Reviewerly/exHarmony huggingface dataset:
Towards Robust DNNs: An Taylor Expansion-Based Method for Generating Powerful Adversarial Examples.

Although Deep Neural Networks(DNNs) have achieved successful applications in many fields, they are vulnerable to adversarial examples.Adversarial training is one of the most effective methods to improve the robustness of DNNs, and it is generally considered as solving a saddle point problem that minimizes risk and maximizes perturbation.Therefore, powerful adversarial examples can effectively replicate the situation of perturbation maximization to solve the saddle point problem.The method proposed in this paper approximates the output of DNNs in the input neighborhood by using the Taylor expansion, and then optimizes it by using the Lagrange multiplier method to generate adversarial examples. If it is used for adversarial training, the DNNs can be effectively regularized and the defects of the model can be improved.
A Brief Analysis of the Status Quo and Trend of Wearable Smart Jewellery Devices Design

With the recent bustling development of the world’s smart manufacturing industry, the big-scale of the smart jewellery market has been received great attention as well. Through analysing representative products, the purpose of this research is to discover the deficiencies of existing wearable accessories and figure out how can better combine the traditional products design and new technologies together to improve these defects. The findings of this research reveal that there are certain deficiencies of existing wearable accessories products, in terms of functions, forms and among others. This study concludes that it is necessary for the jewellery designers to be aware and the response to the industry trend; taking the use of the original target that smart wearable technology is to explore a new way of human-computer interactive way, through which the personalised and exclusive services can be provided to the end-users so as to better satisfy the needs of individual costumers.
Semantical Knowledge Guided Salient Object Detection with Multiple Proposals

In recent years, the area of salient object detection has developed rapidly duo to the revival of deep learning techniques, especially the emergence of deep Convolutional Neural Networks (CNNs), which has greatly boosted the detection result. Although CNNs can be used to perceive the salient objects, it is difficult to work for images with complex background, which is also a major problem faced by most work. In this paper, we introduce a salient object detection method using high-level features with semantic meaning. In order to obtain the accurate region and edge of all the salient objects in an image, our model has two designs: (1) utilizing multiple proposals as the semantical knowledge prior to enhance the power of locate objects, that are most likely to cover the entire salient regions of an image, and (2) using several attention modules to improve the representation ability of our model, and using abundant low-level feature information extracted by the encoder of the network to assist its decoder to obtain the precise saliency map relatively. In addition, our model can make full use of multi-level features and semantical knowledge, so the saliency map we got is very excellent. The experiments shows that our approach achieves state-of-the-art performance on four public benchmarks, and produces significant improvements over existing well-known methods.
Training-free Monocular 3D Event Detection System for Traffic Surveillance

We focus on the problem of detecting traffic events in a surveillance scenario, including the detection of both vehicle actions and traffic collisions. Existing event detection systems are mostly learning-based and have achieved convincing performance when a large amount of training data is available. However, in real-world scenarios, collecting sufficient labeled training data is expensive and sometimes impossible (e.g. for traffic collision detection). Moreover, the conventional 2D representation of surveillance views is easily affected by occlusions and different camera views in nature. To deal with the aforementioned problems, in this paper, we propose a training-free monocular 3D event detection system for traffic surveillance. Our system firstly projects the vehicles into the 3D Euclidean space and estimates their kinematic states. Then we develop multiple simple yet effective ways to identify the events based on the kinematic patterns, which need no further training. Consequently, our system is robust to the occlusions and the viewpoint changes. Exclusive experiments report the superior result of our method on large-scale real-world surveillance datasets, which validates the effectiveness of our proposed system.
Mask-FgS: Feature Global Networks and Supsampling for Instance Segmentation

The instance segmentation based on deep learning can combine the results of semantic segmentation and target detection to solve more powerful segmentation tasks. In this paper, we address the instance segmentation task with a network called Mask-FgS, which is based on Mask R-CNN. In addition, in our model, the output of the network is still with three branches: box prediction, semantic segmentation, and classification score. Our network proposes a new feature extraction module that improves the performance of the feature pyramid and provides significant improvements in both accuracy and speed. Furthermore, Supsampling replaces traditional bilinear upsampling in the network on mask branches. The proposed network performs experiments in the instance segmentation branch of the coco data set, and the performance of detection and segmentation is better than the current instance segmentation networks.
Obstacle Avoidance Method Based on Multi-source Information Fusion Intended for Robots Operating in Different Environments

Obstacle avoidance is a precondition for a smooth robot moving. However, current methods for obstacle avoidance are deficient in different information and sensitive to environmental conditions. In order to overcome the two mentioned shortcomings, this paper proposes an automatic obstacle avoidance method based on multi-source information fusion. A multi-source information platform is constructed based on the multi-core embedded system, and an artificial intelligence algorithm is used to construct the environment factor acquisition model. Also, a multi-source information fusion method based on fuzzy theory is proposed. Finally, a prototype test platform is constructed to conduct practical experiments on obstacle avoidance for different environments. Experimental results show that the presented method has the advantage of high obstacle identification rate.
Libraries and the REF: how do librarians contribute to research excellence?

