OpenRaiser/Intern-Atlas huggingface dataset:
Artificial intelligence prediction of stock prices using social media
Venue: arXiv cs.AI 2021
Year: 2021

The primary objective of this work is to develop a Neural Network based on LSTM to predict stock market movements using tweets. Word embeddings, used in the LSTM network, are initialised using Stanford's GloVe embeddings, pretrained specifically on 2 billion tweets. To overcome the limited size of the dataset, an augmentation strategy is proposed to split each input sequence into 150 subsets. To achieve further improvements in the original configuration, hyperparameter optimisation is performed. The effects of variation in hyperparameters such as dropout rate, batch size, and LSTM hidden state output size are assessed individually. Furthermore, an exhaustive set of parameter combinations is examined to determine the optimal model configuration. The best performance on the validation dataset is achieved by hyperparameter combination 0.4,8,100 for the dropout, batch size, and hidden units respectively. The final testing accuracy of the model is 76.14%.
A blockchain-based pattern for confidential and pseudo-anonymous contract enforcement
Venue: arXiv cs.CR 2021
Year: 2021

Blockchain has been praised for its capacity to hold data in a decentralized and tamper-proof way. It also supports the execution of code through blockchain's smart contracts, adding automation of actions to the network with high trustability. However, as smart contracts are visible by anybody on the network, the business data and logic may be at risk, thus companies could be reluctant to use such technology. This paper aims to propose a pattern that allows the execution of automatable legal contract clauses, where its execution states are stored in an on-chain smart-contract and the logic needed to enforce it wraps it off-chain. An engine completes this pattern by running a business process that corresponds to the legal contract. We then propose a pattern-based solution based on a real-life use case: transportation of refrigerated goods. We argue that this pattern guarantees companies pseudonymity and data confidentiality while ensuring that an audit trail can be reconstituted through the blockchain smart-contract to identify misbehavior or errors. This paper paves the way for a future possible implementation of the solution described, as well as its evaluation.
Online Packing to Minimize Area or Perimeter
Venue: arXiv cs.CG 2021
Year: 2021

We consider online packing problems where we get a stream of axis-parallel rectangles. The rectangles have to be placed in the plane without overlapping, and each rectangle must be placed without knowing the subsequent rectangles. The goal is to minimize the perimeter or the area of the axis-parallel bounding box of the rectangles. We either allow rotations by 90 degrees or translations only. For the perimeter version we give algorithms with an absolute competitive ratio slightly less than 4 when only translations are allowed and when rotations are also allowed. We then turn our attention to minimizing the area and show that the asymptotic competitive ratio of any algorithm is at least $Ω(\sqrt{n})$, where $n$ is the number of rectangles in the stream, and this holds with and without rotations. We then present algorithms that match this bound in both cases. We also show that the competitive ratio cannot be bounded as a function of OPT. We then consider two special cases. The first is when all the given rectangles have aspect ratios bounded by some constant. The particular variant where all the rectangles are squares and we want to minimize the area of the bounding square has been studied before and an algorithm with a competitive ratio of 8 has been given [Fekete and Hoffmann, Algorithmica, 2017]. We improve the analysis of the algorithm and show that the ratio is at most 6, which is tight. The second special case is when all edges have length at least 1. Here, the $Ω(\sqrt n)$ lower bound still holds, and we turn our attention to lower bounds depending on OPT. We show that any algorithm has an asymptotic competitive ratio of at least $Ω(\sqrt{OPT})$ for the translational case and $Ω(\sqrt[4]{OPT})$ when rotations are allowed. For both versions, we give algorithms that match the respective lower bounds.
Checking Robustness Between Weak Transactional Consistency Models
Venue: arXiv cs.PL 2021
Year: 2021

Concurrent accesses to databases are typically encapsulated in transactions in order to enable isolation from other concurrent computations and resilience to failures. Modern databases provide transactions with various semantics corresponding to different trade-offs between consistency and availability. Since a weaker consistency model provides better performance, an important issue is investigating the weakest level of consistency needed by a given program (to satisfy its specification). As a way of dealing with this issue, we investigate the problem of checking whether a given program has the same set of behaviors when replacing a consistency model with a weaker one. This property known as robustness generally implies that any specification of the program is preserved when weakening the consistency. We focus on the robustness problem for consistency models which are weaker than standard serializability, namely, causal consistency, prefix consistency, and snapshot isolation. We show that checking robustness between these models is polynomial time reducible to a state reachability problem under serializability. We use this reduction to also derive a pragmatic proof technique based on Lipton's reduction theory that allows to prove programs robust. We have applied our techniques to several challenging applications drawn from the literature of distributed systems and databases.
Query Abandonment Prediction with Recurrent Neural Models of Mouse Cursor Movements
Venue: arXiv cs.IR 2021
Year: 2021

