Reviewerly/exHarmony huggingface dataset:
Virtex 7 FPGA Implementation of 256 Bit Key AES Algorithm with Key Schedule and Sub Bytes Block Optimization

Hardware Security plays a major role in most of the applications which include net banking, e-commerce, military, satellite, wireless communications, electronic gadgets, digital image processing, etc. Cryptography is associated with the process of converting ordinary plain text into unintelligible text and vice versa. There are three types of cryptographic techniques; Symmetric key cryptography, Hash functions and Public key cryptography. Symmetric key algorithms namely Advanced Encryption Standard (AES), and Data Encryption Standard use the same key for encryption and decryption. It is much faster, easy to implement and requires less processing power. The proposed 256-bit AES algorithm is highly optimized in Key schedule and Sub bytes blocks, for Area and Power. The optimization has been done by reusing the S-box block. We are optimizing the algorithm with a new approach where internal operations are 32-bit operations, as compared to 128-bit operations. The proposed implementation helps in re-using the same hardware in a pipelined fashion which results in an area reduction by 72% using slice registers, 62% using slice LUT's and 61% using LUT-FF Pairs. This in turn results in a power reduction by 78% in a FPGA implementation. The throughput (Mbps) of the proposed implementation using Virtex-7 (xc7vx485tffg1157) FPGA improved by 10%.
Selecting Useful Knowledge from Previous Tasks for Future Learning in a Single Network

Continual learning can learn new tasks incrementally while avoiding catastrophic forgetting. Recent work has shown that packing multiple tasks into a single network incrementally by iterative pruning and re-training network is a promising method. We build upon this idea and propose an improved version of PackNet. Specifically, we propose a novel gradient-based threshold method to reuse the knowledge of the previous tasks selectively when learning new tasks. Our experiments on a variety of classification tasks and different network architectures demonstrate that our method obtains competitive results when compared to PackNet.
The superposition characteristics of primary halo in the Daping gold deposit, Yunnan Province, China and its significance for exploration

One of main issues in geochemical exploration is that primary haloes of some deeply buried mineral deposits cannot be investigated, or that anomalies of these haloes are too weak to be analyzed. The superposition characteristics of the primary haloes can help us to identify the axial variation of the indicator elements in ore bodies more easily, and then provide a strong indication of the presence of buried ore bodies. This study uses multi-element lithogeochemical data (n = 924) to study the primary haloes of the Daping gold mineralization and the elements associated with the mineralization. First, correlation coefficient matrix was used to study the correlation among the elements in the samples with an Au grade greater than 1 ppm, and the results were used to study the element combination of the deposit. Secondly, Gregorian's method was used to calculate the axial zonality series of the indicator elements. Then, based on the content of 18 elements in 11 existing middle mining sections, the axial variation of the indicator elements was studied. Finally, based on the concentration zonality of the elements and the axial variation of the indicator elements, four deep targets of No.V1-2-3 gold-bearing quartz vein were proposed. Buried ore bodies were found by two boreholes in the No. II predicted target area, which demonstrated usefulness of the methods in this study. The results of this paper provide an example for the application of the superposition characteristics of primary haloes in deep mineral exploration for similar types of gold mineralization in other areas.
Multi-View Digital Mammography Mass Classification: A Convolutional Neural Network Model Approach

Breast cancer (BC) is a disease in which malignant (cancer) cells form in the tissues of the breast. Among various breast screening techniques, mammography is the current gold standard for early detection of BC. In this paper, a two-stage breast mass classification model is innovatively proposed. In specific, stage-one is a feature extraction stage in which mammogram features are extracted and selected by a pre-trained convolutional neural network (CNN) model. Then, in stage-two, CNN-extracted mammogram features are classified as benign or malignant cases by a two-layered neural network classifier. Our proposed method has been evaluated on the subset of the INbreast database. The results show that our model obtains better performance than previous advanced classification methods.
Local Attention Sequence Model for Video Object Detection

Video object detection still faces several difficulties and challenges. For example, the imbalance of positive and negative samples leads to low information processing efficiency, and detection performance declines in abnormal situations in video. This paper examines video object detection based on local attention to address such challenges. We propose a local attention sequence model and optimized the parameter and calculation of ConvGRU. It could process spatial and temporal information in videos more efficiently and ultimately improve detection performance under abnormal conditions. The experiments on ImageNet VID show that our method could improve the detection accuracy by 5.3%, and the visualization results show that the method is adaptive to different abnormal conditions, thereby improving the reliability of video object detection.
Multimodal Navigation for Autonomous Service Robots

