Reviewerly/exHarmony huggingface dataset:
Multiple-Instance ranking based deep hashing for multi-Label image retrieval

Hashing methods have been widely applied to approximate nearest neighbor search in large-scale image retrieval due to its fast search speed and efficient storage space. In practice, most images are with multiple category-aware objects, i.e., multi-label images. This paper focuses on hash code learning for multi-label image retrieval. Most existing hashing methods directly extract one patch such as a downsized crop from each image as a training example, which ignores the multi-label characteristic of images and leads to suboptimal representations for multi-label images. Some researches have proved that each multi-label image follows a multi-instance assumption, where each image is represented as a bag of category-aware proposals (instances). However, existing multiple-instance learning methods use predefined statistical functions with limited learning capability to construct bag features, and they are designed for classification or pairwise-similarity preserving. Thus, directly applying existing multiple-instance learning methods into deep hashing framework still leads to suboptimal hash codes for retrieval. In this paper, we pose hashing learning for multi-label image retrieval as a problem of multiple-instance ranking learning. To solve this problem, we present an end-to-end deep hashing framework, referred to as Deep Multiple-Instance Ranking based Hashing (DMIRH). In DMIRH, we design a category-aware bag feature construction module, which jointly assigns the learned instances into categories and aggregates the selected instance features into a bag feature representation that can capture the multi-label information of each image. In addition, we propose a novel learning objective, which consists of an Inner Product distance based quantization loss to control the hash quality and a listwise ranking loss to preserve the ranking relationships. Experimental results on public benchmarks show the superiority of DMIRH over several state-of-the-art hashing methods.
User recommendation method based on joint probability matrix decomposition in CPS networks

In recent years, with the rapid development of the Internet, various virtual communities continue to emerge, and the phenomenon of user groups working together is gradually increasing. People begin to pay more attention to group-oriented recommendation. Most of existing group recommendation methods are improved on the memory-based collaborative filtering recommendation method, or think that the members of the group are independent of each other, ignoring the impact of association among the members of the group on the results of group recommendation. In this paper, a group recommendation method based on joint probability matrix decomposition is proposed to better model the group recommendation problem. Firstly, the user-plus-person group information is used to calculate the correlation between users. Secondly, the user correlation matrix is integrated into the process of probability matrix decomposition to get the individual prediction score. Finally, the group-to-item prediction score is obtained by using the common synthesis strategy in group-oriented recommendation problem. Furthermore, the proposed method is compared with existing group recommendation methods. Experiments on CiteULike dataset show that the proposed method achieves better recommendation results in accuracy, recall and other evaluation indicators.
Shilling Attacks Detection in Collaborative Recommender System: Challenges and Promise

The reliability of the recommender system is highly essential for the continuity of any system. Fake and malicious users may be spoiling system predictions reliability by inserting and injecting fake profiles called "shilling attacks" into the target recommender system. Thus, the detection of these attacks is necessary for any recommender system. Therefore, several shilling attacks detection approaches have proposed. In this work, we propose a survey for the recent detection methods, which pick up famous shilling attack models against the collaborative filtering recommender systems.
Comparing maintainability index, SIG Method, and SQALE for technical debt identification

Many techniques have emerged to evaluate software Technical Debt (TD). However, differences in reporting TD are not yet studied widely, as they can give different perceptions about the evolution of TD in projects. The goal of this paper is to compare three TD identification techniques: i. Maintainability Index (MI), ii. SIG TD models and iii. SQALE analysis. Considering 17 large open source Python libraries, we compare TD measurements time series in terms of trends in different sets of releases (major, minor, micro). While all methods report generally growing trends of TD over time, MI, SIG TD, and SQALE all report different patterns of TD evolution.
A type- and scope-safe universe of syntaxes with binding: their semantics and proofs

Abstract The syntax of almost every programming language includes a notion of binder and corresponding bound occurrences, along with the accompanying notions of α-equivalence, capture-avoiding substitution, typing contexts, runtime environments, and so on. In the past, implementing and reasoning about programming languages required careful handling to maintain the correct behaviour of bound variables. Modern programming languages include features that enable constraints like scope safety to be expressed in types. Nevertheless, the programmer is still forced to write the same boilerplate over again for each new implementation of a scope-safe operation (e.g., renaming, substitution, desugaring, printing), and then again for correctness proofs. We present an expressive universe of syntaxes with binding and demonstrate how to (1) implement scope-safe traversals once and for all by generic programming; and (2) how to derive properties of these traversals by generic proving. Our universe description, generic traversals and proofs, and our examples have all been formalised in Agda and are available in the accompanying material available online at https://github.com/gallais/generic-syntax .
Clone Detection in Test Code: An Empirical Evaluation

