Reviewerly/exHarmony huggingface dataset:
A Parallel Processing Architecture to Optimize Runtime in Aggregated SPARQL Queries

The search for information becomes a primordial need nowadays and it is possible that the information sought cannot be found by searching in a single data source, actually, an information may require collecting its parts from several distributed data sources. Our work aims to set up an aggregated search engine able to respond to a query by collecting data from independent data sources via a single user interface, and query processing in our system goes through several steps before returning final answers. Process speed is one of the main qualities of any search engine, and this speed can be affected if the search engine interacts with several data sources, which is the case of our work. In this regard, we propose in this paper a solution to optimize runtime in our aggregated search system, firstly, we present runtime evaluation of each process step in order to identify the costliest in terms of execution time, then, we propose a parallel processing architecture to optimize runtime without any data loss. The experimental results confirm the efficiency of our proposed architecture.
A Novel Approach to Populate Multimedia Knowledge Graph via Deep Learning and Semantic Analysis

The growth of data in volume and complexity needs automatic tools to manage and process information. Semantic Web Technologies are a silver bullet in this context due to their capacity to transform human-readable contents into machine-readable ones. Knowledge graphs and the related ontologies represent essential tools for managing very large knowledge bases. The population process of these knowledge structures is composed of expensive and time-consuming tasks, and we propose a novel approach to automate the population step. Our approach is based on novel techniques based on semantic analysis and deep learning using NoSQL technologies. Several results to show the effectiveness of our approach is also reported.
Randomized Quantization with Exact Error Distribution

We design a randomized scalar quantization scheme, where the quantization error is independent of the source and follows any given unimodal distribution (e.g. Gaussian distribution) exactly. We characterize the optimal encoding length of the quantization, and show that our scheme is optimal. This can also be regarded as a one-shot channel simulation setting, where the channel to be simulated is an additive noise channel. Potential applications include neural compression and coupling from the past.
The Classical Capacity of Quantum Jackson Networks with Waiting Time-Dependent Erasures

We study the fundamental limits of classical communication using quantum states that decohere as they traverse through a network of queues. We consider a network of Markovian queues, known as a Jackson network, with a single source or multiple sources and a single destination. Qubits are communicated through this network with inevitable buffering at intermediate nodes. We model each node as a ‘queue-channel,’ wherein as the qubits wait in buffer, they continue to interact with the environment and suffer a waiting time-dependent noise. Focusing on erasures, we first obtain explicit classical capacity expressions for simple topologies such as tandem queue-channel and parallel queue-channel. Using these as building blocks, we characterize the classical capacity of a general quantum Jackson network with waiting time-dependent erasures. Throughout, we study two types of quantum networks, namely, (i) Repeater-assisted and (ii) Repeater-less. We also obtain optimal pumping rates and routing probabilities to maximize capacity in simple topologies. More broadly, our work quantifies the impact of delay-induced decoherence on the fundamental limits of classical communication over quantum networks.
DiPLe: Learning Directed Collaboration Graphs for Peer-to-Peer Personalized Learning

We study fully decentralized learning in which agents learn collaborative, yet personalized prediction models. Specifically, when learners’ local datasets are non-IID, a collaboratively trained global model (such as those learned through most federated learning algorithms to minimize the sum of losses across all agents) may sacrifice the local performance on agents’ private datasets. To address this issue and enable personalized learning, we propose DiPLe : an algorithm for Directed Personalized Learning. Through our algorithm, each agent identifies "relevant" agents with whom to exchange model information. This leads to a weighted and directed collaboration graph. Agents repeatedly update this graph, and then exchange information with neighboring agents on this learned graph, to collaboratively train their personalized models. We provide analytical results on the generalization error bounds and convergence of our proposed learning method. We verify the performance of DiPLe through numerical experiments, and show its advantages in terms of personalization compared to a number of existing federated learning and personalized learning algorithms.
Do Neural Networks Compress Manifolds Optimally?

