Reviewerly/exHarmony huggingface dataset:
Image Encryption Algorithm Based on Fractional Order Chaotic System

This paper proposes an image encryption algorithm based on fractional order chaotic system, Josephus problem principle and circular diffusion. According to the principle of Josephus problem, the original image pixels are scrambled to different positions, and the global diffusion of the original image is achieved by cyclic diffusion. The three-dimensional fractional order chaotic system provides the random sequence for image encryption algorithm. Experimental results show that the system has good statistical characteristics and can resist security attacks.
Learning Abstract Representations Through Lossy Compression of Multimodal Signals

A key competence for open-ended learning is the formation of increasingly abstract representations useful for driving complex behavior. Abstract representations ignore specific details and facilitate generalization. Here, we consider the learning of abstract representations in a multimodal setting with two or more input modalities. We treat the problem as a lossy compression problem and show that generic lossy compression of multimodal sensory input naturally extracts abstract representations that tend to strip away modalitiy specific details and preferentially retain information that is shared across the different modalities. Specifically, we propose an architecture that is able to extract information common to different modalities based on the compression abilities of generic autoencoder neural networks. We test the architecture with two tasks that allow: 1) the precise manipulation of the amount of information contained in and shared across different modalities and 2) testing the method on a simulated robot with visual and proprioceptive inputs. Our results show the validity of the proposed approach and demonstrate the applicability to embodied agents.
ECLIPSE: Enhanced Compiling Method for Pedersen-Committed zkSNARK Engines

We advance the state-of-the art for zero-knowledge commit-and-prove SNARKs (CP-SNARKs). CP-SNARKs are an important class of SNARKs which, using commitments as "glue", allow to efficiently combine proof systems—e.g., general-purpose SNARKs (an efficient way to prove statements about circuits) and $$\varSigma $$ -protocols (an efficient way to prove statements about group operations). Thus, CP-SNARKs allow to efficiently provide zero-knowledge proofs for composite statements such as $$h=H(g^{x})$$ for some hash-function H. Our main contribution is providing the first construction of CP-SNARKs where the proof size is succinct in the number of commitments. We achieve our result by providing a general technique to compile Algebraic Holographic Proofs (AHP) (an underlying abstraction used in many modern SNARKs) with special "decomposition" properties into an efficient CP-SNARK. We then show that some of the most efficient AHP constructions—Marlin, PLONK, and Sonic—satisfy our compilation requirements. Our resulting SNARKs achieve universal and updatable reference strings, which are highly desirable features as they greatly reduce the trust needed in the SNARK setup phase.
Automatic Essay Scoring for Indonesian Short Answers using Siamese Manhattan Long Short-Term Memory

Sistem Penilaian Esai Otomatis (SIMPLE-O) is developed by the Department of Electrical Engineering Universitas Indonesia to assess Indonesian short essay answers. This paper discusses the development of SIMPLE-O using Siamese Manhattan Long Short-Term Memory. Answers from students and lecturers are taken as the input of the system. Both answers are processed with identical LSTM layers. Then, the similarity between them is calculated with a simple similarity equation to determine the score. The average accuracy for the training phase is 92.82 and 84.03 for the validation
Quality-Aware Transcoding Task Allocation Under Limited Power in Live-Streaming Systems

Transcoding in video live-streaming systems requires a lot of computation and, hence, a lot of power. Putting a limit on the power drawn by each of the transcoding processors in a server reduces the overall power consumption, but it also hinders the efficient allocation of transcoding tasks. We address this with a dynamic programming algorithm, together with a heuristic, which maximizes total processing capacity while limiting power consumption in a server with heterogeneous processors. A further greedy algorithm determines the bitrates, at which content is transcoded for each channel, and allocates transcoding tasks to processors, while taking video quality, popularity, and workload balance into account. The initial assumption is that all contents are transcoded to all bitrates for every channel. Then, the algorithm gradually reduces the number of versions to be produced by transcoding, while minimizing the consequent reduction in popularity-weighted video quality, as well as balancing the workload across processors. Experimental results show that our scheme improves aggregate popularity-weighted video quality under a power constraint by 3.82%–39.12%, compared to benchmark methods.
ARcode: Programming for Youngsters Through AR

