The constant increase in the amount and complexity of information obtained from IT data networkelements, for its correct monitoring and management, is a reality. The same happens to data net-works in electrical systems that provide effective supervision and control of substations and hydro-electric plants. Contributing to this fact is the growing number of installations and new environmentsmonitored by such data networks and the constant evolution of the technologies involved. This sit-uation potentially leads to incomplete and/or contradictory data, issues that must be addressed inorder to maintain a good level of monitoring and, consequently, management of these systems. Inthis paper, a prototype of an expert system is developed to monitor the status of equipment of datanetworks in electrical systems, which deals with inconsistencies without trivialising the inferences.This is accomplished in the context of the remote control of hydroelectric plants and substationsby a Regional Operation Centre (ROC). The expert system is developed with algorithms definedupon a combination of Fuzzy logic and Paraconsistent Annotated Logic with Annotation of TwoValues (PAL2v) in order to analyse uncertain signals and generate the operating conditions (faulty,normal, unstable or inconsistent / indeterminate) of the equipment that are identified as importantfor the remote control of hydroelectric plants and substations. A prototype of this expert systemwas installed on a virtualised server with CLP500 software (from the EFACEC manufacturer) thatwas applied to investigate scenarios consisting of a Regional (Brazilian) Operation Centre, with aGeneric Substation and a Generic Hydroelectric Plant, representing a remote control environment.

Last year Qualcomm started rolling out its first chips for Android phones that supported upgradeable GPU drivers to optimize performance, so now it's doing a similar thing for on-device AI and machine learning. Droid-Life points out that during Google I/O, Google and Qualcomm have announced updatable neural network API drivers, representing a new model that will roll out along with Android 12. While NN API drivers have usually been updated along with major OS updates, now the companies say they can roll out quickly via Google Play Services. Even better, the updates will be available for older chipsets and multiple versions of Android. In an I/O presentation about advancements in machine learning, Google developers said the NN API could boost performance as though the phone had two additional CPU cores, while using less power and creating less heat.

Massive multiple-input multiple-output can obtain more performance gain by exploiting the downlink channel state information (CSI) at the base station (BS). Therefore, studying CSI feedback with limited communication resources in frequency-division duplexing systems is of great importance. Recently, deep learning (DL)-based CSI feedback has shown considerable potential. However, the existing DL-based explicit feedback schemes are difficult to deploy because current fifth-generation mobile communication protocols and systems are designed based on an implicit feedback mechanism. In this paper, we propose a DL-based implicit feedback architecture to inherit the low-overhead characteristic, which uses neural networks (NNs) to replace the precoding matrix indicator (PMI) encoding and decoding modules. By using environment information, the NNs can achieve a more refined mapping between the precoding matrix and the PMI compared with codebooks. The correlation between subbands is also used to further improve the feedback performance. Simulation results show that, for a single resource block (RB), the proposed architecture can save 25.0% and 40.0% of overhead compared with Type I codebook under two antenna configurations, respectively. For a wideband system with 52 RBs, overhead can be saved by 30.7% and 48.0% compared with Type II codebook when ignoring and considering extracting subband correlation, respectively.

Qualcomm has officially launched the Snapdragon 778G 5G processor, which will power the Honor 50 series and other upcoming mid-to-high-end devices. It joins the Snapdragon 780G, which Qualcomm introduced in March, as one of the company's options for upper mid-range phones. The 778G SoC uses Kryo 670 CPU, which Qualcomm says can enhance overall CPU performance by 40 percent. Meanwhile, its Adreno 642L GPU is designed to deliver up to 40 percent faster graphics rendering compared to the previous generation. The chipset comes with the latest (6th generation) Qualcomm AI Engine, as well.

In this paper, we study problem of efficient service relocation (i.e., changing assigned data center for a selected client node) in elastic optical networks (EONs) in order to increase network performance (measured by the volume of accepted traffic). To this end, we first propose novel traffic model for cloud ready transport networks. The model takes into account four flow types (i.e., city-to-city, city-to-data center, data center-to-data center and data center-to-data center) while the flow characteristics are based on real economical and geographical parameters of the cities related to network nodes. Then, we propose dedicated flow allocation algorithm that can be supported by the service relocation process. We also introduce 21 different relocation policies, which use three types of data for decision making - network topological characteristics, rejection history and traffic prediction. Eventually, we perform extensive numerical experiments in order to: (i) tune proposed optimization approaches and (ii) evaluate and compare their efficiency and select the best one. The results of the investigation prove high efficiency of the proposed policies. The propoerly designed relocation policy allowed to allocate up to 3% more traffic (compared to the allocation without that policy). The results also reveal that the most efficient relocation policy bases its decisions on two types of data simultaneously - the rejection history and traffic prediction.

