Miracle-dz/UniFAR-FTUs huggingface dataset:
Caracterization and Rheological Behaviour of a Used Insulating Oil and a Transformer Sludge

It is not always possible to avoid electrical equipment transformer failures even under constant control. At least 80% of transformers failures causes are due to a deterioration of insulating oil in the transformers. The most serious consequences are the ones related to sludge accumulation on the windings and in the solid insulation. This contamination leads to a mechanical insulation failure, which often causes noise at substations, damage to insulating surfaces and partial discharges. A structural characterization and rheological behavior of a sample of the used oil and sludge has been determined.
Handcraft and Learned Feature Extraction Techniques for Robust Face Recognition : A Review

In this paper, we will review face representation techniques that are used in face recognition process. There are two types of feature extraction: handcraft and learned features. PCA and LBP are handcraft feature extraction while the DeepFace, generating from convolutional neural network, is learned feature. PCA is an orthogonal transformation where a set of observations is converted to the principal components. The first few principal components have the largest variance hence represented images with small number of features. LBP is a local binary pattern which encodes local image into a binary pattern. LBP tolerates against changes in gray scale variations. By allowing the deep learning to automatically discover the image representations from raw data therefore DeepFace is a learned feature. In some cases, data may be unable to define specific feature especially for face representation. DeepFace is an alternative technique where features are generated through training/learning process without relying on specific algorithms. Learned features significantly outperform the handcraft one where the test set is unseen. PCA, LBP and DeepFace will be compared in terms of accuracy and computational time.
An Efficient Content Based Image Retrieval Scheme with Preserving the Security of Images*

In this paper, an efficient content based image retrieval scheme is proposed which incorporates the security of images in a retrieval system. The main contribution of the proposed scheme is two folded that is to develop an efficient image retrieval process, and to provide security to the retrieved images. To achieve this goal, features from the foreground region as well as background features are extracted, where more importance is given to the foreground region which is identified based on Itti Koch Saliency Map. From the obtained foreground region texture features along with color features are extracted. Simultaneously, the background is divided into four regions and color features are extracted. Finally, the foreground and background features are combined to perform image retrieval process. Now to prevent the feature vector from illegitimate users, suitable cryptographic approaches are used to provide security in terms of confidentiality and integrity. To protect the integrity, the hash value of the feature vector is computed and the obtained hash value along with feature vector are enciphered together. This enciphered value is then communicated to the user. The content owner verifies the integrity of the feature and performs the retrieval process. The retrieved results are then watermarked to protect copyright and are further enciphered so that unauthorized user's cannot access the images. This security mechanism ensures that the retrieved images are shared only to the authorized user. The retrieval process is performed on Corel dataset and the results are comparable to the existing state-of-arts methods. Finally, this process is suitable to protect the security of images in a retrieval system.
A Network-Based Recommendation Algorithm

As Internet expanding into offline, the traditional retail industry began to use the personalized recommendation algorithm to increase user stickiness, conversion and business income. Without considering the data segmentation problem, traditional recommendation algorithm did not perform well in the traditional business data. Accordingly, we considered the interest spread characteristic of retail industry behavior, adopted the method of complex network to construct a personalized recommendation algorithm using the segmentation data set. By using a real sales dataset of a large supermarket, we provided an evaluation of our algorithm. The results show that our algorithm have much better performance in accuracy and recall than the traditional ones, but with the disadvantage of being less coverage.
FastCool: Leakage Aware Dynamic Thermal Management of 3D Memories

3D memory systems offer several advantages in terms of area, bandwidth, and energy efficiency. However, thermal issues arising out of higher power densities have limited their widespread use. While prior works have looked at reducing dynamic power through reduced memory accesses, in these memories, both leakage and dynamic power consumption are comparable. Furthermore, as the temperature rises the leakage power increases, creating a thermal-leakage loop. We study the impact of leakage power on 3D memory temperature and propose turning OFF hot channels to meet thermal constraints. Data is migrated to a 2D memory before closing a 3D channel. We introduce an analytical model to assess the 2D memory delay and use the model to guide data migration decisions. Our experiments show that the proposed optimization improves performance by 27% on an average (up to 66%) over state-of-the-art strategies.
Allowing Switching off Periphery Voltage Island Instead of Doing it per Instance Through Periphery VDD Collapse in SRAMs

In typical SoC Designs, having multiple SRAM cores on single chip has demanded switching off idle cores to attain lowest possible power down leakage because of SRAMs. This paper introduces a novel method called Periphery VDD Collapse to address this aspect by achieving nearly zero periphery leakage when in power down mode. By virtue of having switches outside memory, this feature allows designers to switch off supply for a voltage island containing multiple cores instead of doing it per memory. A novel level shifter interface is implemented to characteristically isolate periphery from bit cell array and allow periphery to be switched off while retaining data present in bit cell array. Experimental results show very minimal change in power and leakage compared to the case when power gates are embedded inside, but saves up to 10% area after being moved outside on chip.
Reconstruction of Finite-Alphabet Block-Sparse Signals From MAP Support Detection

