OpenRaiser/Intern-Atlas huggingface dataset:
Personalized Chatbot Trustworthiness Ratings
Venue: arXiv cs.SI 2020
Year: 2020

Conversation agents, commonly referred to as chatbots, are increasingly deployed in many domains to allow people to have a natural interaction while trying to solve a specific problem. Given their widespread use, it is important to provide their users with methods and tools to increase users awareness of various properties of the chatbots, including non-functional properties that users may consider important in order to trust a specific chatbot. For example, users may want to use chatbots that are not biased, that do not use abusive language, that do not leak information to other users, and that respond in a style which is appropriate for the user's cognitive level. In this paper, we address the setting where a chatbot cannot be modified, its training data cannot be accessed, and yet a neutral party wants to assess and communicate its trustworthiness to a user, tailored to the user's priorities over the various trust issues. Such a rating can help users choose among alternative chatbots, developers test their systems, business leaders price their offering, and regulators set policies. We envision a personalized rating methodology for chatbots that relies on separate rating modules for each issue, and users' detected priority orderings among the relevant trust issues, to generate an aggregate personalized rating for the trustworthiness of a chatbot. The method is independent of the specific trust issues and is parametric to the aggregation procedure, thereby allowing for seamless generalization. We illustrate its general use, integrate it with a live chatbot, and evaluate it on four dialog datasets and representative user profiles, validated with user surveys.
Classifying Suspicious Content in Tor Darknet
Venue: arXiv cs.CV 2020
Year: 2020

One of the tasks of law enforcement agencies is to find evidence of criminal activity in the Darknet. However, visiting thousands of domains to locate visual information containing illegal acts manually requires a considerable amount of time and resources. Furthermore, the background of the images can pose a challenge when performing classification. To solve this problem, in this paper, we explore the automatic classification Tor Darknet images using Semantic Attention Keypoint Filtering, a strategy that filters non-significant features at a pixel level that do not belong to the object of interest, by combining saliency maps with Bag of Visual Words (BoVW). We evaluated SAKF on a custom Tor image dataset against CNN features: MobileNet v1 and Resnet50, and BoVW using dense SIFT descriptors, achieving a result of 87.98% accuracy and outperforming all other approaches.
The K-Centre Problem for Necklaces
Venue: arXiv cs.DS 2020
Year: 2020

In graph theory, the objective of the k-centre problem is to find a set of $k$ vertices for which the largest distance of any vertex to its closest vertex in the $k$-set is minimised. In this paper, we introduce the $k$-centre problem for sets of necklaces, i.e. the equivalence classes of words under the cyclic shift. This can be seen as the k-centre problem on the complete weighted graph where every necklace is represented by a vertex, and each edge has a weight given by the overlap distance between any pair of necklaces. Similar to the graph case, the goal is to choose $k$ necklaces such that the distance from any word in the language and its nearest centre is minimised. However, in a case of k-centre problem for languages the size of associated graph maybe exponential in relation to the description of the language, i.e., the length of the words l and the size of the alphabet q. We derive several approximation algorithms for the $k$-centre problem on necklaces, with logarithmic approximation factor in the context of l and k, and within a constant factor for a more restricted case.
The Effectiveness of Discretization in Forecasting: An Empirical Study on Neural Time Series Models
Venue: arXiv cs.LG 2020
Year: 2020

Time series modeling techniques based on deep learning have seen many advancements in recent years, especially in data-abundant settings and with the central aim of learning global models that can extract patterns across multiple time series. While the crucial importance of appropriate data pre-processing and scaling has often been noted in prior work, most studies focus on improving model architectures. In this paper we empirically investigate the effect of data input and output transformations on the predictive performance of several neural forecasting architectures. In particular, we investigate the effectiveness of several forms of data binning, i.e. converting real-valued time series into categorical ones, when combined with feed-forward, recurrent neural networks, and convolution-based sequence models. In many non-forecasting applications where these models have been very successful, the model inputs and outputs are categorical (e.g. words from a fixed vocabulary in natural language processing applications or quantized pixel color intensities in computer vision). For forecasting applications, where the time series are typically real-valued, various ad-hoc data transformations have been proposed, but have not been systematically compared. To remedy this, we evaluate the forecasting accuracy of instances of the aforementioned model classes when combined with different types of data scaling and binning. We find that binning almost always improves performance (compared to using normalized real-valued inputs), but that the particular type of binning chosen is of lesser importance.
Multi-head Watson-Crick quantum finite automata
Venue: arXiv cs.FL 2020
Year: 2020

Watson-Crick quantum finite automata were introduced by Ganguly et.al. by combining properties of DNA and Quantum automata. In this paper we introduce a multi-head version of the above automaton. We further show that the multi-head variant is computationally more powerful than one-way multi-head reversible finite automata. In fact we also show that the multi-head variant accepts a language which is not accepted by any one-way multi-head deterministic finite automata.
Rational Consensus
Venue: arXiv cs.DC 2020
Year: 2020

