Reviewerly/exHarmony huggingface dataset:
SBSR Solution Evaluation: Methods and Challenges Classification

Search-based software engineering has made substantial progress in recent decades. It is an approach to software engineering in which search-based optimization algorithms are used to solve software engineering problems. Search-based software refactoring (SBSR) is used to improve the quality and maintainability of the software by finding a proper sequence of refactorings using search-based optimization algorithms. Fitness functions utilized to evaluate refactoring solutions in search-based algorithms play a vital role. Indeed, the more accurate and more effective the fitness function is, the more reliable the provided solutions will be. In this paper, we plan to accurately assess this area from the perspective of metrics used in fitness functions to evaluate solutions. To this end, first we propose a classification of metrics used for solution evaluation. Then, the challenges of each category are investigated. Understanding the challenges and ways to handle them can lead to an important comparison and assessment of the presented approaches. It will also direct researchers to accurately recognizing and improving existing approaches in the future.
Recipes for Breaking Data Free

How do the specific, predefined ways data brokers like Garmin or Fitbit render personal biometric data for us hinder-or enhance-our ability to find meaning in our data? Using a Garmin activity tracker as a platform, I present a series of recipes for alternative modes to experience personal data. Recipes are sets of instructions people can follow or remix to create personal, novel data interactions. These recipes highlight how the under-used medium of sound can be a creative material for producing meaning. When we allow our personal data to be brokered by companies like Garmin, we exchange the hidden labor of data representation for an easy-to-access personal data experience; but in doing so, we forfeit the ability to do unexpected things with our data. By exposing these tradeoffs, these recipes encourage us to reclaim control of our own data and embrace the effortful process of data representation as a sense-making practice.
Comparing Different Word Embeddings for Multiword Expression Identification

The identification of Multi-Word Expressions (MWEs) is central to resolving ambiguity of phrases. Recent works show that deep learning methods outperform statistical and lexical based approaches. The deep learning approaches mostly use word2vec embedding; our paper aims at comparing the use of word2vec, GloVe, and a combination of the two word embeddings in identifying MWEs. GloVe, and the combination of word2vec and GloVe were marginally better in terms of F-score, identifying more unique words, and identifying words not seen in the train data. GloVe was marginally better at identifying Verbal Multi-Word Expressions (VMWEs) which tend to be the hardest group of MWEs because they can be gappy, which is caused by interleaving of words that are part of the MWE and words that are not part of the MWE. The major purpose of the paper is to compare the use of different word embeddings in identifying MWEs and not to suggest improvements to the state-of-the-art. Future work using different dimensions of word embedding vectors and use of fasttext are suggested.
<i>Chic</i>

Large-scale machine learning (ML) models are routinely trained in a distributed fashion, due to their increasing complexity and data sizes. In a shared cluster handling multiple distributed learning workloads with a parameter server framework, it is important to determine the adequate number of concurrent workers and parameter servers for each ML workload over time, in order to minimize the average completion time and increase resource utilization. Existing schedulers for machine learning workloads involve meticulously designed heuristics. However, as the execution environment is highly complex and dynamic, it is challenging to construct an accurate model to make online decisions. In this paper, we design an experience-driven approach that learns to manage the cluster directly from experience rather than using a mathematical model. We propose Chic, a scheduler that is tailored for scheduling machine learning workloads in a cluster by leveraging deep reinforcement learning techniques. With our design of the state space, action space, and reward function, Chic trains a deep neural network with a modified version of the cross-entropy method to approximate the policy for assigning workers and parameter servers for future workloads based on the experience of the agent. Furthermore, a simplified version named Chic-Pair with a shorter training time for the policy is purposed by assigning workers and parameter servers in a pair. We compare Chic and Pair with state-of-the-art heuristics, and our results show that Chic and Chic-Pair are able to reduce the average training time significantly for machine learning workloads under a wide variety of conditions.
Secure two-party computation of solid triangle area and tetrahedral volume based on cloud platform

With the emergence and widespread application of cloud computing, the use of cloud platforms to solve the problem of secure multi-party computation has emerged as a new research direction. The traditional computation of a solid geometry is performed through mutual interactions between two parties, which is not suitable in an untrusted cloud computing environment. In this paper, we first design a basic protocol for a secure Euclidean distance calculation that is suitable for cloud platforms and can serve as a building block for other protocols on cloud platforms. Using the solution of the Euclidean distance problem as such a building block, we provide a new method that converts the problems of calculating solid triangular areas and solid tetrahedral volumes into the calculation of distances and determinants in three-dimensional space. Then, we discuss solid point-line distance calculations, which extent the idea of the spatial geometry security problem. We present protocols for the above problems and prove that the proposed protocols can resist conspiracy among users and the untrusted cloud platform so that they can effectively ensure the privacy of the users. We also analyze the performances of these solutions. The analysis results show that our scheme is more versatile.
Prediction of Employee Attrition Using GWO and PSO Optimised Models of C5.0 Used with Association Rules and Analysis of Optimisers

