Reviewerly/exHarmony huggingface dataset:
Analysing factorizations of action-value networks for cooperative multi-agent reinforcement learning

Recent years have seen the application of deep reinforcement learning techniques to cooperative multi-agent systems, with great empirical success. However, given the lack of theoretical insight, it remains unclear what the employed neural networks are learning, or how we should enhance their learning power to address the problems on which they fail. In this work, we empirically investigate the learning power of various network architectures on a series of one-shot games. Despite their simplicity, these games capture many of the crucial problems that arise in the multi-agent setting, such as an exponential number of joint actions or the lack of an explicit coordination mechanism. Our results extend those in Castellini et al. (Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, AAMAS'19.International Foundation for Autonomous Agents and Multiagent Systems, pp 1862-1864, 2019) and quantify how well various approaches can represent the requisite value functions, and help us identify the reasons that can impede good performance, like sparsity of the values or too tight coordination requirements.
Learning from Audience Interaction: Multi-Instance Multi-Label Topic Model for Video Shots Annotating

In recent years, audiences can find their interested TV play or movie videos by labels easily. However, for finding shots with certain semantic content in these videos, it is still a problem to annotate video shots by labels. Some existing approaches train models with annotated shots which cost a lot in labeling manually. Some other methods in solving this kind of task assume that the content of a video is only limited in the labels of the video. They ignore that the labels of a video are too coarse-grained to cover all content of the video. In this paper, we propose a multi-label, multi-instance topic model to annotate video shots by video labels. In a multi-label, multi-instance framework, video shots can be regarded as instances and shot labels are learned from labels in video level which makes the cost of labeling cheaper. On the other hand, our model learns label semantics by controlling the relationship between video labels and shots to solve coarse-grained problem. Furthermore, we also learn keywords for every video. The experiments on a large-scale real-world dataset show that our model outperforms other baseline models substantially.
RGB Guided Depth Map Super-Resolution with Coupled U-Net

The depth maps captured by RGB-D cameras usually are of low resolution, entailing recent efforts to develop depth super-resolution (DSR) methods. However, several problems remain in existing DSR methods. First, conventional DSR methods often suffer from unexpected artifacts. Secondly, high-resolution (HR) RGB features and low-resolution (LR) depth features are often fused in shallow layers only. Thirdly, only the last layer of features is used for reconstruction. To address the above problems, we propose Coupled U-Net (CU-Net), a new color image guided DSR method built on two U-Net branches for HR color images and LR depth maps, respectively. The CU-Net embeds a dual skip connection structure to leverage the feature interaction of the two branches, and a multi-scale fusion to fuse the deeper and multi-scale features of two branch decoders for more effective feature reconstruction. Moreover, a channel attention module is proposed to eliminate artifacts. Extensive experiments show that the proposed CU-Net outperforms state-of-the-art methods.
Side Effect Alerts Generation from EHR in Polish

The paper addresses the problem of extending an existing and widely used program for Polish public healthcare with a function for detecting possible occurrences of drug side effects. The task is performed in two steps. First, we extract information that binds names of drugs with side effects and their frequency. In the next step, we look for similar phrases in the list of side effect phrases. For all words in phrases, we use Polish Wordnet to find similar ones, and check if phrases with replaced words exist in the list. For long side effect phrases, which never occur in patient records, we look for simpler internal side effect phrases to generate alarms. Finally, we evaluate to what extent this action increases the efficiency of side effect alarms.
Multi-cloud Solution Design for Migrating a Portfolio of Applications to the Cloud

Migrating applications to the cloud is rapidly increasing in many organizations as it enables them to take advantages of the cloud, such as the lower costs and accessibility of data. Moreover, such organizations typically try to avoid sticking to a single cloud provider and rather prefer to be able to spread out their applications across different providers. However, there are many challenges in achieving this. First, many of the applications that are required to be moved to the cloud might be legacy applications that do not have good documentation, and so it is not trivial to even assess whether it is feasible to move them to the cloud or not. Moreover, such legacy applications might need a significant architecture overhaul to achieve the task of moving them to the cloud. Large client may have significant percentage of applications in this category. So, one has to evaluate cloud feasibility and understand whether there is a need to re-architect application based on what services providers are able to offer. Second, clients usually define multiple features, encryption/security level, and other service level requirements they expect in the providers they will migrate each of their applications to. Thus, choosing the right providers for different application is another challenging task here. In this work-in-progress paper, we present a novel methodology for preparing such a cloud migration solution, where we perform text mining on application data to evaluate cloud-migration feasibility and then recommend the optimal solution using a mathematical optimization model. We illustrate our approach with an example use case.
An Orthogonal Learning Design Whale Optimization Algorithm with Clustering Mechanism

