Reviewerly/exHarmony huggingface dataset:
TrackMPNN: A Message Passing Graph Neural Architecture for Multi-Object Tracking

This study follows many classical approaches to multi-object tracking (MOT) that model the problem using dynamic graphical data structures, and adapts this formulation to make it amenable to modern neural networks. Our main contributions in this work are the creation of a framework based on dynamic undirected graphs that represent the data association problem over multiple timesteps, and a message passing graph neural network (MPNN) that operates on these graphs to produce the desired likelihood for every association therein. We also provide solutions and propositions for the computational problems that need to be addressed to create a memory-efficient, real-time, online algorithm that can reason over multiple timesteps, correct previous mistakes, update beliefs, and handle missed/false detections. To demonstrate the efficacy of our approach, we only use the 2D box location and object category ID to construct the descriptor for each object instance. Despite this, our model performs on par with state-of-the-art approaches that make use of additional sensors, as well as multiple hand-crafted and/or learned features. This illustrates that given the right problem formulation and model design, raw bounding boxes (and their kinematics) from any off-the-shelf detector are sufficient to achieve competitive tracking results on challenging MOT benchmarks.
Discovering Dialog Structure Graph for Open-Domain Dialog Generation

Learning interpretable dialog structure from human-human dialogs yields basic insights into the structure of conversation, and also provides background knowledge to facilitate dialog generation. In this paper, we conduct unsupervised discovery of dialog structure from chitchat corpora, and then leverage it to facilitate dialog generation in downstream systems. To this end, we present a Discrete Variational Auto-Encoder with Graph Neural Network (DVAE-GNN), to discover a unified human-readable dialog structure. The structure is a two-layer directed graph that contains session-level semantics in the upper-layer vertices, utterance-level semantics in the lower-layer vertices, and edges among these semantic vertices. In particular, we integrate GNN into DVAE to fine-tune utterance-level semantics for more effective recognition of session-level semantic vertex. Furthermore, to alleviate the difficulty of discovering a large number of utterance-level semantics, we design a coupling mechanism that binds each utterance-level semantic vertex with a distinct phrase to provide prior semantics. Experimental results on two benchmark corpora confirm that DVAE-GNN can discover meaningful dialog structure, and the use of dialog structure graph as background knowledge can facilitate a graph grounded conversational system to conduct coherent multi-turn dialog generation.
Noisy Simultaneous Signal Parameter Estimation with Generalized Phase Calculation Approaches

This paper presents estimates of weak signal parameters in a noisy radio signal estimation process for the situation in which there are two simultaneous signals with a weak signal that takes on a value of 855 MHz, the signal-to-interference ratio is 10−4, and the weak signal-to- noise ratio takes on three values (20, 30, and 40 dB). Real time estimates are extracted from zero crossing clusters in a generalization of James Tsui’s phase measurement approach for weak signals in the presence of a strong signal. Results are presented for four pairs of zero crossing clusters, 1, 3 , 2, 4 , 3, 5 , and 6, 8 for the the situation in which there are two simultaneous signals. Real-time estimates obtained with this approach are within 1.2 10−4 of the weak signal frequency, and the error is within 120 parts per million of the weak signal he capability to detect simultaneous signals in the presence of noise is desired for tronic warfare receivers.
PREDICTING DEPRESSION LEVEL USING SOCIAL MEDIA POSTS

Depression is a major concern snowballing day by day. There can be various causes of depression but mental illness is the main problem. A lot of people suffer from depression and a very few of them go through treatment. One out of six people between ages 10 to 19 years are suffering from depression. At its worst, depression can lead to suicide. Depression reduces user’s ability to do work study or socialize. One solution to this problem is study of individual’s behaviour through social media. We could know a person’s opinion, thinking, mood etc. through his social media. These attributes of user can be collected from different social networking sites like Instagram, Facebook, and Twitter etc. Social networking sites can be used as an analysis tool to predict depression level. Our projects aim is to gather information of user from their social media posts and predict his depression level.
Cryptographic Approaches for Privacy-Preserving Machine Learning

The characteristics of the new generation of artificial intelligence technology are shown as follows: with the help of GPU computing, cloud computing and other high-performance distributed computing capabilities, machine learning algorithms represented by deep learning algorithms are used for learning and training on big data to simulate, extend and expand human intelligence. Different data sources and computing physical locations make the current machine learning face serious privacy leakage problem, so the Privacy Protection of Machine (PPM) Learning has become a widely concerned research area. Using cryptography technology to solve the problem of privacy in machine learning is an important technology to protect the privacy of machine learning. Cryptographic tools used in privacy-preserving machine learning are introduced, such as general Secure Multi-Party Computing (SMPC), privacy protection set operation and Homomorphic Encryption (HE), describes the status and developments applying the tools to solving the problems of privacy protection in various stages of machine learning, such as data processing, model training, model testing, and data prediction.
A multifunctional quantum teleportation network

