Reviewerly/exHarmony huggingface dataset:
A New Outsourced Data Deletion Scheme with Public Verifiability

In the cloud storage, the data owner will lose the direct control over his outsourced data, and all the operations over the outsourced data may be executed by corresponding remote cloud server, such as cloud data deletion operation. However, the selfish cloud server might maliciously reserve the data copy for financial interests, and deliberately send a false deletion result to cheat the data owner. In this paper, we design an IBF-based publicly verifiable cloud data deletion scheme. The proposed scheme enables the cloud server to delete the data and return a proof. Then the data owner can check the deletion result by verifying the returned deletion proof. Besides, the proposed scheme can realize public verifiability by applying the primitive of invertible bloom filter. Finally, we can prove that our proposed protocol not only can reach the expected security properties but also can achieve the practicality and high-efficiency.
Towards Considering User Privacy Preferences in Smart Water Management

Adaptive systems are based on user preferences and needs, while sensing and context awareness are also essential features. Recently, the notion of smart homes requires the collection of different data from users, in order to provide them with a personalized user experience. In this context, smart water management can facilitate activities within the smart home. User privacy protection is vital in this environment to provide adaptive data collection and usage. In this paper, we introduce our vision towards user privacy protection in this setting by specifying and considering user privacy preferences, and we present our ongoing work and its initial results. The current work is being conducted in the framework of the TAMIT research project. This initial work will serve as a basis for the integration of user privacy management within TAMIT and can be a useful source of information for the management of user privacy preferences in similar platforms.
An Efficient Architecture to Ensure Data Integrity in ERP Systems

In digital era, Cloud based web solutions plays a vital role in private sectors as well as the government sectors namely CSR (Common Service Centers), UIDAI etc., spreads widely around the globe. The critical issue behind this services are ensuring the data quality and the integrity of the data in the database. As far as the web services are concerned, the accounting and the payment related web services focusses on such integrity issues as the money matters in it. The integrity of the data can be ensuring by the strict audit policy. The audit policy incorporates certain auditing tools to assure the correctness in the data through which the integrity is ensured. Each and every data auditing technique have their own structure to audit the data. The algorithm behind the auditing techniques vary based on the way of verifying the data in the database. In this research, focuses on the query based data auditing algorithm implementing in CampusStack on top of the existing data auditing algorithms for cloud data auditing which provides the single output that fulfills the integrity and privacy issues. The time complexity and the space complexity has to be analyzed with the existing auditing techniques as well as the integrity has to be ensured.
Universality of Logarithmic Loss in Fixed-Length Lossy Compression

We established a universality of logarithmic loss over a finite alphabet as a distortion criterion in fixed-length lossy compression. For any fixed-length lossy-compression problem under an arbitrary distortion criterion, we show that there is an equivalent lossy-compression problem under logarithmic loss. The equivalence is in the strong sense that we show that finding good schemes in corresponding lossy compression under logarithmic loss is essentially equivalent to finding good schemes in the original problem. This equivalence relation also provides an algebraic structure in the reconstruction alphabet, which allows us to use known techniques in the clustering literature. Furthermore, our result naturally suggests a new clustering algorithm in the categorical data-clustering problem.
On the Privacy of Matrix Masking-Based Verifiable (Outsourced) Computation

Privacy-preserving verifiable (outsourced) computation (PPVC) is a useful technique for a resource-constrained client to outsource computationally heavy but sensitive tasks to a computationally powerful but untrusted worker and to obtain expected correct results from the worker. In this paper, we analyze the privacy property of three matrix masking-based PPVC protocols, which have recently been published in IEEE Transactions on Cloud Computing [2] , [3] . To do this, we present a formal definition of a privacy model for a PPVC protocol (see Definition 1 ), and then prove that neither of those three PPVC protocols holds privacy under this model. We also review the comments by Cao et al. [1] on two of the three protocols and show an issue in their comments.
Simultaneous Clustering and Optimization for Evolving Datasets

Simultaneous clustering and optimization (SCO) has recently drawn much attention due to its wide range of practical applications. Many methods have been previously proposed to solve this problem and obtain the optimal model. However, when a dataset evolves over time, those existing methods have to update the model frequently to guarantee accuracy; such updating is computationally infeasible. In this paper, we propose a new formulation of SCO to handle evolving datasets. Specifically, we propose a new variant of the alternating direction method of multipliers (ADMM) to solve this problem efficiently. The guarantee of model accuracy is analyzed theoretically for two specific tasks: ridge regression and convex clustering. Extensive empirical studies confirm the effectiveness of our method.
Joint Manifold Learning Based Distributed Sensor Fusion of Image and Radio-Frequency Data

