Reviewerly/exHarmony huggingface dataset:
Unsupervised depth prediction from monocular sequences: Improving performances through instance segmentation

Depth is a valuable piece of information for robots and autonomous vehicles. Indeed, it enables them to move in space and avoid obstacles. Nevertheless, depth alone is not enough to let them interact with their surroundings. They also need to locate the different objects that are present in their environment. In this paper, we propose a deep learning model that solves unsupervised monocular depth estimation and supervised instance segmentation at the same time with a common architecture. The first task is solved through novel view synthesis while the second is solved by minimising an embedding loss function. Our approach is motivated by the idea that knowing where objects are in the scene could improve the depth estimation of unsupervised monocular depth models. We tested our architecture on two datasets, Kitti and Cityscapes and reached state-of-the-art depth estimation results while solving a second task.
Preliminary analysis learning media based on edupark physics with scientific methods on Padang beach

Abstract West Sumatera is one of the provinces surrounded by oceans that have long beaches that have physical concepts that can be directly observed. The beach is a tourist destination for domestic and foreign tourists who can be used as edupark in the process of learning physics. Edupark is an educational park that educators should be able to use as a learning resource for students. However, educators rarely use coastal tourism as a learning resource. The use of local tourist destinations can be used in learning Physics by implementing the development of science and technology that is developing now. According to the needs of students, so that information technology-based learning media can be used. The use of instructional media aims to help students understand the physical concepts found on the coast of Padang Indonesia, especially in light material and optical instruments. The data used in the study were obtained through interviews and questionnaires with educators and students. Based on this preliminary research conducted, it is known that students are interested in using learning media in the process of learning physics.
Passive and Active Wireless Device Secure Identification

Secure wireless device identification is necessary if we want to ensure that any transmitted data reach only a desired receiver. However the fact that wireless communications are by nature broadcast creates unique challenges such as identity theft, eavesdropping for data interception, jamming attacks to disrupt legitimate transmissions, etc. This paper proposes a new integrated radioprint framework (IRID) that has two main components. First, we propose a machine learning-based radio identification solution that relies on hardware variabilities of internal components of the transmitter caused during manufacturing, allowing us to achieve passive device identification. Second, we introduce a new kind of covert channel, based on variations in the emitted signal strength, which allows us to implement unique active device identification. We evaluate our proposed framework on an experimental test-bed of 20 identical WiFi devices. Although our experiments deal only with IEEE 802.11b, the approach can easily be extended to any wireless protocol. The experimental results show that our proposed solution can differentiate between network devices with accuracy in excess of 99% on the basis of a standard-compliant implementation.
Secure development tools and techniques need more research that will increase their impact and effectiveness in practice

Secure development is an important and pressing problem.
3D Shape Measurement of Complex Surface by Combining Fringe Projection and Phase Measuring Deflectometry

There are many components having both diffused and specular surfaces together, which called as complex object. The existing fringe projection profilometry and deflectometry can only measure 3D (three-dimensional) shape of diffused and specular surfaces, respectively. This paper presents a novel method to measure 3D shape of complex objects by combining fringe projection profilometry and DPMD (direct phase measuring deflectometry) technique. The specular parts of a complex object reflect fringe pattern of two LCD (Liquid Crystal Display) screens and the other parts diffuse the projected fringe pattern from a DLP (digital light processing) projector. All the specular reflected and diffused deformed fringe patterns are simultaneously captured by multiple color channels of a color camera from a different viewpoint. The reflected and diffused fringe patterns on the measured complex object are easily extracted from the captured color images. The absolute phase information is calculated from the extracted fringe patterns. After 3D calibration to build up the relationship between phase and depth, 3D shape data of the measured complex object are obtained. An artificial complex step having diffused and specular surfaces has been manufactured to verify the proposed method. Experimental results show that this method can measure complex surfaces effectively.
A General Algorithm for Time-Optimal Trajectory Generation Subject to Minimum and Maximum Constraints

This paper presents a new algorithm which generates time-optimal trajectories given a path as input. The algorithm improves on previous approaches by generically handling a broader class of constraints on the dynamics. It eliminates the need for heuristics to select trajectory segments that are part of the optimal trajectory through an exhaustive, but efficient search. We also present an algorithm for computing all achievable velocities at the end of a path given an initial range of velocities. This algorithm effectively computes bundles of feasible trajectories for a given path and is a first step toward a new generation of more efficient kinodynamic motion planning algorithms. We present results for both algorithms using a simulated WAM arm with a Barrett hand subject to dynamics constraints on joint torque, joint velocity, momentum, and end effector velocity. The new algorithms are compared with a state-of-the-art alternative approach.
A Verification Method for Security and Safety of IoT Applications Through DSM Language and Lustre

