Reviewerly/exHarmony huggingface dataset:
Enhanced Privacy Preserving Group Nearest Neighbor Search

Group k-nearest neighbor (kGNN) search allows a group of n mobile users to jointly retrieve k points from a location-based service provider (LSP) that minimizes the aggregate distance to them. We identify four protection objectives in the privacy preserving kGNN search: (i) every user's location should be protected from LSP; (ii) the group's query and the query answer should be protected from LSP; (iii) LSP's private database information should be protected from users; (iv) every user's location should be protected from other users in the group. We design two privacy preserving solutions under two types of threat model to the privacy preserving kGNN search in the full user collusion environment, where any n - 1 users in the group may collude to infer the location of the remaining user. Our solutions do not rely on heavy pre-computation on LSP like previous works. Though we consider kGNN, the proposed privacy preserving solutions can be easily adopted to any group query as it treats the query answering (i.e., kGNN) as a black box. Theoretical and experimental analysis suggest that our solutions are highly efficient in both communication cost and user computational cost while incurring some reasonable overhead on LSP.
How does overconfidence affect information security investment and information security performance?

(2021). How does overconfidence affect information security investment and information security performance? Enterprise Information Systems: Vol. 15, Security, privacy and forensics in the enterprise information, pp. 474-491.
Explainable Bayesian Network Query Results via Natural Language Generation Systems

Bayesian networks (BNs) are an important modelling technique used to support certain types of decision making in law and forensics. Their value lies in their ability to infer the rational implications of probabilistic knowledge and beliefs, a task that human decision makers struggle with. However, their use is controversial. One of the main obstacles to the more widespread use of BNs is the difficulty to acquire good explanations of the results obtained with BNs. While useful techniques exist to visualise, verbalise or abstract BNs and the inner workings of belief propagation algorithms, these techniques provide generic, one-size-fits-all explanations, that have, thus far, failed to stem the criticism of lack of explainable BN results. Building on the qualified support graph method introduced in earlier work, this paper outlines how a natural language generation system can be constructed to explain Bayesian inference. This constitutes a novel approach to BN explanation that has the potential to produce more focussed and compelling explanations of Bayesian inference as the narratives such a system produces can be tailored to address specific communicative goals and, by extension, the needs of the user.
Symbol-Level Stochastic Chase Decoding of Reed-Solomon and BCH Codes

This paper proposes the symbol-level-stochastic Chase decoding algorithm (S-SCA) for the Reed-Solomon (RS) and Bose-Chaudhuri-Hocquenghem (BCH) codes, which is a soft-input soft-output (SISO) decoder. By the efficient usage of void space between constellation points for q-ary modulations and using soft information at the input of the decoder, the S-SCA is capable of outperforming conventional symbol-levelChase algorithm (S-CA) with a less computational cost. Since the S-SCA starts with the randomized generation of likely testvectors, it reduces the complexity to polynomial order and also it does not need to find the least reliable symbols to generate testvectors. The symbol-level-search bitwise-transmission stochastic Chase algorithm (SSBT-SCA) is also introduced for RS codes over binary phase shift keying (BPSK) transmission that is capable of generating symbol-level test-vectors with reduced complexity and to better mitigate burst errors. Simulation results show that by increasing the number of test-vectors, the performance of the algorithm can asymptotically approach the maximum-likelihood (ML) bound. The S-SCA provides near 2 dB decoding gain in comparison with S-CA for a (31, 25) RS code using 32-QAM, when 1024 test-vectors are used. Furthermore, the algorithm provides near 3 dB additional gain with 1024 test-vectors compared with S-CA that uses 65536 iterations when a (255, 239) RS code is used in an additive white Gaussian noise (AWGN) channel. For the Rayleigh fading channel and the same code, the algorithm provides more than 5 dB gain. Furthermore, for (63, 57) BCH codes and 8-PSK modulation, the proposed algorithm provides 3 dB gain with less complexity.
New covering codes of radius R, codimension tR and $$tR+\frac{R}{2}$$, and saturating sets in projective spaces

