Reviewerly/exHarmony huggingface dataset:
Semantic Intelligence in Big Data Applications

Today, data are growing at a tremendous rate, and according to the International Data Corporation, it is expected they will reach 175 zettabytes by 2025. The International Data Corporation also forecasts that more than 150B devices will be connected across the globe by 2025, most of which will be creating data in real time, while 90 zettabytes of data will be created by Internet of things (IoT) devices. This vast amount of data creates several new opportunities for modern enterprises, especially for analyzing enterprise value chains in a broader sense. In order to leverage the potential of real data and build smart applications on top of sensory data, IoT-based systems integrate domain knowledge and context-relevant information. Semantic intelligence is the process of bridging the semantic gap between human and computer comprehension by teaching a machine to think in terms of object-oriented concepts in the same way as a human does. Semantic intelligence technologies are the most important component in developing artificially intelligent knowledge-based systems, since they assist machines in contextually and intelligently integrating and processing resources. This chapter aims at demystifying semantic intelligence in distributed, enterprise, and Web-based information systems. It also discusses prominent tools that leverage semantics, handle large data at scale, and address challenges (e.g., heterogeneity, interoperability, and machine learning explainability) in different industrial applications.
A Systematic Study as Foundation for a Variability Modeling Body of Knowledge

In software product line engineering, engineers and researchers use variability models to explicitly represent the commonalities and variability of software systems to foster systematic reuse. Variability modeling has been a field of extensive research for over three decades, including Systematic Literature Reviews (SLRs) and Systematic Mapping Studies (SMSs) to categorize and compare different approaches. Much effort goes into such (secondary) studies, partly because they are often done from scratch and searching for relevant studies for specific research questions is tedious. Systematic reuse of search results would benefit the community by improving the efficiency and quality of such studies. In this paper, we report on creating a curated data set of 78 key SLR/SMS publications and primary studies (e.g., surveys) on variability modeling by conducting a tertiary SMS on variability modeling. When using such a curated paper data set for a secondary study, we estimate researchers can save up to 50 percent effort in the search phase. We present the publicly available data set, which includes categorization of the studies and provides update mechanisms. We see our data set as a foundation for building a Variability Modeling Body of Knowledge (VMBoK). We illustrate the efficient use of the data set in two SLR examples. We argue that our process and the data set can be useful for various research communities to improve the efficiency and quality of secondary (and tertiary) studies.
Scalable Distributed Planning for Multi-Robot, Multi-Target Tracking

In multi-robot multi-target tracking, robots coordinate to monitor groups of targets moving about an environment. We approach planning for such scenarios by formulating a receding-horizon, multi-robot sensing problem with a mutual information objective. Such problems are NP-Hard in general. Yet, our objective is submodular which enables certain greedy planners to guarantee constant-factor suboptimality. However, these greedy planners require robots to plan their actions in sequence, one robot at a time, so planning time is at least proportional to the number of robots. Solving these problems becomes intractable for large teams, even for distributed implementations. Our prior work proposed a distributed planner (RSP) which reduces this number of sequential steps to a constant, even for large numbers of robots, by allowing robots to plan in parallel while ignoring some of each others’ decisions. Although that analysis is not applicable to target tracking, we prove a similar guarantee, that RSP planning approaches performance guarantees for fully sequential planners, by employing a novel bound which takes advantage of the independence of target motions to quantify effective redundancy between robots’ observations and actions. Further, we present analysis that explicitly accounts for features of practical implementations including approximations to the objective and anytime planning. Simulation results—available via open source release—for target tracking with ranging sensors demonstrate that our planners consistently approach the performance of sequential planning (in terms of position uncertainty) given only 2–8 planning steps and for as many as 96 robots with a 24x reduction in the number of sequential steps in planning. Thus, this work makes planning for multi-robot target tracking tractable at much larger scales than before, for practical planners and general tracking problems.
A Knowledge-Enhanced Dialogue Model Based on Multi-Hop Information with Graph Attention

