Miracle-dz/UniFAR-FTUs huggingface dataset:
Architecture-aware online failure prediction for software systems

Failures at runtime in complex software systems are inevitable because these systems usually contain a large number of components. Having all components working perfectly at the same time is, if at all possible, very difficult. Hardware components can fail and software components can still have hidden faults waiting to be triggered at runtime and cause the system to fail. Existing online failure prediction approaches predict failures by observing the errors or the symptoms that indicate looming problems. This observable data is used to create models that can predict whether the system will transition into a failing state. However, these models usually represent the whole system as a monolith without considering their internal components. This thesis proposes an architecture-aware online failure prediction approach, called Hora. The Hora approach improves online failure prediction by combining the results of failure prediction with the architectural knowledge about the system. The task of failure prediction is split into predicting the failure of each individual component, in contrast to predicting the whole system failure. Suitable prediction techniques can be employed for different types of components. The architectural knowledge is used to deduce the dependencies between components which can reflect how a failure of one component can affect the others. The failure prediction and the component
DePedo: Anti Periodic Negative-Step Movement Pedometer with Deep Convolutional Neural Networks

Pedometer is an enabling technique for smartphone- based pedestrian positioning systems. Because the sensor drifts, these algorithms can only estimate moving distances from step counts. In order to detect step events, researchers have tried to leverage the peak detection and the periodicity attribute of step acceleration signals. However, many human behaviors are having acceleration peaks and periodic, causing traditional detectors error- prone when the phone is shaken periodically leading state-of-the-art system to high false positive ratio and consequently to big mistake of distance estimations. Based on the acceleration feature analysis of step events, we present a deep convolution neural network based step detection scheme to improve the pedometer robustness. Finally, the proposed step detection algorithm is tested in a realistic situation, showing a high anti periodic negative-step movement capability.
MEETING BOT: Reinforcement Learning for Dialogue Based Meeting Scheduling

In this paper we present Meeting Bot, a reinforcement learning based conversational system that interacts with multiple users to schedule meetings. The system is able to interpret user utterences and map them to preferred time slots, which are then fed to a reinforcement learning (RL) system with the goal of converging on an agreeable time slot. The RL system is able to adapt to user preferences and environmental changes in meeting arrival rate while still scheduling effectively. Learning is performed via policy gradient with exploration, by utilizing an MLP as an approximator of the policy function. Results demonstrate that the system outperforms standard scheduling algorithms in terms of overall scheduling efficiency. Additionally, the system is able to adapt its strategy to situations when users consistently reject or accept meetings in certain slots (such as Friday afternoon versus Thursday morning), or when the meeting is called by members who are at a more senior designation.
Design of Coalition Resistant Credit Score Functions for Online Discussion Forums

We consider the problem of designing a robust credit score function in the context of online discussion forums. Credit score function assigns a real-valued credit score to each participant based on activities on the forum. A credit score of a participant quantifies the usefulness of contribution made by her. However, participants can manipulate a credit score function by forming coalitions, i.e., by strategically awarding upvotes, likes, etc. among a subset of agents to maximize their credit scores. We propose a coalition resistant credit score function which discourages such strategic endorsements. We use community detection algorithms to identify close-knit communities in the graph of interactions and characterize coalition identifying community detection metric. In particular, we show that modularity is coalition identifying and provide theoretical guarantees on modularity based credit score function. Finally, we validate our theoretical findings with simulations on illustrative datasets.
NICT Self-Training Approach to Neural Machine Translation at NMT-2018

This paper describes the NICT neural machine translation system submitted at the NMT-2018 shared task. A characteristic of our approach is the introduction of self-training. Since our self-training does not change the model structure, it does not influence the efficiency of translation, such as the translation speed. The experimental results showed that the translation quality improved not only in the sequence-to-sequence (seq-to-seq) models but also in the transformer models.
Cecal Microbiome Analyses on Wild Japanese Rock Ptarmigans (Lagopus muta japonica) Reveals High Level of Coexistence of Lactic Acid Bacteria and Lactate-Utilizing Bacteria

