Reviewerly/exHarmony huggingface dataset:
Technical Debt Prioritization in Telecommunication Applications: Why the Actual Refactoring Deviates from the Plan and How to Remediate It? Case Study in the COVID Era

This paper focuses on application of a technical debt prioritisation technique in telecommunication software managing a fleet of devices for a video surveillance system. Technical debt for this application was gathered, categorised and prioritised according to the Continuous Debt Valuation Approach (CoDVA), previously proposed by the authors. The following research question was posed: Is prioritising technical debt reduction based on CoDVA effective (i.e., executed as per plan, bringing tangible benefits)? The outbreak of COVID-19 pandemic caused unprecedented disturbance to the engineering organisations worldwide, therefore the technical debt identification phase had to be adapted to cope with a switch to forced working-from-home mode. This was achieved by applying the Wisdom of Crowds method, ensuring broad participation of engineers, and providing a fairly complete picture of the accrued technical debt. Nevertheless, the actual technical debt reduction activities did not follow exactly the expected guidelines. The three main causes of this phenomenon were discovered: continuous refactoring approach, sizing of technical debt items, and the broadened scope of refactoring activities. Therefore, as a result of this case study we propose to adopt a specific broadened definition of technical debt and follow a few rules for defining its scope and granularity.
Automated Story Generation as Question-Answering

Neural language model-based approaches to automated story generation suffer from two important limitations. First, language model-based story generators generally do not work toward a given goal or ending. Second, they often lose coherence as the story gets longer. We propose a novel approach to automated story generation that treats the problem as one of generative question-answering. Our proposed story generation system starts with sentences encapsulating the final event of the story. The system then iteratively (1) analyzes the text describing the most recent event, (2) generates a question about "why" a character is doing the thing they are doing in the event, and then (3) attempts to generate another, preceding event that answers this question.
Implementation of Shamir’s Secret Sharing Scheme in Cloud Computing

With the increase in the use of cloud systems, security on cloud systems has become one of the most important items that underlined.Due to the nature of the cloud systems, it is difficult to manage multiuser systems and access control devices, resulting in some security vulnerabilities.In this paper we studied on Cloud Computing with Shamir's Secret Sharing Scheme with Advanced Encryption Standard (AES) algorithm for encryption and decryption process.For encryption and decryption process AES in Cryptool 1.4.41 has been used.For implementing Shamir Secret Sharing Scheme, Python language has been chosen and Spyder 3.8 has been chosen for environment.As a result, we successfully demonstrate the Shamir's Scheme implementation with file operations.
Unsupervised dynamic modeling of medical image transformation

Spatiotemporal imaging has applications in e.g. cardiac diagnostics, surgical guidance, and radiotherapy monitoring, In this paper, we explain the temporal motion by identifying the underlying dynamics, only based on the sequential images. Our dynamical model maps the inputs of observed high-dimensional sequential images to a low-dimensional latent space wherein a linear relationship between a hidden state process and the lower-dimensional representation of the inputs holds. For this, we use a conditional variational auto-encoder (CVAE) to nonlinearly map the higher-dimensional image to a lower-dimensional space, wherein we model the dynamics with a linear Gaussian state-space model (LG-SSM). The model, a modified version of the Kalman variational auto-encoder, is end-to-end trainable, and the weights, both in the CVAE and LG-SSM, are simultaneously updated by maximizing the evidence lower bound of the marginal likelihood. In contrast to the original model, we explain the motion with a spatial transformation from one image to another. This results in sharper reconstructions and the possibility of transferring auxiliary information, such as segmentation, through the image sequence. Our experiments, on cardiac ultrasound time series, show that the dynamic model outperforms traditional image registration in execution time, to a similar performance. Further, our model offers the possibility to impute and extrapolate for missing samples.
Are your dependencies code reviewed?: Measuring code review coverage in dependency updates

As modern software extensively uses free open source packages as dependencies, developers have to regularly pull in new third-party code through frequent updates. However, without a proper review of every incoming change, vulnerable and malicious code can sneak into the codebase through these dependencies. The goal of this study is to aid developers in securely accepting dependency updates by measuring if the code changes in an update have passed through a code review process. We implement Depdive, an update audit tool for packages in Crates.io, npm, PyPI, and RubyGems registry. Depdive first (i) identifies the files and the code changes in an update that cannot be traced back to the package's source repository, i.e., \textit{phantom artifacts}; and then (ii) measures what portion of changes in the update, excluding the phantom artifacts, has passed through a code review process, i.e., \textit{code review coverage}. Using Depdive, we present an empirical study across the latest ten updates of the most downloaded 1000 packages in each of the four registries. We further evaluated our results through a maintainer agreement survey. We find the updates are typically only partially code-reviewed (52.5\% of the time). Further, only 9.0\% of the packages had all their updates in our data set fully code-reviewed, indicating that even the most used packages can introduce non-reviewed code in the software supply chain. We also observe that updates either tend to have high \textit{CRC} or low \textit{CRC}, suggesting that packages at the opposite end of the spectrum may require a separate set of treatments.
Deconfounded Imitation Learning

