Miracle-dz/UniFAR-FTUs huggingface dataset:
A new look at clustering through the lens of deep convolutional neural networks

Classification and clustering have been studied separately in machine learning and computer vision. Inspired by the recent success of deep learning models in solving various vision problems (e.g., object recognition, semantic segmentation) and the fact that humans serve as the gold standard in assessing clustering algorithms, here, we advocate for a unified treatment of the two problems and suggest that hierarchical frameworks that progressively build complex patterns on top of the simpler ones (e.g., convolutional neural networks) offer a promising solution. We do not dwell much on the learning mechanisms in these frameworks as they are still a matter of debate, with respect to biological constraints. Instead, we emphasize on the compositionality of the real world structures and objects. In particular, we show that CNNs, trained end to end using back propagation with noisy labels, are able to cluster data points belonging to several overlapping shapes, and do so much better than the state of the art algorithms. The main takeaway lesson from our study is that mechanisms of human vision, particularly the hierarchal organization of the visual ventral stream should be taken into account in clustering algorithms (e.g., for learning representations in an unsupervised manner or with minimum supervision) to reach human level clustering performance. This, by no means, suggests that other methods do not hold merits. For example, methods relying on pairwise affinities (e.g., spectral clustering) have been very successful in many cases but still fail in some cases (e.g., overlapping clusters).
Comparative study of LSA vs Word2vec embeddings in small corpora: a case study in dreams database

Word embeddings have been extensively studied in large text datasets. However, only a few studies analyze semantic representations of small corpora, particularly relevant in single-person text production studies. In the present paper, we compare Skip-gram and LSA capabilities in this scenario, and we test both techniques to extract relevant semantic patterns in single-series dreams reports. LSA showed better performance than Skip-gram in small size training corpus in two semantic tests. As a study case, we show that LSA can capture relevant words associations in dream reports series, even in cases of small number of dreams or low-frequency words. We propose that LSA can be used to explore words associations in dreams reports, which could bring new insight into this classic research area of psychology
Perspective-Aware CNN For Crowd Counting

Crowd counting is the task of estimating pedestrian numbers in crowd images. Modern crowd counting methods employ deep neural networks to estimate crowd counts via crowd density regressions. A major challenge of this task lies in the drastic changes of scales and perspectives in images. Representative approaches usually utilize different (large) sized filters and conduct patch-based estimations to tackle it, which is however computationally expensive. In this paper, we propose a perspective-aware convolutional neural network (PACNN) with a single backbone of small filters (e.g. 3 x 3). It directly predicts a perspective map in the network and encodes it as a perspective-aware weighting layer to adaptively combine the density outputs from multi-scale feature maps. The weights are learned at every pixel of the map such that the final combination is robust to perspective changes and pedestrian size variations. We conduct extensive experiments on the ShanghaiTech, WorldExpo'10 and UCF_CC_50 datasets, and demonstrate that PACNN achieves state-of-the-art results and runs as fast as the fastest.
Modeling Latent Attention Within Neural Networks

Deep neural networks are able to solve tasks across a variety of domains and modalities of data. Despite many empirical successes, we lack the ability to clearly understand and interpret the learned internal mechanisms that contribute to such effective behaviors or, more critically, failure modes. In this work, we present a general method for visualizing an arbitrary neural network's inner mechanisms and their power and limitations. Our dataset-centric method produces visualizations of how a trained network attends to components of its inputs. The computed "attention masks" support improved interpretability by highlighting which input attributes are critical in determining output. We demonstrate the effectiveness of our framework on a variety of deep neural network architectures in domains from computer vision, natural language processing, and reinforcement learning. The primary contribution of our approach is an interpretable visualization of attention that provides unique insights into the network's underlying decision-making process irrespective of the data modality.
Forecasting Hand and Object Locations in Future Frames

This paper presents an approach to forecast future locations of human hands and objects. Given an image frame, the goal is to predict presence and location of hands and objects in the future frame (e.g., 5 seconds later), even when they are not visible in the current frame. The key idea is that (1) an intermediate representation of a convolutional object recognition model abstracts scene information in its frame and that (2) we can predict (i.e., regress) such representations corresponding to the future frames based on that of the current frame. We design a new two-stream convolutional neural network (CNN) architecture for videos by extending the state-of-the-art convolutional object detection network, and present a new fully convolutional regression network for predicting future scene representations. Our experiments confirm that combining the regressed future representation with our detection network allows reliable estimation of future hands and objects in videos
Parallax: Automatic Data-Parallel Training of Deep Neural Networks

