Miracle-dz/UniFAR-FTUs huggingface dataset:
Statistical methods for the analysis of RNA sequencing data

The next generation sequencing technology, RNA-sequencing (RNA-seq), has an increasing popularity over traditional microarrays in transcriptome analyses. Statistical methods used for gene expression analyses with these two technologies are different because the array-based technology measures intensities using continuous distributions, whereas RNA-seq provides absolute quantification of gene expression using counts of reads. There is a need for reliable statistical methods to exploit the information from the rapidly evolving sequencing technologies and limited work has been done on expression analysis of time-course RNA-seq data. Functional clustering is an important method for examining gene expression patterns and thus discovering co-expressed genes to better understand the biological systems. Clusteringbased approaches to analyze repeated digital gene expression measures are in demand. In this dissertation, we propose a model-based clustering method for identifying gene expression patterns in time-course RNA-seq data. Our approach employs a longitudinal negative binomial mixture model to postulate the over-dispersed time-course gene count data. The effectiveness of the proposed clustering method is assessed using simulated data and is illustrated by real data from time-course genomic experiments. Due to the complexity and size of genomic data, the choice of good starting values is an important issue to the proposed clustering algorithm. There is a need for a reliable initialization strategy for cluster-wise regression specifically for time-course discrete count data. We modify existing common initialization procedures to suit our model-based clustering algorithm and the procedures are evaluated through a simulation study on artificial datasets and are applied to real genomic examples to identify the optimal initialization method. Another common issue in gene expression analysis is the presence of missing values in the datasets. Various treatments to missing values in genomic datasets have been developed but limited work has been done on RNA-seq data. In the current work, we examine the performance of various imputation methods and their impact on the clustering of time-course RNA-seq data. We develop a cluster-based imputation method which is specifically suitable for dealing with missing values in RNA-seq datasets. Simulation studies are provided to assess the performance of the proposed imputation approach.
A 622 Mb/s 32/spl times/32 scalable shared buffer ATM switch with searchable address queue

The advanced 0.5-/spl mu/m CMOS technology makes it possible to integrate a huge amount of memories and enables us to apply sophisticated architecture. The implementation of the ATM switch chipset, using new architectures named funnel-structured expandable architecture and the searchable address queueing scheme, is described. A 622 Mb/s 32/spl times/8 element switch consists of one buffer LSI and one control LSI. A 622 Mb/s 32/spl times/32 switch which comprises four element switches can be installed in one board. The switch has delay-priority control, cell-loss priority control, multicasting function and hierarchical queueing function to accommodate 156 Mb/s, 622 Mb/s and 2.4 Gb/s interfaces.
Interval Multi-Objective Optimal Scheduling for Redundant Residential Microgrid With VESS

Optimal scheduling of building microgrid could yield economical savings and reduce the pollutants emission, while ensuring the comfort level of users. A multi-objective optimal scheduling problem is studied for one kind of building microgrid—redundant residential microgrid (RR-microgrid). The radiant floor heating/cooling system widely utilized in residential buildings is treated as the virtual energy storage system (VESS) to dig up the considerable thermal storage capacity. Interval number is employed to describe the random fluctuation of uncertainty factors, such as renewable energy power, electric load, and weather conditions in the process of microgrid operation, while the exchange power with external grid and the output power of micro-gas turbines are, respectively, taken as the mean to smooth the electric power fluctuation, and two different optimization scheduling models which take the operating cost (OC), thermal comfort level (TCL), and pollutant emission (PE) as the optimization objectives are developed, and an improved non-dominate sorting genetic algorithm II (NSGA-II) is proposed to search the Pareto front of the scheduling models. The case study for heating in winter is performed and the results demonstrate the effectiveness of the proposed optimal scheduling method.
Ordonnancement d'entités pour la rencontre du web des documents et du web des données

