Reviewerly/exHarmony huggingface dataset:
Knowledge Graph Based Question Answering System for Financial Securities

Knowledge graphs offer a powerful framework to structure and represent financial information in a flexible way by describing real world entities, such as financial securities, and their interrelations in the form of a graph. Semantic question answering systems allow to retrieve information from a knowledge graph using natural language questions and thus eliminate the need to be proficient in a formal query language. In this work, we present a proof-of-concept design for a financial knowledge graph and with it a semantic question answering framework specifically targeted for the finance domain. Our implemented approach uses a span-based joint entity and relation extraction model with BERT embeddings to translate a single-fact natural language question into its corresponding formal query representation. By employing a joint extraction model, we alleviate the concern of error propagation present in standard pipelined approaches for classification-based question answering. The presented framework is tested on a synthetic dataset derived from the instances of the implemented financial knowledge graph. Our empirical findings indicate very promising results with a F1-score of 84.60% for relation classification and 97.18% for entity detection.
Application of Deep Learning Techniques on Sign Language Recognition—A Survey

Sign language recognition systems are developed to facilitate communication between signers and non-signers. Recent field of research is intended to focus on effectively recognizing signs under computing power constraints. The work primarily includes recognizing sign languages using discrete cosine transforms, principal component analysis, and hidden Markov models. Researchers have used a wide variety of machine learning and deep learning techniques such as artificial neural network, convolutional neural network, minimum distance classifier, three-dimensional residual convolutional neural networks, bidirectional long short-term memory networks, ‘CaffeNet’ convolutional neural network, and so on. Some researchers have used hand trajectories, depth-sensing cameras, etc., to detect the motion. This paper reviews the literature that has been carried out to recognize the most widely used sign languages like Indian sign language, American sign language, Persian sign language, etc., using machine learning and deep learning techniques. This paper draws similarities and differences between various sign languages and their algorithms to infer which techniques are best suited for Indian sign language recognition.
Generative Relation Linking for Question Answering over Knowledge Bases

Relation linking is essential to enable question answering over knowledge bases. Although there are various efforts to improve relation linking performance, the current state-of-the-art methods do not achieve optimal results, therefore, negatively impacting the overall end-to-end question answering performance. In this work, we propose a novel approach for relation linking framing it as a generative problem facilitating the use of pre-trained sequence-to-sequence models. We extend such sequence-to-sequence models with the idea of infusing structured data from the target knowledge base, primarily to enable these models to handle the nuances of the knowledge base. Moreover, we train the model with the aim to generate a structured output consisting of a list of argument-relation pairs, enabling a knowledge validation step. We compared our method against the existing relation linking systems on four different datasets derived from DBpedia and Wikidata. Our method reports large improvements over the state-of-the-art while using a much simpler model that can be easily adapted to different knowledge bases.
Lightweight Convolutional Neural Network for Foreground Segmentation

Foreground segmentation is to segment moving objects from a video. Convolutional neural network based foreground segmentation techniques show great potential in foreground segmentation for complex scenes, but is limited for real-time applications. Model scale is one of the obstructions. In this paper, we bring up a lightweight network improving direction for foreground segmentation without reducing the precision performance, and propose two end-to-end convolutional neural network to cut down the foreground segmentation model scale and evaluate the lightweight models using Change Detection 2014 dataset. The results show that our lightweight network improving direction for foreground segmentation is efficient and our network structure could downscale the model size to a much smaller one while remain the precision performance.
MLS Group Messaging: How Zero-Knowledge Can Secure Updates

The Messaging Layer Security (MLS) protocol currently developed by the Internet Engineering Task Force (IETF) aims at providing a secure group messaging solution. MLS aims for end-to-end security, including Forward Secrecy and Post Compromise Secrecy, properties well studied for one-to-one protocols. It proposes a tree-based regular asynchronous update of the group secrets, where a single user can alone perform a complete update. A main drawback is that a malicious user can create a denial of service attack by sending invalid update information.
Body Motion Detection Technology in Video

The detection and recognition of human body movement involves the research direction of multi-disciplinary knowledge, including digital image processing, computer vision and other professional fields. This paper first introduces three common moving object detection algorithms: background subtraction, inter frame difference and optical flow. On the basis of frame difference method and image edge detection algorithm, a dynamic edge point preserving method is proposed to realize moving object detection. This method combines the motion information and edge information of the picture, and can obtain the edge information of moving object in video sequence more accurately, which is more suitable for human body’s body motion detection.
A dataset for Sentiment analysis of Entities in News headlines (SEN)