The length function $$\ell _q(r,R)$$ is the smallest length of a q-ary linear code of codimension r and covering radius R. In this work we obtain new constructive upper bounds on $$\ell _q(r,R)$$ for all $$R\ge 4$$ , $$r=tR$$ , $$t\ge 2$$ , and also for all even $$R\ge 2$$ , $$r=tR+\frac{R}{2}$$ , $$t\ge 1$$ . The new bounds are provided by infinite families of new covering codes with fixed R and increasing codimension. The new bounds improve upon the known ones. We propose a general regular construction (called “Line+Ovals”) of a minimal $$\rho $$ -saturating $$((\rho +1)q+1)$$ -set in the projective space $$\mathrm {PG}(2\rho +1,q)$$ for all $$\rho \ge 0$$ . Such a set corresponds to an $$[Rq+1,Rq+1-2R,3]_qR$$ locally optimal code of covering radius $$R=\rho +1$$ . Basing on combinatorial properties of these codes regarding to spherical capsules, we give constructions for code codimension lifting and obtain infinite families of new surface-covering codes with codimension $$r=tR$$ , $$t\ge 2$$ . In addition, we obtain new 1-saturating sets in the projective plane $$\mathrm {PG}(2,q^2)$$ and, basing on them, construct infinite code families with fixed even radius $$R\ge 2$$ and codimension $$r=tR+\frac{R}{2}$$ , $$t\ge 1$$ .
Structured Label Inference for Visual Understanding

Visual data such as images and videos contain a rich source of structured semantic labels as well as a wide range of interacting components. Visual content could be assigned with fine-grained labels describing major components, coarse-grained labels depicting high level abstractions, or a set of labels revealing attributes. Such categorization over different, interacting layers of labels evinces the potential for a graph-based encoding of label information. In this paper, we exploit this rich structure for performing graph-based inference in label space for a number of tasks: multi-label image and video classification and action detection in untrimmed videos. We consider the use of the Bidirectional Inference Neural Network (BINN) and Structured Inference Neural Network (SINN) for performing graph-based inference in label space and propose a Long Short-Term Memory (LSTM) based extension for exploiting activity progression on untrimmed videos. The methods were evaluated on (i) the Animal with Attributes (AwA), Scene Understanding (SUN) and NUS-WIDE datasets for multi-label image classification, (ii) the first two releases of the YouTube-8M large scale dataset for multi-label video classification, and (iii) the THUMOS'14 and MultiTHUMOS video datasets for action detection. Our results demonstrate the effectiveness of structured label inference in these challenging tasks, achieving significant improvements against baselines.
Regularizing Output Distribution of Abstractive Chinese Social Media Text Summarization for Improved Semantic Consistency

Abstractive text summarization is a highly difficult problem, and the sequence-to-sequence model has shown success in improving the performance on the task. However, the generated summaries are often inconsistent with the source content in semantics. In such cases, when generating summaries, the model selects semantically unrelated words with respect to the source content as the most probable output. The problem can be attributed to heuristically constructed training data, where summaries can be unrelated to the source content, thus containing semantically unrelated words and spurious word correspondence. In this article, we propose a regularization approach for the sequence-to-sequence model and make use of what the model has learned to regularize the learning objective to alleviate the effect of the problem. In addition, we propose a practical human evaluation method to address the problem that the existing automatic evaluation method does not evaluate the semantic consistency with the source content properly. Experimental results demonstrate the effectiveness of the proposed approach, which outperforms almost all the existing models. Especially, the proposed approach improves the semantic consistency by 4% in terms of human evaluation.
RKDOS: A Relative Kernel Density-based Outlier Score

This article proposes a novel outlier detection algorithm called Relative Kernel Density-based Outlier Score (RKDOS) to detect local outliers. The proposed algorithm uses a weighted kernel density estimation (WKDE) method with an adaptive kernel width for density estimation at the location of an object based on its extended nearest neighbors. For density estimation, we consider both Reverse Nearest Neighbors (RNN) and k-Nearest Neighbors (kNN) of an object. To achieve smoothness in the measure, the Gaussian kernel function is adopted. Further, to improve discriminating power between normal and abnormal samples, we use an adaptive kernel width concept. Extensive experiments on both synthetic and real data sets have shown that our proposed algorithm has better detection performance over some popular existing outlier detection approaches.
Secure Computing Enclaves Using FPGAs

A new scheme for securing users' data and applications in public clouds and data centers using Field Programmable Gate Arrays (FPGAs) has been developed. This scheme incorporates all necessary protocols, hardware, and software components to provide protection against many known potential attacks including internal attacks. It achieves perfect forward secrecy, provides FPGA authentication and integrity checks, and securely establishes a symmetric session key between the user and the FPGA. A complete prototype has been implemented to show the feasibility of the proposed scheme with current FPGAs. Experimental results showed that an FPGA-based compute node can be set up in a cloud in 3.36s; 12.6 times faster than booting a medium-size conventional Virtual Machine (VM) on the same cloud. Based on the average global Internet speed, the time it takes to set up the FPGA-based machine from anywhere in the world was estimated to be 15s. Also, running an experimental secure image processing application on the FPGA took 50 percent less time than running the same application on a conventional state-of-the art processor (without a secure container).
Identifying Extremism in Text Using Deep Learning