Quantum teleportation is one of the most basic quantum protocols, which transfers an unknown quantum state from one location to another through local operation and classical communication by using shared quantum entanglement without physical transfer of the information carrier. And it has been widely used in various quantum information protocols such as entanglement swapping, quantum repeaters, quantum gate teleportation, quantum computation based on measurement, and quantum teleportation networks, which have important application value in quantum computation and quantum information. Quantum teleportation is a naturally bipartite process, in which an unknown quantum state can only be transmitted from one node to another. With the further development of quantum information research, it is necessary to transfer quantum states or quantum information among more and more nodes. Multipartite quantum protocols are expected to form fundamental components for larger-scale quantum communication and computation. A bipartite quantum teleportation should be extended to a multipartite protocol known as a quantum teleportation network. In this paper, a multifunctional quantum teleportation network is proposed theoretically. We first propose a special method of constructing four-partite quantum resources in continuous variables (CVs), and based on this, construct two different types of CV quantum teleportation networks. One type of network contains just one quantum teleportation process consisting of a sender, a receiver and two controllers. In this type of network, the unknown quantum state can be recovered at any other node according to the requirement after the measurement in the input node, which enriches the transfer direction and transfer mode of the unknown quantum state. And meanwhile, the two controllers can control the transfer of a quantum state from the sender to the receiver by restricting the sender and receiver’s access to their information, which makes the quantum teleportation network controllable. The other type of network has two quantum teleportation processes, each containing only a sender, a receiver and no controllers, which increases the number of quantum states that can be transmitted. Then we analyze the dependence of the fidelity of each quantum teleportation network on different physical parameters, and compare the characteristics, advantages and disadvantages among different types of quantum teleportation networks. The scheme for constructing a multifunctional quantum teleportation network in this paper shows some advantages, such as the greater number of quantum nodes, diversity of types, simple operation procedure. And all these advantages provide a broader application prospect for establishing larger and more complex quantum information networks in the future and quicken the pace of the application of quantum information.
Detection of Unusual Targets in Traffic Images Based on One-Class Extreme Machine Learning

In traffic image target detection, unusual targets like a running dog has not been paid sufficient attention. The mature detection methods for general targets cannot be directly applied to detect unusual targets, owing to their high complexity, poor feature expression ability, and requirement for numerous manual labels. To effectively detect unusual targets in traffic images, this paper proposes a multi-level semi-supervised one-class extreme learning machine (ML-S2OCELM). Specifically, the extreme learning machine (ELM) was chosen as the basis to develop a classifier, whose variables could be calculated directly at the cost of limited computing resources. The hypergraph Laplacian array was employed to improve the depiction of data smoothness, making semi-supervised classification more accurate. Furthermore, a stack auto-encoder (AE) was introduced to implement a multi-level neural network (NN), which can extract discriminative eigenvectors with suitable dimensions. Experiments show that the proposed method can efficiently screen out traffic images with unusual targets with only a few positive labels. The research results provide a time-efficient, and resource-saving instrument for feature expression and target detection.
Optimal Clustering in Anisotropic Gaussian Mixture Models

We study the clustering task under anisotropic Gaussian Mixture Models where the covariance matrices from different clusters are unknown and are not necessarily the identical matrix. We characterize the dependence of signal-to-noise ratios on the cluster centers and covariance matrices and obtain the minimax lower bound for the clustering problem. In addition, we propose a computationally feasible procedure and prove it achieves the optimal rate within a few iterations. The proposed procedure is a hard EM type algorithm, and it can also be seen as a variant of the Lloyd's algorithm that is adjusted to the anisotropic covariance matrices.
Sentences Similarity Based on Deep Structured Semantic Model and Semantic Role Labeling

This paper proposes a method of sentences similarity based on deep semantic model and semantic role labeling. Deep semantic model is to learn word representation in a semantic space using deep learning technology, then we use generated vectors to calculate sentences similarity. At the same time, we label sentences with semantic roles and calculate similarity based on semantic roles. Finally, those two kinds of sentences similarity are linearly combined as final sentences similarity. In this paper, experiments are carried out on the SemEva1-2017 and SemEva1-2016 task 1, and Pearson correlation coefficients reach 85% and 63% respectively. Experimental results show that our proposed method outperforms existing methods demonstrating the effectiveness of our approach.
Interprocedural Shape Analysis Using Separation Logic-Based Transformer Summaries

