Reviewerly/exHarmony huggingface dataset:
Mpchecker: Use-After-Free Vulnerabilities Protection Based on Multi-Level Pointers

Highly efficient languages, such as C/C++, have low-level control over memory. Due to the lack of validity detection for pointers and garbage collection for memory, developers are responsible for dynamic memory management by explicitly allocating and deallocating memory. However, explicit memory management brings a large number of memory safety-related vulnerabilities, such as use-after-free. The threat of use-after-free vulnerabilities has become more and more serious due to their high level of the severity and quick emergence of the number. In this paper, a dynamic defense system is proposed against use-after-free exploits by introducing an approach based on multi-level pointers that insert intermediate pointers between a heap object and its related pointers. First, the relationship between a heap object to be protected, and the related pointers pointing to it, is established by combing with intermediate pointers. Then, all of the accesses to this object via its related pointers can only be achieved through these intermediate pointers. Finally, to prevent the dangling pointers from being dereferenced to this object, all the intermediate pointers related to this object are invalidated when it is freed so that any access to a freed object can be prevented due to the invalidated intermediate pointers. The prototype system MPChecker is implemented, which can prevent use-after-free exploits for C/C++ multi-threaded programs. Compared with the related methods, MPChecker can protect pointers that are copied in a type-unsafe way from being de-referenced to freed objects. In addition, it can also defend against dangling pointers located on the whole memory, including the stack, the heap, and global memory, rather than the heap only. The defense capability is proved by protecting against two exploits to a real-world program, comparing the support of type-unsafe copy with a self-written program. The performance evaluation of MPChecker with some benchmarks, multi-threaded programs, and real-world programs, shows its comparable efficiency.
Automatic classification of linear modulations using multiple receivers in the presence of symbol timing uncertainty

Modulation classification of signals collected from multiple receivers over additive white Gaussian noise channels is considered. It is assumed that the channel between the transmitter and each receiver is subject to unknown flat block fading and symbol timing uncertainty. Following the hybrid likelihood framework, the likelihood function of the waveforms observed at the receivers is marginalised over the distribution of the transmitted symbol sequence. Then, the parameter estimates obtained by the particle swarm optimisation technique are substituted into the average likelihood expression in order to compute the test statistic for each assumed modulation scheme. A classification decision is declared in favour of the modulation scheme with the highest overall likelihood score. Numerical examples are provided to evaluate the performance of the proposed classifier as a function of the average received signal-to-noise ratio for different number of receivers and varying quality of the initial parameter estimates.
Internet, Apps, and Tweets: Enhancing Clinical Learning Through Just-in-Time Training

Students work in fast-paced environments that do not allow them the time to thumb through books for needed clinical information. At times such as these, just-in-time training using mobile technology comes to the rescue. Apps, tweets, and the Internet are all in the hands of nursing students and can decrease their cognitive load while enhancing clinical learning.
Personality-Aware Collaborative Learning: Models and Explanations

Personality traits have been demonstrated as one of the effective human factors in the process of decision making. Personality-aware recommendation models have been built for different applications. However, the models and research for educations are still under investigation. In this paper, we utilize the educational learning as a case study, exploit and summarize different approaches which take advantage of personality traits in collaborative personalized recommendations. Furthermore, we extend the existing personality-aware recommendation models and propose two other alternative recommendation algorithms which can utilize the personality traits. We perform empirical comparisons and studies over an educational data set, and explain the effects of personality from the perspective of the characteristics of each algorithm, in order to discover more insights in the data and models.
Prototype of the intraoral dental scanner based on a laser scanning system

Peculiarities of volumetric models construction of the dentitions and individual teeth using the monochromatic light are discussed. Numerical representation of the sensing setup comprising a rotatable objective table is deduced. Computation outputs using the mathematical representation of the described setup are presented. Real-task performance of the developed laser scanning technique is confirmed.
Image resizing using fuzzy inferences

Here, the authors present a fuzzy-based approach for image resizing. The authors introduce a new approach for improving the gradient map and saliency map. Authors’ approach first constructs three different sizes of structural elements, which are used for the operation of close–open filtering to process the input image to obtain three smooth images. Then, the authors use edge detection to process these images, and merge them to obtain an improved gradient map. Besides, a saliency map of the input image is calculated. The authors use hedges in fuzzy logic to strengthen the values of the significant pixels and reduce the values of the background pixels. Hence, the authors can obtain an improved saliency map. After that, the authors introduce a technique to generate a weighted map and then to obtain weighted gradient and saliency maps. Finally, the authors use fuzzy-based approach combining the weighted gradient and saliency maps to obtain an importance map. The map is used when the authors use the seam carving to adjust image size. Experimental results show authors’ approach using the importance map can produce better image resizing results.
Modeling User Actions in Job Search