Prediction of employee attrition based on five selected attributes which are Gender, Distance from Home, Environment Satisfaction, Work–Life Balance and Education Field out of 36 variables present in the data-set. Application of Grey Wolf Optimisation (GWO) Algorithm and Particle Swarm Optimisation (PSO) on the model of Decision Tree Algorithm "C5.0" which is fed in the inputs of Associated Rules, using this optimised algorithm for the prediction of employee attrition using IBM Watson Human Resource Employee Attrition Data. After comparing the efficiency of GWO and PSO, we have come to a conclusion that time to predict an employee attrition and consumption of RAM have been optimised with GWO. Employee Attrition is one of the major problems faced by companies nowadays. Sometimes, when the long-term working employees leave the company, it affects the relationship of the company with the client and in turn affects the revenue of the company if the person replacing the old employee is not able manage a good rapport with the client. The paper can be used to frame better work policies which will help both the employer and employee. It can be seen as a mirror to the working conditions of the employees.
Implementation of Controlled‐NOT Gate by Lyapunov Control

Abstract A scheme to implement the controlled‐NOT (CNOT) gate for quantum systems is proposed, which is based on Lyapunov control. The scheme does not require precise control of the interaction time since the system is stable when the control fields vanish. In particular, the control fields can be easily obtained by most initial states. As an example, the CNOT gate is realized for two atoms trapped in an optical cavity by exploiting two disturbance cases. Compared to continuous disturbance, the fidelity of the CNOT gate is higher under impulsive disturbance, however, interaction times are much longer. Numerical simulations indicate that the scheme is robust against variations of control parameters and decoherence caused by atomic spontaneous emission and cavity decay. Therefore, the scheme may provide useful applications in quantum computation.
Differentially Private Event Sequences over Infinite Streams with Relaxed Privacy Guarantee

Continuous publication of statistics over user-generated streams can provide timely data monitoring and analysis for various applications. Nonetheless, such published statistics may reveal the details of individuals' sensitive status or activities. To guarantee the privacy for event occurrences in data streams, based on the known privacy standard of $$\varepsilon $$ -differential privacy, w-event privacy has been proposed to hide multiple events occurring at continuous time instances. Nonetheless, the too strict requirement of w-event privacy makes it hard to achieve effective privacy protection with high data utility in many real-world scenarios. To this end, in this paper we propose a novel notion of average w-event privacy and the first Lyapunov optimization-based privacy-preserving scheme on infinite streams, aiming to obtain higher data utility while satisfying a relatively stable privacy guarantee for whole streams. In particular, we first formulate both our proposed privacy definition and the utility loss function of statistics publishing in a stream setting. We then design a Lyapunov optimization-based scheme with a detailed algorithm to maximize the publishing data utility under the requirement of our privacy notion. Finally, we conduct extensive experiments on both synthetic and real-world datasets to confirm the effectiveness of our scheme.
Adding Voices to Support Web Navigation Among a Low Digital Literacy Group

This paper presents our initial experiences introducing and refining a voice-annotated web application to a group of people who have limited experience with technology. We conducted a two-week training program for a group of sex-trafficking survivors in Nepal to help them sell their handicrafts on an online marketplace. We incorporate voices in two senses: by providing naturalistic, extended voice annotation with colloquial phrasing and intonation and by using the survivors' words and images in the interface. The colloquial phrases helped in use and we saw the survivors adopting the phrases to explain the system. We contemplate design approaches to make web applications accessible to people with limited digital fluency.
How compatible are our discourse annotation frameworks? Insights from mapping RST-DT and PDTB annotations