In this paper, an orthogonal learning (OL) design whale optimization algorithm (WOA) with clustering mechanism, named OLWOA, is proposed to solve the complex continuous problems. In the proposed algorithm, the OL, as an effective strategy to utilize prior search information (experience), is utilized to overcome the disadvantages of the basic WOA, which converges slowly and falls into local optimum easily. The clustering-based mechanism guides the humpback whales to search toward an interesting area by propagating the information of good solutions from one cluster to another cluster. The experimental results reveal the effectiveness and significance of the OL and the clustering-based learning mechanism in the proposed algorithm.
Research on the Influence of Online Multimedia Corpus Indexing System on English Teaching in Colleges and Universities

English is the most important international common language in the world today. With the development of corpus linguistics, emerging multimedia technology and advanced network development, computer-aided language teaching software is not only widely promoted in teaching, but also enables students to learn actively in autonomous learning. Online multimedia corpus realizes corpus retrieval and audio and video positioning. There are prominent advantages in English teaching. Images, sounds and data coexist, and the database can be built by the user, which is different from person to person and not limited by time and space. Users can choose different corpus according to their own interests and hobbies and basic characteristics, and practise listening, speaking, reading, writing and translating. This paper mainly introduces the application of relevant cognitive theories in language learning, and then introduces the application of corpus retrieval system in English teaching. The online multimedia corpus system is designed and its main functions are introduced. According to the application of multimedia corpus in college English teaching, this paper designs and organizes the participants to conduct systematic experiments. The importance and future development of online multimedia corpus in English teaching are also discussed.
Multi-View Face Recognition Using Deep Attention-Based Face Frontalization

Face frontalization has been widely used in face recognition to alleviate distribution discrepancy between multi-view faces. Given a profile face, existing models learn to synthesize a frontal face from the whole region indistinguishably, often resulting in unsatisfactory frontalization caused by a lack of synthetic focus and disturbances of trivial backgrounds. This paper proposes a novel Deep Attention-based Face Frontalization (DAFF) method to address the above issues explicitly. We first inject the 3D spatial prior of the input face into an encoder-decoder model. This process locates the discriminative foreground for decomposing meaningful convolutional embeddings. After that, we propose a novel objective that served as the generator’s geometric guidance to pay more attention to the target’s essential regions. Therefore, we can leverage the attentional constraints to perform recovery refinement at both embedding and texture levels. Extensive experiments show that DAFF achieves satisfactory frontalization and competitive recognition performance under constrained and in-the-wild benchmarks.
On the existence and construction of maximum distance profile convolutional codes

In this paper, we study the conditions for a convolutional code to be MDP in terms of the size of the base field Fq as well as the openness of the MDP property in a given family of convolutional codes. Given (n,k,δ), our main result is an explicit bound depending on (n,k,δ) such that if q is greater than this bound, there exists a (n,k,δ) MDP convolutional code. A similar result is also offered for complete MDP convolutional codes. We show that these bounds are much lower than that those appeared so far in the literature. Finally, we show an explicit and simple construction procedure for MDP convolutional Goppa codes of dimension one.
Evaluation Metrics for Personalized Recommendation Systems

Abstract This paper summarizes and reviews the latest research progress on the evaluation metrics of the recommendation system, expounds from multiple perspectives such as accuracy, diversity, novelty, and conducts an in-depth analysis of their respective advantages and disadvantages and applicable environments, focuses on the three levels of recommended diversity and evaluation criteria, and makes predictions on the development direction of the evaluation metrics of the recommendation system.
Computing infrastructure for scientific research in a digital transformation

The article discusses the main aspects of the importance of developing a high-performance environment for scientific research in the context of digital transformation. Some historical facts are presented that testify to the role played by the Soviet scientists in the development of this sphere. An analysis of the experience of the FRC CSC RAS in the creation of a modern high-performance platform and its use for scientific research is given.
Radial shearing speckle pattern interference system based on a spatial light modulator

