Reviewerly/exHarmony huggingface dataset:
MEER: Online Estimation of Optimal Memory Reservations for Long Lived Containers in In-Memory Cluster Computing

Modern in-memory data-intensive computing systems like Spark create long-lived containers to execute diverse types of applications. They rely on a cluster manager like YARN or Mesos to perform resource allocation to the containers. The cluster manager or scheduler requires users of the containers to reserve resources beforehand. It is a challenge to estimate just right amounts of memory to run the applications before execution, so as to avoid over-or under-provisioning of memory space. We discover a general property of memory reservation elasticity, which allows applications to run with a reservation limit smaller than they would ideally need while only paying a moderate performance penalty. Based on the property, we designed a system, namely MEER, which performs online estimation of minimum necessary amount of memory limit that achieves nearly optimal performance. We referred to it as optimal reservation, which divides memory over-provisioning from under-provisioning. It is non-trivial to efficiently estimate optimal reservations on line through one step without runtime history. MEER uses a two-step approach to dealing with the challenge: 1) Do robust profiling and probability density analysis of applications' memory footprints in two pilot runs. By using confidence levels for the prediction, we reduce the negative effects of container footprints' randomness and achieve a highly accurate online initial estimation (over 80% accuracy) of optimal reservation. 2) By exploiting a self-decay property of the analytical results, MEER adaptively performs iterative search based on a feed-back control mechanism over subsequent recurring executions. We implemented MEER atop of YARN and evaluated the prototype by running 15 benchmark workloads on a 16-node local cluster. Evaluation results show that it achieves an average accuracy of more than 95%. By deploying MEER on schedulers and allocating memory according to the optimal reservations, one could improve cluster memory utilization by about 40%. It reduces individual application execution time by 2 to 6 times on average compared to the state-of-the-art approaches. A 90 times peak speedup for PageRank in comparison with the default Spark/Yarn is observed.
Generating Off-line and On-line Forgeries from On-line Genuine Signatures

Improving the ability of forgery detection in Automatic Signature Verifiers requires databases with an extensive and realistic set of skilled forgeries. However, collecting skilled professional copies is not always possible in many studies, and most the publicly available databases are limited from that point of view. Recently, the Kinematic Theory of Rapid Movements has been used to improve the skillfulness from a given on-line skilled forgery. This paper proposes a method based on the Kinematic Theory of Rapid Movements to generate both on-line and off-line skilled synthetic forgeries from a single on-line genuine specimen. The method consists of three steps: 1. The genuine on-line signature is decomposed as a sum of overlapped lognormal strokes, 2. The trajectory is modified by distorting the virtual target points and the lognormal parameters, and 3. A new velocity profile is automatically synthetized from the 8-connected modified trajectory. Experiments with multiple on-line and off-line signature verifiers show that this method can generate skilled forgeries harder to detect.
A lightweight efficient chaos‐based cryptosystem for constrained‐node networks

Summary Interconnected sensor and actuator objects creating Constrained‐Node Networks (CNN) are among the most prominent technologies of the Internet of things. Deployed to monitor our lives, surroundings, and critical infrastructure, CNN communications must be strongly secured in order to prevent devastating unauthorized access to critical information. This, however, faces severe challenges imposed by CNN constraints in terms of energy, memory, and computational power. To address such conflicting requirements, this paper presents a chaos‐based efficient lightweight secure communication system for CNNs. The proposed cryptosystem performs optimized confusion‐diffusion operations at the byte level allowing it to be fully implemented on an 8‐bit microcontroller. Obtained results from extensive experiments at both statistical and implementation levels confirm the robustness of the proposed cryptosystem against a multitude of attacks. Additionally, the evaluated encryption/decryption speeds along with the low memory and energy consumption grant the proposed cryptosystem attractive lightweight aspects enabling its deployment in very constrained CNN applications having soft real‐time requirements.
Image concept detection in imbalanced datasets with ensemble of convolutional neural networks

Concept detection for a collection of images is an important topic and has recently been an emerging area of studies. Facing an imbalanced dataset is a great challenge in concept detection that has not yet been adequately investigated. To cope with this challenge, this paper proposes an image conce pt detection system based on the Convolutional Neural Network (CNN) method. The proposed method consists of three stages. At the first stage, a new algorithm is proposed to enhance the batch sampling in the CNN. At the second stage, some augmentation methods are used to improve learning process in CNN and at the final stage, a new ensemble of balanced convolutional neural network is presented in order to detect the concepts of images. Using Caltech-101 image dataset, the experimental results demonstrate the effectiveness of the proposed framework for concept detection in imbalanced datasets.
Markov Model of Web Services for Their Performance Based on Phase-Type Expansion