This paper introduces a navigation stack for service robots with different modalities, namely human-in-the-loop navigation with people following or guiding modes, as well as standard waypoint navigation. The work also focuses on dependable localization and obstacle avoidance, since these are important components in a fully functional navigation stack. The proposed approach is based mostly in state of the art algorithms, integrated in a service robot to offer safe and dependable navigation modalities. A novel method to follow a dynamic goal is proposed, enabling the robot to follow goals whose position and orientation may move with time, whether it is a person or a moving object. The method was implemented in a real robot and validated through experiments in the ISRoboNet@Home testbed (domestic environment) and in the competition European Robotics League Smart Cities (coffee shop environment).
Ethics-driven Software Architecture Decision Making

Context. Software systems play a pervasive role in people's lives. Since ethics in software systems is hardly ever considered as a first-class entity, its support of, or hindrance to, values of users and society is usually neglected. Addressing ethics requires both to consider the context in which a software system is used, and the fact that ethical values (e.g., social, cultural and personal) are intrinsically ambiguous and subjective. Software architecture and architecture design decision making are the right levels of abstraction to both consider such a context, and support reasoning about such subjectivities. Goal. We aim to embed ethical aspects in software architecture design decision making. We have studied the state of the art in ethics for software engineering, to identify the ethical aspects influencing software architecture design. We will use the learned ethical aspects to (i) support architecture design decision making with respect to ethics through proposing methods, models, and techniques, and (ii) conduct empirical studies to evaluate them in industrial practice. Method. We will adopt a design science approach for our research which includes three phases: (i) problem understanding, (ii) solution design, and (iii) solution validation. Results. Based on our previous literature study, we build models (and techniques) embedding the ethical aspects to be used in architecture design decision making. Also, we will apply design science research to use and validate these models in a method assisting practitioners and architects to embed ethical aspects in software architecture design.
EmoChannelAttn: Exploring Emotional Construction Towards Multi-Class Emotion Classification

The current multi-class emotion classification studies mainly focus on enhancing word-level and sentence-level semantical and sentimental features by exploiting hand-crafted lexicon dictionaries. In comparison, very limited studies attempt to achieve emotion classification task from the emotion-level perspectives, which are to understand how the emotion of a sentence is constructed. Another limitation of existing works is that they assumed that emotion labels are relatively independent, neglecting the possible relations among different types of emotions. Therefore, in this work, we aim to explore various fine-grained emotions based on domain knowledge to understand the construction details of emotions and the interconnection among emotions. To address the first issue, we propose a novel method named EmoChannel to capture the intensity variation of a particular emotion in time series by incorporating domain knowledge and dimensional sentiment lexicons. The resulting information of 151 available fine-grained emotions is utilized to comprise the sentence-level emotion construction. As for the second issue, we introduce the EmoChannelAttn Network to identify the dependency relationship within all emotions via attention mechanism to enhance emotion classification performance. Our experiments demonstrate that the proposed method gains significant improvements compared with baseline models on several multi-class datasets.
Multimedia Data Summarization Using Joint Integer Linear Programming

In recent years, there has been a massive increase in multimedia data due to the increasing use of social media and communication technology. So, extracting useful information from a large set of data has become difficult and very time consuming. Multimedia Data (Text, Image, Audio, Video) summarization is a very useful technology that overcomes this challenge by eliminating information that is redundant or useless, and extracting only the relevant key details of the events in summaries. A lot of work has been done in this field to generate summaries in the form of text and images, but very limited research has been done to produce a multimodular summary especially on Asynchronous Data. This research work proposes an ILP based model, which takes a multimodal dataset (text, images, videos) as input and generates textual and image video summary as output. The results were obtained by comparing the two basic baseline’s ROUGE values with the proposed model. Results for different modalities have confirmed that the proposed model performs better than the other baseline approaches.
How BERT’s Dropout Fine-Tuning Affects Text Classification?

