bluuebunny/crossref_metadata_2025 huggingface dataset:
Concurrent separation logic
Authors: Brookes Stephen , O'Hearn Peter W.
Published: April 2020

Concurrent Separation Logic (CSL) was originally advanced in papers of the authors published in Theoretical Computer Science for John Reynolds's 70th Birthday Festschrift (2007). Preliminary versions appeared as invited papers in the CONCUR'04 conference proceedings. Foundational work leading to these papers began in 2002. Since then there have been significant developments stemming from CSL, both in theoretical and practical research. In this retrospective paper we describe the main ideas that underpin CSL, placing these ideas into historical context by summarizing the prevailing tendencies in concurrency verification and programming language semantics when the logic was being invented in 2002-2003. We end with a snapshot of the state-of-the-art as of 2016. Along the way we describe some of the main developments in the intervening period, and we attempt to classify the work that has been done, along broad lines. While we do not intend an exhaustive survey, we do hope to provide some general perspective on what has been achieved in the field, what remains to be done, and directions for future work.
DisCoTec 2016 conference report
Authors: Vieira Hugo Torres
Published: April 2020

The 11th International Federated Conference on Distributed Computing Techniques (DisCoTec 2016) took place in Heraklion, Crete (Greece) from June 6 to June 9, 2016. DisCoTec has gathered for the past 11 years three main conferences, that previously where held separately, and hosts co-located workshops with an unifying theme on distributed computing, encompassing research originating in different communities.
A survey on guarded negation
Authors: Segoufin Luc
Published: April 2020

We consider a logical framework building on existential positive formulas and then adding guarded negations and guarded fixpoints, where the guards are atomic formulas containing all free variables. The resulting first-order and fixpoint logics turn out to have nice algorithmic properties and nice expressive power. We survey some of them.
Report on MFPS XXXIII and CALCO 2017
Authors: Bauer Andrej
Published: April 2020

I once heard it said that cousins can be best friends because they understand the quirks of each other's families but are separated enough not to be hindered by sibling rivalry. If this is true, then Mathematical Foundations of Programming Semantics (MFPS) and the Conference on Algebra and Coalgebra in Computer Science (CALCO) are cousins. When it was decided that they would meet again, this time in Ljubljana, Slovenia, one could only expect an enjoyable and productive week in one of the greenest capitals of Europe. The preparations proceeded smoothly under the MFPS program chair Alexandra Silva (University College London), the CALCO program co-chairs Barbara König (University of Duisburg-Essen) and Filippo Bonchi (École normale supérieure de Lyon), and the head local organizer Matija Pretnar (University of Ljubljana).
Undecidability results for probabilistic automata
Authors: Fijalkow Nathanaël
Published: April 2020

The model of probabilistic automata was introduced by Rabin in 1963. Ever since, undecidability results were obtained for this model, showing that although simple, it is very expressive. This paper provides streamlined constructions implying the most important negative results, including the celebrated inapproximability result of Condon and Lipton.
Satisfiability of Xpath on data trees
Authors: Figueira Diego
Published: April 2020

This is a survey on the satisfiability problem for XPath on data trees.
Data trees
are finite trees whose every node carries a label from a finite alphabet and a data value from an infinite domain. XPath is an expressive node selecting language for XML documents, which can be abstracted as data trees. Its satisfiability problem is in general undecidable. However, various fragments of XPath have decidable satisfiability problem, these are fragments defined in terms of the navigational axes which are allowed. We explore the state of the art in terms of decidability and discuss briefly some of the algorithmic techniques involved.
Automatic verification of database-centric systems
Authors: Deutsch Alin , Hull Richard , Li Yuliang , Vianu Victor
Published: April 2020

We present an overview of results on verification of temporal properties of infinite-state transition systems arising from processes that carry and manipulate unbounded data. The techniques bring into play tools from logic, database theory, and model checking. The theoretical results establish the boundaries of decidability and the complexity of verification for various models. We also describe verifier implementations with surprisingly good performance, suggesting that this line of research has real potential for practical impact.
Conference reports
Authors: Pérez Jorge A
Published: April 2020

In this installment of the conference report column, Agata Ciabattoni, Francesco A. Genco, and Revantha Ramanayake (TU Wien) report on the Second SYSMICS workshop on "Substructural logics: semantics, proof theory, and applications", which took place in Vienna (Austria), on 26--28 February 2018.
First-order logic and aperiodic languages
Authors: Straubing Howard
Published: April 2020

A fundamental result about formal languages states:

Theorem 1
A regular language is first-order definable if and only if its syntactic monoid contains no nontrivial groups.