Developers usually combine various existing atomic services into composite services that satisfy complex needs. There are mainly four basic structures in composite Web service which include sequence, branch, parallelism and loop. Besides functional issues, non-functional issues such as performance play an important role in evaluating services. Therefore, before the service deployment, it is necessary to check whether performance will meet its service-level agreement (SLA). In this paper, we use a novel approach to model an expanded homogeneous Continuous Time Markov Chain (HCTMC) of basic Web services structures by Phase-Type (PH) fitting. It is based on observations of the execution times of atomic service. Analysis of experiments results on real Web services verifies the validity of our method.
Surgical Process Identification System using Machine Learning in Awake Surgery for Brain Tumor

During surgery for brain tumor, expert surgeons consider maximum brain tumor removal and minimum postoperative neuroglial complications. At this brain tumor resection, the surgeon resects based on his knowledge and experience, the surgical process, work contents, and duration of surgery vary by cases. It is difficult for the young surgeons and surgical staffs to understand the surgical process involved. Visualization of the surgical process is an effective tool for aiding the understanding among the surgical staff. This paper presents a surgical identification system using intra-operative information and machine learning. We extract surgical process features using navigation system's log, MR images and microscope video. Then, the surgical processes are identified using Hierarchical Hidden Markov Model. The method has been evaluated using past log data (navigation system's log, MR images and microscope video). The accuracy of the identified surgical processes was 84% in 12 processes. This result indicated that the surgical process identification error is a few minutes, high revel accuracy. In addition, this result is a possibility to support understanding of surgical processes for young surgeons and surgical staffs.
Building Trust in Container Environment

Container technology is an emerging virtualization technology that is more efficient and lightweight than virtual machines. This technology is becoming increasingly popular. However, containers are vulnerable to attackers due to various security issues. It is necessary to build trust between users and their containers, as well as between a remote party and the container in the untrusted container environment. The existing trusted computing technologies build a trust chain from hardware to the running operating system. In this paper, we extend the trust chain to containers and build trust in the container environment. We first boot the computer to a trusted operating system. The trusted OS then verifies programs running on the OS to improve security. We design the well-formed signature list to help users to authorize container executables and modify the Linux kernel to verify executables running in containers. Therefore, the user is able to control what programs can run in his containers and trust the containers. Our approach generates a measurement list and creates a vTPM for each container. A remote party can request the measurement list based on our container state challenge protocol. As a result, a remote party is able to know the container state and decide whether to trust the container. We also implement the executables measurement and verification mechanism and evaluate the performance. The results show the container start delay is no more than 3% of the normal container start time and the overhead to measure and verify executables is no more than 1 us in most cases, which is reasonably efficient.
Improvement of Low-Light Image by Convolutional Neural Network

Many researches have been carried out for enhancing low-light images over the past decades. One of the methods is Retinex theory, where reflectance component is recovered and illumination component is attenuated. Recently, hand-crafted approaches for low-light enhancement have been replaced by artificial neural networks. This paper presents a convolutional neural network that can replace the Retinx-based low-light enhancement method. Experiments carried out on 120 low-light images validated the feasibility of the replacement by producing satisfactory reflectance images.
CFI: Control Flow Integrity or Control Flow Interruption?

Runtime memory vulnerabilities, especially present in widely used languages as C and C++, are exploited by attackers to corrupt code pointers and hijack the execution flow of a program running on a target system to force it to behave abnormally. This is the principle of modern Code Reuse Attacks (CRAs) and of famous attack paradigms as Return-Oriented Programming (ROP) and Jump-Oriented Programming (JOP), which have defeated the previous defenses against malicious code injection such as Data Execution Prevention (DEP). Control-Flow Integrity (CFI) is a promising approach to protect against such runtime attacks. Recently, many CFI solutions have been proposed, with both hardware and software implementations. But how can a defense based on complying with a graph calculated a priori efficiently deal with something unpredictable as exceptions and interrupt requests? The present paper focuses on this dichotomy by analysing some of the CFI-based defenses and showing how the unexpected trigger of an interrupt and the sudden execution of an Interrupt Service Routine (ISR) can circumvent them.
Cryptanalysis of a Protocol for Efficient Sorting on SHE Encrypted Data

Sorting on encrypted data using Somewhat Homomorphic Encryption (SHE) schemes is currently inefficient in practice when the number of elements to be sorted is very large. Hence alternate protocols that can efficiently perform computation and sorting on encrypted data is of interest. Recently, Kesarwani et al. (EDBT 2018) proposed a protocol for efficient sorting on data encrypted using an SHE scheme in a model where one of the two non-colluding servers is holding the decryption key. The encrypted data to be sorted is transformed homomorphically by the first server using a randomly chosen monotonic polynomial with possibly large coefficients, and then the non-colluding server holding the decryption key decrypts, sorts, and conveys back the sorted order to the first server without learning the actual values except possibly for the order. In this work we demonstrate an attack on the above protocol that allows the non-colluding server holding the decryption key to recover the original plaintext inputs (up to a constant difference). Though our attack runs in time exponential in the size of plaintext inputs and degree of the polynomial but polynomial in the size of coefficients, we show that our attack is feasible for 32-bit inputs, hence accounting for several real world scenarios. Of independent interest is our algorithm for recovering the integer inputs (up to a constant difference) by observing only the integer polynomial outputs.
On the Investigation of Wireless Signal Identification Using Spectral Correlation Function and SVMs

