Reviewerly/exHarmony huggingface dataset:
StrongChain: Transparent and Collaborative Proof-of-Work Consensus

Bitcoin is the most successful cryptocurrency so far. This is mainly due to its novel consensus algorithm, which is based on proof-of-work combined with a cryptographically-protected data structure and a rewarding scheme that incentivizes nodes to participate. However, despite its unprecedented success Bitcoin suffers from many inefficiencies. For instance, Bitcoin's consensus mechanism has been proved to be incentive-incompatible, its high reward variance causes centralization, and its hardcoded deflation raises questions about its long-term sustainability. In this work, we revise the Bitcoin consensus mechanism by proposing StrongChain, a scheme that introduces transparency and incentivizes participants to collaborate rather than to compete. The core design of our protocol is to reflect and utilize the computing power aggregated on the blockchain which is invisible and "wasted" in Bitcoin today. Introducing relatively easy, although important changes to Bitcoin's design enables us to improve many crucial aspects of Bitcoin-like cryptocurrencies making it more secure, efficient, and profitable for participants. We thoroughly analyze our approach and we present an implementation of StrongChain. The obtained results confirm its efficiency, security, and deployability.
MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation

This paper proposes a recommender system to alleviate the cold-start problem that can estimate user preferences based on only a small number of items. To identify a user's preference in the cold state, existing recommender systems, such as Netflix, initially provide items to a user; we call those items evidence candidates. Recommendations are then made based on the items selected by the user. Previous recommendation studies have two limitations: (1) the users who consumed a few items have poor recommendations and (2) inadequate evidence candidates are used to identify user preferences. We propose a meta-learning-based recommender system called MeLU to overcome these two limitations. From meta-learning, which can rapidly adopt new task with a few examples, MeLU can estimate new user's preferences with a few consumed items. In addition, we provide an evidence candidate selection strategy that determines distinguishing items for customized preference estimation. We validate MeLU with two benchmark datasets, and the proposed model reduces at least 5.92% mean absolute error than two comparative models on the datasets. We also conduct a user study experiment to verify the evidence selection strategy.
Detecting TCP/IP Connections via IPID Hash Collisions

Abstract We present a novel attack for detecting the presence of an active TCP connection between a remote Linux server and an arbitrary client machine. The attack takes advantage of side-channels present in the Linux kernel’s handling of the values used to populate an IPv4 packet’s IPID field and applies to kernel versions of 4.0 and higher. We implement and test this attack and evaluate its real world effectiveness and performance when used on active connections to popular web servers. Our evaluation shows that the attack is capable of correctly detecting the IP-port 4-tuple representing an active TCP connection in 84% of our mock attacks. We also demonstrate how the attack can be used by the middle onion router in a Tor circuit to test whether a given client is connected to the guard entry node associated with a given circuit. In addition we discuss the potential issues an attacker would face when attempting to scale it to real world attacks, as well as possible mitigations against the attack. Our attack does not exhaust any global resource, and therefore challenges the notion that there is a direct one-to-one connection between shared, limited resources and non-trivial network side-channels. This means that simply enumerating global shared resources and considering the ways in which they can be exhausted will not suffice for certifying a kernel TCP/IP network stack to be free of privacy risk side-channels.
Training a New Generation of Engineers for Digital Economy

The domestic engineering industry is going through the stage of system introduction of digital technologies. The purpose of article is to present an experience of the department in the formation of the competences of a future engineer demanded in digital mechanical engineering. The department implements training methods corresponding to basic elements of a national technological initiative matrix such as digital modeling, mechatronics, additive technologies, artificial intelligence in control systems. Students master digital modeling in the end-to-end course of 3D modeling when dealing with design tasks of increasing complexity requiring the synthesis of independent, creative and competitive activities of students. Mechatronic modules are studied and designed in the educational process as the engineering competences demanded by employers. Additive technologies are studied at a laboratory of fast prototyping; the method of training combines team creation of electronic models, receiving a prototype of a product, and presentation of a result at student’s professionally focused competitions. Systems of artificial intelligence define the content of research work in interaction “student – teacher”. The new types of career-guidance work such as popular scientific lectures, excursions and practical work on the hi-tech equipment are highlighted. The authors also dwell on the prospects of training a new generation of mechanics in the conditions of the coming digital economy: the introduction of high-tech teaching aids, the implementation of the experience of immersive environments, gamification of training, the development of competences providing comprehensive skills for servicing virtual production corporations and intelligent devices.
A Syntactic Operator for Forgetting that Satisfies Strong Persistence