Language models pretraining facilitated fitting models on new and small datasets by keeping the previous pretraining knowledge. The task-agnostic models are to be fine-tuned on all NLP tasks. In this paper, we study the fine-tuning effect of BERT on small amount of data for news classification and sentiment analysis. Our experiments highlight the impact of tweaking the dropout hyper-parameters on the classification performance. We conclude that combining the hidden layers and the attention dropouts probabilities reduce overfitting.
Maximizing Explainability with SF-Lasso and Selective Inference for Video and Picture Ads

There is a growing interest in explainable machine learning methods. In our investigation, we have collected heterogeneous features from two series of YouTube video ads and seven series of Instagram picture ads to form our datasets. There are two main challenges that we found in analysing such data: i) multicollinearity and ii) infrequent common features. Due to these issues, standard estimation methods, such as OLS, Lasso, and Elastic-net, are only able to find a small number of significant features. This paper proposes a method called Significant Feature Lasso (SF-Lasso) to maximize model explainability by identifying most of the significant features that affect a target outcome (such as online video and picture ad popularity). Experiments show that SF-Lasso is able to identify much more significant features while maintaining similar prediction accuracy as what Lasso and Elastic-net can obtain. The human evaluation shows that SF-Lasso is better at identifying true features that appeal to ad viewers. We also find that the number of significant features is mainly affected by the model size (i.e., the number of active variables) and the correlations among explanatory variables.
Proxy-Assisted Digital Signing Scheme for Mobile Cloud Computing

This paper proposes a lightweight digital signing scheme for supporting document signing on mobile devices connected to cloud computing. We employ elliptic curve (ECC) digital signature algorithm (ECDSA) for key pair generation done at mobile device and introduce outsourced proxy (OSP) to decrypt the encrypted file and compute hash value of the files stored in the cloud system. In our model, a mobile client invokes fixed-sized message digests to be signed with a private key stored in the device and produces the digital signature. Then, the signature is returned to the proxy for embedding it onto the original file. To this end, the trust between proxy and mobile devices is guaranteed by PKI technique. Based on the lightweight property of ECC and the modular design of our OSP, our scheme delivers the practical solution that allows mobile users to create their own digital signatures onto documents in a secure and efficient way. We also present the implementation details including system development and experimental evaluation to demonstrate the efficiency of our proposed system.
Zero-Shot Estimation of Base Models’ Weights in Ensemble of Machine Reading Comprehension Systems for Robust Generalization

One of the main challenges of the machine reading comprehension (MRC) models is their fragile out-of-domain generalization, which makes these models not properly applicable to real-world general-purpose question answering problems. In this paper, we leverage a zero-shot weighted ensemble method for improving the robustness of out-of-domain generalization in MRC models. In the proposed method, a weight estimation module is used to estimate out-of-domain weights, and an ensemble module aggregate several base models' predictions based on their weights. The experiments indicate that the proposed method not only improves the final accuracy, but also is robust against domain changes.
HGraph: Parallel and Distributed Tool for Large-Scale Graph Processing

Graph are ubiquitous because the fields of application are varied. Well-known examples are social networks, biological networks and path-finding in road networks. Real-world graphs processing is very challenging because of 4V characteristics related to big data. They are huge to process them on single-node and the time complexity is exponential. Unfortunately, due to the lack of research, only a few systems are able to ensure the storage and quick processing of large-scale graphs. In this paper, we propose HGraph, a parallel and distributed tool which handles large-scale graphs. HGraph is build on top of Hadoop and Spark frameworks. The proposed tool provides high scalability and is adapted to easily implement algorithms for various graph problems. Experimental tests performed on real-world graphs showed that HGraph is reliable and achieves significant gain time over the state of the art of graph processing systems.
Decentralized Accreditation of Educational Attainments using Blockchain

Today's world is connected through the digital platforms Independent of Geographical location and to serve the needs of the administration process involved in educational we need the platform that help to work through network in a secured and transparent manner and due to the Blockchain core behaviors like transparent, confidentiality, Immutable, decentralized and distributed nature, the Legacy Administrative process for Students Certificate management and verification can be replaced. The Proposed model also helps to setup a streamlined administration process in the educational institute solve the variety of issues that are there in legacy educational organization/institutes administration process like tracking of records in cloud, fraud certification detection, keeping Identity digital under the control of authentic source and transferring over the internet securely, by eliminating need to verify them by third party sources. In this paper we proposed a system to solve the current cumbersome administration process by reducing the huge paperwork involved and by reducing the cost of double spend for manpower among the participating, reduce the wait time for the involved stake holders like Students, guardians and universities.
Leveraging Global and Background Contrast for Salient Object Detection in Hyperspectral Images

