Reviewerly/exHarmony huggingface dataset:
DRS Parsing as Sequence Labeling

We present the first fully trainable semantic parser for English, German, Italian, and Dutch discourse representation structures (DRSs) that is competitive in accuracy with recent sequence-to-sequence models and at the same time compositional in the sense that the output maps each token to one of a finite set of meaning fragments, and the meaning of the utterance is a function of the meanings of its parts.We argue that this property makes the system more transparent and more useful for humanin-the-loop annotation.We achieve this simply by casting DRS parsing as a sequence labeling task, where tokens are labeled with both fragments (lists of abstracted clauses with relative referent indices indicating unification) and symbols like word senses or names.We give a comprehensive error analysis that highlights areas for future work. 1
Exploring the Value of Multi-View Learning for Session-Aware Query Representation

Recent years have witnessed a growing interest towards learning distributed query representations that are able to capture search intent semantics.Most existing approaches learn query embeddings using relevance supervision making them suited only to document ranking tasks.Besides, they generally consider either user's query reformulations or system's rankings whereas previous findings show that user's query behavior and knowledge change depending on the system's results, intertwine and affect each other during the completion of a search task.In this paper, we explore the value of multi-view learning for generic and unsupervised session-aware query representation learning.First, single-view query embeddings are obtained in separate spaces from query reformulations and document ranking representations using transformers.Then, we investigate the use of linear (CCA) and non linear (UMAP) multi-view learning methods, to align those spaces with the aim of revealing similarity traits in the multi-view shared space.Experimental evaluation is carried out in a query classification and session-based retrieval downstream tasks using respectively the KDD and TREC session datasets.The results show that multiview learning is an effective and controllable approach for unsupervised learning of generic query representations and can reflect search behavior patterns.
Self-Training Based Adversarial Domain Adaptation for Radio Signal Recognition

Deep learning has made great progress in the field of radio signal recognition. However, existing works usually assume that testing and training data own the same feature domain, which may not be the case in real-world scenarios. The existing cross-domain radio signal recognition methods based on domain adaptation only consider the global feature alignment. A novel unsupervised adversarial domain-adaptive learning framework named DASig is proposed to address the cross-domain radio signal recognition issue for the data acquired in different channel conditions. To align the fine-grained class distributions of the target and source domains, we designed an efficient combined loss function and iterative self-training strategy via target domain pseudo-labels. Moreover, a non-shared domain self-attention mechanism is designed to preserve domain-specific features. Extensive experiment comparisons with five state-of-the-art (SOTA) methods on both the modulation datasets and protocol datasets show the superiority of the DASig.
Old Sinhala Newspaper Article Segmentation for Content Recognition Using Image Processing

As an automation approach of the Old Newspaper digitization, the content segmentation plays a major role. This study segments the degraded and mediocre quality old Sinhala newspapers into separate articles together with main elements classification, character segmentation, feature extraction, and character recognition. As a remedial measure for the misspelled word generation, a word correction technique was introduced at the end of the process to improve the accuracy of the article digitization. This paper highlights the first step of this study, where newspaper page segmentation into separate articles through heuristic knowledge embedded approach is carried out. This approach includes image detection, line detection, text area identification, margin detection, and column separation of newspaper pages. The results of this research are intriguingly comparable to other existing literature.
Reward-Free Exploration for Reinforcement Learning

Exploration is widely regarded as one of the most challenging aspects of reinforcement learning (RL), with many naive approaches succumbing to exponential sample complexity. To isolate the challenges of exploration, we propose a new "reward-free RL" framework. In the exploration phase, the agent first collects trajectories from an MDP $\mathcal{M}$ without a pre-specified reward function. After exploration, it is tasked with computing near-optimal policies under for $\mathcal{M}$ for a collection of given reward functions. This framework is particularly suitable when there are many reward functions of interest, or when the reward function is shaped by an external agent to elicit desired behavior. We give an efficient algorithm that conducts $\tilde{\mathcal{O}}(S^2A\mathrm{poly}(H)/\epsilon^2)$ episodes of exploration and returns $\epsilon$-suboptimal policies for an arbitrary number of reward functions. We achieve this by finding exploratory policies that visit each "significant" state with probability proportional to its maximum visitation probability under any possible policy. Moreover, our planning procedure can be instantiated by any black-box approximate planner, such as value iteration or natural policy gradient. We also give a nearly-matching $\Omega(S^2AH^2/\epsilon^2)$ lower bound, demonstrating the near-optimality of our algorithm in this setting.
Assessing Participation: Toward Long-Term Experiences, Trajectories and Maturity