Abstract Whereas the operation of forgetting has recently seen a considerable amount of attention in the context of Answer Set Programming (ASP), most of it has focused on theoretical aspects, leaving the practical issues largely untouched. Recent studies include results about what sets of properties operators should satisfy, as well as the abstract characterization of several operators and their theoretical limits. However, no concrete operators have been investigated. In this paper, we address this issue by presenting the first concrete operator that satisfies strong persistence – a property that seems to best capture the essence of forgetting in the context of ASP – whenever this is possible, and many other important properties. The operator is syntactic, limiting the computation of the forgetting result to manipulating the rules in which the atoms to be forgotten occur, naturally yielding a forgetting result that is close to the original program.
Integrating Decision Sharing with Prediction in Decentralized Planning for Multi-Agent Coordination under Uncertainty

The performance of decentralized multi-agent systems tends to benefit from information sharing and its effective utilization. However, too much or unnecessary sharing may hinder the performance due to the delay, instability and additional overhead of communications. Aiming to a satisfiable coordination performance, one would prefer the cost of communications as less as possible. In this paper, we propose an approach for improving the sharing utilization by integrating information sharing with prediction in decentralized planning. We present a novel planning algorithm by combining decision sharing and prediction based on decentralized Monte Carlo Tree Search called Dec-MCTS-SP. Each agent grows a search tree guided by the rewards calculated by the joint actions, which can not only be sampled from the shared probability distributions over action sequences, but also be predicted by a sufficiently-accurate and computationally-cheap heuristics-based method. Besides, several policies including sparse and discounted UCT and DIY-bonus are leveraged for performance improvement. We have implemented Dec-MCTS-SP in the case study on multi-agent information gathering under threat and uncertainty, which is formulated as Decentralized Partially Observable Markov Decision Process (Dec-POMDP). The factored belief vectors are integrated into Dec-MCTS-SP to handle the uncertainty. Comparing with the random, auction-based algorithm and Dec-MCTS, the evaluation shows that Dec-MCTS-SP can reduce communication cost significantly while still achieving a surprisingly higher coordination performance.
Personal Brand of University Teachers in the Digital Age

Personal brand is becoming extremely important in any field of human activity nowadays: politics, show business, sports, business and science. On the one hand, the educational sphere of developing countries of the former Soviet Union is characterized by private conflicts, including the “teacher-student” field, the low prestige of the teacher’s profession because of the weak financing of this sphere and devaluing the teachers’ work during many years. On the other hand, the orientation of economies to innovative markets, the digital transformation of socio-economic systems necessitate the maintenance of strong, competitive education and science. This is impossible without top universities, high-class research centers, and therefore without unique teachers and researchers. An important task in this context is the formation of a new generation of highly qualified specialists, unique pedagogues and scientists, competitive in the world market. A special role here belongs to personal branding of such specialists. The purpose of this article is to study how a teacher’s personal brand can be built, what opportunities it opens, what are its value and specific features. In addition, the authors explore what factors contribute to the creation of a personal brand in the context of education and science. To solve these research tasks, the authors use methods of analysis, comparison and synthesis of information, survey methods (questioning, interviewing, narrative technique), modeling. One of the most significant results of the study is the model of the teacher’s personal brand developed by the authors, which shows what attributes of the image are of great importance for the personal brand of a teacher and scientist.
Incremental Learning of Planning Actions in Model-Based Reinforcement Learning

The soundness and optimality of a plan depends on the correctness of the domain model. Specifying complete domain models can be difficult when interactions between an agent and its environment are complex. We propose a model-based reinforcement learning (MBRL) approach to solve planning problems with unknown models. The model is learned incrementally over episodes using only experiences from the current episode which suits non-stationary environments. We introduce the novel concept of reliability as an intrinsic motivation for MBRL, and a method to learn from failure to prevent repeated instances of similar failures. Our motivation is to improve the learning efficiency and goal-directedness of MBRL. We evaluate our work with experimental results for three planning domains.
A Neural Network Model for Chinese Sentence Generation with Key Word

