Reviewerly/exHarmony huggingface dataset:
Training Sets for Algorithm Tuning: Exploring the Impact of Structural Distribution

Algorithm tuning, especially in the context of metaheuristic algorithms, is a growing area of research and practice, which concerns configuring the parameters of an algorithm on the basis of a ‘training set’ of problem instances. There is much interest (but little research so far) on the relationship between the training set and generalization performance. In general terms, we expect that, if an algorithm is targeted at problems of a certain type, then the training set of problem instances should represent a diverse collection of problems of that type. However, many questions around this area are still the topic of research. In this paper, we explore how certain structural aspects of the training set problem instances (in contrast, for example, to problem size) affect generalization performance. Specifically, we look at spaces of simple TSP instances where the number of cities is constant, but with different spatial distributions. We find that, when the training problem sets are suitably difficult, an algorithm tuned for a specific spatial distribution tends towards being the best algorithm on test sets following that distribution. Conversely, and significantly, if the target problem set is not from a ‘particularly difficult’ spatial distribution, a better optimizer for that distribution may be produced by choosing a different spatial distribution for the training set. This has implications for the development of real-world optimizers for specific problem classes, such as, for example, vehicle routing problems with particular depot and customer distributions.
Multilayer Convolution Sparse Coding for Expression Recognition

Abstract Facial expression recognition is widely used in various research fields. For facial expression recognition problems, deep neural network methods have a complex structure and poor interpretability, while traditional machine learning methods have less plentiful diverse features and low recognition rates. Therefore, a new Multilayer Convolution Sparse Coding (MCSC) method is proposed for facial expression recognition. The MCSC method deeply extracts the salient features of the human face through a convolutional neural network. Furthermore, it uses a multilayer sparse coding to learn layer by layer to recognize different facial expression features based on sparse coding, which improves the recognition accuracy of facial expressions. Finally, the MCSC method was validated on three public facial expression datasets, i.e. JAFFE, CK +, and Fer2013. We also compared and analyzed 5 feature extraction approaches. The results show that MCSC has the best facial expression recognition performance in the comparison algorithm. Its accuracies of the three data sets reach to 90.8%, 98.2%, and 72.4%, respectively.
Large-Scale Face Image Retrieval Based on Hadoop and Deep Learning

In this paper, we analyzed large-scale face image retrieval method in cloud computing environment. Based on Hadoop platform, a large-scale face retrieval method by using deep learning was established to take advantage of parallel computing of MapReduce, and thus improving the efficiency of facial image retrieval. This method uses Histogram of Oriented Gradients and adopts a deep residual network. Meanwhile, Hadoop was adopted to process data parallelly to improve the retrieval efficiency. CASIA WebFace dataset was chosen for testing the proposed method, and the results of the experiment could demonstrate the effectiveness of the new method.
Agile Planning and Tracking of Software Projects Using the State-Space Approach

All projects are required to be tracked and reported their status information to the management for controlling their execution, and have limited resources in terms of budget and time, so agile projects are. Their tracking is an overhead for the software projects as the tracking is a management activity, not a technical one, for the development of the software product. Various tracking techniques have been developed for tracking of agile projects. We have classified them as heavy-weight and light-weight, in terms of the information maintained, or not, about which phase of the project lifecycle a partially completed task belongs to. Light-weight techniques consider the tasks which have been completed but do not consider the partially completed tasks; while heavy-weight ones consider the tasks which have been completed as well as the state information about which phase of the project lifecycle the partially completed tasks belong to. Light-weight techniques are not effective due to not considering the partially completed tasks and heavy-weight ones are not utilizing the resources efficiently. In this paper, a light-weight tracking technique is designed using the state-space approach, which considers both the completed tasks and the partially completed tasks but without maintaining the state information for the partially completed tasks in order to tracking the projects effectively and utilizing the resources efficiently. The application of the technique, using Monte-Carlo simulation, is demonstrated by tracking a project which was carried out with the agile software development philosophy.
Exhibition projects as a technology of educational activity of higher school

The educational technologies of higher education are changing in the context of the growing influence of creative industries, the priority of the values of over-professional competencies and the expansion of the range of tasks associated with the development of cultural processes in the territories. The article discusses ways of modifying exhibition activities, where proprietary technologies are rapidly developing, trends in digitalization and event practices are strong, including performances, happening, master classes. Universities are involved in creative practices, but such work requires pedagogical logic, a methodology for involving young people in this area. The purpose of the study was to search for reliable methods and resources necessary for a successful strategy for the development of the art space of the university. Analysis of training courses related to exhibition work, an overview of the sites available for the presentation of youth projects, student surveys on motivation in the field of creative initiatives have become methods of obtaining the initial research data. As a result, it is concluded that it is necessary to develop the art space of the university as an independent educational practice, including for the development of open massive online courses and the formation of art collections in the multimedia format.
Radar Sea Clutter Modeling and Reconstruction Based on Statistical Singularity Power Spectrum