Rest assured, we will explain in the next section exactly what the various terms in the statement mean!
A survey of constructive presheaf models of univalence
Authors: Coquand Thierry
Published: April 2020

Any formal system for representing mathematics should address the two questions of how to represent
collections
of mathematical objects and how to decide the laws of
identifications
of these objects. These laws of identifications have become quite subtle. While it has been clear for a long time that it is good mathematical practice to identify
isomorphic
algebraic structures [11], or at least to use only notions and facts about algebraic structures that are invariant under isomorphisms, category theory extends this to the notion of
categorical
equivalences
1
, which themselves have been generalized to higher forms of equivalences [25]. Voevodsky noticed that, by extending some versions of dependent type theory with one further axiom - the
univalence
axiom
- one obtains a formal system in which all notions and operations are automatically invariant under isomorphisms and even under higher notions of equivalence.
A survival guide to presburger arithmetic
Authors: Haase Christoph
Published: April 2020

The first-order theory of the integers with addition and order, commonly known as Presburger arithmetic, has been a central topic in mathematical logic and computer science for almost 90 years. Presburger arithmetic has been the starting point for numerous lines of research in automata theory, model theory and discrete geometry. In formal verification, Presburger arithmetic is the first-choice logic to represent and reason about systems with infinitely many states. This article provides a broad yet concise overview over the history, decision procedures, extensions and geometric properties of Presburger arithmetic.
Complexity column
Authors: Immerman Neil
Published: April 2020

In 1975, Ladner proved that computational complexty is dense in the sense that if
C
0
is strictly contained in
C
1
, for any two complexity classes satisfying standard closure properties, then there is a complexity class
C
0.5
strictly between them. However, naturally occuring algorithmic problems tend to be complete for one of a tiny stable of important complexity classes. This is often observed and leads to the great practical usefulness of the study of algorithms and complexity.
Verification column
Authors: Lazić Ranko
Published: April 2020

In this accessible yet rigorous column, Christel Baier and Clemens Dubslaff provide a timely and valuable overview of the vibrant area of algorithmic synthesis problems based on Markov decision processes. The latter are one of the most prominent stochastic models, both in theory and in applications. The overview is extensive, covering synthesis with non-standard objectives as well as family-based analysis, and indicating several emerging directions.
Automata Column
Authors: Bojańczyk Mikołaj
Published: April 2020

In this issue, the automata column discusses a powerful decision procedure for infinite state systems, which I would call the Hilbert Method, because it uses theorems like Hilbert's Basis Theorem or the Nullstellensatz. The method - which reduces combinatorial objects like words or trees to algebraic objects like numbers and polynomials - has been used to solve important open problems like: Ehrenfeucht's Conjecture, the equivalence problem for HDT0L systems, or the equivalence problem for tree-to-string transducers. Because of its power, generality and simplicity, I think that the method should become a standard part of the toolkit of automata theory for working with infinite state systems, joining established methods like integer programming or well-structured transition systems.
In memory of Oded Maler
Authors: Fränzle Martin , Chen Mingshuai , Kröger Paul
Published: April 2020

Hybrid automata are an elegant formal model seamlessly integrating differential equations representing continuous dynamics with automata capturing switching behavior. Since the introduction of the computational model more than a quarter of a century ago [Maler et al. 1992], its algorithmic verification has been an area of intense research. Within this note, which is dedicated to Oded Maler (1957--2018) as one of the inventors of the model, we are trying to delineate major lines of attack to the reachability problem for hybrid automata. Due to its relation to system safety, the reachability problem is a prototypical verification problem for hybrid discrete-continuous system dynamics.
Women in Logic 2018 workshop report
Authors: Rozman Mihaela , de Paiva Valeria , Felty Amy , Martin Ursula
Published: April 2020

Women in Logic 2018, WiL2018, took place on July 8th 2018 in Oxford, as a workshop of the 33rd annual ACM/IEEE Symposium on Logic in Computer Science (LICS), part of the Federated Logic Conference 2018 (FLoC). It built on the success of the first such event, held in in Reykjavik, Iceland, in 2017, again in association with LICS.
Robustness of Word and Character N-gram Combinations in Detecting Deceptive and Truthful Opinions
Authors: Siagian Al Hafiz Akbar Maulana , Aritsugi Masayoshi
Published: April 2020

Opinions in reviews about the quality of products or services can be important information for readers. Unfortunately, such opinions may include deceptive ones posted for some business reasons. To keep the opinions as a valuable and trusted source of information, we propose an approach to detecting deceptive and truthful opinions. Specifically, we explore the use of word and character n-gram combinations, function words, and word syntactic n-grams (word sn-grams) as features for classifiers to deal with this task. We also consider applying word correction to our utilized dataset. Our experiments show that classification results of using the word and character n-gram combination features could perform better than those of employing other features. Although the experiments indicate that applying the word correction might be insignificant, we note that the deceptive opinions tend to have a smaller number of error words than the truthful ones. To examine robustness of our features, we then perform cross-classification tests. Our latter experiments results suggest that using the word and character n-gram combination features could work well in detecting deceptive and truthful opinions. Interestingly, the latter experimental results also indicate that using the word sn-grams as combination features could give good performance.
On-the-fly Page Migration and Address Reconciliation for Heterogeneous Memory Systems
Authors: Islam Mahzabeen , Adavally Shashank , Scrbak Marko , Kavi Krishna
Published: April 2020

