Reviewerly/exHarmony huggingface dataset:
Working with broken agents: Exploring computational 2D morphometrics for studying the (post)depositional history of potsherds

The study of size and shape of ceramic fragments is a common approach used for unraveling the depositional and post-depositional history of archaeological assemblages. Similar to sediment particles, the size and shape of a potsherd are altered under conditions of abrasion, breakage, or weathering, occurring during or after deposition, which can be the product of both human and non-human agents. Thus, the analysis of dimensional and morphological changes of fragments not only points towards specific (post)depositional processes, but can also shed light on the nature of human interaction with broken pottery. By coupling a traditional sedimentological framework for shape description and a computational approach to 2D morphometrics, in this paper we present a quick, reproducible and accurate method for studying the alteration of shape and size of potsherds. Three main shape descriptors, i.e. sphericity, roundness, and convexity, were tested on a combination of experimental and Early Neolithic potsherds in Northwest Romania from contexts where the conditions of fragment alteration were known or could be safely inferred. In addition, potsherds from an Early Neolithic pit discovered at Călineşti-Oaş-Dâmbul Sfintei Mării (Satu Mare County, Romania) were analysed for determining the pit infilling process and understanding the extent of the interaction between humans and broken pots. Results from morphometric tests show the reliability of the computational technique and shape descriptors for identifying different conditions of alteration of potsherds. The analysis of the Romanian assemblages provide insight into (post)depositional processes, showing downslope movement as the main mechanism behind the infilling of the pit at Călineşti-Oaş-Dâmbul Sfintei Mării. In addition, the potsherds possessed a high level of erosion and fragmentation, highlighting a long span of human interaction with broken materials at this site during Early Neolithic times.
Some results on deep holes of generalized projective Reed-Solomon codes

Let $l\ge 1$ be an integer and $a_1, \ldots, a_l$ be arbitrarily given $l$ distinct elements of the finite field ${\bf F}_q$ of $q$ elements with the odd prime number $p$ as its characteristic. Let $D = {\bf F}_q\backslash\{a_1, \ldots, a_l\}$ and $k$ be an integer such that $2\le k\le q-l-1$. For any $f(x)\in {\bf F}_q[x]$, we let $f(D) = (f(y_1), \ldots, f(y_{q-l}))$ if $D = \{y_1, ..., y_{q-l}\}$ and $c_{k-1}(f(x))$ be the coefficient of $x^{k-1}$ of $f(x)$. In this paper, by using Dür's theorem on the relation between the covering radius and minimum distance of the generalized projective Reed-Solomon code ${\rm GPRS}_q(D, k)$, we show that if $u(x)\in {\bf F}_q[x]$ with $\deg u(x) = k$, then the received word $(u(D), c_{k-1}(u(x)))$ is a deep hole of ${\rm GPRS}_q(D, k)$ if and only if $\sum\limits_{y\in I}y\ne 0$ for any subset $I\subseteq D$ with $\#(I) = k$. We show also that if $j$ is an integer with $1\leq j\leq l$ and $u_j(x): = \lambda_j(x-a_j)^{q-2}+\nu_j x^{k-1}+f_{\leq k-2}^{(j)}(x)$ with $\lambda_j\in {\bf F}_q^*$, $\nu_j\in {\bf F}_q$ and $f_{\leq{k-2}}^{(j)}(x)\in{\bf F}_q[x]$ being a polynomial of degree at most $k-2$, then $(u_j(D), c_{k-1}(u_j(x)))$ is a deep hole of ${\rm GPRS}_q(D, k)$ if and only if $\binom{q-2}{k-1}(-a_j)^{q-1-k}\prod\limits_{y\in I}(a_j-y)+e\ne 0$ for any subset $I\subseteq D$ with $\#(I) = k$, where $e$ is the identity of ${\bf F}_q^*$. Furthermore, $(u({\bf F}_q^*), c_{k-1}(u(x)))$ is not a deep hole of the primitive projective Reed-Solomon code ${\rm PPRS}_q({\bf F}_q^*, k)$ if $\deg u(x) = k$, and $(u({\bf F}_q^*), \delta)$ is a deep hole of ${\rm PPRS}_q({\bf F}_q^*, k)$ if $u(x) = \lambda x^{q-2}+\delta x^{k-1}+f_{\leq{k-2}}(x)$ with $\lambda\in {\bf F}_q^*$ and $\delta\in {\bf F}_q$.
Setting decision thresholds when operating conditions are uncertain

