Reviewerly/exHarmony huggingface dataset:
Semantically Enhanced Multi-scale Feature Pyramid Fusion for Pedestrian Detection

Detecting multi-scale pedestrians (especially small scale ones) is one of the most challenging problems in computer vision community. At present, most existing pedestrian detectors only adopt single-scale feature map in their backbone network for detection, which is not capable of fully taking advantages of multi-scale feature information, and thus resulting in unsatisfactory multi-scale detection performance. To address this issue, we propose in this paper a semantically enhanced multi-scale feature pyramid fusion method that can effectively extract and integrate multi-scale feature maps for multi-scale pedestrian detection. The proposed method consists of two main components: 1) Trapezoidal Path Augmented Module (TPAM) and 2) Multi-scale Feature Fusion Module (MFFM). TPAM aims at extracting higher-level semantic features by the additional higher-level feature layers, where the produced features are enhanced with supplementary higher-level semantic information, so that they can focus more accurately in the pedestrian area, leading to improved detection performance. MFFM aims at integrating multi-scale feature maps coming from TPAM to further take advantages of multi-scale feature information and reduce computational redundancy caused by multiple detection heads. By extensive experimental evaluations on the popular CityPersons and Caltech benchmarks, our proposed method achieves superior performances than previous state of the arts on multi-scale pedestrian detection.
QuickBCC: Quick and Scalable Binary Vulnerable Code Clone Detection

Due to code reuse among software packages, vulnerabilities can propagate from one software package to another. Current code clone detection techniques are useful for preventing and managing such vulnerability propagation. When the source code for a software package is not available, such as when working with proprietary or custom software distributions, binary code clone detection can be used to examine software for flaws. However, existing binary code clone detectors have scalability issues, or are limited in their accurate detection of vulnerable code clones. In this paper, we introduce QuickBCC, a scalable binary code clone detection framework designed for vulnerability scanning. The framework was built on the idea of extracting semantics from vulnerable binaries both before and after security patches, and comparing them to target binaries. In order to improve performance, we created a signature based on the changes between the pre- and post-patched binaries, and implemented a filtering process when comparing the signatures to the target binaries. In addition, we leverage the smallest semantic unit, a strand, to improve accuracy and robustness against compile environments. QuickBCC is highly optimized, capable of preprocessing 5,439 target binaries within 111 min, and is able to match those binaries against 6 signatures in 23 s when running as a multi-threaded application. QuickBCC takes, on average, 3 ms to match one target binary. Comparing performance to other approaches, we found that it outperformed other approaches in terms of performance when detecting well known vulnerabilities with acceptable level of accuracy.
A Process Adaptation Framework for Blockchain-Based Supply Chain Management

Blockchain technology has been applied to supply chains in many industries, such as manufacturing, retail, agriculture, and pharmaceutics. Blockchains can improve supply chain integrity by providing a level playing field for resource-constrained small-to-medium size businesses to participate in a supply chain process and demonstrate trustworthiness. As with many complex and highly connected processes, significant uncertainties could arise in supply chains at the physical level of goods and transports, and the digital level of data and blockchain transactions. Supply chain uncertainties include upstream supply uncertainty, internal process uncertainty and downstream demand uncertainty, which require flexible mechanisms for run-time process adaptation that existing blockchain technologies do not adequately support. In this paper, we propose a framework that enables process adaptation through attaching and detaching process fragments defined in smart contracts to a process instance dynamically at run-time. The proposed framework is compared with an existing static process method and another existing blockchain-based solution supporting process adaptation.
A 3D Real Object Recognition and Localization on SLAM based Augmented Reality Environment

This paper introduces a method for recognizing real world objects in AR(Augmented Reality) environment and visualizing virtual information based on the objects. Existing AR shows high dependence on markers. The use range of the virtual space is limited to narrow marker space and the placement and tracking of virtual objects in a 3D is also limited to the space centered on the marker. The method constructs a map of the space in the form of a point cloud using SLAM, and performs real-world object recognition of the constructed wide AR space in real time. As a result, the degree of freedom of space utilization increases, and it is helpful in applying AR such as information display of real-world objects by accurately recognizing and localizing the location of objects existing in the real-world space.
Consistent Query Answering for Primary Keys on Path Queries

