Hewlett-Packard (India)

In this study, we present a neural network (NN) based recommender system with novel custom loss function developed to recommend fee for its pricelock product.It is a popular add-on product that allows users to lock a flight price and book it later at the same locked price, even if the price increases while flight booking.The core challenge in enabling this product lies in predicting the magnitude of future price changes over time horizons.We formulate this problem as a multi-task learning (MTL) setup, where price change magnitudes are modeled as ordinal categories across several time intervals modeled as heads.Crucially, we address the ordinal nature of price change buckets by introducing a novel loss function called Learnable Soft Ordinal Regression (L-SORD).Our demo showcases how this system improves both predictive accuracy and revenue performance, enabling more effective price recommendations in a high stakes, real world environment.This work highlights the potential of combining MTL architectures with custom loss functions in production grade pricing recommender systems.

Artificial Intelligence (AI) combined with simulations and experiments has great potential to accelerate scientific discovery across technology and pharmaceuticals. However, the gap between simulations and experiments is challenging due to disparities in time and scale, making it difficult to estimate properties like energy and electronic states from experiments, and to provide feedback based on theoretical insights.Our research addresses the challenge by developing unique deep kernel based surrogate models that learns from microscopic images, mapping structural features to energy differences from defect formation. We start with full-training using simulated images to determine optimal settings, establishing a baseline for active learning. Using these settings from the baseline, active learning is trained, and predicts structures along simulation trajectories based on uncertainty and energetic stability, thus reducing data requirements, simulation time and computational costs. The results demonstrate that the model achieves a low average error margin of approximately 0.03 meV, indicating good performance. To enhance feature extraction and reconstruction capabilities, we developed an autoencoder-decoder as additional surrogate to create latent space to capture essential features, enabling precise comparisons between simulations and experiments. The results from this model achieved a reconstruction loss of around 0.2 and accurately reconstructed molecular structures.Overall, this work advances the steering of experiments through computational simulations by employing a surrogate models that actively predicts the trajectories of structural evolution, achieving time-to-solution comparable to experimental measurements.

Thirteen groups of students have started Start Up journey conceiving an idea to address specific problems of farmers. The main ideas include improved seed drills, weed management devices, harvesters, livestock feed, value addition to agri products, apps on labour management, etc. University of Agricultural Sciences, Dharwad has conducted ideation workshops under Institutional Development Plan of National Agricultural Higher Education Project. In order to encourage students to generate idea and develop a product/innovation to address the problems of farming community and other stake holders, ideathon workshop were conducted in all constituent colleges of UAS, Dharwad. The students were oriented about the process and design thinking concepts. Out of 70 student groups who have presented the idea for a specific problem of students, 35 groups have been short listed and 10 groups were successfully developed the prototypes. Among them, four groups have are at the stage of commercial production and registered as a private limited company. The impact assessment result revealed that the ideation workshops were useful in terms of building Entrepreneurship Skills, Self-Confidence and created opportunity to find solutions to the problems. The experience of students in product development revealed that 24.79 per cent students have learnt to design prototypes and 47.01 per cent expressed that they learnt to modify prototypes and make suitable changes. The overall benefit was expressed as high in building solutions by 52.14 percent of students, 49.57 percent gained confidence to startup and over 50 percent gained confidence to manage a startup. The major support expected by students includes financial assistance , moral support , laboratory and other techinical support. The overall benefit realize by students inicludes exposure to the idea development 26.50 percent understanding pros and cons of idea 32.48 percent and confidence building 64.96 percent. It is clear from the study that students can generate effective ideas for the problems of the farmers and other stake holders. There is need for facilitating and guiding the students in the startup journey. Ideation workshops serve as a first step in promotion of startups among the students

A driving force behind the improvement of server efficiency in recent years is the use of SPEC benchmarks. They are used in mandatory government regulations, the ISO/IEC 21836:2020 standard, and product marketing, giving server manufacturers and buyers significant incentive to improve energy efficiency. To produce relevant results, benchmarks need to take into account future trends in hardware and software development, such as the introduction of new accelerators and workloads. To keep pace with the development of the fast moving IT landscape, SPEC plans to introduce a workload bounty program to encourage researchers to develop novel workloads. Submitted workloads will be considered for inclusion in future SPEC Efficiency benchmarks and rewarded. In this paper, we outline the process of energy-efficiency benchmark development. SPEC ensures the development of high-quality benchmarks for government regulations through its extensive experience and collaboration with stakeholders from industry, academia, and governments. One of the tools that emerged from this process is the Chauffeur Worklet Development Kit (WDK), which can be used by researchers to develop next-generation workloads to enhance the real-world relevance of future SPEC benchmarks, a critical element for the benchmarks to contribute to future energy conservation.