With the continuous improvement of the e-commerce ecosystem and the rapid growth of e-commerce data, in the context of the e-commerce ecosystem, consumers ask hundreds of millions of questions every day.In order to improve the timeliness of customer service responses, many systems have begun to use customer service robots to respond to consumer questions, but the current customer service robots tend to respond to specific questions.For many questions that lack background knowledge, they can generate only responses that are biased towards generality and repetitiveness.To better promote the understanding of dialogue and generate more meaningful responses, this paper introduces knowledge information into the research of question answering system by using a knowledge graph.The unique structured knowledge base of the knowledge graph is convenient for knowledge query, can acquire knowledge faster, and improves the background information needed for answering questions.To avoid the lack of information in the dialogue process, this paper proposes the Multi-hop Knowledge Information Enhanced Dialogue-Graph Attention (MKIED-GA) model.The model first retrieves the problem subgraph directly related to the input information from the entire knowledge base and then uses the graph neural network as the knowledge inference module on the subgraph to encode the subgraph.The graph attention mechanism is used to determine the one-hop and two-hop entities that are more relevant to the problem to achieve the aggregation of highly relevant neighbor information.This further enriches the semantic information to provide a better understanding of the meaning of the input question and generate appropriate response information.In the process of generating a response, a multi-attention flow mechanism is used to focus on different information to promote the generation of better responses.Experiments have proved that the model presented in this article can generate more meaningful responses than other models.
Hierarchical Human Action Recognition to Measure the Performance of Manual Labor

Measuring manual-labor performance has been a key element of work scheduling and resource management in many industries.It is performed using a standard data system called Time and Motion Study (TMS).Many industries still rely on direct human effort to execute the TMS methodology which can be time-consuming, error-prone, and expensive.In this paper, we introduce an automatic replacement of the TMS technique that works at two levels of abstraction: primitive and activity actions.We leverage on recent advancements in deep learning methods and employ an encoder-decoder based classifier to recognize primitives and a continuous-time hidden Markov model to recognize activities.We show that our system yields results competitive with those obtained with several common human action recognition models.We also show how our proposed system can help operational decisions by computing productivity indicators such as worker availability, worker performance, and overall labor effectiveness.
Optimized BGP Simulator for Evaluation of Internet Hijacks

Simulating network experiments is critical for inferring insights on the large scale Internet attacks and defences. In this work, we develop a new network simulator for evaluating routing attacks and defences. We compare it to existing simulators demonstrating better performance and higher accuracy in the evaluation of attacks and defences. We apply our simulator for evaluating hijacks of 1M-top Alexa domains and show that about 50% of the targets are vulnerable.
Boosting Transferability of Targeted Adversarial Examples via Hierarchical Generative Networks

Transfer-based adversarial attacks can evaluate model robustness in the black-box setting. Several methods have demonstrated impressive untargeted transferability, however, it is still challenging to efficiently produce targeted transferability. To this end, we develop a simple yet effective framework to craft targeted transfer-based adversarial examples, applying a hierarchical generative network. In particular, we contribute to amortized designs that well adapt to multi-class targeted attacks. Extensive experiments on ImageNet show that our method improves the success rates of targeted black-box attacks by a significant margin over the existing methods—it reaches an average success rate of 29.1% against six diverse models based only on one substitute white-box model, which significantly outperforms the state-of-the-art gradient-based attack methods. Moreover, the proposed method is also more efficient beyond an order of magnitude than gradient-based methods.
Matrix‐variate time series modelling with hidden Markov models

In this paper, a hidden Markov model for modelling matrix‐variate time series data is developed. It relies on matrix‐variate distribution and presents a promising alternative to the existing methods. Simulation study is carefully conducted and uses benchmark tests with pre‐specified overlapping values. Compared with the existing methods, the proposed model demonstrates rather high accuracy in state classification. Results suggest that such an approach is indeed competitive. Interesting applications are presented for real‐life data illustration.
Toward Observation Based Least Restrictive Collision Avoidance Using Deep Meta Reinforcement Learning

This letter presents the Observation-based Least-Restrictive Collision Avoidance Module (OLR-CAM) that can be added to any autonomous robot working in a shared environment and provide a high-level safety layer to the existing policy for each robot. The OLR-CAM takes raw sensory observations as input, evaluates the agents' safety against dynamic and static obstacles, and only intervenes the default policy when needed - in a least-restrictive fashion - to avoid a potential collision. In our approach, we meta-train the OLR-CAM policy within a “2D Navigation Meta World System”. Furthermore, to endow the policy with a notion of safety in multi-agent environments with obstacles, we propose a novel reward function based on a safety value function derived from the Hamilton-Jacobi reachability theory and a local cost map. The proposed reward function does not need any additional information about the environment's map. This facilitates the adoption of the algorithm in a new environment at the meta test stage. The proposed algorithm is fully meta-trained in simulation and tested on a real multi-agent system without any additional training conducted in the real setting. Our results show that the OLR-CAM success rate outperforms a well-known classical baseline approach by 10 percent on average and reduces the interruptions/changes to the preferred velocity by 15 percent.
AliExpress Learning-To-Rank: Maximizing Online Model Performance without Going Online