For efficient placement of data in flat-address heterogeneous memory systems consisting of fast (e.g., 3D-DRAM) and slow memories (e.g., NVM), we present a hardware-based page migration technique. Unlike epoch-based approaches that migrate heavily accessed (“hot”) pages from slow to fast memories at each epoch interval, we migrate a page immediately when it becomes hot (“on-the-fly”), using hardware in user-transparent manner and with minimal OS intervention. The management of physical addresses due to page relocation becomes cumbersome and requires costly OS intervention. We use a small hardware remap table to keep track of new physical addresses of the migrated pages. This limits address reconciliation to occur only at periodic evictions of old remap entries. Also, we propose a hardware-orchestrated light-weight address reconciliation process. For our studied heterogeneous memory system, on-the-fly page migration with hardware-assisted address reconciliation provides 74% and 24% IPC improvements, on average for a set of SPEC CPU2006 workloads when compared to a baseline without any page migration and a system with on-the-fly page migration using OS-based address reconciliation, respectively.

Furthermore, we present an analytical model for classifying applications as page migration friendly (applications that show performance gains from page migration) or unfriendly based on memory access behavior.
Developing a Computing Identity Framework
Authors: Mahadeo Jonathan , Hazari Zahra , Potvin Geoff
Published: April 2020

This paper expands on knowledge of computing identity by building on what is known about prior identity models in science and mathematics education. The model theorizes three primary sub-constructs that contribute to the development of a computing identity: belief in one's performance/competence, interest, and recognition in computing. Drawing on data from a nationally representative survey of more than 1,700 college students at 22 colleges and universities, the study tested the alignment of the theorized model to the measures on the survey. Confirmatory Factor Analysis was used to validate whether the appropriate measures loaded on the three separate sub-constructs. Criterion-related validity was also established by testing whether the computing identity measures predicted the choice of a computer science career. The results reveal that a computing identity proxy based on the theorized measures was a highly significant predictor of students' computer science and information technology career choice (p < 0.0001). In addition, this work also established criterion-related validity by showing gender differences that had been found by prior work in computing. Finally, the theorized measures were found to be reliable and internally consistent. The educational understanding of computing identities may provide an important tool to help researchers and practitioners improve student persistence in computer science.
Transparency about a Robot's Lack of Human Psychological Capacities
Authors: Straten Caroline L. van , Peter Jochen , Kühne Rinaldo , Barco Alex
Published: April 2020

The increasing sophistication of social robots has intensified calls for transparency about robots’ machine nature. Initial research has suggested that providing children with information about robots’ mechanical status does not alter children's humanlike perception of, and relationship formation with, social robots. Against this background, our study experimentally investigated the effects of transparency about a robot's lack of human psychological capacities (intelligence, self-consciousness, emotionality, identity construction, social cognition) on children's perceptions of a robot and their relationship to it. Our sample consisted of 144 children aged 8 to 9 years old who interacted with the Nao robot in either a transparent or a control condition. Transparency decreased children's humanlike perception of the robot in terms of animacy, anthropomorphism, social presence, and perceived similarity. Transparency reduced child-robot relationship formation in terms of decreased trust, while children's feelings of closeness toward the robot were not affected.
Multimodal Physiological Signals for Workload Prediction in Robot-assisted Surgery
Authors: Zhou Tian , Cha Jackie S. , Gonzalez Glebys , Wachs Juan P. , Sundaram Chandru P. , Yu Denny
Published: April 2020

Monitoring surgeon workload during robot-assisted surgery can guide allocation of task demands, adapt system interfaces, and assess the robotic system's usability. Current practices for measuring cognitive load primarily rely on questionnaires that are subjective and disrupt surgical workflow. To address this limitation, a computational framework is demonstrated to predict user workload during telerobotic surgery. This framework leverages wireless sensors to monitor surgeons’ cognitive load and predict their cognitive states. Continuous data across multiple physiological modalities (e.g., heart rate variability, electrodermal, and electroencephalogram activity) were simultaneously recorded for twelve surgeons performing surgical skills tasks on the validated da Vinci Skills Simulator. These surgical tasks varied in difficulty levels, e.g., requiring varying visual processing demand and degree of fine motor control. Collected multimodal physiological signals were fused using independent component analysis, and the predicted results were compared to the ground-truth workload level. Results compared performance of different classifiers, sensor fusion schemes, and physiological modality (i.e., prediction with single vs. multiple modalities). It was found that our multisensor approach outperformed individual signals and can correctly predict cognitive workload levels 83.2% of the time during basic and complex surgical skills tasks.
A Multi-server ORAM Framework with Constant Client Bandwidth Blowup
Authors: Hoang Thang , Yavuz Attila A. , Guajardo Jorge
Published: April 2020