We propose a new radial shearing speckle pattern interference system. The introduction of radial shearing and the four-step temporal phase shift are realized using a spatial light modulator to modulate the phase information of the light wavefront. The modulation mode of the spatial light modulator is controlled by programming, and thus the shearing information can be adjusted quickly and arbitrarily according to the actual measurement needs. The interference system no longer has the requirement of the defect direction in nondestructive testing and can simultaneously realize the accurate detection of defects in all directions. The basic principle of radial shearing speckle pattern interferometry and the introduction method of radial shearing are described, and the system feasibility and practicability are verified by experiments.
Path Ranking Model for Entity Prediction

Knowledge graphs (KGs) often encounter knowledge incompleteness, necessitating a demand for KG completion. Path-based methods are one of the most important approaches to this task. However, since the number of entities is much larger than that of relations in a knowledge graph, existing path-based methods are only used to predict the relations between entity pairs, and are rarely applied to solve the entity prediction task. To address the issue, this paper proposes a new framework called Path Ranking Model (PRM) for the knowledge graph completion task. Our key idea is to exploit both the observable patterns and latent semantic information in relation paths to predict the entities. Extensive experiments on public popular datasets demonstrate the effectiveness of our proposed framework in the entity prediction task.
Data-Driven Information Filtering Framework for Dynamically Hybrid Job Recommendation

Recommender system is a mature concept that periodically evolves. It is relating to more than one branch of knowledge such as data mining, machine learning, information retrieval, natural language processing and human-computer interaction. Nowadays recommender systems impose themselves to be a core model in all the present modern web applications. They are obviously found in numerous industries such as videos recommendation by Youtube and products recommendation by Amazon. Similarly, recommendation systems are incredibly required in the challenging e-recruitment domain. In e-recruitment domain, it is mandatory to build an intelligent system that digs deeper into thousands of jobs to eventually filter out the relevant jobs for each job-seeker. We explain what it needs to build such sophisticated and practical hybrid engine. The engine is practically employed in a dynamic e-recruitment portal and it has already proven its efficiency in the real-world market. We utilized many applied recommendation approaches in other several industries to empower the field of e-recruitment. Our project aims to recommend relevant jobs for job-seekers to solve the problem of disrupting job-seekers with irrelevant jobs to improve users’ satisfaction and loyalty.
Role of Technologies in Revamping the Supply Chain Management of Kirana Stores

This chapter aims to study the challenges and possibilities of technological disruption on supply chain management of neighborhood kirana stores in India. These kirana stores have been the lifeline of consumers across India during the Covid-19 outbreak which saw the closure of most of the other retail formats. While Covid-19 developed a new affiliation for kirana stores among consumers and provided new possibilities for these stores to expand their customer bases and product range, at the same time highlighted the gaps in their supply chain management. This paper would examine the role and assistance which technologies like digital and blockchain technologies can provide to these unorganized small-scale neighborhood kirana stores. The methodology adopted in this study is the interpretivism approach of research. All the interpretations are drawn with the content analysis of relevant research, case studies, and industry reports. Blockchain and digital technologies hold tremendous potential to transform retail omni-supply chains. Although blockchain is a nascent technology, it has huge potential to integrate back-end operations for the supply chain for these stores. However, RFID and other digital technologies have established evidence to manage and improve front-end operation for supply chain management at these stores. The limitation will arise in this study with the nonavailability of primary data and direct interaction with the people involved in the process. Sometimes the method used in the findings of the study may become the limitation when discussed by other researchers with a different method. This study will help the kirana stores and their supplier both to develop a competitive advantage and effective resource planning to ensure the distribution of their products to end consumers.
CURE: Collection for Urdu Information Retrieval Evaluation and Ranking

Urdu is a widely spoken language with 163 million speakers across the globe. Information Retrieval (IR) for Urdu entails special consideration of research community due to its rich morphological features and a large number of speakers. In general, IR evaluation task is not extensively explored for Urdu. The most important missing element is the availability of a standardized evaluation corpus specific to Urdu. In this research work, we propose and construct a standard test collection of Urdu documents for IR evaluation and named it Collection for Urdu Retrieval Evaluation (CURE). We select 1,096 unique documents against 50 diverse queries from a large collection of 0.5 million crawled documents using two IR models. The purpose of test collection is the evaluation of IR models, ranking algorithms, and different natural language processing techniques. Next, we perform binary relevance judgment on the selected documents. We also build two other language resources for lemmatization and query expansion specific to our test collection. Evaluation of test collection is carried out using four retrieval models as well using the stop-words list, lemmatization, and query expansion. Furthermore, error analysis is performed for each query with different NLP techniques. To the best of our knowledge, this work is the first attempt for preparing a standardized information retrieval evaluation test collection for the Urdu language.
Blockchain-Based Trust Auction for Dynamic Virtual Machine Provisioning and Allocation in Clouds