The quality of the decisions made by a machine learning model depends on the data and the operating conditions during deployment. Often, operating conditions such as class distribution and misclassification costs have changed during the time since the model was trained and evaluated. When deploying a binary classifier that outputs scores, once we know the new class distribution and the new cost ratio between false positives and false negatives, there are several methods in the literature to help us choose an appropriate threshold for the classifier’s scores. However, on many occasions, the information that we have about this operating condition is uncertain. Previous work has considered ranges or distributions of operating conditions during deployment, with expected costs being calculated for ranges or intervals, but still the decision for each point is made as if the operating condition were certain. The implications of this assumption have received limited attention: a threshold choice that is best suited without uncertainty may be suboptimal under uncertainty. In this paper we analyse the effect of operating condition uncertainty on the expected loss for different threshold choice methods, both theoretically and experimentally. We model uncertainty as a second conditional distribution over the actual operation condition and study it theoretically in such a way that minimum and maximum uncertainty are both seen as special cases of this general formulation. This is complemented by a thorough experimental analysis investigating how different learning algorithms behave for a range of datasets according to the threshold choice method and the uncertainty level.
Resilient Fiber-based Quantum Key Distribution (QKD) Networks with Secret-key Re-allocation Strategy

Fiber-based QKD is a promising solution for enhancing the security of information networks. A secret-key re-allocation strategy is proposed and verified for the failure recovery of key provisioning services in resilient fiber-based QKD networks.
Good (K-means) clusterings are unique (up to small perturbations)

If we have found a “good” clustering C of a data set, can we prove that C is not far from the (unknown) best clustering Copt of these data? Perhaps surprisingly, the answer to this question is sometimes yes. This paper gives spectral bounds on the distance d(C,Copt) for the case when “goodness” is measured by a quadratic cost, such as the squared distortion of K-means clustering or the Normalized Cut criterion of spectral clustering. The bounds exist only if the data admit a “good”, low-cost clustering. The results in this paper are non-asymptotic and model-free, in the sense that no assumptions are made on the data generating process. The bounds do not depend on undefined constants, and can be computed tractably from the data.
Hyperbolic Function Embedding: Learning Hierarchical Representation for Functions of Source Code in Hyperbolic Space

Recently, source code mining has received increasing attention due to the rapid increase of open-sourced code repositories and the tremendous values implied in this large dataset, which can help us understand the organization of functions or classes in different software and analyze the impact of these organized patterns on the software behaviors. Hence, learning an effective representation model for the functions of source code, from a modern view, is a crucial problem. Considering the inherent hierarchy of functions, we propose a novel hyperbolic function embedding (HFE) method, which can learn a distributed and hierarchical representation for each function via the Poincaré ball model. To achieve this, a function call graph (FCG) is first constructed to model the call relationship among functions. To verify the underlying geometry of FCG, the Ricci curvature model is used. Finally, an HFE model is built to learn the representations that can capture the latent hierarchy of functions in the hyperbolic space, instead of the Euclidean space, which are usually used in those state-of-the-art methods. Moreover, HFE is more compact in terms of lower dimensionality than the existing graph embedding methods. Thus, HFE is more effective in terms of computation and storage. To experimentally evaluate the performance of HFE, two application scenarios, namely, function classification and link prediction, have been applied. HFE achieves up to 7.6% performance improvement compared to the chosen state-of-the-art methods, namely, Node2vec and Struc2vec.
Vision-based malaria parasite image analysis: a systematic review

