OpenRaiser/Intern-Atlas huggingface dataset:
Few-shot brain segmentation from weakly labeled data with deep heteroscedastic multi-task networks
Venue: arXiv cs.LG 2019
Year: 2019

In applications of supervised learning applied to medical image segmentation, the need for large amounts of labeled data typically goes unquestioned. In particular, in the case of brain anatomy segmentation, hundreds or thousands of weakly-labeled volumes are often used as training data. In this paper, we first observe that for many brain structures, a small number of training examples, (n=9), weakly labeled using Freesurfer 6.0, plus simple data augmentation, suffice as training data to achieve high performance, achieving an overall mean Dice coefficient of $0.84 \pm 0.12$ compared to Freesurfer over 28 brain structures in T1-weighted images of $\approx 4000$ 9-10 year-olds from the Adolescent Brain Cognitive Development study. We then examine two varieties of heteroscedastic network as a method for improving classification results. An existing proposal by Kendall and Gal, which uses Monte-Carlo inference to learn to predict the variance of each prediction, yields an overall mean Dice of $0.85 \pm 0.14$ and showed statistically significant improvements over 25 brain structures. Meanwhile a novel heteroscedastic network which directly learns the probability that an example has been mislabeled yielded an overall mean Dice of $0.87 \pm 0.11$ and showed statistically significant improvements over all but one of the brain structures considered. The loss function associated to this network can be interpreted as performing a form of learned label smoothing, where labels are only smoothed where they are judged to be uncertain.
On Polar Coding for Binary Dirty Paper
Venue: arXiv cs.IT 2019
Year: 2019

The problem of communication over binary dirty paper (DP) using nested polar codes is considered. An improved scheme, focusing on low delay, short to moderate blocklength communication is proposed. Successive cancellation list (SCL) decoding with properly defined CRC is used for channel coding, and SCL encoding without CRC is used for source coding. The performance is compared to the best achievable rate of any coding scheme for binary DP using nested codes. A well known problem with nested polar codes for binary DP is the existence of frozen channel code bits that are not frozen in the source code. These bits need to be retransmitted in a second phase of the scheme, thus reducing transmission rate. We observe that the number of these bits is typically either zero or a small number, and provide an improved analysis, compared to that presented in the literature, on the size of this set and on its scaling with respect to the blocklength when the power constraint parameter is sufficiently large or the channel crossover probability sufficiently small.
Group Maturity and Agility, Are They Connected? - A Survey Study
Venue: arXiv cs.SE 2019
Year: 2019

The focus on psychology has increased within software engineering due to the project management innovation "agile development processes". The agile methods do not explicitly consider group development aspects; they simply assume what is described in group psychology as mature groups. This study was conducted with 45 employees and their twelve managers (N=57) from two SAP customers in the US that were working with agile methods, and the data were collected via an online survey. The selected Agility measurement was correlated to a Group Development measurement and showed significant convergent validity, i.e., a more mature team is also a more agile team. This means that the agile methods probably would benefit from taking group development into account when its practices are being introduced.
Composition of Sentence Embeddings:Lessons from Statistical Relational Learning
Venue: arXiv cs.CL 2019
Year: 2019

Various NLP problems -- such as the prediction of sentence similarity, entailment, and discourse relations -- are all instances of the same general task: the modeling of semantic relations between a pair of textual elements. A popular model for such problems is to embed sentences into fixed size vectors, and use composition functions (e.g. concatenation or sum) of those vectors as features for the prediction. At the same time, composition of embeddings has been a main focus within the field of Statistical Relational Learning (SRL) whose goal is to predict relations between entities (typically from knowledge base triples). In this article, we show that previous work on relation prediction between texts implicitly uses compositions from baseline SRL models. We show that such compositions are not expressive enough for several tasks (e.g. natural language inference). We build on recent SRL models to address textual relational problems, showing that they are more expressive, and can alleviate issues from simpler compositions. The resulting models significantly improve the state of the art in both transferable sentence representation learning and relation prediction.
Group development and group maturity when building agile teams: A qualitative and quantitative investigation at eight large companies
Venue: arXiv cs.SE 2019
Year: 2019

