Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed Programming using Role-Parametric Session Types in Go Statically-Typed Endpoint APIs for Dynamically-Instantiated Communication Structures

This paper presents a framework for the static specification and safe programming of message passing protocols where the number and kinds of participants are dynamically instantiated. We develop the first theory of distributed multiparty session types (MPST) to support parameterised protocols with indexed roles—our framework statically infers the different kinds of participants induced by a protocol definition as role variants , and produces decoupled endpoint projections of the protocol onto each variant. This enables safe MPST-based programming of the parameterised endpoints in distributed settings: each endpoint can be implemented separately by different programmers, using different techniques (or languages). We prove the decidability of role variant inference and well-formedness checking, and the correctness of projection. We implement our theory as a toolchain for programming such role-parametric MPST protocols in Go. Our approach is to generate API families of lightweight, protocol-and variant-specific type wrappers for I/O. The APIs ensure a well-typed Go endpoint program (by native Go type checking) will perform only compliant I/O actions w.r. t. the source protocol. We leverage the abstractions of MPST to support the specification and implementation of Go applications involving multiple channels, possibly over mixed transports (e.g., Go channels, TCP), and channel passing via a unified programming interface. We evaluate the applicability and run-time performance of our generated APIs using microbenchmarks and real-world applications.
eJBio: Electronic Journal of Biology

The present study provides substantial evidence of variation among the ITS2 regions of the sample species both at sequence as well as RNA secondary structure level. A bioinformatics approach was exploited to reveal the mystery of conservation and variation in ITS2 in mosquito genera. Multiple sequence alignment revealed conservation within the species and across species. Dimer frequency of all the species showed variance in conservation according to the type of dimer. RNA secondary structures were generated for all the species. To obtain a deep insight into structural similarity and divergence, parameters like structural energy, number of stems, G-C, A-U, G-U pairings were calculated. Out of five types of loops derived by Sribo Program, hairpin and exterior loops were found to be fairly more conserved rather than interior, bulge and multi loops. Tandem repeats with high degree of variation were observed in these specific regions. The unexpected observation was the striking proximity of Aedes longfilamentus and Anopheles culicifacies in the phylogenetic tree where they shared same clade instead of other species of Aedes genera. This finding is quite appealing and intriguing being contrary to the expected outcome and requires further rigorous investigation. These results indicate towards the major variable and conserved regions as well as the stretches of sequences and structural parts where selection pressure varies during ribosome biogenesis.
eJBio: Electronic Journal of Biology

Amygdalus communis, A. haussknechtii, A. arabica, A. orientalis, and A. lycioides belong the family Rosaceae, are Iranian native plants. In this study, antioxidant activity of hydroalcholic extracts from the leaves of A. haussknechtii, A. arabica, A. orientalis and A. lycioides, and five cultivars of A. communis was evaluated by DPPH assay. Results indicated the hydroalcholic extract from the leaves of A. haussknechtii, and A. lycioides had the highest antioxidant activity. The total phenolic content of the extracts ranged from 19 to 30 mg Gallic acid/g dry weight extract. The hydro-alcoholic extract of A. haussknechtii exhibited the highest total phenolic content. These results suggested that the level of antioxidant activity in these plants was varied to a great extent. Our results indicated the extract of A. haussknechtii could be an important dietary source of total phenolic content with high antioxidant capacity.
COUPLING VS . UNIFYING : MODELING TECHN TRANSLATIO Yuqing Gao IBM

As a part of our effort to develop a unified computational framework for speech-to-speech translation, so that sub-optimizations or local optimizations can be avoided, we are developing direct models for speech recognition. In direct model, the focus is on the creation of one single integrated model , rather than a complex series of artifices, therefore various factors such as linguistics and language features, speaker or speaking rate differences, different acoustic conditions, can be applied to the joint optimization. In this paper we discuss how linguistic and semantic constraints are used in phoneme recognition.
Cross-domain Aspect/Sentiment-aware Abstractive Review Summarization

This study takes the lead to study the aspect/sentiment-aware abstractive review summarization in domain adaptation scenario. The proposed model CASAS (neural attentive model for Cross-domain Aspect/Sentiment-aware Abstractive review Summarization) leverages domain classification task, working on datasets of both source and target domains, to recognize the domain information of texts and transfer knowledge from source domains to target domains. The extensive experiments on Amazon reviews demonstrate that CASAS outperforms the compared methods in both out-of-domain and in-domain setups.
Code Generation = A (cid:3) + BURS

