An integrated system to remote monitor and control anaerobic wastewater treatment plants through the internet.

The TELEMAC project brings new methodologies from the Information and Science Technologies field to the world of water treatment. TELEMAC offers an advanced remote management system which adapts to most of the anaerobic wastewater treatment plants that do not benefit from a local expert in wastewater treatment. The TELEMAC system takes advantage of new sensors to better monitor the process dynamics and to run automatic controllers that stabilise the treatment plant, meet the depollution requirements and provide a biogas quality suitable for cogeneration. If the automatic system detects a failure which cannot be solved automatically or locally by a technician, then an expert from the TELEMAC Control Centre is contacted via the internet and manages the problem.     

Silhouette-based human action recognition using SAX-Shapes

Human action recognition is an important problem in Computer Vision. Although most of the existing solutions provide good accuracy results, the methods are often overly complex and computationally expensive, hindering practical applications. In this regard, we introduce the combination of time-series representation for the silhouette and Symbolic Aggregate approXimation (SAX), which we refer to as SAX-Shapes, to address the problem of human action recognition. Given an action sequence, the extracted silhouettes of an actor from every frame are transformed into time series. Each of these time series is then efficiently converted into the symbolic vector: SAX. The set of all these SAX vectors (SAX-Shape) represents the action. We propose a rotation invariant distance function to be used by a random forest algorithm to perform the human action recognition. Requiring only silhouettes of actors, the proposed method is validated on two public datasets. It has an accuracy comparable to the related works and it performs well even in varying rotation.     

Time optimization of ultraviolet–ozone pretreatment for improving wool fabrics properties

Wool fabrics were exposed to ultraviolet (UV)–ozone treatment for different periods. After exposure, the fabrics were analyzed by Fourior transform infrared spectroscopy (FTIR). Also, the crystallinity and amorphousity regions were followed by an X‐ray diffraction technique. The results were correlated by mechanical properties measurements. After a certain period of exposure, the dyeability and printability were improved because these abilities are strongly related to the percentage ratio of crystallinity regions to amorphousity regions in wool fabrics. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 85: 1469–1476, 2002     

IESK-ArDB: a database for handwritten Arabic and an optimized topological segmentation approach

Even though a lot of researches have been conducted in order to solve the problem of unconstrained handwriting recognition, an effective solution is still a serious challenge. In this article, we address two Arabic handwriting recognition-related issues. Firstly, we present IESK-arDB, a new multi-propose off-line Arabic handwritten database. It is publicly available and contains more than 4,000 word images, each equipped with binary version, thinned version as well as a ground truth information stored in separate XML file. Additionally, it contains around 6,000 character images segmented from the database. A letter frequency analysis showed that the database exhibits letter frequencies similar to that of large corpora of digital text, which proof the database usefulness. Secondly, we proposed a multi-phase segmentation approach that starts by detecting and resolving sub-word overlaps, then hypothesizing a large number of segmentation points that are later reduced by a set of heuristic rules. The proposed approach has been successfully tested on IESK-arDB. The results were very promising, indicating the efficiency of the suggested approach.     

A branch and bound algorithm for extracting smallest minimal unsatisfiable subformulas

Explaining the causes of infeasibility of Boolean formulas has practical applications in numerous fields, such as artificial intelligence (repairing inconsistent knowledge bases), formal verification (abstraction refinement and unbounded model checking), and electronic design (diagnosing and correcting infeasibility). Minimal unsatisfiable subformulas (MUSes) provide useful insights into the causes of infeasibility. An unsatisfiable formula often has many MUSes. Based on the application domain, however, MUSes with specific properties might be of interest. In this paper, we tackle the problem of finding a smallest-cardinality MUS (SMUS) of a given formula. An SMUS provides a succinct explanation of infeasibility and is valuable for applications that are heavily affected by the size of the explanation. We present (1) a baseline algorithm for finding an SMUS, founded on earlier work for finding all MUSes, and (2) a new branch-and-bound algorithm called Digger that computes a strong lower bound on the size of an SMUS and splits the problem into more tractable subformulas in a recursive search tree. Using two benchmark suites, we experimentally compare Digger to the baseline algorithm and to an existing incomplete genetic algorithm approach. Digger is shown to be faster in nearly all cases. It is also able to solve far more instances within a given runtime limit than either of the other approaches.     