The agile approach to projects focuses more on close-knit teams than traditional waterfall projects, which means that aspects of group maturity become even more important. This psychological aspect is not much researched in connection to the building of an "agile team." The purpose of this study is to investigate how building agile teams is connected to a group development model taken from social psychology. We conducted ten semi-structured interviews with coaches, Scrum Masters, and managers responsible for the agile process from seven different companies, and collected survey data from 66 group-members from four companies (a total of eight different companies). The survey included an agile measurement tool and the one part of the Group Development Questionnaire. The results show that the practitioners define group developmental aspects as key factors to a successful agile transition. Also, the quantitative measurement of agility was significantly correlated to the group maturity measurement. We conclude that adding these psychological aspects to the description of the "agile team" could increase the understanding of agility and partly help define an "agile team." We propose that future work should develop specific guidelines for how software development teams at different maturity levels might adopt agile principles and practices differently.
Is it Possible to Disregard Obsolete Requirements? - An Initial Experiment on a Potentially New Bias in Software Effort Estimation
Venue: arXiv cs.SE 2019
Year: 2019

Effort estimation is a complex area in decision-making, and is influenced by a diversity of factors that could increase the estimation error. The effects on effort estimation accuracy of having obsolete requirements in specifications have not yet been studied. This study aims at filling that gap. A total of 150 students were asked to provide effort estimates for different amounts of requirements, and one group was explicitly told to disregard some of the given requirements. The results show that even the extra text instructing participants to exclude requirements in the estimation task, had the subjects give higher estimates. The effect of having obsolete requirements in requirements specifications and backlogs in software effort estimation is not taken into account enough today, and this study provides empirical evidence that it possibly should. We also suggest different psychological explanations to the found effect.
Numerical Simulation of Metal Machining Process with Eulerian and Total Lagrangian SPH
Venue: arXiv cs.CE 2019
Year: 2019

This paper presents numerical simulations of metal machining processes with Eulerian and Total Lagrangian Smoothed Particle Hydrodynamics (SPH). Being a mesh-free method, SPH can conveniently handle large deformation and material separation. However, the Eulerian SPH (ESPH) in which the kernel functions are computed based on the current particle positions suffers from the tensile instability. The Total Lagrangian SPH (TLSPH) is free of this instability as the kernel functions are calculated in the reference configurations. In this work, the metals are modelled using the Johnson-Cook constitutive model, which can capture strain hardening and thermal softening in metals. The processing/cutting tools are modelled as rigid bodies, while the metal-tool contact forces are considered using the standard SPH interaction and the particle-particle pinball contact in ESPH and TLSPH, respectively. The two methods are employed to model several cases with impact, pressing, and cutting; the results are compared with reference experimental and numerical results. It is found that both the two SPH methods can capture the salient phenomena in metal processing, e.g. strain localisation, large deformation, and material separation. However, the TLSPH approach provides a better simulation of strain localisation and chip morphology. This work shows that the TLSPH method has the potential to model the metal machining processes efficiently without any numerical instabilities.
Beyond k-induction: Learning from Counterexamples to Bidirectionally Explore the State Space
Venue: arXiv cs.LO 2019
Year: 2019

We describe and evaluate a novel k-induction proof rule called bidirectional k-induction (bkind), which substantially improves the k-induction bug-finding capabilities. Particularly, bkind exploits the counterexamples generated by the over-approximation step to derive new properties and feed them back to the bounded model checking procedure. We also combine an interval invariant generator and bkind to significantly improve the number of correct verification results. Experimental results show that bkind can considerably reduce the verification time compared to the naive k-induction proof rule, since it only requires half the number of steps to find a given safety property violation in an unsafe program. The bkind algorithm outperforms 2LS, another state-of-the-art k-induction verifier, and produces more than twice correct proofs and about 35% more correct alarms than when analysing a large set of public available benchmarks.
Deep Multi-scale Discriminative Networks for Double JPEG Compression Forensics
Venue: arXiv cs.CV 2019
Year: 2019

As JPEG is the most widely used image format, the importance of tampering detection for JPEG images in blind forensics is self-evident. In this area, extracting effective statistical characteristics from a JPEG image for classification remains a challenge. Effective features are designed manually in traditional methods, suggesting that extensive labor-consuming research and derivation is required. In this paper, we propose a novel image tampering detection method based on deep multi-scale discriminative networks (MSD-Nets). The multi-scale module is designed to automatically extract multiple features from the discrete cosine transform (DCT) coefficient histograms of the JPEG image. This module can capture the characteristic information in different scale spaces. In addition, a discriminative module is also utilized to improve the detection effect of the networks in those difficult situations when the first compression quality (QF1) is higher than the second one (QF2). A special network in this module is designed to distinguish the small statistical difference between authentic and tampered regions in these cases. Finally, a probability map can be obtained and the specific tampering area is located using the last classification results. Extensive experiments demonstrate the superiority of our proposed method in both quantitative and qualitative metrics when compared with state-of-the-art approaches.
Energy Efficient Software Matching in Vehicular Fog
Venue: arXiv cs.NI 2019
Year: 2019

