Reviewerly/exHarmony huggingface dataset:
Data Mining of Job Requirements in Online Job Advertisements Using Machine Learning and SDCA Logistic Regression

There are currently many job portals offering job positions in the form of job advertisements. In this article, we are proposing an approach to mine data from job advertisements on job portals. Mainly, it would concern job requirements mining from individual job advertisements. Our proposed system consists of a data mining module, a machine learning module, and a postprocessing module. The machine learning module is based on the SDCA logistic regression. The postprocessing module includes several approaches to increase the success rate of the job requirements identification. The proposed system was verified on 20 most searched IT job positions from the selected job portal. In total, 9971 job advertisements were analyzed. Our system’s verification is finding all job requirements in 80% of analyzed advertisements. The detected job requirements were also compared with the Open Skills database. Based on this database and the extension of IT job positions with other typical job skills, we created a list of the most frequent job skills in selected IT job positions. The main contribution is the development of a universal system to detect job requirements in job advertisements. The proposed approach can be used not only for IT positions, but also for various job positions. The presented data mining module can also be used for various job portals.
Adaptive HVS objectivity-based watermarking scheme for copyright protection

During copyright protection of multimedia recourses, the imperceptibility and robustness of digital watermarks can be improved through basic rules of the Human Visual System (HVS). This trend is based on the physiological researches begun a century ago. All watermarking methods employ the limitations of human vision in order to reduce a probability of unauthorized extraction of the watermark. In this study, we propose to increase the payload in invisible regions of the image saving the imperceptibility and robustness to attacks by optimizing the embedding parameters using the genetic algorithm and the gray-wolf algorithm. The experiments have been conducted with high-resolution images which are now widely distributed as multimedia content transmitted over the Internet.
Proof Search on Bilateralist Judgments over Non-deterministic Semantics

The bilateralist approach to logical consequence maintains that judgments of different qualities should be taken into account in determining what-follows-from-what. We argue that such an approach may be actualized by a two-dimensional notion of entailment induced by semantic structures that also accommodate non-deterministic and partial interpretations, and propose a proof-theoretical apparatus to reason over bilateralist judgments using symmetrical two-dimensional analytical Hilbert-style calculi. We also provide a proof-search algorithm for finite analytic calculi that runs in at most exponential time, in general, and in polynomial time when only rules having at most one formula in the succedent are present in the concerned calculus.
Deadlock Avoidance and Solution Time Optimization for Discrete-Time Control Systems via Control Barrier Functions

Safety is a critical component for dynamic systems. Simultaneously imposing multiple dynamic constraints on the dynamic systems for safety adjustment is a challenging problem. In recent years, control barrier functions (CBFs) have been proposed to deal with the safety problem of dynamic systems. In general, CBFs are combined with control Lyapunov functions (CLFs) by quadratic programming (QP) to construct optimal controller to guarantee the safe trajectory tracking of dynamic systems. In this paper, an augmented CBF is proposed to guarantee the safety of a class of discrete-time control systems. Also, control input perturbation (CIP) algorithm is provided to solve the deadlock problem. Additionally, in order to improve the efficiency of solving QP, switching control is used to reduce the solution time of QP greatly. Finally, the effectiveness of the proposed methods is verified by adjusting the set point of the 3D double integral (3DDI) dynamic system.
Secure Multiparty Computation in the Bounded Storage Model

Most cryptography is based on assumptions such as factoring and discrete log, which assume an adversary has bounded computational power. With the recent development in quantum computing as well as concern with everlasting security, there is an interest in coming up with information-theoretic constructions in the bounded storage model.In this model, an adversary is computationally unbounded but has limited space. Past works have constructed schemes such as key exchange and bit commitment in this model. In this work, we expand the functionalities further by building a semi-honest MPC protocol in the bounded storage model. We use the hardness of the parity learning problem (recently shown by Ran Raz (FOCS ’16) without any cryptographic assumptions) to prove the security of our construction, following the work by Guan and Zhandry (EUROCRYPT ’19).
Distribution Knowledge Embedding for Graph Pooling