A system called BURS that is based on term rewrite systems and a search algorithm A (cid:3) are combined to produce a code generator that generates optimal code. The theory underlying BURS is re-developed, formalised and explained in this work. The search algorithm uses a cost heuristic that is derived from the term rewrite system to direct the search. The advantage of using a search algorithm is that we need to compute only those costs that may be part of an optimal rewrite sequence.
Towards a Life Cycle Oriented Business Intelligence Success Model

Information System (IS) success has been an essential research issue in the IS community for nearly two generations of IS researchers. For the Business Intelligence (BI) domain we see the lack of a comprehensive, life cycle oriented success model. Based on an extensive review of BI success related literature, we propose a methodology for creating such success models in general and demonstrate the feasibility of the proposed approach within the context of BI. A life cycle oriented BI success model is presented including an outline of the constructs, their relationships, and associated measurement items. From a practical perspective, the results provide assistance for the future development of BI solutions in organizations. The paper deepens the understanding of success model adaptation and extension from a theoretical perspective.
on Leveraging Applications of Formal Methods, Veriﬁcation and Validation -

: The development of complex systems involves several domain experts and several design models corresponding to different analyses ( views ) of the same system. No explicit information regarding the characteristics of the performed sys-tem analyses is given. We propose a stepwise approach to keep trace of the process that allows a system designer to build a point of view ﬁrst, by explicitly deﬁning the features of an analysis ( view ) and second, by explicitly deﬁning the required concepts and properties borrowed from a design model. The objective is to trigger a given model analysis. Strengthening the design models with domain knowledge is highly recommended before performing such analysis. We propose a model annotation methodology for this purpose. These approaches are deployed using Model Driven Engineering (MDE) techniques and illustrated on an didactic case study.
Development of a Land Resources Protection Model

Effective and intensive development of agricultural industry is impossible without a system of measures to protect agricultural lands from different types of degradation. On the one hand, it is necessary to provide a maximum yield of agricultural crops and, on the other hand, to maintain and increase soil fertility, as well as to prevent negative human impact on the environment. More extensive soil fertility recovery requires a system of measures: application of fertilizers, agricultural and ameliorative practices, stimulation of humification and others. Humus is a key element of soil fertility. It accelerates biochemical and physiological processes, metabolism and total energy of processes in a plant body, intensifies nutrient enrichment, thus increasing the crop and its quality. Soil exhaustion due to humus reduction may result in a disaster for the agro industrial complex of a region. Soil fertility is the national heritage and the country’s strategic reserve. This article suggests that the problem should be solved by creating the Agro Landscape - Technology system and studying the responses of the simulated system on changes in its characteristics and initial conditions according to the considered methods of control over working operations. The methodology being designed provides practical methods of making forecasts and using them in solving management problems where many interrelated (system) factors and phenomena should be considered and vast forecast data should be used.
CorLayer: A Transparent Link Correlation Layer for Energy-Efficient Broadcast

Recent work has shown that wireless links are not independent, and that transmissions from a transmitter to multiple receivers are correlated. This finding has profound implications for the performance of network protocols such as broadcast, multicast, opportunistic routing, and network coding. In this paper, we show how link correlation can significantly impact broadcast. We present the design and implementation of CorLayer, a general supporting layer for energy-efficient reliable broadcast that carefully blacklists certain poorly correlated wireless links. The design uses only one-hop information, which makes it work in a fully distributed manner and introduces minimal communication overhead. The highlight of our work is CorLayer's broad applicability and effectiveness. We integrate CorLayer transparently with 16 state-of-the-art broadcast protocols specified in 13 publications on three physical testbeds running TelosB, MICAz, and GreenOrbs nodes, respectively. The experimental results show that CorLayer significantly improves energy efficiency across a wide spectrum of broadcast protocols and that the total number of packet transmissions can be reduced consistently by 47% on average.
Image Segmentation and Recognition