Librarians support research in a wide variety of ways. However, librarians may not always receive recognition for the valuable contributions they make to the research life cycle or research environments. In higher education institutions (HEIs), librarians face competition from other professional support services in addition to external organizations and suppliers. This article provides an analysis of submissions made by HEIs to the UK’s Research Excellence Framework (REF) in 2014 and demonstrates that the work of librarians is rarely acknowledged in substantive ways, particularly in STEM disciplines. It provides an overview of the multiple ways in which librarians contribute to research environments as well as how they can practically contribute to the REF 2021 submission process and other research assessment processes. Librarians have developed a wide range of responsive and innovative support services over the last decade and REF 2021 is demonstrated to be an opportunity for librarians to gain recognition for the important role they play in UK HEIs.
Semantic-Aware Techniques Enhanced Recommendations in Social Network

This paper proposed the semantic-aware method to enhance the unknown but interesting recommendations. That is, the recommender system aims to suggest unknown but interesting groups to the target user, people should have the same interests or characteristics in the groups and the groups we recommended are high probably the target user like. The previous work, UBI, discovers unknown but interesting items based on the social behaviors of users. However, the work did not consider the information that the user really likes, e.g., the fan pages that user already joined. The information that user really likes is an important hint to recommend unknown but really interesting groups to the target user by merging social objects (such as fan pages and groups). We propose a semantic representation method of the social objects and integrate the semantic features into the UBI algorithm. Experimental results show that our method has better recommendations with unknown but user interesting items.
Secret Text Embedding Capacity Improvement (Approach 3)

In this chapter, the comprehensive procedure to improve the capacity of secret text is discussed. Two Steganography techniques were proposed which employed JPEG compression on grayscale image to hide secret text.
Steganalysis on All Approaches/Vulnerability Analysis of Stego Image(s)

Stego image quality, secret text embedding capacity, computational time and security are the main challenges involved for steganography methods.
Improved Cohort Intelligence Optimization Algorithm (Approach 4)

Nowadays the level of information security has been enhanced by various concepts such as cryptography, steganography [9] along with nature-inspired optimization algorithms [4, 7, 8]. However, in today’s world computational cost (time and function evaluations) plays a vital role in the success of any scientific method. The optimization algorithms, such as CICC and M-MRSLS were already implemented and applied for JPEG image steganography for $$ 8 \times 8 $$ as well as $$ 16 \times 16 $$ quantization table, respectively. Although results were satisfactory in terms of image quality and capacity, the computational time was high for most of the test images.
Data Driven Domain Appraisal: Extracting Information from Short Dense Texts

Domain names can be traded via online market places and auctions. Speculation can be lucrative, with high value transactions reaching into tens of millions of dollars. This paper proposes a framework for automated domain name appraisal and evaluates several formulations of the problem with real world data. A dynamic nonlinear valuation modelling process is defined using machine learning techniques. Attributes or value factors are derived from short domain name text strings as well as a variety of other contextual data able to be obtained online from open sources. A data set of 9.975 million domains is used for evaluation and results show that search engine query data is a primary driver of value but using extracted text features can facilitate higher performance in distinguishing high value domains particularly when used with ensemble learning.
Cohort Intelligence with Cognitive Computing (CICC) and Modified-Multi Random Start Local Search (M-MRSLS) Optimization Algorithms for JPEG Image Steganography (Approach 1 and Approach 2)

The JPEG image format (Miano in Compressed image file formats: JPEG, PNG, GIF, XBM, BMP. Addison-Wesley Professional, Boston, pp 1–264, 1999 [7]) is commonly used in various steganography techniques such as least significant bit insertion, masking, and filtering, transformations, etc. The algorithm of CI is inspired by the natural and social tendency of learning from one another. It has already been applied and tested for solving unconstrained, constrained and NP-hard combinatorial problems.
Detecting Attended Visual Targets in Video

