a sufficient condition for monotonicity. Experiments show that monotonicity in several benchmarks is automatically detected, and monotonicity can speed up parameter synthesis up to orders of magnitude faster than a symbolic baseline.