Discourse-annotated corpora are an important resource for the community, but they are often annotated according to different frameworks. This makes joint usage of the annotations difficult, preventing researchers from searching the corpora in a unified way, or using all annotated data jointly to train computational systems. Several theoretical proposals have recently been made for mapping the relational labels of different frameworks to each other, but these proposals have so far not been validated against existing annotations. The two largest discourse relation annotated resources, the Penn Discourse Treebank and the Rhetorical Structure Theory Discourse Treebank, have however been annotated on the same texts, allowing for a direct comparison of the annotation layers. We propose a method for automatically aligning the discourse segments, and then evaluate existing mapping proposals by comparing the empirically observed against the proposed mappings. Our analysis highlights the influence of segmentation on subsequent discourse relation labelling, and shows that while agreement between frameworks is reasonable for explicit relations, agreement on implicit relations is low. We identify several sources of systematic discrepancies between the two annotation schemes and discuss consequences for future annotation and for usage of the existing resources.
Soft Bigram Similarity to Identify Confusable Drug Names

Look-alike and Sound-alike drug names are related to medication errors where doctors, nurses, and pharmacists prescribe and administer the wrong medication. Bisim similarity is reported as the best orthographic measure to identifying confusable drug names, but it lacks from a similarity scale between the bigrams of a drug name. In this paper, we propose a Soft-Bisim similarity measure that extends to the Bisim to soften the comparison scale between the Bigrams of a drug name for improving the detection of confusable drug names. In the experimentation, Soft-Bisim outperforms others 17 similarity measures for 396,900 pairs of drug names. In addition, the average of four measures is outperformed when Bisim is replaced by Soft-Bisim similarity.
Incisions in virtual reality simulators: assessing user performance

The use of simulators based on virtual reality has become more common in different areas. In the field of health sciences, their use ranges from the training of future professionals to planning and performing surgical procedures in real cases. Different procedures can be trained through these programs, one of them being the surgical incision. It was identified that although there are different applications capable of simulating incisions, there was no specific method to assess the performance of the user who was involved in this task, something that would be fundamental for the learning process. The structure and the variables used in the proposed assessment system for surgical incisions performed in simulators were obtained from the literature of the area and the decision models support vector machine and classical logic were chosen after performing tests with a data set of simulated incision examples. The assessment system is presented as the main result and a simulation of a specific type of incision (submental) was developed to validate it. Satisfactory results were obtained in the tests, with 74 to 97% of accuracy, depending on the test configuration. The tests indicate that the system in its current format is a good candidate for testing and eventual use with human users in a training environment.
Visual Siamese Clustering for Cosmetic Product Recommendation

We investigate the problem of a visual similarity-based recommender system, where cosmetic products are recommended based on the preferences of people who share similarity of visual features. In this work we train a Siamese convolutional neural network, using our own dataset of cropped eye regions from images of 91 female subjects, such that it learns to output feature vectors that place images of the same subject close together in high-dimensional space. We evaluate the trained network based on its ability to correctly identify existing subjects from unseen images, and then assess its capability to find visually similar matches amongst the existing subjects when an image of a new subject is input.
Data Anonymization for Hiding Personal Tendency in Set-Valued Database Publication

Set-valued database publication has been increasing its importance recently due to its benefit for various applications such as marketing analysis and advertising. However, publishing a raw set-valued database may cause individual privacy breach such as the leakage of sensitive information like personal tendencies when data recipients perform data analysis. Even though imposing data anonymization methods such as suppression-based methods and random data swapping methods to such a database can successfully hide personal tendency, it induces item loss from records and causes significant distortion in record structure that degrades database utility. To avoid the problems, we proposed a method based on swapping technique where an individual’s items in a record are swapped to items of the other record. Our swapping technique is distinct from existing one called random data swapping which yields much structure distortion. Even though the technique results in inaccuracy at a record level, it can preserve every single item in a database from loss. Thus, data recipients may obtain all the item information in an anonymized database. In addition, by carefully selecting a pair of records for item swapping, we can avoid excessive record structure distortion that leads to alter database content immensely. More importantly, such a strategy allows one to successfully hide personal tendency without sacrificing a lot of database utility.
PerDREP

In contrast to the one-size-fits-all approach to medicine, precision medicine will allow targeted prescriptions based on the specific profile of the patient thereby avoiding adverse reactions and ineffective but expensive treatments. Longitudinal observational data such as Electronic Health Records (EHRs) have become an emerging data source for personalized medicine. In this paper, we propose a unified computational framework, called PerDREP, to predict the unique response patterns of each individual patient from EHR data. PerDREP models individual responses of each patient to the drug exposure by introducing a linear system to account for patients' heterogeneity, and incorporates a patient similarity graph as a network regularization. We formulate PerDREP as a convex optimization problem and develop an iterative gradient descent method to solve it. In the experiments, we identify the effect of drugs on Glycated hemoglobin test results. The experimental results provide evidence that the proposed method is not only more accurate than state-of-the-art methods, but is also able to automatically cluster patients into multiple coherent groups, thus paving the way for personalized medicine.
Abnormal Behavior Recognition Based on Transfer Learning