Development of Internet of Things (IoT) brings a variety of IoT applications that involve housing, navigation, payment, and healthcare. Since IoT applications play an important role in our lives, security is critical to these applications and must be guaranteed. In order to realize this, the paper proposes a pre-execution verification method for downloaded IoT applications, which meets security and safety requirements of users using model checking. The model checking requires special models for verification, which is difficult to describe for developers. So we introduce a domain-specific modeling language (DSML) to describe IoT application and a generator from the DSML into the model to pre-execution verification and execution. Also, as a case study, we provide a study of our method used in a smart house application, which is one of the most representative examples in IoT applications.
Research on Automatic Exploitation of House of Spirit Heap Overflow Vulnerability

Existing vulnerability detection systems cannot effectively detect and utilize house of spirit type heap overflow vulnerabilities. This article summarizes the characteristics of the house of spirit attack, establishes a house of spirit utilization model, and forms a prototype system HCRAX based on this. HCRAX uses stain analysis and symbolic execution technology to monitor the key information of symbolic data that reaches the trigger point of the vulnerability, construct data constraints that trigger the house of spirit vulnerability, and solve the constraint conditions to determine whether it is possible to generate code that automatically exploits the vulnerability. By comparing with CRAX, HCRAX can realize the automatic exploitation of the house of spirit loopholes in some test programs, but none of them can be implemented, which proves the rationality of the model.
Clustering with nature-inspired metaheuristics

Clustering or cluster analysis represents one of the most important tasks of data analysis. It essentially uncovers groups (so-called clusters) in unlabeled data – with elements in the same group sharing similar values of the dataset's features. Clustering belongs to the group of unsupervised machine learning problems. It can be formulated as an optimization task which in many of its forms is known to be NP-hard. Thus it is frequently tackled with metaheuristics – also inspired by mechanisms found in nature. This chapter covers the application of nature-inspired algorithms in clustering. First a clustering task is defined, together with its variants which introduce additional factors into a grouping task. Then we characterize strategies for tackling this problem with heuristic optimization algorithms and present examples of this paradigm. Finally we provide some results stemming from our experiments with nature-inspired techniques in this field.
Investigating optimum refactoring sequence using hill-climbing algorithm

The surface indication of a deeper problem in the source code is bad smells that required to be removed as soon as possible. Not all smells are harmful, but if ignored, they automatically become faults which will further deteriorate the quality of the software. Therefore, this damage can be prevented with the help of refactoring that changes the internal attributes without affecting its external behavior. Every class of the source code contains numerous types of bad smells; therefore, more than one refactoring technique needs to be applied to clean the code. In this paper, we have tried to figure out the most optimum refactoring sequence that should be applied in a critically affected class of an open-source software (OSS), jTDS, to maximize its maintainability value with minimal effort. The results indicate improvement in maintainability value by 14.06% after the application of refactoring techniques. This study would be beneficial for the software maintenance team as this will encourage them to complete their work with much ease and will make them more efficient.
ICTs, Environmental Activism and Community Mobilization in Senegal, Kenya and South Africa

Within the broader trends of neoliberal globalization, this chapter explores the emergence of information and communication technologies (ICTs) in sub-Saharan Africa mediascapes, particularly in terms of how the affordances of these information sharing platforms are shaping citizens’ capacity to define and address their environmental realities. It presents three short case studies, VUMA Earth in Senegal, #Water 4 Cape Town in South Africa, and farming apps in Kenya, to illustrate how ICTs have engendered citizen-driven, multi-stakeholder environmental communication to adapt to climate change and related local ecological issues. As these cases reveal, ICTs have permitted citizens to mobilize in new ways through the elaboration of local and regional virtual communities and knowledge sharing to identify and tackle—not just become aware of—ecological problems. The chapter concludes by digesting the lessons of these examples of environmental communication, noting that while ICTs are quickly becoming essential tools for public involvement in sound environmental stewardship, they are also part of modernization, development, and globalization discourses that have been, and typically still are, anchored to exploitative relationships.
An elastic net regularized matrix factorization technique for recommender systems