Preservation of indigenous gastrointestinal microbiota is critical for successful captive breeding of endangered wild animals, yet its biology is poorly understood. Here, we compared the cecal microbial composition of wild living Japanese rock ptarmigans (Lagopus muta japonica) in different locations of Japanese mountains, and the dominant cecal microbial structure of wild Japanese rock ptarmigans is elucidated. Coriobacteraceae and Lachnospraceae were the two dominant bacterial families in all samples analyzed. At the genus level, 10 genera Olsenella, Actinomyces, Megasphaera, Slackia, Cloacibacillus, Bifidobacterium, Escherichia, Dialister, Megamonas, and Bilophila were dominant. These results reveal the high level of coexistence of lactic acid bacteria (Olsenella and Bifidobacterium) and lactate-utilizing bacteria (Megasphaera). This coexistence should be taken into account for the successful breeding of captive Japanese rock ptarmigans in the national conservation program.
Move with Me: Scalably Keeping Virtual Objects Close to Users on the Move

The upcoming Cloud-Fog interplay is expected to grant service providers more degrees of freedom in the implementation and management of their service portfolios. However, recent advances in Mobile Internet have developed a growing need to support user mobility - as users move, the Fog counterparts of services that require close proximity may call for migration(s) to meet the desired quality of service (QoS). With the state-of-the-art virtualization technologies, next-generation Cloud/Fog services are being implemented in modular software (i.e., as a graph/chain of portable virtual objects (VOs)) that can be migrated around the Telco infrastructure, and yet scalability is still an open issue, especially with the inter-datacenter bulk live migration of VOs. In this perspective, a VO clustering and migration policy that jointly considers user proximity and inter-VO affinity is proposed to scalably support user mobility, while allowing service differentiation among users. Results confirm that introducing migrations improve the QoS to always meet or exceed the requirements, as compared to static service placement, and considering VO clusters as aggregate entities will initiate around 40% less migrations, on average - an improvement that increases with inter-VO affinity and could potentially simplify service management when supporting user mobility.
Multiuser MIMO Short-Packet Communications: Time-Sharing or Zero-Forcing Beamforming?

In this paper, we investigate a multiuser MIMO system consisting of one multiple-antenna access point (AP) and multiple single-antenna users under short-packet communication scenarios. Two fundamental schemes, i.e., time division multiple access (TDMA) and zero-forcing beamforming (ZFB), are considered for the users to receive individual information from the AP in the downlink. To analyze the performance of TDMA and ZFB in finite blocklength regime, we derive approximate closed-form expressions of the block error rate (BLER) for each user for both schemes. Simple asymptotic expressions at high signal-to- noise ratio (SNR) are also derived. Based on the derived expressions, we then formulate sum-BLER minimization problems in terms of blocklength allocation to each user in TDMA scheme and power allocation in ZFB scheme. We resolve the formulated problems by performing their convexity analysis. We finally validate our theoretical analysis and compare the performance of TDMA and ZFB schemes by simulation results, which show that TDMA and ZFB can outperform each other in different system setups.
Study on the Direct Relationship between Macroscopic Electrical Parameters and Microscopic Channel Percolative Properties in Nanoscale MOSFETs

In this paper, based on the quantitatively characterized factor of channel current percolation path (PP), the local current fluctuations characteristics in device channel can be directly determined by I-V curves only, which links the microscopic PPs to macroscopic device electrical parameters. The results indicate that the newly-defined “killer ratio” of PP is highly correlated with subthreshold swing degradation rate in both planar devices and FinFETs. It is also found that the current in PP area increases slower with V_{g} than the current in non-PP area, which is verified through TCAD and SPICE simulations. The explanation of the physical nature of correlated behavior sheds new light on understanding statistical variability and reliability in nanoscale devices.
Frustrated, Polite, or Formal: Quantifying Feelings and Tone in Email