Artificial Intelligence (AI) and Deep Learning (DL) algorithms are currently applied to a wide range of products and solutions. DL training jobs are highly resource demanding and they experience great benefits when exploiting AI accelerators (e.g., GPUs). However, the effective management of GPU-powered clusters comes with great challenges. Among these, efficient scheduling and resource allocation solutions are crucial to maximize performance and minimize Data Centers operational costs. In this paper we propose ANDREAS, an advanced scheduling solution that tackles these problems jointly, aiming at optimizing DL training runtime workloads and their energy consumption in accelerated clusters. Experiments based on simulation demostrate that we can achieve a cost reduction between 30 and 62% on average with respect to first-principle methods while the validation on a real cluster shows a worst case deviation below 13% between actual and predicted costs, proving the effectiveness of ANDREAS solution in practical scenarios.

The world's economy is at a tipping point as digital technologies continue to be embedded into both working and personal lives at an unprecedented rate. By 2023, digitally transformed enterprises will account for more than half of global Gross Domestic Product (GDP). Two overarching factors will drive this trend: the proliferation of digital devices and the rising prominence of the millennial and zoomer (Generation Z) user base. These digital-savvy generations account for 75% of the population in the Middle East today. By 2025, the number of connected devices globally is predicted to reach 100 billion, more than 12 times the number of people in this world.

Federated learning has attracted attention in recent years for collaboratively training data on distributed devices with privacy-preservation. The limited network capacity of mobile and IoT devices has been seen as one of the major challenges for cross-device federated learning. Recent solutions have been focusing on threshold-based client selection schemes to guarantee the communication efficiency. However, we find this approach can cause biased client selection and results in deteriorated performance. Moreover, we find that the challenge of network limit may be overstated in some cases and the packet loss is not always harmful. In this paper, we explore the loss tolerant federated learning (LT-FL) in terms of aggregation, fairness, and personalization. We use ThrowRightAway (TRA) to accelerate the data uploading for low-bandwidth-devices by intentionally ignoring some packet losses. The results suggest that, with proper integration, TRA and other algorithms can together guarantee the personalization and fairness performance in the face of packet loss below a certain fraction (10%-30%).

ElectrifAi, one of the world's leading companies in practical artificial intelligence (AI) and pre-built machine learning (ML) models, today announced that it has achieved Amazon Web Services (AWS) Machine Learning Competency status in the new Applied Artificial Intelligence (Applied AI) category. This designation recognizes that ElectrifAi has demonstrated deep experience and expertise in building or integrating practical ML solutions on AWS. AWS Partners recognized as part of the AWS Machine Learning Competency expansion help customers take advantage of intelligent solutions to drive business optimization, customer revenue growth and cost reduction. This is done by creating, automating, and managing end-to-end ML workflows with machine intelligence. The AI and ML driven applications are maturing rapidly and creating new demands on enterprises.

Alarm root cause analysis is a significant component in the day-to-day telecommunication network maintenance, and it is critical for efficient and accurate fault localization and failure recovery. In practice, accurate and self-adjustable alarm root cause analysis is a great challenge due to network complexity and vast amounts of alarms. A popular approach for failure root cause identification is to construct a graph with approximate edges, commonly based on either event co-occurrences or conditional independence tests. However, considerable expert knowledge is typically required for edge pruning. We propose a novel data-driven framework for root cause alarm localization, combining both causal inference and network embedding techniques. In this framework, we design a hybrid causal graph learning method (HPCI), which combines Hawkes Process with Conditional Independence tests, as well as propose a novel Causal Propagation-Based Embedding algorithm (CPBE) to infer edge weights. We subsequently discover root cause alarms in a real-time data stream by applying an influence maximization algorithm on the weighted graph. We evaluate our method on artificial data and real-world telecom data, showing a significant improvement over the best baselines.