On-line news portals play a very important role in the information society. Fair media should present reliable and objective information. In practice there is an observable positive or negative bias concerning named entities (e.g. politicians) mentioned in the on-line news headlines. In this paper we present SEN - a novel publicly available human-labelled dataset for training and testing machine learning algorithms for the problem. It consists of 3819 human-labelled political news headlines coming from several major on-line media outlets in English and Polish. We also describe the process of preparing the dataset and present its analysis, including entity and annotator bias analysis, and some insights into possible challenges of the task of entity-level analysis of the news.
The application of selected modern artificial intelligence techniques in an exemplary strategy game

In the paper requirements for artificial intelligence algorithms designed for modern strategy computer games are analyzed. The selected techniques used to fulfill those requirements are described in detail. Then the exemplary game, which was designed and implemented using the selected algorithms for path-finding, decision-making, tactical and strategic reasoning, and team coordination is presented. Finally, the results of experiments conducted with the use of the created game are analyzed. The results prove the efficiency of selected techniques in creating a strategically challenging game.
Multiple Instance Learning with Auxiliary Task Weighting for Multiple Myeloma Classification

Whole body magnetic resonance imaging (WB-MRI) is the recommended modality for diagnosis of multiple myeloma (MM). WB-MRI is used to detect sites of disease across the entire skeletal system, but it requires significant expertise and is time-consuming to report due to the great number of images. To aid radiological reading, we propose an auxiliary task-based multiple instance learning approach (ATMIL) for MM classification with the ability to localize sites of disease. This approach is appealing as it only requires patient-level annotations where an attention mechanism is used to identify local regions with active disease. We borrow ideas from multi-task learning and define an auxiliary task with adaptive reweighting to support and improve learning efficiency in the presence of data scarcity. We validate our approach on both synthetic and real multi-center clinical data. We show that the MIL attention module provides a mechanism to localize bone regions while the adaptive reweighting of the auxiliary task considerably improves the performance.
HyperTeNet: Hypergraph and Transformer-based Neural Network for Personalized List Continuation

The personalized list continuation (PLC) task is to curate the next items to user-generated lists (ordered sequence of items) in a personalized way. The main challenge in this task is understanding the ternary relationships among the interacting entities (users, items, and lists) that the existing works do not consider. Further, they do not take into account the multi-hop relationships among entities of the same type. In addition, capturing the sequential information amongst the items already present in the list also plays a vital role in determining the next relevant items that get curated.In this work, we propose HyperTeNet - a self-attention hypergraph and Transformer-based neural network architecture for the personalized list continuation task to address the challenges mentioned above. We use graph convolutions to learn the multi-hop relationship among the entities of the same type and leverage a self-attention-based hypergraph neural network to learn the ternary relationships among the interacting entities via hyperlink prediction in a 3-uniform hypergraph. Further, the entity embeddings are shared with a Transformer-based architecture and are learned through an alternating optimization procedure. As a result, this network also learns the sequential information needed to curate the next items to be added to the list. Experimental results demonstrate that HyperTeNet significantly outperforms the other state-of-the-art models on real-world datasets. Our implementation is available online <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/mvijaikumar/HyperTeNet
Allocation and Migration of Virtual Machines Using Machine Learning

Cloud computing promises the advent of a new era of service boosted by means of virtualization technology.The process of virtualization means creation of virtual infrastructure, devices, servers and computing resources needed to deploy an application smoothly.This extensively practiced technology involves selecting an efficient Virtual Machine (VM) to complete the task by transferring applications from Physical Machines (PM) to VM or from VM to VM.The whole process is very challenging not only in terms of computation but also in terms of energy and memory.This research paper presents an energy aware VM allocation and migration approach to meet the challenges faced by the growing number of cloud data centres.Machine Learning (ML) based Artificial Bee Colony (ABC) is used to rank the VM with respect to the load while considering the energy efficiency as a crucial parameter.The most efficient virtual machines are further selected and thus depending on the dynamics of the load and energy, applications are migrated from one VM to another.The simulation analysis is performed in Matlab and it shows that this research work results in more reduction in energy consumption as compared to existing studies.
Low-latency area-efficient systolic bit-parallel GF(2<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e2975" altimg="si154.svg"><mml:msup><mml:mrow /><mml:mrow><mml:mi>m</mml:mi></mml:mrow></mml:msup></mml:math>) multiplier for a narrow class of trinomials

