OpenRaiser/Intern-Atlas huggingface dataset:
Content Popularity Prediction Towards Location-Aware Mobile Edge Caching
Venue: arXiv cs.NI 2018
Year: 2018

Mobile edge caching enables content delivery within the radio access network, which effectively alleviates the backhaul burden and reduces response time. To fully exploit edge storage resources, the most popular contents should be identified and cached. Observing that user demands on certain contents vary greatly at different locations, this paper devises location-customized caching schemes to maximize the total content hit rate. Specifically, a linear model is used to estimate the future content hit rate. For the case where the model noise is zero-mean, a ridge regression based online algorithm with positive perturbation is proposed. Regret analysis indicates that the proposed algorithm asymptotically approaches the optimal caching strategy in the long run. When the noise structure is unknown, an $H_{\infty}$ filter based online algorithm is further proposed by taking a prescribed threshold as input, which guarantees prediction accuracy even under the worst-case noise process. Both online algorithms require no training phases, and hence are robust to the time-varying user demands. The underlying causes of estimation errors of both algorithms are numerically analyzed. Moreover, extensive experiments on real world dataset are conducted to validate the applicability of the proposed algorithms. It is demonstrated that those algorithms can be applied to scenarios with different noise features, and are able to make adaptive caching decisions, achieving content hit rate that is comparable to that via the hindsight optimal strategy.
Sleep Stage Classification: Scalability Evaluations of Distributed Approaches
Venue: arXiv cs.DC 2018
Year: 2018

Processing and analyzing of massive clinical data are resource intensive and time consuming with traditional analytic tools. Electroencephalogram (EEG) is one of the major technologies in detecting and diagnosing various brain disorders, and produces huge volume big data to process. In this study, we propose a big data framework to diagnose sleep disorders by classifying the sleep stages from EEG signals. The framework is developed with open source SparkMlib Libraries. We also tested and evaluated the proposed framework by measuring the scalabilities of well-known classification algorithms on physionet sleep records.
Vectorization of Large Amounts of Raster Satellite Images in a Distributed Architecture Using HIPI
Venue: arXiv cs.DC 2018
Year: 2018

Vectorization process focus on grouping pixels of a raster image into raw line segments, and forming lines, polylines or poligons. To vectorize massive raster images regarding resource and performane problems, weuse a distributed HIPI image processing interface based on MapReduce approach. Apache Hadoop is placed at the core of the framework. To realize such a system, we first define mapper function, and then its input and output formats. In this paper, mappers convert raster mosaics into vector counterparts. Reduc functions are not needed for vectorization. Vector representations of raster images is expected to give better performance in distributed computations by reducing the negative effects of bandwidth problem and horizontal scalability analysis is done.
The Complexity of Leader Election: A Chasm at Diameter Two
Venue: arXiv cs.DC 2018
Year: 2018

This paper focuses on studying the message complexity of implicit leader election in synchronous distributed networks of diameter two. Kutten et al.\ [JACM 2015] showed a fundamental lower bound of $Ω(m)$ ($m$ is the number of edges in the network) on the message complexity of (implicit) leader election that applied also to Monte Carlo randomized algorithms with constant success probability; this lower bound applies for graphs that have diameter at least three. On the other hand, for complete graphs (i.e., graphs with diameter one), Kutten et al.\ [TCS 2015] established a tight bound of $\tildeΘ(\sqrt{n})$ on the message complexity of randomized leader election ($n$ is the number of nodes in the network). For graphs of diameter two, the complexity was not known. In this paper, we settle this complexity by showing a tight bound of $\tildeΘ(n)$ on the message complexity of leader election in diameter-two networks. Together with the two previous results of Kutten et al., our results fully characterize the message complexity of leader election vis-à-vis the graph diameter.
vDLT: A Service-Oriented Blockchain System with Virtualization and Decoupled Management/Control and Execution
Venue: arXiv cs.NI 2018
Year: 2018

A wide range of services and applications can be improved and/or solved by using distributed ledger technology (DLT). These services and applications have widely varying quality of service (QoS) requirements. However, most existing DLT systems do not distinguish different QoS requirements, resulting in significant performance issues such as poor scalability and high cost. In this work, we present vDLT -- a service-oriented blockchain system with virtualization and decoupled management/control and execution. In vDLT, services and applications are classified into different classes according to their QoS requirements, including confirmation latency, throughput, cost, security, privacy, etc. This is a paradigm shift from the existing "blockchain-oriented" DLT systems to next generation "service-oriented" DLT systems. Different QoS requirements are fulfilled by advanced schemes inspired by the development of the traditional Internet, including classification, queuing, virtualization, resource allocation and orchestration, and hierarchical architecture. In addition, management/control and execution of smart contracts are decoupled to support QoS provisioning, improve decentralization, and facilitate evolution in vDLT. With virtualization, different virtual DLT systems with widely varying characteristics can be dynamically created and operated to accommodate different services and applications.
Exploring Gap Filling as a Cheaper Alternative to Reading Comprehension Questionnaires when Evaluating Machine Translation for Gisting
Venue: arXiv cs.CL 2018
Year: 2018

