Reviewerly/exHarmony huggingface dataset:
Developing a monitoring system for Cloud-based distributed data-centers

Nowadays more and more datacenters cooperate each others to achieve a common and more complex goal. New advanced functionalities are required to support experts during recovery and managing activities, like anomaly detection and fault pattern recognition. The proposed solution provides an active support to problem solving for datacenter management teams by providing automatically the root-cause of detected anomalies. The project has been developed in Bari using the datacenter ReCaS as testbed. Big Data solutions have been selected to properly handle the complexity and size of the data. Features like open source, big community, horizontal scalability and high availability have been considered and tools belonging to the Hadoop ecosystem have been selected. The collected information is sent to a combination of Apache Flume and Apache Kafka, used as transport layer, in turn delivering data to databases and processing components. Apache Spark has been selected as analysis component. Different kind of databases have been considered in order to satisfy multiple requirements: Hadoop Distributed File System, Neo4j, InfluxDB and Elasticsearch. Grafana and Kibana are used to show data in a dedicated dashboards. The Root-cause analysis engine has been implemented using custom machine learning algorithms. Finally, results are forwarded to experts by email or Slack, using Riemann.
BottleSum: Unsupervised and Self-supervised Sentence Summarization using the Information Bottleneck Principle

The principle of the Information Bottleneck (Tishby et al. 1999) is to produce a summary of information X optimized to predict some other relevant information Y. In this paper, we propose a novel approach to unsupervised sentence summarization by mapping the Information Bottleneck principle to a conditional language modelling objective: given a sentence, our approach seeks a compressed sentence that can best predict the next sentence. Our iterative algorithm under the Information Bottleneck objective searches gradually shorter subsequences of the given sentence while maximizing the probability of the next sentence conditioned on the summary. Using only pretrained language models with no direct supervision, our approach can efficiently perform extractive sentence summarization over a large corpus. Building on our unsupervised extractive summarization (BottleSumEx), we then present a new approach to self-supervised abstractive summarization (BottleSumSelf), where a transformer-based language model is trained on the output summaries of our unsupervised method. Empirical results demonstrate that our extractive method outperforms other unsupervised models on multiple automatic metrics. In addition, we find that our self-supervised abstractive model outperforms unsupervised baselines (including our own) by human evaluation along multiple attributes.
Prediction of Illiteracy Rates in Indonesia Using Time Series

Illiteracy eradication will increase the quality of human resources; therefore, it should be a priority program for every country. This study applies time series analysis to predict illiteracy rates in Indonesia by using data from the Indonesia Statistic Centre from 2003 to 2017. As data on illiteracy rates are non-stationary, a differencing process is required. The results of this study indicate that the best model to predict illiteracy rates in Indonesia is ARIMA (3,3,1), which shows there are three processes of differencing to obtain stationary data. The results indicate that the point forecast of the illiteracy rate in 2025 is 1.51. Further, the results of the forecasting also reveal that over the next ten years there will a downward trend in illiteracy rates in Indonesia, with the average of the forecast points being 2.32 percent. This shows that continuity and commitment to the implementation of illiteracy eradication programs are required.
Hardware Modification for Improved Eye Tracking with the Pupil Labs Virtual-Reality Integration

Integrated eye tracking in virtual reality is a crucial tool for the field of vision science, and one that frees the stimulus from the stationary 2D plane defined by conventional desktop displays, allowing for the monitoring of task-coordinated movements of the eyes, head, and body, interaction via gaze, and the use of gaze-contingent displays. The Pupil Labs integration into the HTC Vive and Vive Pro is a notable solution, because it is extremely cost effective, and completely open source. One notable limitation, however is that the lack of space between the user’s face and the helmet optics has required that clip-on eye cameras are positioned below the helmet optics, looking up at the eyes at an extreme angle, and this affects the quality of the Pupil Labs eye tracking algorithm. Here, we present the results of a DIY hardware modification that replaces the large 120 Hz eye cameras positioned outside the optics with smaller 200 Hz eye cameras positioned behind the optics, inside the body of the Vive or Vive Pro, and aimed at a specially coated “hot” mirror positioned between the lens and the display. This mirror allows the visible light from the display to pass through to the viewer while reflecting an infrared image of the eye to the eye cameras above. This design provides a clear an on-axis eye image that is well-suited for computer-vision detection of the pupil, a key feature used by Pupil Labs’ eye tracking software. Here, we report on the accuracy and precision of the integrated eye tracking system, and provide instruction and resources to interested researchers looking to modify their own integrations.
Library Book Sharing Network (LBSN)

This paper consists of all related information of the Library Book Share Network, from perspective of students and librarians.It then explores some of the problems and issues faced by students, if they are unable to find their desired book in their campus library.The paper stresses the need of how Library Network is important and how it will benefit students and will decrease work load of librarians.The paper emphasizes on sharing books among each other within the network.
DECoVaC: Design of Experiments with Controlled Variability Components