Duplicated test code (a.k.a. test code clones) has a negative impact on test comprehension and maintenance. Moreover, the typical structure of unit test code induces structural similarity, increasing the amount of duplication. Yet, most research on software clones and clone detection tools is focused on production code, often ignoring test code. In this paper we fill this gap by comparing four different clone detection tools (NiCad, CPD, iClones, TCORE) against the test code of three open-source projects. Our analysis confirms the prevalence of test code clones, as we observed between 23% and 29% test code duplication. We also show that most of the tools suffer from false negatives (NiCad = 83%, CPD = 84%, iClones = 21%, TCORE = 65%), which leaves ample room for improvement. These results indicate that further research on test clone detection is warranted.
Revocable Attribute-Based Data Storage in Mobile Clouds

It is becoming fashionable for people to access data outsourced to clouds with mobile devices. To protect data security and privacy, attribute-based encryption (ABE) has been widely used in cloud storage systems. However, one of the main efficiency drawbacks of ABE is the high computation overheads at mobile devices during user revocation and file access. To address this issue, we propose a revocable attribute-based data storage (RADS) scheme equipped with several attracting features. First, our RADS scheme achieves a fine-grained access control mechanism, by which file owners do not need to explicitly specify authorized visitors to their outsourced files. Second, our RADS scheme allows mobile users to authorize the cloud service provider (CSP) to share costly computations in file access, without exposing the file content. Third, our RADS scheme offloads the operations of access-credential update and file re-encryption during revocation process to CSP, leaving all non-revoked users undisturbed. The revocation of RADS achieves a strong data protection, i.e., revoked users can access neither newly uploaded files nor old ones. The security and efficiency of the RADS scheme are validated via both analysis and experimental results.
Image Inpainting Based on Inside–Outside Attention and Wavelet Decomposition

Recently developed deep learning-based image inpainting methods have suggested their potential applications in filling large missing regions displaying plausible content. Nevertheless, most existing studies either merely considered the external information of the missing regions or relied on the region context to yield semantically coherent patches while overlooking the semantic relevance and feature continuity exhibited by missing regions; these demerits are likely to cause a discontinuous contextual structure and blurry texture details. In this work, a novel inside-outside attention layer (IOA) was proposed, capable of exploiting unmasked image features as references as well as learning the affinity between hole features to predict more consistent semantic information. The adversarial loss attributed exclusively on the natural image level cannot adequately generate a sharp texture detail. To address this problem, a texture component discriminator was introduced via wavelet decomposition to enhance the specific performance. Several experiments were performed on the CelebA and Places2 datasets. As revealed from the results, in contrast to the existing research, the proposed method is capable of restoring images with complex structures and significantly enhancing plausible structure and visual quality.
ECOGreen: Electricity Cost Optimization for Green Datacenters in Emerging Power Markets

Modern datacenters need to tackle efficiently the increasing demand for computing resources while minimizing energy usage and monetary costs.Power market operators have recently introduced emerging demand-response programs, in which electricity consumers regulate their power usage following provider requests to reduce monetary costs.Among different programs, regulation service (RS) reserves are particularly promising for datacenters due to the high credit gain possibilities and datacenters' flexibility in regulating their power consumption.Therefore, it is essential to develop bidding strategies for datacenters to participate in emerging power markets together with power management policies that are aware of power market requirements at runtime.In this paper we propose ECOGreen, a holistic strategy to jointly optimize the datacenter RS problem and virtual machine (VM) allocation that satisfies the hour-ahead power market constraints in the presence of electrical energy storage (EES) and renewable energy.We first find the best power and reserve bidding values as well as the number of active servers in a fast analytical way that works well in practice.Then, we present an online adaptive policy that modulates datacenter power consumption by controlling VMs CPU resource limits and efficiently utilizing demand-side EES and renewable power, while guaranteeing quality-of-service (QoS) constraints.Our results demonstrate that ECOGreen can provide 76% of the datacenter power consumption on average as reserves to the market, due to largely operating on renewable sources and EES.This translates into ECOGreen saving up to 71% electricity costs when compared to other state-of-the-art datacenter electricity cost minimization techniques that participate in the power market.
Multiscale Conditional Regularization for Convolutional Neural Networks