A popular application of machine translation (MT) is gisting: MT is consumed as is to make sense of text in a foreign language. Evaluation of the usefulness of MT for gisting is surprisingly uncommon. The classical method uses reading comprehension questionnaires (RCQ), in which informants are asked to answer professionally-written questions in their language about a foreign text that has been machine-translated into their language. Recently, gap-filling (GF), a form of cloze testing, has been proposed as a cheaper alternative to RCQ. In GF, certain words are removed from reference translations and readers are asked to fill the gaps left using the machine-translated text as a hint. This paper reports, for thefirst time, a comparative evaluation, using both RCQ and GF, of translations from multiple MT systems for the same foreign texts, and a systematic study on the effect of variables such as gap density, gap-selection strategies, and document context in GF. The main findings of the study are: (a) both RCQ and GF clearly identify MT to be useful, (b) global RCQ and GF rankings for the MT systems are mostly in agreement, (c) GF scores vary very widely across informants, making comparisons among MT systems hard, and (d) unlike RCQ, which is framed around documents, GF evaluation can be framed at the sentence level. These findings support the use of GF as a cheaper alternative to RCQ.
Chittron: An Automatic Bangla Image Captioning System
Venue: arXiv cs.CL 2018
Year: 2018

Automatic image caption generation aims to produce an accurate description of an image in natural language automatically. However, Bangla, the fifth most widely spoken language in the world, is lagging considerably in the research and development of such domain. Besides, while there are many established data sets to related to image annotation in English, no such resource exists for Bangla yet. Hence, this paper outlines the development of "Chittron", an automatic image captioning system in Bangla. Moreover, to address the data set availability issue, a collection of 16,000 Bangladeshi contextual images has been accumulated and manually annotated in Bangla. This data set is then used to train a model which integrates a pre-trained VGG16 image embedding model with stacked LSTM layers. The model is trained to predict the caption when the input is an image, one word at a time. The results show that the model has successfully been able to learn a working language model and to generate captions of images quite accurately in many cases. The results are evaluated mainly qualitatively. However, BLEU scores are also reported. It is expected that a better result can be obtained with a bigger and more varied data set.
Towards an Intelligent Edge: Wireless Communication Meets Machine Learning
Venue: arXiv cs.IT 2018
Year: 2018

The recent revival of artificial intelligence (AI) is revolutionizing almost every branch of science and technology. Given the ubiquitous smart mobile gadgets and Internet of Things (IoT) devices, it is expected that a majority of intelligent applications will be deployed at the edge of wireless networks. This trend has generated strong interests in realizing an "intelligent edge" to support AI-enabled applications at various edge devices. Accordingly, a new research area, called edge learning, emerges, which crosses and revolutionizes two disciplines: wireless communication and machine learning. A major theme in edge learning is to overcome the limited computing power, as well as limited data, at each edge device. This is accomplished by leveraging the mobile edge computing (MEC) platform and exploiting the massive data distributed over a large number of edge devices. In such systems, learning from distributed data and communicating between the edge server and devices are two critical and coupled aspects, and their fusion poses many new research challenges. This article advocates a new set of design principles for wireless communication in edge learning, collectively called learning-driven communication. Illustrative examples are provided to demonstrate the effectiveness of these design principles, and unique research opportunities are identified.
Development of a Medical Tele-Management System for Post-Discharge Patients of Chronic Diseases in Resource-Constrained Settings
Venue: arXiv cs.CY 2018
Year: 2018

Medical tele-management is an emerging field of study in telemedicine that proposes an interactive and proactive disease management approach which combines tele-monitoring and tele-consultation services through an information and communications technology (ICT) supported partnership. On one hand, chronic diseases require frequent and continuous monitoring to avoid complications; on the other hand is the need for a platform where health workers in rural settlements can consult or interact with their counterparts in urban areas to reduce isolation. However, telemedicine systems exist singly either as tele-monitoring or tele-consultation systems or majorly in developed countries with dedicated and adequate ICT resources and infrastructure. This work developed a combined tele-monitoring and tele-consultation system for the management of chronic diseases within an information and communication technology resource-constrained setting. This is achieved through the development of a multi-tiered framework and model deployed to provide medical tele-management services for rural African communities. The developed system achieved reliability score, availability, uptime and downtime of 0.9, 99.65%, 99.65% and 0.21% respectively. The evaluation results of the post-implementation task revealed that response means of 4.20 and 4.22 were achieved for the system degree of relevance (SDR) and system ease of use (SEU), respectively on a rating scale of 1 to 5.
Capturing Knowledge Triggering in Collaborative Settings
Venue: arXiv cs.CY 2018
Year: 2018