Building knowledge on participation successes and failures is essential to enhance the overall quality and accountability of participatory processes. This paper relates to participatory assessment conducted in four cities, where 12 participatory workshops were organized, bringing together more than 230 participants. On-the-spot feedback was collected from the participants and generated 203 logbook entries, which helped define participant-related variables. Those variables in turn unfolded unique participatory trajectories for each participant. Four retrospective focus groups were then organized to bring qualitative, in-depth understanding to the participants’ expectations and (dis)satisfactions all along the participatory processes. On the basis of these empirical data, we developed a contextual, analytical tool to review participation in a longitudinal way. This qualitative tool articulates several intertwined influences: the level of satisfaction, the level of expectations and participatory background from the participants’ perspectives, as well as the participatory maturity from the organizing agency’s perspective. We argue that evaluating participation in the long term and in a transversal way, focusing on agencies’ and participants’ trajectories rather than uniquely on on-the-spot experiences, provides additional meaning to criteria applied to participation evaluation and teaches us more about participation quality and efficiency than repeated assessments of disconnected and isolated initiatives.
Designing evaluation framework for the empirical assessment of COVID-19 mobile apps in Pakistan

The significant proliferation in the mobile health applications (Apps) amidst Coronaviruses disease 2019 (COVID-19) resulted in decision making problems for healthcare professionals, decision makers and mobile users in Pakistan. This decision making process is also hampered by mobile app trade-offs, multiple features support, evolving healthcare needs and varying vendors. In this regard, evaluation model for mobile apps is presented which completes in three different phases. In first phase, features-based criteria is designed by leveraging Delphi method, and twenty (20) mobile apps are selected from app stores. In second stage, empirical evaluation is performed by using hybrid multi criteria decision approaches like CRiteria Importance Through Inter-criteria Correlation (CRITIC) method has been used for assigning weights to criteria features; and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method has been used for assessment of mobile app alternatives. In last step, decision making is performed to select the best mobile app for COVID-19 situations. The results suggest that proposed model can be adopted as a guideline by mobile app subscribers, patients and healthcare professionals.
N-Sliced Informed RRT*: Intelligent Sampling-Based Path Planning In High Eccentricity Informed Ellipsis

Many algorithms have been developed based on the Rapidly-exploring Random Tree (RRT). Among them, the Informed-RRT* stands out, because it aims to sample only a particular elliptical sub-region of the map to facilitate the optimal path convergence process. This way, it can find an optimal path in a shorter time by reducing the search area. However, this algorithm loses its benefits and acts similar to the the RRT* algorithm on multi-curve paths. In this study, we propose the n-Sliced Informed RRT*(nSIRRT*) to address this issue by dividing the reference path into a certain number of path segments and optimizing these segments one by one. Using this approximation, we addressed the convergence problem of Informed-RRT* in multi curved paths. Results showed that our method is more sample-efficient and converges the optimal trajectory faster than Informed-RRT* method. This method optimizes the path by focusing the sampling area around small segments on the path. Therefore generated trajectory converges optimal results faster.
Integrating Photovoltaics and Sun Blinds for Smart Shading Systems

For a mountainous country in which the exposed areas are mainly used for agriculture, there is a tendency to install PV systems on the buildings. In 2014, in Austria this share was 87.5%. The trend towards PV is accelerated by the plan to use only renewable energies for the electricity mix in Austria by 2030. For this, it is necessary to expand PV as a key technology and to make new installation options accessible. In this context, the PowerShade project investigates efficient, cost-effective, reliable, and universally applicable power-generating shading solutions. It integrates PV into commercially available sun protection systems. The challenging integration requires flexible, lightweight, robust, and economic applications while providing specific performance, durability, and optical appearance requirements. In this paper we present the first prototype results that generate still less than silicon-based applications. Furthermore, we demonstrate that regardless of potential improvement, the current state of the art still has technological gaps and economic barriers.
ZugChain: Blockchain-Based Juridical Data Recording in Railway Systems