This paper presents a joint manifold learning based distributed sensor fusion approach for image and radio frequency (RF) data. A typical scenario includes several objects (with RF emitters), which are observed by a network of platforms with Medium Wavelength Infrared (MWIR) cameras and/or RF Doppler sensors. Based on a joint manifold learning (JML) sensor fusion approach, we propose to design and implement a distributed heterogeneous data fusion approach for improved Detection, Classification, and Identification (DCI) of targets and entities in dynamic environments with constrained communications. We design and implement distributed JML using diffusion and consensus approaches. In our distributed mechanism, we first partition the JML matrices into submatrices for each platform. For every platform, these submatrices represent the mapping from the sensor data to its contribution in the final fused result. Each node processes the local measurements using submatrices and shares the results with a limited number of neighbors. A prototype is constructed from drones, onboard processing capabilities (Intel Next Unit of Computing, NUC), cameras, and radars to demonstrate the proposed distributed data fusion approach. Supportive results are achieved from field tests and compared with centralized JML framework.
A New Ergodic HMM-Based Face Recognition Using DWT and Half of the Face

Dealing with disguises, illumination and expression variations are important and challenging problems in the face recognition area. Considering the axis-symmetrical structure of the face, we propose a face recognition algorithm using the ergodic Hidden Markov Model (HMM) as a classifier and the image of half of the face; while the Discrete Wavelet Transform (DWT) is applied to generate observation vectors. We evaluate the proposed method on AR, Yale and Faces94 face datasets. The results represent the superiority of our method, compared with some other state-of-the-art methods, in terms of recognition rates, computational complexity, and memory consumption.
The Compact ROV with Variable Center of Gravity and its Control

During Remotely Operated vehicles (ROVs) work it is often required surveying shipwrecks and sunken aircrafts and its inner condition as well as different types of narrow underwater tunnels and caves that requires wide pitch and roll angles of the vehicle to be controlled. A classical ROV has high stability and unable to work in such conditions. Thus, it is necessary to design the new type of high maneuverable ROV that provides effective working within limited space. The new small-sized ROV is presented in the article. The vehicle gives effective motion control within a wide range of pitch and roll orientation. The main feature of the vehicle is the Variable Center of Gravity (VCG) system. The article contains overview of the hydrostatic model for the vehicle with the variable center of gravity. The design of the vehicle and its systems are presented in the article. In-situ ROV poll test in water pool are presented.
Structure learning for relational logistic regression: an ensemble approach

We consider the problem of learning Relational Logistic Regression (RLR). Unlike standard logistic regression, the features of RLR are first-order formulae with associated weight vectors instead of scalar weights. We turn the problem of learning RLR to learning these vector-weighted formulae and develop a learning algorithm based on the recently successful functional-gradient boosting methods for probabilistic logic models. We derive the functional gradients and show how weights can be learned simultaneously in an efficient manner. Our empirical evaluation on standard data sets demonstrates the superiority of our approach over other methods for learning RLR.
Bayesian Diagnosis Tracing: Application of Procedural Misconceptions in Knowledge Tracing

Bayesian diagnosis tracing model (BDT) replaces the generic “wrong” response in the classical Bayesian knowledge tracing model (BKT) with a vector of procedure misconceptions. Using a novel dataset with actual student responses, this paper shows the BDT model has better interpretability of the latent factor and minor improvement in out-sample predictability in some specification than the BKT model.
Natural Language-Based Knowledge Extraction in Healthcare Domain

There is a growing amount of data in the databases of hospitals. These data could be exploited to alleviate the decision-making process of hospital managers, physicians and researchers. However, these types of end-users often lack the expertise necessary for extracting those data from the database. Several approaches exist in the field of how to allow non-programmers writing queries in a convenient manner, but none of them has yet reached fully satisfactory results. This paper sketches a solution to this problem by introducing means for writing queries in a keywords-containing natural language thus alleviating the query writing process for the end-user. Introducing this approach in the knowledge management system of the organization would greatly benefit the domain experts by allowing them to carry out the decision-making process in a more rapid and less erroneous manner.
Image enhancement in embedded devices for internet of things

Summary This paper proposes a new color interpolation method which can be used in embedded devices for IoT system. In this work, we use regression approach for generating and designing filters to restore color image. The filters are designed with four sizes, 5x5 training filter, 7x7 training filter, 9x9 training filter, and 11x11 training filter. The obtained filters are tested in 25 LC dataset to assess the performance. Experimental results inform that the proposed filters provide outstanding performance when they are compared with conventional methods. As compared with the other methods, the proposed filters produce the best average interpolation performance both objectively and visually.
Smooth path planning for redundant robots on collision avoidance