Most successful search queries do not result in a click if the user can satisfy their information needs directly on the SERP. Modeling query abandonment in the absence of click-through data is challenging because search engines must rely on other behavioral signals to understand the underlying search intent. We show that mouse cursor movements make a valuable, low-cost behavioral signal that can discriminate good and bad abandonment. We model mouse movements on SERPs using recurrent neural nets and explore several data representations that do not rely on expensive hand-crafted features and do not depend on a particular SERP structure. We also experiment with data resampling and augmentation techniques that we adopt for sequential data. Our results can help search providers to gauge user satisfaction for queries without clicks and ultimately contribute to a better understanding of search engine performance.
Lexical semantic change for Ancient Greek and Latin
Venue: arXiv cs.CL 2021
Year: 2021

Change and its precondition, variation, are inherent in languages. Over time, new words enter the lexicon, others become obsolete, and existing words acquire new senses. Associating a word's correct meaning in its historical context is a central challenge in diachronic research. Historical corpora of classical languages, such as Ancient Greek and Latin, typically come with rich metadata, and existing models are limited by their inability to exploit contextual information beyond the document timestamp. While embedding-based methods feature among the current state of the art systems, they are lacking in the interpretative power. In contrast, Bayesian models provide explicit and interpretable representations of semantic change phenomena. In this chapter we build on GASC, a recent computational approach to semantic change based on a dynamic Bayesian mixture model. In this model, the evolution of word senses over time is based not only on distributional information of lexical nature, but also on text genres. We provide a systematic comparison of dynamic Bayesian mixture models for semantic change with state-of-the-art embedding-based models. On top of providing a full description of meaning change over time, we show that Bayesian mixture models are highly competitive approaches to detect binary semantic change in both Ancient Greek and Latin.
A shallow neural model for relation prediction
Venue: arXiv cs.LG 2021
Year: 2021

Knowledge graph completion refers to predicting missing triples. Most approaches achieve this goal by predicting entities, given an entity and a relation. We predict missing triples via the relation prediction. To this end, we frame the relation prediction problem as a multi-label classification problem and propose a shallow neural model (SHALLOM) that accurately infers missing relations from entities. SHALLOM is analogous to C-BOW as both approaches predict a central token (p) given surrounding tokens ((s,o)). Our experiments indicate that SHALLOM outperforms state-of-the-art approaches on the FB15K-237 and WN18RR with margins of up to $3\%$ and $8\%$ (absolute), respectively, while requiring a maximum training time of 8 minutes on these datasets. We ensure the reproducibility of our results by providing an open-source implementation including training and evaluation scripts at {\url{https://github.com/dice-group/Shallom}.}
Streaming Models for Joint Speech Recognition and Translation
Venue: arXiv cs.CL 2021
Year: 2021

Using end-to-end models for speech translation (ST) has increasingly been the focus of the ST community. These models condense the previously cascaded systems by directly converting sound waves into translated text. However, cascaded models have the advantage of including automatic speech recognition output, useful for a variety of practical ST systems that often display transcripts to the user alongside the translations. To bridge this gap, recent work has shown initial progress into the feasibility for end-to-end models to produce both of these outputs. However, all previous work has only looked at this problem from the consecutive perspective, leaving uncertainty on whether these approaches are effective in the more challenging streaming setting. We develop an end-to-end streaming ST model based on a re-translation approach and compare against standard cascading approaches. We also introduce a novel inference method for the joint case, interleaving both transcript and translation in generation and removing the need to use separate decoders. Our evaluation across a range of metrics capturing accuracy, latency, and consistency shows that our end-to-end models are statistically similar to cascading models, while having half the number of parameters. We also find that both systems provide strong translation quality at low latency, keeping 99% of consecutive quality at a lag of just under a second.
Graphical Models for Financial Time Series and Portfolio Selection
Venue: arXiv cs.LG 2021
Year: 2021

We examine a variety of graphical models to construct optimal portfolios. Graphical models such as PCA-KMeans, autoencoders, dynamic clustering, and structural learning can capture the time varying patterns in the covariance matrix and allow the creation of an optimal and robust portfolio. We compared the resulting portfolios from the different models with baseline methods. In many cases our graphical strategies generated steadily increasing returns with low risk and outgrew the S&P 500 index. This work suggests that graphical models can effectively learn the temporal dependencies in time series data and are proved useful in asset management.
Where does the Stimulus go? Deep Generative Model for Commercial Banking Deposits
Venue: arXiv cs.LG 2021
Year: 2021

