Reviewerly/exHarmony huggingface dataset:
What Exactly Determines the Type? Inferring Types with Context

Closed-source programs lack crucial information vital for code analysis because that information is stripped on compilation to achieve smaller executable size. Variable type information is fundamental in this process. In this paper, we implement a system called CATI (Context-Assisted Type Inference), which locates variables from stripped binaries and infers 19 types from variables. Experiments show that it infers variable type with 71.2% accuracy on unseen binaries.
Human-machine model of decision makings for predictions

This paper presents a model to design, describe and analyze the human machine system of systems (SoS) for decision makings involving predictions, particularly about future. The hypernetwork model is used, which is suitable to describe and analyze systems consisting of humans and machines, because the model allows simultaneous representations of multiple viewpoints, dynamic hierarchical structure and variable boundaries.
Finding Big Leaks with Big Data: Case Studies from an Internet-of-Things Leak Detection Platform

This paper presents results from an “Internet of Things” leak detection technology that remotely and non-invasively monitors pipelines at the scale of a distribution network. Findings are presented from the deployment of 10,000+ acoustic leak detection nodes with dozens of utility partners. The design and installation of the nodes, the type of data collected, and the approach to data analysis is outlined. A series of case studies are described where the technology has found leaks that failed to surface: large or significant leaks, leaks that were difficult to pinpoint, and unexpected signals found in the process. Both the data analysis and utility perspectives are shown, comparing remotely collected acoustic data to observations from field technicians. The successes and limitations of the technology are both discussed.
Artists Identification Using Convolutional Sparse Autoencoder

This paper describes a method to identify artists with a similar painting technique using a semi-unsupervised learning approach. In this paper, we compared Van Gogh and Paul Gauguin since both use impasto painting technique. The contribution of this paper lies in the use of Convolutional Sparse Autoencoder to classify Van Gogh and Paul Gauguin's paintings. The dataset consists of 450 Paul Gauguin's paintings and 716 Van Gogh's paintings. The best result was achieved by using 100 hidden neurons with test accuracy 63.85%, precision 64.12%, and recall 63.85%.
Implementing the Shortest Time Route Search Algorithm in Semarang Using the Best First Search Method

Abstract The purpose of this research was to find the shortest route so that the search for the destination is more effective and efficient to avoid traffic jams using the best first search method. the best first search method is a search method by selecting the best value from different data. The aim was to find the shortest time route between regions in Semarang using the Best First Search algorithm according to the distance of each region, road width, and traffic density of motorized vehicles to get better time efficiency. Using the best first search usually tries to estimate the distance at the end of the path with a solution. The shortest time search system in the city of Semarang on traffic density, road width to determine the desired time efficiency. Analysis of the Best First Search algorithm by using the output of the path used as input in the search for the shortest route, between the starting point to the destination point. Conducting trials with the software as a result of recording data according to the field. the impact of the system on searching for route research becomes solution for road users to reach their destination a short time.
Active stereo-vision 3D perception system for precise autonomous vehicle hitching

This paper presents a low-cost, low-power 3D optical sensor that is fully integrated for feedback control of an autonomous vehicle to a hitch. The hitching problem is an important challenge for vehicles in agricultural and transportation industries, yet few studies have been carried out in literature. We developed a perception system that combines low-accuracy classical stereo vision sensing with higher accuracy active sensing, as well as incorporating adaptive control strategies to tackle the hitching problem. We tested our proposed method on an RC car to approach a model hitch target. Our experiments demonstrated that our system can successfully control the vehicle from within ±35° of perpendicular to the hitch, to a final position with an overall standard deviation of 3.0 mm of lateral error and 1.5° of angular error.
Bit Manipulation: Conditional Statement using Bit-wise operators with C++

All of information that manipulated by a computer is represented in the form of bits, so in the programming language it is necessary to understand bitwise operations at the first. This paper aims to create a concept of making Conditional Statements with Bitwise operators in C ++. By doing so, we hope that people is easy to understand the operation behind conditional statements. A conditional operator is also known as a ternary operator. It takes three operands. A conditional operator is closely related with if else statement. The method used is a literature study studying the bit manipulation algorithm in the C ++ language. The results obtained are a function using bitwise operations in C ++ that implement conditional statements.
A Bi-criteria Analysis for Fuzzy C-means Problem