Edge computing in Internet of Things (IoT) requires high-performance as well as cost-effective hardware implementations to accommodate large number of computations. Implementation of security in IoT edge devices is indispensable, and attaining security is possible using Elliptic curve cryptography (ECC) algorithms. Finite field GF(2m) multiplication is a performance-critical operation in these algorithms, hence, it requires efficient hardware implementations. Systolic structures for GF(2m) multiplier are suitable for high-performance applications as they allow high-throughput implementations. Many systolic GF(2m) multipliers have been proposed in the literature that can achieve high throughput rates, however, they require large area and high latency. In this paper, we propose a polynomial basis GF(2m) systolic multiplier applicable for a narrow class of trinomials which includes both the NIST (National Institute of Standards and Technology) recommended trinomials for m=233 and 409 fields. Analytical comparisons with the related multipliers for m=409 show that the multiplier proposed in this paper reduces the latency by 32% and area by 5%. The proposed multiplier is synthesized using synopsis tools targeting for an ASIC implementation and the comparisons are also presented. The proposed low-latency area-efficient multiplier can be used in the implementation of hardware security for low-cost IoT edge devices.
Polynomial Representation Is Tricky: Maliciously Secure Private Set Intersection Revisited

Private Set Intersection protocols (PSIs) allow parties to compute the intersection of their private sets, such that nothing about the sets’ elements beyond the intersection is revealed. PSIs have a variety of applications, primarily in efficiently supporting data sharing in a privacy-preserving manner. At Eurocrypt 2019, Ghosh and Nilges proposed three efficient PSIs based on the polynomial representation of sets and proved their security against active adversaries. In this work, we show that these three PSIs are susceptible to several serious attacks. The attacks let an adversary (1) learn the correct intersection while making its victim believe that the intersection is empty, (2) learn a certain element of its victim’s set beyond the intersection, and (3) delete multiple elements of its victim’s input set. We explain why the proofs did not identify these attacks and propose a set of mitigations.
Automatic Polygon Layout for Primal-Dual Visualization of Hypergraphs

N-ary relationships, which relate $N$ entities where $N$ is not necessarily two, can be visually represented as polygons whose vertices are the entities of the relationships. Manually generating a high-quality layout using this representation is labor-intensive. In this paper, we provide an automatic polygon layout generation algorithm for the visualization of N-ary relationships. At the core of our algorithm is a set of objective functions motivated by a number of design principles that we have identified. These objective functions are then used in an optimization framework that we develop to achieve high-quality layouts. Recognizing the duality between entities and relationships in the data, we provide a second visualization in which the roles of entities and relationships in the original data are reversed. This can lead to additional insight about the data. Furthermore, we enhance our framework for a joint optimization on the primal layout (original data) and the dual layout (where the roles of entities and relationships are reversed). This allows users to inspect their data using two complementary views. We apply our visualization approach to a number of datasets that include co-authorship data and social contact pattern data.
Dense Contrastive Visual-Linguistic Pretraining

Inspired by the success of BERT, several multimodal representation learning approaches have been proposed that jointly represent image and text. These approaches achieve superior performance by capturing high-level semantic information from large-scale multimodal pretraining. In particular, LXMERT and UNITER adopt visual region feature regression and label classification as pretext tasks. However, they tend to suffer from the problems of noisy labels and sparse semantic annotations, based on the visual features having been pretrained on a crowdsourced dataset with limited and inconsistent semantic labeling. To overcome these issues, we propose unbiased Dense Contrastive Visual-Linguistic Pretraining (DCVLP), which replaces the region regression and classification with cross-modality region contrastive learning that requires no annotations. Two data augmentation strategies (Mask Perturbation and Intra-Inter-Adversarial Perturbation) are developed to improve the quality of negative samples used in contrastive learning. Overall, DCVLP allows cross-modality dense region contrastive learning in a self-supervised setting independent of any object annotations. We compare our method against prior visual-linguistic pretraining frameworks to validate the superiority of dense contrastive learning on multimodal representation learning.
Real Time Sarcasm Detection on Twitter using Ensemble Methods

Sarcasm means saying the opposite of what you mean in order to make fun of someone and a type of humour that responds to a situation. Sarcasm reorganisation approach is quite beneficial to enhancing automated sentiment analysis data from microblogging and social media sites. The term “sentiment analysis” relates to the study of internet users reported feelings and viewpoints in a particular group, as well as their identification and aggregation. One of the most complicated problems in sentiment analysis is detecting sarcasm. It's a tough task to classify sarcastic sentence forms. This work uses two hybrid machine learning approaches, namely Stacked Generalization and Boosting ensemble methods with Support Vector Machine (SVM), Random Forest (RF) and KNN as base classifiers and Logistic Regression (LR) as Meta classifiers to detect real-time sarcasm on Twitter.
Research on Routing Strategy in Cluster Deduplication System