Graph-level representation learning is the pivotal step for downstream tasks that operate on the whole graph. The most common approach to this problem is graph pooling, where node features are typically averaged or summed to obtain the graph representations. However, pooling operations like averaging or summing inevitably cause severe information missing, which may severely downgrade the final performance. In this paper, we argue what is crucial to graph-level downstream tasks includes not only the topological structure but also the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">distribution</i> from which nodes are sampled. Therefore, powered by existing Graph Neural Networks (GNN), we propose a new plug-and-play pooling module, termed as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Distribution Knowledge Embedding</i> (DKEPool), where graphs are viewed as distributions on top of GNNs and the pooling goal is to summarize the entire distribution information instead of retaining a certain feature vector by simple predefined pooling operations. A DKEPool network <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">de facto</i> disassembles representation learning into two stages, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">structure learning</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">distribution learning</i> . Structure learning follows a recursive neighborhood aggregation scheme to update node features where structure information is obtained. Distribution learning, on the other hand, omits node interconnections and focuses more on the distribution depicted by all the nodes. Extensive experiments on graph classification tasks demonstrate that the proposed DKEPool significantly and consistently outperforms the state-of-the-art methods. The code is avaliable at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/chenchkx/dkepool</uri>
Requirements Elicitation in the Context of Software for Low-Functioning Autistic People: An Initial Proposal of Specific Supporting Artifacts

Requirements engineering is a crucial stage for the success of any software, as it allows a better understanding of the needs and desires of the customers or future users. In software development for autistic users, inexperienced software engineers face difficulties in the requirements engineering stage, as there are few specific techniques and artifacts to gather requirements in such context. The challenges increase when considering low-functioning autistic people, who have the most severe limitations of the disorder. Considering the above, in this paper, we present two artifacts to support the elicitation of requirements in software projects for autistic users. We developed a set of interviewing questions based on the opinion of both software development practitioners and caregivers. Also, we have drawn inspiration in a commercial canvas model to synthesize relevant data obtained with the interviews. When validating the artifacts, we got indicators of the feasibility of applying the artifacts in an actual development project. By developing and integrating these artifacts, we intend to support the requirements engineering stage and continue their development and study in further contexts.
Real-Time Distributed System for Pedestrians’ Fuzzy Safe Navigation in Urban Environment

Our modern society has long known the scourge of road accidents. This phenomenon leads to serious material and human losses. Building systems to reduce or eliminate these accidents, especially those relating to pedestrians, has become a priority for cities. The objective of this paper is to develop a real-time distributed solution for the safe navigation assistance of pedestrians. We provide fuzzy indicators of pedestrians’ risk exposure to road accidents which we integrate into a safest fuzzy path finding service. This work also concerns the development of real-time distributed pipeline architecture to collect the positions of pedestrians and vehicles in real time using a GPS tracker. The data collected will be visualized and stored for analysis and exploration purposes. To perform distributed processing in real time, we use a real-time architecture orchestrated by reactive technologies based on containerized micro-services.
Blending Pruning Criteria for Convolutional Neural Networks

The advancement of convolutional neural networks (CNNs) on various vision applications has attracted lots of attention. Yet the majority of CNNs are unable to satisfy the strict requirement for real-world deployment. To overcome this, the recent popular network pruning is an effective method to reduce the redundancy of the models. However, the ranking of filters according to their “importance” on different pruning criteria may be inconsistent. One filter could be important according to a certain criterion, while it is unnecessary according to another one, which indicates that each criterion is only a partial view of the comprehensive “importance”. From this motivation, we propose a novel framework to integrate the existing filter pruning criteria by exploring the criteria diversity. The proposed framework contains two stages: Criteria Clustering and Filters Importance Calibration. First, we condense the pruning criteria via layerwise clustering based on the rank of “importance” score. Second, within each cluster, we propose a calibration factor to adjust their significance for each selected blending candidates and search for the optimal blending criterion via Evolutionary Algorithm. Quantitative results on the CIFAR-100 and ImageNet benchmarks show that our framework outperforms the state-of-the-art baselines, regrading to the compact model performance after pruning.
Clothes-Independent Identity Feature Learning for Long-Term Person Re-identification

Long-term person re-identification (Re-ID) aims to retrieve the same pedestrian captured by different cameras over a long-duration, which is faced with the challenge of changing clothes. Existing traditional person ReID methods always assume that pedestrians hardly change clothes and focus clothes-dependent identity feature, thus they cannot achieve ideal recognition performance if this assumption is untenable. To alleviate the influence of clothes-changing, this paper proposes a dual-attribute fusion network (DAFN) learning clothes-independent identity feature. In DAFN, the original RGB image, gray-scale image and contour image of a pedestrian are utilized as the input. With the help of our proposed clothes-independent self-attention modules (CSM), the discriminative clothes-independent identity feature can be extracted. At the same time, lightweight feature-enhanced self-attention modules (FSM) are designed in DAFN to improve the robustness of feature representation. Empirical studies show that the DAFN proposed in this paper achieves state-of-the-art performance on long-term person ReID benchmark.
Recognizing Chinese Discourse Relations Based on Multi-Perspective and Hierarchical Modeling