In collaborative knowledge building settings, the existing knowledge in the system is perceived to set stage for the manifestation of more knowledge, termed as the phenomenon of triggering. Although the literature points to a few theories supporting the existence of this phenomenon, these have never been validated in real collaborative environments, thus questioning their general prevalence. In this work, we provide a mechanized way to observe the presence of triggering in knowledge building environments. We implement the method on the most-edited articles of Wikipedia and show how the existing factoids lead to the inclusion of more factoids in these articles. The proposed technique may further be used in other collaborative knowledge building settings as well. The insights obtained from the study will help the portal designers to build portals enabling optimal triggering.
Post-Quantum Secure Cryptographic Algorithms
Venue: arXiv cs.CR 2018
Year: 2018

An overview on current developments in post quantum cryptography
A study on users' privacy perception with smart devices
Venue: arXiv cs.CY 2018
Year: 2018

Nowadays, privacy has become a very serious issue with smart and mobile platforms. Users tend to allow intrusive apps access much sensible information without really knowing the potential threats. To solve this issue several solutions (e.g. GDPR) have been provided. Our claim is that the users currently are not sufficiently involved in this process for being able to use such solutions. To do this we developed an application that provides a form of awareness to the users and we asked them to reply a set of questions. Our conclusions are that users must be better informed of the risks and value of their personal information.
On the Role of Event Boundaries in Egocentric Activity Recognition from Photostreams
Venue: arXiv cs.CV 2018
Year: 2018

Event boundaries play a crucial role as a pre-processing step for detection, localization, and recognition tasks of human activities in videos. Typically, although their intrinsic subjectiveness, temporal bounds are provided manually as input for training action recognition algorithms. However, their role for activity recognition in the domain of egocentric photostreams has been so far neglected. In this paper, we provide insights of how automatically computed boundaries can impact activity recognition results in the emerging domain of egocentric photostreams. Furthermore, we collected a new annotated dataset acquired by 15 people by a wearable photo-camera and we used it to show the generalization capabilities of several deep learning based architectures to unseen users.
Query Log Compression for Workload Analytics
Venue: arXiv cs.DB 2018
Year: 2018

Analyzing database access logs is a key part of performance tuning, intrusion detection, benchmark development, and many other database administration tasks. Unfortunately, it is common for production databases to deal with millions or even more queries each day, so these logs must be summarized before they can be used. Designing an appropriate summary encoding requires trading off between conciseness and information content. For example: simple workload sampling may miss rare, but high impact queries. In this paper, we present LogR, a lossy log compression scheme suitable use for many automated log analytics tools, as well as for human inspection. We formalize and analyze the space/fidelity trade-off in the context of a broader family of "pattern" and "pattern mixture" log encodings to which LogR belongs. We show through a series of experiments that LogR compressed encodings can be created efficiently, come with provable information-theoretic bounds on their accuracy, and outperform state-of-art log summarization strategies.
A logic-algebraic tool for reasoning with Knowledge-Based Systems
Venue: arXiv cs.LO 2018
Year: 2018

A detailed exposition of foundations of a logic-algebraic model for reasoning with knowledge bases specified by propositional (Boolean) logic is presented. The model is conceived from the logical translation of usual derivatives on polynomials (on residue rings) which is used to design a new inference rule of algebro-geometric inspiration. Soundness and (refutational) completeness of the rule are proved. Some applications of the tools introduced in the paper are shown.
ViewpointS: towards a Collective Brain
Venue: arXiv cs.MA 2018
Year: 2018

Tracing knowledge acquisition and linking learning events to interaction between peers is a major challenge of our times. We have conceived, designed and evaluated a new paradigm for constructing and using collective knowledge by Web interactions that we called ViewpointS. By exploiting the similarity with Edelman's Theory of Neuronal Group Selection (TNGS), we conjecture that it may be metaphorically considered a Collective Brain, especially effective in the case of trans-disciplinary representations. Far from being without doubts, in the paper we present the reasons (and the limits) of our proposal that aims to become a useful integrating tool for future quantitative explorations of individual as well as collective learning at different degrees of granu-larity. We are therefore challenging each of the current approaches: the logical one in the semantic Web, the statistical one in mining and deep learning, the social one in recommender systems based on authority and trust; not in each of their own preferred field of operation, rather in their integration weaknesses far from the holistic and dynamic behavior of the human brain.
Affordance Extraction and Inference based on Semantic Role Labeling
Venue: arXiv cs.CL 2018
Year: 2018