In this paper, the radar clutter reconstruction model based on singularity power spectrum (SPS) and instantaneous singularity exponent (ISE) is proposed. The proposed ISE-SPS model consists of power measure channel and dimension measure channel. The multi-scale wavelet coefficients are estimated based on the coupling of the two channels to reconstruct the radar sea clutter. Experiment based on the Ice Multi-parameter Imaging X-Band (IPIX) indicates that the proposed model behaves better than the N-partitioned random multiplicative model (NRMM), with about 32% increment in terms of correlation coefficient and normalized MSE of MFS and SPS.
A Fuzzy Keywords Enabled Ranked Searchable CP-ABE Scheme for a Public Cloud Environment

More and more people and enterprises are willing to store their encrypted data in the cloud. In this context, as an important cryptographic technology, searchable encryption (SE) plays an crucial role in retrieving the data for data owners. Furthermore, the encrypted data are desired to be shared to some data users without destroying the users’ privacy. Therefore, in this paper, we proposed a novel fuzzy keywords enabled ranked searchable ciphertext-policy attribute-based encryption (FRSCP-ABE) scheme to address above problems. Firstly, we combined ciphertext-policy attribute-based encryption (CP-ABE) with searchable encryption (SE) to search and utilize the outsourced encrypted data for data owners efficiently. Secondly, we obtain meaningful and accurate searching results by considering the errors in the documents. Furthermore, we took advantage of the probabilistic trapdoor to resist distinguishability attacks. Finally, we improved the accuracy of searching results by employing the weighted zone score to rank the documents. Our theoretical analysis indicated that our proposal realized fuzzy keywords search and more accurate searching result rank, also guaranteed the data security and saved a large amount of storage and computation costs.
A Self-calibration of a stationary camera in the case of variable intrinsic parameters

In this article, we propose a robust, flexible and fast system for auto-calibration from two pairs of points of interest matched to two images taken by a mobile camera, characterized by variable parameters, we proposed a new method of auto-calibration based on the use of two unknown scene points with their projections on the two images. To test the effectiveness of our algorithm, we applied it to two types of synthetic and real data. The proposed system achieves satisfactory results within a reasonable period.
Experience as an Architect in an Agile Environment

Innovation is created inside of development teams. There is no thinktank above software development teams which can give them all the guidance necessary to develop efficiently software, which meets the requirements of stakeholders. The chapter describes experience of the author creating a community of (software architecture) practice to allow development teams freedom in their technology and methodology decision without losing big picture and guidance of experienced architects. Architectural work becomes recommendation and service instead of command and control.
Binary classification on compositional data

Due to boundedness and sum constraint, compositional data are often transformed by logratio transformation and their transformed data are put into traditional binary classification or discriminant analysis. However, it may be problematic to directly apply traditional multivariate approaches to the transformed data because class distributions are not Gaussian and Bayes decision boundary are not polynomial on the transformed space. In this study, we propose to use flexible classification approaches to transformed data for compositional data classification. Empirical studies using synthetic and real examples demonstrate that flexible approaches outperform traditional multivariate classification or discriminant analysis.
Quest for Sustainable Growth, Does Foreign Direct Investment Still Matter? Evidence in Africa

The debate on the role of Foreign Direct Investment in promoting rapid growth and development of the developing economies remain inconclusive. This paper examined whether FDI still matters in African Countries over the period of 1990 to 2017, with the proper utilization of panel data estimation technique on the annual country data that were sourced from world Governance and Development Indicators. Using random and fixed effect model, the results reveal that some important variables such as coefficient of trade openness, rule of law, political stability, capital formation and population positively determined economic growth in Africa countries, account for about 2, 1, 65, 170, and 396.7 percent increase in economic growth. While, FDI and inflation were found to have negative impact on economic growth accounting for 21.4 and 2 percent fall in economic growth over the study period. The study then recommends amongst others formulation and implementation of policies that encourage domestic investment in the continents.
Context-Aware Face Detection for Occluded Faces