Users of online job search tools interact with result pages in three different ways: via impressions, via clicks, and via applications. We investigate the relationship between these three kinds of interaction using logs provided by Seek.com , an Australian-based job search service. Our focus is on understanding the extent to which the three interaction types can be used to predict each other. In particular we examine models for inferring impressions from clicks, thereby providing system designers with new options for evaluating search result pages.
A Distributed Brain Storm Optimization for Numerical Optimization and Graph Planarization

Brain storm optimization is a newly proposed meta-heuristic that has shown great success in many applications. However, it still suffers from slow convergence speed and population premature problems. To address these issues, this paper proposes a distributed brain storm optimization algorithm, namely DBSO, for both continuous and discrete optimization applications. In DBSO, problem solutions are located on several isolated groups, and they communicate with each other by a leader and literal learning strategies. By doing so, both exploitation and exploration abilities of the algorithm can be greatly enhanced. Experiments are conducted based on a set of continuous numerical benchmark functions and discrete graph planarization instances. The extensive experiments and statistical analysis indicate that DBSO can perform better or competitive results than other methods in terms of solution accuracy and convergence speed.
Adaptively Single-Key Secure Constrained PRFs for $$\mathrm {NC}^1$$

We present a construction of an adaptively single-key secure constrained PRF (CPRF) for $$\mathbf {NC}^1$$ assuming the existence of indistinguishability obfuscation (IO) and the subgroup hiding assumption over a (pairing-free) composite order group. This is the first construction of such a CPRF in the standard model without relying on a complexity leveraging argument. To achieve this, we first introduce the notion of partitionable CPRF, which is a CPRF accommodated with partitioning techniques and combine it with shadow copy techniques often used in the dual system encryption methodology. We present a construction of partitionable CPRF for $$\mathbf {NC}^1$$ based on IO and the subgroup hiding assumption over a (pairing-free) group. We finally prove that an adaptively single-key secure CPRF for $$\mathbf {NC}^1$$ can be obtained from a partitionable CPRF for $$\mathbf {NC}^1$$ and IO.
A Novel Algorithm for Automatic Text Summarization System Using Lexical Chain

In the field of text classification and information retrieval, the process of text summarization has always been an important aspect. It decreases the size of text and preserves its information content by providing a shorter illustration. This illustration has the major portion of data, i.e., the most vital information and it is no longer than the half of source data. In addition, it is the best solution for information overloading problem as we do not have to scan through each line of long length documents and still receive the foremost important information. For the betterment of this process, we have proposed an algorithm utilizing lexical chain calculation and it is implemented using Eclipse Java Development Tool, enterprise edition for web developers. Along with WordNet API, this method also included the nouns and proper nouns in the computation of lexical chains. MAX tagger is used for part-of-speech tagging and statistical calculations. By the execution of proposed methodology, it is clear that it is a better approach which resulted in better output in terms of (i) Execution time, as compared to the existing algorithm; (ii) Improved matching of words between the human-generated summary and proposed algorithm-generated summary; (iii) Better recall, that are commonly used criteria for summary evaluation.
Towards fast and parameter-independent support vector data description for image and video segmentation

Machine learning has become a pillar of today’s expert and intelligent systems where a special attention has been drawn to unsupervised methods. Support vector data description is one of the most interesting methods proposed in this context that addresses two main challenges: the absence of ground truth labels and the presence of outliers in data. It is well known that the ultimate problem has a great impact on a system performance since it typically prevents and limits its generalization and hence its failure. Even though the strengths of the Support vector data description method, its application into multi class and large scale context, as computer vision systems, raises bottleneck issues due to its runtime complexity and mono-class context application. We propose, in this paper, an approach inspired from the movement behavior of particle swarm and relies on the use of core set paradigm to overcome the aforementioned drawbacks of Support vector data description method. Indeed, since the proposed method uses some parameters corresponding to kernel function and swarm’s movement formulation, we present and study in this paper some strategies to ensure a dynamic adaptation of these parameters. The main qualities of the proposed method is that it provides a fast scheme for clustering a set of points without neither a prior knowledge on the number of naturally occurring cluster in the data nor an assumption on clusters shapes. A quantitative and comparative performance assessment is carried out over the Berkeley and BuffaloXiph datasets. The results witness the efficiency of the method in terms of both classification accuracy and computational performance. These quantitative assessments reinforce the significance as well as the importance of embedding the proposed method in other intelligent systems application areas.
TargetFinder