Standard imitation learning can fail when the expert demonstrators have different sensory inputs than the imitating agent. This is because partial observability gives rise to hidden confounders in the causal graph. We break down the space of confounded imitation learning problems and identify three settings with different data requirements in which the correct imitation policy can be identified. We then introduce an algorithm for deconfounded imitation learning, which trains an inference model jointly with a latent-conditional policy. At test time, the agent alternates between updating its belief over the latent and acting under the belief. We show in theory and practice that this algorithm converges to the correct interventional policy, solves the confounding issue, and can under certain assumptions achieve an asymptotically optimal imitation performance.
Query Processing at Snapchat: How we Handle Query Completion, Suggestion and Localization

From the Publisher:Software is a commodity being sold across diverse language and cultural groups, whether in the commercial marketplace, or as customized applications. Developers must structure their applications so that they can be readily and cheaply localized for sale in this range of markets. Obvious differences such as scripts and languages must be understood as well as a range of more subtle cultural conventions. Further topics covered include: the overall architecture for internationalized products and an outline of an internationalization API; the use of computational linguistics methods; quality assurance, testing and documentation. Appendices contain summaries of the facilities available for localization on major platforms, characteristics of European languages, commercial tools and further reading. The book is aimed at small and medium sized software producers, and the IT departments of multinational corporations.
Arabic Text Mining

The rapid growth of the internet has increased the number of online texts. This led to the rapid growth of the number of online texts in the Arabic language. The enormous amount of text must be organized into classes to make the analysis process and text retrieval easier. Text classification is, therefore, a key component of text mining. There are numerous systems and approaches for categorizing literature in English, European (French, German, Spanish), and Asian (Chinese, Japanese). In contrast, there are relatively few studies on categorizing Arabic literature due to the difficulty of the Arabic language. In this work, a brief explanation of key ideas relevant to Arabic text mining are introduced then a new classification system for the Arabic language is presented using light stemming and Classifier Na\"ive Bayesian (CNB). Texts from two classes: politics and sports, are included in our corpus. Some texts are added to the system, and the system correctly classified them, demonstrating the effectiveness of the system.
Intriguing Properties of Compression on Multilingual Models

Multilingual models are often particularly dependent on scaling to generalize to a growing number of languages. Compression techniques are widely relied upon to reconcile the growth in model size with real world resource constraints, but compression can have a disparate effect on model performance for low-resource languages. It is thus crucial to understand the trade-offs between scale, multilingualism, and compression. In this work, we propose an experimental framework to characterize the impact of sparsifying multilingual pre-trained language models during fine-tuning. Applying this framework to mBERT named entity recognition models across 40 languages, we find that compression confers several intriguing and previously unknown generalization properties. In contrast to prior findings, we find that compression may improve model robustness over dense models. We additionally observe that under certain sparsification regimes compression may aid, rather than disproportionately impact the performance of low-resource languages.
Analog quantum variational embedding classifier

Quantum machine learning has the potential to provide powerful algorithms for artificial intelligence. The pursuit of quantum advantage in quantum machine learning is an active area of research. For current noisy, intermediate-scale quantum (NISQ) computers, various quantum-classical hybrid algorithms have been proposed. One such previously proposed hybrid algorithm is a gate-based variational embedding classifier, which is composed of a classical neural network and a parameterized gate-based quantum circuit. We propose a quantum variational embedding classifier based on an analog quantum computer, where control signals vary continuously in time. In our algorithm, the classical data is transformed into the parameters of the time-varying Hamiltonian of the analog quantum computer by a linear transformation. The nonlinearity needed for a nonlinear classification problem is purely provided by the analog quantum computer through the nonlinear dependence of the final quantum state on the control parameters of the Hamiltonian. We performed numerical simulations that demonstrate the effectiveness of our algorithm for performing binary and multi-class classification on linearly inseparable datasets such as concentric circles and MNIST digits. Our classifier can reach accuracy comparable with the best classical classifiers. We find the performance of our classifier can be increased by increasing the number of qubits until the performance saturates and fluctuates. Moreover, the number of optimization parameters of our classifier scales linearly with the number of qubits. The increase of number of training parameters when the size increases is therefore not as fast as that of neural network. Our algorithm presents the possibility of using current quantum annealers for solving practical machine-learning problems, and it could also be useful to explore quantum advantage in quantum machine learning.
One Person, One Model--Learning Compound Router for Sequential Recommendation