Using a word to make a sentence is a basic skill for human. It reflects the abilities of creativity and the degree of mastery of language skills of human. If the machine has this ability, which is used in nature language processing, it will be beneficial to solve some sentence generation problems, specially for summarization generation, machine translate, automatic question and answer and other application scenarios. Long Short-Term Memory(LSTM), is a neural network model broadly used in data processing and predictions. But in the field of text generation, there still are many limits. Most text generation based on LSTM, can only generate word on one direction from start word; or it can not keep the key word in the result. Our paper is to explore a way for LSTM to generate the context before and after the key word. This paper will provide a kind of LSTM model to achieve the function, generating sentence from key word and keeping the key word in the result sentence for Chinese.
Differentiable Physics and Stable Modes for Tool-Use and Manipulation Planning - Extended Abtract

We propose to formulate physical reasoning and manipulation planning as an optimization problem that integrates first order logic, which we call Logic-Geometric Programming.
A Comparative Study of Distributional and Symbolic Paradigms for Relational Learning

Many real-world domains can be expressed as graphs and, more generally, as multi-relational knowledge graphs. Though reasoning and learning with knowledge graphs has traditionally been addressed by symbolic approaches such as Statistical relational learning, recent methods in (deep) representation learning have shown promising results for specialised tasks such as knowledge base completion. These approaches, also known as distributional, abandon the traditional symbolic paradigm by replacing symbols with vectors in Euclidean space. With few exceptions, symbolic and distributional approaches are explored in different communities and little is known about their respective strengths and weaknesses. In this work, we compare distributional and symbolic relational learning approaches on various standard relational classification and knowledge base completion tasks. Furthermore, we analyse the properties of the datasets and relate them to the performance of the methods in the comparison. The results reveal possible indicators that could help in choosing one approach over the other for particular knowledge graphs.
Spatially and Temporally Efficient Non-local Attention Network for Video-based Person Re-Identification

Video-based person re-identification (Re-ID) aims at matching video sequences of pedestrians across non-overlapping cameras. It is a practical yet challenging task of how to embed spatial and temporal information of a video into its feature representation. While most existing methods learn the video characteristics by aggregating image-wise features and designing attention mechanisms in Neural Networks, they only explore the correlation between frames at high-level features. In this work, we target at refining the intermediate features as well as high-level features with non-local attention operations and make two contributions. (i) We propose a Non-local Video Attention Network (NVAN) to incorporate video characteristics into the representation at multiple feature levels. (ii) We further introduce a Spatially and Temporally Efficient Non-local Video Attention Network (STE-NVAN) to reduce the computation complexity by exploring spatial and temporal redundancy presented in pedestrian videos. Extensive experiments show that our NVAN outperforms state-of-the-arts by 3.8% in rank-1 accuracy on MARS dataset and confirms our STE-NVAN displays a much superior computation footprint compared to existing methods.
PEMBUATAN MEDIA PEMBELAJARAN E-LEARNING BERBASIS MOODLE PADA MATAKULIAH PENGANTAR APLIKASI KOMPUTER UNIVERSITAS SERAMBI MEKKAH

The article provides a general description of the Learning Application of Moodle-based e-learning and its feasibility to students of the PGSD FKIP at Serambi Mekkah University in the Introduction to Computer Applications subject. Data collection was done through examining questionnaire and interviewing students. There were various problems faced by students based for example students complaint that the access were a little slow when retrieved simultaneously. However, the content, design and technical feasibility analysis were good and it can be used in the Introduction to Application Computer subject for PGSD students at the Serambi Mekkah University.
OpenHI: open platform for histopathological image annotation

Consolidating semantically rich annotation on digital histopathological images known as whole-slide images requires a software capable of handling such type of biomedical data with support for procedures which align with existing pathological protocols. Demands for large-scale annotated histopathological datasets are on the raise since they are needed for developments of artificial intelligence techniques to promote automated diagnosis, mass screening, phenotype-genotype association study, etc. This paper presents an open platform for efficient collaborative histopathological image annotation with standardised semantic enrichment at a pixel-level precision named OpenHI (Open Histopathological Image). The framework's responsive processing algorithm can perform large-scale histopathological image annotation and serve as biomedical data infrastructure for digital pathology. Its web-based design is highly configurable and could be extended to annotate histopathological image of various oncological types. The framework is open-source and fully documented.
An Auditable and Secure Model for Permissioned Blockchain

