Reviewerly/exHarmony huggingface dataset:
Asymmetric quantum codes from skew cyclic codes over B1

Skew cyclic codes over Bk = 𝔽pr [v1, …, vk]/〈vi2 − vi, vivj – vjvi〉 have been studied by Irwansyah et al in 2018. In this paper we use B1 = 𝔽p2 [v]/〈v2 – v〉 and the Gray map φ : B1→𝔽p22 defined by φ(a + bv) = (a, a + b) for all a + bv ∈ B1 and extend φ to Φ : B1n→𝔽p22n. If C is a skew cyclic code over B1, we can get Φ(C) ⊆ 𝔽p22n a skew cyclic code if n is odd and a skew 2-quasicyclic code if n is even. Then by using the map S introduced by Ezerman et al in 2011, we get a (4n, p2k, 2d) additive code over 𝔽p2 and we use this to construct an additive asymmetric quantum code.
Function Norms for Neural Networks

Deep neural networks (DNNs) have become increasingly important due to their excellent empirical performance on a wide range of problems. However, regularization is generally achieved by indirect means, largely due to the complex set of functions defined by a network and the difficulty in measuring function complexity. There exists no method in the literature for additive regularization based on a norm of the function, as is classically considered in statistical learning theory. In this work, we study the tractability of function norms for deep neural networks with ReLU activations. We provide, to the best of our knowledge, the first proof in the literature of the NP-hardness of computing function norms of DNNs of 3 or more layers. We also highlight a fundamental difference between shallow and deep networks. In the light on these results, we propose a new regularization strategy based on approximate function norms, and show its efficiency on a segmentation task with a DNN.
Neural Question Generation using Interrogative Phrases

Question Generation (QG) is the task of generating questions from a given passage.One of the key requirements of QG is to generate a question such that it results in a target answer.Previous works used a target answer to obtain a desired question.However, we also want to specify how to ask questions and improve the quality of generated questions.In this study, we explore the use of interrogative phrases as additional sources to control QG.By providing interrogative phrases, we expect that QG can generate a more reliable sequence of words subsequent to an interrogative phrase.We present a baseline sequenceto-sequence model with the attention, copy, and coverage mechanisms, and show that the simple baseline achieves state-of-the-art performance.The experiments demonstrate that interrogative phrases contribute to improving the performance of QG.In addition, we report the superiority of using interrogative phrases in human evaluation.Finally, we show that a question answering system can provide target answers more correctly when the questions are generated with interrogative phrases. 1
OVERSCAN: OAuth 2.0 Scanner for Missing Parameters

The websites are developed rapidly and wildly used by people around the world. The main reason is the increase of the immense number of internet users, which results in the security control of accessing sensitive information is necessary. The authorization server as the one security aspect which controls the access permission to the system. Many authentication protocols were proposed to meet these functional requirements. The open-standard authorization (OAuth) protocol is one of the well-known solutions widely used. However, many developers still misuse this protocol, which can cause security breaches. This paper proposes a tool named OVERSCAN, which is an OAuth2.0 scanner for misused or missing parameters. The experiments of using OVERSCAN have been conducted over 45 samples supporting OAuth2.0 protocol. The results show that 84.4% of samples lack significant parameters which can cause security problems.
An efficient resource sharing technique for multi-tenant databases

Multi-tenancy is one of the key components of cloud computing environment. Multi-tenant database system in SaaS (Software as a Service) has gained a lot of attention in academics, research and business arena. These database systems provide scalability and economic benefits for both cloud service providers and customers(organizations/companies referred as tenants) by sharing same resources and infrastructure in isolation of shared databases, network and computing resources with Service level agreement (SLA) compliances. In a multitenant scenario, active tenants compete for resources in order to access the database. If one tenant blocks up the resources, the performance of all the other tenants may be restricted and a fair sharing of the resources may be compromised. The performance of tenants must not be affected by resource-intensive activities and volatile workloads of other tenants. Moreover, the prime goal of providers is to accomplish low cost of operation, satisfying specific schemas/SLAs of each tenant. Consequently, there is a need to design and develop effective and dynamic resource sharing algorithms which can handle above mentioned issues. This work presents a model embracing a query classification and worker sorting technique to efficiently share I/O, CPU and Memory thus enhancing dynamic resource sharing and improvising the utilization of idle instances proficiently. The model is referred as Multi-Tenant Dynamic Resource Scheduling Model (MTDRSM) .The MTDRSM support workload execution of different benchmark such as TPC-C(Transaction Processing Performance Council), YCSB(The Yahoo! Cloud Serving Benchmark)etc. and on different database such as MySQL, Oracle, H2 database etc. Experiments are conducted for different benchmarks with and without SLA compliances to evaluate the performance of MTDRSM in terms of latency and throughput achieved. The experiments show significant performance improvement over existing Mute Bench model in terms of latency and throughput.
Transparent Encryption Technique for Trusted Computing