This paper addresses finite-alphabet block-sparse signal recovery by considering support detection and data estimation separately. To this aim, we propose a maximum a posteriori (MAP) support detection criterion that takes into account the finite alphabet of the signal as a constraint. We then incorporate the MAP criterion in a compressed sensing detector based on a greedy algorithm for support estimation. We also propose to consider the finite-alphabet property of the signal in the bound-constrained least-squares optimization algorithm for data estimation. The MAP support detection criterion is investigated in two different contexts: independent linear modulation symbols and dependent binary continuous phase modulation (CPM) symbols. The simulations are carried out in the context of sporadic multiuser communications and show the efficiency of proposed algorithms compared to selected state-of-the-art algorithms both in terms of support detection and data estimation.
Investigation of Multi-Protocol Label Switching for Intelligent Transportation Systems

Intelligent Transportation Systems have many applications, which may be applicable to human safety, traffic management, entertainment and comfort of the users, etc. Most of these applications require better Quality-of-Service provisioning and improved performance by the underlying communication network. Multi-Protocol Label Switching is an efficient and effective technique for forwarding packets along with supporting Quality-of-Service. This work present: an approach that involves integration of Multi-Protocol Label Switching into Intelligent Transportation Systems and investigates its applicability and work-ability in such systems. It would enable harvesting inbuilt capabilities of Multi-Protocol Label Switching in ITS.
Power and Area Efficient Approximate Heterogeneous 8T SRAM for Multimedia Applications

Approximate computing paradigm has emerged as one of the key research fields in area and energy efficient CMOS circuit design for error-tolerant applications like data mining, scientific computing, multimedia applications, etc. Multimedia applications (such as H.264) which use SRAM as storage consume a significant amount of power. In this paper, we propose heterogeneous 8T SRAM memory architectures without and with truncation (2-bit) as storage for low power multimedia applications in smartphones. To the best of our knowledge, this is the first work where both heterogeneous SRAM design and bit-truncation techniques have been simultaneously used to obtain low power memory design for multimedia applications. We show that the proposed techniques provide high image quality even at low power and low area budget of 0.3 uW/pixel and 5.2 um^2/pixel at 0.5 V and 20 MHz in UMC 28nm. The proposed memory architecture is compared with existing heterogeneous 6T, hybrid 8T/6T, all-identical 6T, and all-identical 8T SRAM memory. The results show that proposed memory architectures perform cumulatively better than existing techniques in terms of dynamic power, leakage power & area. The use of proposed memory for storage of an image has much higher Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Metric (SSIM) than existing SRAM techniques. The results are verified by Spice simulation performed in UMC 28 nm CMOS technology.
Altered Thymidine Metabolism Due to Defects of Thymidine Phosphorylase*

Mitochondrialneurogastrointestinalencephalomyopathy (MNGIE) is an autosomal recessive human disease due to mutations in the thymidine phosphorylase (TP) gene. TP enzyme catalyzes the reversible phosphorolysis of thymidine to thymine and 2-deoxy-d-ribose 1-phosphate. We present evidence that thymidine metabolism is altered in MNGIE. TP activities in buffy coats were reduced drastically in all 27 MNGIE patients compared with 19 controls. All MNGIE patients had much higher plasma levels of thymidine than normal individuals and asymptomatic TP mutation carriers. In two patients, the renal clearance of thymidine was ∼20% that of creatinine, and because hemodialysis demonstrated that thymidine is ultrafiltratable, most of the filtered thymidine is likely to be reabsorbed by the kidney. In vitro, fibroblasts from controls catabolized thymidine in medium; by contrast, MNGIE fibroblasts released thymidine. In MNGIE, severe impairment of TP enzyme activity leads to increased plasma thymidine. In patients who are suspected of having MNGIE, determination of TP activity in buffy coats and thymidine levels in plasma are diagnostic. We hypothesize that excess thymidine alters mitochondrial nucleoside and nucleotide pools leading to impaired mitochondrial DNA replication, repair, or both. Therapies to reduce thymidine levels may be beneficial to MNGIE patients.
Measuring the satisfaction of intensive care unit patient families in Morocco: A regression tree analysis*