Recognizing discourse relations is crucial for understanding semantic and logical connections between two discourse units in the text. The most of existing work only considers the single-level and single-perspective discourse relationship between discourse units, which will lead to inconsistencies between recognizing classes and sub-classes of the discourse relations and cannot distinguish very similar relations in semantics. Therefore, in this paper, we propose a Multi-perspective and Hierarchical Model (MHM) that can model hierarchical relationships and capture the implicit connections between discourse units from multi-perspective (i.e., rhetorical, co-referential, and temporal) to strengthen the distinction between different discourse relations. Specifically, we first build a hierarchical classification module with contrastive learning to mine the finer semantic in two-level discourse relations and recognize relations consistently. Then, we introduce recognizing co-referential and temporal relations as auxiliary tasks and build a mapping from rhetorical relations to them, modeling multi-perspective relationships between discourse units. The experimental results on MCDTB and CDTB show that our model achieves the best performance.
Implementation of English “Online and Offline” Hybrid Teaching Recommendation Platform Based on Reinforcement Learning

At present, there is a serious disconnect between online teaching and offline teaching in English MOOC large-scale hybrid teaching recommendation platform, which is mainly due to the problems of cold start and matrix sparsity in the recommendation algorithm, and it is difficult to fully tap the user's interest characteristics because it only considers the user's rating but neglects the user's personalized evaluation. In order to solve the above problems, this paper proposes to use reinforcement learning thought and user evaluation factors to realize the online and offline hybrid English teaching recommendation platform. First, the idea of value function estimation in reinforcement learning is introduced, and the difference between user state value functions is used to replace the previous similarity calculation method, thus alleviating the matrix sparsity problem. The learning rate is used to control the convergence speed of the weight vector in the user state value function to alleviate the cold start problem. Second, by adding the learning of the user evaluation vector to the value function estimation of the state value function, the state value function of the user can be estimated approximately and the discrimination degree of the target user can be reflected. Experimental results show that the proposed recommendation algorithm can effectively alleviate the cold start and matrix sparsity problems existing in the current collaborative filtering recommendation algorithm and can dig deep into the characteristics of users' interests and further improve the accuracy of scoring prediction.
Visibility detection and prediction of foggy highway based on lane line detection and Winters additive model

Highway fog is one of the main reasons of causing a traffic accident, so the fog highway visibility detection is very important. In the existing methods, it is necessary to set an appropriate target in advance for visibility calculation and most of the methods are based on neural network for visibility prediction, which restricts the efficiency of visibility detection and prediction. In view of the above problems, this paper proposes a highway visibility detection and prediction method based on lane detection and Winters additive model in foggy days. This method is based on the dark channel prior theory to obtain the highway image after fog removal and extract the coordinates of the end points of the lane line from the image. The visibility of highway in foggy days is calculated with the lane line as the reference. The visibility time series were fitted and predicted based on Winters additive model. The experimental results show that the method can accurately calculate and predict the highway visibility in foggy days without the need of camera calibration.
Interpretable Visual Understanding with Cognitive Attention Network

While image understanding on recognition-level has achieved remarkable advancements, reliable visual scene understanding requires comprehensive image understanding on recognition-level but also cognition-level, which calls for exploiting the multi-source information as well as learning different levels of understanding and extensive commonsense knowledge. In this paper, we propose a novel Cognitive Attention Network (CAN) for visual commonsense reasoning to achieve interpretable visual understanding. Specifically, we first introduce an image-text fusion module to fuse information from images and text collectively. Second, a novel inference module is designed to encode commonsense among image, query and response. Extensive experiments on large-scale Visual Commonsense Reasoning (VCR) benchmark dataset demonstrate the effectiveness of our approach. The implementation is publicly available at https://github.com/tanjatang/CAN.
Holistic Attention on Pooling Based Cascaded Partial Decoder for Real- Time Salient Object Detection