This paper examines deposits of individuals ("retail") and large companies ("wholesale") in the U.S. banking industry, and how these deposit types are impacted by macroeconomic factors, such as quantitative easing (QE). Actual data for deposits by holder are unavailable. We use a dataset on banks' financial information and probabilistic generative model to predict industry retail-wholesale deposit split from 2000 to 2020. Our model assumes account balances arise from separate retail and wholesale lognormal distributions and fit parameters of distributions by minimizing error between actual bank metrics and simulated metrics using the model's generative process. We use time-series regression to forward predict retail-wholesale deposits as function of loans, retail loans, and reserve balances at Fed banks. We find increase in reserves (representing QE) increases wholesale but not retail deposits, and increase in loans increase both wholesale and retail deposits evenly. The result shows that QE following the 2008 financial crisis benefited large companies more than average individuals, a relevant finding for economic decision making. In addition, this work benefits bank management strategy by providing forecasting capability for retail-wholesale deposits.
Expression Recognition Analysis in the Wild
Venue: arXiv cs.CV 2021
Year: 2021

Facial Expression Recognition(FER) is one of the most important topic in Human-Computer interactions(HCI). In this work we report details and experimental results about a facial expression recognition method based on state-of-the-art methods. We fine-tuned a SeNet deep learning architecture pre-trained on the well-known VGGFace2 dataset, on the AffWild2 facial expression recognition dataset. The main goal of this work is to define a baseline for a novel method we are going to propose in the near future. This paper is also required by the Affective Behavior Analysis in-the-wild (ABAW) competition in order to evaluate on the test set this approach. The results reported here are on the validation set and are related on the Expression Challenge part (seven basic emotion recognition) of the competition. We will update them as soon as the actual results on the test set will be published on the leaderboard.
Brazilian Favela Women: How Your Standard Solutions for Technology Abuse Might Actually Harm Them
Venue: arXiv cs.CY 2021
Year: 2021

Brazil is home to over 200M people, the majority of which have access to the Internet. Over 11M Brazilians live in favelas, or informal settlements with no outside government regulation, often ruled by narcos or militias. Victims of intimate partner violence (IPV) in these communities are made extra vulnerable not only by lack of access to resources, but by the added layer of violence caused by criminal activity and police confrontations. In this paper, we use an unintended harms framework to analyze the unique online privacy needs of favela women and present research questions that we urge tech abuse researchers to consider.
Resilient Virtualized Systems Using ReHype
Venue: arXiv cs.SE 2021
Year: 2021

System-level virtualization introduces critical vulnerabilities to failures of the software components that implement virtualization -- the virtualization infrastructure (VI). To mitigate the impact of such failures, we introduce a resilient VI (RVI) that can recover individual VI components from failure, caused by hardware or software faults, transparently to the hosted virtual machines (VMs). Much of the focus is on the ReHype mechanism for recovery from hypervisor failures, that can lead to state corruption and to inconsistencies among the states of system components. ReHype's implementation for the Xen hypervisor was done incrementally, using fault injection results to identify sources of critical corruption and inconsistencies. This implementation involved 900 LOC, with memory space overhead of 2.1MB. Fault injection campaigns, with a variety of fault types, show that ReHype can successfully recover, in less than 750ms, from over 88% of detected hypervisor failures. In addition to ReHype, recovery mechanisms for the other VI components are described. The overall effectiveness of our RVI is evaluated hosting a Web service application, on a cluster of VMs. With faults in any VI component, for over 87% of detected failures, our recovery mechanisms allow services provided by the application to be continuously maintained despite the resulting failures of VI components.
Rate of Prefix-free Codes in LQG Control Systems with Side Information
Venue: arXiv cs.IT 2021
Year: 2021

In this work, we study an LQG control system where one of two feedback channels is discrete and incurs a communication cost. We assume that a decoder (co-located with the controller) can make noiseless measurements of a subset of the state vector (referred to as side information) meanwhile a remote encoder (co-located with a sensor) can make arbitrary measurements of the entire state vector, but must convey its measurements to the decoder over a noiseless binary channel. Use of the channel incurs a communication cost, quantified as the time-averaged expected length of prefix-free binary codeword. We study the tradeoff between the communication cost and control performance. The formulation motivates a constrained directed information minimization problem, which can be solved via convex optimization. Using the optimization, we propose a quantizer design and a subsequent achievability result.
AI-Empowered VNF Migration as a Cost-Loss-Effective Solution for Network Resilience
Venue: arXiv cs.NI 2021
Year: 2021