Reproducible research in Machine Learning has seen a salutary abundance of progress lately: workflows, transparency, and statistical analysis of validation and test performance. We build on these efforts and take them further. We offer a principled experimental design methodology, based on linear mixed models, to study and separate the effects of multiple factors of variation in machine learning experiments. This approach allows to account for the effects of architecture, optimizer, hyper-parameters, intentional randomization, as well as unintended lack of determinism across reruns. We illustrate that methodology by analyzing Matching Networks, Prototypical Networks and TADAM on the miniImagenet dataset.
Simple yet Effective Bridge Reasoning for Open-Domain Multi-Hop Question Answering

A key challenge of multi-hop question answering (QA) in the open-domain setting is to accurately retrieve the supporting passages from a large corpus. Existing work on open-domain QA typically relies on off-the-shelf information retrieval (IR) techniques to retrieve \textbf{answer passages}, i.e., the passages containing the groundtruth answers. However, IR-based approaches are insufficient for multi-hop questions, as the topic of the second or further hops is not explicitly covered by the question. To resolve this issue, we introduce a new sub-problem of open-domain multi-hop QA, which aims to recognize the bridge (\emph{i.e.}, the anchor that links to the answer passage) from the context of a set of start passages with a reading comprehension model. This model, the \textbf{bridge reasoner}, is trained with a weakly supervised signal and produces the candidate answer passages for the \textbf{passage reader} to extract the answer. On the full-wiki HotpotQA benchmark, we significantly improve the baseline method by 14 point F1. Without using any memory-inefficient contextual embeddings, our result is also competitive with the state-of-the-art that applies BERT in multiple modules.
Quantum algorithm for finding the negative curvature direction in non-convex optimization

We present an efficient quantum algorithm aiming to find the negative curvature direction for escaping the saddle point, which is the critical subroutine for many second-order non-convex optimization algorithms. We prove that our algorithm could produce the target state corresponding to the negative curvature direction with query complexity O(polylog(d) /ε), where d is the dimension of the optimization function. The quantum negative curvature finding algorithm is exponentially faster than any known classical method which takes time at least O(d /\sqrtε). Moreover, we propose an efficient quantum algorithm to achieve the classical read-out of the target state. Our classical read-out algorithm runs exponentially faster on the degree of d than existing counterparts.
Quantum Adiabatic Feature Selection

Dimensionality reduction is the fundamental problem for machine learning and pattern recognition. During data preprocessing, the feature selection is often demanded to reduce the computational complexity. The problem of feature selection is categorized as a NP optimization problem. Exhaustive search of huge set of features takes huge amount of time on classical computer. In the present paper we discuss the role of quantum adiabatic computation to perform feature selection with bi-quadratic optimization and provide a quantum feature selection algorithm. Our algorithm runs with the quantum adiabatic time complexity bound $O(1/g_{min}^{2})$, which is better than classical approach for bi-quadratic feature selection.
Proof Pearl: Magic Wand as Frame

Separation logic adds two connectives to assertion languages: separating conjunction * ("star") and its adjoint, separating implication -* ("magic wand"). Comparatively, separating implication is less widely used. This paper demonstrates that by using magic wand to express frames that relate mutable local portions of data structures to global portions, we can exploit its power while proofs are still easily understandable. Many useful separation logic theorems about partial data structures can now be proved by simple automated tactics, which were usually proved by induction. This magic-wand-as-frame technique is especially useful when formalizing the proofs by a high order logic. We verify binary search tree insert in Coq as an example to demonstrate this proof technique.
SGSI project at CNAF

The Italian Tier1 center is mainly focused on LHC and physics experiments in general. Recently we tried to widen our area of activity and established a collaboration with the University of Bologna to set-up an area inside our computing center for hosting experiments with high demands of security and privacy requirements on stored data. The first experiment we are going to host is Harmony, a project part of IMI’s Big Data for Better Outcomes programme (IMI stands for Innovative Medicines Initiative). In order to be able to accept this kind of data we had to make a subset of our computing center compliant with the ISO 27001 regulation. In this article we will describe the SGSI project (Sistema Gestione Sicurezza Informazioni, Information Security Management System) with details of all the processes we have been through in order to become ISO 27001 compliant, with a particular focus on the separation of the project dedicated resources from all the others hosted in the center. We will also describe the software solutions adopted to allow this project to accept in the future any experiment or collaboration in need for this kind of security procedures.
Multidimensional quantum illumination via direct measurement of spectro-temporal correlations

