Reviewerly/exHarmony huggingface dataset:
Multi-view Dynamic Heterogeneous Information Network Embedding

Abstract Most existing heterogeneous information network (HIN) embedding methods focus on static environments while neglecting the evolving characteristic of real-world networks. Although several dynamic embedding methods have been proposed, they are merely designed for homogeneous networks and cannot be directly applied in heterogeneous environments. To tackle above challenges, we propose a novel framework for incorporating temporal information into HIN embedding, named multi-view dynamic HIN embedding (MDHNE), which can efficiently preserve evolution patterns of implicit relationships from different views in updating node vectors over time. We first transform HIN to a series of homogeneous networks corresponding to different views. Then our proposed MDHNE applies recurrent neural network (RNN) to incorporate evolving pattern of complex network structure and semantic relationships between nodes into latent embedding spaces, and thus the node vectors from multiple views can be learned and updated when HIN evolves over time. Moreover, we come up with an attention-based fusion mechanism, which can automatically infer weights of latent vectors corresponding to different views by minimizing the objective function specific for different mining tasks. Extensive experiments clearly demonstrate that our MDHNE model outperforms state-of-the-art baselines on three real-world dynamic datasets for different network mining tasks.
Performance of deep neural networks on low-power IoT devices

Advances in deep learning have revolutionized machine learning by solving complex tasks such as image, speech, and text recognition. However, training and inference of deep neural networks are resource-intensive. Recently, researchers made efforts to bring inference to IoT edge and sensor devices which have become the prime data sources nowadays. However, running deep neural networks on low-power IoT devices is challenging due to their resource-constraints in memory, compute power, and energy. This paper presents a benchmark to grasp these trade-offs by evaluating three representative deep learning frameworks: uTensor, TF-Lite-Micro, and CMSIS-NN. Our benchmark reveals significant differences and trade-offs for each framework and its tool-chain: (1) We find that uTensor is the most straightforward framework to use, followed by TF-Micro, and then CMSIS-NN. (2) Our evaluation shows large differences in energy, RAM, and Flash footprints. For example, in terms of energy, CMSIS-NN is the most efficient, followed by TF-Micro and then uTensor, each with a significant gap.
Natural Language Processing through BERT for Identifying Gender-Based Violence Messages on Social Media

The purpose of this article is to provide a natural language processing model to classify messages on social media that contain gender violence. The applied methodology considers performing a fine tune process to BERT, so as the output model can be trained with labeled messages -as violence and nonviolence-on the Reddit and Twitter platforms. The performance obtained in terms of area under the curve, accuracy, sensitivity and specificity were 0.9603, 0.8909, 0.8826 and 0.8989 respectively.
Vision-Based Multi-Modal Framework for Action Recognition

Human activity recognition plays a central role in the development of intelligent systems for video surveillance, public security, health care and home monitoring, where detection and recognition of activities can improve the quality of life and security of humans. Typically, automated, intuitive and real-time systems are required to recognize human activities and identify accurately unusual behaviors in order to prevent dangerous situations. In this work, we explore the combination of three modalities (RGB, depth and skeleton data) to design a robust multi-modal framework for vision-based human activity recognition. Especially, spatial information, body shape/posture and temporal evolution of actions are highlighted using illustrative representations obtained from a combination of dynamic RGB images, dynamic depth images and skeleton data representations. Therefore, each video is represented with three images that summarize the ongoing action. Our framework takes advantage of transfer learning from pre-trained models to extract significant features from these newly created images. Next, we fuse extracted features using Canonical Correlation Analysis and train a Long Short-Term Memory network to classify actions from visual descriptive images. Experimental results demonstrated the reliability of our feature-fusion framework that allows us to capture highly significant features and enables us to achieve the state-of-the-art performance on the public UTD-MHAD and NTU RGB+D datasets.
An Attempt for Energy Conscious Scheduling Based on Subjective Particle Swarm Optimization in Cloud Computing Environment