We provide a game-theoretic analysis of consensus, assuming that processes are controlled by rational agents and may fail by crashing. We consider agents that \emph{care only about consensus}: that is, (a) an agent's utility depends only on the consensus value achieved (and not, for example, on the number of messages the agent sends) and (b) agents strictly prefer reaching consensus to not reaching consensus. We show that, under these assumptions, there is no \emph{ex post Nash Equilibrium}, even with only one failure. Roughly speaking, this means that there must always exist a \emph{failure pattern} (a description of who fails, when they fail, and which agents they do not send messages to in the round that they fail) and initial preferences for which an agent can gain by deviating. On the other hand, if we assume that there is a distribution $π$ on the failure patterns and initial preferences, then under minimal assumptions on $π$, there is a Nash equilibrium that tolerates $f$ failures (i.e., $π$ puts probability 1 on there being at most $f$ failures) if $f+1 < n$ (where $n$ is the total number of agents). Moreover, we show that a slight extension of the Nash equilibrium strategy is also a \emph{sequential} equilibrium (under the same assumptions about the distribution $π$).
Discriminative Dictionary Design for Action Classification in Still Images and Videos
Venue: arXiv cs.CV 2020
Year: 2020

In this paper, we address the problem of action recognition from still images and videos. Traditional local features such as SIFT, STIP etc. invariably pose two potential problems: 1) they are not evenly distributed in different entities of a given category and 2) many of such features are not exclusive of the visual concept the entities represent. In order to generate a dictionary taking the aforementioned issues into account, we propose a novel discriminative method for identifying robust and category specific local features which maximize the class separability to a greater extent. Specifically, we pose the selection of potent local descriptors as filtering based feature selection problem which ranks the local features per category based on a novel measure of distinctiveness. The underlying visual entities are subsequently represented based on the learned dictionary and this stage is followed by action classification using the random forest model followed by label propagation refinement. The framework is validated on the action recognition datasets based on still images (Stanford-40) as well as videos (UCF-50) and exhibits superior performances than the representative methods from the literature.
A Clarified Typology of Core-Periphery Structure in Networks
Venue: arXiv cs.SI 2020
Year: 2020

Core-periphery structure, the arrangement of a network into a dense core and sparse periphery, is a versatile descriptor of various social, biological, and technological networks. In practice, different core-periphery algorithms are often applied interchangeably, despite the fact that they can yield inconsistent descriptions of core-periphery structure. For example, two of the most widely used algorithms, the k-cores decomposition and the classic two-block model of Borgatti and Everett, extract fundamentally different structures: the latter partitions a network into a binary hub-and-spoke layout, while the former divides it into a layered hierarchy. We introduce a core-periphery typology to clarify these differences, along with Bayesian stochastic block modeling techniques to classify networks in accordance with this typology. Empirically, we find a rich diversity of core-periphery structure among networks. Through a detailed case study, we demonstrate the importance of acknowledging this diversity and situating networks within the core-periphery typology when conducting domain-specific analyses.
Is MAP Decoding All You Need? The Inadequacy of the Mode in Neural Machine Translation
Venue: arXiv cs.CL 2020
Year: 2020

Recent studies have revealed a number of pathologies of neural machine translation (NMT) systems. Hypotheses explaining these mostly suggest there is something fundamentally wrong with NMT as a model or its training algorithm, maximum likelihood estimation (MLE). Most of this evidence was gathered using maximum a posteriori (MAP) decoding, a decision rule aimed at identifying the highest-scoring translation, i.e. the mode. We argue that the evidence corroborates the inadequacy of MAP decoding more than casts doubt on the model and its training algorithm. In this work, we show that translation distributions do reproduce various statistics of the data well, but that beam search strays from such statistics. We show that some of the known pathologies and biases of NMT are due to MAP decoding and not to NMT's statistical assumptions nor MLE. In particular, we show that the most likely translations under the model accumulate so little probability mass that the mode can be considered essentially arbitrary. We therefore advocate for the use of decision rules that take into account the translation distribution holistically. We show that an approximation to minimum Bayes risk decoding gives competitive results confirming that NMT models do capture important aspects of translation well in expectation.
Adversarial Canonical Correlation Analysis
Venue: arXiv cs.LG 2020
Year: 2020

