According to both academic and industry perspectives, the Fourth Industrial Revolution has brought about a paradigm shift in the manufacturing sector enabling companies to enhance their competitiveness in the global market. To achieve this goal, manufacturing companies will need to undertake a deep digital transformation, primarily by introducing advanced Information Technology into traditionally less digitalized departments, such as shop floors, where Operational Technology currently dominate. For the full achievement of Industry 4.0 revolution objectives, practitioners believe in the strong requirement of a progressive and tight integration between IT and OT departments. In the depicted scenario, communication technologies are expected to play a pivotal role in facilitating the integration process, but other more recent and advanced IT have also proven helpful. In particular, the topic of IT/OT integration has attracted significant attention from various research communities that have sought to identify both the opportunities and challenges associated with its implementation. Although some good surveys of those works have appeared in the literature, to the best of our knowledge, no comprehensive review has yet been conducted that is fully dedicated to the topic of IT/OT convergence. In this paper, we propose a holistic approach to examine the various dimensions of IT/OT integration, which we classify into five interconnected realms, Communication, IT-Driven Support to OT, Human Centricity, Advanced Industrial Control Systems, and cybersecurity. Furthermore, we develop a realm-oriented taxonomy to organize the surveyed works in a structured manner, offering readers a clear overview of the current state of the literature, along with insights into unexplored opportunities and future directions for IT/OT integration.

Data is a critical resource for machine learning (ML), yet working with data remains a key friction point. This paper introduces Croissant, a metadata format for datasets that creates a shared representation across ML tools, frameworks, and platforms. Croissant makes datasets more discoverable, portable, and interoperable, thereby addressing significant challenges in ML data management. Croissant is already supported by several popular dataset repositories, spanning hundreds of thousands of datasets, enabling easy loading into the most commonly-used ML frameworks, regardless of where the data is stored. Our initial evaluation by human raters shows that Croissant metadata is readable, understandable, complete, yet concise.

The ubiquity and remarkable technological progress of wearable consumer devices and mobile-computing platforms (smart phone, smart watch, tablet), along with the multitude of sensor modalities available, have enabled continuous monitoring of patients and their daily activities. Such rich, longitudinal information can be mined for physiological and behavioral signatures of cognitive impairment and provide new avenues for detecting MCI in a timely and cost-effective manner. In this work, we present a platform for remote and unobtrusive monitoring of symptoms related to cognitive impairment using several consumer-grade smart devices. We demonstrate how the platform has been used to collect a total of 16TB of data during the Lilly Exploratory Digital Assessment Study, a 12-week feasibility study which monitored 31 people with cognitive impairment and 82 without cognitive impairment in free living conditions. We describe how careful data unification, time-alignment, and imputation techniques can handle missing data rates inherent in real-world settings and ultimately show utility of these disparate data in differentiating symptomatics from healthy controls based on features computed purely from device data.

Many behaviors that lead to worsened health outcomes are modifiable, social, and visible. Social influence has thus the potential to foster adoption of habits that promote health and improve disease management. In this study, we consider the evolution of the physical activity of 44.5 thousand Fitbit users as they interact on the Fitbit social network, in relation to their health status. The users collectively recorded 9.3 million days of steps over the period of a year through a Fitbit device. 7,515 of the users also self-reported whether they were diagnosed with a major chronic condition. A time-aggregated analysis shows that ego net size, average alter physical activity, gender, and body mass index (BMI) are significantly predictive of ego physical activity. For users who self-reported chronic conditions, the direction and effect size of associations varied depending on the condition, with diabetic users specifically showing almost a 6-fold increase in additional daily steps for each additional social tie. Subsequently, we consider the co-evolution of activity and friendship longitudinally on a month by month basis. We show that the fluctuations in average alter activity significantly predict fluctuations in ego activity. By leveraging a class of novel non-parametric statistical tests we investigate the causal factors in these fluctuations. We find that under certain stationarity assumptions, non-null causal dependence exists between ego and alter's activity, even in the presence of unobserved stationary individual traits. We believe that our findings provide evidence that the study of online social networks have the potential to improve our understanding of factors affecting adoption of positive habits, especially in the context of chronic condition management.

We study the k-BALANCED PARTITIONING problem in which the vertices of a graph are to be partitioned into k sets of size at most ceil(n/k) while minimising the cut size, which is the number of edges connecting vertices in different sets. The problem is well studied for general graphs, for which it cannot be approximated within any factor in polynomial time. However, little is known about restricted graph classes. We show that for trees k-BALANCED PARTITIONING remains surprisingly hard. In particular, approximating the cut size is APX-hard even if the maximum degree of the tree is constant. If instead the diameter of the tree is bounded by a constant, we show that it is NP-hard to approximate the cut size within n^c, for any constant c<1. In the face of the hardness results, we show that allowing near-balanced solutions, in which there are at most (1+eps)ceil(n/k) vertices in any of the k sets, admits a PTAS for trees. Remarkably, the computed cut size is no larger than that of an optimal balanced solution. In the final section of our paper, we harness results on embedding graph metrics into tree metrics to extend our PTAS for trees to general graphs. In addition to being conceptually simpler and easier to analyse, our scheme improves the best factor known on the cut size of near-balanced solutions from O(log^{1.5}(n)/eps^2) [Andreev and Räcke TCS 2006] to 0(log n), for weighted graphs. This also settles a question posed by Andreev and Räcke of whether an algorithm with approximation guarantees on the cut size independent from eps exists.