Email conversations are the primary mode of communication in enterprises. The email content expresses an individual’s needs, requirements and intentions. Affective information in the email text can be used to get an insight into the sender’s mood or emotion. We present a novel approach to model human frustration in text. We identify linguistic features that influence human perception of frustration and model it as a supervised learning task. The paper provides a detailed comparison across traditional regression and word distribution-based models. We report a mean-squared error (MSE) of 0.018 against human-annotated frustration for the best performing model. The approach establishes the importance of affect features in frustration prediction for email data. We further evaluate the efficacy of the proposed feature set and model in predicting other tone or affects in text, namely formality and politeness; results demonstrate a comparable performance against the state-of-the-art baselines.
Real-Time Planning for Traffic Signal Control

Unusual events, such as sporting events, road works, and nat-ural disasters, can overwhelm city infrastructure such as traf-ﬁc networks, which have limited capacity. Recent work on automating trafﬁc ﬂow to reduce congestion and its effects, such as pollution, has formulated the problem of responding to sensed congestion using hybrid domain-independent planning. This approach employs complex domain-independent solvers and requires them to formulate a complete multi-step plan to eliminate the congestion before the ﬁrst response to the congestion can be initiated. In this short paper, we present a much simpler, domain-dependent solver that uses a receding-horizon approach based on real-time heuristic search to select appropriate actions incrementally. Empirical results comparing this approach to previous work are encouraging, demonstrating reasonable behavior and much better scalability. These results suggest that smart cities can leverage real-time planning to control trafﬁc signals on a city-wide scale.
Pedagogical Agents: Back to the Future

Back in the 1990s we started work on pedagogical agents, a new user interface paradigm for interactive learning environments. Pedagogical agents are autonomous characters that inhabit learning environments and can engage with learners in rich, face-to-face interactions. Building on this work, in 2000 we, together with our colleague, Jeff Rickel, published an article on pedagogical agents that surveyed this new paradigm and discussed its potential. We made the case that pedagogical agents that interact with learners in natural, life-like ways can help learning environments achieve improved learning outcomes. This article has been widely cited, and was a winner of the 2017 IFAAMAS Award for Influential Papers in Autonomous Agents and Multiagent Systems (IFAAMAS, 2017). On the occasion of receiving the IFAAMAS award, and after twenty years of work on pedagogical agents, we decided to take another look at the future of the field. We’ll start by revisiting our predictions for pedagogical agents back in 2000, and examine which of those predictions panned out. Then, informed what we have learned since then, we will take another look at emerging trends and the future of pedagogical agents. Advances in natural language dialogue, affective computing, machine learning, virtual environments, and robotics are making possible even more lifelike and effective pedagogical agents, with potentially profound effects on the way people learn.
Human Mesenchymal Stem Cells Expressing Erythropoietin Enhance Survivability of Retinal Neurons Against Oxidative Stress: An In Vitro Study

Retinal degeneration is a prominent feature in ocular disorders. In exploring possible treatments, Mesenchymal Stem Cells (MSCs) have been recognized to yield therapeutic role for retinal degenerative diseases. Studies have also displayed that erythropoietin (EPO) administration into degenerative retina models confers significant neuroprotective actions in limiting pathological cell death. In this study, we aimed to use MSCs to deliver EPO and to evaluate the ability of EPO to rescue retinal neurons from dying upon reactive oxidative stress induction. We derived human MSCs from Wharton’s jelly (hWJMSCs) of the umbilical cord and cells were transduced with lentivirus particles encoding EPO and a reporter gene of green fluorescent protein (GFP). The supernatants of both transduced and non-transduced cells were collected and used as a pre-conditioning medium for Y79 retinoblastoma cells (retinal neuron cell line) following exposure to glutamate induction. Retinal cells exposed to glutamate showed reduced mitochondrial depolarization and enhanced improvement in cell viability when incubated with pre-conditioned media of transduced cells. Our results established a proof-of-concept that MSCs could be used as a candidate for the delivery of EPO therapeutic gene in the treatment of retinal degenerations.
Does the recurrent laryngeal nerve recover function after initial dysfunction in patients undergoing thyroidectomy?