Background: Malaria is one of the classic neglected serious diseases in many developing countries. The early stage of disease detection, accurate parasite count, detection of the aggressiveness of the disease, technical limitations, lack of expertise in malaria diagnosis and smart tools, lack of good quality healthcare services, funds so on are the challenges found during malaria diagnosis that requires a deeper analysis. Objectives: This paper aims to give a review of the automated diagnosis or visual inspection of malaria parasites using histology images of thin or thick blood film smears. Methods and Results: Various computer -aided diagnosis techniques are in use to solve tasks meticulously in a stratified description paradigm using non-linear transformation architectures. Conclusion: This work elaborates a comprehensive study of various computer vision diagnostic approaches already proposed in this field with a future direction for better quicker malaria identification.
A Real-Time Street Actions Detection

Human action detection in real time is one of the most important and challenging problems in computer vision.Nowadays, CCTV cameras exist everywhere in our lives.However, the contents of these cameras are monitored and analyzed using human operator.This paper proposes a real time human action detection approach which efficiently detects basic and common actions in the street such as stopping, walking, running, group stopping, group walking, and group running.The proposed approach measures the object movement type based on three techniques: YOLO object detection, Kalman Filter and Homography.Real videos from CCTV camera and BEHAVE dataset are used to test the proposed method.The experimental results show that the proposed method is very effective and accurate to detect basic human actions in the street.The accuracies of the proposed method on the tested videos are 96.9% and 88.4% for the BEHAVE and the created CCTV datasets, respectively.The proposed approach runs in real time with more than 50 fps for BEHAVE dataset and 32 fps for the created CCTV datasets.
Depth wise Distribution of Heavy Metals in Different Soil Series of Northwestern India

A field survey and laboratory analysis carried out on "Depth wise Distribution of Heavy Metals in Different Soil Series of Northwestern India" was conducted during 2017-18 in five well established soil series which are comes under Aeolian and upper alluvial plain of Hisar district.The profile samples up to depth of 90 cm.i.e. 0-15, 15-30, 30-60, 60-90 cm, using GPS were collected and analyzed for total concentrations of heavy metals (Pb, Cr, Cd, Co and Ni).The total lead content was initially increased with depth then further decreased in Balsamand.In Isarwal and Rawalwas it was increased with depth but in Barwala decreased with depth.In Ladwa soil series first increased then decreased.The total chromium content of Balsamand first increased with depth then decreased.In Isarwal and Rawalwas it was decreased with depth.In Barwala and Ladwa series initially decreased then further increased.The total cadmium of Balsamand and Isarwal first increased with depth then decreased.In Isarwal increased and decreased in Barwala.In Ladwa series initially decreased then further increased.The total cobalt was increased with depth in Balsamand, Isarwal and Rawalwas series.In Barwala decreased with depth and in Ladwa increased first then decreased.The total nickel content was increased with depth in Balsamand, Isarwal and Rawalwas series.In Barwala decreased with depth and in Ladwa first increased then further decreased.The non uniform distribution of total heavy metals contents were found in all series.
Implementation of Efficient Speech Recognition System on Mobile Device for Hindi and English Language

Speech recognition or speech to text conversion has rapidly gained a lot of interest by large organizations in order to ease the process of human to machine communication.Optimization of the speech recognition process is of utmost importance, due to the fact that real-time users want to perform actions based on the input speech given by them, and these actions sometime define the lifestyle of the users and thus the process of speech to text conversion should be carried out accurately.Here`s the plan to improve the accuracy of this process with the help of natural language processing and speech analysis.Some existing speech recognition software's of Google, Amazon, and Microsoft tend to have an accuracy of more than 90% in real time speech detection.This system combines the speech recognition approach used by these softwares and joined with language processing to improve the overall accuracy of the process with the help of phonetic analysis.Proposed Phonetic Model supports multi-lingual speech recognition and observed that the accuracy of this system is 90% for Hindi and English speech to text recognition.The Hindi WordNet database provided by IIT Mumbai used in this research work for Hindi speech to text conversion.
Semantic Frame Embeddings for Detecting Relations between Software Requirements