Oblivious Random Access Machine (ORAM) allows a client to hide the access pattern when accessing sensitive data on a remote server. It is known that there exists a logarithmic communication lower bound on any passive ORAM construction, where the server only acts as the storage service. This overhead, however, was shown costly for some applications. Several active ORAM schemes with server computation have been proposed to overcome this limitation. However, they mostly rely on costly homomorphic encryptions, whose performance is worse than passive ORAM. In this article, we propose S
3
ORAM, a new multi-server ORAM framework, which features
O
(1) client bandwidth blowup and low client storage without relying on costly cryptographic primitives. Our key idea is to harness Shamir Secret Sharing and a multi-party multiplication protocol on applicable binary tree-ORAM paradigms. This strategy allows the client to instruct the server(s) to perform secure and efficient computation on his/her behalf with a low intervention thereby, achieving a constant client bandwidth blowup and low server computational overhead. Our framework can also work atop a general
k
-ary tree ORAM structure (
k
≥ 2). We fully implemented our framework, and strictly evaluated its performance on a commodity cloud platform (Amazon EC2). Our comprehensive experiments confirmed the efficiency of S
3
ORAM framework, where it is approximately 10× faster than the most efficient passive ORAM (i.e., Path-ORAM) for a moderate network bandwidth while being three orders of magnitude faster than active ORAM with
O
(1) bandwidth blowup (i.e., Onion-ORAM). We have open-sourced the implementation of our framework for public testing and adaptation.
Feel Free to Interrupt
Authors: Attia Sameh , Betz Vaughn
Published: April 2020

Saving and restoring an FPGA task state in an orderly manner is essential to enable hardware checkpointing, which is highly desirable to improve the ability to debug cloud-scale hardware services, and context switching, which allows multiple users to share FPGA resources. However, these features require task interruption, and stopping a task at an arbitrary time can cause several hazards including deadlock and data loss. In this article, we build a context saving and restoring simulator to simulate and identify these hazards. In addition, we derive design rules that should be followed to achieve safe task interruption. Finally, we propose task wrappers that can be placed around an FPGA task to implement these rules. The timing and area overheads added by these wrappers are very small; they add 1.8% area and no timing overhead to a full Memcached system. Taken together, these design rules and wrappers enable safe checkpointing and context switching in a wide variety of FPGA tasks, including those with multiple clocks, multi-cycle I/O transactions, and interface dependencies.
“The Best of Both Worlds!”
Authors: Eraslan Sukru , Yesilada Yeliz , Harper Simon
Published: April 2020

Web pages are composed of different kinds of elements (menus, adverts, etc.). Segmenting pages into their elements has long been important in understanding how people experience those pages and in making those experiences “better.” Many approaches have been proposed that relate the resultant elements with the underlying source code; however, they do not consider users’ interactions. Another group of approaches analyses eye movements of users to discover areas that interest or attract them (i.e., areas of interest or AOIs). Although these approaches consider how users interact with web pages, they do not relate AOIs with the underlying source code. We propose a novel approach that integrates web page and eye tracking data driven approaches for automatic AOI detection. This approach segments an entire web page into its AOIs by considering users’ interactions and relates AOIs with the underlying source code. Based on the Adjusted Rand Index measure, our approach provides the most similar segmentation to the ground-truth segmentation compared to its individual components.
An Outsourcing Model for Alert Analysis in a Cybersecurity Operations Center
Authors: Shah Ankit , Ganesan Rajesh , Jajodia Sushil , Cam Hasan
Published: April 2020

A typical Cybersecurity Operations Center (CSOC) is a service organization. It hires and trains analysts, whose task is to perform analysis of alerts that were generated while monitoring the client’s networks. Due to ever-increasing financial and infrastructure burden on a CSOC driven by the rapidly growing demand for security services, it would become prohibitively expensive to continually expand the size of a CSOC to meet the demands in the future. An alternative solution is to outsource the alert analysis process to on-demand analysts, to provide scalable CSOC service to its clients with features, such as (1) higher throughput, (2) higher quality, and (3) more economical service than the current in-house service. The current outsourcing model is not cost effective and an exact optimization model is computationally inefficient. This article presents a novel two-step sequential mixed integer programming optimization method that is used in the development of a new decision-support business model for outsourcing the alert analysis process. It is demonstrated that through this model, a CSOC can effectively deliver its alert management services with the above-mentioned features. Results indicate that the model is scalable, computationally viable, real-time implementable, and can deliver CSOC services that meet the service-level agreement (SLA) between the CSOC and its client. In addition, the article provides valuable insights into the cost of operating the new business process outsourcing model for cybersecurity services.
Improving the Accuracy of the Video Popularity Prediction Models through User Grouping and Video Popularity Classification
Authors: Hassanpour Masoud , Hoseinitabatabaei Seyed Amir , Barnaghi Payam , Tafazolli Rahim
Published: April 2020