Fuzzy C-means problem has a broad application prospect as a branch of clustering problem. This paper deeply explores the fuzzy C-means bi-criteria problem in two different algorithms and extends the previous known $$O(k^{2}\mathrm{ln}~k)$$ and $$O(k\mathrm{ln}~k)$$ performance guarantee. It is shown that for any constant $$\beta \ge 1$$ , selecting $$\beta k$$ cluster centers can achieve $$O(k^{2})$$ and O(k) approximation. Preliminary numerical experiments are proposed to support the theoretical results of the paper, in which we run these algorithms on real data sets with different parameter values.
VPN+ Towards Detection and Remediation of Information Leakage on Smartphones

Smartphones carry a plethora of sensitive and personally identifiable information (PII) such as email addresses, GPS coordinates, names, and phone numbers. A common occurrence in the design of many popular smartphone applications is to harvest this user data for consumer market analysis and targeted advertising. Transmitting sensitive PII data without the user's explicit knowledge has been given the name “information leakage Unfortunately, the permission systems employed by modern smartphone OSes are too coarse grained, presenting an “all or nothing” choice to users making it largely insufficient to defend against information leakage attacks. In this paper we propose a network-filtering based solution, which uses an entirely on-device VPN to capture and scan network packets for PII data. Our novelty is a specially designed string searching algorithm used to scan network packets, and a Naive Bayes classifier to learn and predict the user's desired action when information leakage occurs. We evaluate and compare our work to other recent literature. We achieve 2MB/s throughput with our string searching algorithm and ~66% accuracy with our Naive Bayes classifier after building a training set of only 50 observations.
Impact of Memory DoS Attacks on Cloud Applications and Real-Time Detection Schemes

Even though memory-based denial-of-service attacks can cause severe performance degradations on co-located virtual machines, a previous detection scheme against such attacks cannot accurately detect the attacks and also generates high detection delay and high performance overhead since it assumes that cache-related statistics of an application follow the same probability distribution at all times, which may not be true for all types of applications. In this paper, we present the experimental results showing the impacts of memory DoS attacks on different types of cloud-based applications. Based on these results, we propose two lightweight, responsive Statistical based Detection Schemes (SDS/B and SDS/P) that can detect such attacks accurately. SDS/B constructs a profile of normal range of cache-related statistics for all applications and use statistical methods to infer an attack when the real-time collected statistics exceed this normal range, while SDS/P exploits the increased periods of access patterns for periodic applications to infer an attack. Our evaluation results show that SDS/B and SDS/P outperform the state-of-the-art detection scheme, e.g., with 65% higher specificity, 40% shorter detection delay, and 7% less performance overhead.
Image Transmission over V2V Channel with Moving Scatterer

Image transmission over some different communication channels have already been done. The transmission has to strictly obey channel characteristic so the receiver can preserve quality of received image. Vehicular channel becomes one of communication channel that occasionally studied to transmit image data. In this paper, this channel hereby is studied in term of image transmission by considering the presence of moving scatterer around transmitter and receiver. The effects of number of scatterer that move with certain velocity are examined regarding received image quality by measuring the performance according to SNR, BER and signal constellation. Simulation results shown that an increase in amount of moving scatterer over signal constellation caused symbol position more interfered by noises. Moreover, when 16 scatterer was used with BPSK modulation has caused Inter Symbol Interference (ISI). Meanwhile, the quality of received image at that value were poor and hardly recognized visually. When SNR 20 dB was given, the difference of BER between 2 scatterer and 16 scatterer was 0.11.
A Framework for Sedimentological Data Structure Extraction in Gold Mining Facies Delineation

Quartz-pebble conglomerate (reef) horizons of the Witwatersrand Basin are the most important sources of gold (Au) in South Africa. Concentration of gold to economic ore-grades in the Witwatersrand-type gold deposits has been linked to sedimentary processes having operated as principal driving forces. Therefore, in Witwatersrand-type gold deposits, sedimentological data are often utilized to make qualitative assessment of in-situ gold-grade. This paper presents a framework that prescribes a normative and principled analysis of sedimentological data in order to quantify in-situ gold-grades. Its significance is the provision of a scalable process for extracting correlational structures, regardless of the attribute value-types. The framework applies network analysis, and a bespoke clustering algorithm to support visual analysis of spatial correlation between sedimentological data samples. A key result is the preservation of the semantic interpretation of the resulting cluster assignments via a topologically invariant transformation. This is achieved by applying non-uniform weighting to the attributes to emphasize the influence of non-numeric attributes in cluster assignment. Additionally, sedimentological attributes that are strongly correlated with gold-grade are identified by adopting total variance reduction from decision tree regression as a guiding principle. Using this approach, and data collected from a mining company located in the Witwatersrand Basin, spatial correlation structures defined over sedimentological data attributes are reported. As a byproduct, observations that lack spatial correlation with the rest of the data are identified. These isolated observations can inform quality assurance and quality control departments about potential irregularities in the reporting of sedimentological data within exploration and mining companies.
Driver Attention Area Extraction Method Based on Deep Network Feature Visualization