Artifical Neural-Network-based (ANN-based) lossy compressors have recently obtained striking results on several sources. Their success may be ascribed to an ability to identify the structure of low-dimensional manifolds in high-dimensional ambient spaces. Indeed, prior work has shown that ANN-based compressors can achieve the optimal entropy-distortion curve for some such sources. In contrast, we determine the optimal entropy-distortion tradeoffs for two low-dimensional manifolds with circular structure and show that state-of-the-art ANN-based compressors fail to optimally compress them.
The linear complexity of sequences with low autocorrelation from interleaved technique and period pq

In this paper, we consider the quaternary sequences with period pq where p and q are two odd primes. These sequences are constructed by interleaving the quaternary power residue sequence of period p according to the quadratic residue with respect to q. We derive the linear complexity of these sequences over the finite field of order four and the finite ring of order four. It is shown that the considered quaternary sequences have a sufficiently large linear complexity to resist Berlekamp-Massey algorithm or Reeds and Sloane algorithm attack effectively.
Efficient Private Storage of Sparse Machine Learning Data

We consider the problem of maintaining sparsity in private distributed storage of confidential machine learning data. In many applications, e.g., face recognition, the data used in machine learning algorithms is represented by sparse matrices which can be stored and processed efficiently. However, mechanisms maintaining perfect information-theoretic privacy require encoding the sparse matrices into randomized dense matrices. It has been shown that, under some restrictions on the storage nodes, sparsity can be maintained at the expense of relaxing the perfect information-theoretic privacy requirement, i.e., allowing some information leakage. In this work, we lift the restrictions imposed on the storage nodes and show that there exists a trade-off between sparsity and the achievable privacy guarantees. We focus on the setting of non-colluding nodes and construct a coding scheme that encodes the sparse input matrices into matrices with the desired sparsity level while limiting the information leakage.
Weakly Secure Coded Distributed Computing with Group-based Function Assignment

This paper considers a distributed computing system where nodes are grouped such that nodes in the same group compute the same function. To complete a computing task distributedly, nodes need to exchange their local computation results with each other, which incurs communication cost and security concerns. The objective of this work is to study the tradeoff between computation and communication and avoid exchanged information leakage to eavesdroppers. Given a fixed computation load, we derive lower bounds on the communication load for 1-group and 2-group systems. New coding schemes are proposed and shown to be weakly secure and achieve the optimal tradeoffs for 1-group systems and for 2-group systems with large computation load.
Multi-Message Pliable Private Information Retrieval

We formulate a new variant of the private information retrieval (PIR) problem where the user is pliable, i.e., interested in any message from a desired subset of the available dataset, denoted as pliable private information retrieval (PPIR). We consider the setup where a dataset consisting of f messages is replicated in n noncolluding databases and classified into Γ classes. For this setup, the user wishes to retrieve any λ ≥ 1 messages from multiple desired classes, while revealing no information about the identity of the desired classes to the databases. We term this problem multi-message PPIR (M-PPIR) and introduce the single-message PPIR (PPIR) problem as an elementary special case of M-PPIR. We first derive converse bounds on the M-PPIR download rate, followed by achievable schemes. As a result, we show that the PPIR capacity for f messages and Γ classes matches the PIR capacity with n noncolluding databases and Γ messages. Thus, enabling flexibility, i.e., pliability, where privacy is only guaranteed for classes, but not for messages as in classical PIR, allows to trade-off privacy versus download rate. A similar insight is shown to hold for the general case of M-PPIR.
Cryptanalysis of random phase encryption based on collinear holographic data storage system

This paper analyzed the security of random phase encryption holographic storage technology. Taking binary random phase as an example, the recorded hologram is continually readout by series guessing reference. The experiment showed that the correlation coefficient between readout information and the recorded information was firstly decreased and then increased when the phase correct ratio of guessing reference is increased from 0% to 100%. The recorded information can’t be readout at all when the phase correct ratio of guessing reference range from 40% to 60%. Since the guessing reference with phase correct ratio between 40% and 60% has occupied majority guessing cases, the recorded information can’t be cracked in most cases. This indicates the high security of the random phase encryption storage technique.
Investigating thresholding techniques in a real predictive maintenance scenario

We deal with a real predictive maintenance (PdM) scenario in an Industry 4.0 setting. With a help of the Sibyl platform, we can monitor live data from key components of a Philips factory equipment; in this work, we focus on a cold-forming press. Due to the dynamic environment of the operation of this press, unsupervised anomaly detection techniques are used to timely detect the wear, where early anomalies are interpreted as warning signs of a forthcoming failure. Typically such techniques assign an anomaly score, and the problem we face is how to appropriately set a threshold for this score. We introduce and compare four generally applicable thresholding techniques, two of which are dynamic, i.e., they continuously refine the threshold during the episode lifetime. We discuss the properties of these techniques and quantitatively evaluate their behavior in our case study.
Balancing Security and Privacy in Genomic Range Queries

