Reviewerly/exHarmony huggingface dataset:
GitCanary: A Tool for Analyzing Student Contributions in Group Programming Assignments

Courses in computer science curricula often involve group programming assignments. In this paper, we present the GitCanary tool for monitoring project progress and member contributions using development productivity metrics. The tool is developed to support teachers and students with an overview that they can use during their guidance sessions. Feedback on the tool and its metrics was collected from students and teachers using interviews and questionnaires after utilizing it in a software development course. The results that the tool provides were found to be valuable in project guidance and to promote transparency in work distribution.
Towards physical interaction-based sequential mobility assistance using latent generative model of movement state

In this paper, we address sequential mobility assistance for daily elderly care through physical human–robot interaction. The goal of this work is to develop a robotic assistive system to provide physical support in daily life such as movement transition, e.g. sit-to-stand and walking. Using a mobile human support robotic platform, we propose an unsupervised learning-based approach to providing desirable physical support through an adaptive impedance parameter selection strategy according to the recognized user's movement state in an online manner. Using a latent generative model with a long short-term memory-based variational autoencoder, we first estimate the probability of the user's current movement state based on the sensory information in a low dimensional latent space. Then, the desired impedance parameters are selected adaptively according to the estimated movement state. One of the benefits of such an unsupervised learning approach is that no labeling is necessary in the training phase. Furthermore, our proposed framework is capable of detecting possible novel states such as falling over based on the obtained latent space. In order to demonstrate the proof of concept of our proposed approach, we present the experimental results of performance evaluations of online movement state recognition as well as novel movement detection.
Handling bidirectional control flow

Pressed by the difficulty of writing asynchronous, event-driven code, mainstream languages have recently been building in support for a variety of advanced control-flow features. Meanwhile, experimental language designs have suggested effect handlers as a unifying solution to programmer-defined control effects, subsuming exceptions, generators, and async–await. However, despite these trends, complex control flow—in particular, control flow that exhibits a bidirectional pattern—remains challenging to manage. We introduce bidirectional algebraic effects, a new programming abstraction that supports bidirectional control transfer in a more natural way. Handlers of bidirectional effects can raise further effects to transfer control back to the site where the initiating effect was raised, and can use themselves to handle their own effects. We present applications of this expressive power, which falls out naturally as we push toward the unification of effectful programming with object-oriented programming. We pin down the mechanism and the unification formally using a core language that makes generalizations to effect operations and effect handlers. The usual propagation semantics of control effects such as exceptions conflicts with modular reasoning in the presence of effect polymorphism—it breaks parametricity. Bidirectionality exacerbates the problem. Hence, we set out to show the core language, which builds on the existing tunneling semantics for algebraic effects, is not only type-safe (no effects go unhandled), but also abstraction-safe (no effects are accidentally handled). We devise a step-indexed logical-relations model, and construct its parametricity and soundness proofs. These core results are fully mechanized in Coq. While a full-featured compiler is left to future work, experiments show that as a first-class language feature, bidirectional handlers can be implemented efficiently.
Efficient Method for Mining High-Utility Itemsets Using High-Average Utility Measure

Mining high-utility itemsets (HUIs) based on high-average utility measure is an important task in the data mining field. However, many of the existing algorithms are performing the mining process sequentially and do not utilize the widely available multi-core processors, thus requiring long execution times. To address this issue, we propose an extended version of the HAUI-Miner algorithm, namely pHAUI-Miner. The algorithm applies multi-thread parallel processing to significantly reduce the mining time. Experimental evaluations on standard databases have shown the effectiveness of the proposed algorithm over the original and sequential method.
A model for detecting faults in build specifications

Incremental and parallel builds are crucial features of modern build systems. Parallelism enables fast builds by running independent tasks simultaneously, while incrementality saves time and computing resources by processing the build operations that were affected by a particular code change. Writing build definitions that lead to error-free incremental and parallel builds is a challenging task. This is mainly because developers are often unable to predict the effects of build operations on the file system and how different build operations interact with each other. Faulty build scripts may seriously degrade the reliability of automated builds, as they cause build failures, and non-deterministic and incorrect outputs. To reason about arbitrary build executions, we present BuildFS, a generally-applicable model that takes into account the specification (as declared in build scripts) and the actual behavior (low-level file system operation) of build operations. We then formally define different types of faults related to incremental and parallel builds in terms of the conditions under which a file system operation violates the specification of a build operation. Our testing approach, which relies on the proposed model, analyzes the execution of single full build, translates it into BuildFS, and uncovers faults by checking for corresponding violations. We evaluate the effectiveness, efficiency, and applicability of our approach by examining 612 Make and Gradle projects. Notably, thanks to our treatment of build executions, our method is the first to handle JVM-oriented build systems. The results indicate that our approach is (1) able to uncover several important issues (247 issues found in 47 open-source projects have been confirmed and fixed by the upstream developers), and (2) much faster than a state-of-the-art tool for Make builds (the median and average speedup is 39X and 74X respectively).
Evaluation of 24-Hours forecasts of global solar irradiation from IFS, GFS and McClear models