We study the data complexity of consistent query answering (CQA) on databases that may violate the primary key constraints. A repair is a maximal consistent subset of the database. For a Boolean query q, the problem CERTAINTY(q) takes a database as input, and asks whether or not each repair satisfies the query q. It is known that for any self-join-free Boolean conjunctive query q, CERTAINTY(q) is in FO, L-complete, or coNP-complete. In particular, CERTAINTY(q) is in FO for any self-join-free Boolean path query q. In this paper, we show that if self-joins are allowed, then the complexity of CERTAINTY(q) for Boolean path queries q exhibits a tetrachotomy between FO, NL-complete, PTIME-complete, and coNP-complete. Moreover, it is decidable, in polynomial time in the size of the query q, which of the four cases applies.
Colour Image Encryption using Expand-Shrink Operation in Chaos Encryption Algorithm

Abstract In recent decades, chaos based encryption algorithm is widely used in image encryption. Most of the chaos based encryption algorithm operate at bit level, which is ineffective in high resolution digital images. In this research paper, a new colour image encryption algorithm is proposed by using expand-shrink operation for confusion and diffusion operation in chaos algorithm. The proposed algorithm consists of confusion-diffusion strategy operated at pixel level. Original colour image is expanded based on intrinsic features of bit planes. Expanded image undergo confusion diffusion operation using Hilbert scan method. In addition, for diffusion operation uses circle map that generates random image. In the experimental section, the proposed algorithm achieved better performance in image encryption on various digital images.
A Deep Learning-Based Trust Assessment Method for Cloud Users

Attacks launched from the inside of the cloud are threats not only to the cloud users but also to the cloud infrastructures. Although with trusted computing the cloud service providers can guarantee the trust and security of the cloud environment for the users, the trustworthiness of users is not properly assessed. Inspired by the concept of variable trust, the main contribution of this paper is that we propose a trust assessment method for cloud users based on deep learning. Firstly, we extract users’ activities from system logs and employ stacked LSTM (long short-term memory) neural network to model normal activity patterns to build trust profiles for different users. Secondly, the trust profile is capable of predicting future behavioural actions of the specific user, and by calculating the similarity between predicted actions and actual actions the trustworthiness of the user will be assessed with a baseline to detect the trust state of the cloud user dynamically. And in the end, we design and conduct experiments on a public dataset. The results of experiments indicate that when the user is in abnormal state, there are notable differences between predicted actions and user’s actual actions, which proves the efficiency of the proposed method.
Secure Scalable Blockchain for Sealed-Bid Auction in Energy Trading

In this paper, we design and develop a scalable blockchain-based auction system for energy trading that outsources the execution of computation-intensive transactions to an off-chain network. The system is designed to promote peer-to-peer energy trading between electrical vehicles and energy suppliers. The idea is to reduce the burden on the blockchain by entrusting an off-chain network with the auction process. The off-chain network is composed of a small subset of nodes (validators) chosen randomly on-chain. The selected validators act as auctioneers to receive bids from energy suppliers, decide about the auction winner, and then announce it to the blockchain. In order to ensure the integrity of the decision made off-chain about the winner, the proposed winner is validated on-chain only if no dispute is raised by any of the bidders who participated in the auction. To secure the dispute process, the validators need to issue an acknowledgment receipt to each bidder using a Proof-of-Reception protocol, which consists of exchanging a digital receipt signed by at least one validator.
Football Player Posture Detection Method Combining Foreground Detection and Neural Networks

In recent years, with the rapid development of artificial intelligence, information technology, intelligent digital video surveillance systems, real-time sports competition playback, and other technologies have emerged one after another, making the advantages of deep learning-based football posture detection tasks become more obvious. Related models and methods have been applied to the research field of sports posture estimation and have achieved great improvement, surpassing the traditional football posture estimation method based on manual design features in one fell swoop. In addition, the application of video foreground detection has developed rapidly and has great application value in sports analysis. Therefore, this paper proposes a novel football motion detection approach combining foreground detection and deep learning for real-time detection of football player posture. The main task of foreground target detection is to extract the interesting foreground target in the real monitoring scene and use it as the target of interest for subsequent analysis. Then, we propose a triple DetectNet detection framework based on deep learning technology, which can quickly and robustly realize the three-dimensional pose estimation of multiperson motion. For input, the triple DetectNet framework uses three neural networks and is executed in three stages; the first stage is to use the DetectNet (DN) network to detect the bounding box of each person separately, the second stage uses the 2DPoseNet (2DPN) network to estimate each of the corresponding two-dimensional poses of the individual, and the third stage uses the 3DPoseNet (3DPN) network to obtain the 3D pose of the person. This paper also conducted experiments on four datasets, and the results proved the superiority and success of this algorithm.
A Pipelined Point Cloud Based Neural Network Processor for 3-D Vision With Large-Scale Max Pooling Layer Prediction