Objective:Meeting the needs of patients’ family members becomes an essential part of responsibilities of intensive care unit physicians. The aim of this study was to evaluate the satisfaction of patients’ family members using the Arabic version of the Society of Critical Care Medicine’s Family Needs Assessment questionnaire and to assess the predictors of family satisfaction using the classification and regression tree method. Design:The authors conducted a prospective study. Setting:This study was conducted at a 12-bed medical intensive care unit in Morocco. Patients:Family representatives (n = 194) of consecutive patients with a length of stay >48 hrs were included in the study. Intervention:Intervention was the Society of Critical Care Medicine’s Family Needs Assessment questionnaire. Measurements and Main Results:Demographic data for relatives included age, gender, relationship with patients, education level, and intensive care unit commuting time. Clinical data for patients included age, gender, diagnoses, intensive care unit length of stay, Acute Physiology and Chronic Health Evaluation, MacCabe index, Therapeutic Interventioning Scoring System, and mechanical ventilation. The Arabic version of the Society of Critical Care Medicine’s Family Needs Assessment questionnaire was administered between the third and fifth days after admission. Of family representatives, 81% declared being satisfied with information provided by physicians, 27% would like more information about the diagnosis, 30% about prognosis, and 45% about treatment. In univariate analysis, family satisfaction (small Society of Critical Care Medicine’s Family Needs Assessment questionnaire score) increased with a lower family education level (p = .005), when the information was given by a senior physician (p = .014), and when the Society of Critical Care Medicine’s Family Needs Assessment questionnaire was administered by an investigator (p = .002). Multivariate analysis (classification and regression tree) showed that the education level was the predominant factor contributing to the Society of Critical Care Medicine’s Family Needs Assessment questionnaire score. Society of Critical Care Medicine’s Family Needs Assessment questionnaire increased (greater satisfaction) with a higher education level. Other factors of great satisfaction included the senior physician providing the information, and Acute Physiology and Chronic Health Evaluation <15. Conclusions:Satisfaction of intensive care unit patients’ families in a Moroccan sample using the classification and regression tree was dependent on relatives’ education level, communication presented by senior caregiver, and low Acute Physiology and Chronic Health Evaluation score. These data underline cultural specificities of the study and suggest that caregivers should develop structured communication programs considering satisfaction predictors.
On the Impact of Sweep Radius and Energy Limitation on Sweep Coverage in Wireless Sensor Networks

Sweep coverage is an important problem in Wireless Sensor Networks (WSNs). Technically, sweep coverage makes use of mobile sensor nodes that move around to collect sensing data from Points of Interest (POIs) at a low cost. Since POIs can often be sensed remotely, mobile sensor nodes do not have to arrive at the location of POIs to gather sensing data. Sweep radius, the maximum distance between a mobile sensor node and a POI that enables sensing, is an important factor in sweep coverage planning. In addition, because mobile sensor nodes are typically powered by batteries, they tend to have a limited lifetime. To continue the coverage, mobile sensor nodes have to periodically return to the base station to replenish their energy. In this paper, sweep coverage based on sweep radius and energy limitation is formulated as the ($t, T, R$)-SCBR problem. To tackle the ($t, T, R$)-SCBR problem, a centralized algorithm (i.e. CPS) and a distributed algorithm (i.e. DPP) are proposed. Through extensive simulations, we found that the proposed algorithms significantly outperform the existing schemes.
A Simple Test for Quickly Measuring Shielding Effectiveness of Connectors Using Isotropic Broadband Electric Field Probe

The shielding of coaxial cables and connectors is an important parameter for the electromagnetic compatibility of electro-electronic systems. It is of paramount importance the detection of failures as soon as possible in order to consider improved systems and avoid unexpected costs. Within this context, this paper presents a simple procedure to measure the electric field radiated by connectors, with and without solder. A virtual simulation was performed using the commercial package FEKO with a good correlation between simulation and measurement.
Analysis of Blockchain Solutions for IoT: A Systematic Literature Review

The Internet of Things (IoT) aims at connecting things to the Internet in a peer-to-peer paradigm for data collecting and data sharing in our daily life. A blockchain is an immutable append-only ledger maintained by a peer-to-peer network, where the whole network needs to reach a consensus on the transactional data stored on the ledger. With the decentralization nature, the design of IoT and blockchain aligns with each other well. Blockchain has been integrated with the IoT to solve the existing IoT problems. Our research focuses on analyzing the solutions proposed in academia and the methodologies used to integrate blockchain with the IoT. Through conducting a systematic literature review (SLR) on peer-reviewed, published articles on blockchain-based solutions for IoT, we gather the knowledge on current technical approaches implemented to integrate blockchain into the IoT. Majority of the research in this space is either at a conceptual level or at a very early stage. However, we only found 35 published papers with the real implementation of blockchain in the IoT platforms. We elicit the challenges of the IoT that were being addressed, and the detailed design of the blockchain-based solutions from two perspectives, namely data management and thing management. The evaluation methods and metrics used by those works are also being recorded and analyzed. In addition to the analysis of the literature, we provide our insights on improving the existing solutions and research methodology based on our expertise and experience on the blockchain.
Energy Management Strategy for Zone Cooling Load Demand Reduction with Occupancy Thermal Comfort Margin