The current intelligent driving technology based on image data is being widely used. However, the analysis of traffic accidents occurred in intelligent driving vehicles shows that there is an explanatory difference between the intelligent driving system based on image data and the driver’s understanding of the target information in the image. In addition, driving behavior is the driver’s response based on the analysis of road information, which is not available in the current intelligent driving system. In order to solve this problem, our paper proposes a driver attention area extraction method based on deep network feature visualization. In our method, we construct a Driver Behavior Information Network (DBIN) to map the relation between image information and driving behavior. Then we use the Deep Network Feature Visualization method (DNFV) to determine the driver’s attention area. The experimental results show that our method can extract effective road information from a real traffic scene picture and obtain the driver’s attention area. Our method can provide a useful theoretical basis and related technology of visual perception for future intelligent driving systems, driving training and assisted driving systems.
Detecting Control Flow Similarities Using Machine Learning Techniques

In this work, methods are presented that allow a comparison between control flow paths. The intended use cases for these methods are weak points and bug detection. In existing work, control flow graphs have always been compared with each other to achieve those goals. Nevertheless, vulnerabilities or bugs can be hidden in completely different contexts, i.e. in different parts of the program. Therefore, this work deals with the extraction, coding and comparison of control flow paths. This is because the path of a vulnerability or bug in which the instructions are executed is always similar.
Improving Search Snippets in Context-Aware Web Search Scenarios

As an essential part in web search, search snippets usually provide result previews for users to either gather useful information or make click-through decisions. In complex search scenarios, users may need to submit multiple queries to search systems until their information needs are satisfied. As user intents tend to be ambiguous, incorporating contextual information for user modeling has been proved effective in many session-level tasks. Therefore, the generation of search snippets may also benefit from the integration of context information. However, to our best knowledge, most existing snippet generation methods ignore user interaction and focus merely on the query content. Whether it is useful of exploiting session contexts to improve search snippets still remains inscrutable. To this end, we propose a snippet generation model which considers session contexts. The proposed method utilizes the query sequence as well as users' interaction behaviors within a session to model users' session-level information needs. We also adopt practical log-based search data to evaluate the performance of the proposed method. Experiment results based on both expert annotation and user preference test show the effectiveness of considering contextual information in search snippet generation.
The Design Of Visual Media Based On Ict To Improve The Beginning Writing Of Autistic Spectrum Disorder Students In Elementary School

Abstract The purpose of this research is to produce the design of visual media development based on ICT (Information Communication and Technology) to improve the students’ initial writing of Autistic Spectrum Disorder (ASD) which is suitable to be used to support the learning process of writing Autistic Spectrum Disorder (ASD) students. This research uses research and development (R&amp;D) model from Borg &amp; Gall which is simplified into 3 stages, namely, (1) potential and problems, (2) data collection, and (3) product design. Data collection techniques in this study are observation, interviews and documentation. The results of this study is the design of the development of visual media based on ICT (Information Communication and Technology) to improve the beginning writing skills of Autistic Spectrum Disorder (ASD) students in elementary School.
Geometry-Based Automated Recognition of Objects on Satellite Images of Sub-meter Resolution

The paper considers an algorithm for automated classification of mobile small size objects on multispectral satellite images of submeter spatial resolution using radiometric and geometric features. It ensures recognizing the desired classes of objects with high accuracy regardless of their orientation in the image. The geometric features of the objects classified in the binary image included the area of the object, the lengths of the principal and auxiliary axes of inertia, the eccentricity of the ellipse with the main moments of inertia, the area of a convex polygon described near the object, the equivalent diameter of a circle with the same area, and the convexity coefficient.
Heterogeneous Distributed SRAM Configuration for Energy-Efficient Deep CNN Accelerators

Convolutional Neural Networks (CNNs) are often the first choice for visual recognition systems due to their high, even superhuman, recognition accuracy. The memory configuration of CNN accelerators highly impacts their area and energy efficiency, and employing on-chip memories such as SRAMs is unavoidable. SRAMs can reduce the number of energy-hungry DRAM accesses by storing a large amount of data locally. In this paper, we propose a new on-chip memory configuration, for a certain class of CNN accelerators that divides the memories into two groups. The first group consists of shallow but wide SRAMs into which parallel computational units accumulate intermediate results. The second group includes narrow but deep SRAMs shared between adjacent computational units to store then transfer final results to the external DRAM without interrupting the computation process. Implementation results show that the proposed configuration reduces the area by 21 % and improves the energy efficiency by 18% compared to designs which use an ordinary ping-pong structure for SRAM-DRAM data transfer.
The Concept of Information in the Documentation and Information Science Fields