The employment of high-performance servers and GPU accelerators for training deep neural network models have greatly accelerated recent advances in machine learning (ML). ML frameworks, such as TensorFlow, MXNet, and Caffe2, have emerged to assist ML researchers to train their models in a distributed fashion. However, correctly and efficiently utilizing multiple machines and GPUs is still not a straightforward task for framework users due to the non-trivial correctness and performance challenges that arise in the distribution process. This paper introduces Parallax, a tool for automatic parallelization of deep learning training in distributed environments. Parallax not only handles the subtle correctness issues, but also leverages various optimizations to minimize the communication overhead caused by scaling out. Experiments show that Parallax built atop TensorFlow achieves scalable training throughput on multiple CNN and RNN models, while requiring little effort from its users.
Fighting biases with dynamic boosting

While gradient boosting algorithms are the workhorse of modern industrial machine learning and data science, all current implementations are susceptible to a non-trivial but damaging form of label leakage. It results in a systematic bias in pointwise gradient estimates that lead to reduced accuracy. This paper formally analyzes the issue and presents solutions that produce unbiased pointwise gradient estimates. Experimental results demonstrate that our open-source implementation of gradient boosting that incorporates the proposed algorithm produces state-of-the-art results outperforming popular gradient boosting implementations.
AND: Autoregressive Novelty Detectors

We propose an unsupervised model for novelty detection. The subject is treated as a density estimation problem, in which a deep neural network is employed to learn a parametric function that maximizes probabilities of training samples. This is achieved by equipping an autoencoder with a novel module, responsible for the maximization of compressed codes' likelihood by means of autoregression. We illustrate design choices and proper layers to perform autoregressive density estimation when dealing with both image and video inputs. Despite a very general formulation, our model shows promising results in diverse one-class novelty detection and video anomaly detection benchmarks.
Interoperability governance: a definition and insights from case studies in Europe

Interoperability has become a crucial value in European e-government developments, as promoted by the Digital Single Market strategy and the Tallinn Declaration. The European Union and its Member States have made considerable investments in improving the understanding of interoperability and in developing interoperable building blocks to support cross-border data exchange and public service provisioning. This includes recent updates of the European Interoperability Framework (EIF) and European Interoperability Reference Architecture (EIRA), as well as the publication of a number of generic and domain specific architecture and solutions building blocks such as digital identification or electronic delivery services. While in the previous version of the EIF, interoperability governance was not clearly developed, the new version of 2017 puts interoperability governance as a concept that spans across the different interoperability layers (legal, organizational, semantic and technical) and that builds the frame for interoperability overall. In this paper, we develop a definition of interoperability governance from a literature review and we put forward a model to investigate interoperability governance models at European and Member State levels. Based on several case studies of EU institutions and Member States, we could draw recommendations for what the key aspects of interoperability governance are to successfully diffuse interoperability into public service provisioning.
Catalan: A Comprehensive Grammar

Catalan: A Comprehensive Grammar is a complete reference guide to modern Catalan grammar, presenting an accessible and systematic description of the modern language. It is the only comprehensive grammar of the language available in English The Grammar reflects the current reality of Catalan by acknowledging regional diversity and features Balearic and Valencian varieties alongside the language used in the Barcelona region. The combined specialist knowledge of the author team ensures a balanced coverage of modern Catalan. Features include: * comprehensive coverage of all parts of speech * a wealth of authentic examples illustrating language points * attention to areas of particular difficulty for those whose first language is English * full cross-referencing * detailed index.
Smart cities in the era of artificial intelligence and internet of things: literature review from 1990 to 2017

The concept of smart city is considered as the new engine for economic and social growths since it is supported by the rapid development of information and communication technologies. However, each technology not only brings its advantages, but also the challenges that cities have to face in order to implement it. So, this paper addresses two research questions : « What are the most important technologies that drive the development of smart cities ? » and « what are the challenges that cities will face when adopting these technologies ? » Relying on a literature review of studies published between 1990 and 2017, the ensuing results show that Artificial Intelligence and Internet of Things represent the most used technologies for smart cities. So, the focus of this paper will be on these two technologies by showing their advantages and their challenges.
Predicting the Runtime of Memory Intensive Batch Workloads