The point cloud data provides useful geometric information to 3-D intelligent systems such as autonomous driving, 3-D reconstruction, and hand pose estimation (HPE). Many mobile devices have implemented the 3-D intelligent system with their limited hardware resources. However, previous processors were not designed for accelerating the point cloud based neural network (PNN) which consists of sampling-grouping layers (SGLs) and convolution layers (CLs). In this article, a pipelined PNN processor is proposed for low latency PNN-based 3-D intelligent systems in mobile devices. The processor adopts the pipelined heterogeneous architecture to accelerate both SGLs and CLs in PNNs. The window-based sampling-grouping (WSG) algorithm boosts up the throughput <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 2.34 higher in SGLs by directly sampling and grouping the 3-D point cloud data from the depth image. Furthermore, the max pooling (MP) prediction core (MPPC) predicts the large-scale (64- and 128-to-1) MP layers, which increases the throughput by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 1.31 higher. Besides, the MP prediction on the tiled data can hide the latency of the MPPC and solve the bank conflict problem on the in-out memories in the convolution core (CC). As a result, the processor successfully demonstrates the PNN-based HPE system resulting in 4.45 ms of the processing time with 8.24 mm of HPE error and 266 mW of power consumption.
Enhancing the Student Learning Experience by Adopting TDD and BDD in Course Projects

A demonstration of the application and contribution of Test-Driven Development (TDD) and Behavior-Driven Development (BDD) in the student learning experience in the context of a Software Engineering course. Background: Software testing is an activity for ensuring software quality. Although teaching testing rigorously to students is a priority in academia, undergraduate students often encounter difficulties performing testing tasks effectively. Intended Outcomes: To increase satisfaction rate and course grades, and reduce delivery time. Application Design: We experimented with the Software Engineering Laboratory (LES) course of a Private University in the Bachelor of Computer Science and Information Systems courses. This experiment corroborated the learning difficulties of students. Collected data were assessed both quantitatively and qualitatively. Findings: Backed up with statistical tests, the results showed a reduction in student absences, higher student satisfaction rate, and higher grades in the courses. Furthermore, our approach allowed students to deliver a product in a short period, representing a possibility of adoption of BDD due to their successful learning experience. Finally, we aim to foster a discussion of appropriate teaching methods of software testing.
Aria: Tolerating Skewed Workloads in Secure In-memory Key-value Stores

The recent advent of the hardware trusted execution environment (TEE), e.g., Intel SGX, enables encrypted and integrity-verified in-memory key-value (KV) stores. However, due to the architectural limitations of the hardware, it is non-trivial to build a secure in-memory KV store with SGX without compromising the performance. The reason comes from (i) the limited memory capacity the SGX TEE provides, and (ii) being unaware of the access patterns of skewed workloads, which are commonly seen in the real world.In this paper, we present Aria, a secure in-memory KV store based on SGX. Our goal is to utilize the limited resource while still achieving high performance. Aria places KV pairs and index structures directly in the untrusted memory and introduces the security metadata in the TEE to conduct protection. The core component of Aria is Secure Cache, a software-based cache layer, which uses the limited memory resource to guarantee the confidentiality and integrity (including freshness) of Aria. Secure Cache keeps the frequently accessed security metadata in the TEE memory at fine-granularity and evicts rarely-used ones to the untrusted memory. With Secure Cache, we have the opportunities to explore strategies that are impossible in SGX implementation. By decoupling the security metadata management from the index structure, Aria supports various index schemes. We implement Aria with the indexes of both a hash table and a B-tree. Experiments show that Aria improves throughput by up to 104% compared to the state-of-the-art system.
Designing a mobile app to trace Covid-19 using social networks

In this paper, we introduce our new design of mobile social app. This app can be used to trace the spread of Covid-19 among the app users while in social gathering. In the state of the art, we studied several existing social apps from around the world to trace/track Covid-19 and compare them with our new social app. In this comparisons, a number of parameters are defined to cover wide range of aspects related to current pandemic. These aspects covers existing of technologies (Cloud, Bluetooth, Mobile Device), governments (Support, Use, Enforcement), and app users (Health, Social, Privacy). From the users prospective, the new design takes into account the effects of wearing masks have on lowering the rate and informing the user of the spread of the Covid-19 pandemic in their surrounding communities. From the development prospective, the new design was described without hassling with the platform details of app implementation. Our evaluation of the design support the development of a social app that adhere to existing technologies, government policies, and user trust.
Wavefront matching method as a deep neural network and mutual use of their techniques