Exciting recent advances in genome sequencing, coupled with greatly reduced storage and computation costs, make genomic testing increasingly accessible to individuals. Already today, one’s digitized DNA can be easily obtained from a sequencing lab and later used to conduct numerous tests by engaging with a testing facility. Due to the inherent sensitivity of genetic material and the often-proprietary nature of genomic tests, privacy is a natural and crucial issue. While genomic privacy received a great deal of attention within and outside the research community, genomic security has not been sufficiently studied. This is surprising since the usage of fake or altered genomes can have grave consequences, such as erroneous drug prescriptions and genetic test outcomes. Unfortunately, in the genomic domain, privacy and security (as often happens) are at odds with each other. In this article, we attempt to reconcile security with privacy in genomic testing by designing a novel technique for a secure and private genomic range query protocol between a genomic testing facility and an individual user. The proposed technique ensures authenticity and completeness of user-supplied genomic material while maintaining its privacy by releasing only the minimum thereof. To confirm its broad usability, we show how to apply the proposed technique to a previously proposed genomic private substring matching protocol. Experiments show that the proposed technique offers good performance and is quite practical. Furthermore, we generalize the genomic range query problem to sparse integer sets and discuss potential use cases.
Faulty diagnostics model in e-commerce using AI

Risk administration has increased due to increased online orders to avoid sales invoices. Failure to pay a bill within 90 days of receipt constitutes overdue payment. The credit rating is used to determine the probability that consumers will fail. The CR has been thoroughly studied and several learning algorithms have been proposed. The main goal is to create a CR model for the role of Risk Solution Services (RSS), an industry standard for predicting the parameters of customer default in e-commerce risk assessment. The risk assessment included a general concept, exclusion criteria, and details of the ordering process. The most recent design should operate both independently and in conjunction with the NRC Primary Risk Audit as it is intended to replace the overall screening risk assessment. This article is about a CR implementation of Genetic Programming (GP) with Artificial Intelligence (AI). The dataset includes RSS-enabled purchase requisitions. The results show that the GP pre-risk control model goes beyond the generic CR model in terms of classification accuracy. A system with more discriminatory capacity is produced by combining GP models with the NRC's major risk assessment.
Learning-on-learning approach for modeling

Neural networks based on back-propagation learning algorithms and gradient descent algorithms are the first and the easiest tools developed for machine learning. They are still widespread nowadays, so much so by exploiting a huge number of different coding languages, between which MatLab, Python or Java, we have the possibility of using these training tools. But as highlighted in the past, these traditional neural networks suffer from their slow convergence rate. Aim of this paper is to revisit an algorithm to improve the speed of the learning phase, by exploiting the power of parallel computing to train a suitable number of auxiliary neural networks which work concurrently with the principal network. The implementation of the proposed algorithm in MatLab is shown in order to make evident the main difference with the traditional learning algorithms. Several examples, related to modeling of technological datasets from industrial environment, confirm the suitability of the proposed procedure.
Prediction of DHF (Dengue Hemorrhagic Fever) Severity Using Random Forest, KNN, Decision Tree and Naïve Bayes

Dengue Hemorrhagic Fever is an acute viral infectious disease caused by the dengue virus. Transmitted through the bite of Aedes Mosquitoes and divided into 4 severity. Severity 1 and 2 are characterized by a decrease in platelet levels below normal and there is a change in hemoconcentration, while Severity 3 and 4 are included in Dengue Shock Syndrome (DSS). The purpose of the study was to classify the severity of dengue, based on age, gender, platelets, body temperature, bleeding, and also doctor’s recommendation. The advancement of information technology is the reason for the development of medical science, especially with artificial intelligence. Abundant medical data becomes a source that can be processed in improving the quality of medical information. The method chosen is Random Forest, kNN, Decision Tree and Naïve Bayes. The dataset used is the medical records of all DHF patients for 30 months in the period Jan 2019 to June 2021, at the Nadhifa Al Ghiffari Clinic in Bojongsoang District, Bandung Regency.
Decision Support System for Selection of Zakat Mustahik Using Analytical Network Process Method

Indonesia as a country with the largest Muslim population in the world has a very large potential for state revenue from zakat instruments. Zakat as a financial distribution can be used to reduce poverty and improve the welfare of the people, whereby the property can be distributed to eligible individuals. To achieve this goal, it is necessary to distribute zakat that is fair and in accordance with the needs of mustahik. The existence of determining indicators to assess the level of welfare of mustahik is needed for the proper distribution of zakat. Decision support system for selection of zakat mustahik is used by amil zakat institution as decision makers to assist in decision making. Based on flexibility and eficiency, the Analytical Network Process (ANP) method is more reliable than Analytic Hierarchy Process (AHP) method and other multi criteria decision making methods. ANP is superior to AHP in simplicity, connectivity, more objective comparisons, more accurate predictions, more stable results and robust. Therefore, in this study we use ANP method to determine the best mustahik priority scale with health and wealth criteria. The sub-criteria for health are family head’s health condition, condition of pregnant women, serious illness, and medical ability. While the sub-criteria of wealth are income, job, age, the number of dependents, number of children attending school, item ownership, and home condition. The stages of this research are problem identification, data collection, design of priority determination methods, decision support system using ANP, and implementation. Based on the results we obtained that the order of priority of the mustahik which is A1, A2 and A3. This value data is taken directly from the supermatrix boundary which is the highest normal value of 0,48496 achieved by A1 as the best mustahik.
On the Validity of Internet Speed Test Tools and Broadband Measurement Programs