In this work we investigate quantum-enhanced target detection in the presence of large background noise using multidimensional quantum correlations between photon pairs generated through spontaneous parametric down-conversion. Until now similar experiments have only utilized one of the photon pairs' many degrees of freedom such as temporal correlations and photon number correlations. Here, we utilized both temporal and spectral correlations of the photon pairs and achieved over an order of magnitude reduction to the background noise and in turn significant reduction to data acquisition time when compared to utilizing only temporal modes. We believe this work represents an important step in realizing a practical, real-time quantum-enhanced target detection system. The demonstrated technique will also be of importance in many other quantum sensing applications and quantum communications.
Exploring Reciprocal Attention for Salient Object Detection by Cooperative Learning

Typically, objects with the same semantics are not always prominent in images containing different backgrounds. Motivated by this observation that accurately salient object detection is related to both foreground and background, we proposed a novel cooperative attention mechanism that jointly considers reciprocal relationships between background and foreground for efficient salient object detection. Concretely, we first aggregate the features at each side-out of traditional dilated FCN to extract the initial foreground and background local responses respectively. Then taking these responses as input, reciprocal attention module adaptively models the nonlocal dependencies between any two pixels of the foreground and background features, which is then aggregated with local features in a mutual reinforced way so as to enhance each branch to generate more discriminative foreground and background saliency map. Besides, cooperative losses are particularly designed to guide the multi-task learning of foreground and background branches, which encourages our network to obtain more complementary predictions with clear boundaries. At last, a simple but effective fusion strategy is utilized to produce the final saliency map. Comprehensive experimental results on five benchmark datasets demonstrate that our proposed method performs favorably against the state-of-the-art approaches in terms of all compared evaluation metrics.
SEMANTIC SEGMENTATION OF AIRBORNE IMAGES AND CORRESPONDING DIGITAL SURFACE MODELS – ADDITIONAL INPUT DATA OR ADDITIONAL TASK?

Abstract. We analyze the effects of additional height data for semantic segmentation of aerial images with a convolutional encoder-decoder network. Besides a merely image-based semantic segmentation, we trained the same network with height as additional input and furthermore, we defined a multi-task model, where we trained the network to estimate the relative height of objects in parallel to semantic segmentation on the image data only. Our findings are, that excellent results are possible for image data only and additional height information has no significant effect – neither when employed as extra input nor when used for multi-task training, even with differently weighted losses. Based on our results, we, thus, hypothesize that a strong encoder-decoder network implicitly learns the correlation of object categories and relative heights.
Techniques and Applications for Crawling, Ingesting and Analyzing Blockchain Data

As the public Ethereum network surpasses half a billion transactions and enterprise Blockchain systems becoming highly capable of meeting the demands of global deployments, production Blockchain applications are fast becoming commonplace across a diverse range of business and scientific verticals. In this paper, we reflect on work we have been conducting recently surrounding the ingestion, retrieval and analysis of Blockchain data. We describe the scaling and semantic challenges when extracting Blockchain data in a way that preserves the original metadata of each transaction by cross referencing the Smart Contract interface with the on-chain data. We then discuss a scientific use case in the area of Scientific workflows by describing how we can harvest data from tasks and dependencies in a generic way. We then discuss how crawled public blockchain data can be analyzed using two unsupervised machine learning algorithms, which are designed to identify outlier accounts or smart contracts in the system. We compare and contrast the two machine learning methods and cross correlate with public Websites to illustrate the effectiveness such approaches.
Whitney Numbers of Combinatorial Geometries and Higher-Weight Dowling Lattices

We study the Whitney numbers of the first kind of combinatorial geometries, in connection with the theory of error-correcting codes.The first part of the paper is devoted to general results relating the M\" obius functions of nested atomistic lattices, extending some classical theorems in combinatorics.We then specialize our results to restriction geometries, i.e., to sublattices \scrA of the lattice of subspaces of an \BbbF q-linear space, say, X, generated by a set of projective points A \subsete X.In this context, we introduce the notion of subspace distribution and show that partial knowledge of the latter is equivalent to partial knowledge of the Whitney numbers of \scrA .This refines a classical result by Dowling.The most interesting applications of our results are to be seen in the theory of higherweight Dowling lattices (HWDLs), to which we devote the second and most substantive part of the paper.These combinatorial geometries were introduced by Dowling in 1971 in connection with fundamental problems in coding theory, most notably the famous MDS conjecture.They were further studied by, among others, Zaslavsky, Bonin, Kung, Brini, and Games.To date, still very little is known about these lattices and the techniques to compute their Whitney numbers have not been discovered yet.In this paper, we bring forward the theory of HWDLs, computing their Whitney numbers for new infinite families of parameters.We also show that the second Whitney numbers of HWDLs are polynomials in the underlying field size q, whose coefficients are curious expressions involving the Bernoulli numbers.In passing, we obtain new results intersecting coding theory and enumerative combinatorics.
Using Latent Codes for Class Imbalance Problem in Unsupervised Domain Adaptation