Common-sense reasoning is becoming increasingly important for the advancement of Natural Language Processing. While word embeddings have been very successful, they cannot explain which aspects of 'coffee' and 'tea' make them similar, or how they could be related to 'shop'. In this paper, we propose an explicit word representation that builds upon the Distributional Hypothesis to represent meaning from semantic roles, and allow inference of relations from their meshing, as supported by the affordance-based Indexical Hypothesis. We find that our model improves the state-of-the-art on unsupervised word similarity tasks while allowing for direct inference of new relations from the same vector space.
IoU is not submodular
Venue: arXiv cs.LG 2018
Year: 2018

This short article aims at demonstrate that the Intersection over Union (or Jaccard index) is not a submodular function. This mistake has been made in an article which is cited and used as a foundation in another article. The Intersection of Union is widely used in machine learning as a cost function especially for imbalance data and semantic segmentation.
Agile Metrics for a University Software Engineering Course
Venue: arXiv cs.SE 2018
Year: 2018

Teaching agile software development by pairing lectures with hands-on projects has become the norm. This approach poses the problem of grading and evaluating practical project work as well as process conformance during development. Yet, few best practices exist for measuring the success of students in implementing agile practices. Most university courses rely on observations during the course or final oral exams. In this paper, we propose a set of metrics which give insights into the adherence to agile practices in teams. The metrics identify instances in development data, e.g. commits or user stories, where agile processes were not followed. The identified violations can serve as starting points for further investigation and team discussions. With contextual knowledge of the violation, the executed process or the metric itself can be refined. The metrics reflect our experiences with running a software engineering course over the last five years. They measure aspects which students frequently have issues with and that diminish process adoption and student engagement. We present the proposed metrics, which were tested in the latest course installment, alongside tutoring, lectures, and oral exams.
Endorsements on Social Media: An Empirical Study of Affiliate Marketing Disclosures on YouTube and Pinterest
Venue: arXiv cs.HC 2018
Year: 2018

Online advertisements that masquerade as non-advertising content pose numerous risks to users. Such hidden advertisements appear on social media platforms when content creators or "influencers" endorse products and brands in their content. While the Federal Trade Commission (FTC) requires content creators to disclose their endorsements in order to prevent deception and harm to users, we do not know whether and how content creators comply with the FTC's guidelines. In this paper, we studied disclosures within affiliate marketing, an endorsement-based advertising strategy used by social media content creators. We examined whether content creators follow the FTC's disclosure guidelines, how they word the disclosures, and whether these disclosures help users identify affiliate marketing content as advertisements. To do so, we first measured the prevalence of and identified the types of disclosures in over 500,000 YouTube videos and 2.1 million Pinterest pins. We then conducted a user study with 1,791 participants to test the efficacy of these disclosures. Our findings reveal that only about 10% of affiliate marketing content on both platforms contains any disclosures at all. Further, users fail to understand shorter, non-explanatory disclosures. Based on our findings, we make various design and policy suggestions to help improve advertising disclosure practices on social media platforms.
Channel Characterization for Chip-scale Wireless Communications within Computing Packages
Venue: arXiv cs.ET 2018
Year: 2018

Wireless Network-on-Chip (WNoC) appears as a promising alternative to conventional interconnect fabrics for chip-scale communications. WNoC takes advantage of an overlaid network composed by a set of millimeter-wave antennas to reduce latency and increase throughput in the communication between cores. Similarly, wireless inter-chip communication has been also proposed to improve the information transfer between processors, memory, and accelerators in multi-chip settings. However, the wireless channel remains largely unknown in both scenarios, especially in the presence of realistic chip packages. This work addresses the issue by accurately modeling flip-chip packages and investigating the propagation both its interior and its surroundings. Through parametric studies, package configurations that minimize path loss are obtained and the trade-offs observed when applying such optimizations are discussed. Single-chip and multi-chip architectures are compared in terms of the path loss exponent, confirming that the amount of bulk silicon found in the pathway between transmitter and receiver is the main determinant of losses.
Typed Linear Algebra for Efficient Analytical Querying
Venue: arXiv cs.DB 2018
Year: 2018

This paper uses typed linear algebra (LA) to represent data and perform analytical querying in a single, unified framework. The typed approach offers strong type checking (as in modern programming languages) and a diagrammatic way of expressing queries (paths in LA diagrams). A kernel of LA operators has been implemented so that paths extracted from LA diagrams can be executed. The approach is validated and evaluated taking TPC-H benchmark queries as reference. The performance of the LA-based approach is compared with popular database competitors (PostgreSQL and MySQL).