Reviewerly/exHarmony huggingface dataset:
SPACE: Unsupervised Object-Oriented Scene Representation via Spatial Attention and Decomposition

The ability to decompose complex multi-object scenes into meaningful abstractions like objects is fundamental to achieve higher-level cognition. Previous approaches for unsupervised object-oriented scene representation learning are either based on spatial-attention or scene-mixture approaches and limited in scalability which is a main obstacle towards modeling real-world scenes. In this paper, we propose a generative latent variable model, called SPACE, that provides a uniﬁed probabilistic modeling framework that combines the best of spatial-attention and scene-mixture approaches. SPACE can explicitly provide factorized object representations for foreground objects while also decomposing background segments of complex morphology. Previous models are good at either of these, but not both. SPACE also resolves the scalability problems of previous methods by incorporating parallel spatial-attention and thus is applicable to scenes with a large number of objects without performance degradations. We show through experiments on Atari and 3D-Rooms that SPACE achieves the above properties consistently in comparison to SPAIR, IODINE, and GENESIS. Results of our experiments can be found on our project website: https://sites.google.com/view/space-project-page
entity2rec: Property-specific knowledge graph embeddings for item recommendation

Knowledge graphs have shown to be highly beneficial to recommender systems, providing an ideal data structure to generate hybrid recommendations using both content-based and collaborative filtering. Most knowledge-aware recommender systems are based on manually engineered features, typically relying on path counting and/or on random walks. Recently, knowledge graph embeddings have proven to be extremely effective at learning features for prediction tasks, reducing the complexity and time required to manually design effective features. In this work, we present entity2rec, which learns user-item relatedness for item recommendation through property-specific knowledge graph embeddings. A key element of entity2rec is the construction of property-specific subgraphs. Through an extensive evaluation on three datasets, we show that: (1) hybrid property-specific subgraphs consistently enhance the quality of recommendations with respect to collaborative and content-based subgraphs; (2) entity2rec generates accurate and non-obvious recommendations, compared to a set of state-of-the-art recommender systems, achieving high accuracy, serendipity and novelty. More in detail, entity2rec is particularly effective when the dataset is sparse and has a low popularity bias; (3) entity2rec is easily interpretable and can thus be configured for a particular recommendation problem.
Strategic Mapping of Youth Unemployment With Interval-Valued Intuitionistic Hesitant Fuzzy DEMATEL Based on 2-Tuple Linguistic Values

This study aims to identify the factors that affect youth unemployment in emerging countries. For this purpose, 3 dimensions and 12 criteria are selected as a result of literature review. The analysis process has 3 different steps. Firstly, interval-valued intuitionistic fuzzy sets are created with the help of 2-tuple linguistic data. Additionally, relation matrix is generated by considering these fuzzy sets. In the second process, defuzzification process is occurred. Finally, the dimensions and criteria are weighted with Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach by using defuzzified data sets. The findings indicate that economic and social inequalities play the most significant role for youth unemployment in emerging countries. On the other side, it is also identified that economic crisis and insufficient education conditions are also important issues which lead to youth unemployment in these countries. Hence, it is recommended that governments should implement fair tax management practices in these countries to minimize economic and social inequalities. Furthermore, education conditions should be improved in the countries. In this framework, an effective education plan can be designed by cooperating with companies in the industry. Thus, labor needs in industry can be identified and education system can be designed according to the needs in the market. With the help of these implementations, it can be much easier for young people to find a job.
Blockchain Technology for Information Security of the Energy Internet: Fundamentals, Features, Strategy and Application

In order to ensure the information security, most of the important information including the data of advanced metering infrastructure (AMI) in the energy internet is currently transmitted and exchanged through the intranet or the carrier communication. The former increases the cost of network construction, and the latter is susceptible to interference and attacks in the process of information dissemination. The blockchain is an emerging decentralized architecture and distributed computing paradigm. Under the premise that these nodes do not need mutual trust, the blockchain can implement trusted peer-to-peer communication for protecting the important information by adopting distributed consensus mechanisms, encryption algorithms, point-to-point transmission and smart contracts. In response to the above issues, this paper firstly analyzes the information security problems existing in the energy internet from the four perspectives of system control layer, device access, market transaction and user privacy. Then blockchain technology is introduced, and its working principles and technical characteristics are analyzed. Based on the technical characteristics, we propose the multilevel and multichain information transmission model for the weak centralization of scheduling and the decentralization of transaction. Furthermore, we discuss that the information transmission model helps solve some of the information security issues from the four perspectives of system control, device access, market transaction and user privacy. Application examples are used to illustrate the technical features that benefited from the blockchain for the information security of the energy internet.
ConTExT: A Generic Approach for Mitigating Spectre