Detecting of occluded faces is a real challenge for face detection in the computer vision area. This challenge tends to be higher when the occlusion is more than half of the face. Regardless to the great success of recent face detection systems, face detection with occlusion is not yet saturated and requires more attention. We proposed in this work an occluded faces dataset in high degree of occlusion along with context-based labelling, which is a useful technique that let CNN to learn more features during training. The experimental benchmark result showed weak performance of current face detection models in our proposed dataset.
USING ARTIFICIAL NEURAL NETWORKS FOR PREDICTING NEW EPILEPSY CASES AT KWEKWE GENERAL HOSPITAL IN ZIMBABWE

Epilepsy is responsible for an enormous amount of “untold” suffering around the globe. Fortunately, effective and cost efficient treatment is available for the management of this neurological disorder. Unfortunately, in developing countries such as Zimbabwe, up to 90% of the people who have this condition, and sometimes even more, are excluded from care and consequently remain in the shadow of this treatment gap. In Zimbabwe, we have all reasons to consider epilepsy a healthcare priority. This study employed monthly time series data on epilepsy cases recorded and managed at Kwekwe General Hospital (KGH) from Janaury 2010 to December 2019, in order to predict epilepsy case-loads over the period January 2020 to December 2021. The popular ANN (12, 12, 1) model was applied. Residual analysis of this model indicated that it was really stable and thus suitable for predicting epilepsy case volumes at KGH over the out-of-sample period. The results of the study indicate that epilepsy cases will generally decline over the out-of-sample period. Amongst other recommendations, the study suggested the need for timely treatment of epileptic patients in order to save life.
A Robust Non-Blind Watermarking for Biomedical Images Based on Chaos

The advent of the Internet in these last years encouraged a considerable traffic of digital images. In the sanitary field, precisely in telemedicine branch, medical images play a very important role for therapeutic diagnoses. Thus, it is necessary to protect medical images data before transmission over the network to preserve their security and prevent unauthorized access. In this paper, a secure algorithm for biomedical images encryption scheme based on the combination of watermarking technique and chaotic function is proposed. In the proposed method, to achieve high security level performances, a non-blind hybrid watermarking technique with audio signal, Discrete Wavelet Transform is used; smoothness is also used as selected criteria; the iterations obtained by the chaotic sequences are essential and allow a good realization of the encryption process. One of the main advantages of chaos-based encryption schemes is the generation of a large number of key spaces to resist brute force attacks from the encryption algorithm. The experimental results presented in this paper attest to the invisibility and robustness of the proposed algorithm combining watermarking and chaos-based encryption.
A new decomposition law for inverses modular multiplicative on (ℤ/ϱℤ)

Abstract In modular arithmetic, we find a valuable concept, the so-called “modular multiplicative inverse” (symbolized by MMI). In precise words, if ℤ /ϱ ℤ denoted the residue system modulo ϱ , the (MMI) of a ∈ ℤ /ϱ ℤ, if it exists, is a − 1 ∈ ℤ /ϱ ℤ, such that <?CDATA $a \times a^{-1} \equiv 1 \quad \bmod \varrho$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mi>a</mml:mi> <mml:mo>×</mml:mo> <mml:msup> <mml:mrow> <mml:mi>a</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>−</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msup> <mml:mo>≡</mml:mo> <mml:mn>1</mml:mn> <mml:mspace width="1em" /> <mml:mo lspace="thickmathspace" rspace="thickmathspace">mod</mml:mo> <mml:mi>ϱ</mml:mi> </mml:mrow> </mml:math> , where p ≡ q mod ϱ is the usual modular representation of q ∈ ℤ /ϱ ℤ. This very special element it is one of the most wideley used mathematical concept in science, engineering and subject areas include the particle physics, the analysis and design of algorithms, data structures, databases, and computer architecture. In this paper, we establish a promising decomposition law for (MMI). In this respect, the main purpose of this paper it is shown that it is possible to express the (MMI) in terms of certain modular multiplicative inverse operators (MMIO) all, well-defined on Group of units pre-established. The main point to note here is that the result of this paper, which expands a result originally presented in [3], does not include any special conditions on the parameters involved.
Unsupervised Feature Learning for Whatsapp network Data packets using Autoencoder