Shape analyses aim at inferring semantic invariants related to the data-structures that programs manipulate. To achieve that, they typically abstract the set of reachable states. By contrast, abstractions for transformation relations between input states and output states not only provide a finer description of program executions but also enable the composition of the effect of program fragments so as to make the analysis modular. However, few logics can efficiently capture such transformation relations. In this paper, we propose to use connectors inspired by separation logic to describe memory state transformations and to represent procedure summaries. Based on this abstraction, we design a top-down interprocedural analysis using shape transformation relations as procedure summaries. Finally, we report on implementation and evaluation.
SpeechTrans@SMM4H’20: Impact of Preprocessing and N-grams on Automatic Classification of Tweets That Mention Medications

This paper describes our system developed for automatically classifying tweets that mention medications. We used the Decision Tree classifier for this task. We have shown that using some elementary preprocessing steps and TF-IDF n-grams led to acceptable classifier performance. Indeed, the F1-score recorded was 74.58% in the development phase and 63.70% in the test phase.
Elements of Quantum Information Theory

In this Chapter we review some fundamental concepts of quantum mechanics and linear algebra using the Dirac notation and the density operator formalism, including the notions of qubits, entanglement and general quantum operations (Sects. 2.1–2.5). We then introduce the entropies characterizing information-processing tasks which commonly occur in quantum cryptography, in particular the Shannon and von Neumann entropy (Sect. 2.6), the min- and max-entropy (Sect. 2.7) and their smooth versions (Sect. 2.8). We emphasize the operational meaning of the smooth min- and max-entropy for the cryptographic tasks of error correction and privacy amplification in Sects. 2.9 and 2.10, respectively. We additionally elaborate on the notion of distance between quantum states and its relation to their distinguishability in Sect. 2.11 in the Appendix of this Chapter. The content of this Chapter is mostly inspired by the following literature: [1–4].
Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networks

This paper studies a federated learning (FL) system, where \textit{multiple} FL services co-exist in a wireless network and share common wireless resources. It fills the void of wireless resource allocation for multiple simultaneous FL services in the existing literature. Our method designs a two-level resource allocation framework comprising \emph{intra-service} resource allocation and \emph{inter-service} resource allocation. The intra-service resource allocation problem aims to minimize the length of FL rounds by optimizing the bandwidth allocation among the clients of each FL service. Based on this, an inter-service resource allocation problem is further considered, which distributes bandwidth resources among multiple simultaneous FL services. We consider both cooperative and selfish providers of the FL services. For cooperative FL service providers, we design a distributed bandwidth allocation algorithm to optimize the overall performance of multiple FL services, meanwhile cater to the fairness among FL services and the privacy of clients. For selfish FL service providers, a new auction scheme is designed with the FL service owners as the bidders and the network provider as the auctioneer. The designed auction scheme strikes a balance between the overall FL performance and fairness. Our simulation results show that the proposed algorithms outperform other benchmarks under various network conditions.
Temporary Free Access

Many universities rapidly transitioned to online instruction in the spring of 2020, and content providers responded by offering temporary free access to a wide range of sources. Despite having no direct cost, free access comes with several hidden costs, including the staff time used to manage temporary access. With many campuses expecting online instruction to continue at least through the end of the year, teaching faculty and library staff now have to build solutions for online learning without the resources that got them through the spring. While transitioning to Open Educational Resources and Open Access publications may take a large effort upfront to redesign courses and workflows, being able to rely on permanent access would be a significant advantage.
The DeepMind Chinese–English Document Translation System at WMT2020

This paper describes the DeepMind submission to the Chinese→English constrained data track of the WMT2020 Shared Task on News Translation. The submission employs a noisy channel factorization as the backbone of a document translation system. This approach allows the flexible combination of a number of independent component models which are further augmented with back-translation, distillation, fine-tuning with in-domain data, Monte-Carlo Tree Search decoding, and improved uncertainty estimation. In order to address persistent issues with the premature truncation of long sequences we included specialized length models and sentence segmentation techniques. Our final system provides a 9.9 BLEU points improvement over a baseline Transformer on our test set (newstest 2019).
Accept a Challenge: The Multi-Agent Programming Contest