Salient object detection in hyperspectral images has attracted increasing attention, thanks to the rich spectral information that is beneficial to distinguishing salient objects from cluttered background. Most existing methods adapt the color components of Itti's model to spectral domain to detect salient objects. However, these methods typically calculate a single saliency map for the foreground, which fails to suppress background clusters effectively. In this paper, we propose a simple yet effective model for salient object detection. Specifically, we first segment a whole hyperspectral image into superpixels. Then, we calculate global and background contrast values for each superpixel based on its spectral features, and also compute a center prior to incorporate spatial location of each superpixel. The final saliency score is obtained through production of the above three values to fully capture spectral-spatial structures of the image. Extensive experiments on real hyperspectral images show that the proposed method outperforms several state-of-the-arts, which well demonstrates its effectiveness. Keywords-hyperspectral image; salient object detection; global contrast; background contrast.
Universal Representation for Code

Learning from source code usually requires a large amount of labeled data. Despite the possible scarcity of labeled data, the trained model is highly task-specific and lacks transferability to different tasks. In this work, we present effective pre-training strategies on top of a novel graph-based code representation, to produce universal representations for code. Specifically, our graph-based representation captures important semantics between code elements (e.g., control flow and data flow). We pre-train graph neural networks on the representation to extract universal code properties. The pre-trained model then enables the possibility of fine-tuning to support various downstream applications. We evaluate our model on two real-world datasets – spanning over 30M Java methods and 770K Python methods. Through visualization, we reveal discriminative properties in our universal code representation. By comparing multiple benchmarks, we demonstrate that the proposed framework achieves state-of-the-art results on method name prediction and code graph link prediction.
Meta-Learning with Attention for Improved Few-Shot Learning

We consider few-shot learning (FSL), where a model learns from very few labeled examples such that it can generalize to unseen examples. Model-agnostic meta-learning (MAML) has been proposed to solve FSL. However, the low performance of MAML suggests its difficulty in tackle diverse tasks, due to the restriction of sharing a single model initialization for fast adaptation. In this paper, we propose meta-learning with attention mechanisms. Our method meta-learns attention modules to instantiate task-specific model initialization for fast adaptation, which can obtain high-quality solution to a new task using few gradient descent steps. To further improve generalization during inference, we propose to incorporate an entropy regularizer into the adaptation objective to penalize the Shannon entropy of prediction probability. Extensive experiments under various FSL scenarios show that our method achieves state-of-the-art performance on the mini-ImageNet and tiered-ImageNet.
Performance characterization of video analytics workloads in heterogeneous edge infrastructures

Summary Powered by deep learning, video analytic applications process millions of camera feeds in real‐time to extract meaningful information from their surroundings. And this number grows by the minute. To avoid saturating the backhaul network and provide lower latencies, a distributed and heterogeneous edge cloud is postulated as a key enabler for widespread video analytics. This article provides a complete characterization of end‐to‐end video analytics across a set of hardware platforms and different neural network architectures. Each platform is selected to fill a different gap in a distributed, shared, and heterogeneous infrastructure. Moreover, we analyze how performance scales on each of these platforms with respect to the amount of resources dedicated to video analytics. Finally, we extract the key conclusions of the characterization to build an experimental model to estimate performance and cost of end‐to‐end video analytics in different edge scenarios. Our experiments show that managing video analytics workloads efficiently requires awareness of both, the platforms in which these are executed, and the full end‐to‐end pipeline. To the best of our knowledge, this is the first work that provides a complete characterization of end‐to‐end video analytics in heterogeneous edge platforms.
A Data Warehouse of Wi-Fi Sessions for Contact Tracing and Outbreak Investigation

The COVID-19 pandemic has spurred the development of a large number of automated and semi-automated contact tracing frameworks. Many of these are reactive and require active client participation, such as installing a specific contact tracing app on the clients’ smartphones, and they are often unable to scale in time to reach the requisite critical mass adoption. To be better prepared for the emergence and re-emergence of coronavirus epidemics, we seek to leverage on the availability of common existing digital infrastructure such as the increasingly ubiquitous Wi-Fi networks that can be readily activated to assist in large-scale contact tracing. We present and discuss the design, implementation, and deployment of a data warehouse of Wi-Fi sessions for contact tracing and disease outbreak investigation. We discuss the conceptual design of the data warehouse and present the logical model that implements the conceptual model. We describe the data staging procedures and discuss the analysis of the Wi-Fi session data for mobility-based contact tracing and disease outbreak investigation. Finally, we present the case where the data warehouse of Wi-Fi sessions is experimentally deployed at full scale on a large local university campus in Singapore.