We address the problem of severe class imbalance in unsupervised domain adaptation, when the class spaces in source and target domains diverge considerably. Till recently, domain adaptation methods assumed the aligned class spaces, such that reducing distribution divergence makes the transfer between domains easier. Such an alignment assumption is invalidated in real world scenarios where some source classes are often under-represented or simply absent in the target domain. We revise the current approaches to class imbalance and propose a new one that uses latent codes in the adversarial domain adaptation framework. We show how the latent codes can be used to disentangle the silent structure of the target domain and to identify under-represented classes. We show how to learn the latent code reconstruction jointly with the domain invariant representation and use them to accurately estimate the target labels.
A Novel Squeeze YOLO-based Real-time People Counting Approach

Real-time people counting based on videos is one of the most popular projects in the construction of smart cities. To develop an accurate people counting approach, deep learning can be used as it greatly improves the accuracy of machine learning-based approaches. To this end, we have previously proposed an accurate you only look once (YOLO)-based people counting approach, dubbed YOLO-PC. However, the model of YOLO-PC was very large with an excessive number of parameters, thus it requires large storage space on the device and makes transmission on internet a time consuming task. In this paper, a new real-time people counting method named as squeeze YOLO-based people counting (S-YOLO-PC) is proposed. S-YOLO-PC uses the fire layer of SqueezeNet to optimise the network structure, which reduces the number of parameters used in the model without decreasing its accuracy. Based on the obtained the experimental results, S-YOLO-PC reduces the number of model parameters by 11.5% and 9% compared to YOLO and YOLO-PC, respectively. S-YOLO-PC can also detect and count people with 41 frames per second (FPS) with the average precision (AP) of person of 72%.
The influence on Mandelstam by Goethe: “The Octaves” as “The Poems on the Cognition”

The article considers “The Octaves” by O.E. Mandelstam as the unique complex of genre and theme connected not only with the literary but also with the philosophical influence of J.W. Goethe on the Mandelstam’s poetry. It is proved that Mandelstam enters the consensual dialogue with Goethe because at the time of creating the studied cycle of poems, at the beginning of 1930s, Mandelstam’s interest in scientific theories, especially in biology and history reached its pinnacle. Mandelstam’s “The Octaves”, “Lamarck” are compared with scientific works of J.W. Goethe as well as with his sciencerelated poetry, in particular with “Metamorphosis of Plants”. Imaginative homology of both poets, convergence of their plots and trope functions are demonstrated. All this undoubtedly proves that Mandelshtam was affected by Goethe’s beliefs reflected in his ideology and ideopoetic. It is shown how the feeling of spiritual connection with Goethe which Mandelstam had, especially after his journey to Armenia (and acquaintance with biologist B. Kuzin) is converted into aesthetic world view and poetic principles. It brightly manifests itself in lyrical ontology of such “The Octaves” like: “Both Schubert on the waters and Mozart in birds’ chirping...”, “When having destroyed a draft...”, “Oh, Moslem-butterfly...”, “And the jagged bough of a maple-tree...”.
Read, Attend and Comment: A Deep Architecture for Automatic News Comment Generation

Automatic news comment generation is a new testbed for techniques of natural language generation. In this paper, we propose a "read-attend-comment" procedure for news comment generation and formalize the procedure with a reading network and a generation network. The reading network comprehends a news article and distills some important points from it, then the generation network creates a comment by attending to the extracted discrete points and the news title. We optimize the model in an end-to-end manner by maximizing a variational lower bound of the true objective using the back-propagation algorithm. Experimental results on two datasets indicate that our model can significantly outperform existing methods in terms of both automatic evaluation and human judgment.
VirtSC

Self-checksumming (SC) is a tamper-proofing technique that ensures certain program segments (code) in memory hash to known values at runtime. SC has few restrictions on application and hence can protect a vast majority of programs. The code verification in SC requires computation of the expected hashes after compilation, as the machine-code is not known before. This means the expected hash values need to be adjusted in the binary executable, hence combining SC with other protections is limited due to this adjustment step. However, obfuscation protections are often necessary, as SC protections can be otherwise easily detected and disabled via pattern matching. In this paper, we present a layered protection using virtualization obfuscation, yielding an architecture-agnostic SC protection that requires no post-compilation adjustment. We evaluate the performance of our scheme using a dataset of 25 real-world programs (MiBench and 3 CLI games). Our results show that the SC scheme induces an average overhead of 43% for a complete protection (100% coverage). The overhead is tolerable for less CPU-intensive programs (e.g. games) and when only parts of programs (e.g. license checking) are protected. However, large overheads stemming from the virtualization obfuscation were encountered.