This paper presents a new algorithm for programming secondary tasks for redundant robots under and out of the collision imminence. For the collision avoidance, the new algorithm uses the concept of Assur virtual chains, together with interpolations based on cubic Bézier polynomials, taking advantage of its properties in terms of continuities, for smoothing the transitions between tasks. Out of collision condition, the redundancy is addressed by the method of Adaptive Extended Jacobian. This set of strategies, acting together, results in a stable algorithm and in smooth trajectory profiles for redundant robots avoiding collisions. An application using a redundant P3R-planar robot is developed, and graphical comparative analyzes are presented.
Unintended Consequences of Wearable Fitness Devices in Corporate Wellness Programs

In an effort to reduce insurance costs and improve the health and well-being of employees, corporations are using wearable fitness trackers in tandem with Corporate Wellness Programs. Research shows that CWP improve employee fitness levels, job satisfaction, and well-being. While prior studies indicate that wearable fitness trackers improve user well-being, the potential negative impacts or unintended consequences of introducing these devices in the workplace needs additional investigation. We conduct a case study at a company using wearable fitness trackers in its wellness program. Our initial findings suggest that introducing wearable fitness trackers to wellness programs has a negative relationship with job satisfaction and employee well-being. These findings have implications for wearable technology research and for practitioners utilizing wearables in the workplace.
Facial Attribute Editing using Semantic Segmentation

Recently, great success has been made in facial attribute editing by using adversarial learning. However, most existing studies focus on how to generate an image with high fidelity while ignore where to manipulate. Hence, in this paper, generative adversarial network with semantic masks (SM-GAN), a new framework for accurate facial attribute editing is proposed. Specifically, the proposed framework is constructed by combining GAN with semantic segmentation network. Here, the semantic segmentation network provides the mask with respect to attribute-related region so that the editing only occurs in expected areas. Qualitative and quantitative experiments on public dataset CelebA demonstrate that the proposed approach can not only generate realistic attribute editing results, but also preserve attribute-irrelevant areas unchanged.
A 3D channel body interpretation via multiple attributes and supervoxel graph cut

Channels have always been vital geologic features in the exploration of hydrocarbon reservoirs, which makes the interpretation of channels an important task. Many different seismic attributes have been proposed to help the process of channel interpretation. A single seismic attribute could not fully and accurately reflect the geologic structure and edge details of a channel. Therefore, interpretation on a single attribute causes inaccurate segmentation. A 3D channel body interpretation method based on multiple attributes and supervoxel graph cut is applied in this paper, which identifies and segments the channel geologic body with fuzzy boundaries, poor continuity, or even data loss more accurately. First, a nonlinear dimensionality reduction method (locally linear embedding with geodesic distance) is applied to fuse a variety of seismic attributes to make channels clearer. Then, a graph-cut method based on the super geologic voxel is introduced, which reduces the computational complexity of segmentation and generates supervoxels more fitted to the edge of the channel body. Finally, a smooth 3D surface of the channel is obtained through the isosurface extraction. We use the data of a work area in northwest China and Parihaka-3D to evaluate the performance of our method. Our results show that, compared with other methods, the information provided by the fusion attribute is more complete, and the edge continuity of the channel is improved. The 3D channel bodies obtained by our method are clear and continuous. In the case of a complex channel body, our method can also work well.
Survey on Memory Corruption Mitigation

Memory corruption is one of the oldest problems in computer security. This problem has existed for many years and a number of solutions have been proposed, but memory corruption attacks continue to pose a serious threat. From the attacker's point of view, this paper corresponds the memory corruption mitigation with the attacker's behavior. It mainly introduces the memory corruption mitigation on Windows and Linux, analyses the limitations of each protection mechanism, and briefly describes bypassing techniques. Besides, we identify some concepts in memory corruption prevention, and provide suggestions on improving the existing mechanisms.
Exploiting the Spam Correlations in Scalable Online Social Spam Detection