We describe an algorithm dubbed Suppression via Disk Covering (SDC) to efficiently select a set of strong, spatially distributed key-points, and we show that selecting keypoint in this way significantly improves visual tracking. We also describe two efficient implementation schemes for the popular Adaptive Non-Maximal Suppression algorithm, and show empirically that SDC is significantly faster while providing the same improvements with respect to tracking robustness. In our particular application, using SDC to filter the output of an inexpensive (but, by itself, less reliable) keypoint detector (FAST) results in higher tracking robustness at significantly lower total cost than using a computationally more expensive detector.

We investigate the complexity of shortest paths in time-dependent graphs, in which the costs of edges vary as a function of time, and as a result the shortest path between two nodes s and d can change over time. Our main result is that when the edge cost functions are (polynomial-size) piecewise linear, the shortest path from s to d can change nΘ(log n) times, settling a several-year-old conjecture of Dean [Technical Reports, 1999, 2004]. We also show that the complexity is polynomial if the slopes of the linear function come from a restricted class, present an output-sensitive algorithm for the general case, and describe a scheme for a (1 + ε)-approximation of the travel time function in near-quadratic space. Finally, despite the fact that the arrival time function may have superpolynomial complexity, we show that a minimum delay path for any departure time interval can be computed in polynomial time.

Monitoring the performance of large shared computing systems such as the cloud computing infrastructure raises many challenging algorithmic problems. One common problem is to track users with the largest deviation from the norm (outliers), for some measure of performance. Taking a stream-computing perspective, we can think of each user's performance profile as a stream of numbers (such as response times), and the aggregate performance profile of the shared infrastructure as a “braid” of these intermixed streams. The monitoring system's goal then is to untangle this braid sufficiently to track the top k outliers. This paper investigates the space complexity of one-pass algorithms for approximating outliers of this kind, proves lower bounds using multi-party communication complexity, and proposes small-memory heuristic algorithms. On one hand, stream outliers are easily tracked for simple measures, such as max or min, but our theoretical results rule out even good approximations for most of the natural measures such as average, median, or the quantiles. On the other hand, we show through simulation that our proposed heuristics perform quite well for a variety of synthetic data.

In this paper, we present an abstract framework for online approximation of time-series data that yields a unified set of algorithms for several popular models: data streams, amnesic approximation, and out-of-order stream approximation. Our framework essentially develops a popular greedy method of bucket-merging into a more generic form, for which we can prove space-quality approximation bounds. When specialized to piecewise linear bucket approximations and commonly used error metrics, such as L2or L¿, our framework leads to provable error bounds where none were known before, offers new results, or yields simpler and unified algorithms. The conceptual simplicity of our scheme translates into highly practical implementations, as borne out in our simulation studies: the algorithms produce near-optimal approximations, require very small memory footprints, and run extremely fast.

In this paper, we propose an approach to complexity reduction of Mamdani-type fuzzy rule-based systems (FRBSs) based on removing logical redundancies. We first generate an FRBS from data by applying a simplified version of the well-known Wang and Mendel method. Then, we represent the FRBS as a multi-valued logic relation. Finally, we apply MVSIS, a tool for circuit minimization and simulation, to minimize the relation and consequently to reduce complexity of the associated FRBS. Unlike similar previous approaches proposed in the literature, the use of MVSIS let us deal with nondeterminism, that is, let us manage rules with the same antecedent but different consequents. To allow nondeterminism guarantees to achieve a higher (or at least not worse) complexity reduction than the one achievable from removing nondeterminism as soon as it appears. We apply our approach to six popular benchmarks. Results show a considerable complexity reduction associated only sporadically with consistent accuracy degradation. Moreover, quite surprisingly, the complexity reduction often comes together with an improvement in the classification accuracy.

We report on a new experimental analysis of high-order entropy-compressed suffix arrays, which retains the theoretical performance of previous work and represents an improvement in practice. Our experiments indicate that the resulting text index offers state-of-the-art compression. In particular, we require roughly 20% of the original text size---without requiring a separate instance of the text. We can additionally use a simple notion to encode and decode block-sorting transforms (such as the Burrows--Wheeler transform), achieving a compression ratio comparable to that of bzip2. We also provide a compressed representation of suffix trees (and their associated text) in a total space that is comparable to that of the text alone compressed with gzip.

We report on a simple encoding format called wzip for decompressing block-sorting transforms, such as the Burrows-Wheeler transform (BWT). Our compressor uses the simple notions of gamma encoding and RLE, organized with a wavelet tree, to achieve a slightly better compression ratio than bzip2 in less time. In fact, our compression/decompression time is dependent on H/sub h/, the hth order empirical entropy. This relationship of performance to the compressibility of data is a key new idea among compression algorithms. Another key contribution of our compressor is its simplicity. Our compressor can also operate as a full-text index with a small amount of data, while still preserving backward compatibility with just the compressor.