Along with the development of Internet of Things (IoT) and the rise of fog computing, more new joint technologies have been proposed. Vehicular Ad-hoc Networks (VANET) are one of the emergent technologies that come with a very promising role, where the spare processing capabilities of vehicles can be exploited. In this paper, we propose a fog architecture to provide services for end users based on a cluster of static vehicles in a parking lot referred to as a vehicular fog. The proposed vehicular fog architecture is connected to the central data center through an optical infrastructure. As the processing requests from users require specific software packages that may not be available in all vehicles, we study the software matching problem of task assignments in vehicular fog. The goal of this paper is to examine the effect of software packages variety in vehicles on the assignment decision and the overall power consumption. A mixed integer linear programming (MILP) model was utilized to optimize the power consumption of the overall architecture, considering different numbers of software packages in the vehicular fog. The results reveal a power saving of up to 27% when vehicles are uploaded with four or more different software packages out of a library of ten software packages in this example.
Determining input variable ranges in Industry 4.0: A heuristic for estimating the domain of a real-valued function or trained regression model given an output range
Venue: arXiv cs.LG 2019
Year: 2019

Industrial process control systems try to keep an output variable within a given tolerance around a target value. PID control systems have been widely used in industry to control input variables in order to reach this goal. However, this kind of Transfer Function based approach cannot be extended to complex processes where input data might be non-numeric, high dimensional, sparse, etc. In such cases, there is still a need for determining the subspace of input data that produces an output within a given range. This paper presents a non-stochastic heuristic to determine input values for a mathematical function or trained regression model given an output range. The proposed method creates a synthetic training data set of input combinations with a class label that indicates whether the output is within the given target range or not. Then, a decision tree classifier is used to determine the subspace of input data of interest. This method is more general than a traditional controller as the target range for the output does not have to be centered around a reference value and it can be applied given a regression model of the output variable, which may have categorical variables as inputs and may be high dimensional, sparse... The proposed heuristic is validated with a proof of concept on a real use case where the quality of a lamination factory is established to identify the suitable subspace of production variable values.
Frustratingly Poor Performance of Reading Comprehension Models on Non-adversarial Examples
Venue: arXiv cs.CL 2019
Year: 2019

When humans learn to perform a difficult task (say, reading comprehension (RC) over longer passages), it is typically the case that their performance improves significantly on an easier version of this task (say, RC over shorter passages). Ideally, we would want an intelligent agent to also exhibit such a behavior. However, on experimenting with state of the art RC models using the standard RACE dataset, we observe that this is not true. Specifically, we see counter-intuitive results wherein even when we show frustratingly easy examples to the model at test time, there is hardly any improvement in its performance. We refer to this as non-adversarial evaluation as opposed to adversarial evaluation. Such non-adversarial examples allow us to assess the utility of specialized neural components. For example, we show that even for easy examples where the answer is clearly embedded in the passage, the neural components designed for paying attention to relevant portions of the passage fail to serve their intended purpose. We believe that the non-adversarial dataset created as a part of this work would complement the research on adversarial evaluation and give a more realistic assessment of the ability of RC models. All the datasets and codes developed as a part of this work will be made publicly available.
On Topological and Metrical Properties of Stabilizing Feedback Gains: the MIMO Case
Venue: arXiv cs.SY 2019
Year: 2019

In this paper, we discuss various topological and metrical aspects of the set of stabilizing static feedback gains for multiple-input-multiple-output (MIMO) linear-time-invariant (LTI) systems, in both continuous and discrete-time. Recently, connectivity properties of this set (for continuous time) have been reported in the literature, along with a discussion on how this connectivity is affected by restricting the feedback gain to linear subspaces. We show that analogous to the continuous-time case, one can construct instances where the set of stabilizing feedback gains for discrete time LTI systems has exponentially many connected components.
20 Years of DDoS: a Call to Action
Venue: arXiv cs.NI 2019
Year: 2019

Botnet Distributed Denial of Service (DDoS) attacks are now 20 years old; what has changed in that time? Their disruptive presence, their volume, distribution across the globe, and the relative ease of launching them have all been trending in favor of attackers. Our increases in network capacity and our architectural design principles are making our online world richer, but are favoring attackers at least as much as Internet services. The DDoS mitigation techniques have been evolving but they are losing ground to the increasing sophistication and diversification of the attacks that have moved from the network to the application level, and we are operationally falling behind attackers. It is time to ask fundamental questions: are there core design issues in our network architecture that fundamentally enable DDoS attacks? How can our network infrastructure be enhanced to address the principles that enable the DDoS problem? How can we incentivize the development and deployment of the necessary changes? In this article, we want to sound an alarm and issue a call to action to the research community. We propose that basic research and principled analyses are badly needed, because the status quo does not paint a pretty picture for the future.
Compact Error-Resilient Self-Assembly of Recursively Defined Patterns
Venue: arXiv cs.DS 2019
Year: 2019