In order to identify abnormal behavior in image samples, a transfer learning recognition model based on Inception-V3 neural network is proposed in this paper. The whole process consists of two parts: 1) Inception-V3 neural network is used to extract the features of image samples.2) The abnormal behavior recognition model is established to classify the obtained features. In this paper, three-layer feedforward neural network algorithm is used to compare with other common machine learning classification models (k-nearest neighbor algorithm, random forest, LightGBM, SVM, two-layer feedforward neural network).The results show that the performance of anomalous behavior recognition based on three-layer feedforward neural network is better than other classification models.
Enhancing the long-term performance of recommender system

Recommender system is a critically important tool in online commercial system and provide users with personalized recommendation on items. So far, numerous recommendation algorithms have been made to further improve the recommendation performance in a single-step recommendation, while the long-term recommendation performance is neglected. In this paper, we proposed an approach called Adjustment of Recommendation List (ARL) to enhance the long-term recommendation accuracy. In order to observe the long-term accuracy, we developed an evolution model of network to simulate the interaction between the recommender system and user's behaviour. The result shows that not only long-term recommendation accuracy can be enhanced significantly but the diversity of item in online system maintains healthy. Notably, an optimal parameter n* of ARL existed in long-term recommendation, indicating that there is a trade-off between keeping diversity of item and user's preference to maximize the long-term recommendation accuracy. Finally, we confirmed that the optimal parameter n* is stable during evolving network, which reveals the robustness of ARL method.
Susceptibility to Fitness App's Persuasive Features

The incidence of physical inactivity, obesity and non-communicable diseases is on the rise globally due to the sedentary lifestyles occasioned by modernity and technology. As a means of tackling the inactivity problem, which is almost becoming a global epidemic, research has shown that persuasive technology holds bright prospects. However, in the physical activity domain, there is limited research on users' persuasion profiles and the differences between users who are currently exercising (acting users) and those who have the intentions to exercise in the future (non-acting users). To bridge this gap, we conducted a study among 190 participants resident in two individualist countries to determine the susceptibility profile of both user types and their differences. We based our study on storyboards, illustrating six commonly employed persuasive features in fitness apps. The results of our analysis showed that both user types are most likely to be susceptible to Goal-Setting/Self-Monitoring, followed by Reward and Competition, and least likely to be susceptible to Cooperation, Social Comparison and Social Learning. In particular, acting users are more likely to be susceptible to Social Learning than non-acting us-ers. Overall, our findings suggest that, irrespective of user type, personal features will be more likely effective than social features among users from individualist cultures. We discuss the implications of our findings in the context of fitness apps design.
Cross-modal retrieval in challenging scenarios using attributes

Cross-modal retrieval is an important field of research today because of the abundance of multi-media data. In this work, we attempt to address two challenging scenarios that we may encounter in real-life cross-modal retrieval, but which are relatively unexplored in literature. First, due to the ever-increasing number of new categories of data, cross-modal algorithms should be able to generalize to categories which it has not seen during training. Second, the data that is available during testing may be degraded (for example, it has low resolution or noise) as compared to those available during training. Here, we evaluate how these adverse conditions affect the performance of the state-of-the-art cross-modal approaches. We also propose a unified framework that can handle all these diverse and challenging scenarios without any modification. In the proposed approach, the data from different modalities are projected into a common semantic preserving latent space in which semantic relations as given by the class-name embeddings (attributes) are preserved. Extensive experiments on diverse cross-modal data including image-text, RGB-depth and comparison with the state-of-the-art approaches show the usefulness of the proposed approach for these challenging scenarios.
On the Hardness of the Mersenne Low Hamming Ratio Assumption

In a recent paper [1], Aggarwal, Joux, Prakash, and Santha (AJPS) describe an ingenious public-key cryptosystem mimicking NTRU over the integers. This algorithm relies on the properties of Mersenne primes rather than polynomial rings. The security of the AJPS cryptosystem relies on the conjectured hardness of the Mersenne Low Hamming Ratio Assumption, defined in [1]. This work shows that AJPS' security estimates are too optimistic and describes an algorithm allowing to recover the secret key from the public key much faster than foreseen in [1]. In particular, our algorithm is experimentally practical (within the reach of the computational capabilities of a large organization), at least for the parameter choice $$\{n=1279,h=17\}$$ conjectured in [1] as corresponding to a $$2^{120}$$ security level. The algorithm is fully parallelizable.