The identification of saliency is one of the fundamental computer vision problems. Convolutional Neural Network (CNN) has recently become the most commonly employed and potent detection strategy in which feature maps from various layers are combined without distinction. The current salient object detection frameworks use the multi-level aggregation of pre-trained neural networks. We resolve saliency identification via a cascaded partial decoder convolutional neural network with a holistic attention framework while focusing on extending the pooling function. Our framework is a partial decoder that discards relatively large resolution features for the acceleration of shallow layers. On the other hand, we noticed that deeper layers feature integration to achieve a reasonably accurate saliency map. We employed a pooling-based module based on a U-shaped architecture with a holistic attention model to gradually optimize the highly-developed semantic features of the backbone network. Experimental findings on six benchmark data sets show that we are more reliably locating the salient objects with sensitive information, thus improving their accuracy considerably compared to 22 prior art. Our method is as fast as it does to process an image of size 352×352 at a pace more significant than the different state-of-the-art architectures.
Attention Feature Network Extraction Combined with the Generation Algorithm of Multimedia Image Description

In view of the issue that the features of the images in the shallow layer cannot be fully utilized when the image description is generated and the target association of the image cannot be sufficiently obtained, a generation method for the description of the acquisition of attention images is put forward in this paper. The proportions of the features of images at various depths are autonomously assigned based on the content data of the language model, and the images thus generated are all pictures with image features with attention. In this way, the effect of description generation of images has been improved. After the testing of the database, the results indicate that the calculation method of the algorithm put forward in this paper is more accurate than the top-down multimedia image algorithm generated by a single attention.
Arabic factoid Question-Answering system for Islamic sciences using normalized corpora

Factoid Question-Answering (QA) Systems were developed to provide an accurate answer to a factoid question expressed in a natural language. The prime knowledge resources for most of factoid QA systems are online databases. However, the unstructured information in these resources rises the complexity of the information retrieval task. In this paper, we aim to develop an Arabic QA system for factoid questions specialized in Islamic sciences as prophetic tradition (Hadith), Hadith narrator and Quran interpretation (Tafsir). In fact, many questions in the Islamic research fields are focusing on Tafsir and Hadith text. In addition, a number of those interrogations focus on the chain of narrators who transmitted the Hadith. Besides, analyzing the narrator profile is considered an important and enquired task in hadith science. Furthermore, the proposed QA system is based on a normalized database specified in Text Encoding Initiative (TEI) standard. To achieve this, we propose a method composed of three phases to retrieve an accurate answer for the user question. The first phase is the question analysis. The second is the information search. The third phase is the answer processing. Besides, we implant the proposed QA system with a graphic interface allowing the interaction with the user. Finally, we experiment our prototype on 100 questions in the theme of Hadith, narrator and Tafsir text. The QA system succeeds to generate accurate responses for 92% of the entered questions.
FAST: Secure and High Performance Format-Preserving Encryption and Tokenization

We propose a new construction for format-preserving encryption. Our design provides the flexibility for use in format-preserving encryption (FPE) and for static table-driven tokenization. Our algorithm is a substitution-permutation network based on random Sboxes. Using pseudorandom generators and pseudorandom functions, we prove a strong adaptive security based on the super-pseudorandom permutation assumption of our core design. We obtain empirical parameters to reach this assumption. We suggest parameters for quantum security.
Domain Knowledge and Adaptive Serious Games: Exploring the Relationship of Learner Ability and Affect Adaptability

Detection and responding to a player’s affect are important for serious games. A method for this purpose was tested within Chem-o-crypt, a game that teaches chemical equation balancing. The game automatically detects boredom, flow, and frustration using the Affdex SDK from Affectiva. The sensed affective state is then used to adapt the game play in an attempt to engage the player in the game. A randomized controlled experiment incorporating a Dynamic Bayesian Network that compared results from groups with the affect-sensitive states vs those without revealed that measuring affect and adapting the game improved learning for low domain-knowledge participants.
Limits and Challenges of Embedding-based Question Answering in Export Control Expert System

In this paper, we report our initial findings from creating a question answering module for a dialog-based expert system which aims at advising users on export control regulations. We describe problems of data scarcity and knowledge transfer showing results of preliminary trials with utilizing contextual embeddings to extend keyword-based matching in our conversational expert system. We analyze pros and cons of the neural NLP techniques in systems which require precise information, for which no labelled data is available and which deal with less-resourced languages (Japanese in our case). Data scraped from governmental guidelines is presented and problems specific to legal documents in this area are discussed. Using examples from the preliminary experiments with deep learning-based methods we show when they fail and when they might be useful for further development of NLP systems operating on legal data. We also discuss evaluation-related problems and risks of imprecise output.