This article proposes a novel approach for enhancing the video popularity prediction models. Using the proposed approach, we enhance three popularity prediction techniques that outperform the accuracy of the prior state-of-the-art solutions. The major components of the proposed approach are two novel mechanisms for ”
user grouping
” and ”
content classification
.” The user grouping method is an unsupervised clustering approach that divides the users into an adequate number of user groups with similar interests. The content classification approach identifies the classes of videos with similar popularity growth trends. To predict the popularity of the newly-released videos, our proposed popularity prediction model trains its parameters in each user group and its associated video popularity classes. Evaluations are performed through a 5-fold cross validation and on a dataset containing one month video request records of 26,706 users of BBC iPlayer. Using the proposed grouping technique, user groups of similar interest and up to two video popularity classes for each user group were detected. Our analysis shows that the accuracy of the proposed solution outperforms the state-of-the-art, including Szabo-Huberman (SH), Multivariate Linear (ML), and Multivariate linear Radial Basis Functions (MRBF) models by an average of 45%, 33%, and 24%, respectively. Finally, we discuss how various systems in the network and service management domain such as cache deployment, advertising, and video broadcasting technologies benefit from our findings to illustrate the implications.
A Burmese (Myanmar) Treebank
Authors: Ding Chenchen , Yee Sann Su Su , Pa Win Pa , Soe Khin Mar , Utiyama Masao , Sumita Eiichiro
Published: April 2020

A 20,000-sentence Burmese (Myanmar) treebank on news articles has been released under a CC BY-NC-SA license. Complete phrase structure annotation was developed for each sentence from the morphologically annotated data prepared in previous work of Ding et al. [1]. As the final result of the Burmese component in the
Asian Language Treebank Project
, this is the first large-scale, open-access treebank for the Burmese language. The annotation details and features of this treebank are presented.
Strings at MOSCA
Authors: Hague Matthew
Published: April 2020

The first edition of MOSCA, the Meeting on String Constraints and Applications was held in May 2019 in the otherworldly location of Bertinoro [D'Antoni et al. 2019]. For one week, 43 participants presented surveys and discussed recent research into the analysis of string constraints.
Conference reports
Authors: Pérez Jorge A
Published: April 2020

In this installment of the conference report column, Alberto Lluch Lafuente reports on DisCoTec 2019, the 14th International Federated Conference on Distributed Computing Techniques, of which he was the general chair. This event took place in Kongens Lyngby, Denmark, in June 17--21, 2019.
1
Korean Part-of-speech Tagging Based on Morpheme Generation
Authors: Song Hyun-Je , Park Seong-Bae
Published: April 2020

Two major problems of Korean part-of-speech (POS) tagging are that the word-spacing unit is not mapped one-to-one to a POS tag and that morphemes should be recovered during POS tagging. Therefore, this article proposes a novel two-step Korean POS tagger that solves the problems. This tagger first generates a sequence of lemmatized and recovered morphemes that can be mapped one-to-one to a POS tag using an encoder-decoder architecture derived from a POS-tagged corpus. Then, the POS tag of each morpheme in the generated sequence is finally determined by a standard sequence labeling method. Since the knowledge for segmenting and recovering morphemes is extracted automatically from a POS-tagged corpus by an encoder-decoder architecture, the POS tagger is constructed without a dictionary nor handcrafted linguistic rules. The experimental results on a standard dataset show that the proposed method outperforms existing POS taggers with its state-of-the-art performance.
Loanword Identification in Low-Resource Languages with Minimal Supervision
Authors: Mi Chenggang , Xie Lei , Zhang Yanning
Published: April 2020

Bilingual resources play a very important role in many natural language processing tasks, especially the tasks in cross-lingual scenarios. However, it is expensive and time consuming to build such resources. Lexical borrowing happens in almost every language. This inspires us to detect these loanwords effectively, and to use the “loanword (in receipt language)”-“donor word (in donor language)” to extend the bilingual resource for NLP tasks in low-resource languages. In this article, we propose a novel method to identify loanwords in Uyghur. The most important advantage of this method is that the model only relies on large amount of monolingual corpora and only a small scale of annotated data. Our loanword identification model includes two parts: loanword candidate generation and loanword prediction. In the first part, we use two large-scale monolingual corpora and a small bilingual dictionary to train a cross-lingual embedding model. Since semantic unrelated words often cannot be treated as loanword pairs, a loanword candidate list will be generated according to this model and a word list in Uyghur. In the second part, we predict from the preceding candidates based on a log-linear model that integrates several features such as pronunciation similarity, part-of-speech tags, and hybrid language modeling. To evaluate the effectiveness of our proposed method, we conduct two types of experiments: loanword identification and OOV translation. Experimental results showed that (1) our proposed method achieved significant F1 improvements compared to other models in all four loanword identification tasks in Uyghur, and (2) after extending the existing translation models with loanword identification results, OOV rates in several language pairs reduced significantly and the translation performance improved.
A Survey of Figurative Language and Its Computational Detection in Online Social Networks
Authors: Abulaish Muhammad , Kamal Ashraf , Zaki Mohammed J.
Published: April 2020

