Reviewerly/exHarmony huggingface dataset:
Investigation on Privacy Preserving using K-Anonymity Techniques

In the current world, day by day the data growth and the investigation about that information increased due to the pervasiveness of computing devices, but people are reluctant to share their information on online portals or surveys fearing safety because sensitive information such as credit card information, medical conditions and other personal information in the wrong hands can mean danger to the society. These days privacy preserving has become a setback for storing data in data repository so for that reason data in the repository should be made undistinguishable, data is encrypted while storing and later decrypted when needed for analysis purpose in data mining. While storing the raw data of the individuals it is important to remove person-identifiable information such as name, employee id. However, the other attributes pertaining to the person should be encrypted so the methodologies used to implement. These methodologies can make data in the repository secure and PPDM task can made easier.
Accurate <scp>automatic detection</scp> of acute lymphatic leukemia using a refined simple classification

Abstract An improved classification technique is presented to identify automatically the acute lymphatic leukemia (ALL) subtypes. An adaptive segmentation procedure is performed on peripheral blood smear images to extract the main features (10 geometric features) from the segmented images of white blood cell (WBC), nucleus, and cytoplasm. To show the importance of the different extracted features for the diagnostic accuracy, a comprehensive study is made on all the possible permutation cases of the features using powerful classifiers which are K‐nearest neighbor (KNN) at different metric functions, support vector machine (SVM) with different kernels, and artificial neural network (ANN). This procedure enables us to construct a feature map depending only on least number of features which lead to the highest diagnostic accuracy. It is found that the features map regarding the vacuoles in the cytoplasm and the regularity of the nucleus membrane gives the highest accurate results. The automatic classification for ALL subtypes based only on these two effective features is assessed using the receiver operating characteristic (ROC) curve and F 1 ‐score measures. It is confirmed that the present technique is highly accurate, and saves the effort and time of training.
Convolutional Neural Networks With Dynamic Regularization

Regularization is commonly used for alleviating overfitting in machine learning. For convolutional neural networks (CNNs), regularization methods, such as DropBlock and Shake-Shake, have illustrated the improvement in the generalization performance. However, these methods lack a self-adaptive ability throughout training. That is, the regularization strength is fixed to a predefined schedule, and manual adjustments are required to adapt to various network architectures. In this article, we propose a dynamic regularization method for CNNs. Specifically, we model the regularization strength as a function of the training loss. According to the change of the training loss, our method can dynamically adjust the regularization strength in the training procedure, thereby balancing the underfitting and overfitting of CNNs. With dynamic regularization, a large-scale model is automatically regularized by the strong perturbation, and vice versa. Experimental results show that the proposed method can improve the generalization capability on off-the-shelf network architectures and outperform state-of-the-art regularization methods.
Visual feedback from a virtual body modulates motor illusion induced by tendon vibration

Frequency-specific tendon vibration (TV) elicits illusory kinesthetic sensations around the vibrated body parts. Studies indicate that vision plays a fundamental role in modulating such illusions. In our current study, we used immersive virtual reality (IVR) to investigate the role of body-related visual feedback in modulating illusory sensation of movement in the left arm. Thirty healthy participants were asked to evaluate the onset of motor illusion and four illusion-related features (vividness, duration, extension and aftereffect), in the presence and absence of real and virtual visual feedback. Additionally, subjective reports of the embodiment illusion (the sense of embodying a virtual surrogate) were collected in virtual conditions. Results showed a progressive decrease in the perception of the motor illusion along a continuum ranging from the absence of visual feedback (maximal illusory perception) to the observation of one’s own real arm (minimal illusory perception). Interestingly, the appearance of the virtual limbs affected the movement illusion differently. Specifically, TV evoked a stronger kinesthetic illusion when observing the virtual hand detached from the limb than during the observation of the virtual full limb and virtual object. This suggests that a closer visual resemblance between the virtual and real limb results in a greater effect on proprioceptive processing. However, no significant correlation was found between the illusion of arm movement and the illusion of embodiment, indicating that the two phenomena may not be directly related. These findings provide new insight into the role of body-related visual feedback in modulating motor illusions.
Data-driven inference of representation invariants