Cloud computing uses virtualization technology to provide users with different types of resources in the form of services. The third party plays a crucial role in coordinating cloud market between cloud providers and users. As for providing services or trading, the extra broker fees are required for the middleman because the third party facilitates transactions. Moreover, there is no guarantee that the third party is trusted, which can lead to information leakage, data tampering, and unfair trading. Blockchain technology is an emerging technology that can store and communicate data between entities that unnecessarily trust each other. To resolve the problems, this paper presents the blockchain-based trust and fair system and develops the smart contract of auction and transaction. The prototype system is implemented based on the Hyperledger Fabric. The experimental results prove the feasibility of the scheme.
A Combination Model of Robust Principal Component Analysis and Multiple Kernel Learning for Cancer Patient Stratification

In recent years, bioinformatics has been significantly contributing to patient stratification that is very crucial for early detection of cancer diseases. In particular, stratification or classification of patients is to divide patients into subgroups that will be offered effective treatment regimens. However, current methods have to face two major challenges in analyzing large biomedical datasets when stratifying cancer patients. Firstly, the datasets are very big with a high number of features. Secondly, because the public data is available and heterogeneous, there is a great need of combining multiple data sources, providing more comprehensive and informative datasets. A variety of methods has been proposed to tackle these challenges, but they have often solved one or the other separately. Handling noisy data encountered another difficulty in data integration. In this paper, we have proposed an efficient model, combining of the robust principal component analysis-based dimensionality reduction and feature extraction with classification based on multiple kernel learning. The proposed method resolved the above-mentioned problems in cancer patient stratification. The model obtained high accuracy with 92.92% and significant statistical tests. These results hold great promise, supporting cancer research, diagnosis, and treatment.
Artifact: Masa: Responsive Multi-DNN Inference on the Edge

This artifact is a guideline how the Edgecaffe framework, presented in [1], can be used. Edgecaffe is an open-source Deep Neural Network framework for efficient multi-network inference on edge devices. This framework enables the layer by layer execution and fine-grained control during inference of Deep Neural Networks. Edgecaffe is created to give more fine grained-control over the execution during inference than offered by the original code of Caffe [2]. Edgecaffe made it possible for Masa to outperform Deepeye [3] and normal bulk execution. Besides the core implementation of Edgecaffe, the repository holds additional tools, Queue Runner and ModelSplitter, that make more convenient to run experiments and prepare newly trained networks.
Analysis of Quality of Experience (QoE) in Video Streaming Over Wi-Fi in Real Time

Over the years, in wireless and mobile networks, video traffic is becoming more dominant. In order to assess the users’ satisfaction of the services, a measure has to be considered which depicts the delight or annoyance of the users’ experience with the services. Quality of Experience is one such measure which focuses on the experience of the users with the services delivered, unlike quality of service (QoS) which focuses on the media or network itself. In addition to video transmission, Quality of Experience introduces a user experience-driven strategy that focuses on the contextual and human factors. This is helpful because it expresses user experience both objectively and subjectively. Hence, in order enhance viewers’ experience, measuring the Quality of Experience of the services along with network and a system factor proves to be beneficial. We aim to analyze the Quality of Experience of users in the university. The data gives insight about the various parameters that affect transmission of video or any data in that regard. The quality of the transferred videos is assessed by the end users by rating their experience. We aim to provide objective and subjective measure of Quality of Experience by analyzing the factors affecting Quality of Experience and the users’ experience, respectively.
Hybridization of TrellisNet with CNN

This paper is an introduction to how hybridization is performed on a sequential model namely TrellisNet that affects its accuracy. TrellisNet is a temporal convolution Network with a special architecture. An attempt is made to combine this network with the Convolution Layer to improve Trellis Net performance. For this task, here EMNIST, which is an extension of MNIST that contains English handwritten letters and digits is used. The goal is to demonstrate the efficiency and performance of the current combined model. This also gives us an insight of performance this model as compared to other popular models used for Handwritten character recognition. TrellisNet with generalized weight matrices is a truncated Recurrent Network. Thus, a Convolution Layer followed by the defined TrellisNet is designed for achieving the targeted results aimed in this paper. This stacked architecture has delivered encouraging results. The results establish the characteristics of the model and show the problems this model can address.