Speed is etched into our psyche – consumers like to buy broadband or mobile service from the “fastest” provider; there is a whole industry of speed testing services; governments invest millions of dollars in national speed monitoring programs; and Internet Service Providers (ISPs) use speed test rankings in their advertising. Given how strongly speed shapes consumer, industry, and government behaviour, one would expect speed testing and comparison methodologies to be thoroughly studied and documented; however, this is not so. In this paper, we first highlight how test conditions skew results. Using popular speed test tools, we highlight some of the factors – test duration, number of threads, congestion control algorithm, and server locations – that significantly impact outcomes. Speed results should therefore only be interpreted in the strict context of their test conditions, and any generalisations should be discouraged and discarded. Our second contribution shows that national monitoring programs that rank ISPs on speed are largely flawed. By analysing public data from Australia and the U.K., we show that sampling imbalances across ISPs in terms of the test conditions (e.g., access technologies and locations) lead to biased results. Our study urges the community to restrain the emphasis placed on speed testing.
Mitigating Congestion with Decentralized Traffic Shaping for Adaptive Video Streaming over ICN

With a significant increase in video traffic on the Internet, Information Centric Networking (ICN), which improves the communication quality through decentralized content-based control, has been attracting attention. To improve the quality of experience (QoE) on video streaming, adaptive video streaming is applied to ICN. In adaptive streaming over ICN, the Adaptive Bitrate (ABR) algorithm, which adjusts a bitrate of the video segment according to a bandwidth estimation, is mainly responsible for a congestion control. However, since a bitrate is adjusted every time just before downloading a segment, even if congestion occurs in a network, it takes time for congestion avoidance until the current segment download finishes. To avoid congestion early, this paper presents decentralized congestion control in which a distributed server or router mitigates communication traffic immediately according to explicit congestion detection using transmission queue occupancy. Then, we propose the decentralized traffic shaping system in which the server or router increases a transmission interval between packets to be sent during congestion. Thus, the proposed method encourages the ABR algorithm to avoid congestion quickly by the reduced communication traffic according to explicit congestion detection. An experiment using the proposed method was implemented on ns-3 network simulator and results of experiments showed that play stall could be reduced without decreasing QoE.
The Audibility Problem and Indirect Listening

There is a strong intuition that we can listen to works of music, yet musical ontologies on which works of music are abstract objects, perhaps most notably, type theories of music, seem to imply that this is impossible. This problem has received relatively little attention in the literature. I here explore and develop a solution suggested by Julian Dodd and argue that it has at least two problematic consequences, namely (i) that some works of music cannot be listened to unless one has already studied the relevant score or through a similar process learned roughly what the work requires of its performances and (ii) that it is possible for two people to listen to different musical works while listening to the same performance.
Sentiment Analysis of Product Reviews as Customer Recommendations in Shopee Philippines Using Hybrid Approach

Due to the Coronavirus (Covid-19) pandemic, there was a positive shift in online shopping. On an e-commerce website like Shopee, consumers may post comments under the products they have purchased. This research study aims to conduct sentiment analysis on product reviews as customer recommendations in Shopee Philippines. The product reviews were first scraped from Shopee. After which, it is preprocessed and then annotated using VADER. The customers’ sentiments were analyzed using Multinomial Naive Bayes (MNB) and Support Vector Machine (SVM). The confusion matrix and a classification report were then used to determine the accuracy value, the precision value, the recall value, and the F-measure value of the results from the models. Lastly, the results from a survey would justify the model’s results to customer recommendations. The final results of the research study show how reviews with positive, negative, and neutral sentiments can affect a product’s condition to be recommended to other consumers or not. Based on the analysis of the product reviews, 83.6% are positive, 9.1% are negative, and 7.3% are neutral. The SVM model is found to be a better model than MNB which got an 83% accuracy score. The survey results which validated the model’s results have found that 75.8% of the respondents would recommend a store or a product based on the number of positive reviews.