In this chapter the main approaches to the concept of information are presented, as they have been set forth in the documentation and information science fields from the middle to the late twentieth century. To explain them, several authors have grouped them into three paradigms—physical or systems-oriented, subjective or cognitive, and socio-cognitive—which we will describe, focusing on the main representatives of each. Finally, we argue that the socio-cognitive paradigm, with its emphasis on the social context of information practices (beliefs, assumptions, behaviors, and evaluations, among others), provides the best epistemological framework for understanding the research on information literacy in higher education.
Centroids Triplet Network and Temporally-Consistent Embeddings for In-Situ Object Recognition

This work proposes learning to recognize objects from a small number of training examples collected and deployed in-situ. That is, from data collected where the objects are commonly placed or being used, perhaps after first encountering them, the learning algorithm immediately is able to recognize them again. We refer to this method-ology as in-situ learning, and it opposes to the conventional methodology of using complex data acquisition mechanisms, such as rotating tables or synthetic data, to build a large-scale dataset for training convolutional neural networks (ConvNets). To learn in-situ, we propose a novel loss function that generates discriminative features for known and unseen objects, by utilizing a regularization term that reduces the distance between features and their manifold centroid. Additionally, we propose a temporal filter that is particularly useful to quickly react to appearing objects on the scene, which depending on the distance between neighboring video-frame features, it applies a weighted average between the current and the previous frame. Our framework achieves state-of-the-art accuracy for in-situ and on-the-fly learning, for the case of known objects achieves an average increase in accuracy of 3.01%, an increase of 3.3% for novel objects, and an average increase of 7.07% for the combined case, compared with the closest baseline. Utilizing the temporal filtering, led to a further increase in accuracy against nuisances of 7.32% for the known and novels objects case.
A Dynamical System Approach to Real-time Three-Dimensional Concave Obstacle Avoidance

In this paper, we propose an approach based on Dynamical System (DS) to avoid three-dimensional concave obstacles in real-time. Most three-dimensional concave obstacles can be divided into multiple parts, each of which can be approximated by a convex ellipsoid, and the ellipsoids intersect in pairs. The key of this method is that when the trajectory computed according to the deformed DS reaches a point on the intersection line between two of the convex ellipsoids, the trajectory evolves along the intersection line. We prove that the proposed approach can ensure the impenetrability of the concave obstacles. We validate the effectiveness of the method in the simulations on several different concave objects and experiments on the arm of the UBTECH humanoid robot.
Damage evaluation on 3Dimensional Carbon Fibre laminates by speckle interferometry

In this work, the evaluation of damaged areas due to low velocity impact tests at different impact energy values on Carbon Fibre (CF) laminates with 3D fabric, made by infusion technology and reinforced with epoxy resin, were investigated by NDI (Non-destructive Investigation) technique based on speckle interferometry. Composites are characterized by several interacting failure modes such as matrix breakage, fibre failure and delaminations, which can be simultaneously induced by low velocity impacts. It can be very difficult to detect by visual inspections of the structures such phenomena. The aim of this study is to evaluate the damaged area due to an impact event. CF composite laminates were impact damaged with four energy levels (5, 10, 20, 30J) at room temperature, by drop weight impact machine. The results showed that ESPI technique was able to identify barely visible low-velocity impact damages.
Parallelization of Classical Numerical optimization in Quantum Variational Algorithms

Numerical optimization has been extensively used in many real-world applications related to Scientific Computing, Artificial Intelligence and, more recently, Quantum Computing. However, existing optimizers conduct their internal computations sequentially, which affects their performance. We observed a general pattern that enabled us to parallelize such internal computations and achieve significant speedup. We designed a novel parallelization algorithm for optimizers, which consists of pattern detection, prediction, precomputation, and caching. Importantly, our design does not require any change to the optimizers. Instead, it simply modifies the function to be optimized, thereby leading to several engineering advantages, including simplicity, modularity and portability. We implemented this solution and included it in the Qiskit Aqua open-source project. In this paper, we present an evaluation on both standard benchmarks and real-world quantum-computing applications. The evaluation results confirm that our approach (1) incurs negligible overhead, (2) effectively speeds up optimization, and (3) does not affect the accuracy of the results or the convergence of the optimizers.