Most existing LTR approaches follow a supervised learning paradigm from offline data collected from the online system. However, it has been noticed that previous LTR models can have good performances over offline validation data but have poor online performances, which implies a possible large inconsistency between the offline and online evaluation. We investigate and confirm in this paper that such inconsistency exists and can have a significant impact on AliExpress Search. Reasons for the inconsistency include the ignorance of item context. Therefore, this paper proposes an evaluator-generator framework for LTR with item context. The framework consists of an evaluator that generalizes to evaluate recommendations involving the context, and a generator that maximizes the evaluator score by reinforcement learning, and a discriminator that ensures the generalization of the evaluator. Extensive experiments in simulation environments and AliExpress Search online system show that, firstly, the classic data-based metrics on the offline dataset can show significant inconsistency with online performance. Secondly, the proposed evaluator score is significantly more consistent with the online performance than common ranking metrics. Finally, as the consequence, our method achieves a significant improvement in terms of Conversion Rate over the industrial-level fine-tuned model in online A/B tests.
Towards Tackling Common Web Application Vulnerabilities Using Secure Design Patterns

Many software vulnerabilities originate during the design stage of the software development process. Secure design patterns can address vulnerabilities in the design level. However, few solid research studies have been done on applying secure pat-terns to tackle web application vulnerabilities and the researchers found that the security patterns are harder for developers to use than conventional design patterns. This paper presents an approach for selecting appropriate secure design patterns to tackle web application vulnerabilities. In this pilot study, we focus on two most common web application vulnerabilities: SQL injection (SQLi) and Cross-site scripting (XSS). The paper also uses a case study to demonstrate the implementation of the chosen pattern in redesigning a vulnerable application. The results from the evaluation show that the proposed pattern can effectively address SQLi and XSS vulnerabilities. Although SQLi and XSS are the targeted vulnerabilities in the approach, based on the success of this study, we believe that the approach is promising to be applied more generally.
Data-Driven Model for Emotion Detection in Russian Texts

An important task in the field of automatic data analysis is detecting emotions in texts. The paper presents the approach of emotion recognition for text data in Russian. To conduct an emotion analysis, a method was created based on vector representations of words obtained by the ELMo language model, and subsequent processing by an ensemble classifier. To configure and test the created method, a specially prepared dataset of texts for five basic emotions - joy, sadness, anger, fear, and surprise - is used. The dataset was prepared using a crowdsourcing platform and a home-grown procedure for collecting and controlling annotators' markup. The overall accuracy is 0.78 (by the F1-macro score), which is currently the new state of the art for Russian. The results can be used for a wide range of tasks, for example: monitoring social moods, generating control signals for mobile robotic systems, etc.
Differential Fault Attack on GIFT

GIFT, a lightweight block cipher proposed at CHES2017, has been widely cryptanalyzed this years. This paper studies the differential diffusion characteristics of round function of GIFT at first, and proposes a random nibble-based differential fault attack. The key recovery scheme is developed on the statistical properties we found for the differential distribution table of the S-box. A lot of experiments had been done and experimental results show that one round key can be retrieved with an average of 20.24 and 44.96 fault injections for GIFT-64 and GIFT-128 respectively. Further analysis shows that a certain number of fault injections recover most key bits. So we demonstrate an improved fault attack combined with the method of exhaustive search, which shows that the master key can be recovered by performing 216 and 217 computations and injecting 31 and 32 faults on an average for GIFT-64 and GIFT-128 respectively.
An Approach for Documents Clustering Using K-Means Algorithm

Clustering is a process to form the same group as per the similarity values. Document clustering has given importance to the information retrieval and data mining process. This proposed work gives the improvement in efficiency and accuracy of the document clustering. This paper proposed a work which following phase: preprocessing, term document matrix and applying clustering algorithm. Initially, it shows flowchart and later calculates the TF, IDF and TF-IDF values. Later on, K-means clustering takes place to form the multiple clusters as per similarity measures. As per TF values of given keywords that show the 2D and 3D graphically representation.
Open-World Knowledge Graph Completion Benchmarks for Knowledge Discovery

The construction and completion of knowledge graphs in industrial settings has gained traction over the past years. However, modelling a specific domain is often entailed with significant cost. This can be alleviated by including other knowledge sources such as text—a challenge known as open-world knowledge graph completion. Although knowledge graph completion has drawn significant attention from the research community over the past years, we argue that academic benchmarks fall short at two key characteristics of industrial conditions: (1) open-world entities are drawn randomly in benchmarks although in practice they are more volatile than closed-world entities, and (2) textual descriptions of entities are not concise. This paper's mission is to bring academia and industry closer by proposing Inductive Reasoning with Text (IRT), an approach to create open-world evaluation benchmarks from given knowledge graphs. Two graphs, one based on Freebase and another derived from Wikidata, are created, analysed, and enhanced with textual descriptions according to the above assumptions. We evaluate a modular system that can tether any vector space knowledge graph completion model and a transformer-based text encoder to align sentence and entity representations. We show the difficulty of learning with such scattered text in contrast to text provided by other benchmarks and lay out a solid baseline study for future model benchmarking.
Can Alexa be a Team Player?