The advances of the Linked Open Data (LOD) initiative are giving rise to a more structured web of data. Indeed, a few datasets act as hubs (e.g., DBpedia) connecting many other datasets. They also made possible new web services for entity detection inside plain text (e.g., DBpedia Spotlight), thus allowing for new applications that will benefit from a combination of the web of documents and the web of data. To ease the emergence of these new use-cases, we propose a query-biased algorithm for the ranking of entities detected inside a web page. Our algorithm combine link analysis with dimensionality reduction. We use crowdsourcing for building a publicly available and reusable dataset on which we compare our algorithm to the state of the art. Finally, we use this algorithm for the construction of semantic snippets for which we evaluate the usability and the usefulness with a crowdsourcing-based approach.
D-TCAM: A High-Performance Distributed RAM Based TCAM Architecture on FPGAs

Ternary content-addressable memory (TCAM) is a high-speed searching device that searches the entire memory in parallel in deterministic time, unlike random-access memory (RAM), which searches sequentially. A network router classifies and forwards a data packet with the aid of a TCAM that stores the routing data in a table. Field-programmable gate arrays (FPGAs), due to its hardware-like performance and software-like reconfigurability, are widely used in networking systems where TCAM is an essential component. TCAM is not included in modern FPGAs, which leads to the emulation of TCAM using available resources on FPGA. Several emulated TCAM designs are presented but they lack the efficient utilization of FPGA’s hardware resources. In this paper, we present a novel TCAM architecture, the distributed RAM based TCAM (D-TCAM), using D-CAM as a building block. One D-CAM block implements a 48-bytes TCAM using 64 lookup tables (LUTs), that is cascaded horizontally and vertically to increase the width and depth of TCAM, respectively. A sample size of $512\times 144$ is implemented on Xilinx Virtex-6 FPGA, which reduced the hardware utilization by 60% compared to the state-of-the-art FPGA-based TCAMs. Similarly, by exploiting the LUT-flip-flip (LUT-FF) pair nature of Xilinx FPGAs, the proposed TCAM architecture improves throughput by 58.8% without any additional hardware cost.
Aspect Sentiment Classification Towards Question-Answering with Reinforced Bidirectional Attention Network

In the literature, existing studies on aspect sentiment classification (ASC) focus on individual non-interactive reviews. This paper extends the research to interactive reviews and proposes a new research task, namely Aspect Sentiment Classification towards Question-Answering (ASC-QA), for real-world applications. This new task aims to predict sentiment polarities for specific aspects from interactive QA style reviews. In particular, a high-quality annotated corpus is constructed for ASC-QA to facilitate corresponding research. On this basis, a Reinforced Bidirectional Attention Network (RBAN) approach is proposed to address two inherent challenges in ASC-QA, i.e., semantic matching between question and answer, and data noise. Experimental results demonstrate the great advantage of the proposed approach to ASC-QA against several state-of-the-art baselines.
Implementasi Video Telephony melalui Jaringan WiMAX menggunakan Controlled Delay

Worldwide Interoperability for Microwave Access atau yang disingkat dengan WiMAX merupakan teknologi jaringan Wireless yang memiliki jangkauan yang luas hingga 50 km dengan kecepatan akses hingga 70 Mbps. WiMAX terintegrasi dengan konsep QoS. Konsep QoS yang dimiliki oleh WiMAX berupa service class . WiMAX mendukung aplikasi real- time yang salah satunya adalah video telephony. Video telephony adalah aplikasi real-time yang memungkinkan pengguna dapat bertatap muka dan berbicara secara langsung. Parameter yang mempengaruhi kualitas video telephony adalah delay dan packet loss. Agar kedua parameter ini dapat diminimalisir, penelitian ini akan digunakan mekanisme Controlled Delay pada WiMAX dengan tujuan peningkatan kualitas layanan pada video telephony.
ARTIFICIAL INTELLIGENCE, DATA ANALYTICS AND SPORTS BIOMECHANICS: A NEW ERA OR A FALSE DAWN?