A representation invariant is a property that holds of all values of abstract type produced by a module. Representation invariants play important roles in software engineering and program verification. In this paper, we develop a counterexample-driven algorithm for inferring a representation invariant that is sufficient to imply a desired specification for a module. The key novelty is a type-directed notion of visible inductiveness, which ensures that the algorithm makes progress toward its goal as it alternates between weakening and strengthening candidate invariants. The algorithm is parameterized by an example-based synthesis engine and a verifier, and we prove that it is sound and complete for first-order modules over finite types, assuming that the synthesizer and verifier are as well. We implement these ideas in a tool called Hanoi, which synthesizes representation invariants for recursive data types. Hanoi not only handles invariants for first-order code, but higher-order code as well. In its back end, Hanoi uses an enumerative synthesizer called Myth and an enumerative testing tool as a verifier. Because Hanoi uses testing for verification, it is not sound, though our empirical evaluation shows that it is successful on the benchmarks we investigated.
Quantum Fredkin gate based on synthetic three-body interactions in superconducting circuits

We propose the generation of effective three-body interactions in superconducting circuits by coupling qubits or resonators to a bus qubit. Such interactions are characterized by energy exchange between two qubits or resonators depending on the state of the bus qubit. We show that a controlled-iswap (-$\sqrt{\mathrm{i}swap}$) gate can be naturally implemented based on the three-body interactions and it can be used to construct a quantum Fredkin (controlled-swap) gate. A generalized Fredkin gate which controls the swapping of photons between two resonators can be realized in a similar way. It can be used to generate the entangled state of a high number of photons. This proposal is promising to be demonstrated with superconducting circuits previously reported and will stimulate the implementation of multiqubit quantum gates based on many-body interactions.
Comparative Study of Different Cryptographic Algorithms

With the increasing interconnection of computer networks and sophistication of cyber-attacks, Cryptography is one way to make sure that confidentiality, authentication, integrity, availability, and identification of data user can be maintained as well as security and privacy of data provided to the user.Symmetric key cryptography is a part of the cryptographic technique which ensures high security and confidentiality of data transmitted through the communication channel using a common key for both encryption and decryption.In this paper I have analyzed comparative encryption algorithms in performance, three most useful algorithms: Data Encryption Standard (DES), Triple DES (3DES) also known as Triple Data Encryption Algorithm (TDEA), and Advanced Encryption Standard (AES).They have been analyzed on their ability to secure data, time taken to encrypt data and throughput the algorithm requires.The performance of different algorithms differs according to the inputs.
Investigating the role of disaggregated economic freedom measures and FDI on human development in Africa

Purpose The purpose of the study is to investigate the role of disaggregated economic freedom measures in the foreign direct investment (FDI) and human development nexus. Design/methodology/approach The study uses a panel data of 32 selected African countries from 1996 to 2017. A dynamic ordinary least squares (DOLS) with fixed effects and instrumental variable (IV) econometric techniques was used to address issues of endogeneity and serial correlation commonly associated with panel time series data. Findings The Results indicate that FDI without accounting for absorptive factors has a positive but insignificant effect on human development for the selected African countries. However, FDI has a positive and significant effect on human development when interacted with measures of economic freedom such as investment freedom, business freedom and financial freedom. In contrast, yet plausible, FDI has a negative influence when interacted with property rights, trade freedom, government integrity and tax burden. Practical implications The study posits that to attract FDI into Africa with the purpose of improving human development, relevant absorptive capacities such as business, investment and financial freedom environment are critical. However, excessive capital flight and government interference through taxation and abuse of property rights should be controlled if the continent seeks to promote human development through FDI. Originality/value The novelty and originality of the study, are evident in the use of disaggregated measures of economic freedom as comprehensive absorptive capacities to examine how they complement FDI to impact on human development in Africa.
EMO

Real-time user emotion recognition is highly desirable for many applications on eyewear devices like smart glasses. However, it is very challenging to enable this capability on such devices due to tightly constrained image contents (only eye-area images available from the on-device eye-tracking camera) and computing resources of the embedded system. In this paper, we propose and develop a novel system called EMO that can recognize, on top of a resource-limited eyewear device, real-time emotions of the user who wears it. Unlike most existing solutions that require whole-face images to recognize emotions, EMO only utilizes the single-eye-area images captured by the eye-tracking camera of the eyewear. To achieve this, we design a customized deep-learning network to effectively extract emotional features from input single-eye images and a personalized feature classifier to accurately identify a user's emotions. EMO also exploits the temporal locality and feature similarity among consecutive video frames of the eye-tracking camera to further reduce the recognition latency and system resource usage. We implement EMO on two hardware platforms and conduct comprehensive experimental evaluations. Our results demonstrate that EMO can continuously recognize seven-type emotions at 12.8 frames per second with a mean accuracy of 72.2%, significantly outperforming the state-of-the-art approach, and consume much fewer system resources.
Data librarianship in South African academic and research libraries: a survey