Since coding literacy has become a necessity, acquiring the fundamentals of programming at an early stage can bring countless constructive consequences to many aspects of the youngsters' lives. Educational serious games have recently attracted attention, especially with the post-pandemic surge in homeschooling and distance learning. The majority of the existing serious games are either using the traditional two-dimensional (2D) or three-dimensional (3D) environments. One of the most emerging technologies over the recent years is Augmented Reality (AR). This study describes the findings of a comparative experimental research that questions the impact of the medium of a serious game on the learning process of Computer Science (CS) core concepts for youngsters. The objective of the experiment is the design, implementation and evaluation of an AR serious game that would assist youngsters in learning the basic programming concepts. This game was developed in order to examine the effectiveness of an engaging AR serious game on the learning process for youngsters compared to another implemented 2D version of the same game, in the context of teaching CS.
2D vs 3D Online Writer Identification: A Comparative Study

Nowadays, different automatic systems for writer identification and verification are available. On-line writer identification through automatic analysis of handwriting acquired with a tablet has been widely studied. Furthermore, the recent development of Commercial Off-The-Shelf (COTS) wearables with integrated inertial measurement units (IMUs) recording limbs movement allows the study of handwriting movements executed on the air. The goal of this paper is to compare the performance of an online writer identification system while processing 2D data acquired by a tablet while writing on-paper and 3D data acquired by a smartwatch while writing on-air. To this end, a database of handwriting samples produced by the same writers while writing the same symbols in the two modalities has been built up. The results of the study show a performance gap smaller than 5% between the 2D and 3D top implementations of the system, confirming that 3D handwriting is a promising alternative for developing wearable user authentication system.
An Extensive Web Security Through Cloud Based Double Layer Password Encryption (DLPE) Algorithm for Secured Management Systems

Nowadays , cloud -based technology has been enlarged depends on the human necessities in the world. A lot of technologies is discovered that serve the people in different ways of cloud -based security and best resource allocation. Cloud-based technology is the essential factor to the resources like hardware, software for effective resource utilization . The securing applications enabled security mechanism enables the vital role for cloud -based web security through the secured password. The violation of data by the unauthorized access of users concerns many web developers and application owners . Web security enables the cloud-based password management system that illustrates the data storage and the web passwords access through the "Cloud framework". Web security, End-to-end passwords , and all the browser -based passwords could belong to the analysis of web security . The aim is to enhance system security. Thus, sensitive data are sustained with security and privacy . In this paper , the proposed Password Management via cloud-based web security gets to attain . An efficient Double Layer Password Encryption (DLPE ) algorithm to enable the secured password management system . Text -based passwords continue to be the most popular method of online user identification . They safeguard internet accounts with important assets against harmful attempts on passwords. The security of passwords is dependent on the development of strong passwords and keeping them from being stolen by intruders . The proposed DLPE algorithm perceived the double - layer encryption system as an effective security concern. When the data user accesses the user Login , the OTP generates via mail /SMS , and the original message is encrypted using public key generation. Then the text of data gets doubly encrypted through the cloud framework . The private key is used to decipher the cipher text . If the OTP gets matched , the text is to be decrypted over the text data . When double encryption happens , the detection of data flaws, malicious attacks , application hackers gets reduced and the strong password enabled double-layer encryption attained the secured data access without any malicious attackers . The data integrity , confidentiality enabled password management . The ability to manage a distributed systems policy like the Double Layer Password encryption technique enables password verification for the data used to highly secure the data or information.
DAC–Deep Autoencoder-Based Clustering: A General Deep Learning Framework of Representation Learning

Clustering performs an essential role in many real world applications, such as market research, pattern recognition, data analysis, and image processing. However, due to the high dimensionality of the input feature values, the data being fed to clustering algorithms usually contains noise and thus could lead to in-accurate clustering results. While traditional dimension reduction and feature selection algorithms could be used to address this problem, the simple heuristic rules used in those algorithms are based on some particular assumptions. When those assumptions does not hold, these algorithms then might not work. In this paper, we propose DAC, Deep Autoencoder-based Clustering, a generalized data-driven framework to learn clustering representations using deep neuron networks. Experiment results show that our approach could effectively boost performance of the K-Means clustering algorithm on a variety types of datasets.
Deep Correlation Filter Tracking With Shepherded Instance-Aware Proposals