A cluster deduplication system can coordinate the work of mu ltip le nodes, wh ich can better alleviate the disk index bottleneck existing in the large-scale data backup system.However, there is a problem of isolated islands of information among nodes during data deduplication.When the servers use the query mode to route data, a large amount of system overhead is required to ensure a high deduplication rate and low throughput rate.At the same time, wh ile the servers cannot obtain a h igher deduplication rate if the servers adopt the stateless routing method.Data routing strategy can greatly affect the overall performance of the system.The concept of data frequency is proposed in this paper, and the classified routing strategy is designed.In the metadata server, a byte-shaped Bloom filter for record ing the occurrence frequency of data blocks is maintained to record the occurrence frequency of data blocks.The values in the Bloom filter are counted.Then the frequency of the data blocks is co mpared with the configured threshold value to determine whether the data is "hot data".We use stateful routing to send "clod data" to the storage nodes and use stateless routing to send the hot data to the storage nodes.Experimental results show that the classifying routing algorithm based on the frequency of data can greatly reduce the overhead of the system while guaranteeing the deduplication rate of the deduplication system as well as imp rove system throughput and real-time processing capabilities.Co mpared with the fully stateful routing scheme, our method only loses less than 2% of the deduplication rate, which reduces the communication query overhead by more than 25% and improves the real-time processing capability of the storage system.
Trusted-DNN: A TrustZone-based Adaptive Isolation Strategy for Deep Neural Networks

Deep neural network (DNN) models have been widely deployed on embedded and mobile devices in lots of application fields such as health care, face recognition, driver assistance, etc. These applications usually require privacy or trusted computing protection. However, diverse hardware resources, various transport protocols, and limited computation and storage capacity make it challenging for traditional embedded systems to provide complex security protection mechanism oriented DNN models. To meet the challenges, we propose Trusted-DNN, a TrustZone-based adaptive isolation strategy for DNN models. We first design a normal pattern to exploit TrustZone technology to provide overall protection for running DNNs. To deploy arbitrary DNN models into TrustZone, we then develop a dynamic model partition method, which makes our strategy easily adaptive to various DNN models and devices. Finally, we employ several optimization techniques to reduce the inference latency of Trusted-DNN models. We perform AlexNet on OP-TEE, which is a TrustZone-based secure operating system, based on a Raspberry Pi 3B+ board. The extensive experimental results highlight that the optimized Trusted-DNN can reduce memory footprint by up to 98% compared with the ordinary program and Trusted-DNN only increase inference latency by 22.8%. Our code is available at https://gitee.com/PaintZero/alexnet-tee.
Minimum variance embedded auto-associative kernel extreme learning machine for one-class classification

One-class classification (OCC) needs samples from only a single class to train the classifier. Recently, an auto-associative kernel extreme learning machine was developed for the OCC task. This paper introduces a novel extension of this classifier by embedding minimum variance information within its architecture and is referred to as VAAKELM. The minimum variance embedding forces the network output weights to focus in regions of low variance and reduces the intra-class variance. This leads to a better separation of target samples and outliers, resulting in an improvement in the generalization performance of the classifier. The proposed classifier follows a reconstruction-based approach to OCC and minimizes the reconstruction error by using the kernel extreme learning machine as the base classifier. It uses the deviation in reconstruction error to identify the outliers. We perform experiments on 15 small-size and 10 medium-size one-class benchmark datasets to demonstrate the efficiency of the proposed classifier. We compare the results with 13 existing one-class classifiers by considering the mean $$\hbox {F}_1$$ score as the comparison metric. The experimental results show that VAAKELM consistently performs better than the existing classifiers, making it a viable alternative for the OCC task. The source code is available on the GitHub homepage: https://github.com/PratikMishra/VAAKELM.
Emergent design challenges for embedded systems and paths forward

Modern embedded systems need to cater for several needs depending upon the application domain in which they are deployed. For example, mixed-critically needs to be considered for real-time and safety-critical systems and energy for battery-operated systems. At the same time, many of these systems demand for their reliability and security as well. With electronic systems being used for increasingly varying type of applications, novel challenges have emerged. For example, with the use of embedded systems in increasingly complex applications that execute tasks with varying priorities, mixed-criticality systems present unique challenges to designing reliable systems. The large design space involved in implementing cross-layer reliability in heterogeneous systems, particularly for mixed-critical systems, poses new research problems. Further, malicious security attacks on these systems pose additional extraordinary challenges in the system design. In this paper, we cover both the industry and academia perspectives of the challenges posed by these emergent aspects of system design towards designing high-performance, energy-efficient, reliable and/or secure embedded systems. We also provide our views on paths forward.