Nowadays the network traffic analyses plays a important role in network management. The network management includes Quality of Service, blocking a particular service or application with in the organization network. There are two versions of network traffic analysis existing one is encrypted and other is unencrypted. Instant Message applications such as whatsapp,viber,telegram are generating encrypted network traffic. This type of traffic can be analyzed by analyzing the behavior of network packets flow. The objectives of doing such encrypted traffic analysis include Traffic Clustering, Application Type and Protocol Classification, Anomaly Detection or File Identification. This research is focused on capturing the whatsapp data packets at router level and clustering the packets. Since the packets are captured at router level they dont have any label. It is proposed to apply unsupervised Machine Learning, Deep Learning algorithm such as K Means, PCA, Autoencoder are applied for clustering the network data packets. PCA, Autoencoder are both unsupervised learning approach for dimensionality reduction. The Autoencoder along with K Means algorithm gives good results in clustering the network packets according to their file type(jpg,pdf,mp4).
Parallel Large-Scale Structure from Motion by Distributed Averaging

With the development of computer vision, Structure from Motion (SFM) which recovers sparse point clouds from image sequences has achieved great success. Large-scale scenes cannot be reconstructed with a single compute node so that we introduce a divide-and-conquer framework to solve the distributed SFM problem. First, we attach great importance to the efficiency of image matching and geometric filtering, which takes up a lot of time in the traditional SFM problem. We use the GPS information of images to calculate the GPS neighborhood. The number of image matches is greatly reduced by matching each image with only valid GPS neighbors, and a robust matching relationship is obtained. Second, the calculated matching relationship is used as the initial camera graph to be divided into multiple subgraphs by the clustering algorithm, and local SFM is executed on several computing nodes to register the local cameras. Finally, all local camera poses are integrated and optimized to complete the global camera registration. Our system can solve the structure from motion problem in large-scale scenes with accuracy and efficiency.
On the long-term learning ability of LSTM LMs.

We inspect the long-term learning ability of Long Short-Term Memory language models (LSTM LMs) by evaluating a contextual extension based on the Continuous Bag-of-Words (CBOW) model for both sentence- and discourse-level LSTM LMs and by analyzing its performance. We evaluate on text and speech. Sentence-level models using the long-term contextual module perform comparably to vanilla discourse-level LSTM LMs. On the other hand, the extension does not provide gains for discourse-level models. These findings indicate that discourse-level LSTM LMs already rely on contextual information to perform long-term learning.
Virtual View and Video Synthesis Without Camera Calibration or Depth Map

This paper proposes a method for synthesizing virtual views based on two real views from different perspectives. This method is also readily applicable to video synthesis. This method can quickly generate new virtual views and a smooth video a view transition without camera calibration or depth map. In this method, we first extract the corresponding feature points from the real views by using the SFT algorithm. Secondly, we build a virtual multi-camera model. Then we calculate the coordinates of feature points in each virtual perspective, and project the real views onto this virtual perspective. Finally, the virtual views are synthesized. This method can be applied to most real scenes such as indoor and street scenes.
Swift Controller: A Computer Vision Based Mouse Controller

As technology advances and better virtual interfaces emerge, there is a demand for new kinds of interaction devices. The currently being used devices like keyboard, mouse, pens, etc. have been the most popular among these virtual interfaces. With the advancement of technology where speech and gestures have become the key components to interact with smart devices a replacement for currently used devices is needed. The development of user interfaces influences the changes in Human-Computer Interaction (HCI). This paper focuses to design an input device using computer vision and gesture recognition techniques. This device interacts with a computer using hand gestures, providing an intuitive cost-effective way of performing mouse controls. Various algorithms and techniques are used to make the product user-friendly. We have designed a colored glove that can work in various environments and control the navigation along with functions of the mouse such as left-click, right-click, dragging, and scrolling swiftly. This real-time application allows for practical interaction between users and the system.
Interpreting the compositional truth predicate in models of arithmetic

Abstract We present a construction of a truth class (an interpretation of a compositional truth predicate) in an arbitrary countable recursively saturated model of first-order arithmetic. The construction is fully classical in that it employs nothing more than the classical techniques of formal proof theory.
Face tracking based on low illumination

In the environment of low illuminance, the traditional face tracking is easy to be affected by occlusion, deformation and other factors in the process of tracking, Therefore, in order to achieve accurate face tracking in low illumination environment, an improved compressed sensing tracking method combining HAAR-LBP cascade classifier is proposed in this paper. First, HAAR feature is used for face coarse tracking, then bilateral filtering is used for image enhancement, and finally LBP feature is used for fine tracking. The improved tracking method overcomes the difficulty that the traditional method has poor tracking effect and is easy to lose the target under the condition of low illuminance, and the tracking accuracy is also improved. Accroding to the experiment, it can be easily known that the accuracy of the improved algorithm is about 5% higher than that of the traditional algorithm, and the real-time performance of the improved algorithm is also improved to a certain extent.