With a wide deployment of Multi-Access Edge Computing (MEC) in the Fifth Generation (5G) mobile networks, virtual network functions (VNF) can be flexibly migrated between difference locations, and therewith significantly enhances the network resilience to counter the degradation in quality of service (QoS) due to network function outages. A balance has to be taken carefully, between the loss reduced by VNF migration and the operations cost generated thereby. To achieve this in practical scenarios with realistic user behavior, it calls for models of both cost and user mobility. This paper proposes a novel cost model and a AI-empowered approach for a rational migration of stateful VNFs, which minimizes the sum of operations cost and potential loss caused by outages, and is capable to deal with the complex realistic user mobility patterns.
Load-Balancing for Improving User Responsiveness on Multicore Embedded Systems
Venue: arXiv cs.DC 2021
Year: 2021

Most commercial embedded devices have been deployed with a single processor architecture. The code size and complexity of applications running on embedded devices are rapidly increasing due to the emergence of application business models such as Google Play Store and Apple App Store. As a result, a high-performance multicore CPUs have become a major trend in the embedded market as well as in the personal computer market. Due to this trend, many device manufacturers have been able to adopt more attractive user interfaces and high-performance applications for better user experiences on the multicore systems. In this paper, we describe how to improve the real-time performance by reducing the user waiting time on multicore systems that use a partitioned per-CPU run queue scheduling technique. Rather than focusing on naive load-balancing scheme for equally balanced CPU usage, our approach tries to minimize the cost of task migration by considering the importance level of running tasks and to optimize per-CPU utilization on multicore embedded systems. Consequently, our approach improves the real-time characteristics such as cache efficiency, user responsiveness, and latency. Experimental results under heavy background stress show that our approach reduces the average scheduling latency of an urgent task by 2.3 times.
MAVERICK: Proactively detecting network control plane bugs using structural outlierness
Venue: arXiv cs.CR 2021
Year: 2021

Proactive detection of network configuration bugs is important to ensure its proper functioning and reduce cost of network administrator. In this research, we propose to build the control plane verification engine MAVERICK that detects the bugs in the network control plane i.e., network device configurations and control plane states. MAVERICK automatically infers signatures for the control plane configurations (e.g., ACLs, route-maps, route-policies and so on) and states that allows administrators to automatically detect bugs with minimal human intervention. MAVERICK achieves this by effectively leveraging any structural deviation i.e., outliers in the network configurations that is organized as simple or complexly nested key-value pairs. The outliers that are calculated using signature-based outlier detection mechanism are further characterized for its severity and ranked or re-prioritized according to their criticality. We consider a wide set of heuristics and domain expertise factors for effectively to reduce both false positives and false negatives.Our evaluation on four medium to large-scale enterprise networks show that MAVERICK can automatically detect the bugs present in the network with approximately 75% accuracy. Further-more, With minimal administrator input i.e., with a few minutes of signature re-tuning, MAVERICK allows the administrators to effectively detect approximately 94 - 100% of the bugs present in the network, thereby ranking down less severe bugs and removing false positives.
Outlining Traceability: A Principle for Operationalizing Accountability in Computing Systems
Venue: arXiv cs.CY 2021
Year: 2021

Accountability is widely understood as a goal for well governed computer systems, and is a sought-after value in many governance contexts. But how can it be achieved? Recent work on standards for governable artificial intelligence systems offers a related principle: traceability. Traceability requires establishing not only how a system worked but how it was created and for what purpose, in a way that explains why a system has particular dynamics or behaviors. It connects records of how the system was constructed and what the system did mechanically to the broader goals of governance, in a way that highlights human understanding of that mechanical operation and the decision processes underlying it. We examine the various ways in which the principle of traceability has been articulated in AI principles and other policy documents from around the world, distill from these a set of requirements on software systems driven by the principle, and systematize the technologies available to meet those requirements. From our map of requirements to supporting tools, techniques, and procedures, we identify gaps and needs separating what traceability requires from the toolbox available for practitioners. This map reframes existing discussions around accountability and transparency, using the principle of traceability to show how, when, and why transparency can be deployed to serve accountability goals and thereby improve the normative fidelity of systems and their development processes.