The intrinsic tamper-resistant characteristic of blockchain enables transaction to be permanently stored as immutable record, which provides extremely reliable information traceability for some application scenarios such as supply chain and copyright protection. However, in fact, even the Byzantine fault-tolerant consensus algorithm cannot fundamentally guarantee that transaction information is real and legitimate in reality. Therefore, the normal operation of the blockchain system requires the establishment of a special supervisory body to audit the transaction information, which can ensure that the data sources are credible rather than spurious or illegal. In addition, in the environment of permissioned blockchain, the realization of access mechanism often relies on centralized certificate authority. Unfortunately, a highly centralized management entity is not entirely trustworthy and reliable because it may be corrupt or attacked, which inevitably triggers many information security incidents such as privacy leakage and data loss. In order to realize the secure governance of permissioned blockchain system, a novel multi-supervised permissioned blockchain (MSPB) model is proposed in this paper, which supports auditing and eliminates the centralized entities in the system, including certification authority and auditing authority. We evaluated the security and performance of the proposed model to demonstrate its feasibility, reliability and effectiveness.
Measurement of quality test of aerodynamic profiles in wind turbine blades using laser triangulation technique

Abstract The development of an optical contact instrument for measuring the geometric shape of aerodynamic profiles on blades of small power wind turbines is presented. The instrument uses the triangulation principle, where a structured line laser pattern is projected onto the surface of one of the faces of the blade under test, a camera captures the image of the line and is processed to interpret the distorted form of the projected line. A linear sweep of the instrument makes it possible to measure the profile in another section of the blade. Comparison and evaluation results of two symmetric profiles of the NACA 0012 family are presented, one manufactured in a 3D printer and the other one is a metal profile AF104 of a subsonic wind tunnel. Additionally, three sections of a blade with profile FX 63‐137 of a 1.5 kW wind turbine were evaluated. An aerodynamic analysis shows a reduction in the lift coefficient and in the efficiency of the aerodynamic profile, as well as an increase in the drag coefficient. The sensitivity of the instrument is 0.1 mm on the Z ‐axis.
webweb: a tool for creating, displaying, and sharing interactive network visualizations on the web

webweb is a package for creating interactive and portable visualizations of complex networks in the web browser that can be easily shared.With webweb, users of MATLAB, pure Python, and Python's networkX (Aric A. Hagberg & Pieter J. Swart, 2008) are able to write complex network data directly to a dependency-free html file.When opened in a browser, this file's embedded javascript functions render both an interactive network visualization and a lightweight user interface, allowing solo users to visualize and explore their data, or easily share their visualizations with collaborators with a single file.
A Semantic Sequential Correlation Based LSTM Model for Next POI Recommendation

The widespread of location-based social networks has generated massive check-in sequences in chronological order. Forecasting check-in sequences is significant while challenging due to the check-ins' sparsity problem. Existing methods have followed closely to incorporate spatial and temporal context to alleviate the data sparsity problem, but neglect the semantic sequential correlation between check-ins. Howbeit, incorporating the semantic sequential correlation between check-ins for next POI recommendation encounters the challenges of semantic sequential correlation measurement and sequential behavior modeling. To measure the semantic sequential correlation, we apply a semantic sequential correlation calculation model based on a semantic correlational graph that incorporates the time intervals' influence to calculate the semantic sequential correlation. Then, we apply a novel Long Short-Term Memory (LSTM) framework equipped with two additional semantic gates that takes the additional semantic sequential correlation as the extra input to capture users' sequential behaviors and model their long short-term interest with the restrictions in the semantic level. Finally, we cluster users into different groups as an improvement of our model to achieve a more accurate recommendation. Our proposed model is evaluated on a real-world and large-scale dataset and the experimental results demonstrate that our method outperforms the state-of-the-art methods for next POI recommendation.
Context in Photo Albums

Recent progress in digital photography and storage availability has drastically changed our approach to photo creation. While in the era of film cameras, careful forethought would usually precede the capture of a photo; nowadays, a large number of pictures can be taken with little effort. One of the consequences is the creation of numerous photos depicting the same moment in slightly different ways, which makes the process of organizing photos laborious for the photographer. Nevertheless, photo collection organization is important both for exploring photo albums and for simplifying the ultimate task of selecting the best photos. In this work, we conduct a user study to explore how users tend to organize or cluster similar photos in albums, to what extent different users agree in their clustering decisions, and to investigate how the clustering-defined photo context affects the subsequent photo-selection process. We also propose an automatic hierarchical clustering solution for modeling user clustering decisions. To demonstrate the usefulness of our approach, we apply it to the task of automatic photo evaluation within photo albums and propose a clustering-based context adaptation.