The frequent usage of figurative language on online social networks, especially on Twitter, has the potential to mislead traditional sentiment analysis and recommender systems. Due to the extensive use of slangs, bashes, flames, and non-literal texts, tweets are a great source of figurative language, such as sarcasm, irony, metaphor, simile, hyperbole, humor, and satire. Starting with a brief introduction of figurative language and its various categories, this article presents an in-depth survey of the state-of-the-art techniques for computational detection of seven different figurative language categories, mainly on Twitter. For each figurative language category, we present details about the characterizing features, datasets, and state-of-the-art computational detection approaches. Finally, we discuss open challenges and future directions of research for each figurative language category.
Time-aspect-sentiment Recommendation Models Based on Novel Similarity Measure Methods
Authors: Li Guohui , Chen Qi , Zheng Bolong , Hung Nguyen Quoc Viet , Zhou Pan , Liu Guanfeng
Published: April 2020

The explosive growth of e-commerce has led to the development of the recommendation system. The recommendation system aims to provide a set of items that meet users’ personalized needs through analyzing users’ consumption records. However, the timeliness of purchasing data and the implicity of feedback data pose severe challenges for the existing recommendation methods. To alleviate these challenges, we exploit the user’s consumption records from the perspectives of user and item, by modeling the data on both item and user level, where the item-level value reflects the grade of item, and the user-level value reflects the user’s purchase intention. In this article, we collect the description information and the reviews of the items from public websites, then adopt sentiment analysis techniques to model the similarities on user level and item level, respectively. In particular, we extend the traditional latent factor model and propose two novel methods—
I
tem
L
evel Similarity
M
atrix
F
actorization (ILMF) and
U
ser
L
evel Similarity
M
atrix
F
actorization (ULMF)—by introducing two novel similarity measure methods. In ILMF and ULMF, the consistency between latent factors and explicit aspects is naturally incorporated into learning latent factors of the users and items, such that we can predict the users’ preferences on different items more accurately. Moreover, we propose
I
tem-
U
ser
L
evel Similarity
M
atrix
F
actorization (IULMF), which combines these two methods to study their contributions on the final performance. Experimental evaluations on the real datasets show that our methods outperform the baseline approaches in terms of both the precision and NDCG.
HopliteBuf
Authors: Garg Tushar , Wasly Saud , Pellizzoni Rodolfo , Kapre Nachiket
Published: April 2020

HopliteBuf is a deflection-free, low-cost, and high-speed FPGA overlay Network-on-chip (NoC) with stall-free buffers. It is an FPGA-friendly 2D unidirectional torus topology built on top of HopliteRT overlay NoC. The stall-free buffers in HopliteBuf are supported by static analysis tools based on network calculus that help determine worst-case FIFO occupancy bounds for a prescribed workload. We implement these FIFOs using cheap LUT SRAMs (Xilinx SRL32s and Intel MLABs) to reduce cost. HopliteBuf is a hybrid microarchitecture that combines the performance benefits of conventional buffered NoCs by using stall-free buffers with the cost advantages of deflection-routed NoCs by retaining the lightweight unidirectional torus topology structure. We present two design variants of the HopliteBuf NoC: (1) single corner-turn FIFO (
W
→
S
) and (2) dual corner-turn FIFO (
W
→
S
+
N
). The single corner-turn (
W
→
S
) design is simpler and only introduces a buffering requirement for packets changing dimension from the X ring to the downhill Y ring (or West to South). The dual corner-turn variant requires two FIFOs for turning packets going downhill (
W
→
S
) as well as uphill (
W
→
N
). The dual corner-turn design overcomes the mathematical analysis challenges associated with single corner-turn designs for communication workloads with cyclic dependencies between flow traversal paths at the expense of a small increase in resource cost. Our static analysis delivers bounds that are not only better (in latency) than HopliteRT but also tighter by 2−3×. Across 100 randomly generated flowsets mapped to a 5×5 system size, HopliteBuf is able to route a larger fraction of these flowsets with <128-deep FIFOs, boost worst-case routing latency by ≈ 2× for mutually feasible flowsets, and support a 10% higher injection rate than HopliteRT. At 20% injection rates, HopliteRT is only able to route 1--2% of the flowsets, while HopliteBuf can deliver 40--50% sustainability. When compared to the
W
→
S

bkp

backpressure-based router, we observe that our HopliteBuf solution offers 25--30% better feasibility at 30--40% lower LUT cost.
Conducting Natural Language Inference with Word-Pair-Dependency and Local Context
Authors: Du Qianlong , Zong Chengqing , Su Keh-Yih
Published: April 2020

This article proposes to conduct natural language inference with novel
Enhanced-Relation-Head-Dependent triplets (RHD triplets)
, which are constructed via enhancing each word in the
RHD triplet
with its associated local context. Most previous approaches based on deep neural network (DNN) for this task either perform token alignment without considering syntactic dependency among words, or directly use tree- LSTM to generate passage representation with irrelevant information. To improve token alignment and inference judgment with word-pair-dependency, the
RHD
triplet structure is first proposed. To avoid incorporating irrelevant information, this proposed approach performs comparison directly on each triplet-pair of the given passage-pair (instead of comparing each triplet in a passage with the content merged from the whole opposite passage). Furthermore, to take local context into consideration while conducting token alignment and inference judgment, we also enhance the words of the triplets with their associated local context to improve the performance. Experimental results show that the proposed approach is better than most previous approaches that adopt tree structures, and its performance is comparable to other state-of-the-art approaches (however, our approach is more human comprehensible).
Improving Neural Machine Translation with Linear Interpolation of a Short-Path Unit
Authors: Li Yachao , Li Junhui , Zhang Min , Li Yixin , Zou Peng
Published: April 2020