Purpose The purpose of this study was to establish the current skills base of librarians working in research data management services in academic and research libraries in South Africa. The purpose was also to determine the relevance of courses and programmes that are currently being offered by library and information studies programmes in response to the needs of research data management services and make recommendations on curriculum improvement. Design/methodology/approach About 13 institutions which were considered early adopters of research data management services were identified as participants in an online survey. In addition, a review of Web pages of existing library and information studies schools was carried to establish courses that would support research data management services. Data collected through the two approaches were analysed and presented quantitatively and qualitatively. Findings The findings reveal an environment in a developmental stage, with limited skilled personnel to run research data management services. The findings also show an absence of specific data librarianship courses within existing library and information studies programmes and a very limited scope for the full range of data management courses within professional development programmes. Originality/value The paper provides information on approaches to further develop existing curriculum and contribute to the data management needs and support governments, funders and publishers' requirements for the discoverability and re-use of research data across research domains.
Robust Distributed Kalman Filtering: On the Choice of the Local Tolerance

We propose a distributed Kalman filter for a sensor network under model uncertainty. The distributed scheme is characterized by two communication stages in each time step: in the first stage, the local units exchange their observations and then they can compute their local estimate; in the final stage, the local units exchange their local estimate and compute the final estimate using a diffusion scheme. Each local estimate is computed in order to be optimal according to the least favorable model belonging to a prescribed local ambiguity set. The latter is a ball, in the Kullback-Liebler topology, about the corresponding nominal local model. We propose a strategy to compute the radius, called local tolerance, for each local ambiguity set in the sensor network, rather than keep it constant across the network. Finally, some numerical examples show the effectiveness of the proposed scheme.
MEC for Fair, Reliable and Efficient Media Streaming in Mobile Networks

Beyond the advanced radio capabilities, 5G means a digital transformation, catalyzed by cloud technologies, making the networks agile and broader. However, high and quick dynamics in dense client cells consuming live broadcast contents can cause Quality of Experience (QoE) degradations. Here, inaccurate bandwidth assessment of media players drives to buffering times along with quality fluctuations. Moreover, massive recurrent requests can negatively impact on Content Delivery Network (CDN) performance. Complemented by capillarity and zero-latency features of multi-access edge computing (MEC) systems, 5G infrastructures will expand media services to take QoE to a new level. This paper investigates QoE gains of an MEC enabled infrastructure. The proposed MEC system applies three video delivery mechanisms. First, it enforces the QoE in a congested cell. Second, it shields from CDN degradation for a reliable content distribution. Third, it enhances network core and backhaul efficiency saving CDN traffic. Furthermore, our solution is deployed and tested on a LTE infrastructure. Results for live streams show that the MEC system makes the media players tend to a common and high quality bitrate, and it is able to quickly, transparently and coordinately switch to healthy CDN infrastructures and reduce CDN traffic.
Investigating the relationship between time and predictive model maintenance

Abstract A majority of predictive models should be updated regularly, since the most recent data associated with the model may have a different distribution from that of the original training data. This difference may be critical enough to impact the effectiveness of the machine learning model. In our paper, we investigate the relationship between time and predictive model maintenance. Our work incorporates severely imbalanced big data from three Medicare datasets, namely Part D, DMEPOS, and Combined, that have been used in several fraud detection studies. We build training datasets from year-groupings of 2013, 2014, 2015, 2013–2014, 2014–2015, and 2013–2015. Our test datasets are built from the 2016 data. To mitigate some of the adverse effects from the severe class imbalance in these datasets, the performance of five class ratios obtained by Random Undersampling and five learners is evaluated by the Area Under the Receiver Operating Characteristic Curve metric. The models producing the best values are as follows: Logistic Regression with the 2015 year-grouping at a 99:1 class ratio (Part D); Random Forest with the 2014-2015 year-grouping at a 75:25 class ratio (DMEPOS); and Logistic Regression with the full 2015 year-grouping (Combined). Our experimental results show that the largest training dataset (year-grouping 2013–2015) was not among the selected choices, which indicates that the 2013 data may be outdated. Moreover, we note that because the best model is different for Part D, DMEPOS, and Combined, this suggests that these three datasets may actually be sub-domains requiring unique models within the Medicare fraud detection domain.
A Multivariate Model to Quantify and Mitigate Cybersecurity Risk