Canonical Correlation Analysis (CCA) is a statistical technique used to extract common information from multiple data sources or views. It has been used in various representation learning problems, such as dimensionality reduction, word embedding, and clustering. Recent work has given CCA probabilistic footing in a deep learning context and uses a variational lower bound for the data log likelihood to estimate model parameters. Alternatively, adversarial techniques have arisen in recent years as a powerful alternative to variational Bayesian methods in autoencoders. In this work, we explore straightforward adversarial alternatives to recent work in Deep Variational CCA (VCCA and VCCA-Private) we call ACCA and ACCA-Private and show how these approaches offer a stronger and more flexible way to match the approximate posteriors coming from encoders to much larger classes of priors than the VCCA and VCCA-Private models. This allows new priors for what constitutes a good representation, such as disentangling underlying factors of variation, to be more directly pursued. We offer further analysis on the multi-level disentangling properties of VCCA-Private and ACCA-Private through the use of a newly designed dataset we call Tangled MNIST. We also design a validation criteria for these models that is theoretically grounded, task-agnostic, and works well in practice. Lastly, we fill a minor research gap by deriving an additional variational lower bound for VCCA that allows the representation to use view-specific information from both input views.
Infinitude of Primes Using Formal Language Theory
Venue: arXiv cs.FL 2020
Year: 2020

Formal languages are sets of strings of symbols described by a set of rules specific to them. In this note, we discuss a certain class of formal languages, called regular languages, and put forward some elementary results. The properties of these languages are then employed to prove that there are infinitely many prime numbers.
Deep Reinforcement Learning for High Level Character Control
Venue: arXiv cs.LG 2020
Year: 2020

In this paper, we propose the use of traditional animations, heuristic behavior and reinforcement learning in the creation of intelligent characters for computational media. The traditional animation and heuristic gives artistic control over the behavior while the reinforcement learning adds generalization. The use case presented is a dog character with a high-level controller in a 3D environment which is built around the desired behaviors to be learned, such as fetching an item. As the development of the environment is the key for learning, further analysis is conducted of how to build those learning environments, the effects of environment and agent modeling choices, training procedures and generalization of the learned behavior. This analysis builds insight of the aforementioned factors and may serve as guide in the development of environments in general.
Random Number Generator Attack against the Kirchhoff-Law-Johnson-Noise Secure Key Exchange Protocol
Venue: arXiv cs.CR 2020
Year: 2020

This paper introduces and demonstrates two new attacks against the Kirchhoff-Law-Johnson-Noise (KLJN) secure key exchange scheme. The attacks are based on random number generators with compromised security. First we explore the situation in which Eve knows the seed of both Alice's and Bob's random number generators. We show that in this situation Eve can crack the secure key bit within a fraction of the bit exchange period even if her current and voltage measurements have only a single bit of resolution. In the second attack, we explore the situation in which Eve knows the seed of only Alice's random number generator. We show that in this situation Eve can still crack the secure key bit but she needs to use the whole bit exchange period for the attack. The security of the KLJN key exchange scheme, similarly to other protocols, necessitates that the random number generator outputs are truly random for Eve.
On memfractance of plants and fungi
Venue: arXiv cs.ET 2020
Year: 2020

The key feature of a memristor is that the resistance is a function of its previous resistance, thereby the behaviour of the device is influenced by changing the way in which potential is applied across it. Ultimately, information can be encoded on memristors, which can then be used to implement a number of circuit topologies. Biological substrates have already been shown to exhibit some memristive properties. It is, therefore, logical that all biological media will follow this trend to some degree. In this paper we demonstrate that a range of yet untested specimens exhibit memristive properties, including mediums such as water and dampened wood shavings on which we can cultivate biological specimens. We propose that memristance is not a binary property {0,1}, but rather a continuum on the scale [0,1]. The results imply that there is great potential for hybrid electronic systems that combine traditional electronic typologies with naturally occurring specimens.
Unsupervised segmentation via semantic-apparent feature fusion
Venue: arXiv cs.CV 2020
Year: 2020

Foreground segmentation is an essential task in the field of image understanding. Under unsupervised conditions, different images and instances always have variable expressions, which make it difficult to achieve stable segmentation performance based on fixed rules or single type of feature. In order to solve this problem, the research proposes an unsupervised foreground segmentation method based on semantic-apparent feature fusion (SAFF). Here, we found that key regions of foreground object can be accurately responded via semantic features, while apparent features (represented by saliency and edge) provide richer detailed expression. To combine the advantages of the two type of features, an encoding method for unary region features and binary context features is established, which realizes a comprehensive description of the two types of expressions. Then, a method for adaptive parameter learning is put forward to calculate the most suitable feature weights and generate foreground confidence score map. Furthermore, segmentation network is used to learn foreground common features from different instances. By fusing semantic and apparent features, as well as cascading the modules of intra-image adaptive feature weight learning and inter-image common feature learning, the research achieves performance that significantly exceeds baselines on the PASCAL VOC 2012 dataset.
An analysis on the use of autoencoders for representation learning: fundamentals, learning task case studies, explainability and challenges
Venue: arXiv cs.LG 2020
Year: 2020