Abstract Trusted computing is a technology that has been developed to make a computer safer and more trustworthy. The main purpose of trusted computing is to ensure that only authorized user will be able to use and access information stored inside the computer. Although the current trusted computing technology is capable of providing the level of trustworthiness required, it is still subjected to physical attack. In order to solve this problem, the TPM-UAM model has been proposed. However, in order to provide better protection, it is important that data inside the computer to be encrypted whenever it is not used and decrypted whenever it needs to be used. A number of encryption techniques are available such as RSA, AES, DES/3DES, Twofish, Blowfish, IDEA, SEAL, RC4. However, time taken for encrypting and decrypting data by using these techniques is huge. This paper describes a research work that is currently undertaken in order to develop a new encryption mechanism that can provide very fast and transparent encryption and decryption services for trusted computers.
Noise Removal Based Query Pre-processing to Improve Face Search Performance in Large Scale Video Databases

In most person search systems, for ease of use, a user is often required to provide only raw images that contain person as query examples without specific face location. As a result, a face detector needs to be used. However, current face detectors are robust to pose, thus using all the detected faces as query can hurt search performance. Therefore, having a good stage for removing bad examples in query can directly lead to better performance on the whole system. In this paper, we focus on analyzing how bad face examples affect person search system. Moreover, we propose an automatic bad-face removal method which is stable to the case where bad faces are dominant in a query. Experiments show that our removal method yields better mAP in both image example and shot example setting compared to that of Peking state of the art system.
Automatic Kidney CT Segmentation and Optimization Based on Self-Learning

Medical image processing plays an increasingly important role in clinical diagnosis and treatment. Using the results of kidney CT image segmentation for three-dimensional reconstruction is an intuitive and accurate method for diagnosis. However, the performance of traditional image segmentation algorithms is unsatisfactory due to the large difference between the kidneys of patients and the noise of CT images, and the surface of the model reconstructed will be rough and deformed when the results are directly used for three-dimensional reconstruction. To improve this situation, we propose a segmentation, reconstruction and optimization system(SROS), which combines the auto segmentation of kidney CT sequence images and the optimization of three-dimensional reconstruction. According to the characteristics of U-Net, we improved it to be suitable for accomplishing our task. Firstly, we initialize the size and gray value range of CT image sequence. Secondly, we use sequence images and label images to train the network model. Thirdly, we use the trained network model to segment the sequence images. Finally, we use the model to optimize the reconstruction. The experimental results show that SROS has a good performance in terms of 3D reconstruction accuracy, smoothness and robustness.
Separation of misalignment aberrations in interferometric testing for the surface measurement of the frustum of a cone

The separation of misalignment aberrations is a crucial step in interferometric testing for the acquisition of the real surface. In this paper, a Fizeau interference system and a conical mirror with a certain angle were used to achieve the shape measurement of the inner surface of the frustum of a cone. Based on the ray-tracing method, the relationship between the adjustment errors and misalignment aberrations was established, and the misalignment aberrations could be removed by using the error separation model that accords with the least-square algorithm theory. The simulation and practical measurement results indicate that the approach proposed is valid and feasible.
New trends in computational electromagnetics

Electromagnetics is based on the study of Maxwell&apos;s equations, which are the result of the seminal work of James Clerk Maxwell completed in 1865, after his presentation to the British Royal Society in 1864. It has been over 150 years ago now, and this is a long time compared to the recent leaps and bounds progress made in technological advancements. Nevertheless, electromagnetics is still being continuously researched and studied despite its age. The reason is that electromagnetics is extremely useful and has impacted a large sector of modern technologies.
A Memory- and Accuracy-Aware Gaussian Parameter-Based Stereo Matching Using Confidence Measure

Accurate stereo matching requires a large amount of memory at a high bandwidth, which restricts its use in resource-limited systems such as mobile devices. This problem is compounded by the recent trend of applications requiring significantly high pixel resolution and disparity levels. To alleviate this, we present a memory-efficient and robust stereo matching algorithm. For cost aggregation, we employ the semiglobal parametric approach, which significantly reduces the memory bandwidth by representing the costs of all disparities as a Gaussian mixture model. All costs on multiple paths in an image are aggregated by updating the Gaussian parameters. The aggregation is performed during the scanning in the forward and backward directions. To reduce the amount of memory for the intermediate results during the forward scan, we suggest to store only the Gaussian parameters which contribute significantly to the final disparity selection. We also propose a method to enhance the overall procedure through a learning-based confidence measure. The random forest framework is used to train various features which are extracted from the cost and intensity profile. The experimental results on KITTI dataset show that the proposed method reduces the memory requirement to less than 3 percent of that of semiglobal matching (SGM) while providing a robust depth map compared to those of state-of-the-art SGM-based algorithms.
Data Sharing Technique Modeling for Naive Bayes Classifier for Eligibility Classification of Recipient Students in the Smart Indonesia Program