With the increased model size of convolutional neural networks (CNNs), overfitting has become the main bottleneck to further improve the performance of networks. Currently, the weighting regularization methods have been proposed to address the overfitting problem and they perform satisfactorily. Since these regularization methods cannot be used in all the networks and they are usually not flexible enough in different phases of the training and test processes, this article proposes a multiscale conditional (MSC) regularization method. MSC divides the intermediate features into different scales and then generates new data for each scale features, respectively. In addition, the new data are generated by employing the information from two conditions: 1) each sample feature and 2) each layer pattern. Finally, a self-identity structure is proposed to supplement the features with the generated data. Therefore, MSC can adaptively and efficiently generate much finer and individualized data to make the entire regularization more flexible. Furthermore, MSC is more general and can be applied to all kinds of networks through the proposed self-identity structure. The experimental results on all the benchmark datasets showed that the proposed MSC regularization method achieves the best performances in all the networks.
Evaluating the Four-Way Performance Trade-Off for Data Stream Classification in Edge Computing

Edge computing (EC) is a promising technology capable of bridging the gap between Cloud computing services and the demands of emerging technologies such as the Internet of Things (IoT). Most EC-based solutions, from wearable devices to smart cities architectures, benefit from Machine Learning (ML) methods to perform various tasks, such as classification. In these cases, ML solutions need to deal efficiently with a huge amount of data, while balancing predictive performance, memory and time costs, and energy consumption. The fact that these data usually come in the form of a continuous and evolving data stream makes the scenario even more challenging. Many algorithms have been proposed to cope with data stream classification, e.g., Very Fast Decision Tree (VFDT) and Strict VFDT (SVFDT). Recently, Online Local Boosting (OLBoost) has also been introduced to improve predictive performance without modifying the underlying structure of the decision tree produced by these algorithms. In this work, we compared the four-way relationship among time efficiency, energy consumption, predictive performance, and memory costs, tuning the hyperparameters of VFDT and the two versions of SVFDT with and without OLBoost. Experiments over 6 benchmark datasets using an EC device revealed that VFDT and SVFDT-I were the most energy-friendly algorithms, with SVFDT-I also significantly reducing memory consumption. OLBoost, as expected, improved the predictive performance, but caused a deterioration in memory and energy consumption.
Job Seeker Profile Classification of Twitter Data Using the Naïve Bayes Classifier Algorithm Based on the DISC Method

Human resource staff in a company is a person in charge of finding new workers. To get a qualified new workforce, a human resource staff must be selective toward the appliance in terms of ability and personality. This study provides an alternative perspective for a human resource in getting one's personality data through their tweets on a Twitter account. This study uses the Naive Bayes Classifier algorithm with W-IDF (Weighted-Inverse Document Frequency) weighting to classify the personality of recruits into one of DISC's personality theories, namely Dominance, Influence, Steadiness, and Compliance. By using training data and test data as many as 120 personal Twitter accounts and labelling of words that have been verified by psychologists, obtained personality distribution. The classification of the tweet data is Dominance 90 accounts, Influence 10 accounts, Steadiness 8 accounts and Compliance 12 accounts. Evaluation of the accuracy level of 36.67%.
Learning From Smart Home Data: Methods and Challenges of Data Acquisition and Analysis in Smart Home Solutions

As the adoption rate of commercial smart home solutions increases, it drives the development of novel system features, needed to support advanced user scenarios. Being able to remotely control the household is not enough already, and the efforts are made to provide context-aware and intelligent homes, which detect user activities, learn about user habits, adapt to particular users, create intelligent alarms, seamlessly integrate with remote services, etc. On the other hand, significant efforts have already been made by the research community to identify data mining scenarios applicable to smart home solutions, and to propose and improve the algorithms suitable for this purpose. This article summarizes state-of-the-art research in the field of machine learning for smart home solutions.
Quaternion Projection Rule for Rotor Hopfield Neural Networks

A rotor Hopfield neural network (RHNN) is an extension of a complex-valued Hopfield neural network (CHNN) and has excellent noise tolerance. The RHNN decomposition theorem says that an RHNN decomposes into a CHNN and a symmetric CHNN. For a large number of training patterns, the projection rule for RHNNs generates large self-feedbacks, which deteriorates the noise tolerance. To remove self-feedbacks, we propose a projection rule using quaternions based on the decomposition theorem. Using computer simulations, we show that the quaternion projection rule improves noise tolerance.
Evaluation of Automatic Facial Wrinkle Detection Algorithms