With the proliferation of IoT cameras, it is possible to use crowd-sourced videos to help find interested targets (e.g., crime suspect, lost child, lost vehicle, etc.) on demand. Due to the ubiquity of IoT cameras such as dash mounted cameras and phone camera, the crowdsourced videos have much better spatial coverage compared to only using surveillance cameras, and thus can significantly improve the effectiveness of target search. However, this may raise privacy concerns when workers (owners of IoT cameras) are provided with photos of the target. Also, the videos captured by the workers may be misused to track bystanders. To address this problem, we design and implement TargetFinder, a privacy preserving system for target search through IoT cameras. By exploiting homo-morphic encryption techniques, the server can search for the target on encrypted information. We also propose techniques to allow the requester (e.g., the police) to receive images that include the target, while all other captured images of the bystanders are not revealed. Moreover, the target's face image is not revealed to the server and the participating workers. Due to the high computation overhead of the cryptographic primitives, we develop optimization techniques in order to run our privacy preserving protocol on mobile devices. A real-world demo and extensive evaluations demonstrate the effectiveness of TargetFinder.
VPS-SDN

Cloud datacenters resource request development have altered resource allocation and management efficiency, presenting a pertinent research topic. Hence, we innovatively outline Virtual Physical Switch-Software Defined Network (VPS-SDN), an application-aware resource allocation method, an innovative model tenants portraying Virtual Datacenter (VDC) network requirements, plus network framework and uplink and downlink bandwidth. Contrasting hose abstraction, VPS-SDN engenders models resembling physical network, neither the TAG abstraction modeling the actual pattern of communication of applications. The VPS-SDN model leverage per-tenant network structure and bandwidth requirements concisely and flexibly depicting actual networks. VPS-SDN engenders a heuristic switch-centered Virtual Network (vNetwork) to Physical Network (pNetwork) mapping diagram, permitting tenants to run their applications using cloud networking to simulate an enterprise environment cloud datacenters, thereby promoting comparable outcomes. Simulation results show that our proposed scheme achieves high network utilization and enable the substrate network to satisfy much larger virtual networks.
Decoding Homomorphically Encrypted Flac Audio without Decryption

Homomorphic Encryption (HE) allows processing cipher-text data, but it is a challenge to enable complex methods such as multimedia decompression in the HE domain. In this paper, we propose a novel scheme to enable FLAC (Free Lossless Audio Codec) decompression in the HE domain. FLAC applies linear prediction to predict the current sample and Golomb coding to encode residuals. FLAC decoding relies heavily on dynamic controls that HE does not support due to unknown values of control variables after encryption. Our scheme regularizes dynamic controls in FLAC decoding with static controls by calculating an encrypted matching bit for each possible value of a control variable and producing candidate results as if it were a match. The summation of each possible value’s candidate results multiplied by its matching bit is equivalent to selecting the results of the matched control value. Our FLAC decoding scheme enables Single-Instruction Multiple-Data (SIMD): multiple (e.g., 256) plaintexts are packed and encrypted into a single ciphertext, and decoding one encrypted frame corresponds to decoding multiple plaintext frames. Our scheme is applicable to other audio compression standards based on similar technologies. Experimental results are also reported.
The Kinematics of Containment for N-Dimensional Ellipsoids

Knowing the set of allowable motions of a convex body moving inside a slightly larger one is useful in applications such as automated assembly mechanisms, robot motion planning, etc. The theory behind this is called the “kinematics of containment (KC).” In this article, we show that when the convex bodies are ellipsoids, lower bounds of the KC volume can be constructed using simple convex constraint equations. In particular, we study a subset of the allowable motions for an n-dimensional ellipsoid being fully contained in another. The problem is addressed in both algebraic and geometric ways, and two lower bounds of the allowable motions are proposed. Containment checking processes for a specific configuration of the moving ellipsoid and the calculations of the volume of the proposed lower bounds in the configuration space (C-space) are introduced. Examples for the proposed lower bounds in the 2D and 3D Euclidean space are implemented, and the corresponding volumes in C-space are compared with different shapes of the ellipsoids. Practical applications using the proposed theories in motion planning problems and parts-handling mechanisms are then discussed.
Tangible interface for shape modeling by block assembly of wirelessly connected blocks