Introduction: kidneys filter blood by removing waste and extra water to make urine. The kidney's filtration rate, called the glomerular filtration rate (GFR), shows how well the kidneys are filtering. Chronic Kidney Disease (CKD) is known to be the 6th fastest growing cause of death globally. Early detection of CKD can help to protect kidney function. This paper presents a case of Stage 2 Kidney disease treated successfully by the application of Yoga Prana Vidya healing protocols. Method: This study uses case study method, analysing patient medical records and YPV protocols used in treating the patient for 6 months. Patient feedback also obtained for analysis. Results: Improvements in the main parameters achieved at the end of 6 months of YPV intervention show that eGFR improved from 79 to 95. Total Cholesterol improved from 235 mg/dl to 165 mg/dl. HDL Cholesterol increased from 32 mg/dl to 47 mg/dl and LDL Cholesterol reduced from 150 mg/dl to 94 mg/dl. Patient achieved peace and satisfaction of YPV treatment that worked for normalizing his kidney condition. Conclusion: Using the YPV intervention for this patient, only YPV healing was given and no other form of therapy or treatment was given for stage 2 kidney ailment, which shows that YPV Healing System can be applied not only as complementary treatment but also as an alternative treatment mode in such cases of diseases. Further research may be conducted with wider applications of YPV Healing system to various other kidney ailments.

Emotional Intelligence is an integral part of our life and when it comes to project management it is even more crucial to practice. As project managers we always want to bring the best out of the people we deal with. The best thing about emotional intelligence is that we can develop this skill. We can leverage emotional intelligence to bring such an environment to a workplace where people are being treated as people and not just work resources. By practicing emotional intelligence, a project manager can make sure that the team delivers their best work, they have an environment filled with trust and, they have an open, transparent, supportive work culture. Often, we feel getting carried away with our instant reactions to issues and taking up wrong decisions which we might regret later. This paper talks about our brain circuitry to figure out why we have those common reactions and how we can practice self-regulation and self-mastery.

With the emerging intelligent technology and larger talent gaps training and development are the need of the hour. Organizations use competencies to remain adaptable and competitive in their respective fields. In this paper, we propose a technique to manage competency across a business unit to keep up with the want of the customers and ever-changing technology. The solution is powered by Artificial Intelligence and E-learning. We propose a technique to customize a learning roadmap for an individual by using a machine learning-based hybrid recommendation system. By using the technique managers can identify and optimize the skills required to deliver on an organization’s business strategy. The last section of the paper discusses a technique to auto-rank the algorithms used within a hybrid recommendation system based accuracy of predictions that help reduce the training time of the machine learning model itself. The solution indirectly boosts innovation, job satisfaction, and morale among employees resulting in higher levels of engagement, thus enabling a better productive workforce.

Data analytics and artificial intelligence are extensively used by enterprises today and they increasingly span organization boundaries. Such collaboration between organizations today happens in an ad hoc manner, with very little visibility and systemic control on who is accessing the data, how, and for what purpose. When sharing data and AI models with other organizations, the owners desire the ability to control access, have visibility into the entire data pipeline and lineage, and ensure integrity. In this work, we present a decentralized trusted data and model platform for collaborative AI, that leverages blockchain as an immutable metadata store of data and model resources and operations performed on them, to support and enforce ownership, authenticity, integrity, lineage and auditability properties. Smart contracts enforce policies specified on data, including hierarchical and composite policies that are uniquely enabled by the use of blockchain. We demonstrate that our system is light-weight and can support over 1000 transactions per second with sub-second latency, significantly lower than the time taken to execute data pipelines.

For the rapid increase in resource requirements in large scale Data Centers (DCs), enterprises have brought hyperconverged architecture where the storage pool is built up by the individual storage components associated with different servers, and it is shared among all the Virtual Machines (VMs) or containers through a common network infrastructure. Due to the sharing of network bandwidth among the application generated network traffic and the storage traffic from shared storage infrastructure, quality of service (QoS) performances of networking and storage intensive applications are affected, which further impacts VM or container migrations with dynamic workload scenarios. In this article, we propose NetStor to ensure QoS for various collocated network and storage intensive workloads over a hyperconverged architecture and ensure QoS during VM or container migration. NetStor uses a workload estimation strategy for VMs and containers, and applies the strategic decisions for resource allocation and migration based on environmental learning and workload characterization. NetStor supports a dynamic QoS provisioning that is workload-agnostic catering to both VMs and containers, and is unique to the best of our knowledge. We have implemented NetStor over a hyperconverged DC architecture on a testbed and found that NetStor can enhance network performance significantly compared to other related mechanisms discussed in the literature.

Mobile phones today have become an integral part of every person’s life. With the advent of smartphones, in addition to voice and text communication, people today can browse the internet, log in to social media, purchase merchandises through e-commerce websites and make monetary transactions using mobile phones. Hence usability of the phone plays an essential role in making a purchase decision. This study aims to capture the experience of mobile phone users by measuring their perceptions about attributes of usability and its impact on brand loyalty and re-purchase intention. Data was collected from 301 mobile phone users from the millennial generation and analysed using structural equation modelling. Results indicate that only ‘satisfaction’ derived by using the mobile phone develops brand loyalty, which in turn influences the re-purchase decision. Implications are drawn, and recommendations are made to increase the usability of mobile phones.