The knowledge of the global solar irradiation available at ground level is a key element in many fields, environment, agriculture, architecture, health, and photovoltaic power generation where the knowledge of the amount of radiation available on the earth’s surface a few hours in advance allows investment decision-making, electricity grid management, and better management of the energy produced. Different methods are used to forecast global solar radiation. The objective of this study is to validate three different models for forecasting solar radiation. This paper reports the validation results of the hourly forecasts of the global horizontal irradiation (GHI) of two numerical prediction models: GFS (Global Forecasting System) and IFS (Integrated forecasting System) which are global numerical weather prediction models. As clear sky situations are very frequent in Morocco, the performance of the clear sky model McClear was also evaluated. This model uses forecasts of aerosols, ozone and water vapor as inputs. The validation was done by comparing the 24-hours forecasts of these models with high-performances ground data from four meteorological stations installed in Morocco. The analysis is declined in two parts: an assessment for all skies types as well as for clear skies. In this case, the comparisons were made for automatically selected cloudless moments of all-skies pyranometric measurements. For all-sky conditions, the performances of the two IFS-based models are better than those obtained with the GFS model. In most cases, IFS surpasses GFS with a Mean Bias Error (MBE) ranging from 1.78 W/m2 and 26.19 W/m2 versus -1.96 W/m2 and 49.54 W/m2 for GFS, and a correlation coefficient (CC) between 0.93 and 0.98 for IFS versus 0.83 and 0.95 for GFS. For clear sky conditions, McClear+IFS exhibits the best performances outperforming GFS with an MBE ranging between 7.40 W/m2 and -18.25 W/m2 versus 32.16 W/m2 and -45.16 W/m2 for GFS. Generally, the validation results are satisfactory. The IFS and GFS models proved their ability to capture the temporal variability of GHI. The combination of the McClear and IFS models improved the clear sky forecasts for the whole stations, while a slight improvement was noticed for the all- sky forecasts. 0n the whole, the three models are reliable sources for solar radiation forecasting for Morocco.
Cryptoanalysis of the modified diffractive-imaging-based image encryption by deep learning attack

The conventional diffractive-imaging-based image encryption (CDIE) proposed in 2010 has drawn much attention in the last decade. A new modified diffractive-imaging-based image encryption (MDIE) with good non-linearity was proposed in 2019. Inspired by the cryptoanalysis method based on the convolutional neural networks, we propose a model trained with large numbers of ciphertext-plaintext pairs to crack the new cryptosystem. The proposed model has two blocks, the first block is employed for extracting the features of ciphertext and the second one is used for recovering the plaintext according to these extracted features. Compared with existing cryptoanalysis methods based on the convolutional neural networks, the proposed model has better generalization. We hope this structure can help researchers to solve other optical cryptoanalysis problems. To our knowledge, this is the first time to make the CNN-based attack can be used beyond the MNIST dataset, including both the handwriting dataset and fashion dataset. This work proves the CNN-based attack can be used in general situations. The analysis of validity and robustness is presented. Besides, the experimental results are conducted to validate the proposed method.
A Joint Learning Approach based on Self-Distillation for Keyphrase Extraction from Scientific Documents

Keyphrase extraction is the task of extracting a small set of phrases that best describe a document. Most existing benchmark datasets for the task typically have limited numbers of annotated documents, making it challenging to train increasingly complex neural networks. In contrast, digital libraries store millions of scientific articles online, covering a wide range of topics. While a significant portion of these articles contain keyphrases provided by their authors, most other articles lack such kind of annotations. Therefore, to effectively utilize these large amounts of unlabeled articles, we propose a simple and efficient joint learning approach based on the idea of self-distillation. Experimental results show that our approach consistently improves the performance of baseline models for keyphrase extraction. Furthermore, our best models outperform previous methods for the task, achieving new state-of-the-art results on two public benchmarks: Inspec and SemEval-2017.
Optical multiple images encryption system based on ellipsometry reconstruction