We address the problem of detecting attention targets in video. Our goal is to identify where each person in each frame of a video is looking, and correctly handle the case where the gaze target is out-of-frame. Our novel architecture models the dynamic interaction between the scene and head features and infers time-varying attention targets. We introduce a new annotated dataset, VideoAttentionTarget, containing complex and dynamic patterns of real-world gaze behavior. Our experiments show that our model can effectively infer dynamic attention in videos. In addition, we apply our predicted attention maps to two social gaze behavior recognition tasks, and show that the resulting classifiers significantly outperform existing methods. We achieve state-of-the-art performance on three datasets: GazeFollow (static images), VideoAttentionTarget (videos), and VideoCoAtt (videos), and obtain the first results for automatically classifying clinically-relevant gaze behavior without wearable cameras or eye trackers.
Research and Application of Collecting and Integrating Information Fragment Based on Java

Collecting and sorting out the form information filled by users according to the demands usually waste much time. In this way, it may cause some problems such as high error rate and inconsistent format. In order to research and solve this problem, this paper analyzes some ways of data collection and collation, using JavaEE programming to build an adaptive users’ template interface and linking it to the existing information system, which can generate the required information table by cloud services. This research achieves the function of informational automation collection and integration, building foundation for data analysis and mining.
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The purpose of this study is to investigate the moderating effect of university students??gender including commitment experience on class satisfaction...
Simple and sharp analysis of k-means||

We present a simple analysis of k-means|| (Bahmani et al., PVLDB 2012) -- a distributed variant of the k-means++ algorithm (Arthur and Vassilvitskii, SODA 2007). Moreover, the bound on the number of rounds is improved from $O(\log n)$ to $O(\log n / \log\log n)$, which we show to be tight.
Multiple Action Detection in Videos

In this work, we present the efficient detection of multiple actions occurring simultaneously in streaming video of various real-world applications using a frame differencing-based method for background detection. We compare our method with other modeling methods (such as multi-channel nonlinear SVM) for multiple action detection on various video datasets. We demonstrate through quantitative performance evaluation metrics such as performance accuracy, standard deviation and detection F-score, and the efficacy of the proposed method over those reported in the literature.
Variable Selection in Finite Mixture of Time-Varying Regression Models

In this paper, we research the regression problem of time series data from heterogeneous populations on the basis of the finite mixture regression model.We propose two finite mixed time-varying regression models to solve this.A regularization method for variable selection of the models is proposed, which is a mixture of the appropriate penalty functions and 2 l penalty.A Block-wise minimization maximization (MM) algorithm is used for maximum penalized log quasi-likelihood estimation of these models.The procedure is illustrated by analyzing simulations and with an application to analyze the behavior of urban vehicular traffic of the city of São Paulo in the period from 14 to 18 December 2009, which shows that the proposed models outperform the FMR models.
Truncated Inference for Latent Variable Optimization Problems: Application to Robust Estimation and Learning

Optimization problems with an auxiliary latent variable structure in addition to the main model parameters occur frequently in computer vision and machine learning. The additional latent variables make the underlying optimization task expensive, either in terms of memory (by maintaining the latent variables), or in terms of runtime (repeated exact inference of latent variables). We aim to remove the need to maintain the latent variables and propose two formally justified methods, that dynamically adapt the required accuracy of latent variable inference. These methods have applications in large scale robust estimation and in learning energy-based models from labeled data.
Advances in security research in the Asiacrypt region

Members of the International Association for Cryptologic Research explore regional work and collaboration activities.
LAM-CIoT:Lightweightauthenticationmechanismincloud-basedIoT environmen

Internet of Things (IoT)becomes a new era of the Internet, which consists of several connected physical smart objects (i.e.,sensing devices)through the Internet. IoT has diﬀerent types of applications, such as smart home, wearable devices, smart connected vehicles, industries, and smart cities. Therefore, IoT based applications become the essential parts of our day-to-day life. In a cloud-based IoT environment, cloud platform is used to store the data accessed from the IoT sensors. Such an environment is greatly scalable and it supports real time event processing which is very important in several scenarios (i.e., IoT sensors based surveillance and monitoring). Since some applications incloud-based IoT are very critical, the information collected and sent by IoT sensors must not be leaked during the communication. To accord with this, we design a new lightweight authentication mechanism in cloud-based IoT environment, called LAM-C IoT. By using LAM-C IoT, an authenticated user can access the data of IoT sensors remotely. LAM-C IoT applies eﬃcient “one-way crypto graphic hash functions” along with “bit wise XOR operations”. In addition, fuzzy extractor mechanism is also employed at the user’s end for local biometric veriﬁcation. LAM-CIoT is methodically analyzed for its security part through the formal security using the broadly-accepted “Real-Or-Random (ROR)” model, formal security veriﬁcation using the widely-used “Automated Validation of Internet Security Protocols and Applications (AVISPA)” tool as well as the informal security analysis.The performance analysis shows that LAM-C IoT oﬀers better security, and low communication and computation over heads as compared to the closely related authentication schemes. Finally, LAM-CIoTisevaluatedusingtheNS2networksimulatorforthemeasurement of network performance parameters that envisions the impact of LAM-C IoT on the network performance of LAM-C IoT and other schemes.
The application of $σ$-LFSR in Key-Dependent Feedback Configuration for Word-Oriented Stream Ciphers