In many machine learning tasks, learning a good representation of the data can be the key to building a well-performant solution. This is because most learning algorithms operate with the features in order to find models for the data. For instance, classification performance can improve if the data is mapped to a space where classes are easily separated, and regression can be facilitated by finding a manifold of data in the feature space. As a general rule, features are transformed by means of statistical methods such as principal component analysis, or manifold learning techniques such as Isomap or locally linear embedding. From a plethora of representation learning methods, one of the most versatile tools is the autoencoder. In this paper we aim to demonstrate how to influence its learned representations to achieve the desired learning behavior. To this end, we present a series of learning tasks: data embedding for visualization, image denoising, semantic hashing, detection of abnormal behaviors and instance generation. We model them from the representation learning perspective, following the state of the art methodologies in each field. A solution is proposed for each task employing autoencoders as the only learning method. The theoretical developments are put into practice using a selection of datasets for the different problems and implementing each solution, followed by a discussion of the results in each case study and a brief explanation of other six learning applications. We also explore the current challenges and approaches to explainability in the context of autoencoders. All of this helps conclude that, thanks to alterations in their structure as well as their objective function, autoencoders may be the core of a possible solution to many problems which can be modeled as a transformation of the feature space.
LaCulturaNonSiFerma -- Report su uso e la diffusione degli hashtag delle istituzioni culturali italiane durante il periodo di lockdown
Venue: arXiv cs.CL 2020
Year: 2020

This report presents an analysis of #hashtags used by Italian Cultural Heritage institutions to promote and communicate cultural content during the COVID-19 lock-down period in Italy. Several activities to support and engage users' have been proposed using social media. Most of these activities present one or more #hashtags which help to aggregate content and create a community on specific topics. Results show that on one side Italian institutions have been very proactive in adapting to the pandemic scenario and on the other side users' reacted very positively increasing their participation in the proposed activities.
Undirected Unicast Network Capacity: A Partition Bound
Venue: arXiv cs.IT 2020
Year: 2020

In this paper, we present a new technique to obtain upper bounds on undirected unicast network information capacity. Using this technique, we characterize an upper bound, called partition bound, on the symmetric rate of information flow in undirected unicast networks and give an algorithm to compute it. Two classes of networks are presented for which the bound is tight and the capacity is achievable by routing thus confirming the undirected unicast conjecture for these classes of networks. We also show that the bound can be loose in general and present an approach to tighten it.
Towards Seamless Authentication for Zoom-Based Online Teaching and Meeting
Venue: arXiv cs.CR 2020
Year: 2020

The lockdowns and travel restrictions in current coronavirus pandemic situation has replaced face-to-face teaching and meeting with online teaching and meeting. Recently, the video conferencing tool Zoom has become extremely popular for its simple-to-use feature and low network bandwith requirement. However, Zoom has serious security and privacy issues. Due to weak authentication mechanisms, unauthorized persons are invading Zoom sessions and creating disturbances (known as Zoom bombing). In this paper, we propose a preliminary work towards a seamless authentication mechanism for Zoom-based teaching and meeting. Our method is based on PRNU (Photo Response Non Uniformity)-based camera authentication, which can authenticate the camera of a device used in a Zoom meeting without requiring any assistance from the participants (e.g., needing the participant to provide biometric). Results from a small-scale experiment validates the proposed method.
A Massively Parallel Algorithm for Minimum Weight Vertex Cover
Venue: arXiv cs.DS 2020
Year: 2020

We present a massively parallel algorithm, with near-linear memory per machine, that computes a $(2+\varepsilon)$-approximation of minimum-weight vertex cover in $O(\log\log d)$ rounds, where $d$ is the average degree of the input graph. Our result fills the key remaining gap in the state-of-the-art MPC algorithms for vertex cover and matching problems; two classic optimization problems, which are duals of each other. Concretely, a recent line of work---by Czumaj et al. [STOC'18], Ghaffari et al. [PODC'18], Assadi et al. [SODA'19], and Gamlath et al. [PODC'19]---provides $O(\log\log n)$ time algorithms for $(1+\varepsilon)$-approximate maximum weight matching as well as for $(2+\varepsilon)$-approximate minimum cardinality vertex cover. However, the latter algorithm does not work for the general weighted case of vertex cover, for which the best known algorithm remained at $O(\log n)$ time complexity.
A Neural Network Looks at Leonardo's(?) Salvator Mundi
Venue: arXiv cs.CV 2020
Year: 2020

We use convolutional neural networks (CNNs) to analyze authorship questions surrounding the works of Leonardo da Vinci -- in particular, Salvator Mundi, the world's most expensive painting and among the most controversial. Trained on the works of an artist under study and visually comparable works of other artists, our system can identify likely forgeries and shed light on attribution controversies. Leonardo's few extant paintings test the limits of our system and require corroborative techniques of testing and analysis.