Out-of-order execution and speculative execution are among the biggest contributors to performance and efficiency of modern processors.However, they are inconsiderate, leaking secret data during the transient execution of instructions.Many solutions and hardware fixes have been proposed for mitigating transient-execution attacks.However, they either do not eliminate the leakage entirely or introduce unacceptable performance penalties.In this paper, we propose ConTExT, a Considerate Transient Execution Technique.ConTExT is a minimal and fully backward compatible architecture change.The basic idea of ConTExT is that secrets can enter registers but not transiently leave them.ConTExT transforms Spectre from a problem that cannot be solved purely in software [65], to a problem that is not easy to solve, but solvable in software.For this, ConTExT requires minimal, fully backward-compatible modifications of applications, compilers, operating systems, and the hardware.ConTExT offers full protection for secrets in memory and secrets in registers.With ConTExT-light, we propose a software-only solution of ConTExT for existing commodity CPUs protecting secrets in memory.We evaluate the security and performance of ConTExT.Even when over-approximating with ConTExT-light, we observe no performance overhead for unprotected code and data, and an overhead between 0 % and 338 % for security-critical applications while protecting against all Spectre variants.
Just-In-Time Defect Identification and Localization: A Two-Phase Framework

Defect localization aims to locate buggy program elements (e.g., buggy files, methods or lines of code) based on defect symptoms, e.g., bug reports or program spectrum. However, when we receive the defect symptoms, the defect has been exposed and negative impacts have been introduced. Thus, one challenging task is: whether we can locate buggy program prior to the appearance of the defect symptom (e.g., when buggy program elements are being committed to a version control system). We refer to this type of defect localization as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">“Just-In-Time (JIT) Defect localization”</i> . Although many prior studies have proposed various JIT defect identification methods to identify whether a new change is buggy, these prior methods do not locate the suspicious positions. Thus, JIT defect localization is the next step of JIT defect identification (i.e., after a buggy change is identified, suspicious source code lines are located). To address this problem, we propose a two-phase framework, i.e., JIT defect identification and JIT defect localization. Given a new change, JIT defect identification will identify it as buggy change or clean change first. If a new change is identified as buggy, JIT defect localization will rank the source code lines introduced by the new change according to their suspiciousness scores. The source code lines ranked at the top of the list are estimated as the defect location. For JIT defect identification phase, we use 14 change-level features to build a classifier by following existing approach. For JIT defect localization phase, we propose a JIT defect localization approach that leverages software naturalness with the N-gram model. To evaluate the proposed framework, we conduct an empirical study on 14 open source projects with a total of 177,250 changes. The results show that software naturalness is effective for our JIT defect localization. Our model achieves a reasonable performance, and outperforms the two baselines (i.e., random guess and a static bug finder (i.e., PMD)) by a substantial margin in terms of four ranking measures.
Digital Competences Relationship between Gender and Generation of University Professors

Digital competences can be defined as the set of techno-pedagogical and communicational skills that allow teachers to function effectively within the educational contexts that new technologies generate. This research work was aimed to establish the relationship between the level of university professors’ digital competences concerning their gender and generation. The study was based on a sample of 613 professionals with an undergraduate degree, who came from different areas of Ecuador; they were the attendees to the "Training Program for Leveling Tutors using Virtual Modality" at Tí©cnica del Norte University (Ibarra -Ecuador). This research applied a quantitative and explanatory approach where the non-parametric statistical test of Chi-square was used to corroborate the validity of the data; it was obtained from a survey applied to the participants in this training course. The results show that the level of digital competences is gender independent but generation dependent. Generation Z is the one with the best digital capabilities, with an average of 61.14%. Regarding the gender, a dependence is observed only with two out of five groups of competencies analyzed in this study; the cloud storage and the interaction of social network. The research line of digital competences is extensive, the results should not be generalized or extrapolated without considering the characteristics of the educators and the context, it is suggested to assess the teaching staff digital competencies, considering other types of demographic variables, so that higher education institutions can carry out a comprehensive training planning in digital skills.
Exploiting Smart Contracts for Capability-Based Access Control in the Internet of Things