Dynamic storage and access load balancing for answering range queries in peer-to-peer networks

Recently, many applications have used Peer-to-Peer (P2P) systems to overcome the current problems with client/server systems such as non-scalability, high bandwidth requirement and single point of failure. In this paper, we propose an efficient scheme to support efficient range query processing over structured P2P systems, while balancing both the storage load and access load. The paper proposes a rotating token scheme to balance the storage load by placing joining nodes in appropriate locations in the identifier space to share loads with already overloaded nodes. Then, to support range queries, we utilize an order-preserving mapping function to map keys to nodes in order preserving way and without hashing. This may result in an access load imbalance due to non-uniform distribution of keys in the identifier space. Thus, we propose an adaptive replication scheme to relieve overloaded nodes by shedding some load on other nodes to balance the access load. We derive a formula for estimating the overhead of the proposed adaptive replication scheme. In this study, we carry simulation experiments with synthetic data to measure the performance of the proposed schemes. Our simulation experiments show significant gains in both storage load balancing and access load balancing.     

A novel distributed scheduling scheme for OFDMA uplink using channel information and probabilistic transmission

Distributed uplink scheduling in OFDMA systems is considered. In the proposed model, mobile terminals have the responsibility of making their own transmission decisions. The proposed scheme is based on two dimensional reservation in time and frequency. Terminals use channel state information in order to favor transmissions over certain subchannels, and transmission is done in a probabilistic manner. The proposed approach provides more autonomy to mobile devices in making transmission decisions. Furthermore, it allows avoiding collisions during transmission since it leads to collision detection during the resource reservation phase. The proposed approach is compared to other random access methods and shown to be superior in terms of increasing sum-rate, reducing the number of users in outage, and reducing the collision probability in the reservation phase.     

cTraj: efficient indexing and searching of sequences containing multiple moving objects

Indexing sequences containing multiple moving objects by all features of these objects captured at every clock tick results in huge index structures due to the large number of extracted features in all sampled instances. Thus, the main problems with current systems that index sequences containing multiple moving objects are: huge storage requirements for index structures, slow search time and low accuracy due to lack of representation of the time-varying features of objects. In this paper, a technique called cTraj to address these problems is proposed. For each object in a sequence, cTraj captures the features at sampled instances. Then, it maps the object??s features at each sampled instance from high-dimensional feature space into a point in low-dimensional distance space. The sequence of points of an object in low-dimensional space is considered the time-varying feature trajectory of the object. To reduce storage requirements of an index structure, the sequence of points in each trajectory is represented by a minimum bounding box (MBB). cTraj indexes a sequence by the MBBs of its objects using a spatial access method (SAM), such as an R?tree; thus, greatly reducing storage requirements of the index and speeding up the search time. The cTraj technique does not result in any false dismissal, but the result might contain a few false alarms, which are removed by a two-step refinement process. The experiments show that the proposed cTraj technique produces effective results comparable to those of a sequential method, however much more efficient.     

Impact of Coronavirus Pandemic Crisis on Technologies and Cloud Computing Applications

In light of the coronavirus disease 2019 (COVID-19) outbreak caused by the novel coronavirus, companies and institutions have instructed their employees to work from home as a precautionary measure to reduce the risk of contagion. Employees, however, have been exposed to different security risks because of working from home. Moreover, the rapid global spread of COVID-19 has increased the volume of data generated from various sources. Working from home depends mainly on cloud computing (CC) applications that help employees to efficiently accomplish their tasks. The cloud computing environment (CCE) is an unsung hero in the COVID-19 pandemic crisis. It consists of the fast-paced practices for services that reflect the trend of rapidly deployable applications for maintaining data. Despite the increase in the use of CC applications, there is an ongoing research challenge in the domains of CCE concerning data, guaranteeing security, and the availability of CC applications. This paper, to the best of our knowledge, is the first paper that thoroughly explains the impact of the COVID-19 pandemic on CCE. Additionally, this paper also highlights the security risks of working from home during the COVID-19 pandemic.