Reviewerly/exHarmony huggingface dataset:
Bayesian Deep Learning with Trust and Distrust in Recommendation Systems

Exploiting the selections of social friends and foes can efficiently face the data scarcity of user preferences and the cold-start problem. In this paper, we present a Social Deep Pairwise Learning model, namely SDPL. According to the Bayesian Pairwise Ranking criterion, we design a loss function with multiple ranking criteria based on the selections of users, and those in their friends and foes to improve the accuracy in the top-k recommendation task. We capture the nonlinearity in user preferences and the social information of trust and distrust relationships by designing a deep learning architecture. In each backpropagation step, we perform social negative sampling to meet the multiple ranking criteria of our loss function. Our experiments on a benchmark dataset from Epinions, among the largest publicly available that has been reported in the relevant literature, demonstrate the effectiveness of the proposed approach, outperforming other state-of-the art methods. In addition, we show that our deep learning strategy plays an important role in capturing the nonlinear associations between user preferences and the social information of trust and distrust relationships, and demonstrate that our social negative sampling strategy is a key factor in SDPL.
Post-Quantum Crystography System for Secure Electronic Voting

Abstract Security (privacy, confidentiality and integrity) of pre-electoral, electoral and post electoral phases of the electioneering process is fundamental to the success of Electronic Voting (E-Voting) Systems. Crystography, which is the combination of cryptography and steganography could be a fitting ‘tool kit’ for enhancing the security of sensitive election-related information transmitted over public networks, thereby also ensuring free, fair and credible election/voting. Most of the existing secure e-voting systems are based on public key cryptographic schemes like RSA and Elliptic Curve Cryptography (ECC), whose security depends on the difficulty of solving Integer Factorization Problem (IFP) and Discrete Logarithm problem (DLP) respectively. However, techniques for solving IFP and DLP problems, improves continually. One of such is the quantum algorithm discovered by Peter Shor in 1994, which can solve both IFP and DLP problems in polynomial time. Consequently, the existence of quantum computers in the range of 1000 bits would spell doom to systems based on those problems. This paper presents the development of a new crystographic system that combines Post Quantum Cryptography with steganography to ensure that the security of e-voting is maintained both in classical computing era as well as post-quantum computing era. Our experiments’ results shows that our proposed system performed better than existing ones.
Methodology for risk management related to cyber-security of Unmanned Aircraft Systems

Nowadays, the market of Unmanned Aerial Systems (UAS) develops progressively with a lot of applications such as: infrastructure monitoring, law enforcement, environment research, goods transportation, etc. However, due to the lack of human observation, communication capacities, and protection, a UAS is an appropriate target for a criminal or a terrorist cyber attack. Therefore, risks related to the cyber-security need to be taken into account during the development of a UAS. This document presents a risk management methodology related to cyber-security. The expected result of the application of this methodology is a list of cyber-security requirements which guides the development of cyber-security countermeasures.
WiseNET: An indoor multi-camera multi-space dataset with contextual information and annotations for people detection and tracking

Nowadays, camera networks are part of our every-day life environments, consequently, they represent a massive source of information for monitoring human activities and to propose new services to the building users. To perform human activity monitoring, people must be detected and the analysis has to be done according to the information relative to the environment and the context. Available multi-camera datasets furnish videos with few (or none) information of the environment where the network was deployed. The proposed dataset provides multi-camera multi-space video sets along with the complete contextual information of the environment. The dataset regroups 11 video sets (composed of 62 single videos) recorded using 6 indoor cameras deployed on multiple spaces. The video sets represent more than 1 h of video footage, include 77 people tracks and captured different human actions such as walking around, standing/sitting, motionless, entering/leaving a space and group merging/splitting. Moreover, each video has been manually and automatically annotated to include people detection and tracking meta-information. The automatic people detection annotations were obtained by using different complexity and robustness detectors, from machine learning to state-of-art deep Convolutional Neural Network (CNN) models. Concerning the contextual information, the Industry Foundation Classes (IFC) file that represents the environment's Building Information Modeling (BIM) data is also provided. The BIM/IFC file describes the complete structure of the environment, it's topology and the elements contained in it. To our knowledge, the WiseNET dataset is the first to provide a set of videos along with the complete information of the environment. The WiseNET dataset is publicly available at https://doi.org/10.4121/uuid:c1fb5962-e939-4c51-bfd5-eac6f2935d44, as well as at the project's website http://wisenet.checksem.fr/#/dataset.
DEEPaaS API: a REST API for Machine Learning and Deep Learning models