Due to the rapid penetration of the Internet of Things (IoT) into human life, illegal access to IoT resources (e.g., data and actuators) has greatly threatened our safety. Access control, which specifies who (i.e., subjects) can access what resources (i.e., objects) under what conditions, has been recognized as an effective solution to address this issue. To cope with the distributed and trust-less nature of IoT systems, we propose a decentralized and trustworthy Capability-Based Access Control (CapBAC) scheme by using the Ethereum smart contract technology. In this scheme, a smart contract is created for each object to store and manage the capability tokens (i.e., data structures recording granted access rights) assigned to the related subjects, and also to verify the ownership and validity of the tokens for access control. Different from previous schemes which manage the tokens in units of subjects, i.e., one token per subject, our scheme manages the tokens in units of access rights or actions, i.e., one token per action. Such novel management achieves more fine-grained and flexible capability delegation and also ensures the consistency between the delegation information and the information stored in the tokens. We implemented the proposed CapBAC scheme in a locally constructed Ethereum blockchain network to demonstrate its feasibility. In addition, we measured the monetary cost of our scheme in terms of gas consumption to compare our scheme with the existing Blockchain-Enabled Decentralized Capability-Based Access Control (BlendCAC) scheme proposed by other researchers. The experimental results show that the proposed scheme outperforms the BlendCAC scheme in terms of the flexibility, granularity, and consistency of capability delegation at almost the same monetary cost.
A Framework for Exploring Blockchain Technology in Supply Chain Management

The purpose of the study is to identify and explore different applications of blockchain technology in supply chain management and suggest a framework for their analysis.The study addresses researchers' conflicting opinions regarding the hype of blockchain and clarifies which minor applications are hyped and which are currently more applicable.That helps supply chain practitioners appraise the different applications of blockchains before it is too late to revise a decision regarding their use.The framework was developed based on a systematic literature review of blockchain-related articles, and it was examined with semi-structured interviews with four companies that applied the technology.Findings from the study suggested that traceability and supply chain finance seemed to be the most applicable applications of blockchain technology in supply chain management.But, light was also shed on other applications such as: compliance with standards, supply chain integration and digitalization of the transactions.This study was restricted by the availability of applied cases, as blockchain remains an emerging technology.Therefore, this study cannot be considered a case research, but a literature review followed by a limited number of interviews.However, the study resulted in a conceptual framework that forms the basis for further exploratory studies with more empirical data.
Synchronous Transformers for end-to-end Speech Recognition

For most of the attention-based sequence-to-sequence models, the decoder predicts the output sequence conditioned on the entire input sequence processed by the encoder. The asynchronous problem between the encoding and decoding makes these models difficult to be applied for online speech recognition. In this paper, we propose a model named synchronous transformer to address this problem, which can predict the output sequence chunk by chunk. Once a fixed-length chunk of the input sequence is processed by the encoder, the decoder begins to predict symbols immediately. During training, a forward-backward algorithm is introduced to optimize all the possible alignment paths. Our model is evaluated on a Mandarin dataset AISHELL-1. The experiments show that the synchronous transformer is able to perform encoding and decoding synchronously, and achieves a character error rate of 8.91% on the test set.
A Blockchain-Based Access Control Framework for Cyber-Physical-Social System Big Data

Cyber-Physical-Social System (CPSS) big data is specified as the global historical data which is usually stored in cloud, the local real-time data which is usually stored in the fog-edge server (FeS) of the mobile terminal devices or sensors, and the social data which is usually stored in the social data server (SdS), moreover adopts a centralized access control mechanism to offer users' access strategy which can easily cause CPSS big data to be tampered with and to be leaked. Therefore, a blockchain-based access control scheme called BacCPSS for CPSS big data is proposed. In BacCPSS, account address of the node in blockchain is used as the identity to access CPSS big data, the access control permission for CPSS big data is redefined and stored in blockchain, and processes of authorization, authorization revocation, access control and audit in BacCPSS are designed, and then a lightweight symmetric encryption algorithm is used to achieve privacy-preserving. Finally, a credible experimental model on EOS and Aliyun cloud is built. Results show that BacCPSS is feasible and effective, and can achieve secure access in CPSS while protecting privacy.
Blockchain Mechanism and Symmetric Encryption in A Wireless Sensor Network