Deep learning has brought significant breakthroughs in sequential recommendation (SR) for capturing dynamic user interests. A series of recent research revealed that models with more parameters usually achieve optimal performance for SR tasks, inevitably resulting in great challenges for deploying them in real systems. Following the simple assumption that light networks might already suffice for certain users, in this work, we propose CANet, a conceptually simple yet very scalable framework for assigning adaptive network architecture in an input-dependent manner to reduce unnecessary computation. The core idea of CANet is to route the input user behaviors with a light-weighted router module. Specifically, we first construct the routing space with various submodels parameterized in terms of multiple model dimensions such as the number of layers, hidden size and embedding size. To avoid extra storage overhead of the routing space, we employ a weight-slicing schema to maintain all the submodels in exactly one network. Furthermore, we leverage several solutions to solve the discrete optimization issues caused by the router module. Thanks to them, CANet could adaptively adjust its network architecture for each input in an end-to-end manner, in which the user preference can be effectively captured. To evaluate our work, we conduct extensive experiments on benchmark datasets. Experimental results show that CANet reduces computation by 55 ~ 65% while preserving the accuracy of the original model. Our codes are available at https://github.com/icantnamemyself/CANet.
Linear maps as sufficient criteria for entanglement depth and compatibility in many-body systems

Physical transformations are described by linear maps that are completely positive and trace preserving (CPTP). However, maps that are positive (P) but not completely positive (CP) are instrumental to derive separability/entanglement criteria. Moreover, the properties of such maps can be linked to entanglement properties of the states they detect. Here, we extend the results presented in [Phys. Rev A 93, 042335 (2016)], where sufficient separability criteria for bipartite systems were derived. In particular, we analyze the entanglement depth of an $N$-qubit system by proposing linear maps that, when applied to any state, result in a bi-separable state for the $1:(N-1)$ partitions, i.e., $(N-1)$-entanglement depth. Furthermore, we derive criteria to detect arbitrary $(N-n)$-entanglement depth tailored to states in close vicinity of the completely depolarized state (the normalized identity matrix). We also provide separability (or $1$- entanglement depth) conditions in the symmetric sector, including for diagonal states. Finally, we suggest how similar map techniques can be used to derive sufficient conditions for a set of expectation values to be compatible with separable states or local-hidden-variable theories. We dedicate this paper to the memory of the late Andrzej Kossakowski, our spiritual and intellectual mentor in the field of linear maps.
Learning to Infer from Unlabeled Data: A Semi-supervised Learning Approach for Robust Natural Language Inference

Natural Language Inference (NLI) or Recognizing Textual Entailment (RTE) aims at predicting the relation between a pair of sentences (premise and hypothesis) as entailment, contradiction or semantic independence. Although deep learning models have shown promising performance for NLI in recent years, they rely on large scale expensive human-annotated datasets. Semi-supervised learning (SSL) is a popular technique for reducing the reliance on human annotation by leveraging unlabeled data for training. However, despite its substantial success on single sentence classification tasks where the challenge in making use of unlabeled data is to assign "good enough" pseudo-labels, for NLI tasks, the nature of unlabeled data is more complex: one of the sentences in the pair (usually the hypothesis) along with the class label are missing from the data and require human annotations, which makes SSL for NLI more challenging. In this paper, we propose a novel way to incorporate unlabeled data in SSL for NLI where we use a conditional language model, BART to generate the hypotheses for the unlabeled sentences (used as premises). Our experiments show that our SSL framework successfully exploits unlabeled data and substantially improves the performance of four NLI datasets in low-resource settings. We release our code at: https://github.com/msadat3/SSL_for_NLI.
RANCANG BANGUN APLIKASI ECODUACTION SEBAGAI STRATEGI PENGEMBANGAN KAMPUNG TEMATIK ECO ENZYME BERBASIS SOCIOTECHNOPRENEUR