In this paper, an optical multiple image encryption method based on ellipsometry reconstruction is proposed. In our proposal, the cryptosystem is designed as multiple paths, and each path is used to encrypt one image. In each path, one original image is decomposed into two phase only masks (POMs) using the interference method. Then, every two masks decomposed from the same original image are encoded onto the spatial light modulator (SLM) to generate an elliptically polarized light (EPL) with specific amplitude and polarization. After that, each light passes through a linear polarizer. CCD is used to record the sum of the intensity of these path outputs, which is ciphertext. Jones matrix is used to describe these procedures. Moreover, our proposed encryption system has certain anti-attack ability. Computer simulations are conducted to verify the correctness and validity of our proposed method.
Long Short Term Memory Network-based Interference Recognition for Industrial Internet of Things

Nowadays, the rapid growth of wireless Internet of things (IoT) devices is one of the significant factors leading smart systems in various sectors, such as healthcare, education, and agriculture. This is, of course, not limited to the industrial sector, where the IoT concept is applied for real time monitoring and control of devices instead of human beings. Co-channel interferences occurs when two or more devices are using the same channel. It causes unnecessary contention as the devices will be forced to defer transmissions until the medium is clear causing a loss of throughput. Adjacent channel interference is even more serious and occurs when the devices are on overlapping channels causing corrupted data, which makes indispensable retransmissions. The more devices are added to an environment, the higher the likelihood of interference problem is. Due to a huge number of IoT devices, the interference issue becomes very serious. In this paper, a long short-term memory network-based interference recognition (LSTM-IR) is proposed. This method is integrated into the industrial IoT (IIoT) network in factory environments to mitigate the effect of interferences. The comparative results are done among three interference suppression techniques (IST) including the traditional minimum mean square error (MMSE) approach, the multi-layer perceptron (MLP), and the proposed LSTM-IR. Since the type of transmitting and receiving data is usually a sequencing data type. Therefore, the proposed method with the input data from a fast Fourier transform (FFT) algorithm provides better performances because it is based on an LSTM which is suitable for the sequences of data. The number of the devices in the factory is obviously the key factor because the smaller number of active devices causes less interferences.
Security Attack on Secret Sharing Based Watermarking Using Fractional Fourier Transform and Singular Value Decomposition

The secret sharing based watermarking scheme using the Fractional Fourier Transform (FrFT) and Singular Value Decomposition (SVD) has been proven to yield the outstanding results. This method embeds the singular value matrix into the FrFT transformed sub-bands. The FrFT transformed sub-bands are computed from the Samir Secret Sharing (SSS) scheme. As reported in literature, the aforementioned scheme achieves a good imperceptibility aspect as well as robust against some malicious attacks and geometric distortions. However, it cannot confront the attack presented in this paper. An attacker can simply perform this attack to the FrFT-SVD based on image watermarking scheme to obtain the desired counterfeit watermark image. It demonstrates the vulnerability issue of the FrFT-SVD-based image watermarking scheme.
NLP-CIC at SemEval-2020 Task 9: Analysing Sentiment in Code-switching Language Using a Simple Deep-learning Classifier

Code-switching is a phenomenon in which two or more languages are used in the same message.Nowadays, it is quite common to find messages with languages mixed in social media.This phenomenon presents a challenge for sentiment analysis.In this paper, we use a standard convolutional neural network model to predict the sentiment of tweets in a blend of Spanish and English languages.Our simple approach achieved a F1-score of 0.71 on test set on the competition.We analyze our best model capabilities and perform error analysis to expose important difficulties for classifying sentiment in a code-switching setting.
Pre-trained Language Model Based Active Learning for Sentence Matching

Active learning is able to significantly reduce the annotation cost for data-driven techniques.However, previous active learning approaches for natural language processing mainly depend on the entropy-based uncertainty criterion, and ignore the characteristics of natural language.In this paper, we propose a pre-trained language model based active learning approach for sentence matching.Differing from previous active learning, it can provide linguistic criteria from the pre-trained language model to measure instances and help select more effective instances for annotation.Experiments demonstrate our approach can achieve greater accuracy with fewer labeled training instances.
Evaluating Semantic Accuracy of Data-to-Text Generation with Natural Language Inference.