The early phases of requirements engineering (RE) deal with a vast amount of software requirements (i.e.,requirements that define characteristics of software systems), which are typically expressed in natural language.Analysing such unstructured requirements, usually obtained from stakeholders' inputs, is considered a challenging task due to the inherent ambiguity and inconsistency of natural language.To support such a task, methods based on natural language processing (NLP) can be employed.One of the more recent advances in NLP is the use of word embeddings for capturing contextual information, which can then be applied in word analogy tasks.In this paper, we describe a new resource, i.e., embedding-based representations of semantic frames in FrameNet, which was developed to support the detection of relations between software requirements.Our embeddings, which encapsulate contextual information at the semantic frame level, were trained on a large corpus of requirements (i.e., a collection of more than three million mobile application reviews).The similarity between these frame embeddings is then used as a basis for detecting semantic relatedness between software requirements.Compared with existing resources underpinned by frame embeddings built upon pre-trained vectors, our proposed frame embeddings obtained better performance against judgments of an RE expert.These encouraging results demonstrate the potential of the resource in supporting RE analysis tasks (e.g., traceability), which we plan to investigate as part of our immediate future work.
EFFORTS TO SUPPORT AND EXPAND THE USE OF EDUCATIONAL TECHNOLOGY AS A MEANS OF DELIVERING LEARNING

Indonesian government has made improving basic education a priority, and indicated that it sees technology as a way to provide teachers with the means to teach innovative lessons while closing the performance gap between rural and urban schools and between poorer and richer provinces. The role of technology must be aligned with the existing education ecosystem. It should not replace the traditional education system that involves teachers, schools, universities and parents, albeit offering wide range of information and allowing independent learning. Education is not only about learning in school, but also building a character. That is why the role of technology is intended as a tool to support the learning process, to open access, and improve quality. This paper describes the efforts taken by the Indonesian government in supporting and expanding the use of technology in the field of education in Indonesia. The discussions are supported by related research findings and literature.DOI: doi.org/10.24071/ijiet.2019.030112
Computer-supported form design using keystroke-level modeling with reinforcement learning

The Keystroke-Level Model (KLM) is commonly used to predict a user's task completion times with graphical user interfaces. With KLM, the user's behavior is modeled with a linear function of independent, elementary operators. Each task can be completed with a sequence of operators. The policy, or the assumed sequence that the user executes, is typically pre-specified by the analyst. Using Reinforcement Learning (RL), RL-KLM [4] proposes an algorithmic method to obtain this policy automatically. This approach yields user-like policies in simple but realistic interaction tasks, and offers a quick way to obtain an upper bound for user performance.
Safety in Numbers: On the Need for Robust Diffie-Hellman Parameter Validation

We consider the problem of constructing Diffie-Hellman (DH) parameters which pass standard approaches to parameter validation but for which the Discrete Logarithm Problem (DLP) is relatively easy to solve. We consider both the finite field setting and the elliptic curve setting. For finite fields, we show how to construct DH parameters (p, q, g) for the safe prime setting in which $$p=2q+1$$ is prime, q is relatively smooth but fools random-base Miller-Rabin primality testing with some reasonable probability, and g is of order q mod p. The construction involves modifying and combining known methods for obtaining Carmichael numbers. Concretely, we provide an example with 1024-bit p which passes OpenSSL's Diffie-Hellman validation procedure with probability $$2^{-24}$$ (for versions of OpenSSL prior to 1.1.0i). Here, the largest factor of q has 121 bits, meaning that the DLP can be solved with about $$2^{64}$$ effort using the Pohlig-Hellman algorithm. We go on to explain how this parameter set can be used to mount offline dictionary attacks against PAKE protocols. In the elliptic curve case, we use an algorithm of Bröker and Stevenhagen to construct an elliptic curve E over a finite field $${\mathbb {F}}_p$$ having a specified number of points n. We are able to select n of the form $$h\cdot q$$ such that h is a small co-factor, q is relatively smooth but fools random-base Miller-Rabin primality testing with some reasonable probability, and E has a point of order q. Concretely, we provide example curves at the 128-bit security level with $$h=1$$ , where q passes a single random-base Miller-Rabin primality test with probability 1/4 and where the elliptic curve DLP can be solved with about $$2^{44}$$ effort. Alternatively, we can pass the test with probability 1/8 and solve the elliptic curve DLP with about $$2^{35.5}$$ effort. These ECDH parameter sets lead to similar attacks on PAKE protocols relying on elliptic curves. Our work shows the importance of performing proper (EC)DH parameter validation in cryptographic implementations and/or the wisdom of relying on standardised parameter sets of known provenance.
DEVELOPMENT AND COMPARISON OF MATHEMATIC MOBILE LEARNING BY USING EXELEARNING 2.0 PROGRAM AND MIT INVENTOR 2

