COACT: a query interface language for collaborative databases

Data curation activities in collaborative databases mandate that collaborators interact until they converge and agree on the content of their data. In a previous work, we presented a cloud-based collaborative database system that promotes and enables collaboration and data curation scenarios. Our system classifies different versions of a data item to either pending, approved, or rejected. The approval or rejection of a certain version is done by the database Principle Investigators (or PIs) based on its value. Our system also allows collaborators to view the status of each version and help PIs take decisions by providing feedback based on their experiments and/or opinions. Most importantly, our system provided mechanisms for history tracking of different versions to trace the modifications and approval/rejection done by both collaborators and PIs on different versions of a data item. We labeled our system as Update-Pending-Approval model (or UPA). In this paper, we describe a high level SQL query interface language for PIs and collaborators to interact with the UPA framework. We define a set of UPA keywords that are used as a part of the history tracking mechanism to select specific versions of a data item, and a set of UPA options that select specific versions based on possible future decisions of PIs. We implemented our query interface mechanism on top of Apache Phoenix, taking into consideration that the UPA system was implemented on top of Apache HBase. We test the performance of the UPA query language by executing several queries that contain different complexity levels and discuss their results.     

Partial syndrome-based system-level fault diagnosis using game theory

This paper introduces a novel diagnosis approach, using game theory, to solve the comparison-based system-level fault identification problem in distributed and parallel systems based on the asymmetric comparison model. Under this diagnosis model tasks are assigned to pairs of nodes and the results of executing these tasks are compared. Using the agreements and disagreements among the nodes’ outputs, i.e. the input syndrome, the fault diagnosis algorithm identifies the fault status of the system’s nodes, under the assumption that at most t of these nodes can permanently fail simultaneously. Since the introduction of the comparison model, significant progress has been made in both theory and practice associated with the original model and its offshoots. Nevertheless, the problem of efficiently identifying the set of faulty nodes when not all the comparison outcomes are available to the fault identification algorithm prior to initiating the diagnosis phase, i.e. partial syndromes, remains an outstanding research issue. In this paper, we first show how game theory can be adapted to solve the fault diagnosis problem by maximising the payoffs of all players (nodes). We then demonstrate, using results from a thorough simulation, the effectiveness of this approach in solving the fault identification problem using partial syndromes from randomly generated diagnosable systems of different sizes and under various fault scenarios. We have considered large diagnosable systems, and we have experimented extreme faulty situations by simulating all possible fault sets even those that are less likely to occur in practice. Over all the extensive simulations we have conducted, the new game-theory-based diagnosis algorithm performed very well and provided good diagnosis results, in terms of correctness, latency, and scalability, making it a viable addition or alternative to existing diagnosis algorithms.     

Correction to: A subthreshold low-power CMOS LC-VCO with high immunity to PVT variations

This paper presents a capacitor-free low dropout (LDO) linear regulator based on a dual loop topology. The regulator utilizes two feedback loops to satisfy the challenges of hearing aid devices, which include fast transient performance and small voltage spikes under rapid load-current changes. The proposed design works without the need of a decoupling capacitor connected at the output and operates with a 0–100 pF capacitive load. The design has been taped out in a \(0.18\,\upmu \hbox {m}\) CMOS process. The proposed regulator has a low component count, area of \(0.012\, \hbox {mm}^2\) and is suitable for system-on-chip integration. It regulates the output voltage at 0.9 V from a 1.0–1.4 V supply. The measured results for a current step load from 250 to 500 \(\upmu \hbox {A}\) with a rise and fall time of \(1.5\,\upmu \hbox {s}\) are an overshoot of 26 mV and undershoot of 26 mV with a settling time of \(3.5\,\upmu \hbox {s}\) when \({C_L}\) between 0 and 100 pF. The proposed LDO regulator consumes a quiescent current of only \(10.5\,\upmu \hbox {A}\). The design is suitable for application with a current step edge time of 1 ns while maintaining \(\Delta V_{out}\) of 64 mV.     

A 3.3 V, 10 Bits, Clock-Feedthrough Compensated Switched-Current Second Order Sigma-Delta Modulator

This article presents a low-pass sigma-delta modulator for Analogue-to-Digital conversion. The circuit uses a switched-current technique which presents a well known drawback called clock feedthrough. This phenomenon induces an error on the output signal value. In order to cancel the clock feedthrough effect, we use a new method based on a current feedback loop. The circuit is designed in 0.8 μm AMS “Austria Mikro Systems” single poly CMOS process. Measurements of the modulator are performed under A/D converters characterisation system, and show 55 dB dynamic range at 2.048 MHz sampling rate with 8 kHz input frequency bandwidth. These characteristics are suitable for audio applications.     