A major challenge in evaluating data-to-text (D2T) generation is measuring the semantic accuracy of the generated text, i.e. checking if the output text contains all and only facts supported by the input data. We propose a new metric for evaluating the semantic accuracy of D2T generation based on a neural model pretrained for natural language inference (NLI). We use the NLI model to check textual entailment between the input data and the output text in both directions, allowing us to reveal omissions or hallucinations. Input data are converted to text for NLI using trivial templates. Our experiments on two recent D2T datasets show that our metric can achieve high accuracy in identifying erroneous system outputs.
Manipulating Free-space Optical Beams with a Silicon Photonic Mesh

Photonic integrated meshes made of tuneable interferometers enable implementation of functions programmable on demand and are being envisioned as the optical counterpart of electronic field programmable gate arrays. Several examples of photonic processors capable of performing arbitrary linear operations have been recently proposed, and are expected to find applications in different areas, from the on-chip processing of telecom signals to microwave photonics, and from quantum optics to neural networks. In this work, we use a reconfigurable mesh of silicon photonic Mach-Zehnder Interferometers (MZIs) to manipulate free-space optical beams. We demonstrate beam steering, beam coupling from a free-space optical source to a single mode waveguide and automatic identification of the direction of arrival of a beam from a free-space source.
Deterministic nondestructive state analysis for polarization-spatial-time-bin hyperentanglement with cross-Kerr nonlinearity*

We present a deterministic nondestructive hyperentangled Bell state analysis protocol for photons entangled in three degrees of freedom (DOFs), including polarization, spatial-mode, and time-bin DOFs. The polarization Bell state analyzer and spatial-mode Bell state analyzer are constructed by polarization parity-check quantum nondemolition detector (P-QND) and spatial-mode parity-check quantum nondemolition detector (S-QND) using cross-Kerr nonlinearity, respectively. The time-bin Bell state analyzer is constructed by the swap gate for polarization state and time-bin state of a photon (P-T swap gate) and P-QND. The Bell states analyzer for one DOF will not destruct the Bell states of other two DOFs, so the polarization-spatial-time-bin hyperentangled Bell states can be determinately distinguished without destruction. This deterministic nondestructive state analysis method has useful applications in quantum information protocols.
Data security and integrity in cloud computing : Threats and Solutions

Cloud computing changed the world of Internet. With the help of cloud computing user can easily share, store and retrieve their data from anywhere. Cloud computing is scalable, fast, flexible, and cost-effective technology platform for IT enabled services over the internet. Cloud computing provides hardware, software and infrastructural storage to many users at a time Most of the times cloud users don’t know the exact location of their data or the sources of data stored with their data. In spite of various benefits that are provided by the cloud computing services, its users are very much afraid about the security of their data once it is over the cloud under the control of third-party vendors. Therefore, many data security and integrity concerns like access control, searchable encryption techniques, key management, ownership proofs and remote integrity check arise. This paper discusses the security and integrity of data in cloud computing. It is a study of aspects related to data security. The paper will go in to details of few data protection methods and approaches used throughout the world to ensure maximum data protection by reducing risks and threats. Going ahead the paper also discuss different techniques used for secured data storage on cloud.
Empirical assessment of generating adversarial configurations for software product lines

Software product line (SPL) engineering allows the derivation of products tailored to stakeholders’ needs through the setting of a large number of configuration options. Unfortunately, options and their interactions create a huge configuration space which is either intractable or too costly to explore exhaustively. Instead of covering all products, machine learning (ML) approximates the set of acceptable products (e.g., successful builds, passing tests) out of a training set (a sample of configurations). However, ML techniques can make prediction errors yielding non-acceptable products wasting time, energy and other resources. We apply adversarial machine learning techniques to the world of SPLs and craft new configurations faking to be acceptable configurations but that are not and vice-versa. It allows to diagnose prediction errors and take appropriate actions. We develop two adversarial configuration generators on top of state-of-the-art attack algorithms and capable of synthesizing configurations that are both adversarial and conform to logical constraints. We empirically assess our generators within two case studies: an industrial video synthesizer (MOTIV) and an industry-strength, open-source Web-app configurator (JHipster). For the two cases, our attacks yield (up to) a 100% misclassification rate without sacrificing the logical validity of adversarial configurations. This work lays the foundations of a quality assurance framework for ML-based SPLs.
Blockchain application in supply chain chemical substance reporting - a Delphi study

Blockchains utilise digital ledger technology to enable data to be traced in a more efficient manner than traditional paper-based systems. Smart contracts extend the capabilities of a blockchain by defining specific obligations. Chemical regulations impose the need on industry to record and report the use of hazardous chemicals within products. The process of collating supply chain chemical substance reporting information is a manually intensive and lengthy process in order to identify potential business risks and reporting of information to employees, consumers and chemical regulators. The research question answered in this paper relates to use of a blockchain with a smart contract to enable the automated collation of supply chain chemical substance information. This paper presents the findings from a Delphi study and a proposed 'supply chain chemical substance reporting' (SCCSR) blockchain. The SCCSR blockchain enables industry to implement greater efficiencies in collecting the required chemical substance information.