Abstract The objective of Smart Indonesia Program (Program Indonesia Pintar: PIP) is to help school-aged people from poor / vulnerable / priority families to continue to receive education services to graduate from secondary education, both through formal and non-formal education channels. In its implementation, there are still many fraudulent in the proces of nominating proposal PIP funds and there are still many prospective students who should not receive PIP because they do not meet the technical guidelines provided by the Ministry of Education and Culture to determine the eligibility of prospective recipients of PIP funds can be done by schools and stakeholders, one of them by using classification techniques. One algorithm that is widely used in classification is the Naive Bayes Classifier (NBC) algorithm. In this study three data sharing techniques were used, namely Hold Out 70% training data and 30% testing data, K-Means Clustering, and also 10 Fold Cross Validation. Determination of the best data sharing technique will be determined by looking at the value of Accuracy, Precision, and Recall and also the value of Area Under Curve (AUC) which is illustrated by the Receiver Operating Characteristic (ROC) curve so that the NBC algorithm is generated with 10 Fold Cross Validation has a very good classification level with the values of accuracy, precision, and recall respectively at 97.40%; 100%; and 76.14%.
Watermarking structured light patterns for one-shot, extendable 3D scanning

We present a pattern watermarking framework for system that captures the 3D shape of face, in order to support face dependent applications e.g. face identification. As the face may move during the 3D capture, it is important that the shape is retrieved within as short a time as possible e.g. stereo, one-shot structured light. On the other hand, the face needs to be captured from multiple sides. In order to get a fast, full-face capture without compromised resolution on profile sides. We have devised a method to let multiple projectors and cameras work simultaneously. A projected pattern in combination with a camera allows for a structured light approach. This is beneficial, given the weakly textured surfaces we are dealing with. Yet, where projection patterns overlap, our system automatically changes over to a multi-view approach. In order to let the system automatically detect whether a single projection vs. an overlap is observed, we watermark the different projection patterns while preserving enough textures for correspondence match. In our system, two projectors and two cameras are deployed. Each camera-project pair consists a one-shot structured light set and the two cameras consist a multiview stereo. Our method is fully compatible with now-a-days tensor computation platforms, which provides simplicity for research and development as well as easy-extendability for industrial application and running-time performance optimization. This paper presents watermarking patterns and corresponding detection methods, in tensor computation.
The Potential of Smart Systems in Educational Mobility and Open Education

This article considers the results of Internet users' survey of different ages and different spheres of activity in order to study the awareness of SMART-systems possibilities in educational mobility. Possible sources of information are identified. Participation of respondents in different forms and projects of educational route creation is analyzed. Respondents' opinions on the functions and potential of SMART-systems for the organization of educational mobility in online learning are studied. The identified SMART-systems potential contributes to the understanding of the intellectual technologies development direction for the purpose of open education system improvement.
A survey on outlier detection in Internet of Things big data

In this chapter, more than one criteria are combined for better review and characterization of OD techniques. First, we classified the solutions based on big data phase criteria into data-generation and data-acquisition phases. Then, we categorized the techniques that detect outlier in data-acquisition phase on outlier type into fault detection, event detection, and intrusion detection. We classified the fault-detection techniques based on OD method into statistical based, machine learning, distance based, and density based. Thereafter, based on if the techniques assume the underlying distribution model and estimate the parameters of the model or not, are classified into parametric based and nonparametric based, respectively. We classified the machine learning techniques depending on if the user influences machine-learning technique or not, into supervised and unsupervised techniques, which are classified further based on the analyzing approach. Moreover, we categorized the distance-based and density-based techniques based on distance and density measurements.
El Big Data desde la perspectiva de una muestra de estudiantes de universidades del norte de Perú

Big Data is a reality that is gradually being incorporated and strongly in all fields of society, including in Education, although late. The objective of this study is to descriptively analyze the general perspective that a sample of 104 university students of Education of Peru has on the Big Data applied to Education. The participating sample was non-probabilistic incidentally. A survey study was conducted, using the VABIDAE scale to collect the data. A psychometric study of the scale was done with the collected data, as well as a descriptive statistical analysis. The results show a positive perception of the participants before Big Data as well as an obvious predisposition to use Big Data services in their professional practice as a source of information. It also highlights the absence of a gender gap. Finally, its possible impact on teacher training is discussed.
Agreement in Kimbeere Relative Clauses