Facial wrinkles (considered to be natural features) appear as people get older. Wrinkle detection is an important aspect of applications that depend on facial skin changes, such as face age estimation and soft biometrics. While existing wrinkle detection algorithms focus on forehead horizontal lines, it is necessary to develop new methods to detect all wrinkles (vertical and horizontal) on whole face. Therefore, we evaluated the performance of wrinkle detection algorithms on the whole face and proposed an enhancement technique to improve the performance. More specifically, we used 45 images of the Face Recognition Technology dataset (FERET) and 25 images of the Sudanese dataset. For ground truth annotations, the selected images were manually annotated by the researcher. The experiments showed that the method with enhancement performed better at detecting facial wrinkles when compared to the state-of-the-art methods. When evaluated on FERET, the average Jaccard similarity indices were 56.17%, 31.69% and 15.87% for the enhancement method, Hybrid Hessian Filter and Gabor Filter, respectively.
Iterative learning to rank from explicit relevance feedback

Interactive information retrieval (IIR) models consider the implicit or explicit user feedback in order to understand their intent and improve the quality of retrieved documents. We are proposing an IIR model that combines explicit relevance feedback and learning to rank techniques to improve the quality of retrieved documents. Besides evaluating the proposed method in general information retrieval cases using LETOR datasets, we have applied it to the special case of Community Question Answering (CQA) systems using SemEval challenge data. The proposed method outperforms other existing learning to rank techniques on most of these datasets.
The social media commons: Public sphere, agonism, and algorithmic obligation

This paper takes a unique approach to framing the political obligation social media companies like Twitter and Facebook have in a democratic society by casting the public sphere as a common-pool resource. Over the last decade or so much of our civic discourse has moved to social media platforms such as Facebook and Twitter. This paper argues that just as citizens have an obligation to one another, social media companies have an obligation to promote agonistic forms of civic, political discourse through their algorithms. This algorithmic obligation that social media companies have must be reconciled with social media companies' formulation of their fiduciary shareholder obligations. We posit that a commons based view of social media discourse allows these companies to make both a democratic health and fiduciary interest argument.
Homotopic Convex Transformation: A New Landscape Smoothing Method for the Traveling Salesman Problem

This article proposes a novel landscape smoothing method for the symmetric traveling salesman problem (TSP). We first define the homotopic convex (HC) transformation of a TSP as a convex combination of a well-constructed simple TSP and the original TSP. The simple TSP, called the convex-hull TSP, is constructed by transforming a known local or global optimum. We observe that controlled by the coefficient of the convex combination, with local or global optimum: 1) the landscape of the HC transformed TSP is smoothed in terms that its number of local optima is reduced compared to the original TSP and 2) the fitness distance correlation of the HC transformed TSP is increased. Furthermore, we observe that the smoothing effect of the HC transformation depends highly on the quality of the used optimum. A high-quality optimum leads to a better smoothing effect than a low-quality optimum. We then propose an iterative algorithmic framework in which the proposed HC transformation is combined within a heuristic TSP solver. It works as an escaping scheme from local optima aiming to improve the global searchability of the combined heuristic. Case studies using the 3-Opt and the Lin–Kernighan local search as the heuristic solver show that the resultant algorithms significantly outperform their counterparts and two other smoothing-based TSP heuristic solvers on most of the test instances with up to 20 000 cities.
Independent Language Modeling Architecture for End-To-End ASR

The attention-based end-to-end (E2E) automatic speech recognition (ASR) architecture allows for joint optimization of acoustic and language models within a single network. However, in a vanilla E2E ASR architecture, the decoder sub-network (subnet), which incorporates the role of the language model (LM), is conditioned on the encoder output. This means that the acoustic encoder and the language model are entangled that doesn’t allow language model to be trained separately from external text data. To address this problem, in this work, we propose a new architecture that separates the decoder subnet from the encoder output. In this way, the decoupled subnet becomes an independently trainable LM subnet, which can easily be updated using the external text data. We study two strategies for updating the new architecture. Experimental results show that, 1) the independent LM architecture benefits from external text data, achieving 9.3% and 22.8% relative character and word error rate reduction on Mandarin HKUST and English NSC datasets respectively; 2) the proposed architecture works well with external LM and can be generalized to different amount of labelled data.