The multi-agent programming contest (MAPC), is an annual attempt to motivate people to learn about and develop multi-agent systems to solve a complex challenge. We try to find scenarios, in which multi-agent systems can be suitably applied. These scenarios and the competition in general also often serve researchers as a testbed for their systems and frameworks. We analyze the results and solutions of the contest of 2019 and take a broader look at the agent technology that has been used to solve the competition’s challenges since its inception in 2005, and how it has been applied.
The Reality of Financial Corruption in Kuwait: A Procedure Research According to Corruption Perception Index &amp; Related Rules

The study aimed at highlighting the reality of financial corruption in accordance with corruption perception index and some other related rules in the state of Kuwait. The study covered the international and local reports on the levels of financial corruption in Kuwait and its impacts on the economic development within the period from 2003 to 2019. The results indicated that there is a high indicator of financial corruption in the State of Kuwait that started to increase during the last ten years in all of it aspects including issue of money laundering, bypasses, manipulating the money of sovereign fund to invest in suspicious projects, the real-estate swindle, issues related to investing the money of the Public Institution of Social Insurance (retirement sums) and so on. The researchers got to a group of recommendations and proposals in both penal and Shariaa sides including activating the religious speech in all types of media, tribunes of mosques as well as all religious institutions about the danger of financial corruption on individual and society. It is also by warning people about out God punishment against every spoiler, activating the role of religion members in all attempts of government to fight the financial corruption phenomenon in the state with definitely adherence to the Islamic law principles in all financial issues of the state, agreements and so on. Moreover, it is very necessary to activate the most extreme laws and penalties against perpetrators of financial corruption without and exception.
Random Shadows and Highlights: A new data augmentation method for extreme lighting conditions

In this paper, we propose a new data augmentation method, Random Shadows and Highlights (RSH) to acquire robustness against lighting perturbations. Our method creates random shadows and highlights on images, thus challenging the neural network during the learning process such that it acquires immunity against such input corruptions in real world applications. It is a parameter-learning free method which can be integrated into most vision related learning applications effortlessly. With extensive experimentation, we demonstrate that RSH not only increases the robustness of the models against lighting perturbations, but also reduces over-fitting significantly. Thus RSH should be considered essential for all vision related learning systems. Code is available at: https://github.com/OsamaMazhar/Random-Shadows-Highlights.
The value of transactions in the new data economy

Along with tools for video-calls, cashless payment has been among one of the technologies that gained new momentum during the global pandemic. While paying with a credit or debit card has been an everyday practice for some time, mobile and contactless payments are increasingly welcome and used in many Western countries. However, most of these new payment technologies are not necessarily developed by banks, and more often stem from the so-called Big Tech companies (PayPal, Apple, Google, Amazon). The question of how money alters its meaning with the way that it is circulated lies at the heart of Lana Swartz’s book, New Money: How Payment Became Social Media.
Algorithmic Frameworks for the Detection of High-Density Anomalies

This study explores the concept of high-density anomalies. As opposed to the traditional concept of anomalies as isolated occurrences, high-density anomalies are deviant cases positioned in the most normal regions of the data space. Such anomalies are relevant for various practical use cases, such as misbehavior detection and data quality analysis. Effective methods for identifying them are particularly important when analyzing very large or noisy sets, for which traditional anomaly detection algorithms will return many false positives. In order to be able to identify high-density anomalies, this study introduces several nonparametric algorithmic frameworks for unsupervised detection. These frameworks are able to leverage existing underlying anomaly detection algorithms and offer different solutions for the balancing problem inherent in this detection task. The frameworks are evaluated with both synthetic and real-world datasets, and are compared with existing baseline algorithms for detecting traditional anomalies. The Iterative Partial Push (IPP) framework proves to yield the best detection results.
Declarative User-Item Profiling Based Context-Aware Recommendation

Context-aware recommendation has attracted much attention due to its ability to effectively finding the items that a target likes out of an abundance of online items. Different users may characterize different contexts to items since they also consider different contexts when they select items. Comprehensive identification of the declarative dominant contexts for both items and users can significantly affect the quality of the recommendation, which is often overlooked by the existing research. In this paper, we propose a new recommendation approach, which identifies the dominant contexts as declared by users on their previous transactions. Firstly, we identify the significant contexts from both item and user perspectives and construct the user-item profile in a personalized manner. Secondly, we propose a new context-aware recommendation model that seamlessly incorporates both declarative profiles into the recommendations. Finally, we demonstrate the effectiveness of the proposed method by conducting comprehensive experiments over two real benchmark datasets. The experimental results show that the proposed method outperforms the state-of-the-art methods.
Quantum State Engineering in Multi-Level Systems Using Shortcut to Adiabatic Passage

We extend the shortcut to the adiabatic passage method, proposed by