Signal identification is an important notion that leads to significant performance improvements for adaptive wireless spectrum access techniques. Besides identifying the modulation types and other features, standard-based identification has also an important place in signal identification domain. In this paper, a generalized identification method which utilizes the outputs of spectral correlation function as the training inputs for the support vector machines to distinguish wireless signals is introduced. The proposed method eliminates the dependence on the distinct features to identify different signals. The method's performance is tested using the measurements taken in the laboratory environment and various wireless signals are successfully distinguished from each other. The comparative performance of the proposed method is also quantified by the classification confusion matrix.
Cover-up: a probabilistic privacy-preserving graph database model

A new graph database model is introduced that allows for an efficient and straightforward privacy-preserving mechanism. A probabilistic graph database model is also proposed, perhaps less suitable for lossless storage, but adapted for the use of statistical analysis that preserves the privacy of the individuals behind the data. Parallels are drawn to concepts in combinatorics such as clique complexes and incidence geometries.
A mechanism based on finger-sliding behavior for designing radial menus

Human behavior about sliding their fingers on touch screens needs to be understood for the design of radial menus. For this purpose, data about the angles of finger sliding in performing radial pointing to eight predefined directions were collected. The optimal angle ranges and boundaries for these directions were then determined by the proposed mechanism. Consistent with previous research, results showed that the deviations of slide angles for the four diagonal directions were larger than that for the four orthogonal directions. Therefore, to reduce overall pointing errors, the angle ranges for the diagonal directions should be wider than that for the orthogonal directions. Based on the proposed mechanism, the total probability of pointing errors could be limited to 0.05%, compared to the error of 2.00% if the eight angle ranges were evenly divided by the radii of a regular octagon. Besides the angles, the lengths and starting points of the slides were reported and discussed along with the oblique effect, judder effect, and screen orientation. Possible applications of the proposed mechanism for dynamic and personalized radial menus are addressed. Further research would be worthwhile to examine the effects of screen orientation and the number of predefined directions on pointing performances. Moreover, extending the research to mid-air sliding gestures would be of interest and value in designing three-dimensional radial menus. The proposed mechanism could effectively enhance the accuracy of the selections through radial menus, and could be applied to the remote controls via smart handheld devices.
Weighted Product Method for Selection of Superior Seeds Catfish in the Clarias Gariepinus Types (Sangkuriang)

Sangkuriang catfish is a famous clarias clan fish form his slick and long body non-scaly, with dorsal fins and a long rear fin, which sometimes merges into the tail fin makes it look like a short eel. Catfish also have additional breathing apparatus in the form of modifications from gill arc. There is a pair of fins that are spines bone that is sharp, on the pectoral fin. Some things to consider in order to select superior seedlings of sangkuriang catfish such as size, weight, color, physical defect, even water conditions, which in reality not all catfish farmers know about it. This decision support system using the Weighted Product (WP) method is one of the methods that use the true value in determining the rating of each alternative on each criterion to determine the conclusion of a problem. Based on the results of testing the Selection Decision Support System of the Superior Broodstock Sangkuriang Catfish has an accuracy rate of 87.5%.
Subliminal Hash Channels

Due to their nature, subliminal channels are mostly regarded as being malicious, but due to recent legislation efforts users’ perception might change. Such channels can be used to subvert digital signature protocols without degrading the security of the underlying primitive. Thus, it is natural to find countermeasures and devise subliminal-free signatures. In this paper we discuss state-of-the-art countermeasures and introduce a generic method to bypass them.
Automatic near-infrared hyperspectral image analysis of copper concentrates

The reflection of light on mineral samples have been widely used to obtain information concerning their composition. In particular, visible and near-infrared reflectance spectrum have offered an inexpensive way to obtain information about their mineralogical composition. In this work, near-infrared hyperspectral reflective images of several mineral samples are obtained and analyzed. The average reflective spectrum of Chalcopyrite (CuFeS2), Pyrite (FeS2), Chalcocite (Cu2S), Covellite (CuS), and Slag (FeO-SiO2) packed into pellets were obtained using a near-infrared hyperspectral camera. In order to analyze copper concentrates, a K-Nearest Neighbor classifier was trained to identify its main components. A 10 fold cross validation approach was used to certify the validity of the classifier. The trained classifier provided the mineralogical spatial distribution of the different components in a concentrate sample. An automatic system controlling all the acquisition and image processing stages provides analysis of the concentrate samples. Further work is underway to include additional minerals and to improve implementation issues such as signal filtering. This is the first step towards the design of a low cost system to provide relevant information about the concentrates feeding copper smelters.
Effective Numerical Solution to Two-Dimensional Stochastic Neural Field Equations