Arguably, Python is one of the most widely used programming languages in data analytics, data mining, artificial intelligence, machine learning and deep learning (Nguyen et al., 2019).DEEPaaS API is a Python package that provides a REST API (Fielding, 2000) that can be used to easily expose the underlying model functionality over HTTP.Developers can plug their applications into DEEPaaS API with minimal requirements and modifications, allowing for easier interaction with the underlying functionality, both for training, validating and testing.
Uncertainty Quantification with Generative Models

We develop a generative model-based approach to Bayesian inverse problems, such as image reconstruction from noisy and incomplete images. Our framework addresses two common challenges of Bayesian reconstructions: 1) It makes use of complex, data-driven priors that comprise all available information about the uncorrupted data distribution. 2) It enables computationally tractable uncertainty quantification in the form of posterior analysis in latent and data space. The method is very efficient in that the generative model only has to be trained once on an uncorrupted data set, after that, the procedure can be used for arbitrary corruption types.
Human mitochondrial genome compression using machine learning techniques

In recent years, with the development of high-throughput genome sequencing technologies, a large amount of genome data has been generated, which has caused widespread concern about data storage and transmission costs. However, how to effectively compression genome sequences data remains an unsolved problem.In this paper, we propose a compression method using machine learning techniques (DeepDNA), for compressing human mitochondrial genome data. The experimental results show the effectiveness of our proposed method compared with other on the human mitochondrial genome data.The compression method we proposed can be classified as non-reference based method, but the compression effect is comparable to that of reference based methods. Moreover, our method not only have a well compression results in the population genome with large redundancy, but also in the single genome with small redundancy. The codes of DeepDNA are available at https://github.com/rongjiewang/DeepDNA .
Howard Chu on Lightning Memory-Mapped Database

Gavin Henry: What's the history of LMDB [Lightning Memory-Mapped Database]?
A Mean Quantization Watermarking Scheme for Audio Signals Using Singular-Value Decomposition

Recently, research in audio watermarking technology makes it a promising tool for copyright protection of digital audio files in networked environments. In this paper, we propose a new mean quantization based watermarking mechanism for audio based on the technique due to Singular Value Decomposition (SVD). In this method, the host signal is divided into a number of two-dimensional matrix frames. The SVD is then employed to every frame, and also the Euclidean norm of the Singular Values (SVs) are calculated for every frame. The watermark is then hidden into an audio signal by quantization of the norm of the SVs. The watermark is extracted blindly using the inverse process. Both the subjective and objective tests demonstrate good imperceptibility of the watermark in the audio file. Furthermore, the experimental results show good robustness of the proposed scheme against various common audio attacks and Stirmark benchmark attacks. The false-negative error of the method is very near to zero against Stirmark and audio attacks. The proposed scheme has a high payload, and its performance is superior compared to other related watermarking methods for audio signals. Finally, the proposed method achieves better trade-offs between conflicting requirements of imperceptibility, payload, and robustness.
Steganography via MIDI Files by Adjusting Velocities of Musical Note Sequences With Monotonically Non-Increasing or Non-Decreasing Pitches

Data hiding techniques for steganography, which embed secret data in multimedia imperceptibly, are useful for protecting information security. By taking advantage of the popularity of MIDI files on the Internet, a new data hiding method via MIDI files is proposed, which modifies the velocities of musical note sequences to embed secret data. Initially, musical note sequences with monotonic pitches, each consisting of at least three consecutive notes with pitches either entirely non-decreasing or entirely non-increasing, are found from an input MIDI file. Next, for each of such musical note sequences, a reference velocity is generated for each non-end note in the sequence by a linear interpolation scheme. Then, a number of data bits are embedded into each non-end note by adding the decimal value of the bits to or subtracting the value from the corresponding reference velocity value to yield a new velocity for the note. The new velocity value does not differ much from the original one and fits the velocity trend in the musical note sequence, so that the resulting stego-MIDI file does not yield abnormal note strengths and the musical expression is kept. Moreover, a melody humanization scheme is proposed for modifying the velocity values in strength-invariant MIDI channels to create data embeddability without producing unreasonable melodies. The original MIDI file size is also kept unchanged after data embedding, avoiding attracting attentions from hackers. Experimental results show the feasibility of the proposed method. Also, a comparison with five other methods show that the method has the merit of reducing the resulting melody distortion or file-size change while yielding a reasonable secret-bit embedding rate.
On-Off Control of Range Extender in Extended-Range Electric Vehicle using Bird Swarm Intelligence