Total thyroidectomy with or without central compartment dissection is the treatment of choice for thyroid carcinoma. Extensive dissection along the recurrent laryngeal nerve (RLN) can lead to vocal cord fixity and hoarseness even without nerve sacrifice. Recovery rates after surgery for thyroid cancers have not been well documented. The aim of the study is to analyze the incidence of vocal cord palsy (VCP) and its recovery rates in patients operated for thyroid cancers.
Acceptability of post-mortem imaging among Muslim and non-Muslim communities.

OBJECTIVE: People's views regarding autopsy vary according to their cultural and religious beliefs. This paper aims to determine the opinions towards this procedure among Muslims resident in Libya and Muslims and non-Muslims resident in the UK. Our long-term goal is to improve autopsy rates; whether conventional or through the use of post-mortem imaging. METHODS: 400 questionnaires were distributed to the three communities, interrogating belief about post-mortem investigations. Descriptive statistics and non-parametric statistics were used to analyse the data. RESULTS: Of the 400 distributed questionnaires, there was a high return rate of 320 (80%). All groups felt that children should be buried sooner than adults(p < 0.001), but 77% of Libyan Muslims thought that children should be buried within 12 h of death compared to 16% of UK Muslims and only 7% of UK non-Muslims (p < 0.001). More non-Muslims were unconcerned about a negative impact of traditional autopsy on the dignity of the corpse than Muslims (p < 0.001) and more Muslims responded that autopsy has a negative emotional effect on the family (p < 0.001). Type of death altered what sort of investigations were desired. In the case of homicide, Libyan Muslims were less likely to prefer CT (p < 0.001) or MRI (p = 0.001). Sex had no effect on the results of the survey. CONCLUSION: Post-mortem imaging is acceptable to both Muslims and non-Muslims in Libya and the UK, but Muslims have a significant preference for post-mortem imaging compared to autopsy, except in homicidal cases. ADVANCES IN KNOWLEDGE: (1) The ability of post-mortem imaging to preserve the dignity of the corpse is independent of religion, however, significantly more Muslims feel that autopsy has a negative emotional effect on the family of the deceased. (2) A significant majority of Muslims in Libya prefer to bury children within 12 h of death, while a delay of up to a week is acceptable in UK. (3) Muslims resident in UK have an attitude closer to that of the indigenous (non-Muslim) population and therefore, educational programmes may be successful in changing attitudes of Muslims in Libya and other predominantly Muslim countries.
Kleene Theorems for Free Choice Nets Labelled with Distributed Alphabets

We provided (PNSE'2014) expressions for free choice nets having "distributed choice property" which makes the nets "direct product" representable. In a recent work (PNSE'2016), we gave equivalent syntax for a larger class of free choice nets obtained by dropping distributed choice property. In both these works, the classes of free choice nets were restricted by a "product condition" on the set of final markings. In this paper we do away with this restriction and give expressions for the resultant classes of nets which correspond to "free choice synchronous products and Zielonka automata". For free choice nets with distributed choice property, we give an alternative characterization using properties checkable in polynomial time. Free choice nets we consider are 1-bounded, S-coverable, and are labelled with distributed alphabets, where S-components of the associated S-cover respect the given alphabet distribution.
A Survey on Coexistence in Heterogeneous Wireless Networks in TV White Spaces