In neural machine translation (NMT), the source and target words are at the two ends of a large deep neural network, normally mediated by a series of non-linear activations. The problem with such consequent non-linear activations is that they significantly decrease the magnitude of the gradient in a deep neural network, and thus gradually loosen the interaction between source words and their translations. As a result, a source word may be incorrectly translated into a target word out of its translational equivalents. In this article, we propose
short-path units
(SPUs) to strengthen the association of source and target words by allowing information flow over adjacent layers effectively via linear interpolation. In particular, we enrich three critical NMT components with SPUs: (1) an enriched encoding model with SPU, which interpolates source word embeddings linearly into source annotations; (2) an enriched decoding model with SPU, which enables the source context linearly flow to target-side hidden states; and (3) an enriched output model with SPU, which further allows linear interpolation of target-side hidden states into output states. Experimentation on Chinese-to-English, English-to-German, and low-resource Tibetan-to-Chinese translation tasks demonstrates that the linear interpolation of SPUs significantly improves the overall translation quality by 1.88, 1.43, and 3.75 BLEU, respectively. Moreover, detailed analysis shows that our approaches much strengthen the association of source and target words. From the preceding, we can see that our proposed model is effective both in rich- and low-resource scenarios.
Exploiting Data Resilience in Wireless Network-on-chip Architectures
Authors: Ascia Giuseppe , Catania Vincenzo , Monteleone Salvatore , Palesi Maurizio , Patti Davide , Jose John , Salerno Valerio Mario
Published: April 2020

The emerging wireless Network-on-Chip (WiNoC) architectures are a viable solution for addressing the scalability limitations of manycore architectures in which multi-hop long-range communications strongly impact both the performance and energy figures of the system. The energy consumption of wired links as well as that of radio communications account for a relevant fraction of the overall energy budget. In this article, we extend the approximate computing paradigm to the case of the on-chip communication system in manycore architectures. We present techniques, circuitries, and programming interfaces aimed at reducing the energy consumption of a WiNoC by exploiting the trade-off energy saving vs. application output degradation. The proposed platform—namely, xWiNoC—uses variable voltage swing links and tunable transmitting power wireless interfaces along with a programming interface that allows the programmer to specify those data structures that are error-resilient. Thus, communications induced by the access to such error-resilient data structures are carried out by using links and radio channels that are configured to work in a low energy mode, albeit by exposing a higher bit error rate. xWiNoC is assessed on a set of applications belonging to different domains in which the trade-off energy vs. performance vs. application result quality is discussed. We found that up to 50% of communication energy saving can be obtained with a negligible impact on the application output quality and 3% in application performance degradation.
Grass Leaf Identification Using dbN Wavelet and CILBP
Authors: Han Fan , Qiao Xue , Ma Yubao , Yan Weihong , Wang Xinyu , Pan Xin
Published: April 2020

Grass is one of the most important resources in the ecosystem for the sustainable development of human beings. However, the studies focusing on grass identification, which were traditionally implemented by experts with low efficiency and precision, cannot meet the requirements of modern grassland management. In this study, we proposed cubic interpolation LBP (CILBP) and dbN wavelets for grass identification based on leaf images. A low-frequency component of leaf images decomposed by dbN wavelets was used as the input of CILBP for more subtle texture extraction. The novelty of the proposed method was that CILBP can better describe the texture features from the low-frequency subimage, as compared with the original bilinear LBP. The effectiveness in identification accuracy of the proposed method for grass leaf was demonstrated by the experimental results.
Dietary Supplementation of L-Arginine and N-Carbamylglutamate Attenuated the Hepatic Inflammatory Response and Apoptosis in Suckling Lambs with Intrauterine Growth Retardation
Authors: Zhang Hao , Fan Yaotian , Elsabagh Mabrouk , Guo Shuang , Wang Mengzhi , Jiang Honghua
Published: April 2020