Data centers use their non-peak business hours to run batch jobs in order to maximize the resource utilization. Large data centers may run thousands of batch jobs every day. These jobs typically process large volumes of data and can either be compute or memory intensive. Batch jobs may perform data reconciliation, risk analysis, and carry out analytics that are critical for the business. Hence, it is imperative that these jobs complete within the available time frame. New servers with large number of core and Non-Uniform Memory Access (NUMA) architecture provide very large compute capacity. This means that many batch jobs can be run concurrently to minimize their collective completion time. However, excessive parallelism may create memory access bottleneck and adversely affect the completion time. The objective of this work is to predict the completion time of concurrently running batch jobs. We assume each job to be multithreaded and memory intensive. A prediction model based on memory and CPU contention is proposed. Our predictions are based on server simulation that uses individual batch job data to predict the completion time of concurrently running jobs. The efficacy of our approach is validated using STREAM, a well-known open source synthetic benchmark. We also study the effect of hyper-threading and memory binding on prediction accuracy of our model.
Adoption of Fixed-mobile Convergence in the U.S. Telecommunication Industry

Fixed-mobile convergence (FMC) is a radical innovation for telecommunication companies because it requires them to move into unknown territories that are beyond traditional business lines and underlying technologies. In the meantime, understanding what types of telecommunication companies are better at doing business through the early adoption of FMC has become increasingly important in the converging telecommunication industry. This paper tries to examine several firm characteristics and their influence on the extent of FMC adoption in the US telecommunication industry. The results show that FMC is still in the early stage and that firm scope and size have a significant positive impact on the extent of FMC adoption.
Exploring Usability and Accessibility of Avatar-based Touchless Gestural Interfaces for Autistic People

Many prior works investigated the potential of pervasive technologies and interactive applications to increase access capabilities to digital content for people with disability, particularly Neuro-Developmental Disorders (NDDs). In this paper, we present an exploratory study aimed at understanding if an avatar-based touchless gestural interface is able to foster interest towards digital representations of artworks, e.g. paintings or sculptures usually exhibited in museums, and to make them more accessible for such people. In particular, the study involved three autistic people and a therapist, and allowed us to report the potential of an avatar to communicate the interactivity and stimulate interaction with just a few directions to start, or not at all. We also shortly present and discuss some possible idea for future developments.
Going Beyond the Problem Given: How Human Tutors Use Post-Solution Discussions to Support Transfer

Two studies investigated the role and effectiveness of post-solution, reflective dialogues in physics tutorials. The first study investigated the instructional roles of post-solution discussions, their relationship to problem-solving discussions, and features that predict learning. Seven tutors individually guided 15 students as they worked on problems in the Andes physics tutoring system. Tutors adapted the post-solution discussions to students? ability levels and their performance on the current problem. Qualitative analysis of the transcripts revealed several roles of the post-solution dialogues - most prominently, explaining conceptual knowledge and integrating this knowledge with strategic, problem-solving knowledge. The number of post-solution discussions students had with their tutor, the number of discussions that abstracted from the current problem, and the number of tutor-initiated discussions predicted transfer, as measured by pre-test to post-test gain score on problems similar to those solved in Andes. Several tutorial strategies that are distributed between problem solving and post-solution reflection were identified. A framework for describing distributed plans for reflection is proposed based on these analyses. The second study investigated whether reflection questions such as those asked by the tutors in the first study lead to better conceptual understanding and problem-solving ability, as measured by overall gain scores and gain scores on conceptual and quantitative questions. It also examined whether human tutor-provided feedback on students? responses - with its often multi-exchange, dialectic character - is more effective than a single, canned explanation. Forty-six students solved problems in Andes in one of three conditions: with no reflection questions after problem solving, with reflection questions discussed with human tutors, or with the same reflection questions followed by canned feedback (without a human tutor). Students learned more with reflection questions and feedback than without, but the canned feedback and human tutored conditions did not differ significantly. Hence, overall, these studies support the practice of implementing post-solution reflective activities in intelligent tutoring systems, but call into question the need for natural-language processing techniques to support these activities.
A Comprehensive Study on Bugs in Actor Systems