The idea is to propose a scheduling of work flow applications over a versatile computing system like cloud computing models. Literally, scheduling in a cloud computing environment is tough in terms of optimization which has to take care of several conditions to satisfy a humungous loads of quality of service (QoS) needs. The conventional work mechanisms in scheduling focuses on the constraints in optimization in terms of time or money without focusing on the energy utilization. The key intention is to design a fresh scheme for multi - purpose work flow schedule for clouds and to make use of enhanced particle swarm optimization (EPSO) scheme for optimizing the performance of scheduling. The proposed scheme is intended on energizing voltage and frequency scaling (EVFS) scheme for reducing the energy utilization. This scheme permits the processors to function in varied voltage supplies by sacrifice the timepiece frequencies. This variable energy involve a concession involving the scheduling value and force Assessments were carried out on artificial and actual - planet systematic applications to focus on the perform of the designed scheme.
Tsunami evacuation Geographic Information System (GIS) education as disaster mitigation

Abstract Padang City is the largest city on west coast of Sumatra and capital city of West Sumatra, Indonesia where it directly adjacent to Indian Ocean. Because of its position, the people are shadowed by fear and felt threatened when earthquake occurred. If earthquakes and tsunamis ever happened, the people need information of safe zone that in reach to evacuate their families and their own self. If someday the calamity happened, with proper education system the society will be able to anticipate the occurring events. Researcher create a Geographic Information System (GIS) with A-Star algorithm method. A-Star Algorithm using nearest distance available to reach destination and have heuristic value for consideration. In this system include alternative route, total capacity, and distance to the selected shelter. In this research, researcher using descriptive method to collect data, evacuation route, and tsunami shelter around Purus, Padang. With this system the people are expected to have sufficient information about tsunami evacuation route as means to minimalized casualties.
An Open Source Distributed Virtualization for Enterprise Infrastructure using OVIRT

Virtualization normally suggests the formation of a virtual machine that can virtualize the total of hardware resources, including memory, stockpiling, processors, and organization availability. The necessities referenced from Goldberg and Popek, are that there are three angles to satisfy the virtualization. A proportional domain ought to be given to run a program that appeared differently in relation to a local framework. If the program shows an undeniable lead under virtualization, it may not be recognized as a virtualized situation. An ensured control of virtualized assets ought to be given by virtualization. Having unlimited oversight of assets is essential to guarantee information and assets on each virtual condition from any risks or lead impedance in sharing physical assets. Performance deterioration is normally expected by virtualization, because of the additional assignment for virtualization, anyway satisfactory execution should be accomplished with programming or equipment uphold in taking care of advantaged directions. These points of interest guarantee successful virtualization. In the going with portion, hypervisors and their sorts are clarified with the application level of virtualization. Virtualized resources are additionally introduced in CPU, memory, and I/O exchange. In this paper, we are examining how virtualization happens utilizing the Ovirt motor which incorporates virtualization, the design of virtualization innovation, Virtual Machine just as Hypervisors. Further examining the strategy of virtualization, motivations to utilize virtualization, Importance of Virtualization in conclusion talk about Pros and Cons of Virtualization. This paper shows the significance of virtualization innovation that can streamline IT tasks just as permit IT associations to react quicker to changing business requests.
Boosting Graph Analytics by Tuning Threads and Data Affinity on NUMA Systems

The execution of large real-world graphs, such as web searches and social networks, has been boosting by modern HPC systems. However, their irregular communication patterns and poor data locality impose many challenges, mainly when executed on NUMA systems. As we show in this paper, there is no one-fits-all configuration for threads/data mapping, and the best combination will vary according to the NUMA system, graph algorithm, and input graph at hand. Based on that, we propose Graphith: a framework that automatically enhances graph processing performance by adapting its execution considering the variables mentioned above. Graphith also goes one step further and improves the existing policies: it uses a Genetic Algorithm to fine-tune the thread-to-core allocation combined with data mapping policies. With that, Graphith improves in 21%, on average, the default execution, and is, on average, 7% better than the best possible combination of standard policies.
Area-Efficient Modular Reduction Structure and Memory Access Scheme for NTT

Number theoretic transform based multiplication is commonly used in Post-quantum cryptography, which is the most resource-consuming operation. In this paper, we propose an area-efficient modular reduction structure for generalized Mersenne primes with interval prediction, and a novel memory access scheme which fetches two data at the same side of a butterfly unit simultaneously. By the interval prediction structure, some adders are eliminated in a modular multiplication. When implement it in 3-stage pipeline mode and synthesize it with TSMC 90nm process, this structure achieves approximate 14.9% less area compared with other designs. The proposed memory access scheme is an in-place scheme. It is more regular than other designs and the two pieces of memory share the same address. Based on this characteristic, we construct an address generator which consumes 40% less area.
Eigenvalues of quantum walk induced by recurrence properties of the underlying birth and death process: application to computation of an edge state