Matrix factorization models are becoming increasingly popular in the field of collaborative filtering recommender systems. Recent developments in this area of research use a penalization method, such as the L2 penalty, to restrict overfitting and reduce sparseness. We propose an alternative way of regularizing matrix factorization for recommender systems, i.e., the elastic net. A compromise between the L1 and L2 penalties, the elastic net can be implemented in any coefficient estimation scenario. We evaluate the performance of our model on real-world data, namely the MovieLens 100K dataset. Comparison with two more restrictive models shows that our proposed regularization provides superior accuracy in predictions, as measured by the mean absolute error. Moreover, prediction errors for individual users occur less often, and we are able to accurately predict 95.02% of the ratings with an error of at most two points from the real ratings, given on a scale from 1 to 5. Finally, sensitivity analysis shows the stability of the proposed solution.
Highly Parallel Convolution Method to Compare DNA Sequences with Enforced In/Del and Mutation Tolerance

New error tolerant method for the comparison and analysis of symbol sequences is proposed. The method is based on convolution function calculation, where the function is defined over the binary numeric sequences obtained by the specific transformation of original symbol sequence. The method allows highly parallel implementation and is of great value for insertion/delition mutations search. To calculate the convolution function, fast Fourier transform is used in the method implementation.
Detecting and Bypassing Trivial Computations in Convolutional Neural Networks

Convolutional neural networks (CNNs) recently are able to exceed human accuracy in various application domains such as image recognition, medical diagnosis, and financial analysis. However, the high computational complexity of CNNs incurs high energy consumption on current hardware implementations. Existing solutions such as pruning and quantization typically require retraining or fine-tuning to regain accuracy, which can be cost-prohibitive and time-consuming. This paper proposes a retraining-free approach to reducing the computation workload of CNNs during inference by detecting and bypassing the trivial computations. We define trivial computations as the computations the results of which can be determined without actual computations. The examples include multiplication with 0, +1/-1 and addition with 0 or addition of opposite numbers. Correspondingly, we develop bypass circuits to detect the trivial computations. Once detected, the circuit delivers the predetermined result without an actual computation. Experimental results on MNIST and EMNIST show that the CNNs with bypass circuits can lead to 30.66-33.52% energy savings without any accuracy loss. This technique can be used together with existing techniques such as pruning and quantization because it is totally complimentary to such techniques.
A Systematic Method for Extracting and Analyzing Cloud-Based Compliance Requirements

Business organizations are enforced with requirements presented by regulatory bodies or standards which must be complied with and followed maximally. Failure or in instances slight deviations from these requirements had cost a number of firms and affected their reputations. These could be traced to inefficient techniques employed in analyzing these requirements. This paper presents an efficient pre-analysis and analysis procedure that employs both goal based engineering and cerno-framework approach in acquiring, extracting and classifying requirements as well as presenting algorithms that will diligently take care of previous analysis lapses. The work was applied to the health domain utilizing the Health Insurance Protection and Accountability Act (HIPAA). This was used placing in mind that such industry is fast adopting cloud services with the consideration of the security constraints that are essential in such domain. This approach will aid a full compliance consideration in the management of business or software processes aiding analysts in preventing incomplete adherence to regulatory guidelines or policies and enhancing correctness.
A Blind Metric Based Variational Approach for Ultrasound Image Denoising

In this paper, we propose a blind metric based variational approach for denoising ultrasound images corrupted by speckle noise. We follow a two step procedure where in we first derive an appropriate model for speckle noise reduction following Bayes theorem. There after we propose an enhancement algorithm based on a blind metric iterative framework for further improvement of the initial results. We validate our technique on synthetic as well as real ultrasound images. Experimental results prove the superiority of our technique, both visually and quantitatively, over the well known existing techniques in the domain.
The Elliptical Basis Function Data Descriptor (EBFDD) Network: A One-Class Classification Approach to Anomaly Detection

This paper introduces the Elliptical Basis Function Data Descriptor (EBFDD) network, a one-class classification approach to anomaly detection based on Radial Basis Function (RBF) neural networks. The EBFDD network uses elliptical basis functions, which allows it to learn sophisticated decision boundaries while retaining the advantages of a shallow network. We have proposed a novel cost function, whose minimisation results in a trained anomaly detector that only requires examples of the normal class at training time. The paper includes a large benchmark experiment that evaluates the performance of EBFDD network and compares it to state of the art one-class classification algorithms including the One-Class Support Vector Machine and the Isolation Forest. The experiments show that, overall, the EBFDD network outperforms the state of the art approaches.
Lightweight Secure-MQTT for Internet of Things