Nearly every day we engage with others to accomplish a variety of tasks, from the mundane, like setting a budget, to surgeons in an operating room working with speed and precision to ensure patients receive the best possible outcomes. In these examples, everyone works as a team to achieve desired goals. Regrettably, today's conversational user interfaces (CUIs) are far from this reality of human teamwork, instead focusing on simple, dyadic interactions to satisfy the immediate needs of its owner. However, we envision CUIs becoming collaborative team players within existing human teams, unlocking an array of invaluable applications, from meeting facilitation to team training. Furthermore, such a team player CUI not only completes the tasks assigned to it, but also actively contributes to the strength, wellbeing, and optimal functioning of the team.
Method for Protection of Heterogeneous Data based on Pseudo-Holographic Watermarks

This paper proposes a comprehensive solution for protection of heterogeneous two-dimensional and three-dimensional graphic data from unauthorized distribution and illegitimate changes using pseudo-holographic watermarks. A pseudo-holographic image is a signal, which encodes a binary sequence in the form of sinusoidal functions. The previous research of pseudo-holograms has shown that they are compatible with various popular embedding strategies and besides allow to significantly increase robustness of original watermarking scheme. In this paper, the robustness and information capacity of pseudo-holograms are significantly increased by minimizing the aliasing effect, which occur when the watermark is cropped. For this, a new algorithm for amplitude peak detection is proposed. The approach is based on signal oversampling before DFT calculation and isolating each ring during spectrum analysis. The experimental study has shown the efficiency of a new detection algorithm. The proposed solution allows to select the parameters in such a way as to maintain the resistance to cropping at a given level.
Face Mask Detection Using Deep Learning

With the spread of coronavirus disease 2019 (COVID-19) pandemic throughout the world, social distancing and using a face mask have become crucial to prevent the spreading of this disease. Our goal is to develop a better way to detect face masks. In this paper, we propose a comparison between all available networks, which is an efficient one-stage face mask detector. The detection scheme follows preprocessing, feature extraction, and classification. The mask detector has been built using deep learning, specifically ResNetV2, as the base pre-trained model upon which we have our own CNN. We use OpenCV’s ImageNet to extract faces from video frames and our trained model to classify if the person is wearing a mask or not. We also propose an object removal algorithm to reject prediction below absolute confidence and accept only predictions above it. For the training purpose, we are using the face mask dataset, which consists of 680 images with mask and 686 images without mask. The results show mask detector has an accuracy of 99.9%. We have also used other pre-trained networks like MobileNetV2 as our base network and compared our results. ResNet50 gives us the state-of-the-art performance of face mask detection, which is higher than other face detectors.
A Deep Learning based Traffic Flow Classification with Just a Few Packets

Recently, traffic flow classification has received unprecedented attention due to the introduction of a variety of network applications. Classification plays a crucial role in cyber-security and network management such as resource allocation. The previous studies have shown quite a good performance, but they require a large number of packets in the flow to identify an associated application. In this paper, we propose a deep learning based model for traffic flow classification with just a few packets. We compute five meaningful statistics from the flow and use them as hand-crafted features in the model. Such features when combined with deep learning features, improve the classification accuracy significantly. We evaluate the effectiveness of the model on a real-world traffic dataset that we collected by using a tcpdump utility of Linux. The initial experimental results show that the model can distinguish the traffic types quite accurately with only 15 packets of the flow by carefully extracting the features from the data.
A Quaternion Deterministic Monogenic CNN Layer for Contrast Invariance

Deep learning (DL) is attracting considerable interest as it currently achieves remarkable performance in many branches of science and technology. However, current DL cannot guarantee capabilities of the mammalian visual systems such as lighting changes. This paper proposes a deterministic entry layer capable of classifying images even with low-contrast conditions. We achieve this through an improved version of the quaternion monogenic wavelets. We have simulated the atmospheric degradation of the CIFAR-10 and the Dogs and Cats datasets to generate realistic contrast degradations of the images. The most important result is that the accuracy gained by using our layer is substantially more robust to illumination changes than nets without such a layer.