In this paper, we consider an extended coined Szegedy model and discuss the existence of the point spectrum of induced quantum walks in terms of recurrence properties of the underlying birth and death process. We obtain that if the underlying random walk is not null recurrent, then the point spectrum exists in the induced quantum walks. As an application, we provide a simple computational way of the dispersion relation of the edge state part for the topological phase model driven by quantum walk using the recurrence properties of underlying birth and death process.
A systematic review of disruptive technology within accounting and accounting sector

Disruptive technology offers both opportunities and challenges for intensifying the organisational governance within the lens of the accounting sector. A collaborative decision making becomes necessities for competent and beneficial governance across the accounting sector. Through paradigm shift across the sector, thinking of how to form new governance from a repercussion of disruptive technology facing the sector is crucial. Disruptive technology occurred from the progressing of Big Data, cloud, and artificial intelligence which currently allocated to the transactions in the ledgers that have control over an unprecedented shift and frequently disrupting the works of accounting practitioners. The capabilities to manage and control a huge amount of digitised data in the complex and expeditious journey within these digital age has also promoted the accounting sector to become flexible, have good networking and uses a multi-platform that can support these emerging technologies and the Industrial 4.0. Therefore, the sector crucially needed a knowledgeable and proactive people surrounding as to control outstanding pressure to support futuristic and agile governance practices. The review of this study has a great focus on big data, cloud, artificial intelligence and blockchain within management accounting, auditing & accounting sector with respect of these emerging technologies.
Learning for Graph Matching based Multi-object Tracking in Auto Driving

Abstract Multi-object tracking in autonomous driving aims to represent trajectories of moving objects for planning system of the vehicle. In this paper, we propose a new tracking-bydetection scheme based on deep neural networks for multi-object detection and tracking in autonomous driving scene. We first introduce a light-weight neural network branch for fast object detection, and based on the detection results on each frame, we build two object graphs for consecutive frames separately, where the vertices of the graph represent the objects in the image, and the edges of the graph represent the spatial relations between the objects. We then formulate the multi-object tracking problem as the graph matching process by learning the relevance between objects from another object association network branch. Experiments results on the MOT multi-object tracking dataset show that the proposed object detection and tracking approach achieves comparable results with state-of-the-art deep learning based multi-object tracking methods, and outperforms them in tracking efficiency, which ensures real-time multiobject tracking for autonomous driving.
The Pursuit of Knowledge: Discovering and Localizing Novel Categories using Dual Memory

We tackle object category discovery, which is the problem of discovering and localizing novel objects in a large unlabeled dataset. While existing methods show results on datasets with less cluttered scenes and fewer object in-stances per image, we present our results on the challenging COCO dataset. Moreover, we argue that, rather than discovering new categories from scratch, discovery algorithms can benefit from identifying what is already known and focusing their attention on the unknown. We propose a method that exploits prior knowledge about certain object types to discover new categories by leveraging two memory modules, namely Working and Semantic memory. We show the performance of our detector on the COCO minival dataset to demonstrate its in-the-wild capabilities.
Iterative Label Improvement: Robust Training by Confidence Based Filtering and Dataset Partitioning

State-of-the-art, high capacity deep neural networks not only require large amounts of labelled training data, they are also highly susceptible to labelling errors in this data, typically resulting in large efforts and costs and therefore limiting the applicability of deep learning. To alleviate this issue, we propose a novel meta training and labelling scheme that is able to use inexpensive unlabelled data by taking advantage of the generalization power of deep neural networks. We show experimentally that by solely relying on one network architecture and our proposed scheme of combining self-training with pseudo-labels, both label quality and resulting model accuracy, can be improved significantly. Our method achieves state-of-the-art results, while being architecture agnostic and therefore broadly applicable. Compared to other methods dealing with erroneous labels, our approach does neither require another network to be trained, nor does it necessarily need an additional, highly accurate reference label set. Instead of removing samples from a labelled set, our technique uses additional sensor data without the need for manual labelling. Furthermore, our approach can be used for semi-supervised learning.
MetaplasticNet: Architecture with Probabilistic Metaplastic Synapses for Continual Learning