In this paper, we explore an efficient numerical integration scheme for solving 2D Neural Field Equations(NFEs) in the presence of external stimuli input in both deterministic and stochastic scenarios. The method is based on Galerkin-type spectral approximation and our attention is paid to its efficient implementation because of the partial integro-differential fashion of the NFEs under discussion and two-dimensional spatial domain. The straightforward implementation of the proposed numerical scheme yields a very time consuming method in case of a good spatial resolution. Our efforts are focused on translating the numerical technique into a high-level computer code in MATLAB where the improved efficiency is achieved by vectorizing operations and accessing subarrays via MATLAB's colon notation. The results of numerical experiments are presented.
Age Group and Gender Classification of Unconstrained Faces

Age and Gender classification of unconstrained imaging conditions has attracted an increased recognition as it is applicable in many real-world applications. Recent deep learning-based methods have shown encouraging performance in this field. We, therefore, propose an end-to-end deep learning-based method for robust age group and gender classification of unconstrained images. Particularly, we address the estimations problem with a classification based model that treats age value as a separate class and an independent label. The proposed deep convolutional neural network model learns the relevant informative age and gender representations directly from the image pixel. Technically, the model is first pre-trained on large-scale IMDb-WIKI facial aging dataset, and then fine-tuned on MORPH-II, another large-scale facial aging dataset to learn, and pick up the bias and particularities of each dataset. Finally, it is fine-tuned on the original dataset (OIU-Adience benchmark) with gender and age group labels. The experimental results when analyzed for estimation accuracy on OIU-Adience dataset, show that our model obtains the state-of-the-art performance in both age group and gender classification with an exact and one-off accuracy of 83.1% and 93.8% on age, and also an exact accuracy of 96.2% on gender.
Nearly Optimal Robust Secret Sharing Against Rushing Adversaries

Robust secret sharing is a strengthening of standard secret sharing that allows the shared secret to be recovered even if some of the shares being used in the reconstruction have been adversarially modified. In this work, we study the setting where out of all the n shares, the adversary is allowed to adaptively corrupt and modify up to t shares, where $$n = 2t+1$$ (Note that if the adversary is allowed to modify any more shares, then correct reconstruction would be impossible.). Further, we deal with rushing adversaries, meaning that the adversary is allowed to see the honest parties' shares before modifying its own shares. It is known that when $$n = 2t+1$$ , to share a secret of length m bits and recover it with error less than $$2^{-\lambda }$$ , shares of size at least $$m+\lambda $$ bits are needed. Recently, Bishop, Pastro, Rajaraman, and Wichs (EUROCRYPT 2016) constructed a robust secret sharing scheme with shares of size $$m + O(\lambda \cdot \mathrm{polylog}(n,m,\lambda ))$$ bits that is secure in this setting against non-rushing adversaries. Later, Fehr and Yuan (EUROCRYPT 2019) constructed a scheme that is secure against rushing adversaries, but has shares of size $$m\,+\,O(\lambda \cdot n^{{\varepsilon }}\cdot \mathrm{polylog}(n,m,\lambda ))$$ bits for an arbitrary constant $${\varepsilon }> 0$$ . They also showed a variant of their construction with share size $$m + O(\lambda \cdot \mathrm{polylog}(n,m,\lambda ))$$ bits, but with super-polynomial reconstruction time. We present a robust secret sharing scheme that is simultaneously close-to-optimal in all of these respects – it is secure against rushing adversaries, has shares of size $$m\,+\,O(\lambda \log {n} (\log {n}+\log {m}))$$ bits, and has polynomial-time sharing and reconstruction. Central to our construction is a polynomial-time algorithm for a problem on semi-random graphs that arises naturally in the paradigm of local authentication of shares used by us and in the aforementioned work.
Influence of Augmented Reality on the Achievement and Student Learning Independence

This research focused on the use of innovative learning media by adjusting current student learning style. It is necessary to develop learning activity that's attractive and match with the current student learning style. This study used a quantitative descriptive approach. The aim was to reveal the student learning outcomes whose is supported by innovative learning media. By utilizing it, student gained new learning experiences and the learning became more interesting. The results of this study stated that the use of AR-based innovative learning media was able to create interesting and independent learning that had an impact on learning outcomes.