This paper demonstrates that the two relative markers "-rĩa" and "-ĩngĩ" agree with the head noun in Kimbeere.The agreement is determined by the class of the Head noun.
Chapman-Kolmogorov Relation Based Median String Algorithm for DNA Consensus Classification

Consensus string is the most frequent common pattern in a set of string. Consensus string is an important feature of DNA sequence. Many algorithm have been introduced to discover consensus string. Among them, median string algorithm is the most popular one. Basically, that is a brute force algorithm.DNA sequence is composed of a series of four letter alphabet ∑={a,c,g,t}. If the size of the consensus string is l, then the algorithm generates all the 4l number of l length strings called motifs or l-mer. Then try to fit the motifs one by one with the sequence. In this paper we have discovered a way to reduce the search space using chapman kolmogorov relation. We found that, the proposed system can find the same consensus string within a shorter period of time than the time taken by the median string algorithm. As the l-mer size increases, the proposed system takes much less time than the median string algorithm. For l-mer size 7, we found the proposed system is 47 times faster than the median string algorithm.
STUDENTS’ ABILITY IN UNDERSTANDING SIMPLE PAST TENSE THROUGH WHISPER AND WRITE GAME

This research applied pre-experimental research. The research aimed at finding out whether whisper and write game can increase students’ ability in understanding simple past tense or not. The population of this research was the second grade students of Informatics Department at Cokroaminoto Palopo University. The researcher used purposive sampling technique in determining the students as sample. The sample was class 2F which consisted of 35 students. The instrument of the research was written test. The result shows that the mean score of the posttest is higher than mean score pretest (71.31 &gt; 44.51). Meanwhile, P-Value is 0.00 and α is 0.05. It shows that P-value is smaller than α (0.00 &lt; 0.05). It means that null Hypothesis (H0) is rejected and alternative Hypothesis (H1) is accepted. So, there is significant difference between the result of pretest and posttest after giving the treatment in teaching simple past tense by using whisper and write game. It can be concluded that whisper and write game can increase students’ ability in understanding simple past tense.
Modified MD5 algorithm for low end IoT Edge devices

Advent of the Information Age and Internet of Things has lead to an unprecedented increase in flow of data. This increase in data-flow has been matched with increase in computational power. However, low-end devices, usually implemented as a cost-saving alternative, have not been able to keep up with the increasing security and error detection & correction demands of MD5 or SHA1/2/3 algorithms. We present the performance characteristics of modified versions of MD5 message-digest algorithm which have been altered using two different approaches, reducing lateral register widths or reducing computation cycles. The metrics for comparison are collisions, synthesis reports and time required for message-digest calculation.
Hematological and serum biochemical indices of captive Royal Bengal Tigers (Panthera tigris), Arignar Anna Zoological Park, Vandaloor, Chennai.

The study on Hematological and serum biochemical indices of captive Royal Bengal Tigers (Panthera tigris) was carried out in tigers reared under the captive conditions at Zoo and Rescue centre of Arignar Anna Zoological Park, Vandalur of Tamil Nadu state (12.8781° N, 80.0859°E) from 2015 to April, 2017. Blood samples were collected from 30 tigers from lateral coccygeal vein during the routine clinical examination at squeeze cages in K-EDTA vials .The mean hematological values for Hemoglobin (g%), PCV (%), RBC (106/ µl) and WBC (103/ µl), were found to be 14.43±1.12 (g%), 48.10±0.49 (%)7.37±1.35 (106/ µl) 13.73±0.76 (103/ µl), respectively. Similarly, the mean±S.E. values of erythrocyte indices (MCV in fl, MCH in pg and MCHC in g/dL) were found to be 60.40±0.25 (fl), 32.56±0.34 (pg) and 54.56±0.35 (g/dl), respectively in these tigers investigated. The overall serum biochemical parameters like blood urea nitrogen BUN (mg/dL), creatinine (mg/dL), total protein (g/dL), albumin (g/dL), globulin (g/dL), ALT(IU/L), ALP(IU/L), calcium (mg/dL), phosphorous (mg/dL), cholesterol (mg/dL) and glucose (mg/dL) were estimated to be 43.43±0.55 mg/dL, 2.43±2.11 (mg/dL), 7.45±1.35 (g/dL), 3.08±2.33(g/dL), 2.74±2.28 (g/dL), 103.04±0.30 (IU/L), 78.10±0.40 (IU/L),10.57±1.11 (mg/dL), 8.66±1.30 (mg/dL), 151.83±0.27 (mg/dL) and 59.66±0.40 (mg/dL) respectively. The geographical and meteorological parameters were also assessed in this study. All other components were maintained as same for the tigers including feed and water.