Due to unpredictable thermal comfort requirements and the stochastic nature of occupancy dependent CLD, it is a challenging task to optimally scheduling and sequence control of a chiller plant with thermal energy storage system. One potential way to address this challenge is to design and develop an energy management and control strategy (EMCS) for zone variable air volume (VAV) unit. This paper proposes an EMCS to reduce zone CLD based on zone thermal parameters. The objective is to reduce zone CLD while maintaining temperature and thermal comfort within the threshold limits. In this context, first we develop zone thermal models. Second, we develop an artificial neural network approach and regression approach to predict zone temperature and CLD by tuning the zone parameters. Third, we implement a thermal feedback model to provide the thermal comfort status based on the zone temperature. Finally, we implement control strategies such as demand supply strategy (DSS) and proposed EMCS. The DSS provides required CLD based on occupancy requirements. Then, we implement the proposed EMCS to reduce the zone CLD by controlling the VAV supply air temperature while keeping the thermal comfort margin within the selected thresholds limits. Numerical case studies are conducted based on real data from a commercial building in Singapore to demonstrate the effectiveness of the proposed scheme.
Storage Policies for a Two-Cache System

We study storage policies for a two-cache system where each request for a file arrives at one of the two caches. A request can be served by either one of the two caches or a central server. Serving a request using the cache at which it is received is the most preferred mode of service and is assumed to be free. The other cache can serve the request at a low, but non-zero cost, and the central server can serve it at a high cost. The aim is to design storage policies which minimize the cost of serving incoming requests. Via analysis and simulations, we compare the performance of the optimal caching policy with the locally optimal storage policy which makes decisions only based on the request arrival process at that particular cache.
Selective Sensitization of Useless Sneak-Paths for Test Optimization in Memristor-Arrays

Memristors have shown significant promise in recent times both in logic synthesis and memory-subsystem design. A 2D-crossbar architecture built with memristor arrays provides a convenient platform for storing multi-valued memory states by utilizing the analog variation of current-induced resistance through these cells. The integration of CMOS components and non-CMOS memristor cells further enhances the scope of their applications to various complex system design. However, present-day memristor cells, by virtue of their inherent structural constructs, are prone to various manufacturing defects and sensitive to operational modalities. Therefore, robust and fast testing methods are frequently needed to ensure the correctness of their functionality. Existing techniques for testing memristor arrays are either ad-hoc in nature or suited for application-specific designs, and no attempt has been made to optimize test-time. In this work, we present a graph-based technique to select some sneak-paths and sensitize these to generate test-vectors for a 2D memristor-array. The proposed method relies on Eulerian graph traversal and matching in bipartite graphs to produce tests for detecting stuck-at, slow-to-write, and deep faults in all memristor-cells. Simulation results with LTspice demonstrate the effectiveness and superiority of the proposed method to prior art in terms of test time and fault-coverage.
Rapid Deployment of Anomaly Detection Models for Large Number of Emerging KPI Streams

Internet-based services monitor and detect anomalies on KPIs (Key Performance Indicators, say CPU utilization, number of queries per second, response latency) of their applications and systems in order to keep their services reliable. This paper identifies a common, important, yet little-studied problem of KPI anomaly detection: rapid deployment of anomaly detection models for large number of emerging KPI streams, without manual algorithm selection, parameter tuning, or new anomaly labeling for any newly emerging KPI streams. We propose the first framework ADS (Anomaly Detection through Self-training) that tackles the above problem, via clustering and semi-supervised learning. Our extensive experiments using real-world data show that, with the labels of only the 5 cluster centroids of 70 historical KPI streams, ADS achieves an averaged best F-score of 0.92 on 81 new KPI streams, almost the same as a state-of-art supervised approach, and greatly outperforming a state-of-art unsupervised approach by 61.40% on average.
Directed Fuzzing Based on Program Dynamic Instrumentation

In this paper we present a new approach for directed fuzzing. It enables faster generation of the input data for the target program's specific instructions execution. Existing fuzzing tools randomly generate or mutate input data to increase code coverage. This approach is not effective for analysis of special code regions. The basic idea behind of this paper is to instrument target program in a such way that interesting code fragments were executed as soon as possible. For that propose we detect all the paths in the program which are connecting program's entry point to the considered instructions. Then we apply two type of instrumentations. In the first case we insert coverage collection instructions only in detected paths, which enables fuzzing tool to consider generated or mutated input data valuable if the distances between executed blocks and target points are reduced. In the second case we additionally insert 'exit(0)' instructions in those basic blocks from which target points are unreachable and their execution has no any influence. It allows repeatedly increase fuzzing speed.