The bird swarm algorithm (BSA) is a bio-inspired evolution approach to solving optimization problems. It is derived from the foraging, defense, and flying behavior of bird swarm. This paper proposed a novel version of BSA, named as BSAII. In this version, the spatial distance from the center of the bird swarm instead of fitness function value is used to stand for their intimacy of relationship. We examined the performance of two different representations of defense behavior for BSA algorithms, and compared their experimental results with those of other bio-inspired algorithms. It is evident from the statistical and graphical results highlighted that the BSAII outperforms other algorithms on most of instances, in terms of convergence rate and accuracy of optimal solution. Besides the BSAII was applied to the energy management of extended-range electric vehicles (E-REV). The problem is modified as a constrained global optimal control problem, so as to reduce engine burden and exhaust emissions. According to the experimental results of two cases for the new European driving cycle (NEDC), it is found that turning off the engine ahead of time can effectively reduce its uptime on the premise of completing target distance. It also indicates that the BSAII is suitable for solving such constrained optimization problem.
Modules over monads and operational semantics

This paper is a contribution to the search for efficient and high-level mathematical tools to specify and reason about (abstract) programming languages or calculi. Generalising the reduction monads of Ahrens et al., we introduce transition monads, thus covering new applications such as λμ-calculus, π-calculus, Positive GSOS specifications, differential λ-calculus, and the big-step, simply-typed, call-by-value λ-calculus. Finally, we design a suitable notion of signature for transition monads.
On Approximating Matrix Norms in Data Streams

This paper presents a systematic study of the space complexity of estimating the Schatten p-norms of an n × n matrix in the turnstile streaming model.Both kinds of space complexities, bit complexity and sketching dimension, are considered.Furthermore, two sketching models, general linear sketching and bilinear sketching, are considered.When p is not an even integer, we show that any one-pass algorithm with constant success probability requires near-linear space in terms of bits.This lower bound holds even for sparse matrices, i.e., matrices with O(1) nonzero entries per row and per column.However, when p is an even integer, we give for sparse matrices an upper bound which, up to logarithmic factors, is the same as estimating the pth moment of an n-dimensional vector.These results considerably strengthen lower bounds in previous work for arbitrary (not necessarily sparse) matrices.Similar near-linear lower bounds are obtained for Ky Fan norms, SVD entropy, eigenvalue shrinkers, and M-estimators, many of which could have been solvable in logarithmic space prior to this work.The results for general linear sketches give separations in the sketching complexity of Schatten p-norms with the corresponding vector p-norms, and rule out a table-lookup nearest-neighbor search for p = 1, making progress on a question of Andoni.The results for bilinear sketches are tight for the rank problem and nearly tight for p ≥ 2; the latter is the first general subquadratic upper bound for sketching the Schatten norms.
The real-time elderly fall posture identifying scheme with wearable sensors

The accidental fall is the major risk for elderly especially under unsupervised states. It is necessary to real-time monitor fall postures for elderly. This paper proposes the fall posture identifying scheme with wearable sensors including MPU6050 and flexible graphene/rubber. MPU6050 is located at the waist to monitor the attitude of the body with triaxial accelerometer and gyroscope. The graphene/rubber sensors are located at the knees to monitor the moving actions of the legs. A real-time fall postures identifying algorithm is proposed by the integration of triaxial accelerometer, tilt angles, and the bending angles from the graphene/rubber sensors. A volunteer is engaged to emulate elderly physical behaviors in performing four activities of daily living and six fall postures. Four basic fall down postures can be identified with MPU6050. Integrated with graphene/rubber sensors, two more fall postures are correctly identified by the proposed scheme. Test results show that the accuracy for activities of daily living detection is 93.5% and that for fall posture identifying is 90%. After the fall postures are identified, the proposed system transmits the fall posture to the smart phone carried by the elderly via Bluetooth. Finally, the posture and location are transmitted to the specified mobile phone by short message.
The Effectiveness of Learning Media Folklore Text of North Sumatera Based on Blended Learning by 10th Grade Students of Vocational High SchoolHarapan Mekar-1 Medan

The aim of the study is to find out the effectiveness of learning media folklore text of North Sumatra based on blended learning. This study used research and development of learning media based on blended learning takes the form of online, offline and face-to-face learning media. The result of the study Learning outcomes in the folklore text material of 10th Grade students of Vocational High School Harapan Mekar-1 Medan after using learning media based on blended learning are in the category of "very good" with an average value of 82.81 and the average value before using learning media based on blended learning of 67.65 which is in the "good" category. This proves that student learning outcomes in writing folklore text material using blended learning based learning media is higher with the difference in percentage from pretest to posttest is 15,16.
Optical Image Encryption System Using Several Tilted Planes