INTRODUCTION: In contrast to other sport science disciplines the highly quantitative nature of sport biomechanics has historically required practitioners to apportion significant time to the data collection and analysis phases of applied research and athlete servicing. Historically, this has served to limit the time that can be allocated to results interpretation and translation, and to the critical component of athlete/coach feedback. However, recent advances in wearable technologies and in the data science areas of computer vision, data mining, machine learning and sports analytics, is a literal game-changer for the sports industry and the sports biomechanist in-particular. From compiling the first big data repository of nearly half a million (458,372) sports research motion capture (*.c3d) files for the estimation of ground reaction forces and moments in the field (Johnson et al., 2018), to machine learning methods for auto pose estimation, there is little doubt that there is currently an explosion in the adoption of machine learning techniques in the sports biomechanics domain. With the most important by-product being an upending of the data collection/analysis burden that has hampered the sport biomechanist for so long. From the sports analyst perspective, a myriad of player tracking tools are now available with new players entering the market daily (e.g. Champion Data, MLBAM Statcast, Catapult, PITCHf/x, NFL Next Gen Stats, HITf/x). Yet, arguably, at the present time this type of global tracking offers little by way of obtaining meaningful, actionable and detailed insights aimed at improving performance at the individual level. Similarly, while touted as a panacea to the sports analysis industry there remains a myriad of additional challenges in adopting machine learning techniques at scale for sport biomechanics. These include; 1) training data scientists with sport science expertise to drive questions that leverage the power of these tools, 2) simplifying the data collection time frame required for traditional data science approaches, and 3) developing consensus on the design and framework schema required for the enterprise architecture necessary to undertake this work at scale. This presentation discusses the potential of artificial intelligence and data analytics for sports biomechanics.
Women’s Syntactic Resilience and Men’s Grammatical Luck: Gender-Bias in Part-of-Speech Tagging and Dependency Parsing

Several linguistic studies have shown the prevalence of various lexical and grammatical patterns in texts authored by a person of a particular gender, but models for part-of-speech tagging and dependency parsing have still not adapted to account for these differences. To address this, we annotate the Wall Street Journal part of the Penn Treebank with the gender information of the articles’ authors, and build taggers and parsers trained on this data that show performance differences in text written by men and women. Further analyses reveal numerous part-of-speech tags and syntactic relations whose prediction performances benefit from the prevalence of a specific gender in the training data. The results underscore the importance of accounting for gendered differences in syntactic tasks, and outline future venues for developing more accurate taggers and parsers. We release our data to the research community.
Software Evolution via Topic Modeling: An Analytic Study

Open-source projects continue to evolve resulted in so many versions. Managing, visualizing and understanding an evolving software system are challenging tasks. In this work, we apply Latent Dirichlet Allocation (LDA) to discover source code topics and study their evolution over multiple software versions. We apply LDA to all versions of the system together and then determine how the assignment metric evolves over time. We study the software evolution of two large open-source projects, JEdit and JHotDraw, over twelve versions. The results show that changes in topics across versions are due to actual software changes such as adding, updating, and removing features. Our work suggests that using LDA can open many paths in software evolution research.
A probabilistic approach to stress assignment in Arabic

Although previous studies (e.g., Broselow 1976, McCarthy 1990, Watson 2002) have been successful in analyzing the interplay between metrical or syllabic constrains on stress assignment in Arabic, these analyses are predominately rule/constraint-based. This predisposition toward rule/constraint-based analyses does not adequately address one important variable in language – FREQUENCY, as frequency has been found to have a significant effect on various facets of language acquisition and processing (Ellis 2002). To address this inadequacy of discussion on the role of frequency in the literature, this study argues that while the stress system in Arabic is rule/constraint-governed, it could be probabilistic, bottom-up and frequency-informed as well. The purpose of the study is two-fold: a) to provide a statistical description of frequency cues that are relevant to stress assignment, and b) to examine the viability of using these cues either separately or collectively to predict stress in Arabic. The dataset is constructed from a respected frequency dictionary that is compiled from a 30-million-word corpus (Buckwalter & Parkinson 2010). From the 5000 words included in the dictionary, 4283 multisyllabic words are extracted, corresponding to 105 stress patterns. From these stress patterns, the frequency distribution is calculated for a) the stress pattern, b) the stress position, c) the syllabic structure of the stressed syllable, d) the conditional probability of the stressed syllable and e) the interaction between the number of syllables and aforementioned frequency ques. A brief sample of this analysis is presented in (1) and (2), where “high” indicates that a syllable more is frequently stressed given the corresponding frequency cue, and vice versa.
Terminal container automated guided vehicle based on Lidar navigation