In modern trains, a juridical recording unit logs events that occur during operation. This data is used to reconstruct the exact chain of events in case of failures and crashes. To ensure data recovery after an accident, the recorder is hardened against physical damage and secured against tampering; however, it is a single proprietary device and by no means indestructible.This paper presents ZugChain, a distributed, blockchain-based juridical recording unit that opportunistically utilizes on-train hardware. ZugChain offers high reliability via replication and tamper-resistance due to the nature of blockchains. It implements a permissioned blockchain based on a Byzantine fault-tolerant agreement protocol suitable for diverse communication systems. To utilize the logged data for advanced services, e. g., predictive maintenance, ZugChain securely and continuously exports traces to private data centers. We demonstrate ZugChain's feasibility with an implementation running on real train hardware, where we show that ZugChain orders data within 14 ms using at maximum 15 % of the total available shared CPU resources, thus fulfilling requirements of juridical recorders.
Review of Methods Applying on Facial Alignment

Nowadays, more and more projects and research require people to deal with getting information about people's faces. However, there is no chance to continue the work without knowing classifying organisms on the face, like which parts are eyes and which part is nose. Therefore, face alignment, a method of localizing these facial points, appears to be important. The problem of face alignment can be solved in several ways. Though traditional methods under constrained environments can solve problem of face alignment effectively, there are numerous techniques developed over the last two decades for face alignment, including traditional methods and deep learning-based methods. This paper describes the development process and basic methods of face alignment technology, which are methods based on traditional learning represented by ASM and AAM and deep learning methods represented by CPR and Heatmap. In the end, this paper points out that there are more challenges to facing alignment without constraints in the future. Peoples need to provide more relevant datasets, evaluate metrics and identify the accuracy of different algorithms applied in data sets.
Research on Cryptography as a Service Technique Based on Commercial Cryptography

Cryptography is a cornerstone of network and information security. And it is the most effective, reliable and economical means to achieve network and information security. With the development of cloud computing, network security is facing more severe challenges. It has become a trend to provide cryptographic capability such as cryptographic algorithms, cryptographic devices and cryptosystem as a resource in cloud computing services. This paper provides a solution of Cryptography as a Service based on commercial cryptography, which can be used as a reference for the construction of the Cryptography as a Service platform.
Abusing Trust: Mobile Kernel Subversion via TrustZone Rootkits

The Arm TrustZone is the de facto standard for hardware-backed Trusted Execution Environments (TEEs) on mobile devices, providing isolation for secure computations to be shielded from the normal world, and thus from the rest of the system. Most real-world TEEs are proprietary, difficult-to-inspect, and notoriously insecure: In the past years, it has been demonstrated over and over again that TEEs of millions of devices worldwide, and the Trusted Applications (TAs) they harbor, are often vulnerable to attacks such as control flow hijacking. Not only do we have to trust these TEEs to provide a secure environment for TAs such as keystore and Digital Rights Management (DRM), code running in the secure world provided by the Arm TrustZone also has full access to the memory of the regular operating system (OS). Since Thomas Roth first proposed a TrustZone-based rootkit in 2013, progress regarding such rootkits seems to have stalled in the offensive research community. The biggest challenge for TrustZone rootkits is that no interpretation of normal world memory is available in the secure world. Automated reverse engineering of kernel data structures at runtime is one way to implement rootkit functions. We present mechanisms to engineer the interpretation of Linux kernel memory for malicious subversion and the circumvention of basic protection mechanisms from the secure world. We provide a fully working proof-of-concept rootkit located in the Arm TrustZone to demonstrate the proposed mechanisms. We evaluate and show compatibility of the rootkit across different versions of the Linux kernel despite changing data structures. Our results highlight the feasibility of TrustZone rootkits that potentially survive kernel updates and raise awareness about the real danger of having to put trust into unvetted proprietary vendor code, which, as we show, can easily be abused.
yoU aRe a Liar://A Unified Framework for Cross-Testing URL Parsers

A variety of attacks, including phishing, remote-code execution, server-side request forgery, and hostname redirection, are delivered to users over the web. The beginning of most of the web exploits is an innocent-looking URL. Malformed or misinterpreted URLs can lead to remote code execution attacks as well. The IETF and WHATWG standards organizations define the components of a URL and act as an implementation guide for URL parsers. They state which characters are allowed in each portion of the URL and loosely suggest what to do in case an undefined character is present in the URL. The existence of two standards is the first concern, and the addition of server-side request forgery in the latest version of OWASP Top 10, suggests that neither of these standards is being followed accurately and concisely. Moreover, neither of these specifications describe an exact implementation standard, causing inconsistencies in the way the various parsers interpret the same URL. For example, malicious users can find ways to craft URLs to look like they are pointing to one resource but actually direct the user to different one. This problem is worsened when one application uses two separate parsers for validation and resource fetching.In this paper, we design a framework that unifies the testing suites of 8 URL parsers from popular web-related projects and highlights the inconsistencies between them. We examine and dive deep into the URL parser implementation across the most popular libraries, browsers, and command-line tools, and discover many open areas for exploitation. Our findings include identifying categories of inconsistencies, developing proof-of-concept exploits, and highlighting the need for a comprehensive implementation standard to be developed and enforced at the earliest.
Design Approach in Conference Management System with EZDESK Dashboard for Digital Ecosystem