Metaplasticity, the activity-dependent modification of synaptic plasticity, is an important technique for mitigating catastrophic forgetting in neural networks. Often, continual learning models with metaplasticity require compute-intensive training. In this research, we propose a probabilistic metaplastic synapse with discrete hidden states that alleviates the computational cost. We implement a digital architecture of the network with on-chip training to achieve further power savings. Results show upto ~ 22% and ~ 21% improvement in mean accuracy for Split-MNIST and sequential MNIST-FMNIST benchmarks respectively, compared to previous metaplasticity models. Simulations of the full digital architecture show ~ 53× lower power consumption per weight update with similar accuracy as gradient-based network counterparts.
TagIT: A System for Image Auto-tagging and Clustering

In today's world, most of the things we buy, whether we talk about buying apparels, home decor or kitchen utensils, are through the online mode. E-commerce websites play a major role in providing a platform to the vendors for online selling of products. Generally, the process of selling the product on e-commerce websites passes through three steps: Firstly, vendors input the product information including pictures and tag them manually. Secondly, the e-commerce websites perform refinement of tagging using some efficient automated algorithm. Thirdly, the refining of tagging manually and classification of products is done on the basis of their use, popularity, sales, and many more. In this paper, we propose a two-phase process: First, vendor's input images are taken and passed though the deep neural network-based auto-tagging algorithm that tags the input images based on the different features. Next, with the use of dimensionality reduction and clustering algorithms, the tagged images are clustered into different domains. This process helps the vendors and the service provider to automatically tag their product images and provide recommendations on the basis of domain-specific searches without going through the hassle of manual tagging.
Recommender systems

The major aim of a recommender systems (RSs) is to offer references on numerous products to the consumers. An RS is well-defined as a self-operating system that, given a consumer model and a set of available items such as groceries, books, news, and movies, can offer items that are more fascinating to the consumer. This model generates recommendations by adopting either explicit or implicit data about the user inclinations. Mostly RSs are built on the amount of likeness between either the target user or the target item with all other users or items. By gathering data about the users' favorites for dissimilar items, an RS produces their summaries. These favorites can assist the RS to predict remaining items that might be exciting to the user in the future. Collaborative and content-based filtering is the most frequently used technique to generate recommendations. Collaborative filtering generates recommendations based on past ratings, while content filtering generates recommendations based on ratings and features. Hence, the users' profiles must be accessible to the RS to obtain recommendations. The desirable features of an RS are (i) to be nimble, (ii) disseminated, and (iii) secure. A high-speed RS does not divert the attention of the customer in shopping. A disseminated RS avoids creating centralization, which in turn improves the availability of the items. Lastly, a secure RS protects sensitive information from intruders. From the perceptive of security, there are two major concerns looked for in an RS: (i) protection of the users' personal information, ratings, and storage and (ii) generation of recommendations. Recommenders provide personalized recommendations by taking into consideration not only the users' summaries but also personal information like past item purchasing history, demographics, and online behavior sent to the recommender. Therefore, the users' profiles comprise individual and highly subtle information, such as their summarized browsing history, opinions, likes, and dislikes. It is thus vital to sufficiently secure individual information managed by an RS. The second task that the RS involves is to secure all the possible access paths among the system and the user such as the users' rating data for a store, from the store to the database, from a database to RS, and finally from RS to the users' recommendations. If a security threat occurs between the user and the store access path the intruder might modify the users' ratings; if it occurs in between the store and database the intruder might modify the similarity and aggregate computation; and if it occurs in between the database and recommender server or in between the recommender server and the user the intruder might falsify the entire recommendation process. Approaches to secure the rating matrix, similarity and aggregate computations, encrypting top items, building trusted networks, and detecting attachment paths are the possible ways to divert intruder's attention without conceding the accurate rating information. We address the occurrence of these privacy breaks in the RS, which is possible to study in the approaching direction of the chapter.
High Convergence Rates of CMOS Invertible Logic Circuits Based on Many-Body Hamiltonians

This paper introduces CMOS invertible-logic (CIL) circuits based on many-body Hamiltonians. CIL can realize probabilistic forward and backward operations of a function by annealing a corresponding Hamiltonian using stochastic computing. We have created a Hamiltonian that includes three-body interaction of spins (probabilistic nodes). It provides some degrees of freedom to design a simpler landscape of Hamiltonian (energy) than that of the conventional two-body Hamiltonian. The simpler landscape makes it easier to reach the global minimum energy. The proposed three-body CIL circuits are designed and evaluated with the conventional two- body CIL circuits, resulting in few-times higher convergence rates with negligible area overhead on FPGA.