Abstract Tangible computer-aided design (CAD) that can generate and manipulate 3D models more efficiently than conventional modeling interfaces is a new interface technology between users and computers through tangible physical devices. Because the majority of tangible CAD systems use cameras or projectors, 3D models of complex structures with visual occlusion cannot be generated. In this work, we propose and develop a system that allows users to create complex shapes by assembling wireless blocks. By allowing more freedom in the orientations of blocks, models created by the assembly operation can be used to generate true 3D shapes compared to the 2.5D shapes resulting from previous research. The physical blocks of the proposed system include infrared proximity sensors, inertial magnetic units, Zigbee modules, and Arduino microcontrollers. The status of the block assembly is transmitted to the workstation wirelessly by the Zigbee. The workstation collects the sensor data of the blocks and generates and renders 3D models of the physically assembled blocks. The concept of the proposed tangible CAD system can be used in various application areas where simple, easy-to-use shape modeling interfaces for creating models by assembling small blocks are sufficient. The education and gaming industries are good examples of where this concept can be applied easily. Highlights A 3D shape modeling system through physical block assembly with sensors is developed. Using wireless communication, a degree of freedom of block assembly is increased. The rotation of the block was calculated through the 9-axis sensor and applied to the rendered 3D model. Through efficient data processing, the problems of real-time wireless communication environment are overcome. Users who are not trained in CAD tools can easily create 3D shape models.
Algorithmic cache of sorted tables for feature selection

Feature selection is a mechanism used in Machine Learning to reduce the complexity and improve the speed of the learning process by using a subset of features from the data set. There are several measures which are used to assign a score to a subset of features and, therefore, are able to compare them and decide which one is the best. The bottle neck of consistence measures is having the information of the different examples available to check their class by groups. To handle it, this paper proposes the concept of an algorithmic cache, which stores sorted tables to speed up the access to example information. The work carries out an empirical study using 34 real-world data sets and four representative search strategies combined with different table caching strategies and three sorting methods. The experiments calculate four different consistency and one information measures, showing that the proposed sorted tables cache reduces computation time and it is competitive with hash table structures.
Smartphones enhance the management of learning processes in higher education

Nowadays, students of this generation, specifically seen within higher education, are progressively depending on the means of technology to navigate through their daily lives. A wide variety of smart mobile and tablets devices are used, as digital instruments by university students to support ubiquitous accesses to sources of information and data. Consequently, facilitate the management of students' administrative requirements, e-learning platforms, personal interactions and communication between themselves and their professors.
A general framework for multi-label learning towards class correlations and class imbalance

In multi-label classification settings, one of the most common problems is the massive label output space. To alleviate this, some methods opt to exploit label correlations to reduce the output space during prediction. However, these methods sacrifice efficiency or ignore global label correlations. In addition, label imbalances are another problem that is prevalent in multi-label classification. Current methods of correcting for imbalance oftentimes use single-label methods, which fail to consider label correlations. In this paper, we introduce general frameworks that incorporate topic modeling to seamlessly address both problems. We show that these frameworks can allow even the most naïve methods, such as Binary Relevance, to perform similarly to state-of-the-art methods. Furthermore, we show that our frameworks can also adapt state-of-the-art methods to perform better than the methods by themselves.
Photovoltaic Panel Efficiency Estimation with Artificial Neural Networks: Samples of Adiyaman, Malatya, and Sanliurfa

The amount of electric energy produced by photovoltaic panels depends on air temperature, humidity rate, wind velocity, photovoltaic module temperature, and particularly solar radiation. Being aware of the behaviour patterns of the panels to be used in project and planning works regarding photovoltaic applications will set forth a realistic expense form; therefore, erroneous investments will be avoided, and the country budget will benefit from added value. The power ratings obtained from the photovoltaic panels and the environmental factors were measured and recorded for a year by the measurement stations established in three diverse regions (Adiyaman-Malatya-Sanliurfa). In the developed artificial neural network models, the estimation accuracy was 99.94%. Furthermore, by taking the data of the General Directorate of Meteorology as a reference, models of artificial neural networks were developed using the data from Adiyaman province for training; by using Malatya and Sanliurfa as test data, 99.57% estimation accuracy was achieved. With the artificial neural network models developed as a result of the study, the energy efficiency for the photovoltaic energy systems desired to be established by using meteorological parameters such as temperature, humidity, wind, and solar radiation of various regions anywhere in the world can be estimated with high accuracy.
A hybrid genetic algorithm to reduce the radiation dose in CR images

The quality of computed radiography (CR) images typically relate to patient radiation exposure. The lower the X-ray dose exposure, the higher the level of inherent noise in the CR images. In this work, we address the noise reduction problem by using an estimation of the standard deviation in CR images as an objective function to minimize. We propose a hybrid genetic algorithm for this aim, which produces improved versions of CR images. We also applied an edge-detection method based on the Canny algorithm to preserve the edges of the original CR images. We executed our proposed algorithm for CR images obtained under different radiation exposures. Experimental results show that our solution improves lower radiation CR images reaching a quality as similar to those with higher radiation doses.