This paper investigates correspondence between the wavefront matching method (WFM), a design method for optical circuits, and the residual network (ResNet), one of the deep neural networks (DNNs). "Lesion studies" show that there is a correspondence not only in optimization or learning but also in characteristics. We show that the skip connection, an important concept in ResNets, needs to be modified for the WFM, and propose a new optical skip connection. Considering the WFM as a DNN, we apply the dropout technique, which is common in DNNs, with the optical skip connection. To show the possibility that the physical interpretations and various techniques can be mutually used to develop the WFM and DNNs , we present, as an example, the successful reduction of the over-adaptation in the WFM.
ArtiMarker: A Novel Artificially Inflated Video Marking And Characterization Method on YouTube

YouTube is on demand platform, crowd sourced by design and its huge popularity encourages users to adopt fraudulent methods to inflate viewcounts of their videos, thereby increasing the revenue of the video uploader. It is very important to mark artificially inflated videos as it is one of the stepping stone to encourage false information over web and to gain monetary benefits.To address this issue, in this paper we present "ArtiMarker" a novel artificially inflated video marking and characterization (less,medium,high) method on YouTube based on user level feature.The proposed technique described in the paper will help researchers to understand a new efficient method to mark and characterize artificially inflated videos. The proposed parameters in this paper can also be used in identification of spam videos, fake profiles on YouTube. The performance of proposed work is analyzed and demonstrated using various classifier such as Decision Tree, Function-Based and Bayes.The comparison is based on the testing strategy utilized during the experiments;percentage split,cross validation. It has been observed that Bayes network,J48 and Random Forest are best suited for percentage-split while Random Forest is more suited for cross validation schemes.
Representation based regression for object distance estimation

In this study, we propose a novel approach to predict the distances of the detected objects in an observed scene. The proposed approach modifies the recently proposed Convolutional Support Estimator Networks (CSENs). CSENs are designed to compute a direct mapping for the Support Estimation (SE) task in a representation-based classification problem. We further propose and demonstrate that representation-based methods (sparse or collaborative representation) can be used in well-designed regression problems especially over scarce data. To the best of our knowledge, this is the first representation-based method proposed for performing a regression task by utilizing the modified CSENs; and hence, we name this novel approach as Representation-based Regression (RbR). The initial version of CSENs has a proxy mapping stage (i.e., a coarse estimation for the support set) that is required for the input. In this study, we improve the CSEN model by proposing Compressive Learning CSEN (CL-CSEN) that has the ability to jointly optimize the so-called proxy mapping stage along with convolutional layers. The experimental evaluations using the KITTI 3D Object Detection distance estimation dataset show that the proposed method can achieve a significantly improved distance estimation performance over all competing methods. Finally, the software implementations of the methods are publicly shared at https://github.com/meteahishali/CSENDistance.
An Adversarial Smart Contract Honeypot in Ethereum

A smart contract honeypot is a special type of smart contract. This type of contract seems to have obvious vulnerabilities in contract design. If a user transfers a certain amount of funds to the contract, then the user can withdraw the funds in the contract. However, once users try to take advantage of this seemingly obvious vulnerability, they will fall into a real trap. Consequently, the user’s investment in the contract cannot be retrieved. The honeypot induces other accounts to launch funds, which seriously threatens the security of property on the blockchain. Detection methods for honeypots are available. However, studying the manner by which to defend existing honeypots is insufficient to fight against honeypots. The new honeypots that may appear in the future from the perspective of an attacker must also be predicted. Therefore, we propose a type of adversarial honeypot. The code and behavioral features of honeypots are obtained through a comparative analysis of the 158,568 non-honeypots and 352 honeypots. To build an adversarial honeypot, we try to separately hide these features and make the honeypot bypass the existing detection technology. We construct 18 instances on the basis of the proposed adversarial honeypot and use an open-source honeypot detection tool to detect these instances. The experimental result shows that the proposed honeypot can bypass the detection tool with a 100% ratio. Therefore, this type of honeypot should be given attention, and defensive measures should be proposed as soon as possible.
Efficient representation and counting of antipower factors in words

A k-antipower (for k≥2) is a concatenation of k distinct words of the same length. Fici et al. (ICALP 2016) initiated the study of antipowers occurring as fragments of a word, whereas Badkobeh et al. (Inf. Process. Lett., 2018) presented first algorithms computing such fragments. We address two open problems originating from the latter work and propose efficient algorithms for counting and reporting fragments being k-antipowers. For a length-n word, our algorithms cost O(nklog⁡k) and O(nklog⁡k+C) time, respectively, where C is the number of reported fragments. We also show that the number of different k-antipower factors of a length-n word can be computed in O(nk4log⁡klog⁡n) time. Finally, we present an improved data structure that checks whether a given fragment is a k-antipower for a given integer k≥2. Our main algorithmic tools are runs and maximal gapped repeats.