The Internet of Things (IoT) paradigm allows the connection and exchange of information between millions of smart devices. This paradigm grows and develops exponentially as do the risks and attacks on IoT infrastructures. Security, privacy, reliability, and autonomy are the most important requirements in IoT Systems. If these issues are not guaranteed, the IoT system could be susceptible to malicious users and malicious use. In centralized IoT systems, attacks and risks are greater, especially when data is transmitted between devices and shared with other organizations. To avoid these types of situations, this work presents a decentralized system that guarantees the autonomy and security of an IoT system. The proposed methodology helps to protect data integrity and availability based on the security advantages provided by blockchain and the use of cryptographic tools. The accuracy of the proposed methodology was measured on a temperature and humidity sensing IoT-based Wireless Sensor Network (WSN). The obtained results prove that the proposal fulfils the main requirements of an IoT system. It is autonomous, secure to share and send information between devices and users, has privacy, it is reliable, and the information is available in the infrastructure. Furthermore, this research demonstrates that the proposal is less susceptible to the most frequent attacks against IoT systems, such as linking attack, man in the middle, and Distributed Denial of Service (DDoS) attack.
MobileCount: An efficient encoder-decoder framework for real-time crowd counting

In this work, we propose a computation-efficient encoder-decoder architecture, named MobileCount, which is specifically designed for high-accuracy real-time crowd counting on mobile or embedded devices with limited computation resources. For the encoder part, MobileNetV2 is tailored in order to significantly reduce FLOPs at a little cost of performance drop, which has 4 bottleneck blocks preceded by a max pooling layer of stride 2. The design of decoder is motivated by Light-weight RefineNet, which further boosts counting performance with only a 10% increase of FLOPs. In comparison with state-of-the-arts, our proposed network is able to achieve comparable counting performance with 1/10 FLOPs on a number of benchmarks. At last, we propose a multi-layer knowledge distillation method to further boost the performance of MobileCount without increasing its FLOPs.
Composed Query Image Retrieval Using Locally Bounded Features

Composed query image retrieval is a new problem where the query consists of an image together with a requested modification expressed via a textual sentence. The goal is then to retrieve the images that are generally similar to the query image, but differ according to the requested modification. Previous methods usually consider the image as a whole. In this paper, we propose a novel method that represents the image using a set of local areas in the image. The relationship between each word in the modification text and each area in the image is then explicitly established, allowing the model to accurately correlate the modification text to parts of the image. We conduct extensive experiments on three benchmark datasets. The results show that our method outperforms other state-of-the-art approaches by a considerable margin.
To Test Machine Comprehension, Start by Defining Comprehension

Many tasks aim to measure machine reading comprehension (MRC), often focusing on question types presumed to be difficult. Rarely, however, do task designers start by considering what systems should in fact comprehend. In this paper we make two key contributions. First, we argue that existing approaches do not adequately define comprehension; they are too unsystematic about what content is tested. Second, we present a detailed definition of comprehension—a "Template of Understanding"—for a widely useful class of texts, namely short narratives. We then conduct an experiment that strongly suggests existing systems are not up to the task of narrative understanding as we define it.
An ensemble imbalanced classification method based on model dynamic selection driven by data partition hybrid sampling

In many real-world applications classification problems suffer from class-imbalance. The classification methods for imbalanced data with only data processing or algorithm improvement cannot get satisfied classification performance of the minority class. This paper proposes an ensemble classification method based on model dynamic selection driven by data partition hybrid sampling for imbalanced data. The method includes two core components: the generation of balanced datasets and the dynamic selection of classification models. At the data level a data partition hybrid sampling (DPHS) method is proposed to balance datasets. In particular the data space is divided into four regions according to the majority class proportion in minority class neighborhoods. Then we present a boundary minority class weighted over-sampling (BMW-SMOTE) method where the weight of each minority class instance is calculated by the ratio between the majority class proportion in the neighborhood of the current instance and the sum of all these proportions. The number of synthetic instances is determined by the weight. At the algorithm level we present a model dynamic selection (MDS) strategy. Three ensemble learning models are built. Among them the local regions reinforce and weaken model adopts the balanced dataset obtained by proposed DPHS method for training to strengthen the identification of test instances on the boundary and appropriately weakens the dense distribution of majority class. The model for each test instance is selected adaptively according to the imbalance degree of its neighbors. The experimental results show that the proposed method outperforms typical imbalanced classification methods for F-measure and G-mean.