The Actor model of concurrency provides a convenient way to build large-scale distributed systems, harnessing its inherent parallelism and the message-driven nature. As a result, the Actor model has been widely used in a variety of distributed applications, especially cloud-based applications. However, software bugs in actor systems are not well studied. In this paper, we present a comprehensive analysis on known bugs in actor-based systems. The contributions of this paper are two-fold. First, we have created an actor bug taxonomy, and carefully examined and categorized bugs reported in a number of real-world actor systems. Second, we have compared the characteristics actor bugs with bugs reported in cloud systems in general. Our results shed lights on understanding the benefits and unique challenges for utilizing the Actor model for system development, as well as deployment in open systems like clouds.
Sociolinguistics: An international handbook of the science of language and society (Project announcement)

This article is intended to inform the public about a rather large and representative publishing project in sociolinguistics, and at the same time to ask the readers to send in their critical comments. This handbook of sociolinguistics will be published by Walter de Gruyter (Berlin, New York). The publication date will be some time in I987. The editors will try to find competent contributors for all the 192 articles of the Handbook. The editors would be grateful for any points of criticism. Critical comments should be sent to: Prof. D. Ulrich Ammon, Universitat Duisburg Gesamthochschule, Fachbereich 3 Germanistik, Lotharstrasse 65, 4I00 Duisburg I, Federal Republic of Germany.
How cities categorize datasets in their open data portals: an exploratory analysis

Transparency is a key factor in maintaining the trust of societies in their governments. The open data approach can be used to increase the transparency of public administrations. Hence, thousands of open datasets published by governments, organizations and companies, were made available through portals on internet. In recent years, the growth in the number of portals and the amount of information available has increased users' difficulty in obtaining useful information for conducting analyzes or studies. In a majority of portals the datasets are distributed across selected topics or categories. As such, users may take some time to understand the organization of the datasets in each portal they browse, what hinders a comprehensive and effective access to information. In this work, we conducted a study about how the 100 different portals of the most populous cities in the United States categorize their datasets. As a result, we identified the most relevant categories, based on the frequency of occurrence in these portals. Furthermore, we suggest a method that provides a generic categorization of datasets among several urban data portals that could simplify user interaction with these portals.
Code Authorship and Fault-proneness of Open-Source Android Applications: An Empirical Study

Context: In recent years, many research studies have shown how human factors play a significant role in the quality of software components. Code authorship metrics have been introduced to establish a chain of responsibility and simplify management when assigning tasks in large and distributed software development teams. Researchers have investigated the relationship between code authorship metrics and fault occurrences in software systems. However, we have observed that these studies have only been carried on large software systems having hundreds to thousands of contributors. In our preliminary investigations on Android applications that are considered to be relatively small, we observed that applications systems are not totally owned by a single developer (as one could expect) and that cases of no clear authorship also exist like in large systems. To this end, we do believe that the Android applications could face the same challenges faced by large software systems and could also benefit from such studies. Goal: We investigate the extent to which the findings obtained on large software systems applies to Android applications. Approach: Building on the designs of previous studies, we analyze 278 Android applications carefully selected from GitHub. We extract code authorship metrics from the applications and examine the relationship between code authorship metrics and faults using statistical modeling. Results: Our analyses confirm most of the previous findings, i.e., Android applications with higher levels of code authorship among contributors experience fewer faults.
On Different Search Methods for Systematic Literature Reviews and Maps: Experiences from a Literature Search on Validation and Verification of Emergent Behavior

[Background] Systematic literature reviews and maps have become well-established research methods in software engineering research. Of the three commonly suggested and used search methods: manual search, database search, or snowball search; systematic literature reviews and maps typically employ one or a combination of two or three of those as their search strategy. As systematic literature reviews and maps raise a claim to result in a representative set of relevant papers for a certain area of investigation, it is of importance to understand the impact the search strategy has on achieving this goal. [Aim] This paper contributes a study to compare all three search methods. This study aims at providing evidence as to what advantages and disadvantages of these three search methods are. [Method] We conducted three systematic literature reviews on the same topic, which affects multiple software engineering related disciplines, using different search methods, while keeping other parameters like inclusion and exclusion criteria consistent among all three reviews. [Results] Our results show a similar effectiveness for snowball and database search and the highest efficiency for database searches. However, our literature reviews led to three barely overlapping sets of papers, which in turn led to distinct impressions of the same field. [Conclusion] Our results show that the use of a single search method can lead to a set of included papers, which misrepresents the research field under investigation. Hence, particularly when conducting literature reviews that affect different software engineering sub-disciplines and related disciplines, researchers should not just rely on the single most effective and/or efficient search method.