The whole loading and unloading operation of automated container terminal is carried out by means of mechanization and large-scale production, which requires close cooperation of various operations to realize the high efficiency of loading and unloading process system. Reasonable layout of facilities on container terminals and their organic connection form an organic whole of coordinated and coordinated operations, forming an efficient and perfect assembly line. In order to shorten the berthing time of vehicles, ships and boxes at port terminals, accelerate the turnover of vehicles, ships and boxes, reduce transportation costs and loading and unloading costs, and achieve the best economic benefits. In this paper, the container transshipment vehicle based on lidar navigation is taken as the research object, so that the container autopilot has certain autopilot ability and is suitable for the needs of the automated container terminal.
Ultra-low power and ultra-low voltage RF CMOS circuits and system design techniques

The main focus of this thesis is to mitigate circuit design challenges under ultra-low power (ULP) and ultra-low voltage (ULV) conditions to implement ULP and ULV circuits and systems suitable for low power wireless sensor networks operating from the energy scavenged from the environment. The design of power ecient transceivers requires careful optimization at the circuit level. ULV circuit design challenges are fully investigated in this work to address its importance in low power transceivers operating with energy harvesting. It is shown that ULV supply has severe impacts on the performance of a MOS transistor. New solutions like forward body biasing, new biasing scheme optimized for ULP and ULV design, current-reuse and etc. are introduced to mitigate these performance degradations.Several ULP and ULV wideband RF low noise amplifiers (LNAs) are designed and fabricated to prove the concept. The rst one is a resistive shunt-feedback LNA fabricated in a 90-nm TSMC CMOS using multiple ULP and ULV techniques. This prototype achieves high gure of merit (FoM) while consuming sub-mW power from a 0.5-V supply. The secondone is an inductorless, wideband LNA implemented in an IBM 0.13-um CMOS technology which uses a current-reused tunable active shunt-feedback for input impedance matching.Employing ULP design techniques, this LNA consumes only 400-W while illustrating high FoM. The third fabricated LNA, also implemented in an IBM 0.13-um CMOS, utilizes multiple ULP and ULV design techniques which lead to the best FoM in the literature to the best of the author knowledge, with only 0.25-mW power from a 0.5-V supply. Finally, aUWB current-reuse noise cancelling LNA is implemented that achieves high performance while consuming 410-W from a 0.4-V supply which makes it suitable for systems operating from energy harvesting.As a low power system-level solution, impulse radio ultra-wideband (IR-UWB) technology with short pulses in the time domain which allows the transceiver to be duty cycled, is considered for ULP wireless sensor networks. In this work, a non-coherent chirp-FSK IR-UWB receiver based on continuous time slicing and low power injection locked clock recovery is implemented. The RF front-end of the receiver is implemented in an IBM 0.13m CMOS.It achieves a tuned voltage gain of 20-30-dB with a NF of 7-dB and the static power consumption of only 1.75-mW from a 0.5-V supply voltage. Overall, the proposed techniques, circuits and system improve the power eciency both at circuit and system level and are suitable for ULP and ULV applications such as energy scavenged wireless sensor networks. %%%%L'interet principal de cette these est de reduire les des relies a la conception de circuits operant dans des conditions de ultra-basse puissance (UBP) et ultra-bas voltage (UBV) afin d'implementer des circuits et systemes applicables aux reseaux de capteurs de basse puissance operant a l'aide d'energie recuperee de sources ambiantes. La conception de transmetteur/recepteur requiere une attention particuliere a l'optimisation au…
Attention over Heads: A Multi-Hop Attention for Neural Machine Translation