A well-known technique for optical image encryption is the double random phase encoding (DRPE) technique, which uses two random phase masks (RPMs), one RPM at the input plane of the encryption system and the other RPM at the Fourier plane of the optical system, in order to obtain the encrypted image. In this work, we propose to use tilted planes for the Fourier and the output planes of the optical DRPE encryption system with the purpose of adding two new security keys, which are the angles of the tilted planes. The optical diffraction on a tilted plane is computed using the angular spectrum of plane waves and the coordinate rotation in the Fourier domain. The tilted distributions at the intermediate and output planes of the optical DRPE encryption system are the second RPM and the encrypted image, respectively. The angles of the tilted planes allow improvement to the security of the encrypted image. We perform several numerical simulations with the purpose of demonstrating the validity and feasibility of the proposed image encryption system.
Cross-Task Knowledge Transfer for Query-Based Text Summarization

We demonstrate the viability of knowledge transfer between two related tasks: machine reading comprehension (MRC) and querybased text summarization.Using an MRC model trained on the SQuAD1.1 dataset as a core system component, we first build an extractive query-based summarizer.For better precision, this summarizer also compresses the output of the MRC model using a novel sentence compression technique.We further leverage pre-trained machine translation systems to abstract our extracted summaries.Our models achieve state-of-the-art results on the publicly available CNN/Daily Mail and Debatepedia datasets, and can serve as simple yet powerful baselines for future systems.We also hope that these results will encourage research on transfer learning from large MRC corpora to query-based summarization.
Datalog Reasoning over Compressed RDF Knowledge Bases

Materialisation is often used in RDF systems as a preprocessing step to derive all facts implied by given RDF triples and rules. Although widely used, materialisation considers all possible rule applications and can use a lot of memory for storing the derived facts, which can hinder performance. We present a novel materialisation technique that compresses the RDF triples so that the rules can sometimes be applied to multiple facts at once, and the derived facts can be represented using structure sharing. Our technique can thus require less space, as well as skip certain rule applications. Our experiments show that our technique can be very effective: when the rules are relatively simple, our system is both faster and requires less memory than prominent state-of-the-art RDF systems.
Artificial Soul Optimization - An Invention

The Soul is eternal and exists even after death of a person or animal. The main idea that is captured in this work is that soul continues to exist and takes a different a body after the death. The primary goal of this work is to invent a new field titled "Artificial Soul Optimization (ASO)". The term "Artificial Soul Optimization" is coined in this paper. All the Optimization algorithms which are proposed based on Artificial Souls will come under "Artificial Soul Optimization" Field (ASO Field). In the Particle Swarm Optimization and Artificial Human Optimization, the basic entities in search space are Artificial Birds and Artificial Humans respectively. Similarly, in Artificial Soul Optimization, the basic entities in search space are Artificial Souls. In this work, the ASO Field concepts are added to Particle Swarm Optimization (PSO) algorithm to create a new hybrid algorithm titled "Soul Particle Swarm Optimization (SoPSO). The proposed SoPSO algorithm is applied on various benchmark functions. Results obtained are compared with PSO algorithm. The World's first Hybrid PSO algorithm based on Artificial Souls is created in this work.
Global Entanglement Distribution With Multi-Mode Non-Gaussian Operations

Non-Gaussian operations have been studied intensively in recent years due to their ability to enhance the entanglement of quantum states. However, most previous studies on such operations are carried out in a single-mode setting, even though in reality any quantum state contains multi-mode components in frequency space. Whilst there have been general frameworks developed for multi-mode photon subtraction (PS) and photon addition (PA), an important gap exists in that no framework has thus far been developed for multi-mode photon catalysis (PC). In this work we close that gap. We then apply our newly developed PC framework to the problem of continuous variable (CV) entanglement distribution via quantum-enabled satellites. Due to the high pulse rate envisioned for such systems, multi-mode effects will be to the fore in space-based CV deployments. After determining the entanglement distribution possible via multi-mode PC, we then compare our results with the entanglement distribution possible using multi-mode PS and PA. Our results show that multi-mode PC carried out at the transmitter is the superior non-Gaussian operation for certain regions of the parameter space. The size of this region is highly dependent on the squeezing of the initial state. When carried out at the receiver, multi-mode PC is again found to be the superior non-Gaussian operation for certain regions of the parameter space. In this latter region, the region size is more dependent on the channel loss. Our new results should prove valuable for next-generation deployments of CV quantum-enabled satellites.
Collaborative Edge-Cloud and Edge-Edge Video Analytics

According to YouTube statistics [1], more than 400 hours of content is uploaded to its platform every minute. At this rate, it is estimated that it would take more than 70 years of continuous watch time in order to view all content on YouTube, assuming no more content is uploaded. This raises great challenges when attempting to actively process and analyze video content.