Mathematics mobile learning influences student learning outcomes. Making this application need skills and also the appropriate programs. The first goal in this study is to develop valid and effective mathematics mobile learning. The second goal in this study if using two different programs it would get the same or different results, is it different between the two applications made with different programs? This type of research is research and development (R &amp; D). The first application research subjects were mathematics teachers and 8th grade students of Junior High School in Salatiga consisted of 28 students and the subjects in this study were divided into two, namely students in the 8G class who used the mobile learning application and who did not use it and this study was conducted in the second semester academic year 2014/2015. The second application research subject was 10th-grade students of Senior High School in Salatiga, consisted of 24 students. This research was conducted in the second semester of the academic year 2015/2016. The first application development model was using ASSURE Model and developed with ExeLearning program, while the second application used the ADDIE Model and developed with MIT Inventor 2 program. Data on learning outcomes were obtained by giving initial tests and final tests. Data were analyzed using N-gain enhancement test. The mathematics mobile learning application used the ExeLearning 2.0 program to obtain validation results with very good criteria for the display and material sections while for the improvement of student learning outcomes in the high category amounting to 0.7. The second mathematics mobile learning application obtained very good display validation results and for the material section in the good category, while the increase in learning outcomes obtained an increase in the high category which was 0.71. Both of these applications possessed differences and the characteristics of mobile learning applications also depend on which software is used. It has a unique impression of using the ExeLearning 2.0 application and MIT Inventor 2.
Prediction Model for Road Traffic Accident Based on Random Forest

More than 1.3 million people worldwide die in road traffic accidents every year, and the number of traffic accident deaths in China is increasing by 10% every year. Traffic safety has become an important direction for the development of road transportation systems. At present, research in progress of road traffic accidents is mainly from accidents numbers and the degree of accident damage. Firstly, this paper proposes a random forest prediction model for the prediction of the number of road traffic accidents, which applied to the actual to verify the validity of the model by using the R software to solve it. 1. Predictive
Performance comparison of convolutional neural network based denoising in low dose CT images for various loss functions

Convolutional neural network (CNN) is now the most promising denoising methods for low-dose computed tomography (CT) images. The goal of denoising is to restore original details as well as to reduce noise, and the performance is largely determined by the loss function of the CNN. In this work, we investigate the denoising performance of CNN for three different loss functions in low dose CT images: mean squared error (MSE), perception loss using the pretrained VGG network (VGG loss), and the weighted summation of MSE and VGG losses (VGGMSE loss). CNNs are trained to map the quarter dose CT images to normal dose CT images in a supervised fashion. The image quality of denoised images is evaluated by normalized root mean squared error (NRMSE), structural similarity index (SSIM), mean and standard deviation (SD) of HU values, and the task SNR of non-prewhitening eye filter observer model (NPWE). Our results show that the CNN trained with MSE loss achieves the best performance in NRMSE and SSIM despite significant image blurs. On the other hand, the CNN trained with VGG loss reports the best score in the SD with well-preserved details but has the worst accuracy in the mean HU value. CNN trained with VGGMSE loss shows the best performance in terms of tSNR and the mean HU value and consistently high performance in other metrics. In conclusion, VGGMSE loss can subside the drawbacks of MSE or VGG loss, thus much more effective than them for CT denoising tasks.