In this paper, we propose a multi-hop attention for the Transformer. It refines the attention for an output symbol by integrating that of each head, and consists of two hops. The first hop attention is the scaled dot-product attention which is the same attention mechanism used in the original Transformer. The second hop attention is a combination of multi-layer perceptron (MLP) attention and head gate, which efficiently increases the complexity of the model by adding dependencies between heads. We demonstrate that the translation accuracy of the proposed multi-hop attention outperforms the baseline Transformer significantly, +0.85 BLEU point for the IWSLT-2017 German-to-English task and +2.58 BLEU point for the WMT-2017 German-to-English task. We also find that the number of parameters required for a multi-hop attention is smaller than that for stacking another self-attention layer and the proposed model converges significantly faster than the original Transformer.
Discriminative Learning in the Model Space for Symbolic Sequence Classification

Learning in the model space is an approach that represents sequences with state space models and performs the learning procedure in the space spanned by the “model points. ” To enhance the discriminative capacity and preserve the simplicity and effectiveness of model-based kernel, a discriminative learning method in the model space is proposed for symbolic sequence classification in this paper. Compared with string kernels, the discriminative model-based kernel not only models the temporal information of sequence by a generative mechanism but also considers label information of sequence in the similarity measurement, which is beneficial to sequence classification. On the other hand, although learning in the model space approach has been successfully applied in the cognitive fault diagnosis and the time-series analysis, it is actually unclear how it might perform on the symbolic sequence classification. This paper extends the learning in the model space approach to the symbolic sequence classification problem with a coding scheme on categorical variables. Experimental results and the related analysis confirm the effectiveness of discriminative learning in the model space approach for the symbolic sequence classification. Moreover, we investigate several characteristics related to the performance of the proposed approach, including parameter analysis, Lyapunov analysis etc., and present a guideline on exploiting the discriminative learning in the model space approach in real-world applications.
Softwarization of Large-Scale IoT-based Disasters Management Systems

The Internet of Things (IoT) enables objects to interact and cooperate with each other for reaching common objectives. It is very useful in large-scale disaster management systems where humans are likely to fail when they attempt to perform search and rescue operations in high-risk sites. IoT can indeed play a critical role in all phases of large-scale disasters (i.e. preparedness, relief, and recovery). Network softwarization aims at designing, architecting, deploying, and managing network components primarily based on software programmability properties. It relies on key technologies, such as cloud computing, Network Functions Virtualization (NFV), and Software Defined Networking (SDN). The key benefits are agility and cost efficiency. This thesis proposes softwarization approaches to tackle the key challenges related to large-scale IoT based disaster management systems. A first challenge faced by large-scale IoT disaster management systems is the dynamic formation of an optimal coalition of IoT devices for the tasks at hand. Meeting this challenge is critical for cost efficiency. A second challenge is an interoperability. IoT environments remain highly heterogeneous. However, the IoT devices need to interact. Yet another challenge is Quality of Service (QoS). Disaster management applications are known to be very QoS sensitive, especially when it comes to delay. To tackle the first challenge, we propose a cloud-based architecture that enables the formation of efficient coalitions of IoT devices for search and rescue tasks. The proposed architecture enables the publication and discovery of IoT devices belonging to different cloud providers. It also comes with a coalition formation algorithm. For the second challenge, we propose an NFV and SDN based - architecture for on-the-fly IoT gateway provisioning. The gateway functions are provisioned as Virtual Network Functions (VNFs) that are chained on-the-fly in the IoT domain using SDN. When it comes to the third challenge, we rely on fog computing to meet the QoS and propose algorithms that provision IoT applications components in hybrid NFV based - cloud/fogs. Both stationary and mobile fog nodes are considered. In the case of mobile fog nodes, a Tabu Search-based heuristic is proposed. It finds a near-optimal solution and we numerically show that it is faster than the Integer Linear Programming (ILP) solution by several orders of magnitude.