Visual tracking is a core component of intelligent transportation systems and it is crucial to reduce or avoid traffic accidents. Recently, deep correlation filter (DCF) based trackers have exhibited good tracking performance. However, existing DCF based trackers are still ineffective to cope with large scale variations and severe distortions (e.g., heavy occlusions, significant deformations, large rotations, etc.), leading to the inferior performance. To address these issues, we develop a novel DeepCFIAP++ tracker, which incorporates effective shepherded instance-aware proposals into DCFs. DeepCFIAP++ can not only estimate the target scale at every frame but also re-detect the target in the case of severe distortions. Firstly, we propose to exploit both color and edge cues to generate complementary detection proposals to effectively handle various challenging scenarios. Then, we propose to utilize multi-layer target-specific deep features to rank the generated detection proposals and choose the instance-aware proposals, which will result in more robust tracking performance. Finally, we propose to use the DCFs to shepherd the instance-aware proposals toward their best locations, which will result in more accurate tracking results. Experimental results on five challenging datasets (i.e., OTB2013, OTB2015, VOT2016, VOT2017 and UAV20L) demonstrate that DeepCFIAP++ performs competitively with several other state-of-the-art DCF based trackers.
Securing an MQTT-based Traffic Light Perception System for Autonomous Driving

In this paper, we follow a security-by-design method to implement a secure MQTT-based traffic light perception system for autonomous driving vehicles. This security-by-design method is supported by a security risk management perspective where we analyse security threat information from existing literature as input to evaluate the MQTT assets, security risks and risk treatment decisions at the design phase. We also assess the security of the implemented MQTT system using security tools for MQTT security testing. Thus, showing the need for iterative security risk management as security gaps were identified that would pose challenges especially for decisions on scaling the MQTT system for autonomous driving.
Breathing Sound-based Exercise Intensity Monitoring via Smartphones

Exercise intensity monitoring of physical activities has drawn increasingly attention as the awareness of the exercise intensity is of great importance for a person to achieve optimal training outcomes. For example, over-training could lead to excessive fatigue and loss of motivation for exercise. Traditional exercise intensity monitoring systems utilize GPS data to track the user’s intensity of cardio activities through his/her position and speed. Such systems however become invalid for indoor exercises on stationary fitness equipments such as the treadmill or exercise bike. Recent work in using body-worn sensors to track the user’s heart rate for exercise intensity monitoring usually involves additional wearable sensors which are only available on some particular fitness equipments, and thus are hard to be used in all occasions. This work presents an exercise intensity monitoring system which is capable of detecting a person’s exercise intensity via smartphones. Our system exploits the off-the-shelf smartphone and its headphone to capture the user’s breathing sound. Given the captured acoustic data, our system performs data pre-processing to remove the environmental noise and identify the non-silent acoustic frames based on the signal energy. Our system then conducts breathing event detection for non-silent frames, and further calibrates the detection results by utilizing the high correlation between breathing cycles to improve the detection accuracy. Moreover, our system can estimate the person’s exercise intensity based on features extracted from the frames which contain breathing sound. Our experiments involving 9 subjects over four-month time period demonstrate that our proposed exercise intensity monitoring system is robust and accurate in both indoor and outdoor environments.
An Urgency-Aware and Revenue-Based Itemset Placement Framework for Retail Stores

Placement of items on the shelf space of retail stores signifcantly impacts the revenue of the retailer. Given the prevalence and popularity of medium-to-large-size retail stores, several research efforts have been made towards facilitating item/itemset placement in retail stores for improving retailer revenue. However, they do not consider the issue of urgency of sale of individual items. Hence, they cannot efficiently index, retrieve and place high-revenue itemsets in retail store slots in an urgency-aware manner. Our key contributions are two-fold. First, we introduce the notion of urgency for retail itemset placement. Second, we propose the urgency-aware URI index for efficiently retrieving high-revenue and urgent itemsets of different sizes. We discuss the URIP itemset placement scheme, which exploits URI for improving retailer revenue. We also conduct a performance evaluation with two real datasets to demonstrate that URIP is indeed effective in improving retailer revenue w.r.t. existing schemes.
Optimistic Temporal Difference Learning for <i>2048</i>