We have constructed a system for recognizing multi-character images 1. This is a nontrivial extension of our previous work on single-character images. It is somewhat surprising that a very good single-character recognizer does not in general form a good basis for a multi-character recognizer. The correct solution depends on three key ideas: 1) A method for normalizing probabilities correctly, to preserve information on the quality of the segmentation; 2) A method for giving credit for multiple segmentations that assign the same interpretation to the image; and 3) A method that combines recognition and segmentation into a single adaptive process, trained to maximize the score of the right answer. We also discuss improved ways of analyzing recognizer performance. A major part of this technical report is devoted to giving our methods a good theoretical footing. In particular, we do not start by asserting that maximum likelihood is obviously the right thing to do. Instead, the problem is formalized in terms of a probability measure; the learning algorithm must then be arranged to make this probability conform to the customer’s needs. This formulation can be applied to other segmentation problems such as speech recognition. Our recognizer using these principles works noticeably better than the previous state of the art. This work also appeared, with the same title and authors, in The Mathematics of Generalization: Proceedings of the SFI/CNLS Workshop on Formal Approaches to Supervised Learning, Addison
RATH - A Relational Adaptive Tutoring Hypertext WWW-Environment Based on Knowledge Space Theory

RATH is an adaptive tutoring WWW software prototype combining a mathematical model for the structure of hypertext with the theory of knowledge spaces from mathematical psychology. Using prerequisite relationships between diierent items in a domain of knowledge and using the knowledge about the pupil's current knowledge state RATH presents only those links in a hypertext document to the pupil which point to a document for which he/she fullls all prerequisites and which he/she therefore should be able to understand. In a rst prototype course, this idea is applied to the eld of elementary probability theory. This tiny course is based on prior research of the second author. Thus, RATH is an important step in bringing psychological theories into a working tutorial system.
Open Source Software Projects as Virtual Organizations : Competency Rallying for Software Development

The contribution of this paper is the identification and testing of factors important for the success of Open Source Software (OSS) projects. We present an analysis of OSS communities as virtual organizations and apply Katzy and Crowston’s competency rallying (CR) theory to the case of OSS development projects. CR theory suggests that project participants must develop necessary competencies, identify and understand market opportunities, marshal competencies to meet the opportunity and manage a short-term cooperative process. Using data collected from 7477 OSS projects hosted by the SourceForge system (http://sourceforge.net/), we formulate and test a set of specific hypotheses derived from CR theory. The empirical data analysis supports the majority of these hypotheses, suggesting that CR theory provides a useful lens for studying OSS projects. OSS Projects as Virtual Organizations 3 Open Source Software Projects as Virtual Organizations: Competency Rallying for Software Development
Live Content Generation in Momentum-based Games

The goal of this project is to ﬁnd a procedural content generation (PCG) method for online generation in momentum-based games. To this end, we make our own game called Fast Painting . The content should be solvable, varied, and it should exhibit the desired diﬃculty. In our approach, two reinforcement learning agents, the solver and the generator, are trained in an alternating Markov game. We incentivize solvability and variety of the generated content by giving non-negative variety rewards to the generator when his actions produce recently unseen experiences for the solver. We sabotage the solver by limiting his vision, his ability to act and his training duration in an eﬀort to create easier levels. In a procedure called ghost mode we give a reward for placing level components, that the solver only passes with initial momentum. Our generator produces solvable levels without much training. The variety of its content increases very slowly across millions of training steps. Our attempts at enforcing diﬃculty have had little success. The achieved diﬃculty range is rather small and the more diﬃcult generators still place many easy platforms. It is likely, that more training or an additional diﬃculty reward is needed to make the behaviors of the diﬀerent generators diverge more drastically. The momentum reward seems to result in more interesting levels.
ICEO: a Biological Ontology for Representing and Analyzing the Bacterial Integrative and Conjugative Element

Bacterial integrative and conjugative elements (ICEs) are mobile genetic elements critical to horizontal gene transfer and organism evolution. To better understand and analyze ICEs, it is critical to systematically represent, integrate and classify gene components, functional modules and related information of available ICEs archived in the ICEberg database. Toward this goal, we developed a community-driven ICE ontology (ICEO). ICEO is aligned with the Basic Formal Ontology (BFO) to allow the integration with other ontologies. ICEO reused the existing reliable ontologies, such as Ontology of Gene and Genome, Protein Ontology and NCBITaxon. ICEO now represents the information about over 270 experimentally verified ICEs from 235 bacterial strains. Two query use cases were provided, including a DL query of ICE-contained genes that are also virulence factors and a SPARQL query of ICEs under an upper level taxonomy type of Gammaproteobacteria. Our study demonstrated that ICEO supports computer-assisted reasoning and efficient SPARQL query.
Fast Dimensional Analysis for Root Cause Investigation in Large-Scale Service Environment