Conference management system is a concept for handling and managing relevant file in various activities simultaneously before and after the conference. Actually, the preparation activities at the conference are involving the administrative processes recorded. In this case, the direction and modeling process should consider the user experience (UX) through a simplified design approach to RMS (recognize, materialize and scrutinize), which has been designed to help the development of prototype with standards and usability aspects that align with user requirements as an important factor and motivates overall implementation. EzDesk is an application designed to assist users in application registration and verification of conference registration documents. Meanwhile, a digital ecosystem is a group of interconnected IT resources that can function as a single unit. It consists of suppliers, customers, business partners, applications, third-party data service providers and all their technologies, whose interoperability is the key to the success of the ecosystem.
The Use of Technological Devices: A Descriptive Study of Students in University

In the globalization era of 21<sup>st</sup> century, the most prominent technology development is the technology of communication and information. The development and modernity of the technology of communication and information creates various changes in technology, starting from computer which then changed into gadget form of laptop or notebook, house phone, mobile phone, laptop and iPad. The development of device connected to Internet becomes more needed in daily life, both in socialization and education activity since it significantly affects people&#x0027;s life. Advanced and various technology media can be accessed through Internet by everyone and anywhere. Advanced technology and information causes faster distribution of information. Information can be accepted from one place to another place in almost the same time at different places. The information technology tools that can be used are computer, smartphone, mobile phone and other gadgets that have Internet connection. For people, especially students who belong to the teenagers group, Internet social media is a primary need, which makes the users will always access social media without any absent day.
Prediction of Study Period and Information on Judiciary in Higher Education Using the C5.0 Algorithm

Data Mining is often used to find patterns with certain techniques. Many data mining has been exploited against academic data. In Data Mining, choosing the right method depends on the goal and the process. One method that is often used in data mining is a decision tree. The concept of a decision tree is to convert data into a decision tree. There are several algorithms in implementing decision trees, including the C5.0 algorithm. In previous studies, research has been carried out on the prediction of the study period, but only focused on one final label category, namely the study period. Although several studies have been carried out related to graduation predictions, the novelty of this study uses multi-label, namely the study period and the results of the graduation statement. The purpose of this study will focus on predicting the length of study and student graduation outcomes using the Decision tree method with the C5.0 Algorithm. After implementation, we tested the accuracy by dividing the test data and training data differently. The test is carried out using the r2 score. The accuracy results obtained are 73&#x0025;, and suggestions for further research are to add leave attributes and organizational history which can be a factor in students not graduating on time.
Information Systems Maturity Level Assessment using the HISMM Framework: Case Study of State Hospital in Jakarta

Efforts to deal with several risks have been regulated by law, which certain programs should be initiated by the organization especially for the prevention and control of patient care in the context of hospital management system. The availability of service facilities and medical equipment is important in handling Covid-19 patients as many drawbacks have been found due to new circumstances that many do not predict before, such as the allocation room and time management for separating the patients within treatment. Indeed, the shortcomings in managing the process often related to the utilization of technology infrastructure in containing patients&#x0027; data and the medicine effectively. It is better to identify the issues resides in the hospital before coming up with the solution. Thus, this study want to assess the maturity level of the Information System using the Healthcare Information System Maturity Model (HISMM) as the framework. In the detail, to increase the flexibility of the environment for the sake of study, the investigation was conducted focusing on several attributes as a reference by analysing six dimensions that were relevant namely Data Analysis, Strategy, People, Electronic Medical Records, Information Security, and IT Infrastructure System. The results of this study indicated that State Hospital Jakarta has reached the maturity level at position 4 (four), which is equal to democratic cooperation. The recommendations given in this study are in the form of a roadmap that contains several steps in respected attributes that can support hospitals to reach the next level of maturity in the future.