Fault diagnosis using partial syndromes: a modified Hopfield neural network approach

This paper presents a modified Hopfield neural network (HNN) for solving the system-level fault diagnosis problem which aims at identifying the set of faulty nodes. This problem has been extensively studied in the last three decades. Nevertheless, identifying the set of all faulty nodes using only partial syndromes, i.e. when some of the testing or comparison outcomes are missing prior to initiating the diagnosis phase, remains an outstanding research issue. The new HNN-based diagnosis algorithm does not require any prior learning or knowledge about the system, nor about any faulty situation, hence providing a better generalisation performance. Results from a thorough simulation study demonstrate the effectiveness of the HNN-based fault diagnosis algorithm in terms of diagnosis correctness, diagnosis latency and diagnosis scalability, for randomly generated diagnosable systems of different sizes and under various fault scenarios. We have also conducted extensive simulations using partial syndromes. Simulations showed that the HNN-based diagnosis performed efficiently, i.e. diagnosis correctness was around 99% when at most half of the test or comparison outcomes are missing, making it a viable alternative to existing diagnosis algorithms.     

Interprocedural and Flow-Sensitive Type Analysis for Memory and Type Safety of C Code

The explicit memory management and type conversion endow the C language with flexibility and performance that render it the de facto language for system programming. However, these appealing features come at the cost of programs’ safety. Due to the C language permissiveness, highly skilled but inadvertent programmers often spawn insidious programming errors that yield exploitable code. In this paper, we present a novel type and effect analysis for detecting memory and type errors in C source code. We extend the standard C type system with effect, region, and host annotations that hold valuable safety information. We also define static safety checks to detect safety errors using the aforementioned annotations. Our analysis performs in an intraprocedural phase and an interprocedural phase. The flow-sensitive and alias-sensitive intraprocedural phase propagates type annotations and applies safety checks at each program point. The interprocedural phase generates and propagates unification constraints on type annotations across function boundaries. We present an inference algorithm that automatically infers type annotations and applies safety checks to programs without programmers’ interaction.     

Molecular characterization and role in T cell activation of staphylococcal enterotoxin A binding to the HLA-DR alpha-chain

Superantigens bind to MHC class II-positive cells and stimulate T lymphocytes expressing specific V beta regions of the TCR. Two distinct regions of staphylococcal enterotoxin A superantigen (SEA) have been shown to affect the binding to MHC class II molecules. Results presented here demonstrate for the first time that the SEA-DR interaction can be affected by mutations on the class II alpha-chain. Furthermore, we have precisely mapped the interaction of the SEA N-terminal domain with the alpha1 domain of HLA-DR. Scatchard analysis using DAP cells transfected with mutant class II molecules showed a role for residue DR alpha K39 in the binding of SEA. Also, complementation experiments using mutant SEA molecules revealed an interaction between SEA residue F47 and position alphaQ18 on an outer loop of HLA-DR. These interactions between SEAF47 and the DR alpha-chain are critical, as they allow the recognition by an otherwise nonreactive V beta1+ T cell hybridoma and induction of tyrosine phosphorylation through the TCR.     

VANET QoS-OLSR: QoS-based clustering protocol for Vehicular Ad hoc Networks

In this paper, we address the problem of clustering in Vehicular Ad hoc Networks (VANETs) using Quality of Service Optimized Link State Routing (QoS-OLSR) protocol. Several clustering algorithms have been proposed for VANET and MANET. However, the mobility-based algorithms ignore the Quality of Service requirements that are important for VANET safety, emergency, and multimedia services while the QoS-based algorithms ignore the high speed mobility constraints since they are dedicated for Mobile Ad hoc Networks (MANETs). Our solution is a new QoS-based clustering algorithm that considers a tradeoff between QoS requirements and high speed mobility constraints. The goal is to form stable clusters and maintain the stability during communications and link failures while satisfying the Quality of Service requirements. This is achieved by: (1) considering the high mobility metrics while computing the QoS, (2) using Ant Colony Optimization for MPRs selection, and (3) using MPR recovery algorithm able to select alternatives and keep the network connected in case of link failures. Performance analysis and simulation results show that the proposed model can maintain the network stability, reduce the end-to-end delay, increase the packet delivery ratio, and reduce the communications overhead.