Various forms of terrorism have become increasingly relevant in today’s world. Consequently, the utilization of the web by various terrorist groups to spread propaganda, communicate and organize has increased. However, techniques to effectively identify such material are lacking. This chapter explores an approach which can classify any piece of text as belonging to one of four extremist groups: Sunni Islamic, Antifascist Groups, White Nationalists and Sovereign Citizens. This classification is performed by LSTM models, which will be proven to be much more effective than non-deep learning approaches. This chapter will describe the performance of various models in detail. The process of creating good quality datasets for each extremist category and the unique challenges such a task presents will also be explored.
Semi-Supervised Mix-Hindi Sentiment Analysis using Neural Network

Most of the people in the world now a day would like to share and express their feelings, views, experiences, suggestions and opinions on the web. These opinions are processed by sentiment analysis task and find their polarity.In this paper, we use input text file in Devanagari script stored in UTF-8 encoding scheme. We propose 3 approaches for doing sentiment analysis for Hindi multidomain review. In approach 1, classification of data is done using NN Prediction by using pre-classified words. In approach 2, classification of data is done using IIT-Bombay Hindi SentiWordNet (HSWN). In approach 3, classification of data is done using NN prediction using pre-classified sentences as labeled data. Finally, we report accuracies in every approach. We have different domain (Health, Business, Current affairs, Tourism, Movie, Technology and Product) review dataset manually and randomly collected by us. They contain Mix-Hindi words like (brave), (careful), (mineral), etc., for which we have created dictionary to deal with them. We achieve overall accuracy of 52% in approach 1, 71.5% accuracy in approach 2 and 70.27% accuracy in approach 3.
Leveraging Standards Based Ontological Concepts in Distributed Ledgers: A Healthcare Smart Contract Example

Sharing clinical data using Distributed Ledger Technologies (DLT) is increasing in momentum. Fast Healthcare Interoperability Resources (FHIR) standard provides a standard based shared vocabulary and widely accepted mechanisms enabling healthcare providers to share patient data across institutions. Many DLT based solutions are capitalizing on these standards to enable trustworthy electronic health record sharing among institutions. In this paper, we present our preliminary work on capturing the semantics of the FHIR standard in smart contracts. We also discuss the appropriate data to mine from transaction logs in decentralized ledgers to find any anomalies and information misuses by leveraging these standards-based ontological concepts.
Detecting Social Signals in User-Shared Images for Connection Discovery Using Deep Learning

With the advance of mobile technology and social media, image sharing has become part of our daily lives. For many applications, such as follower/followee recommendation, shared images are an excellent source to discover connections among users who shared them. Shared images on social media are like invitations for user interactions, such as comment, like, and more. Social signals are in those images, and those signals can be objects that interest related users such that they will start to interact. Conventionally, connections among users are discovered through recognizing objects among those shared images, such as a using convolutional neural network (CNN) to extract features that are sensitive to object recognition. However, social signals are not limited to object, they can be colour, textual, or even a concept that may not be captured effectively by conventional CNN. This paper proposes a CNN-based analytic framework to detect social signals among users. The CNN is optimized using a triplet network with user-shared images, and the relationships among users who upload them. It is observed that images from 2 users with a connection have a shorter distance after encoding, than 2 users without a connection. A framework is implemented, which is verified with over 1.7 million images by over 2000 users from two image-oriented social networks, Skyrock and Flickr. It is proven that the proposed analytic framework shows an up to 89% improvement on approaches using object recognition for follower/followee recommendation. To the best of our knowledge, this paper is the first to propose an analytic framework to detect social signals from visual features for connection discovery.
Adversarial Learning for Fine-Grained Image Search

Fine-grained image search is still a challenging problem due to the difficulty in capturing subtle differences regardless of pose variations of objects from fine-grained categories. In practice, a dynamic inventory with new fine-grained categories adds another dimension to this challenge. In this work, we propose an end-to-end network, called FGGAN, that learns discriminative representations by implicitly learning a geometric transformation from multi-view images for fine-grained rigid object retrieval. We integrate a generative adversarial network (GAN) that can automatically handle complex view and pose variations by converting them to a canonical view without any predefined transformations. Moreover, in an open-set scenario, our network is able to better match rigid objects from unseen and unknown fine-grained categories. Extensive experiments on the public CompCars dataset and a newly collected dataset have demonstrated the effectiveness of the proposed method in both closed-set and open-set scenarios.
Person re-Identification with Gradual Background Suppression