Root cause analysis in a large-scale production environment is challenging due to the complexity of services running across global data centers. Due to the distributed nature of a large-scale system, the various hardware, soware, and tooling logs are oen maintained separately, making it dicult to review the logs jointly for detecting issues. Another challenge in reviewing the logs for identifying issues is the scale there could easily be millions of entities, each with hundreds of features. In this paper we present a fast dimensional analysis framework that automates the root cause analysis on structured logs with improved scalability. We rst explore item-sets, i.e. a group of feature values, that could identify groups of samples with sucient support for the target failures using the Apriori algorithm and a subsequent improvement, FP-Growth. ese algorithms were designed for frequent item-set mining and association rule learning over transactional databases. Aer applying them on structured logs, we select the item-sets that are most unique to the target failures based on li. With the use of a large-scale real-time database, we propose preand post-processing techniques and parallelism to further speed up the analysis. We have successfully rolled out this approach for root cause investigation purposes in a large-scale infrastructure. We also present the setup and results from multiple production use-cases in this paper.
Modulating Effects of Mesoscale Oceanic Eddies on Sea Surface Temperature Response to Tropical Cyclones Over the Western North Pacific

The impact of mesoscale oceanic eddies on the temporal and spatial characteristics of sea surface temperature (SST) response to tropical cyclones is investigated in this study based on composite analysis of cyclone‐eddy interactions over the western North Pacific. The occurrence times of maximum cooling, recovery time, and spatial patterns of SST response are specially evaluated. The influence of cold‐core eddies (CCEs) renders the mean occurrence time of maximum SST cooling to become about half a day longer than that in eddy‐free condition, while warm‐core eddies (WCEs) have little effect on this facet. The recovery time of SST cooling also takes longer in presence of CCEs, being overall more pronounced for stronger or slower tropical cyclones. The effect of WCEs on the recovery time is again not significant. The modulation of maximum SST decrease by WCEs for category 2–5 storms is found to be remarkable in the subtropical region but not evident in the tropical region, while the role of CCEs is remarkable in both regions. The CCEs are observed to change the spatial characteristics of SST response, with enhanced SST decrease initially at the right side of storm track. During the recovery period the strengthened SST cooling by CCEs propagates leftward gradually, with a feature similar as both the westward‐propagating eddies and the recovery of cold wake. These results underscore the importance of resolving mesoscale oceanic eddies in coupled numerical models to improve the prediction of storm‐induced SST response.
Updated MINDS report on speech recognition and understanding, Part 2 [DSP Education]

This article is the second part of an updated version of the "MINDS 2006-2007 Report of the Speech Understanding Working Group," one of five reports emanating from two workshops entitled "Meeting of the MINDS: Future Directions for Human Language Technology," sponsored by the U.S. Disruptive Technology Office (DTO). (MINDS is an acronym for "machine translation, information retrieval, natural-language processing, data resources, and speech understanding").
CNN based Malaria Diagnosis from Focus-stack of Blood Smear Images Acquired using Custom-built Slide Scanner

cc ep te d A rti cl e Abstract This paper introduces a focus stacking based approach for automated quantitative detection of Plasmodium falciparum malaria from blood smear. For the detection, a custom designed convolutional neural network (CNN) operating on focus stack of images is used. The cell counting problem is addressed as the segmentation problem and we propose a two level segmentation strategy. Use of CNN operating on focus stack for the detection of malaria is first of its kind, and it not only improved the detection accuracy (both in terms of sensitivity (97.06%) and specificity (98.50%)) but also favoured the processing on cell patches and avoided the need for hand-engineered features. The slide images are acquired with a custom-built portable slide scanner made from low-cost, off-the-shelf components and is suitable for Point-of-Care Diagnostics. The proposed approach of employing sophisticated algorithmic processing together with inexpensive instrumentation can potentially benefit clinicians to enable malaria diagnosis.
ADVANCED DRIVER ASSISTANCE SYSTEM

Lane detection and tracking is one of the key features of advanced driver assistance system. Lane detection is finding the white markings on a dark road. Lane tracking use the previously detected lane markers and adjusts itself according to the motion model. In this paper, review of lane detection and tracking algorithms developed in the last decade is discussed. Several modalities are considered for lane detection which include vision, LIDAR, vehicle odometry information, information from global positioning system and digital maps. The lane detection and tracking is one of the challenging problems in computer vision. Different vision based lane detection techniques are explained in the paper. The performance of different lane detection and tracking algorithms is also compared and studied.