With the advent of analog to digital transition in TV broadcasting, a substantial amount of spectrum has become available in TV bands. To take advantage of this, the idea for cognitive radios was introduced for two major reasons: better utilization of spectrum in urban areas and facilitation of wireless connectivity in rural areas. To achieve these two goals, however, many challenges have to be addressed first. Considering that these frequencies are commonly licensed, besides primary user detection, a serious challenge remains the detection and identification of other secondary devices and networks. The problems arising from this issue concern the coexistence problems happening from having several primary and secondary networks of different technologies cohabiting the same licensed spectrum simultaneously and from many secondary systems/users coexisting at the same place while using identical or different technologies. In this survey we provide a review of existing works and outline new challenges regarding coexistence and self-coexistence in heterogeneous wireless networks in TV White Spaces including a comparative analysis between selected coexistence mechanisms.
Real-time Ambient Fusion of Commodity Tracking Systems for Virtual Reality

Cross-compatibility of virtual reality devices is limited by the difﬁculty of alignment and fusion of data between systems. In this paper, a plugin for ambiently aligning the reference frames of virtual reality tracking systems is presented. The core contribution consists of a procedure for ambient calibration. The procedure describes ambient behaviors for data gathering, system calibration and fault detection. Data is ambiently collected from in-application self-directed movements, and calibration is automatically performed between dependent sensor systems. Sensor fusion is then performed by taking the most accurate data for a given body part amongst all systems. The procedure was applied to aligning a Kinect v2 with an HTC Vive and an Oculus Rift in a variety of common virtual reality scenarios. The results were compared to alignment performed with a gold standard OptiTrack motion capture system. Typical results were 20cm and 4 ◦ of error compared to the ground truth, which compares favorably with the accepted accuracy of the Kinect v2. Data collection for full calibration took on average 13 seconds of in-application, self-directed movement. This work represents an essential development towards plug-and-play sensor fusion for virtual reality technology.
Combining the Augmented Lagrangian Preconditioner with the Simple Schur Complement Approximation

The augmented Lagrangian (AL) preconditioner and its variants have been successfully applied to solve saddle point systems arising from the incompressible Navier--Stokes equations discretized by the finite element method. Attractive features are the purely algebraic construction and robustness with respect to the Reynolds number and mesh refinement. In this paper, we reconsider the application of the AL preconditioner in the context of the stabilized finite volume methods and present the extension to the Reynolds-averaged Navier--Stokes (RANS) equations, which are used to model turbulent flows in industrial applications. Furthermore, we propose a new variant of the AL preconditioner, obtained by substituting the approximation of the Schur complement from the SIMPLE preconditioner into the inverse of the Schur complement for the AL preconditioner. This new variant is applied to both Navier--Stokes and RANS equations to compute laminar and turbulent boundary-layer flows on grids with large aspect ratios. Sp.
Using Venn-Abers predictors to assess cardio-vascular risk

This study investigates a method for predicting compound risk based on in vitro assay data and estimated Cmax, the maximum concentration of a drug in the body. The method makes use of Venn-Abers predictors and Support Vector Machines to compute compound risk with respect to a biological target. The method has been applied to in vitro ion-channel data generated to assess cardiac risk and introduces a more intuitive way to reflect cardiac risk.
Robust Beamforming for SWIPT System with Chance Constraints

The robust beamforming problem in multiple-input single-output (MISO) downlink networks of simultaneous wireless information and power transfer (SWIPT) is studied in this paper. Adopting the time switching fashion to perform energy harvesting and information decoding respectively, we aim at maximizing the sum rate under imperfect channel state information (CSI) and the chance constraints of users' harvested energy. In view of the fact that the constraints for minimal harvested energy is not necessary to meet from time to time, this paper adopts chance constraint to model it and uses the Bernstein inequality to transform it into deterministic constraints equivalently. Recognizing the maximum sum rate problem of imperfect CSI as nonconvex problem, we transform it into finding the expectation of minimum mean square error (MMSE) equivalently in this paper, and an alternative optimization (AO) algorithm is proposed to decompose the optimization problem into two sub- problems: the transmit beamformer design and the division of switching time. The simulation results show the performance gains compared to non-robust state of the art schemes.