Person re-identification plays an important role in public security. However, owing to the interference of background clutters, its performance still needs to be improved. Several mask-based methods aim to solve this problem by totally removing the background clutters, but the promotion is limited because of the mask sharpening effect. In this paper, we propose a novel person re-identification method with Gradual Background Suppression (GBS). The GBS adopts several CNN branches to extract deep features of images with different weight distributions between background and human body. Thus, it can not only reduce the background clutters but also keep the smoothness of target pedestrians. Afterwards, deep features from different CNN branches are integrated with a fusion scheme, and the fused feature is capable of balancing the influence of background clutter and mask sharpening. Extensive experiments have been conducted and the results prove the superiority of the proposed GBS over the background removal approach. Comparing with the state-of-the-art methods, our method achieves remarkable performance with 6.6%, 7.58% and 8.26% improvement of mAP on dataset Market-1501, CUHK03-labeled and CUHK03-detected, respectively.
Soft Theory: A Pragmatic Alternative to Conduct Quantitative Empirical Studies

Practitioners and scholars often face new software engineering phenomena which lack sufficient theoretical grounding. When studying such nascent and emerging topics, it is important to establish an initial and rudimentary understanding, leaving a more precise understanding of underpinning mechanisms till later. Controlled experiments, for example, might lead to insights into the specific mechanisms underpinning a certain practice, such as distributed development, pair programming, and test-driven development. However, at an initial stage of research, such highly controlled studies may not be feasible. In other domains, it may not be clear what the key constructs are, so that effective measurement cannot be done. Instead, researchers might opt for pragmatic alternative research approaches that do not require experimental control or active intervention in a study's setting. In this paper we advocate the use of soft theory (based on soft modeling techniques) for quantitative studies in software engineering research. We discuss the use of soft theory and position it within an existing taxonomy of quantitative data analysis techniques. Soft modeling and soft theory affords us a pragmatic approach to developing inferential and predictive research models, rather than aiming to develop a causal understanding. Soft theory approaches are grounded in robust quantitative data analysis techniques. We argue that these techniques can be effectively used in industry settings which are not amenable to highly controlled studies.
Ai Motion Control – A Generic Approach to Develop Control Policies for Robotic Manipulation Tasks

Abstract Current robotic solutions are able to manage specialized tasks, but they cannot perform intelligent actions which are based on experience. Autonomous robots that are able to succeed in complex environments like production plants need the ability to customize their capabilities. With the usage of artificial intelligence (AI) it is possible to train robot control policies without explicitly programming how to achieve desired goals. We introduce AI Motion Control (AIMC) a generic approach to develop control policies for diverse robots, environments and manipulation tasks. For safety reasons, but also to save investments and development time, motion control policies can first be trained in simulation and then transferred to real applications. This work uses the descriptive study I according to Blessing and Chakrabarti and is about the identification of this research gap. We combine latest motion control and reinforcement learning results and show the potential of AIMC for robotic technologies with industrial use cases.
Subset Reasoning for Event-Based Systems

In highly dynamic domains such as the Internet of Things (IoT), smart industries, smart manufacturing, pervasive health or social media, data is being continuously generated. By combining this generated data with background knowledge and performing expressive reasoning upon this combination, meaningful decisions can be made. Furthermore, this continuously generated data typically originates from multiple heterogeneous sources. Ontologies are ideal for modeling the domain and facilitates the integration of heterogeneous produced data with background knowledge. Furthermore, expressive ontology reasoning allows to infer implicit facts and enables intelligent decision making. The data produced in these domains is often volatile. Time-critical systems, such as IoT Nurse Call systems, require timely processing of the produced IoT data. However, there is still a mismatch between volatile data and expressive ontology reasoning, since the incoming data frequency is often higher than the reasoning time. For this reason, we present an approximation technique that allows to extract a subset of data to speed-up the reasoning process. We demonstrate this technique in a Nurse Call proof of concept where the locations of the nurses are tracked and the most suited nurse is selected when the patient launches a call and in an extension of an existing benchmark. We managed to speed up the reasoning process up to 10 times for small datasets and up to more than 1000 times for large datasets.
A Novel Infrared Image Enhancement Based on Correlation Measurement of Visible Image for Urban Traffic Surveillance Systems

Infrared imaging sensors are widely employed in urban traffic systems since they are not affected by lighting conditions. However, due to the limitation of hardware and imaging environment, it is difficult to obtain infrared (IR) images at the desired quality. IR images always lack detailed information, which leads to unsatisfying IR image enhancement results with the conventional method. Compared with the IR images, the visible (VIS) images contain detailed information, which could help to enhance the quality of the corresponding IR images. In this article, we propose an effective method to enhance IR images by applying the multi-sensors image. First, we adopt the edge-preserving filter to decompose the IR and VIS images into illumination and reflectance components according to Retinex theory. Second, each region in the IR and VIS image is classified into the related region and non-related region according to the correlation between IR and VIS images. Finally, an adaptive fuzzy plateau HE (AFPHE) is utilized to enhance the illumination component, and a strategy is employed to enhance the detail of the IR reflectance component with the help of VIS images. Experimental results demonstrate that the proposed method can effectively improve the contrast and enhance the detail of the IR images.