L-arginine (Arg) is a semiessential amino acid with several physiological functions. N-Carbamylglutamate (NCG) can promote the synthesis of endogenous Arg in mammals. However, the roles of Arg or NCG on hepatic inflammation and apoptosis in suckling lambs suffering from intrauterine growth restriction (IUGR) are still unclear. The current work is aimed at examining the effects of dietary Arg and NCG on inflammatory and hepatocyte apoptosis in IUGR suckling lambs. On day 7 after birth, 48 newborn Hu lambs were selected from a cohort of 432 twin lambs. Normal-birthweight and IUGR Hu lambs were allocated randomly (n=12/group) to control (CON), IUGR, IUGR+1% Arg, or IUGR+0.1% NCG groups. Lambs were fed for 21 days from 7 to 28 days old. Compared with CON lambs, relative protein 53 (P53), apoptosis antigen 1 (Fas), Bcl-2-associated X protein (Bax), caspase-3, cytochrome C, tumor necrosis factor alpha (TNF-α), nuclear factor kappa-B (NF-κB) p65, and NF-κB pp65 protein levels were higher (P<0.05) in liver from IUGR lambs, whereas those in liver from IUGR lambs under Arg or NCG treatment were lower than those in IUGR lambs. These findings indicated that supplementing Arg or NCG reduced the contents of proinflammatory cytokines at the same time when the apoptosis-related pathway was being suppressed, thus suppressing the IUGR-induced apoptosis of hepatic cells.
Impact of Decreased Transmural Conduction Velocity on the Function of the Human Left Ventricle: A Simulation Study
Authors: Vaverka Jiří , Moudr Jiří , Lokaj Petr , Burša Jiří , Pásek Michal
Published: April 2020

This study investigates the impact of reduced transmural conduction velocity (TCV) on output parameters of the human heart. In a healthy heart, the TCV contributes to synchronization of the onset of contraction in individual layers of the left ventricle (LV). However, it is unclear whether the clinically observed decrease of TCV contributes significantly to a reduction of LV contractility. The applied three‐dimensional finite element model of isovolumic contraction of the human LV incorporates transmural gradients in electromechanical delay and myocyte shortening velocity and evaluates the impact of TCV reduction on pressure rise (namely, (dP/dt)max) and on isovolumic contraction duration (IVCD) in a healthy LV. The model outputs are further exploited in the lumped “Windkessel” model of the human cardiovascular system (based on electrohydrodynamic analogy of respective differential equations) to simulate the impact of changes of (dP/dt)max and IVCD on chosen systemic parameters (ejection fraction, LV power, cardiac output, and blood pressure). The simulations have shown that a 50% decrease in TCV prolongs substantially the isovolumic contraction, decelerates slightly the LV pressure rise, increases the LV energy consumption, and reduces the LV power. These negative effects increase progressively with further reduction of TCV. In conclusion, these results suggest that the pumping efficacy of the human LV decreases with lower TCV due to a higher energy consumption and lower LV power. Although the changes induced by the clinically relevant reduction of TCV are not critical for a healthy heart, they may represent an important factor limiting the heart function under disease conditions.
Linear Twin Quadratic Surface Support Vector Regression
Authors: Zhai Qianru , Tian Ye , Zhou Jingyue
Published: April 2020

Twin support vector regression (TSVR) generates two nonparallel hyperplanes by solving a pair of smaller-sized problems instead of a single larger-sized problem in the standard SVR. Due to its efficiency, TSVR is frequently applied in various areas. In this paper, we propose a totally new version of TSVR named Linear Twin Quadratic Surface Support Vector Regression (LTQSSVR), which directly uses two quadratic surfaces in the original space for regression. It is worth noting that our new approach not only avoids the notoriously difficult and time-consuming task for searching a suitable kernel function and its corresponding parameters in the traditional SVR-based method but also achieves a better generalization performance. Besides, in order to make further improvement on the efficiency and robustness of the model, we introduce the 1-norm to measure the error. The linear programming structure of the new model skips the matrix inverse operation and makes it solvable for those huge-sized problems. As we know, the capability of handling large-sized problem is very important in this big data era. In addition, to verify the effectiveness and efficiency of our model, we compare it with some well-known methods. The numerical experiments on 2 artificial data sets and 12 benchmark data sets demonstrate the validity and applicability of our proposed method.
Performance Analysis of Display Field Communication with Advanced Receivers
Authors: Singh Pankaj , Kim Byung Wook , Jung Sung-Yoon
Published: April 2020

Display field communication (DFC) is an imperceptible display-to-camera (D2C) communication approach that provides dual-mode, full-frame, visible light communication capabilities. Unlike conventional screen-to-camera communication approaches, DFC embeds data imperceptibly in the spectral domain of individual video frames. This paper analyzes the practical performance of the DFC scheme with advanced receivers, including zero forcing (ZF), minimum mean square error (MMSE), and maximum likelihood (ML). A 249×262 color image is used for embedding data consisting of eight individual information vectors with their elements 2-QAM and 4-QAM modulated. The color image is separated into three individual channels, i.e., red (R), green (G), and blue (B). A lossy display-camera channel is considered in the presence of Gaussian noise, blooming, and various geometric distortions. Simulation results show that the ML receiver outperforms MMSE and ZF receivers. In addition, independent RGB data channels are evaluated to compare the symbol error rate of each channel. The proposed color DFC algorithm can be a viable candidate for practical scenarios in applications like smart content transmission and for supporting robust communication performance with advanced receivers, while the data embedded in the images remain unobtrusive to the human eye.