A limitation to molecular implementations of tile-based self-assembly systems is the high rate of mismatch errors which has been observed to be between 1% and 10%. Controlling the physical conditions of the system to reduce this intrinsic error rate $ε$ prohibitively slows the growth rate of the system. This has motivated the development of techniques to redundantly encode information in the tiles of a system in such a way that the rate of mismatch errors in the final assembly is reduced even without a reduction in $ε$. Winfree and Bekbolatov, and Chen and Goel, introduced such error-resilient systems that reduce the mismatch error rate to $ε^k$ by replacing each tile in an error-prone system with a $k \times k$ block of tiles in the error-resilient system, but this increases the number of tile types used by a factor of $k^2$, and the scale of the pattern produced by a factor of $k$. Reif, Sahu and Yin, and Sahu and Reif, introduced compact error-resilient systems for the self-assembly of Boolean arrays that reduce the mismatch error rate to $ε^2$ without increasing the scale of the pattern produced. In this paper, we give a technique to design compact error-resilient systems for the self-assembly of the recursively defined patterns introduced by Kautz and Lathrop. We show that our compact error-resilient systems reduce the mismatch error rate to $ε^2$ by using the independent error model introduced by Sahu and Reif. Surprisingly, our error-resilient systems use the same number of tile types as the error-prone system from which they are constructed.
Proving tree algorithms for succinct data structures
Venue: arXiv cs.PL 2019
Year: 2019

Succinct data structures give space-efficient representations of large amounts of data without sacrificing performance. They rely one cleverly designed data representations and algorithms. We present here the formalization in Coq/SSReflect of two different tree-based succinct representations and their accompanying algorithms. One is the Level-Order Unary Degree Sequence, which encodes the structure of a tree in breadth-first order as a sequence of bits, where access operations can be defined in terms of Rank and Select, which work in constant time for static bit sequences. The other represents dynamic bit sequences as binary balanced trees, where Rank and Select present a low logarithmic overhead compared to their static versions, and with efficient insertion and deletion. The two can be stacked to provide a dynamic representation of dictionaries for instance. While both representations are well-known, we believe this to be their first formalization and a needed step towards provably-safe implementations of big data.
Nutty-based Robot Animation -- Principles and Practices
Venue: arXiv cs.RO 2019
Year: 2019

Robot animation is a new form of character animation that extends the traditional process by allowing the animated motion to become more interactive and adaptable during interaction with users in real-world settings. This paper reviews how this new type of character animation has evolved and been shaped from character animation principles and practices. We outline some new paradigms that aim at allowing character animators to become robot animators, and to properly take part in the development of social robots. One such paradigm consists of the 12 principles of robot animation, which describes general concepts that both animators and robot developers should consider in order to properly understand each other. We also introduce the concept of Kinematronics, for specifying the controllable and programmable expressive abilities of robots, and the Nutty Workflow and Pipeline. The Nutty Pipeline introduces the concept of the Programmable Robot Animation Engine, which allows to generate, compose and blend various types of animation sources into a final, interaction-enabled motion that can be rendered on robots in real-time during real-world interactions. The Nutty Motion Filter is described and exemplified as a technique that allows an open-loop motion controller to apply physical limits to the motion while still allowing to tweak the shape and expressivity of the resulting motion. Additionally, we describe some types of tools that can be developed and integrated into Nutty-based workflows and pipelines, which allow animation artists to perform an integral part of the expressive behaviour development within social robots, and thus to evolve from standard (3D) character animators, towards a full-stack type of robot animators.
Signed Network Formation Games and Clustering Balance
Venue: arXiv cs.SI 2019
Year: 2019

We propose a signed network formation game, in which pairs of individuals strategically change the signs of the edges in a complete network. These individuals are members of a social network who strategically reduce cognitive dissonances by changing their interpersonal appraisals. We characterize the best-response dynamics for this game and prove that its implementation \pc{can} dynamically drive the network to a sociologically meaningful sign configuration called clustering balance. In this configuration, agents in the social network form one or more clusters that have positive relationships among their members but negative relationships among members of other clusters. In the past, various researchers in the fields of psycho-sociology, political science, and physics have looked at models that explain the generation of up to two clusters. Our work contributes to these fields by proposing a simple model that generates a broader class of signed networks.