The cost of cybersecurity incidents is large and growing. However, conventional methods for measuring loss and choosing mitigation strategies use simplifying assumptions and are often not supported by cyber attack data. In this paper, we present a multivariate model for different, dependent types of attack and the effect of mitigation strategies on those attacks. Utilising collected cyber attack data and assumptions on mitigation approaches, we look at an example of using the model to optimise the choice of mitigations. We find that the optimal choice of mitigations will depend on the goal—to prevent extreme damages or damage on average. Numerical experiments suggest the dependence aspect is important and can alter final risk estimates by as much as 30%. The methodology can be used to quantify the cost of cyber attacks and support decision making on the choice of optimal mitigation strategies.
Learning spatial-temporal deformable networks for unconstrained face alignment and tracking in videos

Abstract In this paper, we propose a spatial-temporal deformable networks approach to investigate both problems of face alignment in static images and face tracking in videos under unconstrained environments. Unlike conventional feature extractions which cannot explicitly exploit augmented spatial geometry for various facial shapes, in our approach, we propose a deformable hourglass networks (DHGN) method, which aims to learn a deformable mask to reduce the variances of facial deformation and extract attentional facial regions for robust feature representation. However, our DHGN is limited to extract only spatial appearance features from static facial images, which cannot explicitly exploit the temporal consistency information across consecutive frames in videos. For efficient temporal modeling, we further extend our DHGN to a temporal DHGN (T-DHGN) paradigm particularly for video-based face alignment. To this end, our T-DHGN principally incorporates with a temporal relational reasoning module, so that the temporal order relationship among frames is encoded in the relational feature. By doing this, our T-DHGN reasons about the temporal offsets to select a subset of discriminative frames over time steps, thus allowing temporal consistency information memorized to flow across frames for stable landmark tracking in videos. Compared with most state-of-the-art methods, our approach achieves superior performance on folds of widely-evaluated benchmarking datasets. Code will be made publicly available upon publication.
RoSANE: Robust and scalable attributed network embedding for sparse networks

Attributed networks can better describe the real-world complex systems where the interaction or relationship between entities can be represented as networks and the auxiliary information can be represented as node attributes. Attributed Network Embedding (ANE) is attracting much attention. It utilizes network topology and node attributes to jointly learn enhanced low-dimensional node embeddings so as to facilitate various downstream inference tasks. However, the existing ANE methods cannot effectively embed attributed sparse networks which are important real-world scenarios, and/or are not scalable to large-scale networks. To tackle these challenges, we first integrate network topology and node attributes to reconstruct an enriched denser network, and then learn node embeddings upon the denser network. In above two steps, the techniques such as Ball-tree K-Nearest Neighbors and random walks based Skip-Gram model are adopted to guarantee the scalability, which is demonstrated via theoretical complexity analysis. The extensive empirical studies show the effectiveness and efficiency of the proposed method, as well as its robustness to different networks or the same network with different sparsities.
Millennials’ Reading Behavior in the Digital Age: A Case Study of Pakistani University Students

This article uncovers the reading trends of Millennials living in Pakistan by investigating their reading behavior within the digital paradigm. A cross-sectional survey-based quantitative research design was adopted. Masters students (16 years education) from the Higher Education Commission (HEC) recognized universities of Lahore, Pakistan was the study population. A total of 515 masters’ level students from 7 universities participated in the survey. The participants were recruited by employing a two-stage stratified purposive total population sampling technique. The study findings confirmed that despite their preferences for print material, Millennials were using electronic material for reading more frequently. However, the study showed that the reading purpose influences the choice of the reading format. Furthermore, the availability of electronic reading content in the public domain and open access contents may be a reason for increased use of e-content, as free websites were the preferred method of millennials for obtaining reading material. Social networking websites and intelligent search engines like “Google” were also in use and play a role in finding the relevant information and reading e-content. The study shows that the digital environment has a significant impact on the reading behavior of individuals, a fact which needs to be considered by academics, practitioners, and the individuals themselves. It is considered a baseline study that opens various potential directions and avenues for future research.