The huge amount of social spam from large-scale social networks has been a common phenomenon in the contemporary world. The majority of former research focused on improving the efficiency of identifying social spam from a limited size of data in the algorithm side, however, few of them target on the data correlations among large-scale distributed social spam and utilize the benefits from the system side. In this paper, we propose a new scalable system, named SpamHunter, which can utilize the spam correlations from distributed data sources to enhance the performance of large-scale social spam detection. It identifies the correlated social spam from various distributed servers/sources through DHT-based hierarchical functional trees. These functional trees act as bridges among data servers/sources to aggregate, exchange, and communicate the updated and newly emerging social spam with each other. Furthermore, by processing the online social logs instantly, it allows online streaming data to be processed in a distributed manner, which reduces the online detection latency and avoids the inefficiency of outdated spam posts. Our experimental results with real-world social logs demonstrate that SpamHunter reaches 95% F1 score in the spam detection, achieves high efficiency in scaling to a large amount of data servers with low latency.
Discrete and Continuous Logistic p-Fuzzy Models

This manuscript investigates the capacity of the so-called p-fuzzy systems to model both discrete and continuous dynamic systems. Recall that one can apply a p- fuzzy system in order to combine fuzzy rule-based systems (FRBSs) and classical numerical methods to simulate the dynamics of an evolutionary system. Here, we focus on the well-known discrete and continuous Logistic models that can be used to represent several problems of Biomathematics such as dynamic population. We conduct a series of simulations using both continuous and discrete models for several growth rates. We obtain qualitative and quantitative results similar to the analytical solutions, including bifurcations in the discrete case.
Triggering Ontology Alignment Revalidation Based on the Degree of Change Significance on the Ontology Concept Level

Following a common definition, ontologies can be seen as a formal specification of a conceptualisation. However, it cannot be expected that there will be no changes applied to them. Obviously, any application build on top of some ontology needs to adjust to the introduced alterations. For example, a mapping designated between two ontologies (also called an ontology alignment) is valid only if participating ontologies are fixed. In this paper we present a function that can indicate, whether or not, the aforementioned alignment needs updating, in order to follow modifications done to participating ontologies, and to avoid mapping them again from scratch.
Improving the Dense Trajectories Approach Towards Efficient Recognition of Simple Human Activities

Action recognition has been highlighted by its implications on issues of security and integrity. However, although the dense trajectory method has achieved outstanding results, its implementation is associated with a high computational cost, creating the need for faster methods if we want to use these methods to protect our safety and integrity in real-time systems. In this work, we explore the subject keypoints estimation to create what we call action recognition by key trajectories, which is a faster version of the dense trajectories approach. We tested our proposal on the KTH and UCF11 datasets and got as a result that our proposal recognizes subject actions in videos eight times faster with comparable results to the dense trajectories approach. i.e., 94.20% for the Key Trajectories compared to 95.65% of the dense trajectories in the KTH dataset and 80.66% for the Key Trajectories compared to 84.08% of the dense trajectories in the UCF11.
A Generalized Pedagogical Framework for Creating Mixed-Mode Role-Play in Multi-User Virtual Environments

Science students face considerable challenges when attempting to absorb and visualize abstract concepts presented to them in the classroom; educators use a number of methods to support their students in this regard. Our focus is on two such methods currently being used by educators: role-play and 3D simulation; these are designed to immerse the student in the learning process. Both methods attempt to make the invisible, visible. However, the literature demonstrates a lack of research, in particular, into the effectiveness of learning through structured role-play and the impact of this method on students using Multi-User Virtual Environments (MUVEs). This paper exhibits the effects of an interactive role-play learning activity, supported within a MUVE, on the learning process. The activity is generated by a data-driven framework that acts as a template for the creation of the role-play the role-play is generated automatically from pre-defined data stored in a database. The framework is generalizable, which means that it can be used for other role-play subjects by re-configuring the data in the database. This paper aims to demonstrate the advantages of the ‘immersion’ that Virtual Reality (VR) can provide to its users via the means of allowing them to take on the role of an object involved in a message-passing system. This object will be one which is collaborative with other objects in a role-play activity. The role-play activity will be generated by a data-driven pedagogical framework called MMRP.
Employer Preferences for Cybersecurity Skills among Information Systems Graduates

Recognizing the global need for cybersecurity professionals and shedding light on the employers' preference of skills give educators the opportunity to improve their process to prepare future generations for the workforce. The goal of this paper is to acquire a collection of skills employers value when hiring for a new cybersecurity position. Our research plan is split into three phases. First, we will develop an initial list of cybersecurity skills by using a systematic literature review to assess what past research has found. Next, we will validate the importance of these skills through a ratings-based survey of employers. Finally, we will refine and prioritize the validated skills using two preference capturing studies - rank order survey and conjoint analysis. Discussion and implications for future research and academic departments in cybersecurity are provided.