Padang has 11 thematic villages, one of which is the Eco Enzyme Thematic Village, which is held at the Andalas Sepakat Unit Waste Bank office in Andalas Village. The idea of ​​the Eco Enzyme Thematic Village emerged seeing the problem of waste in urban areas, especially the city of Padang which has been piling up in Final Disposal Sites (TPA). In fact, if processed further, there is waste that can produce useful products such as processing with eco-enzymes. However, not many people are able and willing to manage their daily consumption waste into eco-enzyme products. The purpose of this research is to produce an application that can facilitate the community in managing waste into eco-enzyme products to selling finished products and selling the waste itself. This application will consist of 3 superior features, namely educational features, features for selling eco-enzyme and waste products (basic ingredients for eco-enzymes), as well as interaction features between users so that later they can become an active community to encourage education to the wider community. This application is called the "Ecoduaction Application" which is made based on sociotechnopreneur. This research applies the SDLC (System Development Life Cycle) method. The result of this research is an application that bridges the community in taking real action to solve the waste problem as well as being one of the solutions to economic problems in the city of Padang through eco-enzyme processing.
Bridging Speech and Textual Pre-trained Models with Unsupervised ASR

Spoken language understanding (SLU) is a task aiming to extract high-level semantics from spoken utterances. Previous works have investigated the use of speech self-supervised models and textual pre-trained models, which have shown reasonable improvements to various SLU tasks. However, because of the mismatched modalities between speech signals and text tokens, previous methods usually need complex designs of the frameworks. This work proposes a simple yet efficient unsupervised paradigm that connects speech and textual pre-trained models, resulting in an unsupervised speech-to-semantic pre-trained model for various tasks in SLU. To be specific, we propose to use unsupervised automatic speech recognition (ASR) as a connector that bridges different modalities used in speech and textual pre-trained models. Our experiments show that unsupervised ASR itself can improve the representations from speech self-supervised models. More importantly, it is shown as an efficient connector between speech and textual pre-trained models, improving the performances of five different SLU tasks. Notably, on spoken question answering, we reach the state-of-the-art result over the challenging NMSQA benchmark.
Decentralized Policy Optimization

The study of decentralized learning or independent learning in cooperative multi-agent reinforcement learning has a history of decades. Recently empirical studies show that independent PPO (IPPO) can obtain good performance, close to or even better than the methods of centralized training with decentralized execution, in several benchmarks. However, decentralized actor-critic with convergence guarantee is still open. In this paper, we propose \textit{decentralized policy optimization} (DPO), a decentralized actor-critic algorithm with monotonic improvement and convergence guarantee. We derive a novel decentralized surrogate for policy optimization such that the monotonic improvement of joint policy can be guaranteed by each agent \textit{independently} optimizing the surrogate. In practice, this decentralized surrogate can be realized by two adaptive coefficients for policy optimization at each agent. Empirically, we compare DPO with IPPO in a variety of cooperative multi-agent tasks, covering discrete and continuous action spaces, and fully and partially observable environments. The results show DPO outperforms IPPO in most tasks, which can be the evidence for our theoretical results.
ViT-CX: Causal Explanation of Vision Transformers

Despite the popularity of Vision Transformers (ViTs) and eXplainable AI (XAI), only a few explanation methods have been designed specially for ViTs thus far. They mostly use attention weights of the [CLS] token on patch embeddings and often produce unsatisfactory saliency maps. This paper proposes a novel method for explaining ViTs called ViT-CX. It is based on patch embeddings, rather than attentions paid to them, and their causal impacts on the model output. Other characteristics of ViTs such as causal overdetermination are also considered in the design of ViT-CX. The empirical results show that ViT-CX produces more meaningful saliency maps and does a better job revealing all important evidence for the predictions than previous methods. The explanation generated by ViT-CX also shows significantly better faithfulness to the model. The codes and appendix are available at https://github.com/vaynexie/CausalX-ViT.
An exposition of possibility and probability

This paper considers the notion of possible events which are insignificant in probabilistic analysis (i.e. events that have zero probability). The paper discusses the method of modal logic based on "possible worlds" and discusses a mathematical framework for the concepts of possibility, impossibility and certainty that are sometimes (incorrectly) thought to be defined with respect to probability. The relationship between possibility and probability is explored for general probability spaces and for refinements of these spaces conditional on other events, with particular focus on the properties of events having zero probability. We derive conditions under which possibility and significance diverge and conditions under which they can be reconciled as equivalent ideas within certain contexts. We also apply this analysis to discuss issues in possibility and probability in the multinomial model.
Scalable quantum error correction code on a ring topology of qubits

Quantum error correction is an important ingredient for scalable quantum computing. Stabilizer codes are one of the most promising and straightforward ways to correct quantum errors, since they do not require excessive complexity of physical qubits, are convenient for logical operations, and improve performance with increasing the involved qubits number. Here, we propose a linear scalable code of the permutative stabilizers for small distances on the ring architecture, which takes into account the topological features of the superconducting platform. We present the way to construct the quantum circuit of the code and provide numerical simulation that demonstrate the exponential logical error rate suppression.