In this paper, we propose and evaluate a method for generating key-dependent feedback configurations (KDFC) for $σ$-LFSRs. $σ$-LFSRs with such configurations can be applied to any stream cipher that uses a word-based LFSR. Here, a configuration generation algorithm uses the secret key(K) and the initialization vector (IV) to generate a feedback configuration. We have mathematically analysed the feedback configurations generated by this method. As a test case, we have applied this method on SNOW 2.0 and have studied its impact on resistance to various attacks. Further, we have also tested the generated keystream for randomness and have briefly described its implementation and the challenges involved in the same.
On the perceived harmfulness of requirement smells : An empirical study

Technical debt is considered to have negative effects to the long term success of software projects. However, how the debt metaphor applies to requirements engineering is yet not significantly expl ...
Encoder Decoder Based Image Semantic Space Creation for Clothing Items Retrieval

Suggesting clothing items using query images is very convenient in online shopping, however feature extraction is not very easy for the intended query due to other objects and background variations present in these images. We need better feature extractor which extracts features only from intended clothing item in query image and rejects strong features from remaining part. To accomplish this we propose single step encoder-decoder based image translation and feature extraction approach with reverse weight transfer method for creating a common space between catalog of shopping images and query images. We present generated images and retrieved images from test set and compute the exact image retrieval accuracy. We achieved 30.6%, 46.8% and 56.3% exact image retrieval accuracy for top 1, top 3 and top 5 suggestions respectively. We also conducted a subjective survey on similar items retrieval and on an average 3.33 clothing items are relevant out of 5 items retrieved for ~500 responses which shows the effectiveness of this method.
Investigating Next Steps in Static API-Misuse Detection.

Application Programming Interfaces (APIs) often impose constraints such as call order or preconditions. API misuses, i.e., usages violating these constraints, may cause software crashes, data-loss, and vulnerabilities. Researchers developed several approaches to detect API misuses, typically still resulting in low recall and precision. In this work, we investigate ways to improve API-misuse detection. We design MuDetect, an API-misuse detector that builds on the strengths of existing detectors and tries to mitigate their weaknesses. MuDetect uses a new graph representation of API usages that captures different types of API misuses and a systematically designed ranking strategy that effectively improves precision. Evaluation shows that MuDetect identifies real-world API misuses with twice the recall of previous detectors and 2.5x higher precision. It even achieves almost 4x higher precision and recall, when mining patterns across projects, rather than from only the target project.
Accelerated LiDAR data processing algorithm for self‐driving cars on the heterogeneous computing platform

In recent years, light detection and ranging (LiDAR) has been widely used in the field of self-driving cars, and the LiDAR data processing algorithm is the core algorithm used for environment perception in self-driving cars. At the same time, the real-time performance of the LiDAR data processing algorithm is highly demanding in self-driving cars. The LiDAR point cloud is characterised by its high density and uneven distribution, which poses a severe challenge in the implementation and optimisation of data processing algorithms. In view of the distribution characteristics of LiDAR data and the characteristics of the data processing algorithm, this study completes the implementation and optimisation of the LiDAR data processing algorithm on an NVIDIA Tegra X2 computing platform and greatly improves the real-time performance of LiDAR data processing algorithms. The experimental results show that compared with an Intel® Core™ i7 industrial personal computer, the optimised algorithm improves feature extraction by nearly 4.5 times, obstacle clustering by nearly 3.5 times, and the performance of the whole algorithm by 2.3 times.
Discussion of “Statistics = Analytics?”

First, I thank Dr. Jensen for a paper that provides a great overview of the various disciplines that involve analysis of data, and which stimulated much discussion at the 2019 Stu Hunter conference...
Load Balancing with Minimum Makespan in Cloud Computing

Cloud computing is an innovative Internet technology which serves numerous accommodations to different location users. This technology grants a range of computing sources for the proper execution of voluminous-scale tasks with the appropriate size. There is a huge number of servers attached to web supply of several accommodations to users. By overcoming all constraints, it is required that all servers perform a huge amount of tasks in the same period. So it is not admitted to executing each task at the same time. So proper balancing of a huge amount of load is necessary for this area which cuts the makespan with runs each task as a specific way. It is almost impossible to maintain the same number of systems to run equivalent quantity jobs. Often tasks to be run in the cloud environment would be more than attached systems, and sometime, it will happen that some system has to allot more number of tasks than their available servers. To overcome the problem, we will propose an algorithmic method to get optimization load balancing with least makespan.