In this paper, a security mechanism for MQTT-enabled Internet of things (IoT) using a simple thin cryptographic process has been proposed. The key benefits of the proposed security mechanism are the simplicity, resilience to attacks, energy efficiency, and the speed of cryptographic computations such that it can be computed quickly. The encryption of MQTT payload is performed using lightweight operations so that a smaller amount of node energy is consumed in the process. To test the effectiveness of the proposed mechanism, Contiki-based simulation setup is created, wherein the implementation of the proposed solution is performed and tested in Cooja simulator. The analysis depicts that the proposed solution consumes less energy for enciphering data, provides security against possible attacks like spoofing attack and brute-force attack, and has tiny code footprint. It is contemplated that the proposed security solution can be used in protecting message confidentiality in the Internet of things environment and other low-power wireless networks.
Toward an Effective Locality-Sensitive Hashing Search for WMSNs Based on the Neighborhood Rough Set Approach

With the advent of the 5G era, wireless multimedia sensor networks (WMSNs) will be more widely used in security monitoring, environmental monitoring, intelligent transportation, and other applications of the Internet of Things (IoT). Data collected by WMSNs, part of IoT systems, are high-dimensional, multilevel, unstructured, and large-scale complex data. Effective nearest neighbor searching faces the “dimension curse” problem. To this end, by incorporating the neighborhood rough set (NRS) approach, this article proposes a novel and effective locality-sensitive hashing (LSH) method for high-dimensional WMSN data based on the neighborhood (NLSH). This method innovatively combines the neighborhood knowledge representation method with the LSH mechanism and extends the indexing ability of LSH. Moreover, this method does not require any prior knowledge and has good universality. The indexing method of neighborhood bucket building can reduce the number of searches and improve the response speed of the WMSN system. Extensive experiments have been carried out on several real-world multimedia data sets. The results show that a large-scale high-dimensional NLSH search based on the NRS approach can efficiently query the target point and produce very accurate results. The NLSH algorithm based on the NRS approach outperforms other advanced mainstream LSH algorithms.
DeDOP: Deduplication with cross-server Ownership over encrypted data

The data deduplication is a standard practice among cloud service providers. The deduplication process allows storage servers to utilize limited storage capacity efficiently. The deduplication process is challenging when the data are in encrypted form. There are various data deduplication schemes for encrypted data. However, they do not consider a cross-server ownership issue. In this paper, we present a data deduplication scheme, DeDOP, for encrypted data, which also solves cross-server ownership issues. The scheme can detect two almost the same file uploaded by two different users on different storage servers in the system. If the fraction of the data match in two files is more than a certain threshold, then we say files are almost the same. The scheme provides tag consistency, which allows the system to detect data faking attacks where an adversary tries to upload fake data under the different tags in the system.
Constructions of Semi-Bent Functions by Modifying the Supports of Quadratic Boolean Functions

Semi-bent functions have almost maximal nonlinearity. In this paper, two classes of semi-bent functions are constructed by modifying the supports of two quadratic Boolean functions $f_1(x_1,x_2,\cdots,x_n)=\bigoplus\limits^{k}_{i=1}x_{2i-1}x_{2i}$ with $n=2k+1\geq3$ and $f_2(x_1,x_2,\cdots,x_n)=\bigoplus\limits^{k}_{i=1}x_{2i-1}x_{2i}$ with $n=2k+2\geq4$. Meanwhile, the algebraic normal forms of the newly constructed semi-bent functions are determined.
Deep Convolutional Neural Network Classifier of Pulse Repetition Interval Modulations

Electronic support systems obtain valuable information about a pulsed radar through the analysis of its Pulse Repetition Intervals (PRIs). PRI signal is very agile and undergone complex distortion which makes its analysis non-trivial. In this paper, a Convolutional Neural Network (CNN) is proposed for automatic PRI classification. We show that standard min-max and zero-mean unit-variance data normalization is not suitable. We propose a novel normalization providing faster training and lower classification error. We show that a recently proposed CNN scheme is prone to miss-classify higher cardinality stagger so we enhance the scheme by deeper layers, batch normalization and dropout regularization.
MIRCS

The development and use of educational technologies (edutech) have seen some advance over the years in most developing countries. Most Higher Education Institutions' (HEI) in developing countries have result processing software as part of an overall edutech suite. However, adoption or use of the result software has largely been web-based, hence only internet enabled phones (feature or smartphones) or PCs are able to access it. In this paper, we present MIRCS, a low-end USSD-SMS based system designed for checking Higher Education semester results in resource limited settings like Africa. MIRCS can be accessed by students without internet and by using any type of phone, including basic phone. We design the systems working with users from our University, a Higher Education Institution in a developing country and report preliminary results from trials with 784 students. Based on this trial, the system records up to 89% successful access rate from recorded 3136 USSD sessions over four (4) telecommunication networks.