Temporal difference (TD) learning and its variants, such as multistage TD (MS-TD) learning and temporal coherence (TC) learning, have been successfully applied to 2048. These methods rely on the stochasticity of the environment of 2048 for exploration. In this paper, we propose to employ optimistic initialization (OI) to encourage exploration for 2048, and empirically show that the learning quality is significantly improved. This approach optimistically initializes the feature weights to very large values. Since weights tend to be reduced once the states are visited, agents tend to explore those states which are unvisited or visited few times. Our experiments show that both TD and TC learning with OI significantly improve the performance. As a result, the network size required to achieve the same performance is significantly reduced. With additional tunings such as expectimax search, multistage learning, and tile-downgrading technique, our design achieves the state-of-the-art performance, namely an average score of 625 377 and a rate of 72% reaching 32768 tiles. In addition, for sufficiently large tests, 65536 tiles are reached at a rate of 0.02%.
Load Balanced Semantic Aware Distributed RDF Graph

Modern day application development requires efficient management of huge RDF data. The major approaches for RDF data management are Relational and Graph based techniques. As the relational approach suffers from query joins, we propose a semantic aware graph based partitioning method. The partitioned fragments are further allocated in a load balanced way. For efficient query processing, partial replication is implemented. It reduces Inter node Communication thereby accelerating queries on distributed RDF Graph. This approach has been demonstrated in two phases partitioning and Distribution of Linked Observation Data (LOD). The time complexity for partitioning and distribution of Load Balanced Semantic Aware RDF Graph (LBSD) is O(n) where n is the number of triples which is demonstrated by linear increment in algorithm execution time (AET) for LOD data scaled from 1x to 5x. LBSD has been found to behave well till 4x. LBSD is compared with the state of the art relational and graph-based partitioning techniques. LBSD records 71% QET gain when averaged over all the four query types. For most frequent query types, Linear and Star, on an average 65% QET gain is recorded over original configuration for scaling experiments. The optimal replication level has been found to be 12% of original data.
A mobile app for algorithms learning in engineering education: Drag and drop approach

Algorithms, taken in the first year of university, is one of the core courses of the computer science department. Traditional learning systems have some restrictions as they are teacher, class, and time-based. However, distance education is one of the popular concepts of today since it has the flexibility of time and choice of location. Mobile learning, which is a subbranch of distance education provides users opportunities to learn through educational mobile applications. In the present study, an application to run on smart devices running the IOS operating system for engineering students to be used in supporting the basic algorithm education is described. The student can both get theoretical knowledge about concepts of algorithms, and test himself/herself with questions in the question bank. There are three types of questions, such as multiple-choice, true/false, and flowchart designed by the dragging-and-dropping method. The main contribution of this application is that the users can design a flowchart from beginning to end on screen by locating any flowchart components provided on the palette. They also have the opportunity to check their own solutions according to the inserted questions and answers in the database. The application was tested on a group of computer engineering undergraduate students from Mus Alparslan University. In that case, the application is evaluated by both an exam and user surveys. The results of the exam show that the presented application had a positive effect on students' success. Besides, the outcomes of the surveys have also given positive feedback.
Revisiting Double-Spending Attacks on the Bitcoin Blockchain: New Findings

Bitcoin is currently the cryptocurrency with the largest market share. Many previous studies have explored the security of Bitcoin from the perspective of blockchain mining. Especially on the double-spending attacks (DSA), some state-of-the-art studies have proposed various analytical models, aiming to understand the insights behind the double-spending attacks. However, we believe that advanced versions of DSA can be developed to create new threats for the Bitcoin ecosystem. To this end, this paper mainly presents a new type of double-spending attack named Adaptive DSA in the context of the Bitcoin blockchain, and discloses the associated insights. In our analytical model, the double-spending attack is converted into a Markov Decision Process. We then exploit the Stochastic Dynamic Programming (SDP) approach to obtain the optimal attack strategies towards Adaptive DSA. Through the proposed analytical model and the disclosed insights behind Adaptive DSA, we aim to alert the Bitcoin ecosystem that the threat of double-spending attacks is still at a dangerous level.